## What changes were proposed in this pull request?
link doc & example of Interaction
## How was this patch tested?
existing tests
Closes#25027 from zhengruifeng/py_doc_interaction.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The `OVERLAY` function is a `ANSI` `SQL`.
For example:
```
SELECT OVERLAY('abcdef' PLACING '45' FROM 4);
SELECT OVERLAY('yabadoo' PLACING 'daba' FROM 5);
SELECT OVERLAY('yabadoo' PLACING 'daba' FROM 5 FOR 0);
SELECT OVERLAY('babosa' PLACING 'ubb' FROM 2 FOR 4);
```
The results of the above four `SQL` are:
```
abc45f
yabadaba
yabadabadoo
bubba
```
Note: If the input string is null, then the result is null too.
There are some mainstream database support the syntax.
**PostgreSQL:**
https://www.postgresql.org/docs/11/functions-string.html
**Vertica:** https://www.vertica.com/docs/9.2.x/HTML/Content/Authoring/SQLReferenceManual/Functions/String/OVERLAY.htm?zoom_highlight=overlay
**Oracle:**
https://docs.oracle.com/en/database/oracle/oracle-database/19/arpls/UTL_RAW.html#GUID-342E37E7-FE43-4CE1-A0E9-7DAABD000369
**DB2:**
https://www.ibm.com/support/knowledgecenter/SSGMCP_5.3.0/com.ibm.cics.rexx.doc/rexx/overlay.html
There are some show of the PR on my production environment.
```
spark-sql> SELECT OVERLAY('abcdef' PLACING '45' FROM 4);
abc45f
Time taken: 6.385 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY('yabadoo' PLACING 'daba' FROM 5);
yabadaba
Time taken: 0.191 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY('yabadoo' PLACING 'daba' FROM 5 FOR 0);
yabadabadoo
Time taken: 0.186 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY('babosa' PLACING 'ubb' FROM 2 FOR 4);
bubba
Time taken: 0.151 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY(null PLACING '45' FROM 4);
NULL
Time taken: 0.22 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY(null PLACING 'daba' FROM 5);
NULL
Time taken: 0.157 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY(null PLACING 'daba' FROM 5 FOR 0);
NULL
Time taken: 0.254 seconds, Fetched 1 row(s)
spark-sql> SELECT OVERLAY(null PLACING 'ubb' FROM 2 FOR 4);
NULL
Time taken: 0.159 seconds, Fetched 1 row(s)
```
## How was this patch tested?
Exists UT and new UT.
Closes#24918 from beliefer/ansi-sql-overlay.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Spark's `InMemoryFileIndex` contains two places where `FileNotFound` exceptions are caught and logged as warnings (during [directory listing](bcd3b61c4b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InMemoryFileIndex.scala (L274)) and [block location lookup](bcd3b61c4b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InMemoryFileIndex.scala (L333))). This logic was added in #15153 and #21408.
I think that this is a dangerous default behavior because it can mask bugs caused by race conditions (e.g. overwriting a table while it's being read) or S3 consistency issues (there's more discussion on this in the [JIRA ticket](https://issues.apache.org/jira/browse/SPARK-27676)). Failing fast when we detect missing files is not sufficient to make concurrent table reads/writes or S3 listing safe (there are other classes of eventual consistency issues to worry about), but I think it's still beneficial to throw exceptions and fail-fast on the subset of inconsistencies / races that we _can_ detect because that increases the likelihood that an end user will notice the problem and investigate further.
There may be some cases where users _do_ want to ignore missing files, but I think that should be an opt-in behavior via the existing `spark.sql.files.ignoreMissingFiles` flag (the current behavior is itself race-prone because a file might be be deleted between catalog listing and query execution time, triggering FileNotFoundExceptions on executors (which are handled in a way that _does_ respect `ignoreMissingFIles`)).
This PR updates `InMemoryFileIndex` to guard the log-and-ignore-FileNotFoundException behind the existing `spark.sql.files.ignoreMissingFiles` flag.
**Note**: this is a change of default behavior, so I think it needs to be mentioned in release notes.
## How was this patch tested?
New unit tests to simulate file-deletion race conditions, tested with both values of the `ignoreMissingFIles` flag.
Closes#24668 from JoshRosen/SPARK-27676.
Lead-authored-by: Josh Rosen <rosenville@gmail.com>
Co-authored-by: Josh Rosen <joshrosen@stripe.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Event logs are different from the other data in terms of the lifetime. It would be great to have a new configuration for Spark event log compression like `spark.eventLog.compression.codec` .
This PR adds this new configuration as an optional configuration. So, if `spark.eventLog.compression.codec` is not given, `spark.io.compression.codec` will be used.
## How was this patch tested?
Pass the Jenkins with the newly added test case.
Closes#24921 from dongjoon-hyun/SPARK-28118.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
[SPARK-28093](https://issues.apache.org/jira/browse/SPARK-28093) fixed `TRIM/LTRIM/RTRIM('str', 'trimStr')` returns an incorrect value, but that fix introduced a new bug, `TRIM(type trimStr FROM str)` returns an incorrect value. This pr fix this issue.
## How was this patch tested?
unit tests and manual tests:
Before this PR:
```sql
spark-sql> SELECT trim('yxTomxx', 'xyz'), trim(BOTH 'xyz' FROM 'yxTomxx');
Tom z
spark-sql> SELECT trim('xxxbarxxx', 'x'), trim(BOTH 'x' FROM 'xxxbarxxx');
bar
spark-sql> SELECT ltrim('zzzytest', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytest');
test xyz
spark-sql> SELECT ltrim('zzzytestxyz', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytestxyz');
testxyz
spark-sql> SELECT ltrim('xyxXxyLAST WORD', 'xy'), trim(LEADING 'xy' FROM 'xyxXxyLAST WORD');
XxyLAST WORD
spark-sql> SELECT rtrim('testxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'testxxzx');
test xy
spark-sql> SELECT rtrim('xyztestxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'xyztestxxzx');
xyztest
spark-sql> SELECT rtrim('TURNERyxXxy', 'xy'), trim(TRAILING 'xy' FROM 'TURNERyxXxy');
TURNERyxX
```
After this PR:
```sql
spark-sql> SELECT trim('yxTomxx', 'xyz'), trim(BOTH 'xyz' FROM 'yxTomxx');
Tom Tom
spark-sql> SELECT trim('xxxbarxxx', 'x'), trim(BOTH 'x' FROM 'xxxbarxxx');
bar bar
spark-sql> SELECT ltrim('zzzytest', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytest');
test test
spark-sql> SELECT ltrim('zzzytestxyz', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytestxyz');
testxyz testxyz
spark-sql> SELECT ltrim('xyxXxyLAST WORD', 'xy'), trim(LEADING 'xy' FROM 'xyxXxyLAST WORD');
XxyLAST WORD XxyLAST WORD
spark-sql> SELECT rtrim('testxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'testxxzx');
test test
spark-sql> SELECT rtrim('xyztestxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'xyztestxxzx');
xyztest xyztest
spark-sql> SELECT rtrim('TURNERyxXxy', 'xy'), trim(TRAILING 'xy' FROM 'TURNERyxXxy');
TURNERyxX TURNERyxX
```
And PostgreSQL:
```sql
postgres=# SELECT trim('yxTomxx', 'xyz'), trim(BOTH 'xyz' FROM 'yxTomxx');
btrim | btrim
-------+-------
Tom | Tom
(1 row)
postgres=# SELECT trim('xxxbarxxx', 'x'), trim(BOTH 'x' FROM 'xxxbarxxx');
btrim | btrim
-------+-------
bar | bar
(1 row)
postgres=# SELECT ltrim('zzzytest', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytest');
ltrim | ltrim
-------+-------
test | test
(1 row)
postgres=# SELECT ltrim('zzzytestxyz', 'xyz'), trim(LEADING 'xyz' FROM 'zzzytestxyz');
ltrim | ltrim
---------+---------
testxyz | testxyz
(1 row)
postgres=# SELECT ltrim('xyxXxyLAST WORD', 'xy'), trim(LEADING 'xy' FROM 'xyxXxyLAST WORD');
ltrim | ltrim
--------------+--------------
XxyLAST WORD | XxyLAST WORD
(1 row)
postgres=# SELECT rtrim('testxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'testxxzx');
rtrim | rtrim
-------+-------
test | test
(1 row)
postgres=# SELECT rtrim('xyztestxxzx', 'xyz'), trim(TRAILING 'xyz' FROM 'xyztestxxzx');
rtrim | rtrim
---------+---------
xyztest | xyztest
(1 row)
postgres=# SELECT rtrim('TURNERyxXxy', 'xy'), trim(TRAILING 'xy' FROM 'TURNERyxXxy');
rtrim | rtrim
-----------+-----------
TURNERyxX | TURNERyxX
(1 row)
```
Closes#24911 from wangyum/SPARK-28109.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add docs for `SCALAR_ITER` Pandas UDF.
cc: WeichenXu123 HyukjinKwon
## How was this patch tested?
Tested example code manually.
Closes#24897 from mengxr/SPARK-28056.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
This increases the minimum supported version of Pandas to 0.23.2. Using a lower version will raise an error `Pandas >= 0.23.2 must be installed; however, your version was 0.XX`. Also, a workaround for using pyarrow with Pandas 0.19.2 was removed.
## How was this patch tested?
Existing Tests
Closes#24867 from BryanCutler/pyspark-increase-min-pandas-SPARK-28041.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Adding spark.checkpoint.compress configuration parameter to the documentation
## How was this patch tested?
Checked locally for jeykyll html docs. Also validated the html for any issues.
Closes#24883 from sandeepvja/SPARK-24898.
Authored-by: Mellacheruvu Sandeep <mellacheruvu.sandeep@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Currently `ArrayExists` always returns boolean values (if the arguments are not `null`), but it should follow the three-valued boolean logic:
- `true` if the predicate holds at least one `true`
- otherwise, `null` if the predicate holds `null`
- otherwise, `false`
This behavior change is made to match Postgres' equivalent function `ANY/SOME (array)`'s behavior: https://www.postgresql.org/docs/9.6/functions-comparisons.html#AEN21174
## How was this patch tested?
Modified tests and existing tests.
Closes#24873 from ueshin/issues/SPARK-28052/fix_exists.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
We're using an old-ish jQuery, 1.12.4, and should probably update for Spark 3 to keep up in general, but also to keep up with CVEs. In fact, we know of at least one resolved in only 3.4.0+ (https://nvd.nist.gov/vuln/detail/CVE-2019-11358). They may not affect Spark, but, if the update isn't painful, maybe worthwhile in order to make future 3.x updates easier.
jQuery 1 -> 2 doesn't sound like a breaking change, as 2.0 is supposed to maintain compatibility with 1.9+ (https://blog.jquery.com/2013/04/18/jquery-2-0-released/)
2 -> 3 has breaking changes: https://jquery.com/upgrade-guide/3.0/. It's hard to evaluate each one, but the most likely area for problems is in ajax(). However, our usage of jQuery (and plugins) is pretty simple.
Update jquery to 3.4.1; update jquery blockUI and mustache to latest
## How was this patch tested?
Manual testing of docs build (except R docs), worker/master UI, spark application UI.
Note: this really doesn't guarantee it works, as our tests can't test javascript, and this is merely anecdotal testing, although I clicked about every link I could find. There's a risk this breaks a minor part of the UI; it does seem to work fine in the main.
Closes#24843 from srowen/SPARK-28004.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
Current Spark SQL parser can have pretty confusing error messages when parsing an incorrect SELECT SQL statement. The proposed fix has the following effect.
BEFORE:
```
spark-sql> SELECT * FROM test WHERE x NOT NULL;
Error in query:
mismatched input 'FROM' expecting {<EOF>, 'CLUSTER', 'DISTRIBUTE', 'EXCEPT', 'GROUP', 'HAVING', 'INTERSECT', 'LATERAL', 'LIMIT', 'ORDER', 'MINUS', 'SORT', 'UNION', 'WHERE', 'WINDOW'}(line 1, pos 9)
== SQL ==
SELECT * FROM test WHERE x NOT NULL
---------^^^
```
where in fact the error message should be hinted to be near `NOT NULL`.
AFTER:
```
spark-sql> SELECT * FROM test WHERE x NOT NULL;
Error in query:
mismatched input 'NOT' expecting {<EOF>, 'AND', 'CLUSTER', 'DISTRIBUTE', 'EXCEPT', 'GROUP', 'HAVING', 'INTERSECT', 'LIMIT', 'OR', 'ORDER', 'MINUS', 'SORT', 'UNION', 'WINDOW'}(line 1, pos 27)
== SQL ==
SELECT * FROM test WHERE x NOT NULL
---------------------------^^^
```
In fact, this problem is brought by some problematic Spark SQL grammar. There are two kinds of SELECT statements that are supported by Hive (and thereby supported in SparkSQL):
* `FROM table SELECT blahblah SELECT blahblah`
* `SELECT blah FROM table`
*Reference* [HiveQL single-from stmt grammar](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/parse/HiveParser.g)
It is fine when these two SELECT syntaxes are supported separately. However, since we are currently supporting these two kinds of syntaxes in a single ANTLR rule, this can be problematic and therefore leading to confusing parser errors. This is because when a SELECT clause was parsed, it can't tell whether the following FROM clause actually belongs to it or is just the beginning of a new `FROM table SELECT *` statement.
## What changes were proposed in this pull request?
1. Modify ANTLR grammar to fix the above-mentioned problem. This fix is important because the previous problematic grammar does affect a lot of real-world queries. Due to the previous problematic and messy grammar, we refactored the grammar related to `querySpecification`.
2. Modify `AstBuilder` to have separate visitors for `SELECT ... FROM ...` and `FROM ... SELECT ...` statements.
3. Drop the `FROM table` statement, which is supported by accident and is actually parsed in the wrong code path. Both Hive and Presto do not support this syntax.
## How was this patch tested?
Existing UTs and new UTs.
Closes#24809 from yeshengm/parser-refactor.
Authored-by: Yesheng Ma <kimi.ysma@gmail.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
## What changes were proposed in this pull request?
It seems that some users are using Hive 3.0.0. This pr makes it support Hive 3.0 metastore.
## How was this patch tested?
unit tests
Closes#24688 from wangyum/SPARK-26145.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Change the resource config spark.{executor/driver}.resource.{resourceName}.count to .amount to allow future usage of containing both a count and a unit. Right now we only support counts - # of gpus for instance, but in the future we may want to support units for things like memory - 25G. I think making the user only have to specify a single config .amount is better then making them specify 2 separate configs of a .count and then a .unit. Change it now since its a user facing config.
Amount also matches how the spark on yarn configs are setup.
## How was this patch tested?
Unit tests and manually verified on yarn and local cluster mode
Closes#24810 from tgravescs/SPARK-27760-amount.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This is a minor documentation change whereby the https://spark.apache.org/docs/latest/sql-data-sources-avro.html mentions "The date type and naming of record fields should match the input Avro data or Catalyst data,"
The term Catalyst data is confusing. It should instead say, Spark's internal data type such as String
Type or IntegerType.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
There are no code changes; only doc changes.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#24787 from dmatrix/br-orc-ds.doc.changes.
Authored-by: Jules Damji <dmatrix@comcast.net>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
SPARK-27773 has introduced a new metric (counter) numCaughtExceptions to the Spark Dropwizard monitoring system. This PR adds an entry in the monitoring documentation to document this.
Closes#24790 from LucaCanali/addDocFollowingSPARK27773.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
`spark.sql.execution.arrow.enabled` was added when we add PySpark arrow optimization.
Later, in the current master, SparkR arrow optimization was added and it's controlled by the same configuration `spark.sql.execution.arrow.enabled`.
There look two issues about this:
1. `spark.sql.execution.arrow.enabled` in PySpark was added from 2.3.0 whereas SparkR optimization was added 3.0.0. The stability is different so it's problematic when we change the default value for one of both optimization first.
2. Suppose users want to share some JVM by PySpark and SparkR. They are currently forced to use the optimization for all or none if the configuration is set globally.
This PR proposes two separate configuration groups for PySpark and SparkR about Arrow optimization:
- Deprecate `spark.sql.execution.arrow.enabled`
- Add `spark.sql.execution.arrow.pyspark.enabled` (fallback to `spark.sql.execution.arrow.enabled`)
- Add `spark.sql.execution.arrow.sparkr.enabled`
- Deprecate `spark.sql.execution.arrow.fallback.enabled`
- Add `spark.sql.execution.arrow.pyspark.fallback.enabled ` (fallback to `spark.sql.execution.arrow.fallback.enabled`)
Note that `spark.sql.execution.arrow.maxRecordsPerBatch` is used within JVM side for both.
Note that `spark.sql.execution.arrow.fallback.enabled` was added due to behaviour change. We don't need it in SparkR - SparkR side has the automatic fallback.
## How was this patch tested?
Manually tested and some unittests were added.
Closes#24700 from HyukjinKwon/separate-sparkr-arrow.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
I found the docs of `spark.driver.memoryOverhead` and `spark.executor.memoryOverhead` exists a little ambiguity.
For example, the origin docs of `spark.driver.memoryOverhead` start with `The amount of off-heap memory to be allocated per driver in cluster mode`.
But `MemoryManager` also managed a memory area named off-heap used to allocate memory in tungsten mode.
So I think the description of `spark.driver.memoryOverhead` always make confused.
`spark.executor.memoryOverhead` has the same confused with `spark.driver.memoryOverhead`.
## How was this patch tested?
Exists UT.
Closes#24671 from beliefer/improve-docs-of-overhead.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add ability to map the spark resource configs spark.{executor/driver}.resource.{resourceName} to kubernetes Container builder so that we request resources (gpu,s/fpgas/etc) from kubernetes.
Note that the spark configs will overwrite any resource configs users put into a pod template.
I added a generic vendor config which is only used by kubernetes right now. I intentionally didn't put it into the kubernetes config namespace just to avoid adding more config prefixes.
I will add more documentation for this under jira SPARK-27492. I think it will be easier to do all at once to get cohesive story.
## How was this patch tested?
Unit tests and manually testing on k8s cluster.
Closes#24703 from tgravescs/SPARK-27362.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
First, there is currently no public documentation for this setting. So it's hard
to even know that it could be a problem if your application starts failing with
weird shuffle errors.
Second, the javadoc attached to the code was incorrect; the default value just uses
the default value from the JRE, which is 50, instead of having an unbounded queue
as the comment implies.
So use a default that is a "rounded" version of the JRE default, and provide
documentation explaining that this value may need to be adjusted. Also added
a log message that was very helpful in debugging an issue caused by this
problem.
Closes#24732 from vanzin/SPARK-27868.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
As the current approach in OneForOneBlockFetcher, we reuse the OpenBlocks protocol to describe the fetch request for shuffle blocks, and it causes the extension work for shuffle fetching like #19788 and #24110 very awkward.
In this PR, we split the fetch request for shuffle blocks from OpenBlocks which named FetchShuffleBlocks. It's a loose bind with ShuffleBlockId and can easily extend by adding new fields in this protocol.
## How was this patch tested?
Existing and new added UT.
Closes#24565 from xuanyuanking/SPARK-27665.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When the data is read from the sources, the catalyst schema is always nullable. Since Avro uses Union type to represent nullable, when any non-nullable avro file is read and then written out, the schema will always be changed.
This PR provides a solution for users to keep the Avro schema without being forced to use Union type.
## How was this patch tested?
One test is added.
Closes#24682 from dbtsai/avroNull.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
To address https://github.com/apache/spark/pull/24506#discussion_r280964509
## How was this patch tested?
N/A
Closes#24701 from HyukjinKwon/minor-arrow-r-doc.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
ForeachBatch doc is wrongly formatted. This PR formats it.
## How was this patch tested?
```
cd docs
SKIP_API=1 jekyll build
```
Manual webpage check.
Closes#24698 from gaborgsomogyi/foreachbatchdoc.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Added the driver functionality to get the resources.
The user interface is: SparkContext.resources - I called it this to match the TaskContext.resources api proposed in the other PR. Originally it was going to be called SparkContext.getResources but changed to be consistent, if people have strong feelings I can change it.
There are 2 ways the driver can discover what resources it has.
1) user specifies a discoveryScript, this is similar to the executors and is meant for yarn and k8s where they don't tell you what you were allocated but you are running in isolated environment.
2) read the config spark.driver.resource.resourceName.addresses. The config is meant to be used with standalone mode where the Worker will have to assign what GPU addresses the Driver is allowed to use by setting that config.
When the user runs a spark application, if they want the driver to have GPU's they would specify the conf spark.driver.resource.gpu.count=X where x is the number they want. If they are running on yarn or k8s they will also have to specify the discoveryScript as specified above, if they are on standalone mode and cluster is setup properly they wouldn't have to specify anything else. We could potentially get rid of the spark.driver.resources.gpu.addresses config which is really meant to be an internal config for worker to set if the standalone mode Worker wanted to write a discoveryScript out and set that for the user. I'll wait for the jira that implements that to decide if we can remove.
- This PR also has changes to be consistent about using resourceName everywhere.
- change the config names from POSTFIX to SUFFIX to be more consistent with other areas in Spark
- Moved the config checks around a bit since now used by both executor and driver. Note those might overlap a bit with https://github.com/apache/spark/pull/24374 so we will have to figure out which one should go in first.
## How was this patch tested?
Unit tests and manually test the interface.
Closes#24615 from tgravescs/SPARK-27488.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
- solves the current issue with --packages in cluster mode (there is no ticket for it). Also note of some [issues](https://issues.apache.org/jira/browse/SPARK-22657) of the past here when hadoop libs are used at the spark submit side.
- supports spark.jars, spark.files, app jar.
It works as follows:
Spark submit uploads the deps to the HCFS. Then the driver serves the deps via the Spark file server.
No hcfs uris are propagated.
The related design document is [here](https://docs.google.com/document/d/1peg_qVhLaAl4weo5C51jQicPwLclApBsdR1To2fgc48/edit). the next option to add is the RSS but has to be improved given the discussion in the past about it (Spark 2.3).
## How was this patch tested?
- Run integration test suite.
- Run an example using S3:
```
./bin/spark-submit \
...
--packages com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.6 \
--deploy-mode cluster \
--name spark-pi \
--class org.apache.spark.examples.SparkPi \
--conf spark.executor.memory=1G \
--conf spark.kubernetes.namespace=spark \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark-sa \
--conf spark.driver.memory=1G \
--conf spark.executor.instances=2 \
--conf spark.sql.streaming.metricsEnabled=true \
--conf "spark.driver.extraJavaOptions=-Divy.cache.dir=/tmp -Divy.home=/tmp" \
--conf spark.kubernetes.container.image.pullPolicy=Always \
--conf spark.kubernetes.container.image=skonto/spark:k8s-3.0.0 \
--conf spark.kubernetes.file.upload.path=s3a://fdp-stavros-test \
--conf spark.hadoop.fs.s3a.access.key=... \
--conf spark.hadoop.fs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem \
--conf spark.hadoop.fs.s3a.fast.upload=true \
--conf spark.kubernetes.executor.deleteOnTermination=false \
--conf spark.hadoop.fs.s3a.secret.key=... \
--conf spark.files=client:///...resolv.conf \
file:///my.jar **
```
Added integration tests based on [Ceph nano](https://github.com/ceph/cn). Looks very [active](http://www.sebastien-han.fr/blog/2019/02/24/Ceph-nano-is-getting-better-and-better/).
Unfortunately minio needs hadoop >= 2.8.
Closes#23546 from skonto/support-client-deps.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
## What changes were proposed in this pull request?
Use https URL for CRAN repo (and for a Scala download in a Dockerfile)
## How was this patch tested?
Existing tests.
Closes#24664 from srowen/SPARK-27794.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
When turning a Dataset to another Dataset, Spark will up cast the fields in the original Dataset to the type of corresponding fields in the target DataSet.
However, the current upcast behavior is a little weird, we don't allow up casting from string to numeric, but allow non-numeric types as the target, like boolean, date, etc.
As a result, `Seq("str").toDS.as[Int]` fails, but `Seq("str").toDS.as[Boolean]` works and throw NPE during execution.
The motivation of the up cast is to prevent things like runtime NPE, it's more reasonable to make up cast stricter.
This PR does 2 things:
1. rename `Cast.canSafeCast` to `Cast.canUpcast`, and support complex typres
2. remove `Cast.mayTruncate` and replace it with `!Cast.canUpcast`
Note that, the up cast change also affects persistent view resolution. But since we don't support changing column types of an existing table, there is no behavior change here.
## How was this patch tested?
new tests
Closes#21586 from cloud-fan/cast.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
If we refresh a cached table, the table cache will be first uncached and then recache (lazily). Currently, the logic is embedded in CatalogImpl.refreshTable method.
The current implementation does not preserve the cache name and storage level. As a result, cache name and cache level could be changed after a REFERSH. IMHO, it is not what a user would expect.
I would like to fix this behavior by first save the cache name and storage level for recaching the table.
Two unit tests are added to make sure cache name is unchanged upon table refresh. Before applying this patch, the test created for qualified case would fail.
Closes#24221 from William1104/feature/SPARK-27248.
Lead-authored-by: williamwong <william1104@gmail.com>
Co-authored-by: William Wong <william1104@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Tighten up some key links to the project and download pages to use HTTPS
## How was this patch tested?
N/A
Closes#24665 from srowen/HTTPSURLs.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Spark on k8s supports config for specifying the executor cpu requests
(spark.kubernetes.executor.request.cores) but a similar config is missing
for the driver. Instead, currently `spark.driver.cores` value is used for integer value.
Although `pod spec` can have `cpu` for the fine-grained control like the following, this PR proposes additional configuration `spark.kubernetes.driver.request.cores` for driver request cores.
```
resources:
requests:
memory: "64Mi"
cpu: "250m"
```
## How was this patch tested?
Unit tests
Closes#24630 from arunmahadevan/SPARK-27754.
Authored-by: Arun Mahadevan <arunm@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Kafka related Spark parameters has to start with `spark.kafka.` and not with `spark.sql.`. Because of this I've renamed `spark.sql.kafkaConsumerCache.capacity`.
Since Kafka consumer caching is not documented I've added this also.
## How was this patch tested?
Existing + added unit test.
```
cd docs
SKIP_API=1 jekyll build
```
and manual webpage check.
Closes#24590 from gaborgsomogyi/SPARK-27687.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add in GPU and generic resource type allocation to the executors.
Note this is part of a bigger feature for gpu-aware scheduling and is just how the executor find the resources. The general flow :
- users ask for a certain set of resources, for instance number of gpus - each cluster manager has a specific way to do this.
- cluster manager allocates a container or set of resources (standalone mode)
- When spark launches the executor in that container, the executor either has to be told what resources it has or it has to auto discover them.
- Executor has to register with Driver and tell the driver the set of resources it has so the scheduler can use that to schedule tasks that requires a certain amount of each of those resources
In this pr I added configs and arguments to the executor to be able discover resources. The argument to the executor is intended to be used by standalone mode or other cluster managers that don't have isolation so that it can assign specific resources to specific executors in case there are multiple executors on a node. The argument is a file contains JSON Array of ResourceInformation objects.
The discovery script is meant to be used in an isolated environment where the executor only sees the resources it should use.
Note that there will be follow on PRs to add other parts like the scheduler part. See the epic high level jira: https://issues.apache.org/jira/browse/SPARK-24615
## How was this patch tested?
Added unit tests and manually tested.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24406 from tgravescs/gpu-sched-executor-clean.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
The below example works with both Mysql and Hive, however not with spark.
```
mysql> select * from date_test where date_col >= '2000-1-1';
+------------+
| date_col |
+------------+
| 2000-01-01 |
+------------+
```
The reason is that Spark casts both sides to String type during date and string comparison for partial date support. Please find more details in https://issues.apache.org/jira/browse/SPARK-8420.
Based on some tests, the behavior of Date and String comparison in Hive and Mysql:
Hive: Cast to Date, partial date is not supported
Mysql: Cast to Date, certain "partial date" is supported by defining certain date string parse rules. Check out str_to_datetime in https://github.com/mysql/mysql-server/blob/5.5/sql-common/my_time.c
As below date patterns have been supported, the PR is to cast string to date when comparing string and date:
```
`yyyy`
`yyyy-[m]m`
`yyyy-[m]m-[d]d`
`yyyy-[m]m-[d]d `
`yyyy-[m]m-[d]d *`
`yyyy-[m]m-[d]dT*
```
## How was this patch tested?
UT has been added
Closes#24567 from pengbo/SPARK-27638.
Authored-by: mingbo.pb <mingbo.pb@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
added the missing config for structured streaming when using file source.
from the code we have
```
/**
* Maximum age of a file that can be found in this directory, before it is ignored. For the
* first batch all files will be considered valid. If `latestFirst` is set to `true` and
* `maxFilesPerTrigger` is set, then this parameter will be ignored, because old files that are
* valid, and should be processed, may be ignored. Please refer to SPARK-19813 for details.
*
* The max age is specified with respect to the timestamp of the latest file, and not the
* timestamp of the current system. That this means if the last file has timestamp 1000, and the
* current system time is 2000, and max age is 200, the system will purge files older than
* 800 (rather than 1800) from the internal state.
*
* Default to a week.
*/
val maxFileAgeMs: Long =
Utils.timeStringAsMs(parameters.getOrElse("maxFileAge", "7d"))
```
which is not documented.
also the file processing order was not mentioned but in the code we specifically select the file list based on file mtime:
```scala
private val fileSortOrder = if (sourceOptions.latestFirst) {
logWarning(
"""'latestFirst' is true. New files will be processed first, which may affect the watermark
|value. In addition, 'maxFileAge' will be ignored.""".stripMargin)
implicitly[Ordering[Long]].reverse
} else {
implicitly[Ordering[Long]]
}
val files = allFiles.sortBy(_.getModificationTime)(fileSortOrder).map { status =>
(status.getPath.toUri.toString, status.getModificationTime)
}
```
---------
---------
Closes#24548 from linehrr/master.
Lead-authored-by: ryne.yang <ryne.yang@acuityads.com>
Co-authored-by: Ryne Yang <ryne.yang@acuityads.com>
Co-authored-by: linehrr <linehrr@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
For the below three thread configuration items applied to both driver and executor,
spark.rpc.io.serverThreads
spark.rpc.io.clientThreads
spark.rpc.netty.dispatcher.numThreads,
we separate them to driver specifics and executor specifics.
spark.driver.rpc.io.serverThreads < - > spark.executor.rpc.io.serverThreads
spark.driver.rpc.io.clientThreads < - > spark.executor.rpc.io.clientThreads
spark.driver.rpc.netty.dispatcher.numThreads < - > spark.executor.rpc.netty.dispatcher.numThreads
Spark reads these specifics first and fall back to the common configurations.
## How was this patch tested?
We ran the SimpleMap app without shuffle for benchmark purpose to test Spark's scalability in HPC with omini-path NIC which has higher bandwidth than normal ethernet NIC.
Spark's base version is 2.4.0.
Spark ran in the Standalone mode. Driver was in a standalone node.
After the separation, the performance is improved a lot in 256 nodes and 512 nodes. see below test results of SimpleMapTask before and after the enhancement. You can view the tables in the [JIRA](https://issues.apache.org/jira/browse/SPARK-26632) too.
ds: spark.driver.rpc.io.serverThreads
dc: spark.driver.rpc.io.clientThreads
dd: spark.driver.rpc.netty.dispatcher.numThreads
ed: spark.executor.rpc.netty.dispatcher.numThreads
time: Overall Time (s)
old time: Overall Time without Separation (s)
**Before:**
nodes | ds | dc | dd | ed | time
-- |-- | -- | -- | -- | --
128 nodes | 8 | 8 | 8 | 8 | 108
256 nodes | 8 | 8 | 8 | 8 | 196
512 nodes | 8 | 8 | 8 | 8 | 377
**After:**
nodes | ds | dc | dd | ed | time | improvement
-- | -- | -- | -- | -- | -- | --
128 nodes | 15 | 15 | 10 | 30 | 107 | 0.9%
256 nodes | 12 | 15 | 10 | 30 | 159 | 18.8%
512 nodes | 12 | 15 | 10 | 30 | 283 | 24.9%
Closes#23560 from zjf2012/thread_conf_separation.
Authored-by: jiafu.zhang@intel.com <jiafu.zhang@intel.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
We updated our website a long time ago to describe Spark as the unified analytics engine, which is also how Spark is described in the community now. But our README and docs page still use the same description from 2011 ... This patch updates them.
The patch also updates the README example to use more modern APIs, and refer to Structured Streaming rather than Spark Streaming.
Closes#24573 from rxin/consistent-message.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
### Background:
The data source option `pathGlobFilter` is introduced for Binary file format: https://github.com/apache/spark/pull/24354 , which can be used for filtering file names, e.g. reading `.png` files only while there is `.json` files in the same directory.
### Proposal:
Make the option `pathGlobFilter` as a general option for all file sources. The path filtering should happen in the path globbing on Driver.
### Motivation:
Filtering the file path names in file scan tasks on executors is kind of ugly.
### Impact:
1. The splitting of file partitions will be more balanced.
2. The metrics of file scan will be more accurate.
3. Users can use the option for reading other file sources.
## How was this patch tested?
Unit tests
Closes#24518 from gengliangwang/globFilter.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When following the example for using `spark.streams().awaitAnyTermination()`
a valid pyspark code will output the following error:
```
Traceback (most recent call last):
File "pyspark_app.py", line 182, in <module>
spark.streams().awaitAnyTermination()
TypeError: 'StreamingQueryManager' object is not callable
```
Docs URL: https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#managing-streaming-queries
This changes the documentation line to properly call the method under the StreamingQueryManager Class
https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.streaming.StreamingQueryManager
## How was this patch tested?
After changing the syntax, error no longer occurs and pyspark application works
This is only docs change
Closes#24547 from asaf400/patch-1.
Authored-by: Asaf Levy <asaf400@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add a legacy flag to restore the old session init behavior, where SparkConf defaults take precedence over configs in a parent session.
Closes#24540 from jose-torres/oss.
Authored-by: Jose Torres <torres.joseph.f+github@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
The actual implementation doesn't support multi-cluster Kafka connection with delegation token. In this PR I've added this functionality.
What this PR contains:
* New way of configuration
* Multiple delegation token obtain/store/use functionality
* Documentation
* The change works on DStreams also
## How was this patch tested?
Existing + additional unit tests.
Additionally tested on cluster.
Test scenario:
* 2 * 4 node clusters
* The 4-4 nodes are in different kerberos realms
* Cross-Realm trust between the 2 realms
* Yarn
* Kafka broker version 2.1.0
* security.protocol = SASL_SSL
* sasl.mechanism = SCRAM-SHA-512
* Artificial exceptions during processing
* Source reads from realm1 sink writes to realm2
Kafka broker settings:
* delegation.token.expiry.time.ms=600000 (10 min)
* delegation.token.max.lifetime.ms=1200000 (20 min)
* delegation.token.expiry.check.interval.ms=300000 (5 min)
Closes#24305 from gaborgsomogyi/SPARK-27294.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
SQLConf does not load hive-site.xml.So HiveSerDe should fall back to hadoopconf if hive.default.fileformat is not found in SQLConf
## How was this patch tested?
Tested manually.
Added UT
Closes#24489 from sandeep-katta/spark-27555.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This pr add missing options for `sql-migration-guide-hive-compatibility.md`.
## How was this patch tested?
N/A
Closes#24520 from wangyum/SPARK-24360.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR aims to upgrade Maven to 3.6.1 to bring JDK9+ related patches like [MNG-6506](https://issues.apache.org/jira/browse/MNG-6506). For the full release note, please see the following.
- https://maven.apache.org/docs/3.6.1/release-notes.html
This was committed and reverted due to AppVeyor failure. It turns out that the root cause is `PATH` issue. With the updated AppVeyor script, it passed.
https://ci.appveyor.com/project/ApacheSoftwareFoundation/spark/builds/24273412
## How was this patch tested?
Pass the Jenkins and AppVoyer
Closes#24481 from dongjoon-hyun/SPARK-R.
Lead-authored-by: Dongjoon Hyun <dhyun@apple.com>
Co-authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
There are few suspect in the newly added doc. Open this followup to fix it and a typo.
## How was this patch tested?
N/A
Closes#24514 from viirya/SPARK-26924-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR adds SparkR with Arrow optimization documentation.
Note that looks CRAN issue in Arrow side won't look likely fixed soon, IMHO, even after Spark 3.0.
If it happen to be fixed, I will fix this doc too later.
Another note is that Arrow R package itself requires R 3.5+. So, I intentionally didn't note this.
## How was this patch tested?
Manually built and checked.
Closes#24506 from HyukjinKwon/SPARK-26924.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Looks updating documentation from 0.8.0 to 0.12.1 was missed.
## How was this patch tested?
N/A
Closes#24504 from HyukjinKwon/SPARK-27276-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Add user guide for binary file data source.
<img width="826" alt="Screen Shot 2019-04-28 at 10 21 26 PM" src="https://user-images.githubusercontent.com/829644/56877594-0488d300-6a04-11e9-9064-5047dfedd913.png">
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24484 from mengxr/SPARK-27472.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Update the `docs/building-spark.md`. Otherwise:
```
mvn package -DskipTests=true
...
[INFO] --- maven-enforcer-plugin:3.0.0-M2:enforce (enforce-versions) spark-parent_2.12 ---
[WARNING] Rule 0: org.apache.maven.plugins.enforcer.RequireMavenVersion failed with message:
Detected Maven Version: 3.6.0 is not in the allowed range 3.6.1.
...
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-enforcer-plugin:3.0.0-M2:enforce (enforce-versions) on project spark-parent_2.12: Some Enforcer rules have failed. Look above for specific messages explaining why the rule failed. -> [Help 1]
[ERROR]
...
```
## How was this patch tested?
Just test `https://archive.apache.org/dist/maven/maven-3/3.6.1/binaries/apache-maven-3.6.1-bin.zip` is avilable.
Closes#24477 from wangyum/SPARK-27467.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
some minor updates:
- `Execute` action miss `name` message
- typo in SS document
- typo in SQLConf
## How was this patch tested?
N/A
Closes#24466 from uncleGen/minor-fix.
Authored-by: uncleGen <hustyugm@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to use the `TIMESTAMP_MICROS` logical type for timestamps written to parquet files. The type matches semantically to Catalyst's `TimestampType`, and stores microseconds since epoch in UTC time zone. This will allow to avoid conversions of microseconds to nanoseconds and to Julian calendar. Also this will reduce sizes of written parquet files.
## How was this patch tested?
By existing test suites.
Closes#24425 from MaxGekk/parquet-timestamp_micros.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add note about spark.executor.heartbeatInterval change to migration guide
See also https://github.com/apache/spark/pull/24329
## How was this patch tested?
N/A
Closes#24432 from srowen/SPARK-27419.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Corrected the default value in the Documentation for "spark.redaction.regex"
## How was this patch tested?
NA
Closes#24428 from shivusondur/doc2.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fix some broken links in docs
## How was this patch tested?
N/A
Closes#24361 from srowen/BrokenLinks.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR extends the existing BROADCAST join hint (for both broadcast-hash join and broadcast-nested-loop join) by implementing other join strategy hints corresponding to the rest of Spark's existing join strategies: shuffle-hash, sort-merge, cartesian-product. The hint names: SHUFFLE_MERGE, SHUFFLE_HASH, SHUFFLE_REPLICATE_NL are partly different from the code names in order to make them clearer to users and reflect the actual algorithms better.
The hinted strategy will be used for the join with which it is associated if it is applicable/doable.
Conflict resolving rules in case of multiple hints:
1. Conflicts within either side of the join: take the first strategy hint specified in the query, or the top hint node in Dataset. For example, in "select /*+ merge(t1) */ /*+ broadcast(t1) */ k1, v2 from t1 join t2 on t1.k1 = t2.k2", take "merge(t1)"; in ```df1.hint("merge").hint("shuffle_hash").join(df2)```, take "shuffle_hash". This is a general hint conflict resolving strategy, not specific to join strategy hint.
2. Conflicts between two sides of the join:
a) In case of different strategy hints, hints are prioritized as ```BROADCAST``` over ```SHUFFLE_MERGE``` over ```SHUFFLE_HASH``` over ```SHUFFLE_REPLICATE_NL```.
b) In case of same strategy hints but conflicts in build side, choose the build side based on join type and size.
## How was this patch tested?
Added new UTs.
Closes#24164 from maryannxue/join-hints.
Lead-authored-by: maryannxue <maryannxue@apache.org>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to revert 2 commits 06abd06112 and 61561c1c2d, and take current date via `LocalDate.now` in the session time zone. The result is stored as days since epoch `1970-01-01`.
## How was this patch tested?
It was tested by `DateExpressionsSuite`, `DateFunctionsSuite`, `DateTimeUtilsSuite`, and `ComputeCurrentTimeSuite`.
Closes#24330 from MaxGekk/current-date2.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In SQL standard, date type is a union of the `year`, `month` and `day` fields. It's timezone independent, which means it does not represent a specific point in the timeline.
Spark SQL follows the SQL standard, this PR is to make it clear that date type is timezone independent
1. improve the doc to highlight that date is timezone independent.
2. when converting string to date, uses the java time API that can directly parse a `LocalDate` from a string, instead of converting `LocalDate` to a `Instant` at UTC first.
3. when converting date to string, uses the java time API that can directly format a `LocalDate` to a string, instead of converting `LocalDate` to a `Instant` at UTC first.
2 and 3 should not introduce any behavior changes.
## How was this patch tested?
existing tests
Closes#24325 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR updates `AppStatusListener` to flush `LiveEntity` if necessary when receiving `SparkListenerExecutorMetricsUpdate`. This will ensure the staleness of Spark UI doesn't last more than the executor heartbeat interval.
## How was this patch tested?
The new unit test.
Closes#24303 from zsxwing/SPARK-27394.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/24189. It adds a note about parent-session configuration priority.
## How was this patch tested?
Manually built the site and checked.
Closes#24279 from HyukjinKwon/SPARK-27253.
Lead-authored-by: HyukjinKwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Update jquery -> 1.12.4, datatables -> 1.10.18, mustache -> 2.3.12.
Add missing mustache license
## How was this patch tested?
I manually tested the UI locally with the javascript console open and didn't observe any problems or JS errors. The only 'risky' change seems to be mustache, but on reading its release notes, don't think the changes from 0.8.1 to 2.x would affect Spark's simple usage.
Closes#24288 from srowen/SPARK-27358.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This patch fixes wrong HTML tag in configuration.md which breaks the table tag.
This is originally reported in dev mailing list: https://lists.apache.org/thread.html/744bdc83b3935776c8d91bf48fdf80d9a3fed3858391e60e343206f9%3Cdev.spark.apache.org%3E
## How was this patch tested?
This change is one-liner and pretty obvious so I guess we may be able to skip testing.
Closes#24304 from HeartSaVioR/MINOR-configuration-doc-html-tag-error.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Add AL2 license to metadata of all .md files.
This seemed to be the tidiest way as it will get ignored by .md renderers and other tools. Attempts to write them as markdown comments revealed that there is no such standard thing.
## How was this patch tested?
Doc build
Closes#24243 from srowen/SPARK-26918.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This makes the `CurrentDate` expression and `current_date` function independent from time zone settings. New result is number of days since epoch in `UTC` time zone. Previously, Spark shifted the current date (in `UTC` time zone) according the session time zone which violets definition of `DateType` - number of days since epoch (which is an absolute point in time, midnight of Jan 1 1970 in UTC time).
The changes makes `CurrentDate` consistent to `CurrentTimestamp` which is independent from time zone too.
## How was this patch tested?
The changes were tested by existing test suites like `DateExpressionsSuite`.
Closes#24185 from MaxGekk/current-date.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to use the SQL config `spark.sql.session.timeZone` in formatting `TIMESTAMP` literals, and make formatting `DATE` literals independent from time zone. The changes make parsing and formatting `TIMESTAMP`/`DATE` literals consistent, and independent from the default time zone of current JVM.
Also this PR ports `TIMESTAMP`/`DATE` literals formatting on Proleptic Gregorian Calendar via using `TimestampFormatter`/`DateFormatter`.
## How was this patch tested?
Added new tests to `LiteralExpressionSuite`
Closes#24181 from MaxGekk/timezone-aware-literals.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr is a follow-up of #23393.
The HTML in the doc is broken so fixing the broken `code` tag.
## How was this patch tested?
Existing tests.
Closes#24216 from ueshin/issues/SPARK-26288/fix_doc.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This applies some minor updates/cleaning following up SPARK-26928, notably renaming JVMCPU.scala to JVMCPUSource.scala.
## How was this patch tested?
Manually tested
Closes#24201 from LucaCanali/fixupSPARK-26928.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Adding missing spaces after commas.
Closes#24205 from attilapiros/minor-doc-changes.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove Scala 2.11 support in build files and docs, and in various parts of code that accommodated 2.11. See some targeted comments below.
## How was this patch tested?
Existing tests.
Closes#23098 from srowen/SPARK-26132.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
While submitting the spark application, passing multiple configurations not documented clearly, no examples given.it will be better if it can be documented since clarity is less from spark documentation side.
Even when i was browsing i could see few queries raised by users, below provided the reference.
https://community.hortonworks.com/questions/105022/spark-submit-multiple-configurations.html
As part of fixing i had documented the above scenario with an example.
## How was this patch tested?
Manual inspection of the updated document.
Closes#24191 from sujith71955/master_conf.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
As we all know that spark on Yarn uses DB https://github.com/apache/spark/pull/7943 to record RegisteredExecutors information which can be reloaded and used again when the ExternalShuffleService is restarted .
The RegisteredExecutors information can't be recorded both in the mode of spark's standalone and spark on k8s , which will cause the RegisteredExecutors information to be lost ,when the ExternalShuffleService is restarted.
To solve the problem above, a method is proposed and is committed .
## How was this patch tested?
new unit tests
Closes#23393 from weixiuli/SPARK-26288.
Authored-by: weixiuli <weixiuli@jd.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
This pr is a follow-up of #24093 and includes fixes below;
- Lists up all the keywords of Spark only (that is, drops non-keywords there); I listed up all the keywords of ANSI SQL-2011 in the previous commit (SPARK-26215).
- Sorts the keywords in `SqlBase.g4` in a alphabetical order
## How was this patch tested?
Pass Jenkins.
Closes#24125 from maropu/SPARK-27161-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Make it more clear about how Spark categories keywords regarding to the config `spark.sql.parser.ansi.enabled`
## How was this patch tested?
existing tests
Closes#24093 from cloud-fan/parser.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
Add completedStages metircs for namespace=appStatus for monitoring.md.
Closes#24109 from hehuiyuan/hehuiyuan-patch-5.
Authored-by: hehuiyuan <hehuiyuan@ZBMAC-C02WD3K5H.local>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
A sub-task of [SPARK-23206](https://issues.apache.org/jira/browse/SPARK-23206)
Add Executor level metrics to monitoring docs
## How was this patch tested?
jekyll
Closes#24090 from LantaoJin/SPARK-27157.
Authored-by: Lantao Jin <jinlantao@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This pr updated parsing rules in `SqlBase.g4` to support a SQL query below when ANSI mode enabled;
```
SELECT CAST('2017-08-04' AS DATE) + 1 days;
```
The current master cannot parse it though, other dbms-like systems support the syntax (e.g., hive and mysql). Also, the syntax is frequently used in the official TPC-DS queries.
This pr added new tokens as follows;
```
YEAR | YEARS | MONTH | MONTHS | WEEK | WEEKS | DAY | DAYS | HOUR | HOURS | MINUTE
MINUTES | SECOND | SECONDS | MILLISECOND | MILLISECONDS | MICROSECOND | MICROSECONDS
```
Then, it registered the keywords below as the ANSI reserved (this follows SQL-2011);
```
DAY | HOUR | MINUTE | MONTH | SECOND | YEAR
```
## How was this patch tested?
Added tests in `SQLQuerySuite`, `ExpressionParserSuite`, and `TableIdentifierParserSuite`.
Closes#20433 from maropu/SPARK-23264.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
It would be better to change the explanation to this spark.kubernetes.executor.label.[LabelName].
Before:
Note that Spark also adds its own labels to the **driver pod** for bookkeeping purposes.
After modification:
Note that Spark also adds its own labels to the **executor pod** for bookkeeping purposes.
Closes#24054 from hehuiyuan/hehuiyuan-patch-3.
Authored-by: hehuiyuan <hehuiyuan@ZBMAC-C02WD3K5H.local>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In Spark 2.1, we hit a correctness bug. When reading a Hive serde parquet table with the native parquet data source, and the actual file schema doesn't match the table schema in Hive metastore(only upper/lower case difference), the query returns 0 results.
The reason is that, the parquet reader is case sensitive. If we push down filters with column names that don't match the file physical schema case-sensitively, no data will be returned.
To fix this bug, there were 2 solutions proposed at that time:
1. Add a config to optionally disable parquet filter pushdown, and make parquet column pruning case insensitive.
https://github.com/apache/spark/pull/16797
2. Infer the actual schema from data files, when reading Hive serde table with native data source. A config is provided to disable it.
https://github.com/apache/spark/pull/17229
Solution 2 was accepted and merged to Spark 2.1.1
In Spark 2.4, we refactored the parquet data source a little:
1. do parquet filter pushdown with the actual file schema.
https://github.com/apache/spark/pull/21696
2. make parquet filter pushdown case insensitive.
https://github.com/apache/spark/pull/22197
3. make parquet column pruning case insensitive.
https://github.com/apache/spark/pull/22148
With these patches, the correctness bug in Spark 2.1 no longer exists, and the schema inference becomes unnecessary.
To be safe, this PR just changes the default value to NEVER_INFER, so that users can set it back to INFER_AND_SAVE. If we don't receive any bug reports for it, we can remove the related code in the next release.
## How was this patch tested?
existing tests
Closes#24041 from cloud-fan/infer.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Avro is built-in but external data source module since Spark 2.4 but `from_avro` and `to_avro` APIs not yet supported in pyspark.
In this PR I've made them available from pyspark.
## How was this patch tested?
Please see the python API examples what I've added.
cd docs/
SKIP_SCALADOC=1 SKIP_RDOC=1 SKIP_SQLDOC=1 jekyll build
Manual webpage check.
Closes#23797 from gaborgsomogyi/SPARK-26856.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It adds Kafka delegation token support for DStreams. Please be aware as Kafka native sink is not available for DStreams this PR contains delegation token usage only on consumer side.
What this PR contains:
* Usage of token through dynamic JAAS configuration
* `KafkaConfigUpdater` moved to `kafka-0-10-token-provider`
* `KafkaSecurityHelper` functionality moved into `KafkaTokenUtil`
* Documentation
## How was this patch tested?
Existing unit tests + on cluster.
Long running Kafka to file tests on 4 node cluster with randomly thrown artificial exceptions.
Test scenario:
* 4 node cluster
* Yarn
* Kafka broker version 2.1.0
* security.protocol = SASL_SSL
* sasl.mechanism = SCRAM-SHA-512
Kafka broker settings:
* delegation.token.expiry.time.ms=600000 (10 min)
* delegation.token.max.lifetime.ms=1200000 (20 min)
* delegation.token.expiry.check.interval.ms=300000 (5 min)
After each 7.5 minutes new delegation token obtained from Kafka broker (10 min * 0.75).
When token expired after 10 minutes (Spark obtains new one and doesn't renew the old), the brokers expiring thread comes after each 5 minutes (invalidates expired tokens) and artificial exception has been thrown inside the Spark application (such case Spark closes connection), then the latest delegation token picked up correctly.
cd docs/
SKIP_API=1 jekyll build
Manual webpage check.
Closes#23929 from gaborgsomogyi/SPARK-27022.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Make k8s client timeouts configurable. No test suite exists for the client factory class, happy to add one if needed
Closes#23928 from onursatici/os/k8s-client-timeouts.
Lead-authored-by: Onur Satici <osatici@palantir.com>
Co-authored-by: Onur Satici <onursatici@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
spark-standalone documentation do not mention about stop-slave.sh script
## How was this patch tested?
Manually tested the changes
Closes#23960 from ajithme/slavedoc.
Authored-by: Ajith <ajith2489@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to replace `System.currentTimeMillis()` by `Instant.now()` in the `CurrentTimestamp` expression. `Instant.now()` uses the best available clock in the system to take current time. See [JDK-8068730](https://bugs.openjdk.java.net/browse/JDK-8068730) for more details. In JDK8, `Instant.now()` provides results with millisecond resolution but starting from JDK9 resolution of results is increased up to microseconds.
## How was this patch tested?
The changes were tested by `DateTimeUtilsSuite` and by `DateFunctionsSuite`.
Closes#23945 from MaxGekk/current-time.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Hive 2.1.1 cannot read ORC table created by Spark 2.4.0 in default, and I add the information into sql-migration-guide-upgrade.md. for details to see: [SPARK-26932](https://issues.apache.org/jira/browse/SPARK-26932)
doc build
Closes#23944 from haiboself/SPARK-26932.
Authored-by: Bo Hai <haibo-self@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This proposes to add instrumentation for the driver's JVM CPU time via the Spark Dropwizard/Codahale metrics system. It follows directly from previous work SPARK-25228 and shares similar motivations: it is intended as an improvement to be used for Spark performance dashboards and monitoring tools/instrumentation.
Implementation details: this PR takes the code introduced in SPARK-25228 and moves it to a new separate Source JVMCPUSource, which is then used to register the jvmCpuTime gauge metric for both executor and driver.
The registration of the jvmCpuTime metric for the driver is conditional, a new configuration parameter `spark.metrics.cpu.time.driver.enabled` (proposed default: false) is introduced for this purpose.
## How was this patch tested?
Manually tested, using local mode and using YARN.
Closes#23838 from LucaCanali/addCPUTimeMetricDriver.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Since this caveat added to the DStreams documentation it would be good to add to Structured Streaming as well.
## How was this patch tested?
cd docs/
SKIP_API=1 jekyll build
Manual webpage check.
Closes#23974 from gaborgsomogyi/SPARK-26592_.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Spark apps do not need to package Spark. In fact it can cause problems in some cases. Our examples should show depending on Spark as a 'provided' dependency.
Packaging Spark makes the app much bigger by tens of megabytes. It can also bring in conflicting dependencies that wouldn't otherwise be a problem. https://issues.apache.org/jira/browse/SPARK-26146 was what reminded me of this.
## How was this patch tested?
Doc build
Closes#23938 from srowen/Provided.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Introducing new config for initially blacklisted YARN nodes.
## How was this patch tested?
With existing and a new unit test.
Closes#23616 from attilapiros/SPARK-26688.
Lead-authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Co-authored-by: Attila Zsolt Piros <2017933+attilapiros@users.noreply.github.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
[SPARK-23155](https://issues.apache.org/jira/browse/SPARK-23155) enables SHS to set up custom executor log URLs. This patch proposes to extend this feature to to Spark UI as well.
Unlike the approach we did for SHS (replace executor log URLs when executor information is requested so it's like a change of view), here this patch replaces executor log URLs while registering executor, which also affects event log as well. In point of SHS's view, it will be treated as original log url when custom log url is applied to Spark UI.
## How was this patch tested?
Added UT.
Closes#23790 from HeartSaVioR/SPARK-26792.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
SPARK-19185 is resolved so the reference can be removed from the documentation.
## How was this patch tested?
cd docs/
SKIP_API=1 jekyll build
Manual webpage check.
Closes#23959 from gaborgsomogyi/SPARK-27046.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Docs still say that Spark will be available on PyPi "in the future"; just needs to be updated.
## How was this patch tested?
Doc build
Closes#23933 from srowen/SPARK-26807.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently we can use `df.printSchema` to discover the schema information for a query. We should have a way to describe the output schema of a query using SQL interface.
Example:
DESCRIBE SELECT * FROM desc_table
DESCRIBE QUERY SELECT * FROM desc_table
```SQL
spark-sql> create table desc_table (c1 int comment 'c1-comment', c2 decimal comment 'c2-comment', c3 string);
spark-sql> desc select * from desc_table;
c1 int c1-comment
c2 decimal(10,0) c2-comment
c3 string NULL
```
## How was this patch tested?
Added a new test under SQLQueryTestSuite and SparkSqlParserSuite
Closes#23883 from dilipbiswal/dkb_describe_query.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to extend the documentation of the Spark metrics system in the monitoring guide. In particular by:
- adding a list of the available metrics grouped per component instance
- adding information on configuration parameters that can be used to configure the metrics system in alternative to the metrics.properties file
- adding information on the configuration parameters needed to enable certain metrics
- it also propose to add an example of Graphite sink configuration in metrics.properties.template
Closes#23798 from LucaCanali/metricsDocUpdate.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to convert time zone string to `TimeZone` by converting it to `ZoneId` which uses `ZoneOffset` internally. The `ZoneOffset` class of JDK 8 has a cache already: http://hg.openjdk.java.net/jdk8/jdk8/jdk/file/687fd7c7986d/src/share/classes/java/time/ZoneOffset.java#l205 . In this way, there is no need to support cache of time zones in Spark.
The PR removes `computedTimeZones` from `DateTimeUtils`, and uses `ZoneId.of` to convert time zone id string to `ZoneId` and to `TimeZone` at the end.
## How was this patch tested?
The changes were tested by
Closes#23812 from MaxGekk/timezone-cache.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This pr targeted to define reserved/non-reserved keywords for Spark SQL based on the ANSI SQL standards and the other database-like systems (e.g., PostgreSQL). We assume that they basically follow the ANSI SQL-2011 standard, but it is slightly different between each other. Therefore, this pr documented all the keywords in `docs/sql-reserved-and-non-reserved-key-words.md`.
NOTE: This pr only added a small set of keywords as reserved ones and these keywords are reserved in all the ANSI SQL standards (SQL-92, SQL-99, SQL-2003, SQL-2008, SQL-2011, and SQL-2016) and PostgreSQL. This is because there is room to discuss which keyword should be reserved or not, .e.g., interval units (day, hour, minute, second, ...) are reserved in the ANSI SQL standards though, they are not reserved in PostgreSQL. Therefore, we need more researches about the other database-like systems (e.g., Oracle Databases, DB2, SQL server) in follow-up activities.
References:
- The reserved/non-reserved SQL keywords in the ANSI SQL standards: https://developer.mimer.com/wp-content/uploads/2018/05/Standard-SQL-Reserved-Words-Summary.pdf
- SQL Key Words in PostgreSQL: https://www.postgresql.org/docs/current/sql-keywords-appendix.html
## How was this patch tested?
Added tests in `TableIdentifierParserSuite`.
Closes#23259 from maropu/SPARK-26215-WIP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
`HadoopDelegationTokenProvider` has basically the same functionality just like `ServiceCredentialProvider` so the interfaces can be merged.
`YARNHadoopDelegationTokenManager` now loads `ServiceCredentialProvider`s in one step. The drawback of this if one provider fails all others are not loaded. `HadoopDelegationTokenManager` loads `HadoopDelegationTokenProvider`s independently so it provides more robust behaviour.
In this PR I've I've made the following changes:
* Deleted `YARNHadoopDelegationTokenManager` and `ServiceCredentialProvider`
* Made `HadoopDelegationTokenProvider` a `DeveloperApi`
## How was this patch tested?
Existing unit tests.
Closes#23686 from gaborgsomogyi/SPARK-26772.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This patch proposes to change the approach on extracting log urls as well as attributes from YARN executor:
- AS-IS: extract information from `Container` API and include them to container launch context
- TO-BE: let YARN executor self-extracting information
This approach leads us to populate more attributes like nodemanager's IPC port which can let us configure custom log url to JHS log url directly.
## How was this patch tested?
Existing unit tests.
Closes#23706 from HeartSaVioR/SPARK-26790.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Currently, looks, to use `from_avro` and `to_avro` in Java APIs side,
```java
import static org.apache.spark.sql.avro.package$.MODULE$;
MODULE$.to_avro
MODULE$.from_avro
```
This PR targets to deprecate and move both functions under `avro` package into `functions` object like the way of our `org.apache.spark.sql.functions`.
Therefore, Java side can import:
```java
import static org.apache.spark.sql.avro.functions.*;
```
and Scala side can import:
```scala
import org.apache.spark.sql.avro.functions._
```
## How was this patch tested?
Manually tested, and unit tests for Java APIs were added.
Closes#23784 from HyukjinKwon/SPARK-26870.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes to add some pointers of available options of Data source in Spark SQL guide.
## How was this patch tested?
N/A: documentation change
Closes#23742 from peter-gergely-horvath/SPARK-26835.
Authored-by: Peter G. Horvath <peter.gergely.horvath@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This change solves contradiction in structured streaming documentation in formula which tests if specific window will be updated by calculating watermark and comparing with "T" parameter(intermediate state is cleared as (max event time seen by the engine - late threshold > T), otherwise kept(written as "until")). By further examples the "T" seems to be the end of the window, not start like documentation says firstly. For more information please take a look at my question in stackoverflow https://stackoverflow.com/questions/54599594/understanding-window-with-watermark-in-apache-spark-structured-streaming
Can be tested by building documentation.
Closes#23765 from vitektarasenko/master.
Authored-by: Viktor Tarasenko <v.tarasenko@vezet.ru>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Delegation token providers interface now has a parameter `fileSystems` but this is needed only for `HadoopFSDelegationTokenProvider`.
In this PR I've addressed this issue in the following way:
* Removed `fileSystems` parameter from `HadoopDelegationTokenProvider`
* Moved `YarnSparkHadoopUtil.hadoopFSsToAccess` into `HadoopFSDelegationTokenProvider`
* Moved `spark.yarn.stagingDir` into core
* Moved `spark.yarn.access.namenodes` into core and renamed to `spark.kerberos.access.namenodes`
* Moved `spark.yarn.access.hadoopFileSystems` into core and renamed to `spark.kerberos.access.hadoopFileSystems`
## How was this patch tested?
Existing unit tests.
Closes#23698 from gaborgsomogyi/SPARK-26766.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Collapsed notes about using Java 8 API for date/timestamp manipulations and Proleptic Gregorian calendar in the SQL migration guide.
Closes#23722 from MaxGekk/collapse-notes.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Even if `spark.sql.hive.convertMetastoreParquet` is true, when writing to partitioned Hive metastore
Parquet tables, Spark SQL still can not use its own Parquet support instead of Hive SerDe.
Related code:
d53e11ffce/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala (L198)
## How was this patch tested?
N/A
Closes#23671 from 10110346/parquetdoc.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Make .unpersist(), .destroy() non-blocking by default and adjust callers to request blocking only where important.
This also adds an optional blocking argument to Pyspark's RDD.unpersist(), which never had one.
## How was this patch tested?
Existing tests.
Closes#23685 from srowen/SPARK-26771.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add python example for Power Iteration Clustering in spark.ml
## How was this patch tested?
Manually tested
Closes#22996 from huaxingao/spark-25997.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Metrics instance "mesos_cluster" exists in spark, but not mentioned in monitoring.md. This PR add it.
## How was this patch tested?
Manually test.
Closes#23691 from SongYadong/doc_mesos_metrics_inst.
Authored-by: SongYadong <song.yadong1@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR adds a note that explicitly `spark.executor.pyspark.memory` is dependent on resource module's behaviours at Python memory usage.
For instance, I at least see some difference at https://github.com/apache/spark/pull/21977#discussion_r251220966
## How was this patch tested?
Manually built the doc.
Closes#23664 from HyukjinKwon/note-resource-dependent.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Hive 3.1.1 is released. This PR aims to support Hive 3.1.x metastore.
Please note that Hive 3.0.0 Metastore is skipped intentionally.
## How was this patch tested?
Pass the Jenkins with the updated test cases including 3.1.
Closes#23694 from dongjoon-hyun/SPARK-24360-3.1.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch proposes adding a new configuration on SHS: custom executor log URL pattern. This will enable end users to replace executor logs to other than RM provide, like external log service, which enables to serve executor logs when NodeManager becomes unavailable in case of YARN.
End users can build their own of custom executor log URLs with pre-defined patterns which would be vary on each resource manager. This patch adds some patterns to YARN resource manager. (For others, there's even no executor log url available so cannot define patterns as well.)
Please refer the doc change as well as added UTs in this patch to see how to set up the feature.
## How was this patch tested?
Added UT, as well as manual test with YARN cluster
Closes#23260 from HeartSaVioR/SPARK-26311.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This takes over #19621 to add multi-column support to StringIndexer:
1. Supports encoding multiple columns.
2. Previously, when specifying `frequencyDesc` or `frequencyAsc` as `stringOrderType` param in `StringIndexer`, in case of equal frequency, the order of strings is undefined. After this change, the strings with equal frequency are further sorted alphabetically.
## How was this patch tested?
Added tests.
Closes#20146 from viirya/SPARK-11215.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This change addes a new mode for credential renewal that does not require
a keytab; it uses the local ticket cache instead, so it works while the
user keeps the cache valid.
This can be useful for, e.g., people running long spark-shell sessions where
their kerberos login is kept up-to-date.
The main change to enable this behavior is in HadoopDelegationTokenManager,
with a small change in the HDFS token provider. The other changes are to avoid
creating duplicate tokens when submitting the application to YARN; they allow
the tokens from the scheduler to be sent to the YARN AM, reducing the round trips
to HDFS.
For that, the scheduler initialization code was changed a little bit so that
the tokens are available when the YARN client is initialized. That basically
takes care of a long-standing TODO that was in the code to clean up configuration
propagation to the driver's RPC endpoint (in CoarseGrainedSchedulerBackend).
Tested with an app designed to stress this functionality, with both keytab and
cache-based logins. Some basic kerberos tests on k8s also.
Closes#23525 from vanzin/SPARK-26595.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
- Replacing `java.util.Calendar` in `DateTimeUtils. truncTimestamp` and in `DateTimeUtils.getOffsetFromLocalMillis ` by equivalent code using Java 8 API for timestamp manipulations. The reason is `java.util.Calendar` is based on the hybrid calendar (Julian+Gregorian) but *java.time* classes use Proleptic Gregorian calendar which assumes by SQL standard.
- Replacing `Calendar.getInstance()` in `DateTimeUtilsSuite` by similar code in `DateTimeTestUtils` using *java.time* classes
## How was this patch tested?
The changes were tested by existing suites: `DateExpressionsSuite`, `DateFunctionsSuite` and `DateTimeUtilsSuite`.
Closes#23641 from MaxGekk/cleanup-date-time-utils.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is a followup of #16989
The fetch-big-block-to-disk feature is disabled by default, because it's not compatible with external shuffle service prior to Spark 2.2. The client sends stream request to fetch block chunks, and old shuffle service can't support it.
After 2 years, Spark 2.2 has EOL, and now it's safe to turn on this feature by default
## How was this patch tested?
existing tests
Closes#23625 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When reading from empty tables, the optimization `OptimizeMetadataOnlyQuery` may return wrong results:
```
sql("CREATE TABLE t (col1 INT, p1 INT) USING PARQUET PARTITIONED BY (p1)")
sql("INSERT INTO TABLE t PARTITION (p1 = 5) SELECT ID FROM range(1, 1)")
sql("SELECT MAX(p1) FROM t")
```
The result is supposed to be `null`. However, with the optimization the result is `5`.
The rule is originally ported from https://issues.apache.org/jira/browse/HIVE-1003 in #13494. In Hive, the rule is disabled by default in a later release(https://issues.apache.org/jira/browse/HIVE-15397), due to the same problem.
It is hard to completely avoid the correctness issue. Because data sources like Parquet can be metadata-only. Spark can't tell whether it is empty or not without actually reading it. This PR disable the optimization by default.
## How was this patch tested?
Unit test
Closes#23635 from gengliangwang/optimizeMetadata.
Lead-authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the PR, I propose using of the `stringToDate` and `stringToTimestamp` methods in parsing JDBC lower/upper bounds of the partition column if it has `DateType` or `TimestampType`. Since those methods have been ported on Proleptic Gregorian calendar by #23512, the PR switches parsing of JDBC bounds of the partition column on the calendar as well.
## How was this patch tested?
This was tested by `JDBCSuite`.
Closes#23597 from MaxGekk/jdbc-parse-timestamp-bounds.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Documentation and .gitignore
## How was this patch tested?
Manual test that SBT honors the settings in .jvmopts if present
Closes#23615 from sadhen/impr/gitignore.
Authored-by: Darcy Shen <sadhen@zoho.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This enhancement allows for specifying the desired context to use for the initial K8S client auto-configuration. This allows users to more easily access alternative K8S contexts without having to first
explicitly change their current context via kubectl.
Explicitly set my K8S context to a context pointing to a non-existent cluster, then launched Spark jobs with explicitly specified contexts via the new `spark.kubernetes.context` configuration property.
Example Output:
```
> kubectl config current-context
minikube
> minikube status
minikube: Stopped
cluster:
kubectl:
> ./spark-submit --master k8s://https://localhost:6443 --deploy-mode cluster --name spark-pi --class org.apache.spark.examples.SparkPi --conf spark.executor.instances=2 --conf spark.kubernetes.context=docker-for-desktop --conf spark.kubernetes.container.image=rvesse/spark:debian local:///opt/spark/examples/jars/spark-examples_2.11-3.0.0-SNAPSHOT.jar 4
18/10/31 11:57:51 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/10/31 11:57:51 INFO SparkKubernetesClientFactory: Auto-configuring K8S client using context docker-for-desktop from users K8S config file
18/10/31 11:57:52 INFO LoggingPodStatusWatcherImpl: State changed, new state:
pod name: spark-pi-1540987071845-driver
namespace: default
labels: spark-app-selector -> spark-2c4abc226ed3415986eb602bd13f3582, spark-role -> driver
pod uid: 32462cac-dd04-11e8-b6c6-025000000001
creation time: 2018-10-31T11:57:52Z
service account name: default
volumes: spark-local-dir-1, spark-conf-volume, default-token-glpfv
node name: N/A
start time: N/A
phase: Pending
container status: N/A
18/10/31 11:57:52 INFO LoggingPodStatusWatcherImpl: State changed, new state:
pod name: spark-pi-1540987071845-driver
namespace: default
labels: spark-app-selector -> spark-2c4abc226ed3415986eb602bd13f3582, spark-role -> driver
pod uid: 32462cac-dd04-11e8-b6c6-025000000001
creation time: 2018-10-31T11:57:52Z
service account name: default
volumes: spark-local-dir-1, spark-conf-volume, default-token-glpfv
node name: docker-for-desktop
start time: N/A
phase: Pending
container status: N/A
...
18/10/31 11:58:03 INFO LoggingPodStatusWatcherImpl: State changed, new state:
pod name: spark-pi-1540987071845-driver
namespace: default
labels: spark-app-selector -> spark-2c4abc226ed3415986eb602bd13f3582, spark-role -> driver
pod uid: 32462cac-dd04-11e8-b6c6-025000000001
creation time: 2018-10-31T11:57:52Z
service account name: default
volumes: spark-local-dir-1, spark-conf-volume, default-token-glpfv
node name: docker-for-desktop
start time: 2018-10-31T11:57:52Z
phase: Succeeded
container status:
container name: spark-kubernetes-driver
container image: rvesse/spark:debian
container state: terminated
container started at: 2018-10-31T11:57:54Z
container finished at: 2018-10-31T11:58:02Z
exit code: 0
termination reason: Completed
```
Without the `spark.kubernetes.context` setting this will fail because the current context - `minikube` - is pointing to a non-running cluster e.g.
```
> ./spark-submit --master k8s://https://localhost:6443 --deploy-mode cluster --name spark-pi --class org.apache.spark.examples.SparkPi --conf spark.executor.instances=2 --conf spark.kubernetes.container.image=rvesse/spark:debian local:///opt/spark/examples/jars/spark-examples_2.11-3.0.0-SNAPSHOT.jar 4
18/10/31 12:02:30 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/10/31 12:02:30 INFO SparkKubernetesClientFactory: Auto-configuring K8S client using current context from users K8S config file
18/10/31 12:02:31 WARN WatchConnectionManager: Exec Failure
javax.net.ssl.SSLHandshakeException: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.ssl.Alerts.getSSLException(Alerts.java:192)
at sun.security.ssl.SSLSocketImpl.fatal(SSLSocketImpl.java:1949)
at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:302)
at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:296)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1509)
at sun.security.ssl.ClientHandshaker.processMessage(ClientHandshaker.java:216)
at sun.security.ssl.Handshaker.processLoop(Handshaker.java:979)
at sun.security.ssl.Handshaker.process_record(Handshaker.java:914)
at sun.security.ssl.SSLSocketImpl.readRecord(SSLSocketImpl.java:1062)
at sun.security.ssl.SSLSocketImpl.performInitialHandshake(SSLSocketImpl.java:1375)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1403)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1387)
at okhttp3.internal.connection.RealConnection.connectTls(RealConnection.java:281)
at okhttp3.internal.connection.RealConnection.establishProtocol(RealConnection.java:251)
at okhttp3.internal.connection.RealConnection.connect(RealConnection.java:151)
at okhttp3.internal.connection.StreamAllocation.findConnection(StreamAllocation.java:195)
at okhttp3.internal.connection.StreamAllocation.findHealthyConnection(StreamAllocation.java:121)
at okhttp3.internal.connection.StreamAllocation.newStream(StreamAllocation.java:100)
at okhttp3.internal.connection.ConnectInterceptor.intercept(ConnectInterceptor.java:42)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.internal.cache.CacheInterceptor.intercept(CacheInterceptor.java:93)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.internal.http.BridgeInterceptor.intercept(BridgeInterceptor.java:93)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RetryAndFollowUpInterceptor.intercept(RetryAndFollowUpInterceptor.java:120)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.BackwardsCompatibilityInterceptor.intercept(BackwardsCompatibilityInterceptor.java:119)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.ImpersonatorInterceptor.intercept(ImpersonatorInterceptor.java:66)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.HttpClientUtils$2.intercept(HttpClientUtils.java:109)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.RealCall.getResponseWithInterceptorChain(RealCall.java:185)
at okhttp3.RealCall$AsyncCall.execute(RealCall.java:135)
at okhttp3.internal.NamedRunnable.run(NamedRunnable.java:32)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:387)
at sun.security.validator.PKIXValidator.engineValidate(PKIXValidator.java:292)
at sun.security.validator.Validator.validate(Validator.java:260)
at sun.security.ssl.X509TrustManagerImpl.validate(X509TrustManagerImpl.java:324)
at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:229)
at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:124)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1491)
... 39 more
Caused by: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.provider.certpath.SunCertPathBuilder.build(SunCertPathBuilder.java:141)
at sun.security.provider.certpath.SunCertPathBuilder.engineBuild(SunCertPathBuilder.java:126)
at java.security.cert.CertPathBuilder.build(CertPathBuilder.java:280)
at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:382)
... 45 more
Exception in thread "kubernetes-dispatcher-0" Exception in thread "main" java.util.concurrent.RejectedExecutionException: Task java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask611a9c09 rejected from java.util.concurrent.ScheduledThreadPoolExecutor404819e4[Terminated, pool size = 0, active threads = 0, queued tasks = 0, completed tasks = 0]
at java.util.concurrent.ThreadPoolExecutor$AbortPolicy.rejectedExecution(ThreadPoolExecutor.java:2047)
at java.util.concurrent.ThreadPoolExecutor.reject(ThreadPoolExecutor.java:823)
at java.util.concurrent.ScheduledThreadPoolExecutor.delayedExecute(ScheduledThreadPoolExecutor.java:326)
at java.util.concurrent.ScheduledThreadPoolExecutor.schedule(ScheduledThreadPoolExecutor.java:533)
at java.util.concurrent.ScheduledThreadPoolExecutor.submit(ScheduledThreadPoolExecutor.java:632)
at java.util.concurrent.Executors$DelegatedExecutorService.submit(Executors.java:678)
at io.fabric8.kubernetes.client.dsl.internal.WatchConnectionManager.scheduleReconnect(WatchConnectionManager.java:300)
at io.fabric8.kubernetes.client.dsl.internal.WatchConnectionManager.access$800(WatchConnectionManager.java:48)
at io.fabric8.kubernetes.client.dsl.internal.WatchConnectionManager$2.onFailure(WatchConnectionManager.java:213)
at okhttp3.internal.ws.RealWebSocket.failWebSocket(RealWebSocket.java:543)
at okhttp3.internal.ws.RealWebSocket$2.onFailure(RealWebSocket.java:208)
at okhttp3.RealCall$AsyncCall.execute(RealCall.java:148)
at okhttp3.internal.NamedRunnable.run(NamedRunnable.java:32)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
io.fabric8.kubernetes.client.KubernetesClientException: Failed to start websocket
at io.fabric8.kubernetes.client.dsl.internal.WatchConnectionManager$2.onFailure(WatchConnectionManager.java:204)
at okhttp3.internal.ws.RealWebSocket.failWebSocket(RealWebSocket.java:543)
at okhttp3.internal.ws.RealWebSocket$2.onFailure(RealWebSocket.java:208)
at okhttp3.RealCall$AsyncCall.execute(RealCall.java:148)
at okhttp3.internal.NamedRunnable.run(NamedRunnable.java:32)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: javax.net.ssl.SSLHandshakeException: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.ssl.Alerts.getSSLException(Alerts.java:192)
at sun.security.ssl.SSLSocketImpl.fatal(SSLSocketImpl.java:1949)
at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:302)
at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:296)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1509)
at sun.security.ssl.ClientHandshaker.processMessage(ClientHandshaker.java:216)
at sun.security.ssl.Handshaker.processLoop(Handshaker.java:979)
at sun.security.ssl.Handshaker.process_record(Handshaker.java:914)
at sun.security.ssl.SSLSocketImpl.readRecord(SSLSocketImpl.java:1062)
at sun.security.ssl.SSLSocketImpl.performInitialHandshake(SSLSocketImpl.java:1375)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1403)
at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1387)
at okhttp3.internal.connection.RealConnection.connectTls(RealConnection.java:281)
at okhttp3.internal.connection.RealConnection.establishProtocol(RealConnection.java:251)
at okhttp3.internal.connection.RealConnection.connect(RealConnection.java:151)
at okhttp3.internal.connection.StreamAllocation.findConnection(StreamAllocation.java:195)
at okhttp3.internal.connection.StreamAllocation.findHealthyConnection(StreamAllocation.java:121)
at okhttp3.internal.connection.StreamAllocation.newStream(StreamAllocation.java:100)
at okhttp3.internal.connection.ConnectInterceptor.intercept(ConnectInterceptor.java:42)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.internal.cache.CacheInterceptor.intercept(CacheInterceptor.java:93)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.internal.http.BridgeInterceptor.intercept(BridgeInterceptor.java:93)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RetryAndFollowUpInterceptor.intercept(RetryAndFollowUpInterceptor.java:120)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.BackwardsCompatibilityInterceptor.intercept(BackwardsCompatibilityInterceptor.java:119)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.ImpersonatorInterceptor.intercept(ImpersonatorInterceptor.java:66)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at io.fabric8.kubernetes.client.utils.HttpClientUtils$2.intercept(HttpClientUtils.java:109)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:92)
at okhttp3.internal.http.RealInterceptorChain.proceed(RealInterceptorChain.java:67)
at okhttp3.RealCall.getResponseWithInterceptorChain(RealCall.java:185)
at okhttp3.RealCall$AsyncCall.execute(RealCall.java:135)
... 4 more
Caused by: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:387)
at sun.security.validator.PKIXValidator.engineValidate(PKIXValidator.java:292)
at sun.security.validator.Validator.validate(Validator.java:260)
at sun.security.ssl.X509TrustManagerImpl.validate(X509TrustManagerImpl.java:324)
at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:229)
at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:124)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1491)
... 39 more
Caused by: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target
at sun.security.provider.certpath.SunCertPathBuilder.build(SunCertPathBuilder.java:141)
at sun.security.provider.certpath.SunCertPathBuilder.engineBuild(SunCertPathBuilder.java:126)
at java.security.cert.CertPathBuilder.build(CertPathBuilder.java:280)
at sun.security.validator.PKIXValidator.doBuild(PKIXValidator.java:382)
... 45 more
18/10/31 12:02:31 INFO ShutdownHookManager: Shutdown hook called
18/10/31 12:02:31 INFO ShutdownHookManager: Deleting directory /private/var/folders/6b/y1010qp107j9w2dhhy8csvz0000xq3/T/spark-5e649891-8a0f-4f17-bf3a-33b34082eba8
```
Suggested reviews: mccheah liyinan926 - this is the follow up fix to the bug discovered while working on SPARK-25809 (PR #22805)
Closes#22904 from rvesse/SPARK-25887.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
The expressions `DayWeek`, `DayOfWeek`, `WeekDay` and `WeekOfYear` are changed to use Proleptic Gregorian calendar instead of the hybrid one (Julian+Gregorian). This was achieved by using Java 8 API for date/timestamp manipulation, in particular the `LocalDate` class.
Week of year calculation is performed according to ISO-8601. The first week of a week-based-year is the first Monday-based week of the standard ISO year that has at least 4 days in the new year (see https://docs.oracle.com/javase/8/docs/api/java/time/temporal/IsoFields.html).
## How was this patch tested?
The changes were tested by `DateExpressionsSuite` and `DateFunctionsSuite`.
Closes#23594 from MaxGekk/dayweek-gregorian.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
Since 0.11.0, PyArrow supports to raise an error for unsafe cast ([PR](https://github.com/apache/arrow/pull/2504)). We should use it to raise a proper error for pandas udf users when such cast is detected.
Added a SQL config `spark.sql.execution.pandas.arrowSafeTypeConversion` to disable Arrow safe type check.
## How was this patch tested?
Added test and manually test.
Closes#22807 from viirya/SPARK-25811.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Currently, there are some minor inconsistencies in doc compared to the code. In this PR, I am correcting those inconsistencies.
1) Links related to the evaluation metrics in the docs are not working
2) Minor correction in the evaluation metrics formulas in docs.
## How was this patch tested?
NA
Closes#23589 from shahidki31/docCorrection.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Added a new configuration 'spark.mesos.driver.memoryOverhead' for providing the driver memory overhead in mesos cluster mode.
## How was this patch tested?
Verified it manually, Resource Scheduler allocates (drivermemory+ driver memoryOverhead) for driver in mesos cluster mode.
Closes#17726 from devaraj-kavali/SPARK-17928.
Authored-by: Devaraj K <devaraj@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR reverts #22938 per discussion in #23325Closes#23325Closes#23543 from MaxGekk/return-nulls-from-json-parser.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR allows the user to override `kafka.group.id` for better monitoring or security. The user needs to make sure there are not multiple queries or sources using the same group id.
It also fixes a bug that the `groupIdPrefix` option cannot be retrieved.
## How was this patch tested?
The new added unit tests.
Closes#23301 from zsxwing/SPARK-26350.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
The SQL config `spark.sql.legacy.timeParser.enabled` was removed by https://github.com/apache/spark/pull/23495. The PR cleans up the SQL migration guide and the comment for `UnixTimestamp`.
Closes#23529 from MaxGekk/get-rid-off-legacy-parser-followup.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/22732 , we tried our best to keep the behavior of Scala UDF unchanged in Spark 2.4.
However, since Spark 3.0, Scala 2.12 is the default. The trick that was used to keep the behavior unchanged doesn't work with Scala 2.12.
This PR proposes to remove the Scala 2.11 hack, as it's not useful.
## How was this patch tested?
existing tests.
Closes#23498 from cloud-fan/udf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Remove spark.memory.useLegacyMode and StaticMemoryManager. Update tests that used the StaticMemoryManager to equivalent use of UnifiedMemoryManager.
## How was this patch tested?
Existing tests, with modifications to make them work with a different mem manager.
Closes#23457 from srowen/SPARK-26539.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/23043 , we introduced a behavior change: Spark users are not able to distinguish 0.0 and -0.0 anymore.
This PR proposes an alternative fix to the original bug, to retain the difference between 0.0 and -0.0 inside Spark.
The idea is, we can rewrite the window partition key, join key and grouping key during logical phase, to normalize the special floating numbers. Thus only operators care about special floating numbers need to pay the perf overhead, and end users can distinguish -0.0.
## How was this patch tested?
existing test
Closes#23388 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Introducing shared polled ByteBuf allocators.
This feature can be enabled via the "spark.network.sharedByteBufAllocators.enabled" configuration.
When it is on then only two pooled ByteBuf allocators are created:
- one for transport servers where caching is allowed and
- one for transport clients where caching is disabled
This way the cache allowance remains as before.
Both shareable pools are created with numCores parameter set to 0 (which defaults to the available processors) as conf.serverThreads() and conf.clientThreads() are module dependant and the lazy creation of this allocators would lead to unpredicted behaviour.
When "spark.network.sharedByteBufAllocators.enabled" is false then a new allocator is created for every transport client and server separately as was before this PR.
## How was this patch tested?
Existing unit tests.
Closes#23278 from attilapiros/SPARK-24920.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Adding docs for an enhancement that came in late in this PR: #22146
Currently the docs state that we're going to use the first container in a pod template, which was the implementation for some time, until it was improved with 2 new properties.
## How was this patch tested?
I tested that the properties work by combining pod templates with client-mode and a simple pod template.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23155 from aditanase/k8s-readme.
Authored-by: Adrian Tanase <atanase@adobe.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fixed a few typos in the migration guide.
Closes#23465 from MaxGekk/fix-typos-migration-guide.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Added new JSON option `inferTimestamp` (`true` by default) to control inferring of `TimestampType` from string values.
## How was this patch tested?
Add new UT to `JsonInferSchemaSuite`.
Closes#23455 from MaxGekk/json-infer-time-followup.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to switch the `DateFormatClass`, `ToUnixTimestamp`, `FromUnixTime`, `UnixTime` on java.time API for parsing/formatting dates and timestamps. The API has been already implemented by the `Timestamp`/`DateFormatter` classes. One of benefit is those classes support parsing timestamps with microsecond precision. Old behaviour can be switched on via SQL config: `spark.sql.legacy.timeParser.enabled` (`false` by default).
## How was this patch tested?
It was tested by existing test suites - `DateFunctionsSuite`, `DateExpressionsSuite`, `JsonSuite`, `CsvSuite`, `SQLQueryTestSuite` as well as PySpark tests.
Closes#23358 from MaxGekk/new-time-cast.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This patch adds explanation of `why "spark.sql.shuffle.partitions" keeps unchanged in structured streaming`, which couple of users already wondered and some of them even thought it as a bug.
This patch would help other end users to know about such behavior before they find by theirselves and being wondered.
## How was this patch tested?
No need to test because this is a simple addition on guide doc with markdown editor.
Closes#22238 from HeartSaVioR/SPARK-25245.
Lead-authored-by: Jungtaek Lim <kabhwan@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/23178 , to keep binary compability by using abstract class.
## How was this patch tested?
Manual test. I created a simple app with Spark 2.4
```
object TryUDF {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("test").master("local[*]").getOrCreate()
import spark.implicits._
val f1 = udf((i: Int) => i + 1)
println(f1.deterministic)
spark.range(10).select(f1.asNonNullable().apply($"id")).show()
spark.stop()
}
}
```
When I run it with current master, it fails with
```
java.lang.IncompatibleClassChangeError: Found interface org.apache.spark.sql.expressions.UserDefinedFunction, but class was expected
```
When I run it with this PR, it works
Closes#23351 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Added docs for running spark jobs with Mesos on SSL
Closes#23342 from jomach/master.
Lead-authored-by: Jorge Machado <jorge.w.machado@hotmail.com>
Co-authored-by: Jorge Machado <dxc.machado@extaccount.com>
Co-authored-by: Jorge Machado <jorge.machado.ext@kiwigrid.com>
Co-authored-by: Jorge Machado <JorgeWilson.Machado@ext.gfk.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to switch on **java.time API** for parsing timestamps and dates from JSON inputs with microseconds precision. The SQL config `spark.sql.legacy.timeParser.enabled` allow to switch back to previous behavior with using `java.text.SimpleDateFormat`/`FastDateFormat` for parsing/generating timestamps/dates.
## How was this patch tested?
It was tested by `JsonExpressionsSuite`, `JsonFunctionsSuite` and `JsonSuite`.
Closes#23196 from MaxGekk/json-time-parser.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Multiple SparkContexts are discouraged and it has been warning for last 4 years, see SPARK-4180. It could cause arbitrary and mysterious error cases, see SPARK-2243.
Honestly, I didn't even know Spark still allows it, which looks never officially supported, see SPARK-2243.
I believe It should be good timing now to remove this configuration.
## How was this patch tested?
Each doc was manually checked and manually tested:
```
$ ./bin/spark-shell --conf=spark.driver.allowMultipleContexts=true
...
scala> new SparkContext()
org.apache.spark.SparkException: Only one SparkContext should be running in this JVM (see SPARK-2243).The currently running SparkContext was created at:
org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:939)
...
org.apache.spark.SparkContext$.$anonfun$assertNoOtherContextIsRunning$2(SparkContext.scala:2435)
at scala.Option.foreach(Option.scala:274)
at org.apache.spark.SparkContext$.assertNoOtherContextIsRunning(SparkContext.scala:2432)
at org.apache.spark.SparkContext$.markPartiallyConstructed(SparkContext.scala:2509)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:80)
at org.apache.spark.SparkContext.<init>(SparkContext.scala:112)
... 49 elided
```
Closes#23311 from HyukjinKwon/SPARK-26362.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When Kafka delegation token obtained, SCRAM `sasl.mechanism` has to be configured for authentication. This can be configured on the related source/sink which is inconvenient from user perspective. Such granularity is not required and this configuration can be implemented with one central parameter.
In this PR `spark.kafka.sasl.token.mechanism` added to configure this centrally (default: `SCRAM-SHA-512`).
## How was this patch tested?
Existing unit tests + on cluster.
Closes#23274 from gaborgsomogyi/SPARK-26322.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
This proposes an alternative way to load secret keys into a Spark application that is running on Kubernetes. Instead of automatically generating the secret, the secret key can reside in a file that is shared between both the driver and executor containers.
Unit tests.
Closes#23252 from mccheah/auth-secret-with-file.
Authored-by: mcheah <mcheah@palantir.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
In the PR, I propose to return partial results from JSON datasource and JSON functions in the PERMISSIVE mode if some of JSON fields are parsed and converted to desired types successfully. The changes are made only for `StructType`. Whole bad JSON records are placed into the corrupt column specified by the `columnNameOfCorruptRecord` option or SQL config.
Partial results are not returned for malformed JSON input.
## How was this patch tested?
Added new UT which checks converting JSON strings with one invalid and one valid field at the end of the string.
Closes#23253 from MaxGekk/json-bad-record.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add PowerIterationCluster (PIC) in R
## How was this patch tested?
Add test case
Closes#23072 from huaxingao/spark-19827.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
#22148 introduces a behavior change. According to discussion at #22184, this PR updates migration guide when upgrade from Spark 2.3 to 2.4.
## How was this patch tested?
N/A
Closes#23238 from seancxmao/SPARK-25132-doc-2.4.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Update to Scala 2.12.8
## How was this patch tested?
Existing tests.
Closes#23218 from srowen/SPARK-26266.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Parquet files appear to have nullability info when being written, not being read.
## How was this patch tested?
Some test code: (running spark 2.3, but the relevant code in DataSource looks identical on master)
case class NullTest(bo: Boolean, opbol: Option[Boolean])
val testDf = spark.createDataFrame(Seq(NullTest(true, Some(false))))
defined class NullTest
testDf: org.apache.spark.sql.DataFrame = [bo: boolean, opbol: boolean]
testDf.write.parquet("s3://asana-stats/tmp_dima/parquet_check_schema")
spark.read.parquet("s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet4").printSchema()
root
|-- bo: boolean (nullable = true)
|-- opbol: boolean (nullable = true)
Meanwhile, the parquet file formed does have nullable info:
[]batchprod-report000:/tmp/dimakamalov-batch$ aws s3 ls s3://asana-stats/tmp_dima/parquet_check_schema/
2018-10-17 21:03:52 0 _SUCCESS
2018-10-17 21:03:50 504 part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
[]batchprod-report000:/tmp/dimakamalov-batch$ aws s3 cp s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet .
download: s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet to ./part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
[]batchprod-report000:/tmp/dimakamalov-batch$ java -jar parquet-tools-1.8.2.jar schema part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
message spark_schema {
required boolean bo;
optional boolean opbol;
}
Closes#22759 from dima-asana/dima-asana-nullable-parquet-doc.
Authored-by: dima-asana <42555784+dima-asana@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up of #23031 to rename the config name to `spark.sql.legacy.setCommandRejectsSparkCoreConfs`.
## How was this patch tested?
Existing tests.
Closes#23245 from ueshin/issues/SPARK-26060/rename_config.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently if user provides data schema, partition column values are converted as per it. But if the conversion failed, e.g. converting string to int, the column value is null.
This PR proposes to throw exception in such case, instead of converting into null value silently:
1. These null partition column values doesn't make sense to users in most cases. It is better to show the conversion failure, and then users can adjust the schema or ETL jobs to fix it.
2. There are always exceptions on such conversion failure for non-partition data columns. Partition columns should have the same behavior.
We can reproduce the case above as following:
```
/tmp/testDir
├── p=bar
└── p=foo
```
If we run:
```
val schema = StructType(Seq(StructField("p", IntegerType, false)))
spark.read.schema(schema).csv("/tmp/testDir/").show()
```
We will get:
```
+----+
| p|
+----+
|null|
|null|
+----+
```
## How was this patch tested?
Unit test
Closes#23215 from gengliangwang/SPARK-26263.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This change modifies the logic in the SecurityManager to do two
things:
- generate unique app secrets also when k8s is being used
- only store the secret in the user's UGI on YARN
The latter is needed so that k8s won't unnecessarily create
k8s secrets for the UGI credentials when only the auth token
is stored there.
On the k8s side, the secret is propagated to executors using
an environment variable instead. This ensures it works in both
client and cluster mode.
Security doc was updated to mention the feature and clarify that
proper access control in k8s should be enabled for it to be secure.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#23174 from vanzin/SPARK-26194.
## What changes were proposed in this pull request?
Kafka delegation token support implemented in [PR#22598](https://github.com/apache/spark/pull/22598) but that didn't contain documentation because of rapid changes. Because it has been merged in this PR I've documented it.
## How was this patch tested?
jekyll build + manual html check
Closes#23195 from gaborgsomogyi/SPARK-26236.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Updated SQL migration guide according to changes in https://github.com/apache/spark/pull/23120Closes#23235 from MaxGekk/failuresafe-partial-result-followup.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to use **java.time API** for parsing timestamps and dates from CSV content with microseconds precision. The SQL config `spark.sql.legacy.timeParser.enabled` allow to switch back to previous behaviour with using `java.text.SimpleDateFormat`/`FastDateFormat` for parsing/generating timestamps/dates.
## How was this patch tested?
It was tested by `UnivocityParserSuite`, `CsvExpressionsSuite`, `CsvFunctionsSuite` and `CsvSuite`.
Closes#23150 from MaxGekk/time-parser.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
It will throw:
```
requirement failed: When reading JDBC data sources, users need to specify all or none for the following options: 'partitionColumn', 'lowerBound', 'upperBound', and 'numPartitions'
```
and
```
User-defined partition column subq.c1 not found in the JDBC relation ...
```
This PR fix this error example.
## How was this patch tested?
manual tests
Closes#23170 from wangyum/SPARK-24499.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Keeps K8s executor resources present if case of failure or normal termination.
Introduces a new boolean config option: `spark.kubernetes.deleteExecutors`, with default value set to true.
The idea is to update Spark K8s backend structures but leave the resources around.
The assumption is that since entries are not removed from the `removedExecutorsCache` we are immune to updates that refer to the the executor resources previously removed.
The only delete operation not touched is the one in the `doKillExecutors` method.
Reason is right now we dont support [blacklisting](https://issues.apache.org/jira/browse/SPARK-23485) and dynamic allocation with Spark on K8s. In both cases in the future we might want to handle these scenarios although its more complicated.
More tests can be added if approach is approved.
## How was this patch tested?
Manually by running a Spark job and verifying pods are not deleted.
Closes#23136 from skonto/keep_pods.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Yinan Li <ynli@google.com>
## What changes were proposed in this pull request?
`resource` package is a Unix specific package. See https://docs.python.org/2/library/resource.html and https://docs.python.org/3/library/resource.html.
Note that we document Windows support:
> Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS).
This should be backported into branch-2.4 to restore Windows support in Spark 2.4.1.
## How was this patch tested?
Manually mocking the changed logics.
Closes#23055 from HyukjinKwon/SPARK-26080.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It's a bad idea to use case class as public API, as it has a very wide surface. For example, the `copy` method, its fields, the companion object, etc.
For a particular case, `UserDefinedFunction`. It has a private constructor, and I believe we only want users to access a few methods:`apply`, `nullable`, `asNonNullable`, etc.
However, all its fields, and `copy` method, and the companion object are public unexpectedly. As a result, we made many tricks to work around the binary compatibility issues.
This PR proposes to only make interfaces public, and hide implementations behind with a private class. Now `UserDefinedFunction` is a pure trait, and the concrete implementation is `SparkUserDefinedFunction`, which is private.
Changing class to interface is not binary compatible(but source compatible), so 3.0 is a good chance to do it.
This is the first PR to go with this direction. If it's accepted, I'll create a umbrella JIRA and fix all the public case classes.
## How was this patch tested?
existing tests.
Closes#23178 from cloud-fan/udf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR updates maven version from 3.5.4 to 3.6.0. The release note of the 3.6.0 is [here](https://maven.apache.org/docs/3.6.0/release-notes.html).
From [the release note of the 3.6.0](https://maven.apache.org/docs/3.6.0/release-notes.html), the followings are new features:
1. There had been issues related to the project discoverytime which has been increased in previous version which influenced some of our users.
1. The output in the reactor summary has been improved.
1. There was an issue related to the classpath ordering.
## How was this patch tested?
Existing tests
Closes#23177 from kiszk/SPARK-26212.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix Typos.
This PR is the complete version of https://github.com/apache/spark/pull/23145.
## How was this patch tested?
NA
Closes#23185 from kjmrknsn/docUpdate.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently the `SET` command works without any warnings even if the specified key is for `SparkConf` entries and it has no effect because the command does not update `SparkConf`, but the behavior might confuse users. We should track `SparkConf` entries and make the command reject for such entries.
## How was this patch tested?
Added a test and existing tests.
Closes#23031 from ueshin/issues/SPARK-26060/set_command.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Adds USER directives to the Dockerfiles which is configurable via build argument (`spark_uid`) for easy customisation. A `-u` flag is added to `bin/docker-image-tool.sh` to make it easy to customise this e.g.
```
> bin/docker-image-tool.sh -r rvesse -t uid -u 185 build
> bin/docker-image-tool.sh -r rvesse -t uid push
```
If no UID is explicitly specified it defaults to `185` - this is per skonto's suggestion to align with the OpenShift standard reserved UID for Java apps (
https://lists.openshift.redhat.com/openshift-archives/users/2016-March/msg00283.html)
Notes:
- We have to make the `WORKDIR` writable by the root group or otherwise jobs will fail with `AccessDeniedException`
To Do:
- [x] Debug and resolve issue with client mode test
- [x] Consider whether to always propagate `SPARK_USER_NAME` to environment of driver and executor pods so `entrypoint.sh` can insert that into `/etc/passwd` entry
- [x] Rebase once PR #23013 is merged and update documentation accordingly
Built the Docker images with the new Dockerfiles that include the `USER` directives. Ran the Spark on K8S integration tests against the new images. All pass except client mode which I am currently debugging further.
Also manually dropped myself into the resulting container images via `docker run` and checked `id -u` output to see that UID is as expected.
Tried customising the UID from the default via the new `-u` argument to `docker-image-tool.sh` and again checked the resulting image for the correct runtime UID.
cc felixcheung skonto vanzin
Closes#23017 from rvesse/SPARK-26015.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Fix Typos.
## How was this patch tested?
NA
Closes#23145 from kjmrknsn/docUpdate.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
We have deprecated `OneHotEncoder` at Spark 2.3.0 and introduced `OneHotEncoderEstimator`. At 3.0.0, we remove deprecated `OneHotEncoder` and rename `OneHotEncoderEstimator` to `OneHotEncoder`.
TODO: According to ML migration guide, we need to keep `OneHotEncoderEstimator` as an alias after renaming. This is not done at this patch in order to facilitate review.
## How was this patch tested?
Existing tests.
Closes#23100 from viirya/remove_one_hot_encoder.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Currently duplicated map keys are not handled consistently. For example, map look up respects the duplicated key appears first, `Dataset.collect` only keeps the duplicated key appears last, `MapKeys` returns duplicated keys, etc.
This PR proposes to remove duplicated map keys with last wins policy, to follow Java/Scala and Presto. It only applies to built-in functions, as users can create map with duplicated map keys via private APIs anyway.
updated functions: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `StringToMap`, `MapConcat`, `TransformKeys`.
For other places:
1. data source v1 doesn't have this problem, as users need to provide a java/scala map, which can't have duplicated keys.
2. data source v2 may have this problem. I've added a note to `ArrayBasedMapData` to ask the caller to take care of duplicated keys. In the future we should enforce it in the stable data APIs for data source v2.
3. UDF doesn't have this problem, as users need to provide a java/scala map. Same as data source v1.
4. file format. I checked all of them and only parquet does not enforce it. For backward compatibility reasons I change nothing but leave a note saying that the behavior will be undefined if users write map with duplicated keys to parquet files. Maybe we can add a config and fail by default if parquet files have map with duplicated keys. This can be done in followup.
## How was this patch tested?
updated tests and new tests
Closes#23124 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
a followup of https://github.com/apache/spark/pull/23043 . Add a test to show the minor behavior change introduced by #23043 , and add migration guide.
## How was this patch tested?
a new test
Closes#23141 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR adds configurations to use subpaths with Spark on k8s. Subpaths (https://kubernetes.io/docs/concepts/storage/volumes/#using-subpath) allow the user to specify a path within a volume to use instead of the volume's root.
## How was this patch tested?
Added unit tests. Ran SparkPi on a cluster with event logging pointed at a subpath-mount and verified the driver host created and used the subpath.
Closes#23026 from NiharS/k8s_subpath.
Authored-by: Nihar Sheth <niharrsheth@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Allow the Spark Structured Streaming user to specify the prefix of the consumer group (group.id), compared to force consumer group ids of the form `spark-kafka-source-*`
## How was this patch tested?
Unit tests provided by Spark (backwards compatible change, i.e., user can optionally use the functionality)
`mvn test -pl external/kafka-0-10`
Closes#23103 from zouzias/SPARK-26121.
Authored-by: Anastasios Zouzias <anastasios@sqooba.io>
Signed-off-by: cody koeninger <cody@koeninger.org>
## What changes were proposed in this pull request?
This PR is to add back `unionAll`, which is widely used. The name is also consistent with our ANSI SQL. We also have the corresponding `intersectAll` and `exceptAll`, which were introduced in Spark 2.4.
## How was this patch tested?
Added a test case in DataFrameSuite
Closes#23131 from gatorsmile/addBackUnionAll.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The DOI foundation recommends [this new resolver](https://www.doi.org/doi_handbook/3_Resolution.html#3.8). Accordingly, this PR re`sed`s all static DOI links ;-)
## How was this patch tested?
It wasn't, since it seems as safe as a "[typo fix](https://spark.apache.org/contributing.html)".
In case any of the files is included from other projects, and should be updated there, please let me know.
Closes#23129 from katrinleinweber/resolve-DOIs-securely.
Authored-by: Katrin Leinweber <9948149+katrinleinweber@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
"Running on Kubernetes" references `spark.driver.pod.name` few places, and it should be `spark.kubernetes.driver.pod.name`.
## How was this patch tested?
See changes
Closes#23133 from Leemoonsoo/fix-driver-pod-name-prop.
Authored-by: Lee moon soo <moon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
When doing typed aggregation on a Dataset, for struct key type, the key attribute is named as "key". But for non-struct type, the key attribute is named as "value". This key attribute should also be named as "key" for non-struct type.
## How was this patch tested?
Added test.
Closes#23054 from viirya/SPARK-26085.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
An input without valid JSON tokens on the root level will be treated as a bad record, and handled according to `mode`. Previously such input was converted to `null`. After the changes, the input is converted to a row with `null`s in the `PERMISSIVE` mode according the schema. This allows to remove a code in the `from_json` function which can produce `null` as result rows.
## How was this patch tested?
It was tested by existing test suites. Some of them I have to modify (`JsonSuite` for example) because previously bad input was just silently ignored. For now such input is handled according to specified `mode`.
Closes#22938 from MaxGekk/json-nulls.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
bin/docker-image-tool.sh tries to build all docker images (JVM, PySpark
and SparkR) by default. But not all spark distributions are built with
SparkR and hence this script will fail on such distros.
With this change, we make building alternate language binding docker images (PySpark and SparkR) optional. User has to specify dockerfile for those language bindings using -p and -R flags accordingly, to build the binding docker images.
## How was this patch tested?
Tested following scenarios.
*bin/docker-image-tool.sh -r <repo> -t <tag> build* --> Builds only JVM docker image (default behavior)
*bin/docker-image-tool.sh -r <repo> -t <tag> -p kubernetes/dockerfiles/spark/bindings/python/Dockerfile build* --> Builds both JVM and PySpark docker images
*bin/docker-image-tool.sh -r <repo> -t <tag> -p kubernetes/dockerfiles/spark/bindings/python/Dockerfile -R kubernetes/dockerfiles/spark/bindings/R/Dockerfile build* --> Builds JVM, PySpark and SparkR docker images.
Author: Nagaram Prasad Addepally <ram@cloudera.com>
Closes#23053 from ramaddepally/SPARK-25957.
## What changes were proposed in this pull request?
Introducing spark.ui.requestHeaderSize for configuring Jetty's HTTP requestHeaderSize.
This way long authorization field does not lead to HTTP 413.
## How was this patch tested?
Manually with curl (which version must be at least 7.55).
With the original default value (8k limit):
```bash
# Starting history server with default requestHeaderSize
$ ./sbin/start-history-server.sh
starting org.apache.spark.deploy.history.HistoryServer, logging to /Users/attilapiros/github/spark/logs/spark-attilapiros-org.apache.spark.deploy.history.HistoryServer-1-apiros-MBP.lan.out
# Creating huge header
$ echo -n "X-Custom-Header: " > cookie
$ printf 'A%.0s' {1..9500} >> cookie
# HTTP GET with huge header fails with 431
$ curl -H cookie http://458apiros-MBP.lan:18080/
<h1>Bad Message 431</h1><pre>reason: Request Header Fields Too Large</pre>
# The log contains the error
$ tail -1 /Users/attilapiros/github/spark/logs/spark-attilapiros-org.apache.spark.deploy.history.HistoryServer-1-apiros-MBP.lan.out
18/11/19 21:24:28 WARN HttpParser: Header is too large 8193>8192
```
After:
```bash
# Creating the history properties file with the increased requestHeaderSize
$ echo spark.ui.requestHeaderSize=10000 > history.properties
# Starting Spark History Server with the settings
$ ./sbin/start-history-server.sh --properties-file history.properties
starting org.apache.spark.deploy.history.HistoryServer, logging to /Users/attilapiros/github/spark/logs/spark-attilapiros-org.apache.spark.deploy.history.HistoryServer-1-apiros-MBP.lan.out
# HTTP GET with huge header gives back HTML5 (I have added here only just a part of the response)
$ curl -H cookie http://458apiros-MBP.lan:18080/
<!DOCTYPE html><html>
<head>...
<link rel="shortcut icon" href="/static/spark-logo-77x50px-hd.png"></link>
<title>History Server</title>
</head>
<body>
...
```
Closes#23090 from attilapiros/JettyHeaderSize.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Due to implementation limitation, currently Spark can't compare or do equality check between map types. As a result, map values can't appear in EQUAL or comparison expressions, can't be grouping key, etc.
The more important thing is, map loop up needs to do equality check of the map key, and thus can't support map as map key when looking up values from a map. Thus it's not useful to have map as map key.
This PR proposes to stop users from creating maps using map type as key. The list of expressions that are updated: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `MapConcat`, `TransformKeys`. I manually checked all the places that create `MapType`, and came up with this list.
Note that, maps with map type key still exist, via reading from parquet files, converting from scala/java map, etc. This PR is not to completely forbid map as map key, but to avoid creating it by Spark itself.
Motivation: when I was trying to fix the duplicate key problem, I found it's impossible to do it with map type map key. I think it's reasonable to avoid map type map key for builtin functions.
## How was this patch tested?
updated test
Closes#23045 from cloud-fan/map-key.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
[Hive 2.3.4 is released on Nov. 7th](https://hive.apache.org/downloads.html#7-november-2018-release-234-available). This PR aims to support that version.
## How was this patch tested?
Pass the Jenkins with the updated version
Closes#23059 from dongjoon-hyun/SPARK-26091.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Highlights specific security issues to be aware of with Spark on K8S and recommends K8S mechanisms that should be used to secure clusters.
## How was this patch tested?
N/A - Documentation only
CC felixcheung tgravescs skonto
Closes#23013 from rvesse/SPARK-25023.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to bump up the minimum versions of R from 3.1 to 3.4.
R version. 3.1.x is too old. It's released 4.5 years ago. R 3.4.0 is released 1.5 years ago. Considering the timing for Spark 3.0, deprecating lower versions, bumping up R to 3.4 might be reasonable option.
It should be good to deprecate and drop < R 3.4 support.
## How was this patch tested?
Jenkins tests.
Closes#23012 from HyukjinKwon/SPARK-26014.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
Currently, we do not have a mechanism to collect driver logs if a user chooses
to run their application in client mode. This is a big issue as admin teams need
to create their own mechanisms to capture driver logs.
This commit adds a logger which, if enabled, adds a local log appender to the
root logger and asynchronously syncs it an application specific log file on hdfs
(Spark Driver Log Dir).
Additionally, this collects spark-shell driver logs at INFO level by default.
The change is that instead of setting root logger level to WARN, we will set the
consoleAppender threshold to WARN, in case of spark-shell. This ensures that
only WARN logs are printed on CONSOLE but other log appenders still capture INFO
(or the default log level logs).
1. Verified that logs are written to local and remote dir
2. Added a unit test case
3. Verified this for spark-shell, client mode and pyspark.
4. Verified in both non-kerberos and kerberos environment
5. Verified with following unexpected termination conditions: Ctrl + C, Driver
OOM, Large Log Files
6. Ran an application in spark-shell and ensured that driver logs were
captured at INFO level
7. Started the application at WARN level, programmatically changed the level to
INFO and ensured that logs on console were printed at INFO level
Closes#22504 from ankuriitg/ankurgupta/SPARK-25118.
Authored-by: ankurgupta <ankur.gupta@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Every time a task is unschedulable because of the condition where no. of task failures < no. of executors available, we currently abort the taskSet - failing the job. This change tries to acquire new executors so that we can complete the job successfully. We try to acquire a new executor only when we can kill an existing idle executor. We fallback to the older implementation where we abort the job if we cannot find an idle executor.
## How was this patch tested?
I performed some manual tests to check and validate the behavior.
```scala
val rdd = sc.parallelize(Seq(1 to 10), 3)
import org.apache.spark.TaskContext
val mapped = rdd.mapPartitionsWithIndex ( (index, iterator) => { if (index == 2) { Thread.sleep(30 * 1000); val attemptNum = TaskContext.get.attemptNumber; if (attemptNum < 3) throw new Exception("Fail for blacklisting")}; iterator.toList.map (x => x + " -> " + index).iterator } )
mapped.collect
```
Closes#22288 from dhruve/bug/SPARK-22148.
Lead-authored-by: Dhruve Ashar <dhruveashar@gmail.com>
Co-authored-by: Dhruve Ashar <dhruve@users.noreply.github.com>
Co-authored-by: Tom Graves <tgraves@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Fix typos and misspellings, per https://github.com/apache/spark-website/pull/158#issuecomment-435790366
## How was this patch tested?
Existing tests.
Closes#22950 from srowen/Typos.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Change ptats.Stats() to pstats.Stats() for `spark.python.profile.dump` in configuration.md.
## How was this patch tested?
Doc test
Closes#22933 from AlexHagerman/doc_fix.
Authored-by: Alex Hagerman <alex@unexpectedeof.net>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Propose changing the documentation to state that there are 4, not 3, cluster managers available.
## How was this patch tested?
This is a docs-only patch and doesn't need any new testing beyond the normal CI process for Spark.
Closes#22922 from jameslamb/bugfix/cluster_docs.
Authored-by: James Lamb <jaylamb20@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Clarify documentation about security.
## How was this patch tested?
None, just documentation
Closes#22852 from tgravescs/SPARK-25023.
Authored-by: Thomas Graves <tgraves@thirteenroutine.corp.gq1.yahoo.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This turns off hdfs erasure coding by default for event logs, regardless of filesystem defaults. Because this requires apis only available in hadoop 3, this uses reflection. EC isn't a very good choice for event logs, as hflush() is a no-op, and so updates to the file are not visible for a long time. This can still be configured by setting "spark.eventLog.allowErasureCoding=true", which will use filesystem defaults.
## How was this patch tested?
deployed a cluster with the changes with HDFS EC on. By default, event logs didn't use EC, but configuration still would allow EC. Also tried writing to the local fs (which doesn't support EC at all) and things worked fine.
Closes#22881 from squito/SPARK-25855.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Both Spark and Hive support views. However in some cases views created by Hive are not readable by Spark. For example, if column aliases are not specified in view definition queries, both Spark and Hive will generate alias names, but in different ways. In order for Spark to be able to read views created by Hive, users should explicitly specify column aliases in view definition queries.
Given that it's not uncommon that Hive and Spark are used together in enterprise data warehouse, this PR aims to explicitly describe this compatibility issue to help users troubleshoot this issue easily.
## How was this patch tested?
Docs are manually generated and checked locally.
```
SKIP_API=1 jekyll serve
```
Closes#22868 from seancxmao/SPARK-25833.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
New feature to pass podspec files for driver and executor pods.
## How was this patch tested?
new unit and integration tests
- [x] more overwrites in integration tests
- [ ] invalid template integration test, documentation
Author: Onur Satici <osatici@palantir.com>
Author: Yifei Huang <yifeih@palantir.com>
Author: onursatici <onursatici@gmail.com>
Closes#22146 from onursatici/pod-template.
## What changes were proposed in this pull request?
This PR targets to document binary type in "Apache Arrow in Spark".
## How was this patch tested?
Manually built the documentation and checked.
Closes#22871 from HyukjinKwon/SPARK-25179.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Remove deprecated functions which includes:
SQLContext/HiveContext stuff
sparkR.init
jsonFile
parquetFile
registerTempTable
saveAsParquetFile
unionAll
createExternalTable
dropTempTable
## How was this patch tested?
jenkins
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#22843 from felixcheung/rrddapi.
## What changes were proposed in this pull request?
Changed the `kubernetes-client` version and refactored code that broke as a result
## How was this patch tested?
Unit and Integration tests
Closes#22820 from ifilonenko/SPARK-25828.
Authored-by: Ilan Filonenko <ifilondz@gmail.com>
Signed-off-by: Erik Erlandson <eerlands@redhat.com>
## What changes were proposed in this pull request?
Remove SQLContext methods deprecated in 1.4
## How was this patch tested?
Existing tests.
Closes#22815 from srowen/SPARK-25821.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add note about AddJar return value change in migration guide
## How was this patch tested?
n/a
Closes#22826 from srowen/SPARK-25760.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Since Spark 2.2, view definitions are stored in a different way from prior versions. This may cause Spark unable to read views created by prior versions. See [SPARK-25797](https://issues.apache.org/jira/browse/SPARK-25797) for more details.
Basically, we have 2 options.
1) Make Spark 2.2+ able to get older view definitions back. Since the expanded text is buggy and unusable, we have to use original text (this is possible with [SPARK-25459](https://issues.apache.org/jira/browse/SPARK-25459)). However, because older Spark versions don't save the context for the database, we cannot always get correct view definitions without view default database.
2) Recreate the views by `ALTER VIEW AS` or `CREATE OR REPLACE VIEW AS`.
This PR aims to add migration doc to help users troubleshoot this issue by above option 2.
## How was this patch tested?
N/A.
Docs are generated and checked locally
```
cd docs
SKIP_API=1 jekyll serve --watch
```
Closes#22846 from seancxmao/SPARK-25797.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Current the function `from_avro` throws exception on reading corrupt records.
In practice, there could be various reasons of data corruption. It would be good to support `PERMISSIVE` mode and allow the function from_avro to process all the input file/streaming, which is consistent with from_json and from_csv. There is no obvious down side for supporting `PERMISSIVE` mode.
Different from `from_csv` and `from_json`, the default parse mode is `FAILFAST` for the following reasons:
1. Since Avro is structured data format, input data is usually able to be parsed by certain schema. In such case, exposing the problems of input data to users is better than hiding it.
2. For `PERMISSIVE` mode, we have to force the data schema as fully nullable. This seems quite unnecessary for Avro. Reversing non-null schema might archive more perf optimizations in Spark.
3. To be consistent with the behavior in Spark 2.4 .
## How was this patch tested?
Unit test
Manual previewing generated html for the Avro data source doc:
Closes#22814 from gengliangwang/improve_from_avro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Check the `spark.sql.repl.eagerEval.enabled` configuration property in SparkDataFrame `show()` method. If the `SparkSession` has eager execution enabled, the data will be returned to the R client when the data frame is created. So instead of seeing this
```
> df <- createDataFrame(faithful)
> df
SparkDataFrame[eruptions:double, waiting:double]
```
you will see
```
> df <- createDataFrame(faithful)
> df
+---------+-------+
|eruptions|waiting|
+---------+-------+
| 3.6| 79.0|
| 1.8| 54.0|
| 3.333| 74.0|
| 2.283| 62.0|
| 4.533| 85.0|
| 2.883| 55.0|
| 4.7| 88.0|
| 3.6| 85.0|
| 1.95| 51.0|
| 4.35| 85.0|
| 1.833| 54.0|
| 3.917| 84.0|
| 4.2| 78.0|
| 1.75| 47.0|
| 4.7| 83.0|
| 2.167| 52.0|
| 1.75| 62.0|
| 4.8| 84.0|
| 1.6| 52.0|
| 4.25| 79.0|
+---------+-------+
only showing top 20 rows
```
## How was this patch tested?
Manual tests as well as unit tests (one new test case is added).
Author: adrian555 <v2ave10p>
Closes#22455 from adrian555/eager_execution.
## What changes were proposed in this pull request?
As this is targeted for 3.0.0 and Python2 will be deprecated by Jan 1st, 2020, I feel it is appropriate to change the default to Python3. Especially as these projects [found here](https://python3statement.org/) are deprecating their support.
## How was this patch tested?
Unit and Integration tests
Author: Ilan Filonenko <ifilondz@gmail.com>
Closes#22810 from ifilonenko/SPARK-24516.
## What changes were proposed in this pull request?
In the PR, I propose to switch `from_json` on `FailureSafeParser`, and to make the function compatible to `PERMISSIVE` mode by default, and to support the `FAILFAST` mode as well. The `DROPMALFORMED` mode is not supported by `from_json`.
## How was this patch tested?
It was tested by existing `JsonSuite`/`CSVSuite`, `JsonFunctionsSuite` and `JsonExpressionsSuite` as well as new tests for `from_json` which checks different modes.
Closes#22237 from MaxGekk/from_json-failuresafe.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Our current doc does not explain how we are passing the data source specific options to the underlying data source. According to [the review comment](https://github.com/apache/spark/pull/22622#discussion_r222911529), this PR aims to add more detailed information and examples
## How was this patch tested?
Manual.
Closes#22801 from dongjoon-hyun/SPARK-25656.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This takes over original PR at #22019. The original proposal is to have null for float and double types. Later a more reasonable proposal is to disallow empty strings. This patch adds logic to throw exception when finding empty strings for non string types.
## How was this patch tested?
Added test.
Closes#22787 from viirya/SPARK-25040.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
add R API for PrefixSpan
## How was this patch tested?
add test in test_mllib_fpm.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21710 from huaxingao/spark-24207.
## What changes were proposed in this pull request?
Fix minor error in the code "sketch of pregel implementation" of GraphX guide.
This fixed error relates to `[SPARK-12995][GraphX] Remove deprecate APIs from Pregel`
## How was this patch tested?
N/A
Closes#22780 from WeichenXu123/minor_doc_update1.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Since we didn't test Java 9 ~ 11 up to now in the community, fix the document to describe Java 8 only.
## How was this patch tested?
N/A (This is a document only change.)
Closes#22781 from dongjoon-hyun/SPARK-JDK-DOC.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Fix some broken links in the new document. I have clicked through all the links. Hopefully i haven't missed any :-)
## How was this patch tested?
Built using jekyll and verified the links.
Closes#22772 from dilipbiswal/doc_check.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
1. Split the main page of sql-programming-guide into 7 parts:
- Getting Started
- Data Sources
- Performance Turing
- Distributed SQL Engine
- PySpark Usage Guide for Pandas with Apache Arrow
- Migration Guide
- Reference
2. Add left menu for sql-programming-guide, keep first level index for each part in the menu.
## How was this patch tested?
Local test with jekyll build/serve.
Closes#22746 from xuanyuanking/SPARK-24499.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Remove Kafka 0.8 integration
## How was this patch tested?
Existing tests, build scripts
Closes#22703 from srowen/SPARK-25705.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is the work on setting up Secure HDFS interaction with Spark-on-K8S.
The architecture is discussed in this community-wide google [doc](https://docs.google.com/document/d/1RBnXD9jMDjGonOdKJ2bA1lN4AAV_1RwpU_ewFuCNWKg)
This initiative can be broken down into 4 Stages
**STAGE 1**
- [x] Detecting `HADOOP_CONF_DIR` environmental variable and using Config Maps to store all Hadoop config files locally, while also setting `HADOOP_CONF_DIR` locally in the driver / executors
**STAGE 2**
- [x] Grabbing `TGT` from `LTC` or using keytabs+principle and creating a `DT` that will be mounted as a secret or using a pre-populated secret
**STAGE 3**
- [x] Driver
**STAGE 4**
- [x] Executor
## How was this patch tested?
Locally tested on a single-noded, pseudo-distributed Kerberized Hadoop Cluster
- [x] E2E Integration tests https://github.com/apache/spark/pull/22608
- [ ] Unit tests
## Docs and Error Handling?
- [x] Docs
- [x] Error Handling
## Contribution Credit
kimoonkim skonto
Closes#21669 from ifilonenko/secure-hdfs.
Lead-authored-by: Ilan Filonenko <if56@cornell.edu>
Co-authored-by: Ilan Filonenko <ifilondz@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR adds CLI support for YARN custom resources, e.g. GPUs and any other resources YARN defines.
The custom resources are defined with Spark properties, no additional CLI arguments were introduced.
The properties can be defined in the following form:
**AM resources, client mode:**
Format: `spark.yarn.am.resource.<resource-name>`
The property name follows the naming convention of YARN AM cores / memory properties: `spark.yarn.am.memory and spark.yarn.am.cores
`
**Driver resources, cluster mode:**
Format: `spark.yarn.driver.resource.<resource-name>`
The property name follows the naming convention of driver cores / memory properties: `spark.driver.memory and spark.driver.cores.`
**Executor resources:**
Format: `spark.yarn.executor.resource.<resource-name>`
The property name follows the naming convention of executor cores / memory properties: `spark.executor.memory / spark.executor.cores`.
For the driver resources (cluster mode) and executor resources properties, we use the `yarn` prefix here as custom resource types are specific to YARN, currently.
**Validation:**
Please note that a validation logic is added to avoid having requested resources defined in 2 ways, for example defining the following configs:
```
"--conf", "spark.driver.memory=2G",
"--conf", "spark.yarn.driver.resource.memory=1G"
```
will not start execution and will print an error message.
## How was this patch tested?
Unit tests + manual execution with Hadoop2 and Hadoop 3 builds.
Testing have been performed on a real cluster with Spark and YARN configured:
Cluster and client mode
Request Resource Types with lowercase and uppercase units
Start Spark job with only requesting standard resources (mem / cpu)
Error handling cases:
- Request unknown resource type
- Request Resource type (either memory / cpu) with duplicate configs at the same time (e.g. with this config:
```
--conf spark.yarn.am.resource.memory=1G \
--conf spark.yarn.driver.resource.memory=2G \
--conf spark.yarn.executor.resource.memory=3G \
```
), ResourceTypeValidator handles these cases well, so it is not permitted
- Request standard resource (memory / cpu) with the new style configs, e.g. --conf spark.yarn.am.resource.memory=1G, this is not permitted and handled well.
An example about how I ran the testcases:
```
cd ~;export HADOOP_CONF_DIR=/opt/hadoop/etc/hadoop/;
./spark-2.4.0-SNAPSHOT-bin-custom-spark/bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn \
--deploy-mode cluster \
--driver-memory 1G \
--driver-cores 1 \
--executor-memory 1G \
--executor-cores 1 \
--conf spark.logConf=true \
--conf spark.yarn.executor.resource.gpu=3G \
--verbose \
./spark-2.4.0-SNAPSHOT-bin-custom-spark/examples/jars/spark-examples_2.11-2.4.0-SNAPSHOT.jar \
10;
```
Closes#20761 from szyszy/SPARK-20327.
Authored-by: Szilard Nemeth <snemeth@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Many companies have their own enterprise GitHub to manage Spark code. To build and test in those repositories with Jenkins need to modify this script.
So I suggest to add some environment variables to allow regression testing in enterprise Jenkins instead of default Spark repository in GitHub.
## How was this patch tested?
Manually test.
Closes#22678 from LantaoJin/SPARK-25685.
Lead-authored-by: lajin <lajin@ebay.com>
Co-authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
According to the SQL standard, when a query contains `HAVING`, it indicates an aggregate operator. For more details please refer to https://blog.jooq.org/2014/12/04/do-you-really-understand-sqls-group-by-and-having-clauses/
However, in Spark SQL parser, we treat HAVING as a normal filter when there is no GROUP BY, which breaks SQL semantic and lead to wrong result. This PR fixes the parser.
## How was this patch tested?
new test
Closes#22696 from cloud-fan/having.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Removes all vestiges of Flume in the build, for Spark 3.
I don't think this needs Jenkins config changes.
## How was this patch tested?
Existing tests.
Closes#22692 from srowen/SPARK-25598.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove Hadoop 2.6 references and make 2.7 the default.
Obviously, this is for master/3.0.0 only.
After this we can also get rid of the separate test jobs for Hadoop 2.6.
## How was this patch tested?
Existing tests
Closes#22615 from srowen/SPARK-25016.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #18536 and #22545 to update the migration guide.
## How was this patch tested?
Build and check the doc locally.
Closes#22682 from ueshin/issues/SPARK-20946_25525/migration_guide.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Added
- Python foreach
- Scala, Java and Python foreachBatch
- Multiple watermark policy
- The semantics of what changes are allowed to the streaming between restarts.
## How was this patch tested?
No tests
Closes#22627 from tdas/SPARK-25639.
Authored-by: Tathagata Das <tathagata.das1565@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
Documentation is updated with proper classname org.apache.spark.io.ZStdCompressionCodec
## How was this patch tested?
we used the spark.io.compression.codec = org.apache.spark.io.ZStdCompressionCodec
and verified the logs.
Closes#22669 from shivusondur/CompressionIssue.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR aims to fix the failed test of `OracleIntegrationSuite`.
## How was this patch tested?
Existing integration tests.
Closes#22461 from seancxmao/SPARK-25453.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Since we don't fail a job when `AccumulatorV2.merge` fails, we should try to update the remaining accumulators so that they can still report correct values.
## How was this patch tested?
The new unit test.
Closes#22586 from zsxwing/SPARK-25568.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Markdown links are not working inside html table. We should use html link tag.
## How was this patch tested?
Verified in IntelliJ IDEA's markdown editor and online markdown editor.
Closes#22588 from viirya/SPARK-25262-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This adds a missing end markup tag. This should go `master` branch only.
## How was this patch tested?
This is a doc-only change. Manual via `SKIP_API=1 jekyll build`.
Closes#22584 from dongjoon-hyun/SPARK-25262.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This adds a missing markup tag. This should go to `master/branch-2.4`.
## How was this patch tested?
Manual via `SKIP_API=1 jekyll build`.
Closes#22585 from dongjoon-hyun/SPARK-23285.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
SparkSubmit already logs in the user if a keytab is provided, the only issue is that it uses the existing configs which have "yarn" in their name. As such, the configs were changed to:
`spark.kerberos.keytab` and `spark.kerberos.principal`.
## How was this patch tested?
Will be tested with K8S tests, but needs to be tested with Yarn
- [x] K8S Secure HDFS tests
- [x] Yarn Secure HDFS tests vanzin
Closes#22362 from ifilonenko/SPARK-25372.
Authored-by: Ilan Filonenko <if56@cornell.edu>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide.
## How was this patch tested?
N/A
Closes#22453 from seancxmao/SPARK-20937.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
One more legacy config to go ...
Closes#22515 from rxin/allowCreatingManagedTableUsingNonemptyLocation.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
See title. Makes our legacy backward compatibility configs more consistent.
## How was this patch tested?
Make sure all references have been updated:
```
> git grep compareDateTimestampInTimestamp
docs/sql-programming-guide.md: - Since Spark 2.4, Spark compares a DATE type with a TIMESTAMP type after promotes both sides to TIMESTAMP. To set `false` to `spark.sql.legacy.compareDateTimestampInTimestamp` restores the previous behavior. This option will be removed in Spark 3.0.
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala: // if conf.compareDateTimestampInTimestamp is true
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala: => if (conf.compareDateTimestampInTimestamp) Some(TimestampType) else Some(StringType)
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala: => if (conf.compareDateTimestampInTimestamp) Some(TimestampType) else Some(StringType)
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala: buildConf("spark.sql.legacy.compareDateTimestampInTimestamp")
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala: def compareDateTimestampInTimestamp : Boolean = getConf(COMPARE_DATE_TIMESTAMP_IN_TIMESTAMP)
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercionSuite.scala: "spark.sql.legacy.compareDateTimestampInTimestamp" -> convertToTS.toString) {
```
Closes#22508 from rxin/SPARK-23549.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This reverts commit 417ad92502.
We decided to keep the current behaviors unchanged and will consider whether we will deprecate the these functions in 3.0. For more details, see the discussion in https://issues.apache.org/jira/browse/SPARK-23715
## How was this patch tested?
The existing tests.
Closes#22505 from gatorsmile/revertSpark-23715.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Hadoop2.6 and hadoop2.7 do not contain zstd and brotli compressioncodec ,hadoop 3.1 also contains only zstd compressioncodec .
So I think we should remove zstd and brotil for the time being.
**set `spark.sql.parquet.compression.codec=brotli`:**
Caused by: org.apache.parquet.hadoop.BadConfigurationException: Class org.apache.hadoop.io.compress.BrotliCodec was not found
at org.apache.parquet.hadoop.CodecFactory.getCodec(CodecFactory.java:235)
at org.apache.parquet.hadoop.CodecFactory$HeapBytesCompressor.<init>(CodecFactory.java:142)
at org.apache.parquet.hadoop.CodecFactory.createCompressor(CodecFactory.java:206)
at org.apache.parquet.hadoop.CodecFactory.getCompressor(CodecFactory.java:189)
at org.apache.parquet.hadoop.ParquetRecordWriter.<init>(ParquetRecordWriter.java:153)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:411)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:349)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:161)
**set `spark.sql.parquet.compression.codec=zstd`:**
Caused by: org.apache.parquet.hadoop.BadConfigurationException: Class org.apache.hadoop.io.compress.ZStandardCodec was not found
at org.apache.parquet.hadoop.CodecFactory.getCodec(CodecFactory.java:235)
at org.apache.parquet.hadoop.CodecFactory$HeapBytesCompressor.<init>(CodecFactory.java:142)
at org.apache.parquet.hadoop.CodecFactory.createCompressor(CodecFactory.java:206)
at org.apache.parquet.hadoop.CodecFactory.getCompressor(CodecFactory.java:189)
at org.apache.parquet.hadoop.ParquetRecordWriter.<init>(ParquetRecordWriter.java:153)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:411)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:349)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:161)
## How was this patch tested?
Exist unit test
Closes#22358 from 10110346/notsupportzstdandbrotil.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In ArrayContains, we currently cast the right hand side expression to match the element type of the left hand side Array. This may result in down casting and may return wrong result or questionable result.
Example :
```SQL
spark-sql> select array_contains(array(1), 1.34);
true
```
```SQL
spark-sql> select array_contains(array(1), 'foo');
null
```
We should safely coerce both left and right hand side expressions.
## How was this patch tested?
Added tests in DataFrameFunctionsSuite
Closes#22408 from dilipbiswal/SPARK-25417.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR updates the migration guide in order to explain the changes introduced in the behavior of the IN operator with subqueries, in particular, the improved handling of struct attributes in these situations.
## How was this patch tested?
NA
Closes#22469 from mgaido91/SPARK-24341_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
To be more consistent with other statistics based configs.
## How was this patch tested?
N/A - straightforward rename of config option. Used `git grep` to make sure there are no mention of it.
Closes#22457 from rxin/SPARK-24626.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Update `tuning.md` and `rdd-programming-guide.md` to use JDK8.
## How was this patch tested?
manual tests
Closes#22446 from wangyum/java8.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
SPARK-22333 introduced a regression in the resolution of `CURRENT_DATE` and `CURRENT_TIMESTAMP`. Before that ticket, these 2 functions were resolved in a case insensitive way. After, this depends on the value of `spark.sql.caseSensitive`.
The PR restores the previous behavior and makes their resolution case insensitive anyhow. The PR takes over #21217, therefore it closes#21217 and credit for this patch should be given to jamesthomp.
## How was this patch tested?
added UT
Closes#22440 from mgaido91/SPARK-24151.
Lead-authored-by: James Thompson <jamesthomp@users.noreply.github.com>
Co-authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
What changes were proposed in this pull request
Updated the Migration guide for the behavior changes done in the JIRA issue SPARK-23425.
How was this patch tested?
Manually verified.
Closes#22396 from sujith71955/master_newtest.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Add description of Executor Task Metrics to the documentation.
Closes#22397 from LucaCanali/docMonitoringTaskMetrics.
Authored-by: LucaCanali <luca.canali@cern.ch>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This is a rework of #21433 to address some concerns there.
Closes#22398 from michaelmior/long-callsite2.
Authored-by: Michael Mior <mmior@uwaterloo.ca>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose new CSV option `emptyValue` and an update in the SQL Migration Guide which describes how to revert previous behavior when empty strings were not written at all. Since Spark 2.4, empty strings are saved as `""` to distinguish them from saved `null`s.
Closes#22234Closes#22367
## How was this patch tested?
It was tested by `CSVSuite` and new tests added in the PR #22234Closes#22389 from MaxGekk/csv-empty-value-master.
Lead-authored-by: Mario Molina <mmolimar@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add spark.executor.pyspark.memory limit for K8S
## How was this patch tested?
Unit and Integration tests
Closes#22298 from ifilonenko/SPARK-25021.
Authored-by: Ilan Filonenko <if56@cornell.edu>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
How to reproduce permission issue:
```sh
# build spark
./dev/make-distribution.sh --name SPARK-25330 --tgz -Phadoop-2.7 -Phive -Phive-thriftserver -Pyarn
tar -zxf spark-2.4.0-SNAPSHOT-bin-SPARK-25330.tar && cd spark-2.4.0-SNAPSHOT-bin-SPARK-25330
export HADOOP_PROXY_USER=user_a
bin/spark-sql
export HADOOP_PROXY_USER=user_b
bin/spark-sql
```
```java
Exception in thread "main" java.lang.RuntimeException: org.apache.hadoop.security.AccessControlException: Permission denied: user=user_b, access=EXECUTE, inode="/tmp/hive-$%7Buser.name%7D/user_b/668748f2-f6c5-4325-a797-fd0a7ee7f4d4":user_b:hadoop:drwx------
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:319)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkTraverse(FSPermissionChecker.java:259)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:205)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:190)
```
The issue occurred in this commit: feb886f209. This pr revert Hadoop 2.7 to 2.7.3 to avoid this issue.
## How was this patch tested?
unit tests and manual tests.
Closes#22327 from wangyum/SPARK-25330.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
