### What changes were proposed in this pull request?
In the PR, I propose to update the SQL migration guide and clarify semantic of string conversion to typed `TIMESTAMP` and `DATE` literals.
### Why are the changes needed?
This is a follow-up of the PR https://github.com/apache/spark/pull/23541 which changed the behavior of `TIMESTAMP`/`DATE` literals, and can impact on results of user's queries.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
It should be checked by jenkins build.
Closes#26985 from MaxGekk/timestamp-date-constructors-followup.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Implement Factorization Machines as a ml-pipeline component
1. loss function supports: logloss, mse
2. optimizer: GD, adamW
### Why are the changes needed?
Factorization Machines is widely used in advertising and recommendation system to estimate CTR(click-through rate).
Advertising and recommendation system usually has a lot of data, so we need Spark to estimate the CTR, and Factorization Machines are common ml model to estimate CTR.
References:
1. S. Rendle, “Factorization machines,” in Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 995–1000, 2010.
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
run unit tests
Closes#26124 from mob-ai/ml/fm.
Authored-by: zhanjf <zhanjf@mob.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Fixed typo in `docs` directory and in other directories
1. Find typo in `docs` and apply fixes to files in all directories
2. Fix `the the` -> `the`
### Why are the changes needed?
Better readability of documents
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
No test needed
Closes#26976 from kiszk/typo_20191221.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR update document for make Hive 2.3 dependency by default.
### Why are the changes needed?
The documentation is incorrect.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#26919 from wangyum/SPARK-30280.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
add a migration guide for date_add and date_sub to indicates their behavior change. It a followup for #26412
### Why are the changes needed?
add a migration guide
### Does this PR introduce any user-facing change?
yes, doc change
### How was this patch tested?
no
Closes#26932 from yaooqinn/SPARK-29774-f.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When `spark.shuffle.useOldFetchProtocol` is enabled then switching off the direct disk reading of host-local shuffle blocks and falling back to remote block fetching (and this way avoiding the `GetLocalDirsForExecutors` block transfer message which is introduced from Spark 3.0.0).
### Why are the changes needed?
In `[SPARK-27651][Core] Avoid the network when shuffle blocks are fetched from the same host` a new block transfer message is introduced, `GetLocalDirsForExecutors`. This new message could be sent to the external shuffle service and as it is not supported by the previous version of external shuffle service it should be avoided when `spark.shuffle.useOldFetchProtocol` is true.
In the migration guide I changed the exception type as `org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Decoder#fromByteBuffer`
throws a IllegalArgumentException with the given text and uses the message type which is just a simple number (byte). I have checked and this is true for version 2.4.4 too.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This specific case (considering one extra boolean to switch off host local disk reading feature) is not tested but existing tests were run.
Closes#26869 from attilapiros/SPARK-30235.
Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
1. Revert "Preparing development version 3.0.1-SNAPSHOT": 56dcd79
2. Revert "Preparing Spark release v3.0.0-preview2-rc2": c216ef1
### Why are the changes needed?
Shouldn't change master.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test:
https://github.com/apache/spark/compare/5de5e46..wangyum:revert-masterCloses#26915 from wangyum/revert-master.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Include `$SPARK_DIST_CLASSPATH` in class path when launching `CoarseGrainedExecutorBackend` on Kubernetes executors using the provided `entrypoint.sh`
### Why are the changes needed?
For user provided Hadoop, `$SPARK_DIST_CLASSPATH` contains the required jars.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
Kubernetes 1.14, Spark 2.4.4, Hadoop 3.2.1. Adding $SPARK_DIST_CLASSPATH to `-cp ` param of entrypoint.sh enables launching the executors correctly.
Closes#26493 from sshakeri/master.
Authored-by: Shahin Shakeri <shahin.shakeri@pwc.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
The PR adds a new config option to configure an address for the
proxy server, and a new handler that intercepts redirects and replaces
the URL with one pointing at the proxy server. This is needed on top
of the "proxy base path" support because redirects use full URLs, not
just absolute paths from the server's root.
### Why are the changes needed?
Spark's web UI has support for generating links to paths with a
prefix, to support a proxy server, but those do not apply when
the UI is responding with redirects. In that case, Spark is sending
its own URL back to the client, and if it's behind a dumb proxy
server that doesn't do rewriting (like when using stunnel for HTTPS
support) then the client will see the wrong URL and may fail.
### Does this PR introduce any user-facing change?
Yes. It's a new UI option.
### How was this patch tested?
Tested with added unit test, with Spark behind stunnel, and in a
more complicated app using a different HTTPS proxy.
Closes#26873 from vanzin/SPARK-30240.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
- Reverts commit 1f94bf4 and d6be46e
- Switches python to python3 in Docker release image.
### Why are the changes needed?
`dev/make-distribution.sh` and `python/setup.py` are use python3.
https://github.com/apache/spark/pull/26844/files#diff-ba2c046d92a1d2b5b417788bfb5cb5f8L236https://github.com/apache/spark/pull/26330/files#diff-8cf6167d58ce775a08acafcfe6f40966
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test:
```
yumwangubuntu-3513086:~/spark$ dev/create-release/do-release-docker.sh -n -d /home/yumwang/spark-release
Output directory already exists. Overwrite and continue? [y/n] y
Branch [branch-2.4]: master
Current branch version is 3.0.0-SNAPSHOT.
Release [3.0.0]: 3.0.0-preview2
RC # [1]:
This is a dry run. Please confirm the ref that will be built for testing.
Ref [master]:
ASF user [yumwang]:
Full name [Yuming Wang]:
GPG key [yumwangapache.org]: DBD447010C1B4F7DAD3F7DFD6E1B4122F6A3A338
================
Release details:
BRANCH: master
VERSION: 3.0.0-preview2
TAG: v3.0.0-preview2-rc1
NEXT: 3.0.1-SNAPSHOT
ASF USER: yumwang
GPG KEY: DBD447010C1B4F7DAD3F7DFD6E1B4122F6A3A338
FULL NAME: Yuming Wang
E-MAIL: yumwangapache.org
================
Is this info correct [y/n]? y
GPG passphrase:
========================
= Building spark-rm image with tag latest...
Command: docker build -t spark-rm:latest --build-arg UID=110302528 /home/yumwang/spark/dev/create-release/spark-rm
Log file: docker-build.log
Building v3.0.0-preview2-rc1; output will be at /home/yumwang/spark-release/output
gpg: directory '/home/spark-rm/.gnupg' created
gpg: keybox '/home/spark-rm/.gnupg/pubring.kbx' created
gpg: /home/spark-rm/.gnupg/trustdb.gpg: trustdb created
gpg: key 6E1B4122F6A3A338: public key "Yuming Wang <yumwangapache.org>" imported
gpg: key 6E1B4122F6A3A338: secret key imported
gpg: Total number processed: 1
gpg: imported: 1
gpg: secret keys read: 1
gpg: secret keys imported: 1
========================
= Creating release tag v3.0.0-preview2-rc1...
Command: /opt/spark-rm/release-tag.sh
Log file: tag.log
It may take some time for the tag to be synchronized to github.
Press enter when you've verified that the new tag (v3.0.0-preview2-rc1) is available.
========================
= Building Spark...
Command: /opt/spark-rm/release-build.sh package
Log file: build.log
========================
= Building documentation...
Command: /opt/spark-rm/release-build.sh docs
Log file: docs.log
========================
= Publishing release
Command: /opt/spark-rm/release-build.sh publish-release
Log file: publish.log
```
Generated doc:
Closes#26848 from wangyum/SPARK-30216.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch fixes the availability of `minPartitions` option for Kafka source, as it is only supported by micro-batch for now. There's a WIP PR for batch (#25436) as well but there's no progress on the PR so far, so safer to fix the doc first, and let it be added later when we address it with batch case as well.
### Why are the changes needed?
The doc is wrong and misleading.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Just a doc change.
Closes#26849 from HeartSaVioR/MINOR-FIX-minPartition-availability-doc.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose new implementation of `fromDayTimeString` which strictly parses strings in day-time formats to intervals. New implementation accepts only strings that match to a pattern defined by the `from` and `to`. Here is the mapping of user's bounds and patterns:
- `[+|-]D+ H[H]:m[m]:s[s][.SSSSSSSSS]` for **DAY TO SECOND**
- `[+|-]D+ H[H]:m[m]` for **DAY TO MINUTE**
- `[+|-]D+ H[H]` for **DAY TO HOUR**
- `[+|-]H[H]:m[m]s[s][.SSSSSSSSS]` for **HOUR TO SECOND**
- `[+|-]H[H]:m[m]` for **HOUR TO MINUTE**
- `[+|-]m[m]:s[s][.SSSSSSSSS]` for **MINUTE TO SECOND**
Closes#26327Closes#26358
### Why are the changes needed?
- Improve user experience with Spark SQL, and respect to the bound specified by users.
- Behave the same as other broadly used DBMS - Oracle and MySQL.
### Does this PR introduce any user-facing change?
Yes, before:
```sql
spark-sql> SELECT INTERVAL '10 11:12:13.123' HOUR TO MINUTE;
interval 1 weeks 3 days 11 hours 12 minutes
```
After:
```sql
spark-sql> SELECT INTERVAL '10 11:12:13.123' HOUR TO MINUTE;
Error in query:
requirement failed: Interval string must match day-time format of '^(?<sign>[+|-])?(?<hour>\d{1,2}):(?<minute>\d{1,2})$': 10 11:12:13.123(line 1, pos 16)
== SQL ==
SELECT INTERVAL '10 11:12:13.123' HOUR TO MINUTE
----------------^^^
```
### How was this patch tested?
- Added tests to `IntervalUtilsSuite`
- By `ExpressionParserSuite`
- Updated `literals.sql`
Closes#26473 from MaxGekk/strict-from-daytime-string.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Enhancement of the SQL NULL Semantics document: sql-ref-null-semantics.html.
### Why are the changes needed?
Clarify the behavior of `UNKNOWN` for both `EXIST` and `IN` operation.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Doc changes only.
Closes#26837 from xuanyuanking/SPARK-30207.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Follow up of https://github.com/apache/spark/pull/24405
### What changes were proposed in this pull request?
The current implementation of _from_avro_ and _AvroDataToCatalyst_ doesn't allow doing schema evolution since it requires the deserialization of an Avro record with the exact same schema with which it was serialized.
The proposed change is to add a new option `actualSchema` to allow passing the schema used to serialize the records. This allows using a different compatible schema for reading by passing both schemas to _GenericDatumReader_. If no writer's schema is provided, nothing changes from before.
### Why are the changes needed?
Consider the following example.
```
// schema ID: 1
val schema1 = """
{
"type": "record",
"name": "MySchema",
"fields": [
{"name": "col1", "type": "int"},
{"name": "col2", "type": "string"}
]
}
"""
// schema ID: 2
val schema2 = """
{
"type": "record",
"name": "MySchema",
"fields": [
{"name": "col1", "type": "int"},
{"name": "col2", "type": "string"},
{"name": "col3", "type": "string", "default": ""}
]
}
"""
```
The two schemas are compatible - i.e. you can use `schema2` to deserialize events serialized with `schema1`, in which case there will be the field `col3` with the default value.
Now imagine that you have two dataframes (read from batch or streaming), one with Avro events from schema1 and the other with events from schema2. **We want to combine them into one dataframe** for storing or further processing.
With the current `from_avro` function we can only decode each of them with the corresponding schema:
```
scalaval df1 = ... // Avro events created with schema1
df1: org.apache.spark.sql.DataFrame = [eventBytes: binary]
scalaval decodedDf1 = df1.select(from_avro('eventBytes, schema1) as "decoded")
decodedDf1: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string>]
scalaval df2= ... // Avro events created with schema2
df2: org.apache.spark.sql.DataFrame = [eventBytes: binary]
scalaval decodedDf2 = df2.select(from_avro('eventBytes, schema2) as "decoded")
decodedDf2: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string, col3: string>]
```
but then `decodedDf1` and `decodedDf2` have different Spark schemas and we can't union them. Instead, with the proposed change we can decode `df1` in the following way:
```
scalaimport scala.collection.JavaConverters._
scalaval decodedDf1 = df1.select(from_avro(data = 'eventBytes, jsonFormatSchema = schema2, options = Map("actualSchema" -> schema1).asJava) as "decoded")
decodedDf1: org.apache.spark.sql.DataFrame = [decoded: struct<col1: int, col2: string, col3: string>]
```
so that both dataframes have the same schemas and can be merged.
### Does this PR introduce any user-facing change?
This PR allows users to pass a new configuration but it doesn't affect current code.
### How was this patch tested?
A new unit test was added.
Closes#26780 from Fokko/SPARK-27506.
Lead-authored-by: Fokko Driesprong <fokko@apache.org>
Co-authored-by: Gianluca Amori <gianluca.amori@gmail.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This PR switches python to python3 in `make-distribution.sh`.
### Why are the changes needed?
SPARK-29672 changed this
- https://github.com/apache/spark/pull/26330/files#diff-8cf6167d58ce775a08acafcfe6f40966
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26844 from wangyum/SPARK-30211.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR add an optional spark conf for speculation to allow speculative runs for stages where there are only a few tasks.
```
spark.speculation.task.duration.threshold
```
If provided, tasks would be speculatively run if the TaskSet contains less tasks than the number of slots on a single executor and the task is taking longer time than the threshold.
### Why are the changes needed?
This change helps avoid scenarios where there is single executor that could hang forever due to disk issue and we unfortunately assigned the single task in a TaskSet to that executor and cause the whole job to hang forever.
### Does this PR introduce any user-facing change?
yes. If the new config `spark.speculation.task.duration.threshold` is provided and the TaskSet contains less tasks than the number of slots on a single executor and the task is taking longer time than the threshold, then speculative tasks would be submitted for the running tasks in the TaskSet.
### How was this patch tested?
Unit tests are added to TaskSetManagerSuite.
Closes#26614 from yuchenhuo/SPARK-29976.
Authored-by: Yuchen Huo <yuchen.huo@databricks.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Reprocess all PostgreSQL dialect related PRs, listing in order:
- #25158: PostgreSQL integral division support [revert]
- #25170: UT changes for the integral division support [revert]
- #25458: Accept "true", "yes", "1", "false", "no", "0", and unique prefixes as input and trim input for the boolean data type. [revert]
- #25697: Combine below 2 feature tags into "spark.sql.dialect" [revert]
- #26112: Date substraction support [keep the ANSI-compliant part]
- #26444: Rename config "spark.sql.ansi.enabled" to "spark.sql.dialect.spark.ansi.enabled" [revert]
- #26463: Cast to boolean support for PostgreSQL dialect [revert]
- #26584: Make the behavior of Postgre dialect independent of ansi mode config [keep the ANSI-compliant part]
### Why are the changes needed?
As the discussion in http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-PostgreSQL-dialect-td28417.html, we need to remove PostgreSQL dialect form code base for several reasons:
1. The current approach makes the codebase complicated and hard to maintain.
2. Fully migrating PostgreSQL workloads to Spark SQL is not our focus for now.
### Does this PR introduce any user-facing change?
Yes, the config `spark.sql.dialect` will be removed.
### How was this patch tested?
Existing UT.
Closes#26763 from xuanyuanking/SPARK-30125.
Lead-authored-by: Yuanjian Li <xyliyuanjian@gmail.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 PR proposes to add instrumentation of memory usage via the Spark Dropwizard/Codahale metrics system. Memory usage metrics are available via the Executor metrics, recently implemented as detailed in https://issues.apache.org/jira/browse/SPARK-23206.
Additional notes: This takes advantage of the metrics poller introduced in #23767.
## Why are the changes needed?
Executor metrics bring have many useful insights on memory usage, in particular on the usage of storage memory and executor memory. This is useful for troubleshooting. Having the information in the metrics systems allows to add those metrics to Spark performance dashboards and study memory usage as a function of time, as in the example graph https://issues.apache.org/jira/secure/attachment/12962810/Example_dashboard_Spark_Memory_Metrics.PNG
## Does this PR introduce any user-facing change?
Adds `ExecutorMetrics` source to publish executor metrics via the Dropwizard metrics system. Details of the available metrics in docs/monitoring.md
Adds configuration parameter `spark.metrics.executormetrics.source.enabled`
## How was this patch tested?
Tested on YARN cluster and with an existing setup for a Spark dashboard based on InfluxDB and Grafana.
Closes#24132 from LucaCanali/memoryMetricsSource.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
### What changes were proposed in this pull request?
Now, we trim the string when casting string value to those `canCast` types values, e.g. int, double, decimal, interval, date, timestamps, except for boolean.
This behavior makes type cast and coercion inconsistency in Spark.
Not fitting ANSI SQL standard either.
```
If TD is boolean, then
Case:
a) If SD is character string, then SV is replaced by
TRIM ( BOTH ' ' FROM VE )
Case:
i) If the rules for literal in Subclause 5.3, “literal”, can be applied to SV to determine a valid
value of the data type TD, then let TV be that value.
ii) Otherwise, an exception condition is raised: data exception — invalid character value for cast.
b) If SD is boolean, then TV is SV
```
In this pull request, we trim all the whitespaces from both ends of the string before converting it to a bool value. This behavior is as same as others, but a bit different from sql standard, which trim only spaces.
### Why are the changes needed?
Type cast/coercion consistency
### Does this PR introduce any user-facing change?
yes, string with whitespaces in both ends will be trimmed before converted to booleans.
e.g. `select cast('\t true' as boolean)` results `true` now, before this pr it's `null`
### How was this patch tested?
add unit tests
Closes#26776 from yaooqinn/SPARK-30147.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
In this PR, we propose to use the value of `spark.sql.source.default` as the provider for `CREATE TABLE` syntax instead of `hive` in Spark 3.0.
And to help the migration, we introduce a legacy conf `spark.sql.legacy.respectHiveDefaultProvider.enabled` and set its default to `false`.
### Why are the changes needed?
1. Currently, `CREATE TABLE` syntax use hive provider to create table while `DataFrameWriter.saveAsTable` API using the value of `spark.sql.source.default` as a provider to create table. It would be better to make them consistent.
2. User may gets confused in some cases. For example:
```
CREATE TABLE t1 (c1 INT) USING PARQUET;
CREATE TABLE t2 (c1 INT);
```
In these two DDLs, use may think that `t2` should also use parquet as default provider since Spark always advertise parquet as the default format. However, it's hive in this case.
On the other hand, if we omit the USING clause in a CTAS statement, we do pick parquet by default if `spark.sql.hive.convertCATS=true`:
```
CREATE TABLE t3 USING PARQUET AS SELECT 1 AS VALUE;
CREATE TABLE t4 AS SELECT 1 AS VALUE;
```
And these two cases together can be really confusing.
3. Now, Spark SQL is very independent and popular. We do not need to be fully consistent with Hive's behavior.
### Does this PR introduce any user-facing change?
Yes, before this PR, using `CREATE TABLE` syntax will use hive provider. But now, it use the value of `spark.sql.source.default` as its provider.
### How was this patch tested?
Added tests in `DDLParserSuite` and `HiveDDlSuite`.
Closes#26736 from Ngone51/dev-create-table-using-parquet-by-default.
Lead-authored-by: wuyi <yi.wu@databricks.com>
Co-authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to upgrade Maven from 3.6.2 to 3.6.3.
### Why are the changes needed?
This will bring bug fixes like the following.
- MNG-6759 Maven fails to use <repositories> section from dependency when resolving transitive dependencies in some cases
- MNG-6760 ExclusionArtifactFilter result invalid when wildcard exclusion is followed by other exclusions
The following is the full release note.
- https://maven.apache.org/docs/3.6.3/release-notes.html
### Does this PR introduce any user-facing change?
No. (This is a dev-environment change.)
### How was this patch tested?
Pass the Jenkins with both SBT and Maven.
Closes#26770 from dongjoon-hyun/SPARK-30142.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The syntax 'LIKE predicate: ESCAPE clause' is a ANSI SQL.
For example:
```
select 'abcSpark_13sd' LIKE '%Spark\\_%'; //true
select 'abcSpark_13sd' LIKE '%Spark/_%'; //false
select 'abcSpark_13sd' LIKE '%Spark"_%'; //false
select 'abcSpark_13sd' LIKE '%Spark/_%' ESCAPE '/'; //true
select 'abcSpark_13sd' LIKE '%Spark"_%' ESCAPE '"'; //true
select 'abcSpark%13sd' LIKE '%Spark\\%%'; //true
select 'abcSpark%13sd' LIKE '%Spark/%%'; //false
select 'abcSpark%13sd' LIKE '%Spark"%%'; //false
select 'abcSpark%13sd' LIKE '%Spark/%%' ESCAPE '/'; //true
select 'abcSpark%13sd' LIKE '%Spark"%%' ESCAPE '"'; //true
select 'abcSpark\\13sd' LIKE '%Spark\\\\_%'; //true
select 'abcSpark/13sd' LIKE '%Spark//_%'; //false
select 'abcSpark"13sd' LIKE '%Spark""_%'; //false
select 'abcSpark/13sd' LIKE '%Spark//_%' ESCAPE '/'; //true
select 'abcSpark"13sd' LIKE '%Spark""_%' ESCAPE '"'; //true
```
But Spark SQL only supports 'LIKE predicate'.
Note: If the input string or pattern 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-matching.html
**Vertica:**
https://www.vertica.com/docs/9.2.x/HTML/Content/Authoring/SQLReferenceManual/LanguageElements/Predicates/LIKE-predicate.htm?zoom_highlight=like%20escape
**MySQL:**
https://dev.mysql.com/doc/refman/5.6/en/string-comparison-functions.html
**Oracle:**
https://docs.oracle.com/en/database/oracle/oracle-database/19/jjdbc/JDBC-reference-information.html#GUID-5D371A5B-D7F6-42EB-8C0D-D317F3C53708https://docs.oracle.com/en/database/oracle/oracle-database/19/sqlrf/Pattern-matching-Conditions.html#GUID-0779657B-06A8-441F-90C5-044B47862A0A
## How was this patch tested?
Exists UT and new UT.
This PR merged to my production environment and runs above sql:
```
spark-sql> select 'abcSpark_13sd' LIKE '%Spark\\_%';
true
Time taken: 0.119 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark_13sd' LIKE '%Spark/_%';
false
Time taken: 0.103 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark_13sd' LIKE '%Spark"_%';
false
Time taken: 0.096 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark_13sd' LIKE '%Spark/_%' ESCAPE '/';
true
Time taken: 0.096 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark_13sd' LIKE '%Spark"_%' ESCAPE '"';
true
Time taken: 0.092 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark\\%%';
true
Time taken: 0.109 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark/%%';
false
Time taken: 0.1 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark"%%';
false
Time taken: 0.081 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark/%%' ESCAPE '/';
true
Time taken: 0.095 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark%13sd' LIKE '%Spark"%%' ESCAPE '"';
true
Time taken: 0.113 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark\\13sd' LIKE '%Spark\\\\_%';
true
Time taken: 0.078 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark/13sd' LIKE '%Spark//_%';
false
Time taken: 0.067 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark"13sd' LIKE '%Spark""_%';
false
Time taken: 0.084 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark/13sd' LIKE '%Spark//_%' ESCAPE '/';
true
Time taken: 0.091 seconds, Fetched 1 row(s)
spark-sql> select 'abcSpark"13sd' LIKE '%Spark""_%' ESCAPE '"';
true
Time taken: 0.091 seconds, Fetched 1 row(s)
```
I create a table and its schema is:
```
spark-sql> desc formatted gja_test;
key string NULL
value string NULL
other string NULL
# Detailed Table Information
Database test
Table gja_test
Owner test
Created Time Wed Apr 10 11:06:15 CST 2019
Last Access Thu Jan 01 08:00:00 CST 1970
Created By Spark 2.4.1-SNAPSHOT
Type MANAGED
Provider hive
Table Properties [transient_lastDdlTime=1563443838]
Statistics 26 bytes
Location hdfs://namenode.xxx:9000/home/test/hive/warehouse/test.db/gja_test
Serde Library org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat org.apache.hadoop.mapred.TextInputFormat
OutputFormat org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Storage Properties [field.delim= , serialization.format= ]
Partition Provider Catalog
Time taken: 0.642 seconds, Fetched 21 row(s)
```
Table `gja_test` exists three rows of data.
```
spark-sql> select * from gja_test;
a A ao
b B bo
"__ """__ "
Time taken: 0.665 seconds, Fetched 3 row(s)
```
At finally, I test this function:
```
spark-sql> select * from gja_test where key like value escape '"';
"__ """__ "
Time taken: 0.687 seconds, Fetched 1 row(s)
```
Closes#25001 from beliefer/ansi-sql-like.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This patch prevents the cleanup operation in FileStreamSource if the source files belong to the FileStreamSink. This is needed because the output of FileStreamSink can be read with multiple Spark queries and queries will read the files based on the metadata log, which won't reflect the cleanup.
To simplify the logic, the patch only takes care of the case of when the source path without glob pattern refers to the output directory of FileStreamSink, via checking FileStreamSource to see whether it leverages metadata directory or not to list the source files.
### Why are the changes needed?
Without this patch, if end users turn on cleanup option with the path which is the output of FileStreamSink, there may be out of sync between metadata and available files which may break other queries reading the path.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added UT.
Closes#26590 from HeartSaVioR/SPARK-29953.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
### What changes were proposed in this pull request?
This PR adds a note to the docs README showing how to get Jekyll to automatically pick up changes to the Python API docs.
### Why are the changes needed?
`jekyll serve --watch` doesn't watch for changes to the API docs. Without the technique documented in this note, or something equivalent, developers have to manually retrigger a Jekyll build any time they update the Python API docs.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I tested this PR manually by making changes to Python docstrings and confirming that Jekyll automatically picks them up and serves them locally.
Closes#26719 from nchammas/SPARK-30084-watch-api-docs.
Authored-by: Nicholas Chammas <nicholas.chammas@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This proposes to introduce a naming convention for Spark metrics configuration parameters used to enable/disable metrics source reporting using the Dropwizard metrics library: `spark.metrics.sourceNameCamelCase.enabled` and update 2 parameters to use this naming convention.
### Why are the changes needed?
Currently Spark has a few parameters to enable/disable metrics reporting. Their naming pattern is not uniform and this can create confusion. Currently we have:
`spark.metrics.static.sources.enabled`
`spark.app.status.metrics.enabled`
`spark.sql.streaming.metricsEnabled`
### Does this PR introduce any user-facing change?
Update parameters for enabling/disabling metrics reporting new in Spark 3.0: `spark.metrics.static.sources.enabled` -> `spark.metrics.staticSources.enabled`, `spark.app.status.metrics.enabled` -> `spark.metrics.appStatusSource.enabled`.
Note: `spark.sql.streaming.metricsEnabled` is left unchanged as it is already in use in Spark 2.x.
### How was this patch tested?
Manually tested
Closes#26692 from LucaCanali/uniformNamingMetricsEnableParameters.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
`UnaryPositive` only accepts numeric and interval as we defined, but what we do for this in `AstBuider.visitArithmeticUnary` is just bypassing it.
This should not be omitted for the type checking requirement.
### Why are the changes needed?
bug fix, you can find a pre-discussion here https://github.com/apache/spark/pull/26578#discussion_r347350398
### Does this PR introduce any user-facing change?
yes, +non-numeric-or-interval is now invalid.
```
-- !query 14
select +date '1900-01-01'
-- !query 14 schema
struct<DATE '1900-01-01':date>
-- !query 14 output
1900-01-01
-- !query 15
select +timestamp '1900-01-01'
-- !query 15 schema
struct<TIMESTAMP '1900-01-01 00:00:00':timestamp>
-- !query 15 output
1900-01-01 00:00:00
-- !query 16
select +map(1, 2)
-- !query 16 schema
struct<map(1, 2):map<int,int>>
-- !query 16 output
{1:2}
-- !query 17
select +array(1,2)
-- !query 17 schema
struct<array(1, 2):array<int>>
-- !query 17 output
[1,2]
-- !query 18
select -'1'
-- !query 18 schema
struct<(- CAST(1 AS DOUBLE)):double>
-- !query 18 output
-1.0
-- !query 19
select -X'1'
-- !query 19 schema
struct<>
-- !query 19 output
org.apache.spark.sql.AnalysisException
cannot resolve '(- X'01')' due to data type mismatch: argument 1 requires (numeric or interval) type, however, 'X'01'' is of binary type.; line 1 pos 7
-- !query 20
select +X'1'
-- !query 20 schema
struct<X'01':binary>
-- !query 20 output
```
### How was this patch tested?
add ut check
Closes#26716 from yaooqinn/SPARK-30083.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Standardize sql reference
### Why are the changes needed?
To have consistent docs
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Tested using jykyll build --serve
Closes#26721 from huaxingao/spark-30085.
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?
add `.enabled` postfix to `spark.sql.analyzer.failAmbiguousSelfJoin`.
### Why are the changes needed?
to follow the existing naming style
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
not needed
Closes#26694 from cloud-fan/conf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In SPARK-29421 (#26097) , we can specify a different table provider for `CREATE TABLE LIKE` via `USING provider`.
Hive support `STORED AS` new file format syntax:
```sql
CREATE TABLE tbl(a int) STORED AS TEXTFILE;
CREATE TABLE tbl2 LIKE tbl STORED AS PARQUET;
```
For Hive compatibility, we should also support `STORED AS` in `CREATE TABLE LIKE`.
### Why are the changes needed?
See https://github.com/apache/spark/pull/26097#issue-327424759
### Does this PR introduce any user-facing change?
Add a new syntax based on current CTL:
CREATE TABLE tbl2 LIKE tbl [STORED AS hiveFormat];
### How was this patch tested?
Add UTs.
Closes#26466 from LantaoJin/SPARK-29839.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Summarizer support more metrics: sum, std
### Why are the changes needed?
Those metrics are widely used, it will be convenient to directly obtain them other than a conversion.
in `NaiveBayes`: we want the sum of vectors, mean & weightSum need to computed then multiplied
in `StandardScaler`,`AFTSurvivalRegression`,`LinearRegression`,`LinearSVC`,`LogisticRegression`: we need to obtain `variance` and then sqrt it to get std
### Does this PR introduce any user-facing change?
yes, new metrics are exposed to end users
### How was this patch tested?
added testsuites
Closes#26596 from zhengruifeng/summarizer_add_metrics.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
For a literal number with an exponent(e.g. 1e-45, 1E2), we'd parse it to Double by default rather than Decimal. And user could still use `spark.sql.legacy.exponentLiteralToDecimal.enabled=true` to fall back to previous behavior.
### Why are the changes needed?
According to ANSI standard of SQL, we see that the (part of) definition of `literal` :
```
<approximate numeric literal> ::=
<mantissa> E <exponent>
```
which indicates that a literal number with an exponent should be approximate numeric(e.g. Double) rather than exact numeric(e.g. Decimal).
And when we test Presto, we found that Presto also conforms to this standard:
```
presto:default> select typeof(1E2);
_col0
--------
double
(1 row)
```
```
presto:default> select typeof(1.2);
_col0
--------------
decimal(2,1)
(1 row)
```
We also find that, actually, literals like `1E2` are parsed as Double before Spark2.1, but changed to Decimal after #14828 due to *The difference between the two confuses most users* as it said. But we also see support(from DB2 test) of original behavior at #14828 (comment).
Although, we also see that PostgreSQL has its own implementation:
```
postgres=# select pg_typeof(1E2);
pg_typeof
-----------
numeric
(1 row)
postgres=# select pg_typeof(1.2);
pg_typeof
-----------
numeric
(1 row)
```
We still think that Spark should also conform to this standard while considering SQL standard and Spark own history and majority DBMS and also user experience.
### Does this PR introduce any user-facing change?
Yes.
For `1E2`, before this PR:
```
scala> spark.sql("select 1E2")
res0: org.apache.spark.sql.DataFrame = [1E+2: decimal(1,-2)]
```
After this PR:
```
scala> spark.sql("select 1E2")
res0: org.apache.spark.sql.DataFrame = [100.0: double]
```
And for `1E-45`, before this PR:
```
org.apache.spark.sql.catalyst.parser.ParseException:
decimal can only support precision up to 38
== SQL ==
select 1E-45
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:131)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:48)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:76)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:605)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:605)
... 47 elided
```
after this PR:
```
scala> spark.sql("select 1E-45");
res1: org.apache.spark.sql.DataFrame = [1.0E-45: double]
```
And before this PR, user may feel super weird to see that `select 1e40` works but `select 1e-40 fails`. And now, both of them work well.
### How was this patch tested?
updated `literals.sql.out` and `ansi/literals.sql.out`
Closes#26595 from Ngone51/SPARK-29956.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
[HIVE-12063](https://issues.apache.org/jira/browse/HIVE-12063) improved pad decimal numbers with trailing zeros to the scale of the column. The following description is copied from the description of HIVE-12063.
> HIVE-7373 was to address the problems of trimming tailing zeros by Hive, which caused many problems including treating 0.0, 0.00 and so on as 0, which has different precision/scale. Please refer to HIVE-7373 description. However, HIVE-7373 was reverted by HIVE-8745 while the underlying problems remained. HIVE-11835 was resolved recently to address one of the problems, where 0.0, 0.00, and so on cannot be read into decimal(1,1).
However, HIVE-11835 didn't address the problem of showing as 0 in query result for any decimal values such as 0.0, 0.00, etc. This causes confusion as 0 and 0.0 have different precision/scale than 0.
The proposal here is to pad zeros for query result to the type's scale. This not only removes the confusion described above, but also aligns with many other DBs. Internal decimal number representation doesn't change, however.
**Spark SQL**:
```sql
// bin/spark-sql
spark-sql> select cast(1 as decimal(38, 18));
1
spark-sql>
// bin/beeline
0: jdbc:hive2://localhost:10000/default> select cast(1 as decimal(38, 18));
+----------------------------+--+
| CAST(1 AS DECIMAL(38,18)) |
+----------------------------+--+
| 1.000000000000000000 |
+----------------------------+--+
// bin/spark-shell
scala> spark.sql("select cast(1 as decimal(38, 18))").show(false)
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
|1.000000000000000000 |
+-------------------------+
// bin/pyspark
>>> spark.sql("select cast(1 as decimal(38, 18))").show()
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
| 1.000000000000000000|
+-------------------------+
// bin/sparkR
> showDF(sql("SELECT cast(1 as decimal(38, 18))"))
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
| 1.000000000000000000|
+-------------------------+
```
**PostgreSQL**:
```sql
postgres=# select cast(1 as decimal(38, 18));
numeric
----------------------
1.000000000000000000
(1 row)
```
**Presto**:
```sql
presto> select cast(1 as decimal(38, 18));
_col0
----------------------
1.000000000000000000
(1 row)
```
## How was this patch tested?
unit tests and manual test:
```sql
spark-sql> select cast(1 as decimal(38, 18));
1.000000000000000000
```
Spark SQL Upgrading Guide:
Closes#26697 from wangyum/SPARK-28461.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Closes#26690 from huangtianhua/add-note-spark-runs-on-arm64.
Authored-by: huangtianhua <huangtianhua@huawei.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
[HIVE-12063](https://issues.apache.org/jira/browse/HIVE-12063) improved pad decimal numbers with trailing zeros to the scale of the column. The following description is copied from the description of HIVE-12063.
> HIVE-7373 was to address the problems of trimming tailing zeros by Hive, which caused many problems including treating 0.0, 0.00 and so on as 0, which has different precision/scale. Please refer to HIVE-7373 description. However, HIVE-7373 was reverted by HIVE-8745 while the underlying problems remained. HIVE-11835 was resolved recently to address one of the problems, where 0.0, 0.00, and so on cannot be read into decimal(1,1).
However, HIVE-11835 didn't address the problem of showing as 0 in query result for any decimal values such as 0.0, 0.00, etc. This causes confusion as 0 and 0.0 have different precision/scale than 0.
The proposal here is to pad zeros for query result to the type's scale. This not only removes the confusion described above, but also aligns with many other DBs. Internal decimal number representation doesn't change, however.
**Spark SQL**:
```sql
// bin/spark-sql
spark-sql> select cast(1 as decimal(38, 18));
1
spark-sql>
// bin/beeline
0: jdbc:hive2://localhost:10000/default> select cast(1 as decimal(38, 18));
+----------------------------+--+
| CAST(1 AS DECIMAL(38,18)) |
+----------------------------+--+
| 1.000000000000000000 |
+----------------------------+--+
// bin/spark-shell
scala> spark.sql("select cast(1 as decimal(38, 18))").show(false)
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
|1.000000000000000000 |
+-------------------------+
// bin/pyspark
>>> spark.sql("select cast(1 as decimal(38, 18))").show()
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
| 1.000000000000000000|
+-------------------------+
// bin/sparkR
> showDF(sql("SELECT cast(1 as decimal(38, 18))"))
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
| 1.000000000000000000|
+-------------------------+
```
**PostgreSQL**:
```sql
postgres=# select cast(1 as decimal(38, 18));
numeric
----------------------
1.000000000000000000
(1 row)
```
**Presto**:
```sql
presto> select cast(1 as decimal(38, 18));
_col0
----------------------
1.000000000000000000
(1 row)
```
## How was this patch tested?
unit tests and manual test:
```sql
spark-sql> select cast(1 as decimal(38, 18));
1.000000000000000000
```
Spark SQL Upgrading Guide:
Closes#25214 from wangyum/SPARK-28461.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
* Replace hard-coded conf `spark.scheduler.listenerbus.eventqueue` with a constant variable(`LISTENER_BUS_EVENT_QUEUE_PREFIX `) defined in `config/package.scala`.
* Update documentation for `spark.scheduler.listenerbus.eventqueue.capacity` in both `config/package.scala` and `docs/configuration.md`.
### Why are the changes needed?
* Better code maintainability
* Better user guidance of the conf
### Does this PR introduce any user-facing change?
No behavior changes but user will see the updated document.
### How was this patch tested?
Pass Jenkins.
Closes#26676 from Ngone51/SPARK-28574-followup.
Authored-by: wuyi <ngone_5451@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
A java like string trim method trims all whitespaces that less or equal than 0x20. currently, our UTF8String handle the space =0x20 ONLY. This is not suitable for many cases in Spark, like trim for interval strings, date, timestamps, PostgreSQL like cast string to boolean.
### Why are the changes needed?
improve the white spaces handling in UTF8String, also with some bugs fixed
### Does this PR introduce any user-facing change?
yes,
string with `control character` at either end can be convert to date/timestamp and interval now
### How was this patch tested?
add ut
Closes#26626 from yaooqinn/SPARK-29986.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Modify `UTF8String.toInt/toLong` to support trim spaces for both sides before converting it to byte/short/int/long.
With this kind of "cheap" trim can help improve performance for casting string to integrals. The idea is from https://github.com/apache/spark/pull/24872#issuecomment-556917834
### Why are the changes needed?
make the behavior consistent.
### Does this PR introduce any user-facing change?
yes, cast string to an integral type, and binary comparison between string and integrals will trim spaces first. their behavior will be consistent with float and double.
### How was this patch tested?
1. add ut.
2. benchmark tests
the benchmark is modified based on https://github.com/apache/spark/pull/24872#issuecomment-503827016
```scala
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql.execution.benchmark
import org.apache.spark.benchmark.Benchmark
/**
* Benchmark trim the string when casting string type to Boolean/Numeric types.
* To run this benchmark:
* {{{
* 1. without sbt:
* bin/spark-submit --class <this class> --jars <spark core test jar> <spark sql test jar>
* 2. build/sbt "sql/test:runMain <this class>"
* 3. generate result: SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain <this class>"
* Results will be written to "benchmarks/CastBenchmark-results.txt".
* }}}
*/
object CastBenchmark extends SqlBasedBenchmark {
This conversation was marked as resolved by yaooqinn
override def runBenchmarkSuite(mainArgs: Array[String]): Unit = {
val title = "Cast String to Integral"
runBenchmark(title) {
withTempPath { dir =>
val N = 500L << 14
val df = spark.range(N)
val types = Seq("int", "long")
(1 to 5).by(2).foreach { i =>
df.selectExpr(s"concat(id, '${" " * i}') as str")
.write.mode("overwrite").parquet(dir + i.toString)
}
val benchmark = new Benchmark(title, N, minNumIters = 5, output = output)
Seq(true, false).foreach { trim =>
types.foreach { t =>
val str = if (trim) "trim(str)" else "str"
val expr = s"cast($str as $t) as c_$t"
(1 to 5).by(2).foreach { i =>
benchmark.addCase(expr + s" - with $i spaces") { _ =>
spark.read.parquet(dir + i.toString).selectExpr(expr).collect()
}
}
}
}
benchmark.run()
}
}
}
}
```
#### benchmark result.
normal trim v.s. trim in toInt/toLong
```java
================================================================================================
Cast String to Integral
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_231-b11 on Mac OS X 10.15.1
Intel(R) Core(TM) i5-5287U CPU 2.90GHz
Cast String to Integral: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
cast(trim(str) as int) as c_int - with 1 spaces 10220 12994 1337 0.8 1247.5 1.0X
cast(trim(str) as int) as c_int - with 3 spaces 4763 8356 357 1.7 581.4 2.1X
cast(trim(str) as int) as c_int - with 5 spaces 4791 8042 NaN 1.7 584.9 2.1X
cast(trim(str) as long) as c_long - with 1 spaces 4014 6755 NaN 2.0 490.0 2.5X
cast(trim(str) as long) as c_long - with 3 spaces 4737 6938 NaN 1.7 578.2 2.2X
cast(trim(str) as long) as c_long - with 5 spaces 4478 6919 1404 1.8 546.6 2.3X
cast(str as int) as c_int - with 1 spaces 4443 6222 NaN 1.8 542.3 2.3X
cast(str as int) as c_int - with 3 spaces 3659 3842 170 2.2 446.7 2.8X
cast(str as int) as c_int - with 5 spaces 4372 7996 NaN 1.9 533.7 2.3X
cast(str as long) as c_long - with 1 spaces 3866 5838 NaN 2.1 471.9 2.6X
cast(str as long) as c_long - with 3 spaces 3793 5449 NaN 2.2 463.0 2.7X
cast(str as long) as c_long - with 5 spaces 4947 5961 1198 1.7 603.9 2.1X
```
Closes#26622 from yaooqinn/cheapstringtrim.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Impl Complement Naive Bayes Classifier as a `modelType` option in `NaiveBayes`
### Why are the changes needed?
1, it is a better choice for text classification: it is said in [scikit-learn](https://scikit-learn.org/stable/modules/naive_bayes.html#complement-naive-bayes) that 'CNB regularly outperforms MNB (often by a considerable margin) on text classification tasks.'
2, CNB is highly similar to existing MNB, only a small part of existing MNB need to be changed, so it is a easy win to support CNB.
### Does this PR introduce any user-facing change?
yes, a new `modelType` is supported
### How was this patch tested?
added testsuites
Closes#26575 from zhengruifeng/cnb.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Fix the inconsistent behavior of build-in function SQL LEFT/RIGHT.
### Why are the changes needed?
As the comment in https://github.com/apache/spark/pull/26497#discussion_r345708065, Postgre dialect should not be affected by the ANSI mode config.
During reran the existing tests, only the LEFT/RIGHT build-in SQL function broke the assumption. We fix this by following https://www.postgresql.org/docs/12/sql-keywords-appendix.html: `LEFT/RIGHT reserved (can be function or type)`
### Does this PR introduce any user-facing change?
Yes, the Postgre dialect will not be affected by the ANSI mode config.
### How was this patch tested?
Existing UT.
Closes#26584 from xuanyuanking/SPARK-29951.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The Web UI SQL Tab provides information on the executed SQL using plan graphs and by reporting SQL execution plans. Both sources provide useful information. Physical execution plans report Codegen Stage Ids. This PR adds Codegen Stage Ids to the plan graphs.
### Why are the changes needed?
It is useful to have Codegen Stage Id information also reported in plan graphs, this allows to more easily match physical plans and graphs with metrics when troubleshooting SQL execution.
Example snippet to show the proposed change:
Example of the current state:
Physical plan:
### Does this PR introduce any user-facing change?
This PR adds Codegen Stage Id information to SQL plan graphs in the Web UI/SQL Tab.
### How was this patch tested?
Added a test + manually tested
Closes#26519 from LucaCanali/addCodegenStageIdtoWEBUIGraphs.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
support `modelType` `gaussian`
### Why are the changes needed?
current modelTypes do not support continuous data
### Does this PR introduce any user-facing change?
yes, add a `modelType` option
### How was this patch tested?
existing testsuites and added ones
Closes#26413 from zhengruifeng/gnb.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Rename config "spark.sql.ansi.enabled" to "spark.sql.dialect.spark.ansi.enabled"
### Why are the changes needed?
The relation between "spark.sql.ansi.enabled" and "spark.sql.dialect" is confusing, since the "PostgreSQL" dialect should contain the features of "spark.sql.ansi.enabled".
To make things clearer, we can rename the "spark.sql.ansi.enabled" to "spark.sql.dialect.spark.ansi.enabled", thus the option "spark.sql.dialect.spark.ansi.enabled" is only for Spark dialect.
For the casting and arithmetic operations, runtime exceptions should be thrown if "spark.sql.dialect" is "spark" and "spark.sql.dialect.spark.ansi.enabled" is true or "spark.sql.dialect" is PostgresSQL.
### Does this PR introduce any user-facing change?
Yes, the config name changed.
### How was this patch tested?
Existing UT.
Closes#26444 from xuanyuanking/SPARK-29807.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add listener event queue capacity configuration to documentation
### Why are the changes needed?
We some time see many event drops happening in eventLog listener queue. So, instead of increasing all the queues size, using this config we just need to increase eventLog queue capacity.
```
scala> sc.parallelize(1 to 100000, 100000).count()
[Stage 0:=================================================>(98299 + 4) / 100000]19/11/14 20:56:35 ERROR AsyncEventQueue: Dropping event from queue eventLog. This likely means one of the listeners is too slow and cannot keep up with the rate at which tasks are being started by the scheduler.
19/11/14 20:56:35 WARN AsyncEventQueue: Dropped 1 events from eventLog since the application started.
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing tests
Closes#26529 from shahidki31/master1.
Authored-by: shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes to switch `pygments.rb`, which only support Python 2 and seems inactive for the last few years (https://github.com/tmm1/pygments.rb), to Rouge which is pure Ruby code highlighter that is compatible with Pygments.
I thought it would be pretty difficult to change but thankfully Rouge does a great job as the alternative.
### Why are the changes needed?
We're moving to Python 3 and drop Python 2 completely.
### Does this PR introduce any user-facing change?
Maybe a little bit of different syntax style but should not have a notable change.
### How was this patch tested?
Manually tested the build and checked the documentation.
Closes#26521 from HyukjinKwon/SPARK-28752.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Fix broken links
### How was this patch tested?
Tested using jykyll build --serve
Closes#26528 from huaxingao/spark-29901.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add alter view link to drop view
### Why are the changes needed?
create view has links to drop view and alter view
alter view has links to create view and drop view
drop view currently doesn't have a link to alter view. I think it's better to link to alter view as well.
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Tested using jykyll build --serve
Closes#26495 from huaxingao/spark-28798.
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?
Use html files for the links
### Why are the changes needed?
links not working
### Does this PR introduce any user-facing change?
Yes
### How was this patch tested?
Used jekyll build and serve to verify.
Closes#26494 from huaxingao/spark-28795.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
SPARK-29397 added new interfaces for creating driver and executor
plugins. These were added in a new, more isolated package that does
not pollute the main o.a.s package.
The old interface is now redundant. Since it's a DeveloperApi and
we're about to have a new major release, let's remove it instead of
carrying more baggage forward.
Closes#26390 from vanzin/SPARK-29399.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch fixes the edge case of streaming left/right outer join described below:
Suppose query is provided as
`select * from A join B on A.id = B.id AND (A.ts <= B.ts AND B.ts <= A.ts + interval 5 seconds)`
and there're two rows for L1 (from A) and R1 (from B) which ensures L1.id = R1.id and L1.ts = R1.ts.
(we can simply imagine it from self-join)
Then Spark processes L1 and R1 as below:
- row L1 and row R1 are joined at batch 1
- row R1 is evicted at batch 2 due to join and watermark condition, whereas row L1 is not evicted
- row L1 is evicted at batch 3 due to join and watermark condition
When determining outer rows to match with null, Spark applies some assumption commented in codebase, as below:
```
Checking whether the current row matches a key in the right side state, and that key
has any value which satisfies the filter function when joined. If it doesn't,
we know we can join with null, since there was never (including this batch) a match
within the watermark period. If it does, there must have been a match at some point, so
we know we can't join with null.
```
But as explained the edge-case earlier, the assumption is not correct. As we don't have any good assumption to optimize which doesn't have edge-case, we have to track whether such row is matched with others before, and match with null row only when the row is not matched.
To track the matching of row, the patch adds a new state to streaming join state manager, and mark whether the row is matched to others or not. We leverage the information when dealing with eviction of rows which would be candidates to match with null rows.
This approach introduces new state format which is not compatible with old state format - queries with old state format will be still running but they will still have the issue and be required to discard checkpoint and rerun to take this patch in effect.
### Why are the changes needed?
This patch fixes a correctness issue.
### Does this PR introduce any user-facing change?
No for compatibility viewpoint, but we'll encourage end users to discard the old checkpoint and rerun the query if they run stream-stream outer join query with old checkpoint, which might be "yes" for the question.
### How was this patch tested?
Added UT which fails on current Spark and passes with this patch. Also passed existing streaming join UTs.
Closes#26108 from HeartSaVioR/SPARK-26154-shorten-alternative.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
The Spark metrics system produces many different metrics and not all of them are used at the same time. This proposes to introduce a configuration parameter to allow disabling the registration of metrics in the "static sources" category.
### Why are the changes needed?
This allows to reduce the load and clutter on the sink, in the cases when the metrics in question are not needed. The metrics registerd as "static sources" are under the namespaces CodeGenerator and HiveExternalCatalog and can produce a significant amount of data, as they are registered for the driver and executors.
### Does this PR introduce any user-facing change?
It introduces a new configuration parameter `spark.metrics.register.static.sources.enabled`
### How was this patch tested?
Manually tested.
```
$ cat conf/metrics.properties
*.sink.prometheusServlet.class=org.apache.spark.metrics.sink.PrometheusServlet
*.sink.prometheusServlet.path=/metrics/prometheus
master.sink.prometheusServlet.path=/metrics/master/prometheus
applications.sink.prometheusServlet.path=/metrics/applications/prometheus
$ bin/spark-shell
$ curl -s http://localhost:4040/metrics/prometheus/ | grep Hive
metrics_local_1573330115306_driver_HiveExternalCatalog_fileCacheHits_Count 0
metrics_local_1573330115306_driver_HiveExternalCatalog_filesDiscovered_Count 0
metrics_local_1573330115306_driver_HiveExternalCatalog_hiveClientCalls_Count 0
metrics_local_1573330115306_driver_HiveExternalCatalog_parallelListingJobCount_Count 0
metrics_local_1573330115306_driver_HiveExternalCatalog_partitionsFetched_Count 0
$ bin/spark-shell --conf spark.metrics.static.sources.enabled=false
$ curl -s http://localhost:4040/metrics/prometheus/ | grep Hive
```
Closes#26320 from LucaCanali/addConfigRegisterStaticMetrics.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fixing the typos in SQL reference document.
### Why are the changes needed?
For user readability
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Tested manually.
Closes#26424 from jobitmathew/typo.
Authored-by: Jobit Mathew <jobit.mathew@huawei.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This PR supports MERGE INTO in the parser and add the corresponding logical plan. The SQL syntax likes,
```
MERGE INTO [ds_catalog.][multi_part_namespaces.]target_table [AS target_alias]
USING [ds_catalog.][multi_part_namespaces.]source_table | subquery [AS source_alias]
ON <merge_condition>
[ WHEN MATCHED [ AND <condition> ] THEN <matched_action> ]
[ WHEN MATCHED [ AND <condition> ] THEN <matched_action> ]
[ WHEN NOT MATCHED [ AND <condition> ] THEN <not_matched_action> ]
```
where
```
<matched_action> =
DELETE |
UPDATE SET * |
UPDATE SET column1 = value1 [, column2 = value2 ...]
<not_matched_action> =
INSERT * |
INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 ...])
```
### Why are the changes needed?
This is a start work for introduce `MERGE INTO` support for the builtin datasource, and the design work for the `MERGE INTO` support in DSV2.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
New test cases.
Closes#26167 from xianyinxin/SPARK-28893.
Authored-by: xy_xin <xianyin.xxy@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
It adds a note about the required port of a master url in Kubernetes.
Currently a port needs to be specified for the Kubernetes API. Also in case the API is hosted on the HTTPS port. Else the driver might fail with https://medium.com/kidane.weldemariam_75349/thanks-james-on-issuing-spark-submit-i-run-into-this-error-cc507d4f8f0d
Yes, a change to the "Running on Kubernetes" guide.
None - Documentation change
Closes#26426 from Tapped/patch-1.
Authored-by: Emil Sandstø <emilalexer@hotmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR proposes to add **Single threading model design (pinned thread model)** mode which is an experimental mode to sync threads on PVM and JVM. See https://www.py4j.org/advanced_topics.html#using-single-threading-model-pinned-thread
### Multi threading model
Currently, PySpark uses this model. Threads on PVM and JVM are independent. For instance, in a different Python thread, callbacks are received and relevant Python codes are executed. JVM threads are reused when possible.
Py4J will create a new thread every time a command is received and there is no thread available. See the current model we're using - https://www.py4j.org/advanced_topics.html#the-multi-threading-model
One problem in this model is that we can't sync threads on PVM and JVM out of the box. This leads to some problems in particular at some codes related to threading in JVM side. See:
7056e004ee/core/src/main/scala/org/apache/spark/SparkContext.scala (L334)
Due to reusing JVM threads, seems the job groups in Python threads cannot be set in each thread as described in the JIRA.
### Single threading model design (pinned thread model)
This mode pins and syncs the threads on PVM and JVM to work around the problem above. For instance, in the same Python thread, callbacks are received and relevant Python codes are executed. See https://www.py4j.org/advanced_topics.html#the-single-threading-model
Even though this mode can sync threads on PVM and JVM for other thread related code paths,
this might cause another problem: seems unable to inherit properties as below (assuming multi-thread mode still creates new threads when existing threads are busy, I suspect this issue already exists when multiple jobs are submitted in multi-thread mode; however, it can be always seen in single threading mode):
```bash
$ PYSPARK_PIN_THREAD=true ./bin/pyspark
```
```python
import threading
spark.sparkContext.setLocalProperty("a", "hi")
def print_prop():
print(spark.sparkContext.getLocalProperty("a"))
threading.Thread(target=print_prop).start()
```
```
None
```
Unlike Scala side:
```scala
spark.sparkContext.setLocalProperty("a", "hi")
new Thread(new Runnable {
def run() = println(spark.sparkContext.getLocalProperty("a"))
}).start()
```
```
hi
```
This behaviour potentially could cause weird issues but this PR currently does not target this fix this for now since this mode is experimental.
### How does this PR fix?
Basically there are two types of Py4J servers `GatewayServer` and `ClientServer`. The former is for multi threading and the latter is for single threading. This PR adds a switch to use the latter.
In Scala side:
The logic to select a server is encapsulated in `Py4JServer` and use `Py4JServer` at `PythonRunner` for Spark summit and `PythonGatewayServer` for Spark shell. Each uses `ClientServer` when `PYSPARK_PIN_THREAD` is `true` and `GatewayServer` otherwise.
In Python side:
Simply do an if-else to switch the server to talk. It uses `ClientServer` when `PYSPARK_PIN_THREAD` is `true` and `GatewayServer` otherwise.
This is disabled by default for now.
## How was this patch tested?
Manually tested. This can be tested via:
```python
PYSPARK_PIN_THREAD=true ./bin/pyspark
```
and/or
```bash
cd python
./run-tests --python-executables=python --testnames "pyspark.tests.test_pin_thread"
```
Also, ran the Jenkins tests with `PYSPARK_PIN_THREAD` enabled.
Closes#24898 from HyukjinKwon/pinned-thread.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
remove the leading "interval" in `CalendarInterval.toString`.
### Why are the changes needed?
Although it's allowed to have "interval" prefix when casting string to int, it's not recommended.
This is also consistent with pgsql:
```
cloud0fan=# select interval '1' day;
interval
----------
1 day
(1 row)
```
### Does this PR introduce any user-facing change?
yes, when display a dataframe with interval type column, the result is different.
### How was this patch tested?
updated tests.
Closes#26401 from cloud-fan/interval.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Priority for YARN to define pending applications ordering policy, those with higher priority have a better opportunity to be activated. YARN CapacityScheduler only.
### Why are the changes needed?
Ordering pending spark apps
### Does this PR introduce any user-facing change?
add a conf
### How was this patch tested?
add ut
Closes#26255 from yaooqinn/SPARK-29603.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
During creation of array, if CreateArray does not gets any children to set data type for array, it will create an array of null type .
### Why are the changes needed?
When empty array is created, it should be declared as array<null>.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Tested manually
Closes#26324 from amanomer/29462.
Authored-by: Aman Omer <amanomer1996@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds the ability for tasks to request fractional resources, in order to be able to execute more than 1 task per resource. For example, if you have 1 GPU in the executor, and the task configuration is 0.5 GPU/task, the executor can schedule two tasks to run on that 1 GPU.
### Why are the changes needed?
Currently there is no good way to share a resource such that multiple tasks can run on a single unit. This allows multiple tasks to share an executor resource.
### Does this PR introduce any user-facing change?
Yes: There is a configuration change where `spark.task.resource.[resource type].amount` can now be fractional.
### How was this patch tested?
Unit tests and manually on standalone mode, and yarn.
Closes#26078 from abellina/SPARK-29151.
Authored-by: Alessandro Bellina <abellina@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This patch adds the option to clean up files which are completed in previous batch.
`cleanSource` -> "archive" / "delete" / "off"
The default value is "off", which Spark will do nothing.
If "delete" is specified, Spark will simply delete input files. If "archive" is specified, Spark will require additional config `sourceArchiveDir` which will be used to move input files to there. When archiving (via move) the path of input files are retained to the archived paths as sub-path.
Note that it is only applied to "micro-batch", since for batch all input files must be kept to get same result across multiple query executions.
## How was this patch tested?
Added UT. Manual test against local disk as well as HDFS.
Closes#22952 from HeartSaVioR/SPARK-20568.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Co-authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
Spark 2.4 added the ability for executor plugins to be loaded into
Spark (see SPARK-24918). That feature intentionally skipped the
driver to keep changes small, and also because it is possible to
load code into the Spark driver using listeners + configuration.
But that is a bit awkward, because the listener interface does not
provide hooks into a lot of Spark functionality. This change reworks
the executor plugin interface to also extend to the driver.
- there's a "SparkPlugin" main interface that provides APIs to
load driver and executor components.
- custom metric support (added in SPARK-28091) can be used by
plugins to register metrics both in the driver process and in
executors.
- a communication channel now exists that allows the plugin's
executor components to send messages to the plugin's driver
component easily, using the existing Spark RPC system.
The latter was a feature intentionally left out of the original
plugin design (also because it didn't include a driver component).
To avoid polluting the "org.apache.spark" namespace, I added the new
interfaces to the "org.apache.spark.api" package, which seems like
a better place in any case. The actual implementation is kept in
an internal package.
The change includes unit tests for the new interface and features,
but I've also been running a custom plugin that extends the new
API in real applications.
Closes#26170 from vanzin/SPARK-29397.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
re-arrange the parser rules to make it clear that multiple unit TO unit statement like `SELECT INTERVAL '1-1' YEAR TO MONTH '2-2' YEAR TO MONTH` is not allowed.
### Why are the changes needed?
This is definitely an accident that we support such a weird syntax in the past. It's not supported by any other DBs and I can't think of any use case of it. Also no test covers this syntax in the current codebase.
### Does this PR introduce any user-facing change?
Yes, and a migration guide item is added.
### How was this patch tested?
new tests.
Closes#26285 from cloud-fan/syntax.
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 changes the behavior of `Column.getItem` to call `Column.getItem` on Scala side instead of `Column.apply`.
### Why are the changes needed?
The current behavior is not consistent with that of Scala.
In PySpark:
```Python
df = spark.range(2)
map_col = create_map(lit(0), lit(100), lit(1), lit(200))
df.withColumn("mapped", map_col.getItem(col('id'))).show()
# +---+------+
# | id|mapped|
# +---+------+
# | 0| 100|
# | 1| 200|
# +---+------+
```
In Scala:
```Scala
val df = spark.range(2)
val map_col = map(lit(0), lit(100), lit(1), lit(200))
// The following getItem results in the following exception, which is the right behavior:
// java.lang.RuntimeException: Unsupported literal type class org.apache.spark.sql.Column id
// at org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:78)
// at org.apache.spark.sql.Column.getItem(Column.scala:856)
// ... 49 elided
df.withColumn("mapped", map_col.getItem(col("id"))).show
```
### Does this PR introduce any user-facing change?
Yes. If the use wants to pass `Column` object to `getItem`, he/she now needs to use the indexing operator to achieve the previous behavior.
```Python
df = spark.range(2)
map_col = create_map(lit(0), lit(100), lit(1), lit(200))
df.withColumn("mapped", map_col[col('id'))].show()
# +---+------+
# | id|mapped|
# +---+------+
# | 0| 100|
# | 1| 200|
# +---+------+
```
### How was this patch tested?
Existing tests.
Closes#26351 from imback82/spark-29664.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
This PR adds some extra documentation for the new Cogrouped map Pandas udfs. Specifically:
- Updated the usage guide for the new `COGROUPED_MAP` Pandas udfs added in https://github.com/apache/spark/pull/24981
- Updated the docstring for pandas_udf to include the COGROUPED_MAP type as suggested by HyukjinKwon in https://github.com/apache/spark/pull/25939Closes#26110 from d80tb7/SPARK-29126-cogroup-udf-usage-guide.
Authored-by: Chris Martin <chris@cmartinit.co.uk>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the sparkR version number check logic to allow jvm version like `3.0.0-preview`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
### What changes were proposed in this pull request?
This PR aims to deprecate `Python 3.4 ~ 3.5`, which is prior to version 3.6 additionally.
### Why are the changes needed?
Since `Python 3.8` is already out, we will focus on to support Python 3.6/3.7/3.8.
### Does this PR introduce any user-facing change?
Yes. It's highly recommended to use Python 3.6/3.7. We will verify Python 3.8 before Apache Spark 3.0.0 release.
### How was this patch tested?
NA (This is a doc-only change).
Closes#26326 from dongjoon-hyun/SPARK-29668.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fix and use proper html tag in docs
### Why are the changes needed?
Fix documentation format error.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26302 from uncleGen/minor-doc.
Authored-by: uncleGen <hustyugm@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the PySpark version from `3.0.0.dev0` to `3.0.0`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26243 from jiangxb1987/3.0.0-preview-prepare.
Lead-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-29500
`KafkaRowWriter` now supports setting the Kafka partition by reading a "partition" column in the input dataframe.
Code changes in commit nr. 1.
Test changes in commit nr. 2.
Doc changes in commit nr. 3.
tcondie dongjinleekr srowen
### Why are the changes needed?
While it is possible to configure a custom Kafka Partitioner with
`.option("kafka.partitioner.class", "my.custom.Partitioner")`, this is not enough for certain use cases. See the Jira issue.
### Does this PR introduce any user-facing change?
No, as this behaviour is optional.
### How was this patch tested?
Two new UT were added and one was updated.
Closes#26153 from redsk/feature/SPARK-29500.
Authored-by: redsk <nicola.bova@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to deprecate old Java 8 versions prior to 8u92.
### Why are the changes needed?
This is a preparation to use JVM Option `ExitOnOutOfMemoryError`.
- https://www.oracle.com/technetwork/java/javase/8u92-relnotes-2949471.html
### Does this PR introduce any user-facing change?
Yes. It's highly recommended for users to use the latest JDK versions of Java 8/11.
### How was this patch tested?
NA (This is a doc change).
Closes#26249 from dongjoon-hyun/SPARK-29597.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fixes our documentation build to copy minified jquery file instead.
The original file `jquery.js` seems missing as of Scala 2.12 upgrade. Scala 2.12 seems started to use minified `jquery.min.js` instead.
Since we dropped Scala 2.11, we won't have to take care about legacy `jquery.js` anymore.
Note that, there seem multiple weird stuff in the current ScalaDoc (e.g., some pages are weird, it starts from `scala.collection.*` or some pages are missing, or some docs are truncated, some badges look missing). It needs a separate double check and investigation.
This PR targets to make the documentation generation pass in order to unblock Spark 3.0 preview.
### Why are the changes needed?
To fix and make our official documentation build able to run.
### Does this PR introduce any user-facing change?
It will enable to build the documentation in our official way.
**Before:**
```
Making directory api/scala
cp -r ../target/scala-2.12/unidoc/. api/scala
Making directory api/java
cp -r ../target/javaunidoc/. api/java
Updating JavaDoc files for badge post-processing
Copying jquery.js from Scala API to Java API for page post-processing of badges
jekyll 3.8.6 | Error: No such file or directory rb_sysopen - ./api/scala/lib/jquery.js
```
**After:**
```
Making directory api/scala
cp -r ../target/scala-2.12/unidoc/. api/scala
Making directory api/java
cp -r ../target/javaunidoc/. api/java
Updating JavaDoc files for badge post-processing
Copying jquery.min.js from Scala API to Java API for page post-processing of badges
Copying api_javadocs.js to Java API for page post-processing of badges
Appending content of api-javadocs.css to JavaDoc stylesheet.css for badge styles
...
```
### How was this patch tested?
Manually tested via:
```
SKIP_PYTHONDOC=1 SKIP_RDOC=1 SKIP_SQLDOC=1 jekyll build
```
Closes#26228 from HyukjinKwon/SPARK-29569.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
This PR adds `CREATE NAMESPACE` support for V2 catalogs.
### Why are the changes needed?
Currently, you cannot explicitly create namespaces for v2 catalogs.
### Does this PR introduce any user-facing change?
The user can now perform the following:
```SQL
CREATE NAMESPACE mycatalog.ns
```
to create a namespace `ns` inside `mycatalog` V2 catalog.
### How was this patch tested?
Added unit tests.
Closes#26166 from imback82/create_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR remove unnecessary orc version and hive version in doc.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A.
Closes#26146 from denglingang/SPARK-24576.
Lead-authored-by: denglingang <chitin1027@gmail.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fixes the incorrect `EqualNullSafe` symbol in `sql-migration-guide.md`.
### Why are the changes needed?
Fix documentation error.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#26163 from wangyum/EqualNullSafe-symbol.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This patch is a part of [SPARK-28594](https://issues.apache.org/jira/browse/SPARK-28594) and design doc for SPARK-28594 is linked here: https://docs.google.com/document/d/12bdCC4nA58uveRxpeo8k7kGOI2NRTXmXyBOweSi4YcY/edit?usp=sharing
This patch proposes adding new feature to event logging, rolling event log files via configured file size.
Previously event logging is done with single file and related codebase (`EventLoggingListener`/`FsHistoryProvider`) is tightly coupled with it. This patch adds layer on both reader (`EventLogFileReader`) and writer (`EventLogFileWriter`) to decouple implementation details between "handling events" and "how to read/write events from/to file".
This patch adds two properties, `spark.eventLog.rollLog` and `spark.eventLog.rollLog.maxFileSize` which provides configurable behavior of rolling log. The feature is disabled by default, as we only expect huge event log for huge/long-running application. For other cases single event log file would be sufficient and still simpler.
### Why are the changes needed?
This is a part of SPARK-28594 which addresses event log growing infinitely for long-running application.
This patch itself also provides some option for the situation where event log file gets huge and consume their storage. End users may give up replaying their events and want to delete the event log file, but given application is still running and writing the file, it's not safe to delete the file. End users will be able to delete some of old files after applying rolling over event log.
### Does this PR introduce any user-facing change?
No, as the new feature is turned off by default.
### How was this patch tested?
Added unit tests, as well as basic manual tests.
Basic manual tests - ran SHS, ran structured streaming query with roll event log enabled, verified split files are generated as well as SHS can load these files, with handling app status as incomplete/complete.
Closes#25670 from HeartSaVioR/SPARK-28869.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Co-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 PR proposes a few typos:
1. Sparks => Spark's
2. parallize => parallelize
3. doesnt => doesn't
Closes#26140 from plusplusjiajia/fix-typos.
Authored-by: Jiajia Li <jiajia.li@intel.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
When inserting a value into a column with the different data type, Spark performs type coercion. Currently, we support 3 policies for the store assignment rules: ANSI, legacy and strict, which can be set via the option "spark.sql.storeAssignmentPolicy":
1. ANSI: Spark performs the type coercion as per ANSI SQL. In practice, the behavior is mostly the same as PostgreSQL. It disallows certain unreasonable type conversions such as converting `string` to `int` and `double` to `boolean`. It will throw a runtime exception if the value is out-of-range(overflow).
2. Legacy: Spark allows the type coercion as long as it is a valid `Cast`, which is very loose. E.g., converting either `string` to `int` or `double` to `boolean` is allowed. It is the current behavior in Spark 2.x for compatibility with Hive. When inserting an out-of-range value to a integral field, the low-order bits of the value is inserted(the same as Java/Scala numeric type casting). For example, if 257 is inserted to a field of Byte type, the result is 1.
3. Strict: Spark doesn't allow any possible precision loss or data truncation in store assignment, e.g., converting either `double` to `int` or `decimal` to `double` is allowed. The rules are originally for Dataset encoder. As far as I know, no mainstream DBMS is using this policy by default.
Currently, the V1 data source uses "Legacy" policy by default, while V2 uses "Strict". This proposal is to use "ANSI" policy by default for both V1 and V2 in Spark 3.0.
### Why are the changes needed?
Following the ANSI SQL standard is most reasonable among the 3 policies.
### Does this PR introduce any user-facing change?
Yes.
The default store assignment policy is ANSI for both V1 and V2 data sources.
### How was this patch tested?
Unit test
Closes#26107 from gengliangwang/ansiPolicyAsDefault.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add documentation to SQL programming guide to use PyArrow >= 0.15.0 with current versions of Spark.
### Why are the changes needed?
Arrow 0.15.0 introduced a change in format which requires an environment variable to maintain compatibility.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Ran pandas_udfs tests using PyArrow 0.15.0 with environment variable set.
Closes#26045 from BryanCutler/arrow-document-legacy-IPC-fix-SPARK-29367.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
This adds an entry about PrometheusServlet to the documentation, following SPARK-29032
### Why are the changes needed?
The monitoring documentation lists all the available metrics sinks, this should be added to the list for completeness.
Closes#26081 from LucaCanali/FollowupSpark29032.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
This is just a followup on https://github.com/apache/spark/pull/26062 -- see it for more detail.
I think we will eventually find more cases of this. It's hard to get them all at once as there are many different types of compile errors in earlier modules. I'm trying to address them in as a big a chunk as possible.
Closes#26074 from srowen/SPARK-29401.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
The commit 4e6d31f570 changed default behavior of `size()` for the `NULL` input. In this PR, I propose to update the SQL migration guide.
### Why are the changes needed?
To inform users about new behavior of the `size()` function for the `NULL` input.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26066 from MaxGekk/size-null-migration-guide.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Invocations like `sc.parallelize(Array((1,2)))` cause a compile error in 2.13, like:
```
[ERROR] [Error] /Users/seanowen/Documents/spark_2.13/core/src/test/scala/org/apache/spark/ShuffleSuite.scala:47: overloaded method value apply with alternatives:
(x: Unit,xs: Unit*)Array[Unit] <and>
(x: Double,xs: Double*)Array[Double] <and>
(x: Float,xs: Float*)Array[Float] <and>
(x: Long,xs: Long*)Array[Long] <and>
(x: Int,xs: Int*)Array[Int] <and>
(x: Char,xs: Char*)Array[Char] <and>
(x: Short,xs: Short*)Array[Short] <and>
(x: Byte,xs: Byte*)Array[Byte] <and>
(x: Boolean,xs: Boolean*)Array[Boolean]
cannot be applied to ((Int, Int), (Int, Int), (Int, Int), (Int, Int))
```
Using a `Seq` instead appears to resolve it, and is effectively equivalent.
### Why are the changes needed?
To better cross-build for 2.13.
### Does this PR introduce any user-facing change?
None.
### How was this patch tested?
Existing tests.
Closes#26062 from srowen/SPARK-29401.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Fix a config name typo from the resource scheduling user docs. In case users might get confused with the wrong config name, we'd better fix this typo.
### How was this patch tested?
Document change, no need to run test.
Closes#26047 from jiangxb1987/doc.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
Document SHOW CREATE TABLE statement in SQL Reference
### Why are the changes needed?
To complete the SQL reference.
### Does this PR introduce any user-facing change?
Yes.
after the change:
### How was this patch tested?
Tested using jykyll build --serve
Closes#25885 from huaxingao/spark-28813.
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?
This PR proposes:
1. Use `is.data.frame` to check if it is a DataFrame.
2. to install Arrow and test Arrow optimization in AppVeyor build. We're currently not testing this in CI.
### Why are the changes needed?
1. To support SparkR with Arrow 0.14
2. To check if there's any regression and if it works correctly.
### Does this PR introduce any user-facing change?
```r
df <- createDataFrame(mtcars)
collect(dapply(df, function(rdf) { data.frame(rdf$gear + 1) }, structType("gear double")))
```
**Before:**
```
Error in readBin(con, raw(), as.integer(dataLen), endian = "big") :
invalid 'n' argument
```
**After:**
```
gear
1 5
2 5
3 5
4 4
5 4
6 4
7 4
8 5
9 5
...
```
### How was this patch tested?
AppVeyor
Closes#25993 from HyukjinKwon/arrow-r-appveyor.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR exposes USE CATALOG/USE SQL commands as described in this [SPIP](https://docs.google.com/document/d/1jEcvomPiTc5GtB9F7d2RTVVpMY64Qy7INCA_rFEd9HQ/edit#)
It also exposes `currentCatalog` in `CatalogManager`.
Finally, it changes `SHOW NAMESPACES` and `SHOW TABLES` to use the current catalog if no catalog is specified (instead of default catalog).
### Why are the changes needed?
There is currently no mechanism to change current catalog/namespace thru SQL commands.
### Does this PR introduce any user-facing change?
Yes, you can perform the following:
```scala
// Sets the current catalog to 'testcat'
spark.sql("USE CATALOG testcat")
// Sets the current catalog to 'testcat' and current namespace to 'ns1.ns2'.
spark.sql("USE ns1.ns2 IN testcat")
// Now, the following will use 'testcat' as the current catalog and 'ns1.ns2' as the current namespace.
spark.sql("SHOW NAMESPACES")
```
### How was this patch tested?
Added new unit tests.
Closes#25771 from imback82/use_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Please refer [the link on dev. mailing list](https://lists.apache.org/thread.html/cc6489a19316e7382661d305fabd8c21915e5faf6a928b4869ac2b4a%3Cdev.spark.apache.org%3E) to see rationalization of this patch.
This patch adds the functionality to detect the possible correct issue on multiple stateful operations in single streaming query and logs warning message to inform end users.
This patch also documents some notes to inform caveats when using multiple stateful operations in single query, and provide one known alternative.
## How was this patch tested?
Added new UTs in UnsupportedOperationsSuite to test various combination of stateful operators on streaming query.
Closes#24890 from HeartSaVioR/SPARK-28074.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Updated the SQL migration guide regarding to recently supported special date and timestamp values, see https://github.com/apache/spark/pull/25716 and https://github.com/apache/spark/pull/25708.
Closes#25834
### Why are the changes needed?
To let users know about new feature in Spark 3.0.
### Does this PR introduce any user-facing change?
No
Closes#25948 from MaxGekk/special-values-migration-guide.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Changed 'Phive-thriftserver' to ' -Phive-thriftserver'.
### Why are the changes needed?
Typo
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
Manually tested.
Closes#25937 from TomokoKomiyama/fix-build-doc.
Authored-by: Tomoko Komiyama <btkomiyamatm@oss.nttdata.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Copy any "spark.hive.foo=bar" spark properties into hadoop conf as "hive.foo=bar"
### Why are the changes needed?
Providing spark side config entry for hive configurations.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
UT.
Closes#25661 from WeichenXu123/add_hive_conf.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This patch introduces new options "startingOffsetsByTimestamp" and "endingOffsetsByTimestamp" to set specific timestamp per topic (since we're unlikely to set the different value per partition) to let source starts reading from offsets which have equal of greater timestamp, and ends reading until offsets which have equal of greater timestamp.
The new option would be optional of course, and take preference over existing offset options.
## How was this patch tested?
New unit tests added. Also manually tested basic functionality with Kafka 2.0.0 server.
Running query below
```
val df = spark.read.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "spark_26848_test_v1,spark_26848_test_2_v1")
.option("startingOffsetsByTimestamp", """{"spark_26848_test_v1": 1549669142193, "spark_26848_test_2_v1": 1549669240965}""")
.option("endingOffsetsByTimestamp", """{"spark_26848_test_v1": 1549669265676, "spark_26848_test_2_v1": 1549699265676}""")
.load().selectExpr("CAST(value AS STRING)")
df.show()
```
with below records (one string which number part remarks when they're put after such timestamp) in
topic `spark_26848_test_v1`
```
hello1 1549669142193
world1 1549669142193
hellow1 1549669240965
world1 1549669240965
hello1 1549669265676
world1 1549669265676
```
topic `spark_26848_test_2_v1`
```
hello2 1549669142193
world2 1549669142193
hello2 1549669240965
world2 1549669240965
hello2 1549669265676
world2 1549669265676
```
the result of `df.show()` follows:
```
+--------------------+
| value|
+--------------------+
|world1 1549669240965|
|world1 1549669142193|
|world2 1549669240965|
|hello2 1549669240965|
|hellow1 154966924...|
|hello2 1549669265676|
|hello1 1549669142193|
|world2 1549669265676|
+--------------------+
```
Note that endingOffsets (as well as endingOffsetsByTimestamp) are exclusive.
Closes#23747 from HeartSaVioR/SPARK-26848.
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?
This PR supports UPDATE in the parser and add the corresponding logical plan. The SQL syntax is a standard UPDATE statement:
```
UPDATE tableName tableAlias SET colName=value [, colName=value]+ WHERE predicate?
```
### Why are the changes needed?
With this change, we can start to implement UPDATE in builtin sources and think about how to design the update API in DS v2.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
New test cases added.
Closes#25626 from xianyinxin/SPARK-28892.
Authored-by: xy_xin <xianyin.xxy@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Credit to vanzin as he found and commented on this while reviewing #25670 - [comment](https://github.com/apache/spark/pull/25670#discussion_r325383512).
This patch proposes to specify UTF-8 explicitly while reading/writer event log file.
### Why are the changes needed?
The event log file is being read/written as default character set of JVM process which may open the chance to bring some problems on reading event log files from another machines. Spark's de facto standard character set is UTF-8, so it should be explicitly set to.
### Does this PR introduce any user-facing change?
Yes, if end users have been running Spark process with different default charset than "UTF-8", especially their driver JVM processes. No otherwise.
### How was this patch tested?
Existing UTs, as ReplayListenerSuite contains "end-to-end" event logging/reading tests (both uncompressed/compressed).
Closes#25845 from HeartSaVioR/SPARK-29160.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PRs add Java 11 version to the document.
### Why are the changes needed?
Apache Spark 3.0.0 starts to support JDK11 officially.
### Does this PR introduce any user-facing change?
Yes.
### How was this patch tested?
Manually. Doc generation.
Closes#25875 from dongjoon-hyun/SPARK-29196.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Added document reference for USE databse sql command
### Why are the changes needed?
For USE database command usage
### Does this PR introduce any user-facing change?
It is adding the USE database sql command refernce information in the doc
### How was this patch tested?
Attached the test snap
Closes#25572 from shivusondur/jiraUSEDaBa1.
Lead-authored-by: shivusondur <shivusondur@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This PR aims to increase the JVM CodeCacheSize from 0.5G to 1G.
### Why are the changes needed?
After upgrading to `Scala 2.12.10`, the following is observed during building.
```
2019-09-18T20:49:23.5030586Z OpenJDK 64-Bit Server VM warning: CodeCache is full. Compiler has been disabled.
2019-09-18T20:49:23.5032920Z OpenJDK 64-Bit Server VM warning: Try increasing the code cache size using -XX:ReservedCodeCacheSize=
2019-09-18T20:49:23.5034959Z CodeCache: size=524288Kb used=521399Kb max_used=521423Kb free=2888Kb
2019-09-18T20:49:23.5035472Z bounds [0x00007fa62c000000, 0x00007fa64c000000, 0x00007fa64c000000]
2019-09-18T20:49:23.5035781Z total_blobs=156549 nmethods=155863 adapters=592
2019-09-18T20:49:23.5036090Z compilation: disabled (not enough contiguous free space left)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually check the Jenkins or GitHub Action build log (which should not have the above).
Closes#25836 from dongjoon-hyun/SPARK-CODE-CACHE-1G.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Currently, there are new configurations for compatibility with ANSI SQL:
* `spark.sql.parser.ansi.enabled`
* `spark.sql.decimalOperations.nullOnOverflow`
* `spark.sql.failOnIntegralTypeOverflow`
This PR is to add new configuration `spark.sql.ansi.enabled` and remove the 3 options above. When the configuration is true, Spark tries to conform to the ANSI SQL specification. It will be disabled by default.
### Why are the changes needed?
Make it simple and straightforward.
### Does this PR introduce any user-facing change?
The new features for ANSI compatibility will be set via one configuration `spark.sql.ansi.enabled`.
### How was this patch tested?
Existing unit tests.
Closes#25693 from gengliangwang/ansiEnabled.
Lead-authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR upgrade Scala to **2.12.10**.
Release notes:
- Fix regression in large string interpolations with non-String typed splices
- Revert "Generate shallower ASTs in pattern translation"
- Fix regression in classpath when JARs have 'a.b' entries beside 'a/b'
- Faster compiler: 5–10% faster since 2.12.8
- Improved compatibility with JDK 11, 12, and 13
- Experimental support for build pipelining and outline type checking
More details:
https://github.com/scala/scala/releases/tag/v2.12.10https://github.com/scala/scala/releases/tag/v2.12.9
## How was this patch tested?
Existing tests
Closes#25404 from wangyum/SPARK-28683.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This proposes to improve Spark instrumentation by adding a hook for user-defined metrics, extending Spark’s Dropwizard/Codahale metrics system.
The original motivation of this work was to add instrumentation for S3 filesystem access metrics by Spark job. Currently, [[ExecutorSource]] instruments HDFS and local filesystem metrics. Rather than extending the code there, we proposes with this JIRA to add a metrics plugin system which is of more flexible and general use.
Context: The Spark metrics system provides a large variety of metrics, see also , useful to monitor and troubleshoot Spark workloads. A typical workflow is to sink the metrics to a storage system and build dashboards on top of that.
Highlights:
- The metric plugin system makes it easy to implement instrumentation for S3 access by Spark jobs.
- The metrics plugin system allows for easy extensions of how Spark collects HDFS-related workload metrics. This is currently done using the Hadoop Filesystem GetAllStatistics method, which is deprecated in recent versions of Hadoop. Recent versions of Hadoop Filesystem recommend using method GetGlobalStorageStatistics, which also provides several additional metrics. GetGlobalStorageStatistics is not available in Hadoop 2.7 (had been introduced in Hadoop 2.8). Using a metric plugin for Spark would allow an easy way to “opt in” using such new API calls for those deploying suitable Hadoop versions.
- We also have the use case of adding Hadoop filesystem monitoring for a custom Hadoop compliant filesystem in use in our organization (EOS using the XRootD protocol). The metrics plugin infrastructure makes this easy to do. Others may have similar use cases.
- More generally, this method makes it straightforward to plug in Filesystem and other metrics to the Spark monitoring system. Future work on plugin implementation can address extending monitoring to measure usage of external resources (OS, filesystem, network, accelerator cards, etc), that maybe would not normally be considered general enough for inclusion in Apache Spark code, but that can be nevertheless useful for specialized use cases, tests or troubleshooting.
Implementation:
The proposed implementation extends and modifies the work on Executor Plugin of SPARK-24918. Additionally, this is related to recent work on extending Spark executor metrics, such as SPARK-25228.
As discussed during the review, the implementaiton of this feature modifies the Developer API for Executor Plugins, such that the new version is incompatible with the original version in Spark 2.4.
## How was this patch tested?
This modifies existing tests for ExecutorPluginSuite to adapt them to the API changes. In addition, the new funtionality for registering pluginMetrics has been manually tested running Spark on YARN and K8S clusters, in particular for monitoring S3 and for extending HDFS instrumentation with the Hadoop Filesystem “GetGlobalStorageStatistics” metrics. Executor metric plugin example and code used for testing are available, for example at: https://github.com/cerndb/SparkExecutorPluginsCloses#24901 from LucaCanali/executorMetricsPlugin.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
HDFS doesn't update the file size reported by the NM if you just keep
writing to the file; this makes the SHS believe the file is inactive,
and so it may delete it after the configured max age for log files.
This change uses hsync to keep the log file as up to date as possible
when using HDFS. It also disables erasure coding by default for these
logs, since hsync (& friends) does not work with EC.
Tested with a SHS configured to aggressively clean up logs; verified
a spark-shell session kept updating the log, which was not deleted by
the SHS.
Closes#25819 from vanzin/SPARK-29105.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
Updating unit description in configurations, inorder to maintain consistency across configurations.
### Why are the changes needed?
the description does not mention about suffix that can be mentioned while configuring this value.
For better user understanding
### Does this PR introduce any user-facing change?
yes. Doc description
### How was this patch tested?
generated document and checked.
Closes#25689 from PavithraRamachandran/heapsize_config.
Authored-by: Pavithra Ramachandran <pavi.rams@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Document CREATE DATABASE statement in SQL Reference Guide.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
### Before:
There was no documentation for this.
### After:
### How was this patch tested?
Manual Review and Tested using jykyll build --serve
Closes#25595 from sharangk/createDbDoc.
Lead-authored-by: sharangk <sharan.gk@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Currently, there is no migration section for PySpark, SparkCore and Structured Streaming.
It is difficult for users to know what to do when they upgrade.
This PR proposes to create create a "Migration Guide" tap at Spark documentation.
This page will contain migration guides for Spark SQL, PySpark, SparkR, MLlib, Structured Streaming and Core. Basically it is a refactoring.
There are some new information added, which I will leave a comment inlined for easier review.
1. **MLlib**
Merge [ml-guide.html#migration-guide](https://spark.apache.org/docs/latest/ml-guide.html#migration-guide) and [ml-migration-guides.html](https://spark.apache.org/docs/latest/ml-migration-guides.html)
```
'docs/ml-guide.md'
↓ Merge new/old migration guides
'docs/ml-migration-guide.md'
```
2. **PySpark**
Extract PySpark specific items from https://spark.apache.org/docs/latest/sql-migration-guide-upgrade.html
```
'docs/sql-migration-guide-upgrade.md'
↓ Extract PySpark specific items
'docs/pyspark-migration-guide.md'
```
3. **SparkR**
Move [sparkr.html#migration-guide](https://spark.apache.org/docs/latest/sparkr.html#migration-guide) into a separate file, and extract from [sql-migration-guide-upgrade.html](https://spark.apache.org/docs/latest/sql-migration-guide-upgrade.html)
```
'docs/sparkr.md' 'docs/sql-migration-guide-upgrade.md'
Move migration guide section ↘ ↙ Extract SparkR specific items
docs/sparkr-migration-guide.md
```
4. **Core**
Newly created at `'docs/core-migration-guide.md'`. I skimmed resolved JIRAs at 3.0.0 and found some items to note.
5. **Structured Streaming**
Newly created at `'docs/ss-migration-guide.md'`. I skimmed resolved JIRAs at 3.0.0 and found some items to note.
6. **SQL**
Merged [sql-migration-guide-upgrade.html](https://spark.apache.org/docs/latest/sql-migration-guide-upgrade.html) and [sql-migration-guide-hive-compatibility.html](https://spark.apache.org/docs/latest/sql-migration-guide-hive-compatibility.html)
```
'docs/sql-migration-guide-hive-compatibility.md' 'docs/sql-migration-guide-upgrade.md'
Move Hive compatibility section ↘ ↙ Left over after filtering PySpark and SparkR items
'docs/sql-migration-guide.md'
```
### Why are the changes needed?
In order for users in production to effectively migrate to higher versions, and detect behaviour or breaking changes before upgrading and/or migrating.
### Does this PR introduce any user-facing change?
Yes, this changes Spark's documentation at https://spark.apache.org/docs/latest/index.html.
### How was this patch tested?
Manually build the doc. This can be verified as below:
```bash
cd docs
SKIP_API=1 jekyll build
open _site/index.html
```
Closes#25757 from HyukjinKwon/migration-doc.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This update adds support for Kafka Headers functionality in Structured Streaming.
## How was this patch tested?
With following unit tests:
- KafkaRelationSuite: "default starting and ending offsets with headers" (new)
- KafkaSinkSuite: "batch - write to kafka" (updated)
Closes#22282 from dongjinleekr/feature/SPARK-23539.
Lead-authored-by: Lee Dongjin <dongjin@apache.org>
Co-authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Added document for CREATE VIEW command.
### Why are the changes needed?
As a reference to syntax and examples of CREATE VIEW command.
### How was this patch tested?
Documentation update. Verified manually.
Closes#25543 from amanomer/spark-28795.
Lead-authored-by: aman_omer <amanomer1996@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Co-authored-by: Aman Omer <amanomer1996@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Document DROP DATABASE statement in SQL Reference
### Why are the changes needed?
Currently from spark there is no complete sql guide is present, so it is better to document all the sql commands, this jira is sub part of this task.
### Does this PR introduce any user-facing change?
Yes, Before there was no documentation about drop database syntax
After Fix
### How was this patch tested?
tested with jenkyll build
Closes#25554 from sandeep-katta/dropDbDoc.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Document REFRESH TABLE statement in the SQL Reference Guide.
### Why are the changes needed?
Currently there is no documentation in the SPARK SQL to describe how to use this command, it is to address this issue.
### Does this PR introduce any user-facing change?
Yes.
#### Before:
There is no documentation for this.
#### After:
<img width="826" alt="Screen Shot 2019-09-12 at 11 39 21 AM" src="https://user-images.githubusercontent.com/7550280/64811385-01752600-d552-11e9-876d-91ebb005b851.png">
### How was this patch tested?
Using jykll build --serve
Closes#25549 from kevinyu98/spark-28828-refreshTable.
Authored-by: Kevin Yu <qyu@us.ibm.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
JIRA :https://issues.apache.org/jira/browse/SPARK-29050
'a hdfs' change into 'an hdfs'
'an unique' change into 'a unique'
'an url' change into 'a url'
'a error' change into 'an error'
Closes#25756 from dengziming/feature_fix_typos.
Authored-by: dengziming <dengziming@growingio.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Document the resource scheduling feature - https://issues.apache.org/jira/browse/SPARK-24615
Add general docs, yarn, kubernetes, and standalone cluster specific ones.
### Why are the changes needed?
Help users understand the feature
### Does this PR introduce any user-facing change?
docs
### How was this patch tested?
N/A
Closes#25698 from tgravescs/SPARK-27492-gpu-sched-docs.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Add links to IBM Cloud Storage connector in cloud-integration.md
### Why are the changes needed?
This page mentions the connectors to cloud providers. Currently connector to
IBM cloud storage is not specified. This PR adds the necessary links for
completeness.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
<img width="1234" alt="Screen Shot 2019-09-09 at 3 52 44 PM" src="https://user-images.githubusercontent.com/14225158/64571863-11a2c080-d31a-11e9-82e3-78c02675adb9.png">
**After.**
<img width="1234" alt="Screen Shot 2019-09-10 at 8 16 49 AM" src="https://user-images.githubusercontent.com/14225158/64626857-663e4e00-d3a3-11e9-8fa3-15ebf52ea832.png">
### How was this patch tested?
Tested using jykyll build --serve
Closes#25737 from dilipbiswal/ibm-cloud-storage.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Implement the SHOW DATABASES logical and physical plans for data source v2 tables.
### Why are the changes needed?
To support `SHOW DATABASES` SQL commands for v2 tables.
### Does this PR introduce any user-facing change?
`spark.sql("SHOW DATABASES")` will return namespaces if the default catalog is set:
```
+---------------+
| namespace|
+---------------+
| ns1|
| ns1.ns1_1|
|ns1.ns1_1.ns1_2|
+---------------+
```
### How was this patch tested?
Added unit tests to `DataSourceV2SQLSuite`.
Closes#25601 from imback82/show_databases.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
At the moment there are 3 places where communication protocol with Kafka cluster has to be set when delegation token used:
* On delegation token
* On source
* On sink
Most of the time users are using the same protocol on all these places (within one Kafka cluster). It would be better to declare it in one place (delegation token side) and Kafka sources/sinks can take this config over.
In this PR I've I've modified the code in a way that Kafka sources/sinks are taking over delegation token side `security.protocol` configuration when the token and the source/sink matches in `bootstrap.servers` configuration. This default configuration can be overwritten on each source/sink independently by using `kafka.security.protocol` configuration.
### Why are the changes needed?
The actual configuration's default behavior represents the minority of the use-cases and inconvenient.
### Does this PR introduce any user-facing change?
Yes, with this change users need to provide less configuration parameters by default.
### How was this patch tested?
Existing + additional unit tests.
Closes#25631 from gaborgsomogyi/SPARK-28928.
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?
Document CLEAR CACHE statement in SQL Reference
### Why are the changes needed?
To complete SQL Reference
### Does this PR introduce any user-facing change?
Yes
After change:
### How was this patch tested?
Tested using jykyll build --serve
Closes#25541 from huaxingao/spark-28831-n.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
KinesisInputDStream currently does not provide a way to disable
CloudWatch metrics push. Its default level is "DETAILED" which pushes
10s of metrics every 10 seconds. When dealing with multiple streaming
jobs this add up pretty quickly, leading to thousands of dollars in cost.
To address this problem, this PR adds interfaces for accessing
KinesisClientLibConfiguration's `withMetrics` and
`withMetricsEnabledDimensions` methods to KinesisInputDStream
so that users can configure KCL's metrics levels and dimensions.
## How was this patch tested?
By running updated unit tests in KinesisInputDStreamBuilderSuite.
In addition, I ran a Streaming job with MetricsLevel.NONE and confirmed:
* there's no data point for the "Operation", "Operation, ShardId" and "WorkerIdentifier" dimensions on the AWS management console
* there's no DEBUG level message from Amazon KCL, such as "Successfully published xx datums."
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24651 from sekikn/SPARK-27420.
Authored-by: Kengo Seki <sekikn@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This patch adds the description of common SQL metrics in web ui document.
### Why are the changes needed?
The current web ui document describes query plan but does not describe the meaning SQL metrics. For end users, they might not understand the meaning of the metrics.
### Does this PR introduce any user-facing change?
No. This is just documentation change.
### How was this patch tested?
Built the docs locally.
Closes#25658 from viirya/SPARK-28935.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Document DESCRIBE DATABASE statement in SQL Reference
### Why are the changes needed?
To complete the SQL Reference
### Does this PR introduce any user-facing change?
Yes
#### Before
There is no documentation for this command in sql reference
#### After
### How was this patch tested?
Used jekyll build and serve to verify
Closes#25528 from kevinyu98/sql-ref-describe.
Lead-authored-by: Kevin Yu <qyu@us.ibm.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Document UNCACHE TABLE statement in SQL Reference
### Why are the changes needed?
To complete SQL Reference
### Does this PR introduce any user-facing change?
Yes.
After change:
### How was this patch tested?
Tested using jykyll build --serve
Closes#25540 from huaxingao/spark-28830.
Lead-authored-by: Huaxin Gao <huaxing@us.ibm.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Document SHOW TBLPROPERTIES statement in SQL Reference Guide.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
There was no documentation for this.
**After.**
### How was this patch tested?
Tested using jykyll build --serve
Closes#25571 from dilipbiswal/ref-show-tblproperties.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This patch does pooling for both kafka consumers as well as fetched data. The overall benefits of the patch are following:
* Both pools support eviction on idle objects, which will help closing invalid idle objects which topic or partition are no longer be assigned to any tasks.
* It also enables applying different policies on pool, which helps optimization of pooling for each pool.
* We concerned about multiple tasks pointing same topic partition as well as same group id, and existing code can't handle this hence excess seek and fetch could happen. This patch properly handles the case.
* It also makes the code always safe to leverage cache, hence no need to maintain reuseCache parameter.
Moreover, pooling kafka consumers is implemented based on Apache Commons Pool, which also gives couple of benefits:
* We can get rid of synchronization of KafkaDataConsumer object while acquiring and returning InternalKafkaConsumer.
* We can extract the feature of object pool to outside of the class, so that the behaviors of the pool can be tested easily.
* We can get various statistics for the object pool, and also be able to enable JMX for the pool.
FetchedData instances are pooled by custom implementation of pool instead of leveraging Apache Commons Pool, because they have CacheKey as first key and "desired offset" as second key which "desired offset" is changing - I haven't found any general pool implementations supporting this.
This patch brings additional dependency, Apache Commons Pool 2.6.0 into `spark-sql-kafka-0-10` module.
## How was this patch tested?
Existing unit tests as well as new tests for object pool.
Also did some experiment regarding proving concurrent access of consumers for same topic partition.
* Made change on both sides (master and patch) to log when creating Kafka consumer or fetching records from Kafka is happening.
* branches
* master: https://github.com/HeartSaVioR/spark/tree/SPARK-25151-master-ref-debugging
* patch: https://github.com/HeartSaVioR/spark/tree/SPARK-25151-debugging
* Test query (doing self-join)
* https://gist.github.com/HeartSaVioR/d831974c3f25c02846f4b15b8d232cc2
* Ran query from spark-shell, with using `local[*]` to maximize the chance to have concurrent access
* Collected the count of fetch requests on Kafka via command: `grep "creating new Kafka consumer" logfile | wc -l`
* Collected the count of creating Kafka consumers via command: `grep "fetching data from Kafka consumer" logfile | wc -l`
Topic and data distribution is follow:
```
truck_speed_events_stream_spark_25151_v1:0:99440
truck_speed_events_stream_spark_25151_v1:1:99489
truck_speed_events_stream_spark_25151_v1:2:397759
truck_speed_events_stream_spark_25151_v1:3:198917
truck_speed_events_stream_spark_25151_v1:4:99484
truck_speed_events_stream_spark_25151_v1:5:497320
truck_speed_events_stream_spark_25151_v1:6:99430
truck_speed_events_stream_spark_25151_v1:7:397887
truck_speed_events_stream_spark_25151_v1:8:397813
truck_speed_events_stream_spark_25151_v1:9:0
```
The experiment only used smallest 4 partitions (0, 1, 4, 6) from these partitions to finish the query earlier.
The result of experiment is below:
branch | create Kafka consumer | fetch request
-- | -- | --
master | 1986 | 2837
patch | 8 | 1706
Closes#22138 from HeartSaVioR/SPARK-25151.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Co-authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
If MEMORY_OFFHEAP_ENABLED is true, add MEMORY_OFFHEAP_SIZE to resource requested for executor to ensure instance has enough memory to use.
In this pr add a helper method `executorOffHeapMemorySizeAsMb` in `YarnSparkHadoopUtil`.
## How was this patch tested?
Add 3 new test suite to test `YarnSparkHadoopUtil#executorOffHeapMemorySizeAsMb`
Closes#25309 from LuciferYang/spark-28577.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Document DESCRIBE FUNCTION statement in SQL Reference Guide.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
There was no documentation for this.
**After.**
<img width="1234" alt="Screen Shot 2019-09-02 at 11 14 09 PM" src="https://user-images.githubusercontent.com/14225158/64148193-85534380-cdd7-11e9-9c07-5956b5e8276e.png">
<img width="1234" alt="Screen Shot 2019-09-02 at 11 14 29 PM" src="https://user-images.githubusercontent.com/14225158/64148201-8a17f780-cdd7-11e9-93d8-10ad9932977c.png">
<img width="1234" alt="Screen Shot 2019-09-02 at 11 14 42 PM" src="https://user-images.githubusercontent.com/14225158/64148208-8dab7e80-cdd7-11e9-97c5-3a4ce12cac7a.png">
### How was this patch tested?
Tested using jykyll build --serve
Closes#25530 from dilipbiswal/ref-doc-desc-function.
Lead-authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Document SHOW COLUMNS statement in SQL Reference Guide.
### Why are the changes needed?
Currently Spark lacks documentation on the supported SQL constructs causing
confusion among users who sometimes have to look at the code to understand the
usage. This is aimed at addressing this issue.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
There was no documentation for this.
**After.**
<img width="1234" alt="Screen Shot 2019-09-02 at 11 07 48 PM" src="https://user-images.githubusercontent.com/14225158/64148033-0fe77300-cdd7-11e9-93ee-e5951c7ed33c.png">
<img width="1234" alt="Screen Shot 2019-09-02 at 11 08 08 PM" src="https://user-images.githubusercontent.com/14225158/64148039-137afa00-cdd7-11e9-8bec-634ea9d2594c.png">
<img width="1234" alt="Screen Shot 2019-09-02 at 11 11 45 PM" src="https://user-images.githubusercontent.com/14225158/64148046-17a71780-cdd7-11e9-91c3-95a9c97e7a77.png">
### How was this patch tested?
Tested using jykyll build --serve
Closes#25531 from dilipbiswal/ref-doc-show-columns.
Lead-authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
