## What changes were proposed in this pull request?
Partition column name is required to be unique under the same directory. The following paths are invalid partitioned directory:
```
hdfs://host:9000/path/a=1
hdfs://host:9000/path/b=2
```
If case sensitive, the following paths should be invalid too:
```
hdfs://host:9000/path/a=1
hdfs://host:9000/path/A=2
```
Since column 'a' and 'A' are different, and it is wrong to use either one as the column name in partition schema.
Also, there is a `TODO` comment in the code. Currently the Spark doesn't validate such case when `CASE_SENSITIVE` enabled.
This PR is to resolve the problem.
## How was this patch tested?
Add unit test
Closes#23186 from gengliangwang/SPARK-26230.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to change behaviour of `UnivocityParser` and `FailureSafeParser`, and return all fields that were parsed and converted to expected types successfully instead of just returning a row with all `null`s for a bad input in the `PERMISSIVE` mode. For example, for CSV line `0,2013-111-11 12:13:14` and DDL schema `a int, b timestamp`, new result is `Row(0, null)`.
## How was this patch tested?
It was checked by existing tests from `CsvSuite` and `CsvFunctionsSuite`.
Closes#23120 from MaxGekk/failuresafe-partial-result.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When build hash Map with one row of data and run out of memory, we should throw a SparkOutOfMemoryError exception, which is more accurate than SparkException. this PR fix it.
## How was this patch tested?
N / A
Closes#23190 from heary-cao/throwUnsafeHashedRelation.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
How to reproduce this issue:
```scala
scala> val meta = new org.apache.spark.sql.types.MetadataBuilder().putNull("key").build().json
java.lang.NullPointerException
at org.apache.spark.sql.types.Metadata$.org$apache$spark$sql$types$Metadata$$toJsonValue(Metadata.scala:196)
at org.apache.spark.sql.types.Metadata$$anonfun$1.apply(Metadata.scala:180)
```
This pr fix `NullPointerException` when `Metadata` serialize `null` values.
## How was this patch tested?
unit tests
Closes#23164 from wangyum/SPARK-26198.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
`resource` package is a Unix specific package. See https://docs.python.org/2/library/resource.html and https://docs.python.org/3/library/resource.html.
Note that we document Windows support:
> Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS).
This should be backported into branch-2.4 to restore Windows support in Spark 2.4.1.
## How was this patch tested?
Manually mocking the changed logics.
Closes#23055 from HyukjinKwon/SPARK-26080.
Lead-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add headers to empty csv files when header=true, because otherwise these files are invalid when reading.
## How was this patch tested?
Added test for roundtrip of empty dataframe to csv file with headers and back in CSVSuite
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23173 from koertkuipers/feat-empty-csv-with-header.
Authored-by: Koert Kuipers <koert@tresata.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
UnsupportedOperationException messages are not the same with method name.This PR correct these messages.
## How was this patch tested?
NA
Closes#23154 from lcqzte10192193/wid-lcq-1127.
Authored-by: lichaoqun <li.chaoqun@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
It's a bad idea to use case class as public API, as it has a very wide surface. For example, the `copy` method, its fields, the companion object, etc.
For a particular case, `UserDefinedFunction`. It has a private constructor, and I believe we only want users to access a few methods:`apply`, `nullable`, `asNonNullable`, etc.
However, all its fields, and `copy` method, and the companion object are public unexpectedly. As a result, we made many tricks to work around the binary compatibility issues.
This PR proposes to only make interfaces public, and hide implementations behind with a private class. Now `UserDefinedFunction` is a pure trait, and the concrete implementation is `SparkUserDefinedFunction`, which is private.
Changing class to interface is not binary compatible(but source compatible), so 3.0 is a good chance to do it.
This is the first PR to go with this direction. If it's accepted, I'll create a umbrella JIRA and fix all the public case classes.
## How was this patch tested?
existing tests.
Closes#23178 from cloud-fan/udf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose filtering out all empty files inside of `FileSourceScanExec` and exclude them from file splits. It should reduce overhead of opening and reading files without any data, and as consequence datasources will not produce empty partitions for such files.
## How was this patch tested?
Added a test which creates an empty and non-empty files. If empty files are ignored in load, Text datasource in the `wholetext` mode must create only one partition for non-empty file.
Closes#23130 from MaxGekk/ignore-empty-files.
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 is a follow pr of #23176.
`In` and `InSet` are semantically equal, so the tests for `In` should pass with `InSet`, and vice versa.
This combines those test cases.
## How was this patch tested?
The combined tests and existing tests.
Closes#23187 from ueshin/issues/SPARK-26211/in_inset_tests.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a small change for better debugging: to pass query uuid in IncrementalExecution, when we look at the QueryExecution in isolation to trace back the query.
## How was this patch tested?
N/A - just add some field for better debugging.
Closes#23192 from rxin/SPARK-26241.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In an earlier PR, we missed measuring the optimization phase time for streaming queries. This patch adds it.
## How was this patch tested?
Given this is a debugging feature, and it is very convoluted to add tests to verify the phase is set properly, I am not introducing a streaming specific test.
Closes#23193 from rxin/SPARK-26226-1.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Correct some document description errors.
## How was this patch tested?
N/A
Closes#23162 from 10110346/docerror.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR updates maven version from 3.5.4 to 3.6.0. The release note of the 3.6.0 is [here](https://maven.apache.org/docs/3.6.0/release-notes.html).
From [the release note of the 3.6.0](https://maven.apache.org/docs/3.6.0/release-notes.html), the followings are new features:
1. There had been issues related to the project discoverytime which has been increased in previous version which influenced some of our users.
1. The output in the reactor summary has been improved.
1. There was an issue related to the classpath ordering.
## How was this patch tested?
Existing tests
Closes#23177 from kiszk/SPARK-26212.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently, the common `withTempDir` function is used in Spark SQL test cases. To handle `val dir = Utils. createTempDir()` and `Utils. deleteRecursively (dir)`. Unfortunately, the `withTempDir` function cannot be used in the Spark Core test case. This PR Sharing `withTempDir` function in Spark Sql and SparkCore to clean up SparkCore test cases. thanks.
## How was this patch tested?
N / A
Closes#23151 from heary-cao/withCreateTempDir.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fix unionAll doc in SparkR
## How was this patch tested?
Manually ran test
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#23161 from huaxingao/spark-26189.
Signed-off-by: DylanGuedes <djmgguedesgmail.com>
## What changes were proposed in this pull request?
Addition of float, int and list hints for `pyspark.sql` Hint.
## How was this patch tested?
I did manual tests following the same principles used in the Scala version, and also added unit tests.
Closes#20788 from DylanGuedes/jira-21030.
Authored-by: DylanGuedes <djmgguedes@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
There are a few issues with the current configuration types used in
the kubernetes backend:
- they use type parameters for role-specific specialization, which makes
type signatures really noisy throughout the code base.
- they break encapsulation by forcing the code that creates the config
object to remove the configuration from SparkConf before creating the
k8s-specific wrapper.
- they don't provide an easy way for tests to have default values for
fields they do not use.
This change fixes those problems by:
- creating a base config type with role-specific specialization using
inheritance
- encapsulating the logic of parsing SparkConf into k8s-specific views
inside the k8s config classes
- providing some helper code for tests to easily override just the part
of the configs they want.
Most of the change relates to the above, especially cleaning up the
tests. While doing that, I also made some smaller changes elsewhere:
- removed unnecessary type parameters in KubernetesVolumeSpec
- simplified the error detection logic in KubernetesVolumeUtils; all
the call sites would just throw the first exception collected by
that class, since they all called "get" on the "Try" object. Now
the unnecessary wrapping is gone and the exception is just thrown
where it occurs.
- removed a lot of unnecessary mocking from tests.
- changed the kerberos-related code so that less logic needs to live
in the driver builder. In spirit it should be part of the upcoming
work in this series of cleanups, but it made parts of this change
simpler.
Tested with existing unit tests and integration tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#22959 from vanzin/SPARK-25876.
## What changes were proposed in this pull request?
This patch changes the query plan tracker added earlier to report phase timeline, rather than just a duration for each phase. This way, we can easily find time that's unaccounted for.
## How was this patch tested?
Updated test cases to reflect that.
Closes#23183 from rxin/SPARK-26226.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Python with rpc and disk encryption enabled along with a python broadcast variable and just read the value back on the driver side the job failed with:
Traceback (most recent call last): File "broadcast.py", line 37, in <module> words_new.value File "/pyspark.zip/pyspark/broadcast.py", line 137, in value File "pyspark.zip/pyspark/broadcast.py", line 122, in load_from_path File "pyspark.zip/pyspark/broadcast.py", line 128, in load EOFError: Ran out of input
To reproduce use configs: --conf spark.network.crypto.enabled=true --conf spark.io.encryption.enabled=true
Code:
words_new = sc.broadcast(["scala", "java", "hadoop", "spark", "akka"])
words_new.value
print(words_new.value)
## How was this patch tested?
words_new = sc.broadcast([“scala”, “java”, “hadoop”, “spark”, “akka”])
textFile = sc.textFile(“README.md”)
wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word + words_new.value[1], 1)).reduceByKey(lambda a, b: a+b)
count = wordCounts.count()
print(count)
words_new.value
print(words_new.value)
Closes#23166 from redsanket/SPARK-26201.
Authored-by: schintap <schintap@oath.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
Fix Typos.
This PR is the complete version of https://github.com/apache/spark/pull/23145.
## How was this patch tested?
NA
Closes#23185 from kjmrknsn/docUpdate.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is the first step of the data source v2 API refactor [proposal](https://docs.google.com/document/d/1uUmKCpWLdh9vHxP7AWJ9EgbwB_U6T3EJYNjhISGmiQg/edit?usp=sharing)
It adds the new API for batch read, without removing the old APIs, as they are still needed for streaming sources.
More concretely, it adds
1. `TableProvider`, works like an anonymous catalog
2. `Table`, represents a structured data set.
3. `ScanBuilder` and `Scan`, a logical represents of data source scan
4. `Batch`, a physical representation of data source batch scan.
## How was this patch tested?
existing tests
Closes#23086 from cloud-fan/refactor-batch.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR is to fix a regression introduced in: https://github.com/apache/spark/pull/21004/files#r236998030
If user specifies schema, Spark don't need to infer data type for of partition columns, otherwise the data type might not match with the one user provided.
E.g. for partition directory `p=4d`, after data type inference the column value will be `4.0`.
See https://issues.apache.org/jira/browse/SPARK-26188 for more details.
Note that user specified schema **might not cover all the data columns**:
```
val schema = new StructType()
.add("id", StringType)
.add("ex", ArrayType(StringType))
val df = spark.read
.schema(schema)
.format("parquet")
.load(src.toString)
assert(df.schema.toList === List(
StructField("ex", ArrayType(StringType)),
StructField("part", IntegerType), // inferred partitionColumn dataType
StructField("id", StringType))) // used user provided partitionColumn dataType
```
For the missing columns in user specified schema, Spark still need to infer their data types if `partitionColumnTypeInferenceEnabled` is enabled.
To implement the partially inference, refactor `PartitioningUtils.parsePartitions` and pass the user specified schema as parameter to cast partition values.
## How was this patch tested?
Add unit test.
Closes#23165 from gengliangwang/fixFileIndex.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently the `SET` command works without any warnings even if the specified key is for `SparkConf` entries and it has no effect because the command does not update `SparkConf`, but the behavior might confuse users. We should track `SparkConf` entries and make the command reject for such entries.
## How was this patch tested?
Added a test and existing tests.
Closes#23031 from ueshin/issues/SPARK-26060/set_command.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to expose `schema_of_json` and `schema_of_csv` at R side.
**`schema_of_json`**:
```r
json <- '{"name":"Bob"}'
df <- sql("SELECT * FROM range(1)")
head(select(df, schema_of_json(json)))
```
```
schema_of_json({"name":"Bob"})
1 struct<name:string>
```
**`schema_of_csv`**:
```r
csv <- "Amsterdam,2018"
df <- sql("SELECT * FROM range(1)")
head(select(df, schema_of_csv(csv)))
```
```
schema_of_csv(Amsterdam,2018)
1 struct<_c0:string,_c1:int>
```
## How was this patch tested?
Manually tested, unit tests added, documentation manually built and verified.
Closes#22939 from HyukjinKwon/SPARK-25446.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It adds kafka delegation token support for structured streaming. Please see the relevant [SPIP](https://docs.google.com/document/d/1ouRayzaJf_N5VQtGhVq9FURXVmRpXzEEWYHob0ne3NY/edit?usp=sharing)
What this PR contains:
* Configuration parameters for the feature
* Delegation token fetching from broker
* Usage of token through dynamic JAAS configuration
* Minor refactoring in the existing code
What this PR doesn't contain:
* Documentation changes because design can change
## How was this patch tested?
Existing tests + added small amount of additional unit tests.
Because it's an external service integration mainly tested on cluster.
* 4 node cluster
* Kafka broker version 1.1.0
* Topic with 4 partitions
* security.protocol = SASL_SSL
* sasl.mechanism = SCRAM-SHA-256
An example of obtaining a token:
```
18/10/01 01:07:49 INFO kafka010.TokenUtil: TOKENID HMAC OWNER RENEWERS ISSUEDATE EXPIRYDATE MAXDATE
18/10/01 01:07:49 INFO kafka010.TokenUtil: D1-v__Q5T_uHx55rW16Jwg [hidden] User:user [] 2018-10-01T01:07 2018-10-02T01:07 2018-10-08T01:07
18/10/01 01:07:49 INFO security.KafkaDelegationTokenProvider: Get token from Kafka: Kind: KAFKA_DELEGATION_TOKEN, Service: kafka.server.delegation.token, Ident: 44 31 2d 76 5f 5f 51 35 54 5f 75 48 78 35 35 72 57 31 36 4a 77 67
```
An example token usage:
```
18/10/01 01:08:07 INFO kafka010.KafkaSecurityHelper: Scram JAAS params: org.apache.kafka.common.security.scram.ScramLoginModule required tokenauth=true serviceName="kafka" username="D1-v__Q5T_uHx55rW16Jwg" password="[hidden]";
18/10/01 01:08:07 INFO kafka010.KafkaSourceProvider: Delegation token detected, using it for login.
```
Closes#22598 from gaborgsomogyi/SPARK-25501.
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?
In the PR, I propose using of the locale option to parse decimals from CSV input. After the changes, `UnivocityParser` converts input string to `BigDecimal` and to Spark's Decimal by using `java.text.DecimalFormat`.
## How was this patch tested?
Added a test for the `en-US`, `ko-KR`, `ru-RU`, `de-DE` locales.
Closes#22979 from MaxGekk/decimal-parsing-locale.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In `BlockManager`, `getRemoteValues` gets a `ChunkedByteBuffer` (by calling `getRemoteBytes`) and creates an `InputStream` from it. `getRemoteBytes`, in turn, gets a `ManagedBuffer` and converts it to a `ChunkedByteBuffer`.
Instead, expose a `getRemoteManagedBuffer` method so `getRemoteValues` can just get this `ManagedBuffer` and use its `InputStream`.
When reading a remote cache block from disk, this reduces heap memory usage significantly.
Retain `getRemoteBytes` for other callers.
## How was this patch tested?
Imran Rashid wrote an application (https://github.com/squito/spark_2gb_test/blob/master/src/main/scala/com/cloudera/sparktest/LargeBlocks.scala), that among other things, tests reading remote cache blocks. I ran this application, using 2500MB blocks, to test reading a cache block on disk. Without this change, with `--executor-memory 5g`, the test fails with `java.lang.OutOfMemoryError: Java heap space`. With the change, the test passes with `--executor-memory 2g`.
I also ran the unit tests in core. In particular, `DistributedSuite` has a set of tests that exercise the `getRemoteValues` code path. `BlockManagerSuite` has several tests that call `getRemoteBytes`; I left these unchanged, so `getRemoteBytes` still gets exercised.
Closes#23058 from wypoon/SPARK-25905.
Authored-by: Wing Yew Poon <wypoon@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Follow up for https://github.com/apache/spark/pull/23128, move sql read metrics relatives to `SQLShuffleMetricsReporter`, in order to put sql shuffle read metrics relatives closer and avoid possible problem about forgetting update SQLShuffleMetricsReporter while new metrics added by others.
## How was this patch tested?
Existing tests.
Closes#23175 from xuanyuanking/SPARK-26142-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request?
Enhance accuracy of the covariance logic in RowMatrix for function computeCovariance
## How was this patch tested?
Unit test
Accuracy test
Closes#23126 from KyleLi1985/master.
Authored-by: 李亮 <liang.li.work@outlook.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Adds USER directives to the Dockerfiles which is configurable via build argument (`spark_uid`) for easy customisation. A `-u` flag is added to `bin/docker-image-tool.sh` to make it easy to customise this e.g.
```
> bin/docker-image-tool.sh -r rvesse -t uid -u 185 build
> bin/docker-image-tool.sh -r rvesse -t uid push
```
If no UID is explicitly specified it defaults to `185` - this is per skonto's suggestion to align with the OpenShift standard reserved UID for Java apps (
https://lists.openshift.redhat.com/openshift-archives/users/2016-March/msg00283.html)
Notes:
- We have to make the `WORKDIR` writable by the root group or otherwise jobs will fail with `AccessDeniedException`
To Do:
- [x] Debug and resolve issue with client mode test
- [x] Consider whether to always propagate `SPARK_USER_NAME` to environment of driver and executor pods so `entrypoint.sh` can insert that into `/etc/passwd` entry
- [x] Rebase once PR #23013 is merged and update documentation accordingly
Built the Docker images with the new Dockerfiles that include the `USER` directives. Ran the Spark on K8S integration tests against the new images. All pass except client mode which I am currently debugging further.
Also manually dropped myself into the resulting container images via `docker run` and checked `id -u` output to see that UID is as expected.
Tried customising the UID from the default via the new `-u` argument to `docker-image-tool.sh` and again checked the resulting image for the correct runtime UID.
cc felixcheung skonto vanzin
Closes#23017 from rvesse/SPARK-26015.
Authored-by: Rob Vesse <rvesse@dotnetrdf.org>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Fix Typos.
## How was this patch tested?
NA
Closes#23145 from kjmrknsn/docUpdate.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to postpone creation of `OutputStream`/`Univocity`/`JacksonGenerator` till the first row should be written. This prevents creation of empty files for empty partitions. So, no need to open and to read such files back while loading data from the location.
## How was this patch tested?
Added tests for Text, JSON and CSV datasource where empty dataset is written but should not produce any files.
Closes#23052 from MaxGekk/text-empty-files.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add a maven plugin and wrapper script to use scalafmt to format files that differ from git master.
Intention is for contributors to be able to use this to automate fixing code style, not to include it in build pipeline yet.
If this PR is accepted, I'd make a different PR to update the code style section of https://spark.apache.org/contributing.html to mention the script
## How was this patch tested?
Manually tested by modifying a few files and running ./dev/scalafmt then checking that ./dev/scalastyle still passed.
Closes#23148 from koeninger/scalafmt.
Authored-by: cody koeninger <cody@koeninger.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
add following param to instr:
GBTC: validationTol
GBTR: validationTol, validationIndicatorCol
colnames in LiR, LinearSVC, etc
## How was this patch tested?
existing tests
Closes#23122 from zhengruifeng/instr_append_missing_params.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Follow-up to remove extra blank lines in R function descriptions
## How was this patch tested?
N/A
Closes#23167 from srowen/SPARK-26024.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently `InSet` doesn't work properly for binary type, or struct and array type with null value in the set.
Because, as for binary type, the `HashSet` doesn't work properly for `Array[Byte]`, and as for struct and array type with null value in the set, the `ordering` will throw a `NPE`.
## How was this patch tested?
Added a few tests.
Closes#23176 from ueshin/issues/SPARK-26211/inset.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose using of the locale option to parse (and infer) decimals from JSON input. After the changes, `JacksonParser` converts input string to `BigDecimal` and to Spark's Decimal by using `java.text.DecimalFormat`. New behaviour can be switched off via SQL config `spark.sql.legacy.decimalParsing.enabled`.
## How was this patch tested?
Added 2 tests to `JsonExpressionsSuite` for the `en-US`, `ko-KR`, `ru-RU`, `de-DE` locales:
- Inferring decimal type using locale from JSON field values
- Converting JSON field values to specified decimal type using the locales.
Closes#23132 from MaxGekk/json-decimal-parsing-locale.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
We have deprecated `OneHotEncoder` at Spark 2.3.0 and introduced `OneHotEncoderEstimator`. At 3.0.0, we remove deprecated `OneHotEncoder` and rename `OneHotEncoderEstimator` to `OneHotEncoder`.
TODO: According to ML migration guide, we need to keep `OneHotEncoderEstimator` as an alias after renaming. This is not done at this patch in order to facilitate review.
## How was this patch tested?
Existing tests.
Closes#23100 from viirya/remove_one_hot_encoder.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Currently duplicated map keys are not handled consistently. For example, map look up respects the duplicated key appears first, `Dataset.collect` only keeps the duplicated key appears last, `MapKeys` returns duplicated keys, etc.
This PR proposes to remove duplicated map keys with last wins policy, to follow Java/Scala and Presto. It only applies to built-in functions, as users can create map with duplicated map keys via private APIs anyway.
updated functions: `CreateMap`, `MapFromArrays`, `MapFromEntries`, `StringToMap`, `MapConcat`, `TransformKeys`.
For other places:
1. data source v1 doesn't have this problem, as users need to provide a java/scala map, which can't have duplicated keys.
2. data source v2 may have this problem. I've added a note to `ArrayBasedMapData` to ask the caller to take care of duplicated keys. In the future we should enforce it in the stable data APIs for data source v2.
3. UDF doesn't have this problem, as users need to provide a java/scala map. Same as data source v1.
4. file format. I checked all of them and only parquet does not enforce it. For backward compatibility reasons I change nothing but leave a note saying that the behavior will be undefined if users write map with duplicated keys to parquet files. Maybe we can add a config and fail by default if parquet files have map with duplicated keys. This can be done in followup.
## How was this patch tested?
updated tests and new tests
Closes#23124 from cloud-fan/map.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
ignore empty output columns
## How was this patch tested?
added tests
Closes#22991 from zhengruifeng/ovrm_empty_outcol.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR changes the broadcast object in TorrentBroadcast from a strong reference to a weak reference. This allows it to be garbage collected even if the Dataset is held in memory. This is ok, because the broadcast object can always be re-read.
## How was this patch tested?
Tested in Spark shell by taking a heap dump, full repro steps listed in https://issues.apache.org/jira/browse/SPARK-25998.
Closes#22995 from bkrieger/bk/torrent-broadcast-weak.
Authored-by: Brandon Krieger <bkrieger@palantir.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… of hard coded "/" in DependencyUtils
## What changes were proposed in this pull request?
Use Java system property "file.separator" instead of hard coded "/" in DependencyUtils.
## How was this patch tested?
Manual test:
Submit Spark application via REST API that reads data from Elasticsearch using spark-elasticsearch library.
Without fix application fails with error:
18/11/22 10:36:20 ERROR Version: Multiple ES-Hadoop versions detected in the classpath; please use only one
jar:file:/C:/<...>/spark-2.4.0-bin-hadoop2.6/work/driver-20181122103610-0001/myApp-assembly-1.0.jar
jar:file:/C:/<...>/myApp-assembly-1.0.jar
18/11/22 10:36:20 ERROR Main: Application [MyApp] failed:
java.lang.Error: Multiple ES-Hadoop versions detected in the classpath; please use only one
jar:file:/C:/<...>/spark-2.4.0-bin-hadoop2.6/work/driver-20181122103610-0001/myApp-assembly-1.0.jar
jar:file:/C:/<...>/myApp-assembly-1.0.jar
at org.elasticsearch.hadoop.util.Version.<clinit>(Version.java:73)
at org.elasticsearch.hadoop.rest.RestService.findPartitions(RestService.java:214)
at org.elasticsearch.spark.rdd.AbstractEsRDD.esPartitions$lzycompute(AbstractEsRDD.scala:73)
at org.elasticsearch.spark.rdd.AbstractEsRDD.esPartitions(AbstractEsRDD.scala:72)
at org.elasticsearch.spark.rdd.AbstractEsRDD.getPartitions(AbstractEsRDD.scala:44)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.collect(RDD.scala:944)
...
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.worker.DriverWrapper$.main(DriverWrapper.scala:65)
at org.apache.spark.deploy.worker.DriverWrapper.main(DriverWrapper.scala)
With fix application runs successfully.
Closes#23102 from markpavey/JIRA_SPARK-26137_DependencyUtilsFileSeparatorFix.
Authored-by: Mark Pavey <markpavey@exabre.co.uk>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/22326 made a mistake that, not all python UDFs are unevaluable in join condition. Only python UDFs that refer to attributes from both join side are unevaluable.
This PR fixes this mistake.
## How was this patch tested?
a new test
Closes#23153 from cloud-fan/join.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pull request fixes [SPARK-26114](https://issues.apache.org/jira/browse/SPARK-26114) issue that occurs when trying to reduce the number of partitions by means of coalesce without shuffling after shuffle-based transformations.
The leak occurs because of not cleaning up `ExternalSorter`'s `readingIterator` field as it's done for its `map` and `buffer` fields.
Additionally there are changes to the `CompletionIterator` to prevent capturing its `sub`-iterator and holding it even after the completion iterator completes. It is necessary because in some cases, e.g. in case of standard scala's `flatMap` iterator (which is used is `CoalescedRDD`'s `compute` method) the next value of the main iterator is assigned to `flatMap`'s `cur` field only after it is available.
For DAGs where ShuffledRDD is a parent of CoalescedRDD it means that the data should be fetched from the map-side of the shuffle, but the process of fetching this data consumes quite a lot of memory in addition to the memory already consumed by the iterator held by `flatMap`'s `cur` field (until it is reassigned).
For the following data
```scala
import org.apache.hadoop.io._
import org.apache.hadoop.io.compress._
import org.apache.commons.lang._
import org.apache.spark._
// generate 100M records of sample data
sc.makeRDD(1 to 1000, 1000)
.flatMap(item => (1 to 100000)
.map(i => new Text(RandomStringUtils.randomAlphanumeric(3).toLowerCase) -> new Text(RandomStringUtils.randomAlphanumeric(1024))))
.saveAsSequenceFile("/tmp/random-strings", Some(classOf[GzipCodec]))
```
and the following job
```scala
import org.apache.hadoop.io._
import org.apache.spark._
import org.apache.spark.storage._
val rdd = sc.sequenceFile("/tmp/random-strings", classOf[Text], classOf[Text])
rdd
.map(item => item._1.toString -> item._2.toString)
.repartitionAndSortWithinPartitions(new HashPartitioner(1000))
.coalesce(10,false)
.count
```
... executed like the following
```bash
spark-shell \
--num-executors=5 \
--executor-cores=2 \
--master=yarn \
--deploy-mode=client \
--conf spark.executor.memoryOverhead=512 \
--conf spark.executor.memory=1g \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.executor.extraJavaOptions='-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp -Dio.netty.noUnsafe=true'
```
... executors are always failing with OutOfMemoryErrors.
The main issue is multiple leaks of ExternalSorter references.
For example, in case of 2 tasks per executor it is expected to be 2 simultaneous instances of ExternalSorter per executor but heap dump generated on OutOfMemoryError shows that there are more ones.
P.S. This PR does not cover cases with CoGroupedRDDs which use ExternalAppendOnlyMap internally, which itself can lead to OutOfMemoryErrors in many places.
## How was this patch tested?
- Existing unit tests
- New unit tests
- Job executions on the live environment
Here is the screenshot before applying this patch
Here is the screenshot after applying this patch
And in case of reducing the number of executors even more the job is still stable
Closes#23083 from szhem/SPARK-26114-externalsorter-leak.
Authored-by: Sergey Zhemzhitsky <szhemzhitski@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
a followup of https://github.com/apache/spark/pull/23043 . Add a test to show the minor behavior change introduced by #23043 , and add migration guide.
## How was this patch tested?
a new test
Closes#23141 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
