### What changes were proposed in this pull request?
For same SQL
```
SELECT TRANSFORM(a, b, c, null)
ROW FORMAT DELIMITED
USING 'cat'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '&'
FROM (select 1 as a, 2 as b, 3 as c) t
```
In hive:
```
hive> SELECT TRANSFORM(a, b, c, null)
> ROW FORMAT DELIMITED
> USING 'cat'
> ROW FORMAT DELIMITED
> FIELDS TERMINATED BY '&'
> FROM (select 1 as a, 2 as b, 3 as c) t;
OK
123\N NULL
Time taken: 14.519 seconds, Fetched: 1 row(s)
hive> packet_write_wait: Connection to 10.191.58.100 port 32200: Broken pipe
```
In Spark
```
Spark master: local[*], Application Id: local-1609225830376
spark-sql> SELECT TRANSFORM(a, b, c, null)
> ROW FORMAT DELIMITED
> USING 'cat'
> ROW FORMAT DELIMITED
> FIELDS TERMINATED BY '&'
> FROM (select 1 as a, 2 as b, 3 as c) t;
1 2 3 null NULL
Time taken: 4.297 seconds, Fetched 1 row(s)
spark-sql>
```
We should keep same. Change default ROW FORMAT FIELD DELIMIT to `\u0001`
In hive default value is '1' to char is '\u0001'
```
bucket_count -1
column.name.delimiter ,
columns
columns.comments
columns.types
file.inputformat org.apache.hadoop.hive.ql.io.NullRowsInputFormat
```
### Why are the changes needed?
Keep same behavior with hive
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT
Closes#30958 from AngersZhuuuu/SPARK-33930.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Update the SQL migration guide about the changes made by:
- https://github.com/apache/spark/pull/30778
- https://github.com/apache/spark/pull/30711
- https://github.com/apache/spark/pull/30866
### Why are the changes needed?
To inform users about the recent changes in the upcoming releases.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#30925 from MaxGekk/sql-migr-guide-hiveclientimpl.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Support add jar with ivy path
### Why are the changes needed?
Since submit app can support ivy, add jar we can also support ivy now.
### Does this PR introduce _any_ user-facing change?
User can add jar with sql like
```
add jar ivy:://group:artifict:version?exclude=xxx,xxx&transitive=true
add jar ivy:://group:artifict:version?exclude=xxx,xxx&transitive=false
```
core api
```
sparkContext.addJar("ivy:://group:artifict:version?exclude=xxx,xxx&transitive=true")
sparkContext.addJar("ivy:://group:artifict:version?exclude=xxx,xxx&transitive=false")
```
#### Doc Update snapshot
### How was this patch tested?
Added UT
Closes#29966 from AngersZhuuuu/support-add-jar-ivy.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
followup of a3dd8dacee via suggestion https://github.com/apache/spark/pull/30888#discussion_r547822642
### Why are the changes needed?
doc improvement
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
passing GA doc
Closes#30909 from yaooqinn/SPARK-33877-F.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR is a followup of https://github.com/apache/spark/pull/28788, and proposes to update migration guide.
### Why are the changes needed?
To tell users about the behaviour change.
### Does this PR introduce _any_ user-facing change?
Yes, it updates migration guides for users.
### How was this patch tested?
GitHub Actions' documentation build should test it.
Closes#30903 from HyukjinKwon/SPARK-31960-followup.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to enable `spark.storage.replication.proactive` by default for Apache Spark 3.2.0.
### Why are the changes needed?
`spark.storage.replication.proactive` is added by SPARK-15355 at Apache Spark 2.2.0 and has been helpful when the block manager loss occurs frequently like K8s environment.
### Does this PR introduce _any_ user-facing change?
Yes, this will make the Spark jobs more robust.
### How was this patch tested?
Pass the existing UTs.
Closes#30876 from dongjoon-hyun/SPARK-33870.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add `spark.sql.files.minPartitionNum` and it's description to sql-performence-tuning.md.
### Why are the changes needed?
Help user to find it.
### Does this PR introduce _any_ user-facing change?
Yes, it's the doc.
### How was this patch tested?
Pass CI.
Closes#30838 from ulysses-you/SPARK-33840.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This updates SS documentation to document about State Store and task locality.
### Why are the changes needed?
During running some tests for structured streaming, I found state store locality becomes an issue sometimes and it is not very straightforward for end-users. It'd be great if we can document it.
### Does this PR introduce _any_ user-facing change?
No, only doc change.
### How was this patch tested?
No, only doc change.
Closes#30789 from viirya/ss-statestore-doc.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This is a simple change to support allocating a specified amount of overhead memory for the driver's mesos container. This is already supported for executors.
### Why are the changes needed?
This is needed to keep the driver process from exceeding memory limits and being killed off when running on mesos.
### Does this PR introduce _any_ user-facing change?
Yes, it adds a `spark.mesos.driver.memoryOverhead` configuration option. Documentation changes for this option are included in the PR.
### How was this patch tested?
Test cases covering allocation of driver memory overhead are included in the changes.
### Other notes
This is a second attempt to get this change reviewed, accepted and merged. The original pull request was closed as stale back in January: https://github.com/apache/spark/pull/21006.
For this pull request, I took the original change by pmackles, rebased it onto the current master branch, and added a test case that was requested in the original code review.
I'm happy to make any further edits or do anything needed so that this can be included in a future spark release. I keep having to build custom spark distributions so that we can use spark within our mesos clusters.
Closes#30739 from dmcwhorter/dmcwhorter-SPARK-22256.
Lead-authored-by: David McWhorter <david_mcwhorter@premierinc.com>
Co-authored-by: Paul Mackles <pmackles@adobe.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes:
- Respect `PYSPARK_PYTHON` and `PYSPARK_DRIVER_PYTHON` environment variables, or `spark.pyspark.python` and `spark.pyspark.driver.python` configurations in Kubernates just like other cluster types in Spark.
- Depreate `spark.kubernetes.pyspark.pythonVersion` and guide users to set the environment variables and configurations for Python executables.
NOTE that `spark.kubernetes.pyspark.pythonVersion` is already a no-op configuration without this PR. Default is `3` and other values are disallowed.
- In order for Python executable settings to be consistently used, fix `spark.archives` option to unpack into the current working directory in the driver of Kubernates' cluster mode. This behaviour is identical with Yarn's cluster mode. By doing this, users can leverage Conda or virtuenenv in cluster mode as below:
```python
conda create -y -n pyspark_conda_env -c conda-forge pyarrow pandas conda-pack
conda activate pyspark_conda_env
conda pack -f -o pyspark_conda_env.tar.gz
PYSPARK_PYTHON=./environment/bin/python spark-submit --archives pyspark_conda_env.tar.gz#environment app.py
```
- Removed several unused or useless codes such as `extractS3Key` and `renameResourcesToLocalFS`
### Why are the changes needed?
- To provide a consistent support of PySpark by using `PYSPARK_PYTHON` and `PYSPARK_DRIVER_PYTHON` environment variables, or `spark.pyspark.python` and `spark.pyspark.driver.python` configurations.
- To provide Conda and virtualenv support via `spark.archives` options.
### Does this PR introduce _any_ user-facing change?
Yes:
- `spark.kubernetes.pyspark.pythonVersion` is deprecated.
- `PYSPARK_PYTHON` and `PYSPARK_DRIVER_PYTHON` environment variables, and `spark.pyspark.python` and `spark.pyspark.driver.python` configurations are respected.
### How was this patch tested?
Manually tested via:
```bash
minikube delete
minikube start --cpus 12 --memory 16384
kubectl create namespace spark-integration-test
cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: ServiceAccount
metadata:
name: spark
namespace: spark-integration-test
EOF
kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=spark-integration-test:spark --namespace=spark-integration-test
dev/make-distribution.sh --pip --tgz -Pkubernetes
resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --spark-tgz `pwd`/spark-3.2.0-SNAPSHOT-bin-3.2.0.tgz --service-account spark --namespace spark-integration-test
```
Unittests were also added.
Closes#30735 from HyukjinKwon/SPARK-33748.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Addressed comments in PR #30567, including:
1. add test case for SPARK-33647 and SPARK-33142
2. add migration guide
3. add `getRawTempView` and `getRawGlobalTempView` to return the raw view info (i.e. TemporaryViewRelation)
4. other minor code clean
### Why are the changes needed?
Code clean and more test cases
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing and newly added test cases
Closes#30666 from linhongliu-db/SPARK-33142-followup.
Lead-authored-by: Linhong Liu <linhong.liu@databricks.com>
Co-authored-by: Linhong Liu <67896261+linhongliu-db@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently, when casting a string as timestamp type in ANSI mode, Spark throws a runtime exception on parsing error.
However, the result for casting a string to date is always null. We should throw an exception on parsing error as well.
### Why are the changes needed?
Add missing feature for ANSI mode
### Does this PR introduce _any_ user-facing change?
Yes for ANSI mode, Casting string to date will throw an exception on parsing error
### How was this patch tested?
Unit test
Closes#30687 from gengliangwang/castDate.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to escape meta-characters (e.g., \n and \t) in `Dataset.showString`.
Before this PR:
```
scala> Seq("aaa\nbbb\t\tccccc").toDF("value").show()
+--------------+
| value|
+--------------+
|aaa
bbb ccccc|
+--------------+
```
After this PR:
```
+-----------------+
| value|
+-----------------+
|aaa\nbbb\t\tccccc|
+-----------------+
```
### Why are the changes needed?
For better output.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added a unit test.
Closes#30647 from maropu/EscapeMetaInShow.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add make_date/make_timestamp/make_interval into the doc of ANSI Compliance
### Why are the changes needed?
Users can know that these functions throw runtime exceptions under ANSI mode if the result is not valid.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Build doc and check it in browser:
Closes#30683 from gengliangwang/improveDoc.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add migration guide for CHAR VARCHAR types
### Why are the changes needed?
for migration
### Does this PR introduce _any_ user-facing change?
doc change
### How was this patch tested?
passing ci
Closes#30654 from yaooqinn/SPARK-33641-F.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to enable `spark.sql.adaptive.enabled` by default for Apache Spark **3.2.0**.
### Why are the changes needed?
By switching the default for Apache Spark 3.2, the whole community can focus more on the stabilizing this feature in the various situation more seriously.
### Does this PR introduce _any_ user-facing change?
Yes, but this is an improvement and it's supposed to have no bugs.
### How was this patch tested?
Pass the CIs.
Closes#30628 from dongjoon-hyun/SPARK-33679.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to update `master` branch version to 3.2.0-SNAPSHOT.
### Why are the changes needed?
Start to prepare Apache Spark 3.2.0.
### Does this PR introduce _any_ user-facing change?
N/A.
### How was this patch tested?
Pass the CIs.
Closes#30606 from dongjoon-hyun/SPARK-3.2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Update kafka headers documentation, type is not longer a map but an array
[jira](https://issues.apache.org/jira/browse/SPARK-33660)
### Why are the changes needed?
To help users
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
It is only documentation
Closes#30605 from Gschiavon/SPARK-33660-fix-kafka-headers-documentation.
Authored-by: german <germanschiavon@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to make `spark.archives` configuration working in Kubernates.
It works without a problem in standalone cluster but there seems a bug in Kubernates.
It fails to fetch the file on the driver side as below:
```
20/12/03 13:33:53 INFO SparkContext: Added JAR file:/tmp/spark-75004286-c83a-4369-b624-14c5d2d2a748/spark-examples_2.12-3.1.0-SNAPSHOT.jar at spark://spark-test-app-48ae737628cee6f8-driver-svc.spark-integration-test.svc:7078/jars/spark-examples_2.12-3.1.0-SNAPSHOT.jar with timestamp 1607002432558
20/12/03 13:33:53 INFO SparkContext: Added archive file:///tmp/tmp4542734800151332666.txt.tar.gz#test_tar_gz at spark://spark-test-app-48ae737628cee6f8-driver-svc.spark-integration-test.svc:7078/files/tmp4542734800151332666.txt.tar.gz with timestamp 1607002432558
20/12/03 13:33:53 INFO TransportClientFactory: Successfully created connection to spark-test-app-48ae737628cee6f8-driver-svc.spark-integration-test.svc/172.17.0.4:7078 after 83 ms (47 ms spent in bootstraps)
20/12/03 13:33:53 INFO Utils: Fetching spark://spark-test-app-48ae737628cee6f8-driver-svc.spark-integration-test.svc:7078/files/tmp4542734800151332666.txt.tar.gz to /tmp/spark-66573e24-27a3-427c-99f4-36f06d9e9cd5/fetchFileTemp2665785666227461849.tmp
20/12/03 13:33:53 ERROR SparkContext: Error initializing SparkContext.
java.lang.RuntimeException: Stream '/files/tmp4542734800151332666.txt.tar.gz' was not found.
at org.apache.spark.network.client.TransportResponseHandler.handle(TransportResponseHandler.java:242)
at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:142)
at org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:53)
```
This is because `spark.archives` was not actually added on the driver side correctly. The changes here fix it by adding and resolving URIs correctly.
### Why are the changes needed?
`spark.archives` feature can be leveraged for many things such as Conda support. We should make it working in Kubernates as well.
This is a bug fix too.
### Does this PR introduce _any_ user-facing change?
No, this feature is not out yet.
### How was this patch tested?
I manually tested with Minikube 1.15.1. For an environment issue (?), I had to use a custom namespace, service account and roles. `default` service account does not work for me and complains it doesn't have permissions to get/list pods, etc.
```bash
minikube delete
minikube start --cpus 12 --memory 16384
kubectl create namespace spark-integration-test
cat <<EOF | kubectl apply -f -
apiVersion: v1
kind: ServiceAccount
metadata:
name: spark
namespace: spark-integration-test
EOF
kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=spark-integration-test:spark --namespace=spark-integration-test
dev/make-distribution.sh --pip --tgz -Pkubernetes
resource-managers/kubernetes/integration-tests/dev/dev-run-integration-tests.sh --spark-tgz `pwd`/spark-3.1.0-SNAPSHOT-bin-3.2.0.tgz --service-account spark --namespace spark-integration-test
```
Closes#30581 from HyukjinKwon/SPARK-33615.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Suggest users using Datetime conversion functions in the error message of invalid ANSI explicit casting.
### Why are the changes needed?
In ANSI mode, explicit cast between DateTime types and Numeric types is not allowed.
As of now, we have introduced new functions `UNIX_SECONDS`/`UNIX_MILLIS`/`UNIX_MICROS`/`UNIX_DATE`/`DATE_FROM_UNIX_DATE`, we can show suggestions to users so that they can complete these type conversions precisely and easily in ANSI mode.
### Does this PR introduce _any_ user-facing change?
Yes, better error messages
### How was this patch tested?
Unit test
Closes#30603 from gengliangwang/improveErrorMsgOfExplicitCast.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Improve the documentation of SQL configuration `spark.sql.ansi.enabled`
### Why are the changes needed?
As there are more and more new features under the SQL configuration `spark.sql.ansi.enabled`, we should make it more clear about:
1. what exactly it is
2. where can users find all the features of the ANSI mode
3. whether all the features are exactly from the SQL standard
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
It's just doc change.
Closes#30593 from gengliangwang/reviseAnsiDoc.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
SPARK-9767 remove `ConnectionManager` and related files, the configuration `spark.core.connection.ack.wait.timeout` previously used by `ConnectionManager` is no longer used by other Spark code, but it still exists in the `configuration.md`.
So this pr cleans up the useless configuration item spark.core.connection.ack.wait.timeout` from `configuration.md`.
### Why are the changes needed?
Clean up useless configuration from `configuration.md`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#30569 from LuciferYang/SPARK-33631.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Deprecated `KafkaConsumer.poll(long)` API calls may cause infinite wait in the driver. In this PR I've added a new `AdminClient` based offset fetching which is turned off by default. There is a new flag named `spark.sql.streaming.kafka.useDeprecatedOffsetFetching` (default: `true`) which can be set to `false` to reach the newly added functionality. The Structured Streaming migration guide contains more information what migration consideration must be done. Please see the following [doc](https://docs.google.com/document/d/1gAh0pKgZUgyqO2Re3sAy-fdYpe_SxpJ6DkeXE8R1P7E/edit?usp=sharing) for further details.
The PR contains the following changes:
* Added `AdminClient` based offset fetching
* GroupId prefix feature removed from driver but only in `AdminClient` based approach (`AdminClient` doesn't need any GroupId)
* GroupId override feature removed from driver but only in `AdminClient` based approach (`AdminClient` doesn't need any GroupId)
* Additional unit tests
* Code comment changes
* Minor bugfixes here and there
* Removed Kafka auto topic creation feature but only in `AdminClient` based approach (please see doc for rationale). In short, it's super hidden, not sure anybody ever used in production + error prone.
* Added documentation to `ss-migration-guide` and `structured-streaming-kafka-integration`
### Why are the changes needed?
Driver may hang forever.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing + additional unit tests.
Cluster test with simple Kafka topic to another topic query.
Documentation:
```
cd docs/
SKIP_API=1 jekyll build
```
Manual webpage check.
Closes#29729 from gaborgsomogyi/SPARK-32032.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This patch proposes to provide a new option to specify time-to-live (TTL) for output file entries in FileStreamSink. TTL is defined via current timestamp - the last modified time for the file.
This patch will filter out outdated output files in metadata while compacting batches (other batches don't have functionality to clean entries), which helps metadata to not grow linearly, as well as filtered out files will be "eventually" no longer seen in reader queries which leverage File(Stream)Source.
### Why are the changes needed?
The metadata log greatly helps to easily achieve exactly-once but given the output path is open to arbitrary readers, there's no way to compact the metadata log, which ends up growing the metadata file as query runs for long time, especially for compacted batch.
Lots of end users have been reporting the issue: see comments in [SPARK-24295](https://issues.apache.org/jira/browse/SPARK-24295) and [SPARK-29995](https://issues.apache.org/jira/browse/SPARK-29995), and [SPARK-30462](https://issues.apache.org/jira/browse/SPARK-30462).
(There're some reports from end users which include their workarounds: SPARK-24295)
### Does this PR introduce any user-facing change?
No, as the configuration is new and by default it is not applied.
### How was this patch tested?
New UT.
Closes#28363 from HeartSaVioR/SPARK-27188-v2.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
TL;DR:
- This PR completes the support of archives in Spark itself instead of Yarn-only
- It makes `--archives` option work in other cluster modes too and adds `spark.archives` configuration.
- After this PR, PySpark users can leverage Conda to ship Python packages together as below:
```python
conda create -y -n pyspark_env -c conda-forge pyarrow==2.0.0 pandas==1.1.4 conda-pack==0.5.0
conda activate pyspark_env
conda pack -f -o pyspark_env.tar.gz
PYSPARK_DRIVER_PYTHON=python PYSPARK_PYTHON=./environment/bin/python pyspark --archives pyspark_env.tar.gz#environment
```
- Issue a warning that undocumented and hidden behavior of partial archive handling in `spark.files` / `SparkContext.addFile` will be deprecated, and users can use `spark.archives` and `SparkContext.addArchive`.
This PR proposes to add Spark's native `--archives` in Spark submit, and `spark.archives` configuration. Currently, both are supported only in Yarn mode:
```bash
./bin/spark-submit --help
```
```
Options:
...
Spark on YARN only:
--queue QUEUE_NAME The YARN queue to submit to (Default: "default").
--archives ARCHIVES Comma separated list of archives to be extracted into the
working directory of each executor.
```
This `archives` feature is useful often when you have to ship a directory and unpack into executors. One example is native libraries to use e.g. JNI. Another example is to ship Python packages together by Conda environment.
Especially for Conda, PySpark currently does not have a nice way to ship a package that works in general, please see also https://hyukjin-spark.readthedocs.io/en/stable/user_guide/python_packaging.html#using-zipped-virtual-environment (PySpark new documentation demo for 3.1.0).
The neatest way is arguably to use Conda environment by shipping zipped Conda environment but this is currently dependent on this archive feature. NOTE that we are able to use `spark.files` by relying on its undocumented behaviour that untars `tar.gz` but I don't think we should document such ways and promote people to more rely on it.
Also, note that this PR does not target to add the feature parity of `spark.files.overwrite`, `spark.files.useFetchCache`, etc. yet. I documented that this is an experimental feature as well.
### Why are the changes needed?
To complete the feature parity, and to provide a better support of shipping Python libraries together with Conda env.
### Does this PR introduce _any_ user-facing change?
Yes, this makes `--archives` works in Spark instead of Yarn-only, and adds a new configuration `spark.archives`.
### How was this patch tested?
I added unittests. Also, manually tested in standalone cluster, local-cluster, and local modes.
Closes#30486 from HyukjinKwon/native-archive.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR adds the char/varchar type which is kind of a variant of string type:
1. Char type is fixed-length string. When comparing char type values, we need to pad the shorter one to the longer length.
2. Varchar type is string with a length limitation.
To implement the char/varchar semantic, this PR:
1. Do string length check when writing to char/varchar type columns.
2. Do string padding when reading char type columns. We don't do it at the writing side to save storage space.
3. Do string padding when comparing char type column with string literal or another char type column. (string literal is fixed length so should be treated as char type as well)
To simplify the implementation, this PR doesn't propagate char/varchar type info through functions/operators(e.g. `substring`). That said, a column can only be char/varchar type if it's a table column, not a derived column like `SELECT substring(col)`.
To be safe, this PR doesn't add char/varchar type to the query engine(expression input check, internal row framework, codegen framework, etc.). We will replace char/varchar type by string type with metadata (`Attribute.metadata` or `StructField.metadata`) that includes the original type string before it goes into the query engine. That said, the existing code will not see char/varchar type but only string type.
char/varchar type may come from several places:
1. v1 table from hive catalog.
2. v2 table from v2 catalog.
3. user-specified schema in `spark.read.schema` and `spark.readStream.schema`
4. `Column.cast`
5. schema string in places like `from_json`, pandas UDF, etc. These places use SQL parser which replaces char/varchar with string already, even before this PR.
This PR covers all the above cases, implements the length check and padding feature by looking at string type with special metadata.
### Why are the changes needed?
char and varchar are standard SQL types. varchar is widely used in other databases instead of string type.
### Does this PR introduce _any_ user-facing change?
For hive tables: now the table insertion fails if the value exceeds char/varchar length. Previously we truncate the value silently.
For other tables:
1. now char type is allowed.
2. now we have length check when inserting to varchar columns. Previously we write the value as it is.
### How was this patch tested?
new tests
Closes#30412 from cloud-fan/char.
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 intends to fix typos in the sub-modules:
* `bin`
* `core`
* `docs`
* `external`
* `mllib`
* `repl`
* `pom.xml`
Split per srowen https://github.com/apache/spark/pull/30323#issuecomment-728981618
NOTE: The misspellings have been reported at 706a726f87 (commitcomment-44064356)
### Why are the changes needed?
Misspelled words make it harder to read / understand content.
### Does this PR introduce _any_ user-facing change?
There are various fixes to documentation, etc...
### How was this patch tested?
No testing was performed
Closes#30530 from jsoref/spelling-bin-core-docs-external-mllib-repl.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Change "Apache Arrow in Spark" to "Apache Arrow in PySpark"
and the link to “/sql-pyspark-pandas-with-arrow.html#apache-arrow-in-pyspark”
### Why are the changes needed?
When I click on the menu item it doesn't point to the correct page, and from the parent menu I can infer that the correct menu item name and link should be "Apache Arrow in PySpark".
like this:
image
### Does this PR introduce any user-facing change?
Yes, clicking on the menu item will take you to the correct guide page
### How was this patch tested?
Manually build the doc. This can be verified as below:
cd docs
SKIP_API=1 jekyll build
open _site/sql-pyspark-pandas-with-arrow.html
Closes#30466 from liucht-inspur/master.
Authored-by: liucht <liucht@inspur.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add the missing sub-bullets in the left side of `Spark SQL Guide`
### Why are the changes needed?
The three sub-bullets in the left side is not consistent with the contents (five bullets) in the right side.
### Does this PR introduce _any_ user-facing change?
Yes, you can see more lines in the left menu.
### How was this patch tested?
Manually build the doc as follows. This can be verified as attached:
```
cd docs
SKIP_API=1 jekyll build
firefox _site/sql-pyspark-pandas-with-arrow.html
```
Closes#30537 from kiszk/SPARK-33590.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR makes CreateViewCommand/AlterViewAsCommand capturing runtime SQL configs and store them as view properties. These configs will be applied during the parsing and analysis phases of the view resolution. Users can set `spark.sql.legacy.useCurrentConfigsForView` to `true` to restore the behavior before.
### Why are the changes needed?
This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138) that proposes to unify temp view and permanent view behaviors. This PR makes permanent views mimicking the temp view behavior that "fixes" view semantic by directly storing resolved LogicalPlan. For example, if a user uses spark 2.4 to create a view that contains null values from division-by-zero expressions, she may not want that other users' queries which reference her view throw exceptions when running on spark 3.x with ansi mode on.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
added UT + existing UTs (improved)
Closes#30289 from luluorta/SPARK-33141.
Authored-by: luluorta <luluorta@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Datetime parsing should fail if the input string can't be parsed, or the pattern string is invalid, when ANSI mode is enable. This patch should update GetTimeStamp, UnixTimeStamp, ToUnixTimeStamp and Cast.
### Why are the changes needed?
For ANSI mode.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT and Existing UT.
Closes#30442 from leanken/leanken-SPARK-33498.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the discussion https://github.com/apache/spark/pull/30292#issuecomment-725613417, we planned to apply a new API key for each Spark release. However, it turns that DocSearch supports crawling multiple URLs from one website and filtering by fact key: https://docsearch.algolia.com/docs/config-file/#using-regular-expressions
Thanks to the help from shortcuts, our Spark doc supports multiple version now: https://github.com/algolia/docsearch-configs/pull/2868
This PR is to add the fact key in the search script and update the instruction in the comment.
### Why are the changes needed?
To support filtering Spark documentation search results by the current document version.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manual test
Closes#30469 from gengliangwang/apiKeyFollowUp.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Two new options, _modifiiedBefore_ and _modifiedAfter_, is provided expecting a value in 'YYYY-MM-DDTHH:mm:ss' format. _PartioningAwareFileIndex_ considers these options during the process of checking for files, just before considering applied _PathFilters_ such as `pathGlobFilter.` In order to filter file results, a new PathFilter class was derived for this purpose. General house-keeping around classes extending PathFilter was performed for neatness. It became apparent support was needed to handle multiple potential path filters. Logic was introduced for this purpose and the associated tests written.
### Why are the changes needed?
When loading files from a data source, there can often times be thousands of file within a respective file path. In many cases I've seen, we want to start loading from a folder path and ideally be able to begin loading files having modification dates past a certain point. This would mean out of thousands of potential files, only the ones with modification dates greater than the specified timestamp would be considered. This saves a ton of time automatically and reduces significant complexity managing this in code.
### Does this PR introduce _any_ user-facing change?
This PR introduces an option that can be used with batch-based Spark file data sources. A documentation update was made to reflect an example and usage of the new data source option.
**Example Usages**
_Load all CSV files modified after date:_
`spark.read.format("csv").option("modifiedAfter","2020-06-15T05:00:00").load()`
_Load all CSV files modified before date:_
`spark.read.format("csv").option("modifiedBefore","2020-06-15T05:00:00").load()`
_Load all CSV files modified between two dates:_
`spark.read.format("csv").option("modifiedAfter","2019-01-15T05:00:00").option("modifiedBefore","2020-06-15T05:00:00").load()
`
### How was this patch tested?
A handful of unit tests were added to support the positive, negative, and edge case code paths.
It's also live in a handful of our Databricks dev environments. (quoted from cchighman)
Closes#30411 from HeartSaVioR/SPARK-31962.
Lead-authored-by: CC Highman <christopher.highman@microsoft.com>
Co-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
With `ParseUrl`, instead of return null we throw exception if input string is not a vaild url.
### Why are the changes needed?
For ANSI mode.
### Does this PR introduce _any_ user-facing change?
Yes, user will get exception if `set spark.sql.ansi.enabled=true`.
### How was this patch tested?
Add test.
Closes#30399 from ulysses-you/SPARK-33468.
Lead-authored-by: ulysses <youxiduo@weidian.com>
Co-authored-by: ulysses-you <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make the API key of DocSearch configurable and avoid hardcoding in the HTML template
### Why are the changes needed?
After https://github.com/apache/spark/pull/30292, our Spark documentation site supports searching.
However, the default API key always points to the latest release doc. We have to set different API keys for different releases. Otherwise, the search results are always based on the latest documentation(https://spark.apache.org/docs/latest/) even when visiting the documentation of previous releases.
As per discussion in https://github.com/apache/spark/pull/30292#issuecomment-725613417, we should make the API key configurable and set different values for different releases.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manual test
Closes#30409 from gengliangwang/apiKey.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Adds `from_avro` and `to_avro` functions to SparkR.
### Why are the changes needed?
Feature parity.
### Does this PR introduce _any_ user-facing change?
New functions exposed in SparkR API.
### How was this patch tested?
New unit tests.
Closes#30216 from zero323/SPARK-33304.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In section 6.13 of the ANSI SQL standard, there are syntax rules for valid combinations of the source and target data types.
Comparing the ANSI CAST syntax rules with the current default behavior of Spark:
To make Spark's ANSI mode more ANSI SQL Compatible，I propose to disallow the following casting in ANSI mode:
```
TimeStamp <=> Boolean
Date <=> Boolean
Numeric <=> Timestamp
Numeric <=> Date
Numeric <=> Binary
String <=> Array
String <=> Map
String <=> Struct
```
The following castings are considered invalid in ANSI SQL standard, but they are quite straight forward. Let's Allow them for now
```
Numeric <=> Boolean
String <=> Binary
```
### Why are the changes needed?
Better ANSI SQL compliance
### Does this PR introduce _any_ user-facing change?
Yes, the following castings will not be allowed in ANSI mode:
```
TimeStamp <=> Boolean
Date <=> Boolean
Numeric <=> Timestamp
Numeric <=> Date
Numeric <=> Binary
String <=> Array
String <=> Map
String <=> Struct
```
### How was this patch tested?
Unit test
The ANSI Compliance doc preview:
Closes#30260 from gengliangwang/ansiCanCast.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to generalize executor metrics to support user-given file system schemes instead of the fixed `file,hdfs` scheme.
### Why are the changes needed?
For the users using only cloud storages like `S3A`, we need to be able to expose `S3A` metrics. Also, we can skip unused `hdfs` metrics.
### Does this PR introduce _any_ user-facing change?
Yes, but compatible for the existing users which uses `hdfs` and `file` filesystem scheme only.
### How was this patch tested?
Manually do the following.
```
$ build/sbt -Phadoop-cloud package
$ sbin/start-master.sh; sbin/start-slave.sh spark://$(hostname):7077
$ bin/spark-shell --master spark://$(hostname):7077 -c spark.executor.metrics.fileSystemSchemes=file,s3a -c spark.metrics.conf.executor.sink.jmx.class=org.apache.spark.metrics.sink.JmxSink
scala> spark.read.textFile("s3a://dongjoon/README.md").collect()
```
Separately, launch `jconsole` and check `*.executor.filesystem.s3a.*`. Also, confirm that there is no `*.executor.filesystem.hdfs.*`
```
$ jconsole
```
Closes#30405 from dongjoon-hyun/SPARK-33476.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
I push this PR as I could not re-open the stale one https://github.com/apache/spark/pull/20665 .
As for Yarn or Kubernetes, Mesos users should be able to specify priority queues to define a workload management policy for queued drivers in the Mesos Cluster Dispatcher.
This would ensure scheduling order while enqueuing Spark applications for a Mesos cluster.
### Why are the changes needed?
Currently, submitted drivers are kept in order of their submission: the first driver added to the queue will be the first one to be executed (FIFO), regardless of their priority.
See https://issues.apache.org/jira/projects/SPARK/issues/SPARK-23499 for more details.
### Does this PR introduce _any_ user-facing change?
The MesosClusterDispatcher UI shows now Spark jobs along with the queue to which they are submitted.
### How was this patch tested?
Unit tests.
Also, this feature has been in production for 3 years now as we use a modified Spark 2.4.0 since then.
Closes#30352 from pgillet/mesos-scheduler-priority-queue.
Lead-authored-by: Pascal Gillet <pascal.gillet@stack-labs.com>
Co-authored-by: pgillet <pascalgillet@ymail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Instead of returning NULL, throws runtime NoSuchElementException towards invalid key accessing in map-like functions, such as element_at, GetMapValue, when ANSI mode is on.
### Why are the changes needed?
For ANSI mode.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added UT and Existing UT.
Closes#30386 from leanken/leanken-SPARK-33460.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This adds support for Stage level scheduling to kubernetes. Kubernetes can support dynamic allocation via the shuffle tracking option which means we can support stage level scheduling by getting new executors.
The main changes here are having the k8s cluster manager pass the resource profile id into the executors and then the ExecutorsPodsAllocator has to request executors based on the individual resource profiles. I tried to keep code changes here to a minimum. I specifically choose to leave the ExecutorPodsSnapshot the way it was and construct the resource profile to pod states on the fly, with a fast path when not using other resource profiles, to keep the impact to a minimum. This results in the main changes required are just wrapping the allocation logic in a for loop over each profile. The other main change is in the basic feature step we have to look at the resources in the ResourceProfile to request pods with the correct resources. Much of the other logic like in the executor life cycle manager doesn't need to be resource profile.
This also adds support for [SPARK-32661]Spark executors on K8S should request extra memory for off-heap allocations because the stage level scheduling api has support for this and it made sense to make consistent with YARN. This was started with PR https://github.com/apache/spark/pull/29477 but never updated so I just did it here. To do this I moved a few functions around that were now used by both YARN and kubernetes so you will see some changes in Utils.
### Why are the changes needed?
Add the feature to Kubernetes based on customer feedback.
### Does this PR introduce _any_ user-facing change?
Yes the feature now works with K8s, but not underlying API changes.
### How was this patch tested?
Tested manually on kubernetes cluster and with unit tests.
Closes#30204 from tgravescs/stagek8sOrigSnapshotsRebase.
Lead-authored-by: Thomas Graves <tgraves@apache.org>
Co-authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
In [SPARK-33139] we defined `setActionSession` and `clearActiveSession` as deprecated API, it turns out it is widely used, and after discussion, even if without this PR, it should work with unify view feature, it might only be a risk if user really abuse using these two API. So revert the PR is needed.
[SPARK-33139] has two commit, include a follow up. Revert them both.
### Why are the changes needed?
Revert.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#30367 from leanken/leanken-revert-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the last few releases, our Spark documentation https://spark.apache.org/docs/latest/ becomes richer. It would nice to provide a search function to make our users find contents faster.
[DocSearch](https://docsearch.algolia.com/) is entirely free and automated. This PR will use it to provides search function.
The screenshots show below:
### Why are the changes needed?
Let the users of Spark documentation could find the needed information effectively.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
build on my machine and look on brower.
Closes#30292 from beliefer/SPARK-33166.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: beliefer <beliefer@163.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to disable the streaming query with possible correctness issue in chained stateful operators. The behavior can be controlled by a SQL config, so if users understand the risk and still want to run the query, they can disable the check.
### Why are the changes needed?
The possible correctness in chained stateful operators in streaming query is not straightforward for users. From users perspective, it will be considered as a Spark bug. It is also possible the worse case, users are not aware of the correctness issue and use wrong results.
A better approach should be to disable such queries and let users choose to run the query if they understand there is such risk, instead of implicitly running the query and let users to find out correctness issue by themselves and report this known to Spark community.
### Does this PR introduce _any_ user-facing change?
Yes. Streaming query with possible correctness issue will be blocked to run, except for users explicitly disable the SQL config.
### How was this patch tested?
Unit test.
Closes#30210 from viirya/SPARK-33259.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Remove "in cluster mode" from the description of `spark.executor.memoryOverhead`
### Why are the changes needed?
fix correctness issue in documentaion
### Does this PR introduce _any_ user-facing change?
yes, users may not get confused about the description `spark.executor.memoryOverhead`
### How was this patch tested?
pass GA doc generation
Closes#30311 from yaooqinn/minordoc.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
