### 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>
### What changes were proposed in this pull request?
Instead of returning NULL, throws runtime ArrayIndexOutOfBoundsException when ansiMode is enable for `element_at`，`elt`, `GetArrayItem` functions.
### Why are the changes needed?
For ansiMode.
### Does this PR introduce any user-facing change?
When `spark.sql.ansi.enabled` = true, Spark will throw `ArrayIndexOutOfBoundsException` if out-of-range index when accessing array elements
### How was this patch tested?
Added UT and existing UT.
Closes#30297 from leanken/leanken-SPARK-33386.
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?
Update SQL migration guide for SPARK-33290
### Why are the changes needed?
Make the change better documented.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#30256 from sunchao/SPARK-33290-2.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Update join type in stream static joins code examples in structured streaming programming guide.
1) Scala, Java and Python examples have a common issue.
The join keyword is "right_join", it should be "left_outer".
_Reasons:_
a) This code snippet is an example of "left outer join" as the streaming df is on left and static df is on right. Also, right outer join between stream df(left) and static df(right) is not supported.
b) The keyword "right_join/left_join" is unsupported and it should be "right_outer/left_outer".
So, all of these code snippets have been updated to "left_outer".
2) R exmaple is correct, but the example is of "right_outer" with static df (left) and streaming df(right).
It is changed to "left_outer" to make it consistent with other three examples of scala, java and python.
### Why are the changes needed?
To fix the mistake in example code of documentation.
### Does this PR introduce _any_ user-facing change?
Yes, it is a user-facing change (but documentation update only).
**Screenshots 1: Scala/Java/python example (similar issue)**
_Before:_
<img width="941" alt="Screenshot 2020-11-05 at 12 16 09 AM" src="https://user-images.githubusercontent.com/62717942/98155351-19e59400-1efc-11eb-8142-e6a25a5e6497.png">
_After:_
<img width="922" alt="Screenshot 2020-11-05 at 12 17 12 AM" src="https://user-images.githubusercontent.com/62717942/98155503-5d400280-1efc-11eb-96e1-5ba0f3c35c82.png">
**Screenshots 2: R example (Make it consistent with above change)**
_Before:_
<img width="896" alt="Screenshot 2020-11-05 at 12 19 57 AM" src="https://user-images.githubusercontent.com/62717942/98155685-ac863300-1efc-11eb-93bc-b7ca4dd34634.png">
_After:_
<img width="919" alt="Screenshot 2020-11-05 at 12 20 51 AM" src="https://user-images.githubusercontent.com/62717942/98155739-c0ca3000-1efc-11eb-8f95-a7538fa784b7.png">
### How was this patch tested?
The change was tested locally.
1) cd docs/
SKIP_API=1 jekyll build
2) Verify docs/_site/structured-streaming-programming-guide.html file in browser.
Closes#30252 from sarveshdave1/doc-update-stream-static-joins.
Authored-by: Sarvesh Dave <sarveshdave1@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to register the executor source with the Spark metrics system when running in local mode.
### Why are the changes needed?
The Apache Spark metrics system provides many useful insights on the Spark workload.
In particular, the [executor source metrics](https://github.com/apache/spark/blob/master/docs/monitoring.md#component-instance--executor) provide detailed info, including the number of active tasks, I/O metrics, and several task metrics details. The executor source metrics, contrary to other sources (for example ExecutorMetrics source), is not available when running in local mode.
Having executor metrics in local mode can be useful when testing and troubleshooting Spark workloads in a development environment. The metrics can be fed to a dashboard to see the evolution of resource usage and can be used to troubleshoot performance,
as [in this example](https://github.com/cerndb/spark-dashboard).
Currently users will have to deploy on a cluster to be able to collect executor source metrics, while the possibility of having them in local mode is handy for testing.
### Does this PR introduce _any_ user-facing change?
- This PR exposes executor source metrics data when running in local mode.
### How was this patch tested?
- Manually tested by running in local mode and inspecting the metrics listed in http://localhost:4040/metrics/json/
- Also added a test in `SourceConfigSuite`
Closes#28528 from LucaCanali/metricsWithLocalMode.
Authored-by: Luca Canali <luca.canali@cern.ch>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Allow to run the Spark web UI behind a reverse proxy with URLs prefixed by a context root, like www.mydomain.com/spark. In particular, this allows to access multiple Spark clusters through the same virtual host, only distinguishing them by context root, like www.mydomain.com/cluster1, www.mydomain.com/cluster2, and it allows to run the Spark UI in a common cookie domain (for SSO) with other services.
### Why are the changes needed?
This PR is to take over https://github.com/apache/spark/pull/17455.
After changes, Spark allows showing customized prefix URL in all the `href` links of the HTML pages.
### Does this PR introduce _any_ user-facing change?
Yes, all the links of UI pages will be contains the value of `spark.ui.reverseProxyUrl` if it is configurated.
### How was this patch tested?
New HTML Unit tests in MasterSuite
Manual UI testing for master, worker and app UI with an nginx proxy
Spark config:
```
spark.ui.port 8080
spark.ui.reverseProxy=true
spark.ui.reverseProxyUrl=/path/to/spark/
```
nginx config:
```
server {
listen 9000;
set $SPARK_MASTER http://127.0.0.1:8080;
# split spark UI path into prefix and local path within master UI
location ~ ^(/path/to/spark/) {
# strip prefix when forwarding request
rewrite /path/to/spark(/.*) $1 break;
#rewrite /path/to/spark/ "/" ;
# forward to spark master UI
proxy_pass $SPARK_MASTER;
proxy_intercept_errors on;
error_page 301 302 307 = handle_redirects;
}
location handle_redirects {
set $saved_redirect_location '$upstream_http_location';
proxy_pass $saved_redirect_location;
}
}
```
Closes#29820 from gengliangwang/revertProxyURL.
Lead-authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Co-authored-by: Oliver Köth <okoeth@de.ibm.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
this PR renames the blacklisting feature. I ended up using "excludeOnFailure" or "excluded" in most cases but there is a mix. I renamed the BlacklistTracker to HealthTracker, but for the TaskSetBlacklist HealthTracker didn't make sense to me since its not the health of the taskset itself but rather tracking the things its excluded on so I renamed it to be TaskSetExcludeList. Everything else I tried to use the context and in most cases excluded made sense. It made more sense to me then blocked since you are basically excluding those executors and nodes from scheduling tasks on them. Then can be unexcluded later after timeouts and such. The configs I changed the name to use excludeOnFailure which I thought explained it.
I unfortunately couldn't get rid of some of them because its part of the event listener and history files. To keep backwards compatibility I kept the events and some of the parsing so that the history server would still properly read older history files. It is not forward compatible though - meaning a new application write the "Excluded" events so the older history server won't properly read display them as being blacklisted.
A few of the files below are showing up as deleted and recreated even though I did a git mv on them. I'm not sure why.
### Why are the changes needed?
get rid of problematic language
### Does this PR introduce _any_ user-facing change?
Config name changes but the old configs still work but are deprecated.
### How was this patch tested?
updated tests and also manually tested the UI changes and manually tested the history server reading older versions of history files and vice versa.
Closes#29906 from tgravescs/SPARK-32037.
Lead-authored-by: Thomas Graves <tgraves@nvidia.com>
Co-authored-by: Thomas Graves <tgraves@apache.org>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Add a configuration to control the legacy behavior of whether need to pad null value when value size less then schema size.
Since we can't decide whether it's a but and some use need it behavior same as Hive.
### Why are the changes needed?
Provides a compatible choice between historical behavior and Hive
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existed UT
Closes#30156 from AngersZhuuuu/SPARK-33284.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Return schema in SQL format instead of Catalog string from the SchemaOfCsv expression.
### Why are the changes needed?
To unify output of the `schema_of_json()` and `schema_of_csv()`.
### Does this PR introduce _any_ user-facing change?
Yes, they can but `schema_of_csv()` is usually used in combination with `from_csv()`, so, the format of schema shouldn't be much matter.
Before:
```
> SELECT schema_of_csv('1,abc');
struct<_c0:int,_c1:string>
```
After:
```
> SELECT schema_of_csv('1,abc');
STRUCT<`_c0`: INT, `_c1`: STRING>
```
### How was this patch tested?
By existing test suites `CsvFunctionsSuite` and `CsvExpressionsSuite`.
Closes#30180 from MaxGekk/schema_of_csv-sql-schema.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Return schema in SQL format instead of Catalog string from the `SchemaOfJson` expression.
### Why are the changes needed?
In some cases, `from_json()` cannot parse schemas returned by `schema_of_json`, for instance, when JSON fields have spaces (gaps). Such fields will be quoted after the changes, and can be parsed by `from_json()`.
Here is the example:
```scala
val in = Seq("""{"a b": 1}""").toDS()
in.select(from_json('value, schema_of_json("""{"a b": 100}""")) as "parsed")
```
raises the exception:
```
== SQL ==
struct<a b:bigint>
------^^^
at org.apache.spark.sql.catalyst.parser.ParseException.withCommand(ParseDriver.scala:263)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:130)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parseTableSchema(ParseDriver.scala:76)
at org.apache.spark.sql.types.DataType$.fromDDL(DataType.scala:131)
at org.apache.spark.sql.catalyst.expressions.ExprUtils$.evalTypeExpr(ExprUtils.scala:33)
at org.apache.spark.sql.catalyst.expressions.JsonToStructs.<init>(jsonExpressions.scala:537)
at org.apache.spark.sql.functions$.from_json(functions.scala:4141)
```
### Does this PR introduce _any_ user-facing change?
Yes. For example, `schema_of_json` for the input `{"col":0}`.
Before: `struct<col:bigint>`
After: `STRUCT<`col`: BIGINT>`
### How was this patch tested?
By existing test suites `JsonFunctionsSuite` and `JsonExpressionsSuite`.
Closes#30172 from MaxGekk/schema_of_json-sql-schema.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR intends to add a dedicated page for SQL-on-file in SQL documents.
This comes from the comment: https://github.com/apache/spark/pull/30095/files#r508965149
### Why are the changes needed?
For better documentations.
### Does this PR introduce _any_ user-facing change?
<img width="544" alt="Screen Shot 2020-10-28 at 9 56 59" src="https://user-images.githubusercontent.com/692303/97378051-c1fbcb80-1904-11eb-86c0-a88c5269d41c.png">
### How was this patch tested?
N/A
Closes#30165 from maropu/DocForFile.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
The documentation of the Spark SQL null semantics states that "NULL AND False" yields NULL. This is incorrect. "NULL AND False" yields False.
```
Seq[(java.lang.Boolean, java.lang.Boolean)](
(null, false)
)
.toDF("left_operand", "right_operand")
.withColumn("AND", 'left_operand && 'right_operand)
.show(truncate = false)
+------------+-------------+-----+
|left_operand|right_operand|AND |
+------------+-------------+-----+
|null |false |false|
+------------+-------------+-----+
```
I propose the documentation be updated to reflect that "NULL AND False" yields False.
This contribution is my original work and I license it to the project under the project’s open source license.
### Why are the changes needed?
This change improves the accuracy of the documentation.
### Does this PR introduce _any_ user-facing change?
Yes. This PR introduces a fix to the documentation.
### How was this patch tested?
Since this is only a documentation change, no tests were added.
Closes#30161 from stwhit/SPARK-33246.
Authored-by: Stuart White <stuart@spotright.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR proposes to initiate the migration to NumPy documentation style (from reST style) in PySpark docstrings.
This PR also adds one migration example of `SparkContext`.
- **Before:**
...
...
...
- **After:**
...
...
See also https://numpydoc.readthedocs.io/en/latest/format.html
### Why are the changes needed?
There are many reasons for switching to NumPy documentation style.
1. Arguably reST style doesn't fit well when the docstring grows large because it provides (arguably) less structures and syntax.
2. NumPy documentation style provides a better human readable docstring format. For example, notebook users often just do `help(...)` by `pydoc`.
3. NumPy documentation style is pretty commonly used in data science libraries, for example, pandas, numpy, Dask, Koalas,
matplotlib, ... Using NumPy documentation style can give users a consistent documentation style.
### Does this PR introduce _any_ user-facing change?
The dependency itself doesn't change anything user-facing.
The documentation change in `SparkContext` does, as shown above.
### How was this patch tested?
Manually tested via running `cd python` and `make clean html`.
Closes#30149 from HyukjinKwon/SPARK-33243.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
Handle executor failure with multiple containers
Added a spark property spark.kubernetes.executor.checkAllContainers,
with default being false. When it's true, the executor snapshot will
take all containers in the executor into consideration when deciding
whether the executor is in "Running" state, if the pod restart policy is
"Never". Also, added the new spark property to the doc.
### What changes were proposed in this pull request?
Checking of all containers in the executor pod when reporting executor status, if the `spark.kubernetes.executor.checkAllContainers` property is set to true.
### Why are the changes needed?
Currently, a pod remains "running" as long as there is at least one running container. This prevents Spark from noticing when a container has failed in an executor pod with multiple containers. With this change, user can configure the behavior to be different. Namely, if any container in the executor pod has failed, either the executor process or one of its sidecars, the pod is considered to be failed, and it will be rescheduled.
### Does this PR introduce _any_ user-facing change?
Yes, new spark property added.
User is now able to choose whether to turn on this feature using the `spark.kubernetes.executor.checkAllContainers` property.
### How was this patch tested?
Unit test was added and all passed.
I tried to run integration test by following the instruction [here](https://spark.apache.org/developer-tools.html) (section "Testing K8S") and also [here](https://github.com/apache/spark/blob/master/resource-managers/kubernetes/integration-tests/README.md), but I wasn't able to run it smoothly as it fails to talk with minikube cluster. Maybe it's because my minikube version is too new (I'm using v1.13.1)...? Since I've been trying it for two days and still can't make it work, I decided to submit this PR and hopefully the Jenkins test will pass.
Closes#29924 from huskysun/exec-sidecar-failure.
Authored-by: Shiqi Sun <s.sun@salesforce.com>
Signed-off-by: Holden Karau <hkarau@apple.com>
### What changes were proposed in this pull request?
1. Set the default value for the SQL configs `spark.sql.legacy.parquet.int96RebaseModeInWrite` and `spark.sql.legacy.parquet.int96RebaseModeInRead` to `EXCEPTION`.
2. Update the SQL migration guide.
### Why are the changes needed?
Current default value `LEGACY` may lead to shifting timestamps in read or in write. We should leave the decision about rebasing to users.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By existing test suites like `ParquetIOSuite`.
Closes#30121 from MaxGekk/int96-exception-by-default.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Address comments https://github.com/apache/spark/pull/29635#discussion_r507241899 to improve migration guide
### Why are the changes needed?
improve migration guide
### Does this PR introduce _any_ user-facing change?
NO，only doc update
### How was this patch tested?
passing GitHub action
Closes#30113 from yaooqinn/SPARK-32785-F.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This pull request changes the description about `to_avro` and `from_avro` functions to include Python as a supported language as the functions have been supported in Python since Apache Spark 3.0.0 [[SPARK-26856](https://issues.apache.org/jira/browse/SPARK-26856)].
### Why are the changes needed?
Same as above.
### Does this PR introduce _any_ user-facing change?
Yes. The description changed by this pull request is on https://spark.apache.org/docs/latest/sql-data-sources-avro.html#to_avro-and-from_avro.
### How was this patch tested?
Tested manually by building and checking the document in the local environment.
Closes#30105 from kjmrknsn/fix-docs-sql-data-sources-avro.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pull request changes the link to the pickle module page from https://docs.python.org/2/library/pickle.html to https://docs.python.org/3/library/pickle.html in RDD Programming Guide.
### Why are the changes needed?
Since Python 2 is no longer supported and it is preferable to refer to the pickle module page of Python 3.
### Does this PR introduce _any_ user-facing change?
Yes.
Before: the `Pickle` link's destination page was https://docs.python.org/2/library/pickle.html
After: the `Pickle` link's destination page is https://docs.python.org/3/library/pickle.html
### How was this patch tested?
By building the documentation site and check the link's destination page is changed correctly in the local environment.
Closes#30081 from kjmrknsn/docs-fix-pickle-link.
Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a minor change to update structured-streaming-programming-guide and typos in code.
### Why are the changes needed?
Keep the user-facing document correct and updated.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Unit tests.
Closes#30074 from viirya/ss-minor.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138). In order to make SQLConf.get reliable and stable, we need to make sure user can't pollute the SQLConf and SparkSession Context via calling setActiveSession and clearActiveSession.
Change of the PR:
* add legacy config spark.sql.legacy.allowModifyActiveSession to fallback to old behavior if user do need to call these two API.
* by default, if user call these two API, it will throw exception
* add extra two internal and private API setActiveSessionInternal and clearActiveSessionInternal for current internal usage
* change all internal reference to new internal API except for SQLContext.setActive and SQLContext.clearActive
### Why are the changes needed?
Make SQLConf.get reliable and stable.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
* Add UT in SparkSessionBuilderSuite to test the legacy config
* Existing test
Closes#30042 from leanken/leanken-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?
This PR makes `spark.kubernetes.pyspark.pythonVersion` allow only `3`. In other words, it will reject `2` for `Python 2`.
- [x] Configuration description and check is updated.
- [x] Documentation is updated
- [x] Unit test cases are updated.
- [x] Docker image script is updated.
### Why are the changes needed?
After SPARK-32138, Apache Spark 3.1 dropped Python 2 support.
### Does this PR introduce _any_ user-facing change?
Yes, but Python 2 support is already dropped officially.
### How was this patch tested?
Pass the CI.
Closes#30049 from dongjoon-hyun/SPARK-DROP-PYTHON2.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
As [SPARK-13860](https://issues.apache.org/jira/browse/SPARK-13860) stated, TPCDS Query 39 returns wrong results using SparkSQL. The root cause is that when stddev_samp is applied to a single element set, with TPCDS answer, it return null; as in SparkSQL, it return Double.NaN which caused the wrong result.
Add an extra legacy config to fallback into the NaN logical, and return null by default to align with TPCDS standard.
### Why are the changes needed?
SQL correctness issue.
### Does this PR introduce any user-facing change?
Yes. See sql-migration-guide
In Spark 3.1, statistical aggregation function includes `std`, `stddev`, `stddev_samp`, `variance`, `var_samp`, `skewness`, `kurtosis`, `covar_samp`, `corr` will return `NULL` instead of `Double.NaN` when `DivideByZero` occurs during expression evaluation, for example, when `stddev_samp` applied on a single element set. In Spark version 3.0 and earlier, it will return `Double.NaN` in such case. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.statisticalAggregate` to `true`.
### How was this patch tested?
Updated DataFrameAggregateSuite/DataFrameWindowFunctionsSuite to test both default and legacy behavior.
Adjust DataFrameWindowFunctionsSuite/SQLQueryTestSuite and some R case to update to the default return null behavior.
Closes#29983 from leanken/leanken-SPARK-13860.
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?
While explaining pySpark usage, use of repeated synonymous words were causing confusion.
Removed "instead of a JAR" word, to keep it more readable.
### Why are the changes needed?
To keep the docs more readable and easy to understand.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No code changes, minor documentation change only. No tests added.
Closes#29956 from manubatham20/patch-1.
Authored-by: manubatham20 <manubatham2006@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
As of today,
- SPARK-30034 Apache Spark 3.0.0 switched its default Hive execution engine from Hive 1.2 to Hive 2.3. This removes the direct dependency to the forked Hive 1.2.1 in maven repository.
- SPARK-32981 Apache Spark 3.1.0(`master` branch) removed Hive 1.2 related artifacts from Apache Spark binary distributions.
This PR(SPARK-20202) aims to remove the following usage of unofficial Apache Hive fork completely from Apache Spark master for Apache Spark 3.1.0.
```
<hive.group>org.spark-project.hive</hive.group>
<hive.version>1.2.1.spark2</hive.version>
```
For the forked Hive 1.2.1.spark2 users, Apache Spark 2.4(LTS) and 3.0 (~ 2021.12) will provide it.
### Why are the changes needed?
- First, Apache Spark community should not use the unofficial forked release of another Apache project.
- Second, Apache Hive 1.2.1 was released at 2015-06-26 and the forked Hive `1.2.1.spark2` exposed many unfixable bugs in Apache because the forked `1.2.1.spark2` is not maintained at all. Apache Hive 2.3.0 was released at 2017-07-19 and it has been used with less number of bugs compared with `1.2.1.spark2`. Many bugs still exist in `hive-1.2` profile and new Apache Spark unit tests are added with `HiveUtils.isHive23` condition so far.
### Does this PR introduce _any_ user-facing change?
No. This is a dev-only change. PRBuilder will not accept `[test-hive1.2]` on master and `branch-3.1`.
### How was this patch tested?
1. SBT/Hadoop 3.2/Hive 2.3 (https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/129366)
2. SBT/Hadoop 2.7/Hive 2.3 (https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/129382)
3. SBT/Hadoop 3.2/Hive 1.2 (This has not been supported already due to Hive 1.2 doesn't work with Hadoop 3.2.)
4. SBT/Hadoop 2.7/Hive 1.2 (https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/129383, This is rejected)
Closes#29936 from dongjoon-hyun/SPARK-REMOVE-HIVE1.
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 the description how to build Spark for Scala 2.13 with sbt.
In the current doc, how to build Spark for Scala 2.13 with sbt is described like:
But build fails with this command because scala-2.13 profile is not enabled and scala-parallel-collections is absent.
```
[error] /home/kou/work/oss/spark-scala-2.13/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala:23: object parallel is not a member of package collection
```
The correct command should be:
```
build/sbt -Pspark-2.13 compile
```
### Why are the changes needed?
The build command is wrong.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I checked that `sbt -Pspark-2.13` is correct with the following command:
```
build/sbt -Dscala.version=2.13.3 -Phive -Phive-thriftserver -Pyarn -Pkubernetes compile
```
I also build the modified doc and checked the generated html:
Closes#29921 from sarutak/fix-scala-2.13-build-doc.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Fix Typo
### Why are the changes needed?
To maintain consistency.
Correct table name should be used for SELECT command.
### Does this PR introduce _any_ user-facing change?
Yes. Now CREATE FUNCTION doc will show the correct name of table.
### How was this patch tested?
Manually. Doc changes.
Closes#29920 from iRakson/fixTypo.
Authored-by: iRakson <raksonrakesh@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Upgrade to the latest available version of jQuery (3.5.1).
### Why are the changes needed?
There are some CVE-s reported (CVE-2020-11022, CVE-2020-11023) affecting older versions of jQuery. Although Spark UI is read-only and those CVEs doesn't seem to affect Spark, using the latest version of this library can help to handle vulnerability reports of security scans.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual tests and checked the jQuery 3.5 upgrade guide.
Closes#29902 from peter-toth/SPARK-32723-upgrade-to-jquery-3.5.1.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Apache Spark 3.1's default Hadoop profile is `hadoop-3.2`. Instead of having a warning documentation, this PR aims to use a consistent and safer version of Apache Hadoop file output committer algorithm which is `v1`. This will prevent a silent correctness regression during migration from Apache Spark 2.4/3.0 to Apache Spark 3.1.0. Of course, if there is a user-provided configuration, `spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version=2`, that will be used still.
### Why are the changes needed?
Apache Spark provides multiple distributions with Hadoop 2.7 and Hadoop 3.2. `spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version` depends on the Hadoop version. Apache Hadoop 3.0 switches the default algorithm from `v1` to `v2` and now there exists a discussion to remove `v2`. We had better provide a consistent default behavior of `v1` across various Spark distributions.
- [MAPREDUCE-7282](https://issues.apache.org/jira/browse/MAPREDUCE-7282) MR v2 commit algorithm should be deprecated and not the default
### Does this PR introduce _any_ user-facing change?
Yes. This changes the default behavior. Users can override this conf.
### How was this patch tested?
Manual.
**BEFORE (spark-3.0.1-bin-hadoop3.2)**
```scala
scala> sc.version
res0: String = 3.0.1
scala> sc.hadoopConfiguration.get("mapreduce.fileoutputcommitter.algorithm.version")
res1: String = 2
```
**AFTER**
```scala
scala> sc.hadoopConfiguration.get("mapreduce.fileoutputcommitter.algorithm.version")
res0: String = 1
```
Closes#29895 from dongjoon-hyun/SPARK-DEFAUT-COMMITTER.
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 an link in `_layouts/global.html`.
The item `More` in the navigation bar in the WebUI links to `api.html` but it seems to be wrong.
This PR also removes `api.md` because it and `api.html` generated from it are not referred from anywhere.
### Why are the changes needed?
Fix the wrong link.
### Does this PR introduce _any_ user-facing change?
Yes. "More" item no longer links to `api.html`.
### How was this patch tested?
`SKIP_API=1 jekyll build` and confirmed that the item no longer links to `api.html`.
I also confirmed `api.md` and `api.html` are no longer referred from anywhere by the following command.
```
$ grep -Erl "api\.(html|md)" docs
```
Closes#29821 from sarutak/fix-api-doc-link.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
