## What changes were proposed in this pull request?
As mentioned in SPARK-23285, this PR introduces a new configuration property `spark.kubernetes.executor.cores` for specifying the physical CPU cores requested for each executor pod. This is to avoid changing the semantics of `spark.executor.cores` and `spark.task.cpus` and their role in task scheduling, task parallelism, dynamic resource allocation, etc. The new configuration property only determines the physical CPU cores available to an executor. An executor can still run multiple tasks simultaneously by using appropriate values for `spark.executor.cores` and `spark.task.cpus`.
## How was this patch tested?
Unit tests.
felixcheung srowen jiangxb1987 jerryshao mccheah foxish
Author: Yinan Li <ynli@google.com>
Author: Yinan Li <liyinan926@gmail.com>
Closes#20553 from liyinan926/master.
## What changes were proposed in this pull request?
Kubernetes driver and executor pods should request `memory + memoryOverhead` as their resources instead of just `memory`, see https://issues.apache.org/jira/browse/SPARK-23825
## How was this patch tested?
Existing unit tests were adapted.
Author: David Vogelbacher <dvogelbacher@palantir.com>
Closes#20943 from dvogelbacher/spark-23825.
## What changes were proposed in this pull request?
Adding test for default params for `CountVectorizerModel` constructed from vocabulary. This required that the param `maxDF` be added, which was done in SPARK-23615.
## How was this patch tested?
Added an explicit test for CountVectorizerModel in DefaultValuesTests.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20942 from BryanCutler/pyspark-CountVectorizerModel-default-param-test-SPARK-15009.
## What changes were proposed in this pull request?
Address https://github.com/apache/spark/pull/20449#discussion_r172414393, If `resultIter` is already a `InterruptibleIterator`, don't double wrap it.
## How was this patch tested?
Existing tests.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#20920 from jiangxb1987/SPARK-23040.
## What changes were proposed in this pull request?
Currently, the requiredChildDistribution does not specify the partitions. This can cause the weird corner cases where the child's distribution is `SinglePartition` which satisfies the required distribution of `ClusterDistribution(no-num-partition-requirement)`, thus eliminating the shuffle needed to repartition input data into the required number of partitions (i.e. same as state stores). That can lead to "file not found" errors on the state store delta files as the micro-batch-with-no-shuffle will not run certain tasks and therefore not generate the expected state store delta files.
This PR adds the required constraint on the number of partitions.
## How was this patch tested?
Modified test harness to always check that ANY stateful operator should have a constraint on the number of partitions. As part of that, the existing opt-in checks on child output partitioning were removed, as they are redundant.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#20941 from tdas/SPARK-23827.
## What changes were proposed in this pull request?
It may be get `spark.shuffle.service.port` from 9745ec3a61/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala (L459)
Therefore, the client configuration `spark.shuffle.service.port` does not working unless the configuration is `spark.hadoop.spark.shuffle.service.port`.
- This configuration is not working:
```
bin/spark-sql --master yarn --conf spark.shuffle.service.port=7338
```
- This configuration works:
```
bin/spark-sql --master yarn --conf spark.hadoop.spark.shuffle.service.port=7338
```
This PR fix this issue.
## How was this patch tested?
It's difficult to carry out unit testing. But I've tested it manually.
Author: Yuming Wang <yumwang@ebay.com>
Closes#20785 from wangyum/SPARK-23640.
## What changes were proposed in this pull request?
This PR is to improve the test coverage of the original PR https://github.com/apache/spark/pull/20687
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20911 from gatorsmile/addTests.
## What changes were proposed in this pull request?
Roll forward c68ec4e (#20688).
There are two minor test changes required:
* An error which used to be TreeNodeException[ArithmeticException] is no longer wrapped and is now just ArithmeticException.
* The test framework simply does not set the active Spark session. (Or rather, it doesn't do so early enough - I think it only happens when a query is analyzed.) I've added the required logic to SQLTestUtils.
## How was this patch tested?
existing tests
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Author: jerryshao <sshao@hortonworks.com>
Closes#20922 from jose-torres/ratefix.
## What changes were proposed in this pull request?
This PR supports for pushing down filters for DateType in parquet
## How was this patch tested?
Added UT and tested in local.
Author: yucai <yyu1@ebay.com>
Closes#20851 from yucai/SPARK-23727.
## What changes were proposed in this pull request?
isSharedClass returns if some classes can/should be shared or not. It checks if the classes names have some keywords or start with some names. Following the logic, it can occur unintended behaviors when a custom package has `slf4j` inside the package or class name. As I guess, the first intention seems to figure out the class containing `org.slf4j`. It would be better to change the comparison logic to `name.startsWith("org.slf4j")`
## How was this patch tested?
This patch should pass all of the current tests and keep all of the current behaviors. In my case, I'm using ProtobufDeserializer to get a table schema from hive tables. Thus some Protobuf packages and names have `slf4j` inside. Without this patch, it cannot be resolved because of ClassCastException from different classloaders.
Author: Jongyoul Lee <jongyoul@gmail.com>
Closes#20860 from jongyoul/SPARK-23743.
## What changes were proposed in this pull request?
Set default Spark session in the TestSparkSession and TestHiveSparkSession constructors.
## How was this patch tested?
new unit tests
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Closes#20926 from jose-torres/test3.
## What changes were proposed in this pull request?
In SparkSQLCLI, SessionState generates before SparkContext instantiating. When we use --proxy-user to impersonate, it's unable to initializing a metastore client to talk to the secured metastore for no kerberos ticket.
This PR use real user ugi to obtain token for owner before talking to kerberized metastore.
## How was this patch tested?
Manually verified with kerberized hive metasotre / hdfs.
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#20784 from yaooqinn/SPARK-23639.
## What changes were proposed in this pull request?
Changed `LauncherBackend` `set` method so that it checks if the connection is open or not before writing to it (uses `isConnected`).
## How was this patch tested?
None
Author: Sahil Takiar <stakiar@cloudera.com>
Closes#20893 from sahilTakiar/master.
… with dynamic allocation
## What changes were proposed in this pull request?
ignore errors when you are waiting for a broadcast.unpersist. This is handling it the same way as doing rdd.unpersist in https://issues.apache.org/jira/browse/SPARK-22618
## How was this patch tested?
Patch was tested manually against a couple jobs that exhibit this behavior, with the change the application no longer dies due to this and just prints the warning.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Thomas Graves <tgraves@unharmedunarmed.corp.ne1.yahoo.com>
Closes#20924 from tgravescs/SPARK-23806.
## What changes were proposed in this pull request?
This PR proposes to add lineSep option for a configurable line separator in text datasource.
It supports this option by using `LineRecordReader`'s functionality with passing it to the constructor.
The approach is similar with https://github.com/apache/spark/pull/20727; however, one main difference is, it uses text datasource's `lineSep` option to parse line by line in JSON's schema inference.
## How was this patch tested?
Manually tested and unit tests were added.
Author: hyukjinkwon <gurwls223@apache.org>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20877 from HyukjinKwon/linesep-json.
## What changes were proposed in this pull request?
When using Arrow for createDataFrame or toPandas and an error is encountered with fallback disabled, this will raise the same type of error instead of a RuntimeError. This change also allows for the traceback of the error to be retained and prevents the accidental chaining of exceptions with Python 3.
## How was this patch tested?
Updated existing tests to verify error type.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20839 from BryanCutler/arrow-raise-same-error-SPARK-23699.
## What changes were proposed in this pull request?
This PR migrate micro batch rate source to V2 API and rewrite UTs to suite V2 test.
## How was this patch tested?
UTs.
Author: jerryshao <sshao@hortonworks.com>
Closes#20688 from jerryshao/SPARK-23096.
## What changes were proposed in this pull request?
The UUID() expression is stateful and should implement the `Stateful` trait instead of the `Nondeterministic` trait.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20912 from viirya/SPARK-23794.
## What changes were proposed in this pull request?
Adding r2adj in LinearRegressionSummary for Python API.
## How was this patch tested?
Added unit tests to exercise the api calls for the summary classes in tests.py.
Author: Kevin Yu <qyu@us.ibm.com>
Closes#20842 from kevinyu98/spark-23162.
This change basically rewrites the security documentation so that it's
up to date with new features, more correct, and more complete.
Because security is such an important feature, I chose to move all the
relevant configuration documentation to the security page, instead of
having them peppered all over the place in the configuration page. This
allows an almost one-stop shop for security configuration in Spark. The
only exceptions are some YARN-specific minor features which I left in
the YARN page.
I also re-organized the page's topics, since they didn't make a lot of
sense. You had kerberos features described inside paragraphs talking
about UI access control, and other oddities. It should be easier now
to find information about specific Spark security features. I also
enabled TOCs for both the Security and YARN pages, since that makes it
easier to see what is covered.
I removed most of the comments from the SecurityManager javadoc since
they just replicated information in the security doc, with different
levels of out-of-dateness.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20742 from vanzin/SPARK-23572.
This particular test assumed that Hadoop libraries did not support
http as a file system. Hadoop 2.9 does, so the test failed. The test
now forces a non-existent implementation for the http fs, which
forces the expected error.
There were also a couple of other issues in the same test: SparkSubmit
arguments in the wrong order, and the wrong check later when asserting,
which was being masked by the previous issues.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20895 from vanzin/SPARK-23787.
## What changes were proposed in this pull request?
Add documentation about the limitations of `pandas_udf` with keyword arguments and related concepts, like `functools.partial` fn objects.
NOTE: intermediate commits on this PR show some of the steps that can be taken to fix some (but not all) of these pain points.
### Survey of problems we face today:
(Initialize) Note: python 3.6 and spark 2.4snapshot.
```
from pyspark.sql import SparkSession
import inspect, functools
from pyspark.sql.functions import pandas_udf, PandasUDFType, col, lit, udf
spark = SparkSession.builder.getOrCreate()
print(spark.version)
df = spark.range(1,6).withColumn('b', col('id') * 2)
def ok(a,b): return a+b
```
Using a keyword argument at the call site `b=...` (and yes, *full* stack trace below, haha):
```
---> 14 df.withColumn('ok', pandas_udf(f=ok, returnType='bigint')('id', b='id')).show() # no kwargs
TypeError: wrapper() got an unexpected keyword argument 'b'
```
Using partial with a keyword argument where the kw-arg is the first argument of the fn:
*(Aside: kind of interesting that lines 15,16 work great and then 17 explodes)*
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-e9f31b8799c1> in <module>()
15 df.withColumn('ok', pandas_udf(f=functools.partial(ok, 7), returnType='bigint')('id')).show()
16 df.withColumn('ok', pandas_udf(f=functools.partial(ok, b=7), returnType='bigint')('id')).show()
---> 17 df.withColumn('ok', pandas_udf(f=functools.partial(ok, a=7), returnType='bigint')('id')).show()
/Users/stu/ZZ/spark/python/pyspark/sql/functions.py in pandas_udf(f, returnType, functionType)
2378 return functools.partial(_create_udf, returnType=return_type, evalType=eval_type)
2379 else:
-> 2380 return _create_udf(f=f, returnType=return_type, evalType=eval_type)
2381
2382
/Users/stu/ZZ/spark/python/pyspark/sql/udf.py in _create_udf(f, returnType, evalType)
54 argspec.varargs is None:
55 raise ValueError(
---> 56 "Invalid function: 0-arg pandas_udfs are not supported. "
57 "Instead, create a 1-arg pandas_udf and ignore the arg in your function."
58 )
ValueError: Invalid function: 0-arg pandas_udfs are not supported. Instead, create a 1-arg pandas_udf and ignore the arg in your function.
```
Author: Michael (Stu) Stewart <mstewart141@gmail.com>
Closes#20900 from mstewart141/udfkw2.
## What changes were proposed in this pull request?
This cleans up unused imports, mainly from pyspark.sql module. Added a note in function.py that imports `UserDefinedFunction` only to maintain backwards compatibility for using `from pyspark.sql.function import UserDefinedFunction`.
## How was this patch tested?
Existing tests and built docs.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20892 from BryanCutler/pyspark-cleanup-imports-SPARK-23700.
## What changes were proposed in this pull request?
This PR fixes an incorrect comparison in SQL between timestamp and date. This is because both of them are casted to `string` and then are compared lexicographically. This implementation shows `false` regarding this query `spark.sql("select cast('2017-03-01 00:00:00' as timestamp) between cast('2017-02-28' as date) and cast('2017-03-01' as date)").show`.
This PR shows `true` for this query by casting `date("2017-03-01")` to `timestamp("2017-03-01 00:00:00")`.
(Please fill in changes proposed in this fix)
## How was this patch tested?
Added new UTs to `TypeCoercionSuite`.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#20774 from kiszk/SPARK-23549.
## What changes were proposed in this pull request?
This pr added TPCDS v2.7 (latest) queries in `TPCDSQuerySuite` because the current `TPCDSQuerySuite` tests older one (v1.4) and some queries are different from v1.4 and v2.7. Since the original v2.7 queries have the syntaxes that Spark cannot parse, I changed these queries in a following way:
- [date] + 14 days -> date + `INTERVAL` 14 days
- [column name] as "30 days" -> [column name] as \`30 days\`
- Fix some syntax errors, e.g., missing brackets
## How was this patch tested?
Added tests in `TPCDSQuerySuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20343 from maropu/TPCDSV2_7.
## What changes were proposed in this pull request?
The serializability test uses the same MemoryStream instance for 3 different queries. If any of those queries ask it to commit before the others have run, the rest will see empty dataframes. This can fail the test if q3 is affected.
We should use one instance per query instead.
## How was this patch tested?
Existing unit test. If I move q2.processAllAvailable() before starting q3, the test always fails without the fix.
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Closes#20896 from jose-torres/fixrace.
## What changes were proposed in this pull request?
The maxDF parameter is for filtering out frequently occurring terms. This param was recently added to the Scala CountVectorizer and needs to be added to Python also.
## How was this patch tested?
add test
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20777 from huaxingao/spark-23615.
## What changes were proposed in this pull request?
Adds PMML export support to Spark ML pipelines in the style of Spark's DataSource API to allow library authors to add their own model export formats.
Includes a specific implementation for Spark ML linear regression PMML export.
In addition to adding PMML to reach parity with our current MLlib implementation, this approach will allow other libraries & formats (like PFA) to implement and export models with a unified API.
## How was this patch tested?
Basic unit test.
Author: Holden Karau <holdenkarau@google.com>
Author: Holden Karau <holden@pigscanfly.ca>
Closes#19876 from holdenk/SPARK-11171-SPARK-11237-Add-PMML-export-for-ML-KMeans-r2.
## What changes were proposed in this pull request?
Currently when a PySpark Model is transformed, default params that have not been explicitly set are then set on the Java side on the call to `wrapper._transfer_values_to_java`. This incorrectly changes the state of the Param as it should still be marked as a default value only.
## How was this patch tested?
Added a new test to verify that when transferring Params to Java, default params have their state preserved.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#18982 from BryanCutler/pyspark-ml-param-to-java-defaults-SPARK-21685.
## What changes were proposed in this pull request?
Fixes SPARK-23759 by moving connector.start() after connector.setHost()
Problem was created due connector.setHost(hostName) call was after connector.start()
## How was this patch tested?
Patch was tested after build and deployment. This patch requires SPARK_LOCAL_IP environment variable to be set on spark-env.sh
Author: bag_of_tricks <falbani@hortonworks.com>
Closes#20883 from felixalbani/SPARK-23759.
## What changes were proposed in this pull request?
We re-enabled the Scalastyle checker on a line of code. It was previously disabled, but it does not violate any of the rules. So there's no reason to disable the Scalastyle checker here.
## How was this patch tested?
We tested this by running `build/mvn scalastyle:check` after removing the comments that disable the checker. This check passed with no errors or warnings for Spark Core
```
[INFO]
[INFO] ------------------------------------------------------------------------
[INFO] Building Spark Project Core 2.4.0-SNAPSHOT
[INFO] ------------------------------------------------------------------------
[INFO]
[INFO] --- scalastyle-maven-plugin:1.0.0:check (default-cli) spark-core_2.11 ---
Saving to outputFile=<path to local dir>/spark/core/target/scalastyle-output.xml
Processed 485 file(s)
Found 0 errors
Found 0 warnings
Found 0 infos
```
We did not run all tests (with `dev/run-tests`) since this Scalastyle check seemed sufficient.
## Co-contributors:
chialun-yeh
Hrayo712
vpourquie
Author: arucard21 <arucard21@gmail.com>
Closes#20880 from arucard21/scalastyle_util.
## What changes were proposed in this pull request?
The lint failure bugged me:
```R
R/SQLContext.R:715:97: style: Trailing whitespace is superfluous.
#' file-based streaming data source. \code{timeZone} to indicate a timezone to be used to
^
tests/fulltests/test_streaming.R:239:45: style: Commas should always have a space after.
expect_equal(times[order(times$eventTime),][1, 2], 2)
^
lintr checks failed.
```
and I actually saw https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.6-ubuntu-test/500/console too. If I understood correctly, there is a try about moving to Unbuntu one.
## How was this patch tested?
Manually tested by `./dev/lint-r`:
```
...
lintr checks passed.
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#20879 from HyukjinKwon/minor-r-lint.
Currently, the Spark AM relies on the initial set of tokens created by
the submission client to be able to talk to HDFS and other services that
require delegation tokens. This means that after those tokens expire, a
new AM will fail to start (e.g. when there is an application failure and
re-attempts are enabled).
This PR makes it so that the first thing the AM does when the user provides
a principal and keytab is to create new delegation tokens for use. This
makes sure that the AM can be started irrespective of how old the original
token set is. It also allows all of the token management to be done by the
AM - there is no need for the submission client to set configuration values
to tell the AM when to renew tokens.
Note that even though in this case the AM will not be using the delegation
tokens created by the submission client, those tokens still need to be provided
to YARN, since they are used to do log aggregation.
To be able to re-use the code in the AMCredentialRenewal for the above
purposes, I refactored that class a bit so that it can fetch tokens into
a pre-defined UGI, insted of always logging in.
Another issue with re-attempts is that, after the fix that allows the AM
to restart correctly, new executors would get confused about when to
update credentials, because the credential updater used the update time
initially set up by the submission code. This could make the executor
fail to update credentials in time, since that value would be very out
of date in the situation described in the bug.
To fix that, I changed the YARN code to use the new RPC-based mechanism
for distributing tokens to executors. This allowed the old credential
updater code to be removed, and a lot of code in the renewer to be
simplified.
I also made two currently hardcoded values (the renewal time ratio, and
the retry wait) configurable; while this probably never needs to be set
by anyone in a production environment, it helps with testing; that's also
why they're not documented.
Tested on real cluster with a specially crafted application to test this
functionality: checked proper access to HDFS, Hive and HBase in cluster
mode with token renewal on and AM restarts. Tested things still work in
client mode too.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20657 from vanzin/SPARK-23361.
## What changes were proposed in this pull request?
We should provide customized canonicalize plan for `InMemoryRelation` and `InMemoryTableScanExec`. Otherwise, we can wrongly treat two different cached plans as same result. It causes wrongly reused exchange then.
For a test query like this:
```scala
val cached = spark.createDataset(Seq(TestDataUnion(1, 2, 3), TestDataUnion(4, 5, 6))).cache()
val group1 = cached.groupBy("x").agg(min(col("y")) as "value")
val group2 = cached.groupBy("x").agg(min(col("z")) as "value")
group1.union(group2)
```
Canonicalized plans before:
First exchange:
```
Exchange hashpartitioning(none#0, 5)
+- *(1) HashAggregate(keys=[none#0], functions=[partial_min(none#1)], output=[none#0, none#4])
+- *(1) InMemoryTableScan [none#0, none#1]
+- InMemoryRelation [x#4253, y#4254, z#4255], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- LocalTableScan [x#4253, y#4254, z#4255]
```
Second exchange:
```
Exchange hashpartitioning(none#0, 5)
+- *(3) HashAggregate(keys=[none#0], functions=[partial_min(none#1)], output=[none#0, none#4])
+- *(3) InMemoryTableScan [none#0, none#1]
+- InMemoryRelation [x#4253, y#4254, z#4255], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- LocalTableScan [x#4253, y#4254, z#4255]
```
You can find that they have the canonicalized plans are the same, although we use different columns in two `InMemoryTableScan`s.
Canonicalized plan after:
First exchange:
```
Exchange hashpartitioning(none#0, 5)
+- *(1) HashAggregate(keys=[none#0], functions=[partial_min(none#1)], output=[none#0, none#4])
+- *(1) InMemoryTableScan [none#0, none#1]
+- InMemoryRelation [none#0, none#1, none#2], true, 10000, StorageLevel(memory, 1 replicas)
+- LocalTableScan [none#0, none#1, none#2]
```
Second exchange:
```
Exchange hashpartitioning(none#0, 5)
+- *(3) HashAggregate(keys=[none#0], functions=[partial_min(none#1)], output=[none#0, none#4])
+- *(3) InMemoryTableScan [none#0, none#2]
+- InMemoryRelation [none#0, none#1, none#2], true, 10000, StorageLevel(memory, 1 replicas)
+- LocalTableScan [none#0, none#1, none#2]
```
## How was this patch tested?
Added unit test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20831 from viirya/SPARK-23614.
## What changes were proposed in this pull request?
This PR proposes to remove out unused codes, `_ignore_brackets_split` and `_BRACKETS`.
`_ignore_brackets_split` was introduced in d57daf1f77 to refactor and support `toDF("...")`; however, ebc124d4c4 replaced the logics here. Seems `_ignore_brackets_split` is not referred anymore.
`_BRACKETS` was introduced in 880eabec37; however, all other usages were removed out in 648a8626b8.
This is rather a followup for ebc124d4c4 which I missed in that PR.
## How was this patch tested?
Manually tested. Existing tests should cover this. I also double checked by `grep` in the whole repo.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#20878 from HyukjinKwon/minor-remove-unused.
## What changes were proposed in this pull request?
As stated in Jira, there are problems with current `Uuid` expression which uses `java.util.UUID.randomUUID` for UUID generation.
This patch uses the newly added `RandomUUIDGenerator` for UUID generation. So we can make `Uuid` deterministic between retries.
## How was this patch tested?
Added unit tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20861 from viirya/SPARK-23599-2.
## What changes were proposed in this pull request?
Currently we allow writing data frames with empty schema into a file based datasource for certain file formats such as JSON, ORC etc. For formats such as Parquet and Text, we raise error at different times of execution. For text format, we return error from the driver early on in processing where as for format such as parquet, the error is raised from executor.
**Example**
spark.emptyDataFrame.write.format("parquet").mode("overwrite").save(path)
**Results in**
``` SQL
org.apache.parquet.schema.InvalidSchemaException: Cannot write a schema with an empty group: message spark_schema {
}
at org.apache.parquet.schema.TypeUtil$1.visit(TypeUtil.java:27)
at org.apache.parquet.schema.TypeUtil$1.visit(TypeUtil.java:37)
at org.apache.parquet.schema.MessageType.accept(MessageType.java:58)
at org.apache.parquet.schema.TypeUtil.checkValidWriteSchema(TypeUtil.java:23)
at org.apache.parquet.hadoop.ParquetFileWriter.<init>(ParquetFileWriter.java:225)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:342)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:302)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:151)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$SingleDirectoryWriteTask.newOutputWriter(FileFormatWriter.scala:376)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$SingleDirectoryWriteTask.execute(FileFormatWriter.scala:387)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:278)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:276)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1411)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:281)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:206)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:205)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.
```
In this PR, we unify the error processing and raise error on attempt to write empty schema based dataframes into file based datasource (orc, parquet, text , csv, json etc) early on in the processing.
## How was this patch tested?
Unit tests added in FileBasedDatasourceSuite.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#20579 from dilipbiswal/spark-23372.
## What changes were proposed in this pull request?
Fixed `CodegenContext.withSubExprEliminationExprs()` so that it saves/restores CSE state correctly.
## How was this patch tested?
Added new unit test to verify that the old CSE state is indeed saved and restored around the `withSubExprEliminationExprs()` call. Manually verified that this test fails without this patch.
Author: Kris Mok <kris.mok@databricks.com>
Closes#20870 from rednaxelafx/codegen-subexpr-fix.
Firstly, glob resolution will not result in swallowing the remote name part (that is preceded by the `#` sign) in case of `--files` or `--archives` options
Moreover in the special case of multiple resolutions when the remote naming does not make sense and error is returned.
Enhanced current test and wrote additional test for the error case
Author: Mihaly Toth <misutoth@gmail.com>
Closes#20853 from misutoth/glob-with-remote-name.
## What changes were proposed in this pull request?
Fix lint-java from https://github.com/apache/spark/pull/19108 addition of JavaKolmogorovSmirnovTestSuite
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#20875 from jkbradley/kstest-lint-fix.
## What changes were proposed in this pull request?
Add `spark.streaming.backpressure.initialRate` to direct Kafka Streams for Kafka 0.8 and 0.10
This is required in order to be able to use backpressure with huge lags, which cannot be processed at once. Without this parameter `DirectKafkaInputDStream` with backpressure enabled would try to get all the possible data from Kafka before adjusting consumption rate
## How was this patch tested?
- Tests added to `org/apache/spark/streaming/kafka010/DirectKafkaStreamSuite.scala` and `org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala`
- Manual tests on YARN cluster
Author: akonopko <alex.konopko@teamaol.com>
Author: Alexander Konopko <alexander.konopko@gmail.com>
Closes#19431 from akonopko/SPARK-18580-initialrate.
## What changes were proposed in this pull request?
Output metrics were not filled when parquet sink used.
This PR fixes this problem by passing a `BasicWriteJobStatsTracker` in `FileStreamSink`.
## How was this patch tested?
Additional unit test added.
Author: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Closes#20745 from gaborgsomogyi/SPARK-23288.
## What changes were proposed in this pull request?
To fix `scala.MatchError` in `literals.sql.out`, this pr added an entry for `CalendarIntervalType` in `QueryExecution.toHiveStructString`.
## How was this patch tested?
Existing tests and added tests in `literals.sql`
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20872 from maropu/FixIntervalTests.
## What changes were proposed in this pull request?
This PR proposes to add `lineSep` option for a configurable line separator in text datasource.
It supports this option by using `LineRecordReader`'s functionality with passing it to the constructor.
## How was this patch tested?
Manual tests and unit tests were added.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20727 from HyukjinKwon/linesep-text.
## What changes were proposed in this pull request?
Support prediction on single instance for regression and classification related models (i.e., PredictionModel, ClassificationModel and their sub classes).
Add corresponding test cases.
## How was this patch tested?
Test cases added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19381 from WeichenXu123/single_prediction.
## What changes were proposed in this pull request?
Silhouette need to know the number of features. This was taken using `first` and checking the size of the vector. Despite this works fine, if the number of attributes is present in metadata, we can avoid to trigger a job for this and use the metadata value. This can help improving performances of course.
## How was this patch tested?
existing UTs + added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20719 from mgaido91/SPARK-23568.
## What changes were proposed in this pull request?
To drop `exprId`s for `Alias` in user-facing info., this pr added an entry for `Alias` in `NonSQLExpression.sql`
## How was this patch tested?
Added tests in `UDFSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20827 from maropu/SPARK-23666.
