## What changes were proposed in this pull request?
Without this PR some UDAFs like `GenericUDAFPercentileApprox` can throw an exception because expecting a constant parameter (object inspector) as a particular argument.
The exception is thrown because `toPrettySQL` call in `ResolveAliases` analyzer rule transforms a `Literal` parameter to a `PrettyAttribute` which is then transformed to an `ObjectInspector` instead of a `ConstantObjectInspector`.
The exception comes from `getEvaluator` method of `GenericUDAFPercentileApprox` that actually shouldn't be called during `toPrettySQL` transformation. The reason why it is called are the non lazy fields in `HiveUDAFFunction`.
This PR makes all fields of `HiveUDAFFunction` lazy.
## How was this patch tested?
added new UT
Closes#22766 from peter-toth/SPARK-25768.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up PR for #22259. The extra field added in `ScalaUDF` with the original PR was declared optional, but should be indeed required, otherwise callers of `ScalaUDF`'s constructor could ignore this new field and cause the result to be incorrect. This PR makes the new field required and changes its name to `handleNullForInputs`.
#22259 breaks the previous behavior for null-handling of primitive-type input parameters. For example, for `val f = udf({(x: Int, y: Any) => x})`, `f(null, "str")` should return `null` but would return `0` after #22259. In this PR, all UDF methods except `def udf(f: AnyRef, dataType: DataType): UserDefinedFunction` have been restored with the original behavior. The only exception is documented in the Spark SQL migration guide.
In addition, now that we have this extra field indicating if a null-test should be applied on the corresponding input value, we can also make use of this flag to avoid the rule `HandleNullInputsForUDF` being applied infinitely.
## How was this patch tested?
Added UT in UDFSuite
Passed affected existing UTs:
AnalysisSuite
UDFSuite
Closes#22732 from maryannxue/spark-25044-followup.
Lead-authored-by: maryannxue <maryannxue@apache.org>
Co-authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This allows an implementer of Spark Session Extensions to utilize a
method "injectFunction" which will add a new function to the default
Spark Session Catalogue.
## What changes were proposed in this pull request?
Adds a new function to SparkSessionExtensions
def injectFunction(functionDescription: FunctionDescription)
Where function description is a new type
type FunctionDescription = (FunctionIdentifier, FunctionBuilder)
The functions are loaded in BaseSessionBuilder when the function registry does not have a parent
function registry to get loaded from.
## How was this patch tested?
New unit tests are added for the extension in SparkSessionExtensionSuite
Closes#22576 from RussellSpitzer/SPARK-25560.
Authored-by: Russell Spitzer <Russell.Spitzer@gmail.com>
Signed-off-by: Herman van Hovell <hvanhovell@databricks.com>
## What changes were proposed in this pull request?
CSVs with windows style crlf ('\r\n') don't work in multiline mode. They work fine in single line mode because the line separation is done by Hadoop, which can handle all the different types of line separators. This PR fixes it by enabling Univocity's line separator detection in multiline mode, which will detect '\r\n', '\r', or '\n' automatically as it is done by hadoop in single line mode.
## How was this patch tested?
Unit test with a file with crlf line endings.
Closes#22503 from justinuang/fix-clrf-multiline.
Authored-by: Justin Uang <juang@palantir.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR updates the examples for `BisectingKMeans` so that they don't use the deprecated method `computeCost` (see SPARK-25758).
## How was this patch tested?
running examples
Closes#22763 from mgaido91/SPARK-25764.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When the first dropEvent occurs, LastReportTimestamp was printing in the log as
Wed Dec 31 16:00:00 PST 1969
(Dropped 1 events from eventLog since Wed Dec 31 16:00:00 PST 1969.)
The reason is that lastReportTimestamp initialized with 0.
Now log is updated to print "... since the application starts" if 'lastReportTimestamp' == 0.
this will happens first dropEvent occurs.
## How was this patch tested?
Manually verified.
Closes#22677 from shivusondur/AsyncEvent1.
Authored-by: shivusondur <shivusondur@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
1. Split the main page of sql-programming-guide into 7 parts:
- Getting Started
- Data Sources
- Performance Turing
- Distributed SQL Engine
- PySpark Usage Guide for Pandas with Apache Arrow
- Migration Guide
- Reference
2. Add left menu for sql-programming-guide, keep first level index for each part in the menu.
## How was this patch tested?
Local test with jekyll build/serve.
Closes#22746 from xuanyuanking/SPARK-24499.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The PR proposes to deprecate the `computeCost` method on `BisectingKMeans` in favor of the adoption of `ClusteringEvaluator` in order to evaluate the clustering.
## How was this patch tested?
NA
Closes#22756 from mgaido91/SPARK-25758.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
This way the image generated from both environments has the same layout,
with just a difference in contents that should not affect functionality.
Also added some minor error checking to the image script.
Closes#22681 from vanzin/SPARK-25682.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Currently each test in `SQLTest` in PySpark is not cleaned properly.
We should introduce and use more `contextmanager` to be convenient to clean up the context properly.
## How was this patch tested?
Modified tests.
Closes#22762 from ueshin/issues/SPARK-25763/cleanup_sqltests.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Only `AddJarCommand` return `0`, the user will be confused about what it means. This PR sets it to empty.
```sql
spark-sql> add jar /Users/yumwang/spark/sql/hive/src/test/resources/TestUDTF.jar;
ADD JAR /Users/yumwang/spark/sql/hive/src/test/resources/TestUDTF.jar
0
spark-sql>
```
## How was this patch tested?
manual tests
```sql
spark-sql> add jar /Users/yumwang/spark/sql/hive/src/test/resources/TestUDTF.jar;
ADD JAR /Users/yumwang/spark/sql/hive/src/test/resources/TestUDTF.jar
spark-sql>
```
Closes#22747 from wangyum/AddJarCommand.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Also update Kinesis SDK's Jackson to match Spark's
## How was this patch tested?
Existing tests, including Kinesis ones, which ought to be hereby triggered.
This was uncovered, I believe, in https://github.com/apache/spark/pull/22729#issuecomment-430666080Closes#22757 from srowen/SPARK-24601.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Master
## What changes were proposed in this pull request?
Previously Pyspark used the private constructor for SparkSession when
building that object. This resulted in a SparkSession without checking
the sql.extensions parameter for additional session extensions. To fix
this we instead use the Session.builder() path as SparkR uses, this
loads the extensions and allows their use in PySpark.
## How was this patch tested?
An integration test was added which mimics the Scala test for the same feature.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21990 from RussellSpitzer/SPARK-25003-master.
Authored-by: Russell Spitzer <Russell.Spitzer@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
The test fix is to allocate a `Resource` object only after the resource
types have been initialized. Otherwise the YARN classes get in a weird
state and throw a different exception than expected, because the resource
has a different view of the registered resources.
I also removed a test for a null resource since that seems unnecessary
and made the fix more complicated.
All the other changes are just cleanup; basically simplify the tests by
defining what is being tested and deriving the resource type registration
and the SparkConf from that data, instead of having redundant definitions
in the tests.
Ran tests with Hadoop 3 (and also without it).
Closes#22751 from vanzin/SPARK-20327.fix.
Authored-by: Marcelo Vanzin <vanzin@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
Currently if we run
```
sh start-thriftserver.sh -h
```
we get
```
...
Thrift server options:
2018-10-15 21:45:39 INFO HiveThriftServer2:54 - Starting SparkContext
2018-10-15 21:45:40 INFO SparkContext:54 - Running Spark version 2.3.2
2018-10-15 21:45:40 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2018-10-15 21:45:40 ERROR SparkContext:91 - Error initializing SparkContext.
org.apache.spark.SparkException: A master URL must be set in your configuration
at org.apache.spark.SparkContext.<init>(SparkContext.scala:367)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2493)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:934)
at org.apache.spark.sql.SparkSession$Builder$$anonfun$7.apply(SparkSession.scala:925)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:925)
at org.apache.spark.sql.hive.thriftserver.SparkSQLEnv$.init(SparkSQLEnv.scala:48)
at org.apache.spark.sql.hive.thriftserver.HiveThriftServer2$.main(HiveThriftServer2.scala:79)
at org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.main(HiveThriftServer2.scala)
2018-10-15 21:45:40 ERROR Utils:91 - Uncaught exception in thread main
```
After fix, the usage output is clean:
```
...
Thrift server options:
--hiveconf <property=value> Use value for given property
```
Also exit with code 1, to follow other scripts(this is the behavior of parsing option `-h` for other linux commands as well).
## How was this patch tested?
Manual test.
Closes#22727 from gengliangwang/stsUsage.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
When deserializing values of ArrayType with struct elements in java beans, fields of structs get mixed up.
I suggest using struct data types retrieved from resolved input data instead of inferring them from java beans.
## What changes were proposed in this pull request?
MapObjects expression is used to map array elements to java beans. Struct type of elements is inferred from java bean structure and ends up with mixed up field order.
I used UnresolvedMapObjects instead of MapObjects, which allows to provide element type for MapObjects during analysis based on the resolved input data, not on the java bean.
## How was this patch tested?
Added a test case.
Built complete project on travis.
michalsenkyr cloud-fan marmbrus liancheng
Closes#22708 from vofque/SPARK-21402.
Lead-authored-by: Vladimir Kuriatkov <vofque@gmail.com>
Co-authored-by: Vladimir Kuriatkov <Vladimir_Kuriatkov@epam.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The SQL execution listener framework was created from scratch(see https://github.com/apache/spark/pull/9078). It didn't leverage what we already have in the spark listener framework, and one major problem is, the listener runs on the spark execution thread, which means a bad listener can block spark's query processing.
This PR re-implements the SQL execution listener framework. Now `ExecutionListenerManager` is just a normal spark listener, which watches the `SparkListenerSQLExecutionEnd` events and post events to the
user-provided SQL execution listeners.
## How was this patch tested?
existing tests.
Closes#22674 from cloud-fan/listener.
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 correct some comment error:
1. change from "as low a possible" to "as low as possible" in RewriteDistinctAggregates.scala
2. delete redundant word “with” in HiveTableScanExec’s doExecute() method
## How was this patch tested?
Existing unit tests.
Closes#22694 from CarolinePeng/update_comment.
Authored-by: 彭灿00244106 <00244106@zte.intra>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`Literal.value` should have a value a value corresponding to `dataType`. This pr added code to verify it and fixed the existing tests to do so.
## How was this patch tested?
Modified the existing tests.
Closes#22724 from maropu/SPARK-25734.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR adds new function `from_csv()` similar to `from_json()` to parse columns with CSV strings. I added the following methods:
```Scala
def from_csv(e: Column, schema: StructType, options: Map[String, String]): Column
```
and this signature to call it from Python, R and Java:
```Scala
def from_csv(e: Column, schema: String, options: java.util.Map[String, String]): Column
```
## How was this patch tested?
Added new test suites `CsvExpressionsSuite`, `CsvFunctionsSuite` and sql tests.
Closes#22379 from MaxGekk/from_csv.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Set a reasonable poll timeout thats used while consuming topics/partitions from kafka. In the
absence of it, a default of 2 minute is used as the timeout values. And all the negative tests take a minimum of 2 minute to execute.
After this change, we save about 4 minutes in this suite.
## How was this patch tested?
Test fix.
Closes#22670 from dilipbiswal/SPARK-25631.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
- Exposes several metrics regarding application status as a source, useful to scrape them via jmx instead of mining the metrics rest api. Example use case: prometheus + jmx exporter.
- Metrics are gathered when a job ends at the AppStatusListener side, could be more fine-grained but most metrics like tasks completed are also counted by executors. More metrics could be exposed in the future to avoid scraping executors in some scenarios.
- a config option `spark.app.status.metrics.enabled` is added to disable/enable these metrics, by default they are disabled.
This was manually tested with jmx source enabled and prometheus server on k8s:
In the next pic the job delay is shown for repeated pi calculation (Spark action).
Closes#22381 from skonto/add_app_status_metrics.
Authored-by: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Remove Kafka 0.8 integration
## How was this patch tested?
Existing tests, build scripts
Closes#22703 from srowen/SPARK-25705.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
AFAIK multi-column count is not widely supported by the mainstream databases(postgres doesn't support), and the SQL standard doesn't define it clearly, as near as I can tell.
Since Spark supports it, we should clearly document the current behavior and add tests to verify it.
## How was this patch tested?
N/A
Closes#22728 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Only test these 4 cases is enough:
be2238fb50/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetWriteSupport.scala (L269-L279)
## How was this patch tested?
Manual tests on my local machine.
before:
```
- filter pushdown - decimal (13 seconds, 683 milliseconds)
```
after:
```
- filter pushdown - decimal (9 seconds, 713 milliseconds)
```
Closes#22636 from wangyum/SPARK-25629.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
LOAD DATA INPATH didn't work if the defaultFS included a port for hdfs.
Handling this just requires a small change to use the correct URI
constructor.
## How was this patch tested?
Added a unit test, ran all tests via jenkins
Closes#22733 from squito/SPARK-25738.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This PR is a follow-up of https://github.com/apache/spark/pull/22594 . This alternative can avoid the unneeded computation in the hot code path.
- For row-based scan, we keep the original way.
- For the columnar scan, we just need to update the stats after each batch.
## How was this patch tested?
N/A
Closes#22731 from gatorsmile/udpateStatsFileScanRDD.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is the work on setting up Secure HDFS interaction with Spark-on-K8S.
The architecture is discussed in this community-wide google [doc](https://docs.google.com/document/d/1RBnXD9jMDjGonOdKJ2bA1lN4AAV_1RwpU_ewFuCNWKg)
This initiative can be broken down into 4 Stages
**STAGE 1**
- [x] Detecting `HADOOP_CONF_DIR` environmental variable and using Config Maps to store all Hadoop config files locally, while also setting `HADOOP_CONF_DIR` locally in the driver / executors
**STAGE 2**
- [x] Grabbing `TGT` from `LTC` or using keytabs+principle and creating a `DT` that will be mounted as a secret or using a pre-populated secret
**STAGE 3**
- [x] Driver
**STAGE 4**
- [x] Executor
## How was this patch tested?
Locally tested on a single-noded, pseudo-distributed Kerberized Hadoop Cluster
- [x] E2E Integration tests https://github.com/apache/spark/pull/22608
- [ ] Unit tests
## Docs and Error Handling?
- [x] Docs
- [x] Error Handling
## Contribution Credit
kimoonkim skonto
Closes#21669 from ifilonenko/secure-hdfs.
Lead-authored-by: Ilan Filonenko <if56@cornell.edu>
Co-authored-by: Ilan Filonenko <ifilondz@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Project logical operator generates valid constraints using two opposite operations. It substracts child constraints from all constraints, than union child constraints again. I think it may be not necessary.
Aggregate operator has the same problem with Project.
This PR try to remove these two opposite collection operations.
## How was this patch tested?
Related unit tests:
ProjectEstimationSuite
CollapseProjectSuite
PushProjectThroughUnionSuite
UnsafeProjectionBenchmark
GeneratedProjectionSuite
CodeGeneratorWithInterpretedFallbackSuite
TakeOrderedAndProjectSuite
GenerateUnsafeProjectionSuite
BucketedRandomProjectionLSHSuite
RemoveRedundantAliasAndProjectSuite
AggregateBenchmark
AggregateOptimizeSuite
AggregateEstimationSuite
DecimalAggregatesSuite
DateFrameAggregateSuite
ObjectHashAggregateSuite
TwoLevelAggregateHashMapSuite
ObjectHashAggregateExecBenchmark
SingleLevelAggregateHaspMapSuite
TypedImperativeAggregateSuite
RewriteDistinctAggregatesSuite
HashAggregationQuerySuite
HashAggregationQueryWithControlledFallbackSuite
TypedImperativeAggregateSuite
TwoLevelAggregateHashMapWithVectorizedMapSuite
Closes#22706 from SongYadong/generate_constraints.
Authored-by: SongYadong <song.yadong1@zte.com.cn>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The PR addresses [the comment](https://github.com/apache/spark/pull/22715#discussion_r225024084) in the previous one. `outputOrdering` becomes a field of `InMemoryRelation`.
## How was this patch tested?
existing UTs
Closes#22726 from mgaido91/SPARK-25727_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Update the next version of Spark from 2.5 to 3.0
## How was this patch tested?
N/A
Closes#22717 from gatorsmile/followupSPARK-25372.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add `outputOrdering ` to `otherCopyArgs` in InMemoryRelation so that this field will be copied when we doing the tree transformation.
```
val data = Seq(100).toDF("count").cache()
data.queryExecution.optimizedPlan.toJSON
```
The above code can generate the following error:
```
assertion failed: InMemoryRelation fields: output, cacheBuilder, statsOfPlanToCache, outputOrdering, values: List(count#178), CachedRDDBuilder(true,10000,StorageLevel(disk, memory, deserialized, 1 replicas),*(1) Project [value#176 AS count#178]
+- LocalTableScan [value#176]
,None), Statistics(sizeInBytes=12.0 B, hints=none)
java.lang.AssertionError: assertion failed: InMemoryRelation fields: output, cacheBuilder, statsOfPlanToCache, outputOrdering, values: List(count#178), CachedRDDBuilder(true,10000,StorageLevel(disk, memory, deserialized, 1 replicas),*(1) Project [value#176 AS count#178]
+- LocalTableScan [value#176]
,None), Statistics(sizeInBytes=12.0 B, hints=none)
at scala.Predef$.assert(Predef.scala:170)
at org.apache.spark.sql.catalyst.trees.TreeNode.jsonFields(TreeNode.scala:611)
at org.apache.spark.sql.catalyst.trees.TreeNode.org$apache$spark$sql$catalyst$trees$TreeNode$$collectJsonValue$1(TreeNode.scala:599)
at org.apache.spark.sql.catalyst.trees.TreeNode.jsonValue(TreeNode.scala:604)
at org.apache.spark.sql.catalyst.trees.TreeNode.toJSON(TreeNode.scala:590)
```
## How was this patch tested?
Added a test
Closes#22715 from gatorsmile/copyArgs1.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
[SPARK-22479](https://github.com/apache/spark/pull/19708/files#diff-5c22ac5160d3c9d81225c5dd86265d27R31) adds a test case which sometimes fails because the used password string `123` matches `41230802`. This PR aims to fix the flakiness.
- https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/97343/consoleFull
```scala
SaveIntoDataSourceCommandSuite:
- simpleString is redacted *** FAILED ***
"SaveIntoDataSourceCommand .org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider41230802, Map(password -> *********(redacted), url -> *********(redacted), driver -> mydriver), ErrorIfExists
+- Range (0, 1, step=1, splits=Some(2))
" contained "123" (SaveIntoDataSourceCommandSuite.scala:42)
```
## How was this patch tested?
Pass the Jenkins with the updated test case
Closes#22716 from dongjoon-hyun/SPARK-25726.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
improve the code comment added in https://github.com/apache/spark/pull/22702/files
## How was this patch tested?
N/A
Closes#22711 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, if we try run
```
./start-history-server.sh -h
```
We will get such error
```
java.io.FileNotFoundException: File -h does not exist
```
1. This is not User-Friendly. For option `-h` or `--help`, it should be parsed correctly and show the usage of the class/script.
2. We can remove deprecated options for setting event log directory through command line options.
After fix, we can get following output:
```
Usage: ./sbin/start-history-server.sh [options]
Options:
--properties-file FILE Path to a custom Spark properties file.
Default is conf/spark-defaults.conf.
Configuration options can be set by setting the corresponding JVM system property.
History Server options are always available; additional options depend on the provider.
History Server options:
spark.history.ui.port Port where server will listen for connections
(default 18080)
spark.history.acls.enable Whether to enable view acls for all applications
(default false)
spark.history.provider Name of history provider class (defaults to
file system-based provider)
spark.history.retainedApplications Max number of application UIs to keep loaded in memory
(default 50)
FsHistoryProvider options:
spark.history.fs.logDirectory Directory where app logs are stored
(default: file:/tmp/spark-events)
spark.history.fs.updateInterval How often to reload log data from storage
(in seconds, default: 10)
```
## How was this patch tested?
Manual test
Closes#22699 from gengliangwang/refactorSHSUsage.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Avro schema allows recursive reference, e.g. the schema for linked-list in https://avro.apache.org/docs/1.8.2/spec.html#schema_record
```
{
"type": "record",
"name": "LongList",
"aliases": ["LinkedLongs"], // old name for this
"fields" : [
{"name": "value", "type": "long"}, // each element has a long
{"name": "next", "type": ["null", "LongList"]} // optional next element
]
}
```
In current Spark SQL, it is impossible to convert the schema as `StructType` . Run `SchemaConverters.toSqlType(avroSchema)` and we will get stack overflow exception.
We should detect the recursive reference and throw exception for it.
## How was this patch tested?
New unit test case.
Closes#22709 from gengliangwang/avroRecursiveRef.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently `Range` reports metrics in batch granularity. This is acceptable, but it's better if we can make it row granularity without performance penalty.
Before this PR, the metrics are updated when preparing the batch, which is before we actually consume data. In this PR, the metrics are updated after the data are consumed. There are 2 different cases:
1. The data processing loop has a stop check. The metrics are updated when we need to stop.
2. no stop check. The metrics are updated after the loop.
## How was this patch tested?
existing tests and a new benchmark
Closes#22698 from cloud-fan/range.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
```Scala
val df1 = Seq(("abc", 1), (null, 3)).toDF("col1", "col2")
df1.write.mode(SaveMode.Overwrite).parquet("/tmp/test1")
val df2 = spark.read.parquet("/tmp/test1")
df2.filter("col1 = 'abc' OR (col1 != 'abc' AND col2 == 3)").show()
```
Before the PR, it returns both rows. After the fix, it returns `Row ("abc", 1))`. This is to fix the bug in NULL handling in BooleanSimplification. This is a bug introduced in Spark 1.6 release.
## How was this patch tested?
Added test cases
Closes#22702 from gatorsmile/fixBooleanSimplify2.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR adds CLI support for YARN custom resources, e.g. GPUs and any other resources YARN defines.
The custom resources are defined with Spark properties, no additional CLI arguments were introduced.
The properties can be defined in the following form:
**AM resources, client mode:**
Format: `spark.yarn.am.resource.<resource-name>`
The property name follows the naming convention of YARN AM cores / memory properties: `spark.yarn.am.memory and spark.yarn.am.cores
`
**Driver resources, cluster mode:**
Format: `spark.yarn.driver.resource.<resource-name>`
The property name follows the naming convention of driver cores / memory properties: `spark.driver.memory and spark.driver.cores.`
**Executor resources:**
Format: `spark.yarn.executor.resource.<resource-name>`
The property name follows the naming convention of executor cores / memory properties: `spark.executor.memory / spark.executor.cores`.
For the driver resources (cluster mode) and executor resources properties, we use the `yarn` prefix here as custom resource types are specific to YARN, currently.
**Validation:**
Please note that a validation logic is added to avoid having requested resources defined in 2 ways, for example defining the following configs:
```
"--conf", "spark.driver.memory=2G",
"--conf", "spark.yarn.driver.resource.memory=1G"
```
will not start execution and will print an error message.
## How was this patch tested?
Unit tests + manual execution with Hadoop2 and Hadoop 3 builds.
Testing have been performed on a real cluster with Spark and YARN configured:
Cluster and client mode
Request Resource Types with lowercase and uppercase units
Start Spark job with only requesting standard resources (mem / cpu)
Error handling cases:
- Request unknown resource type
- Request Resource type (either memory / cpu) with duplicate configs at the same time (e.g. with this config:
```
--conf spark.yarn.am.resource.memory=1G \
--conf spark.yarn.driver.resource.memory=2G \
--conf spark.yarn.executor.resource.memory=3G \
```
), ResourceTypeValidator handles these cases well, so it is not permitted
- Request standard resource (memory / cpu) with the new style configs, e.g. --conf spark.yarn.am.resource.memory=1G, this is not permitted and handled well.
An example about how I ran the testcases:
```
cd ~;export HADOOP_CONF_DIR=/opt/hadoop/etc/hadoop/;
./spark-2.4.0-SNAPSHOT-bin-custom-spark/bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn \
--deploy-mode cluster \
--driver-memory 1G \
--driver-cores 1 \
--executor-memory 1G \
--executor-cores 1 \
--conf spark.logConf=true \
--conf spark.yarn.executor.resource.gpu=3G \
--verbose \
./spark-2.4.0-SNAPSHOT-bin-custom-spark/examples/jars/spark-examples_2.11-2.4.0-SNAPSHOT.jar \
10;
```
Closes#20761 from szyszy/SPARK-20327.
Authored-by: Szilard Nemeth <snemeth@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Fix old oversight in API: Java `flatMapValues` needs a `FlatMapFunction`
## How was this patch tested?
Existing tests.
Closes#22690 from srowen/SPARK-19287.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Refactor `JoinBenchmark` to use main method.
1. use `spark-submit`:
```console
bin/spark-submit --class org.apache.spark.sql.execution.benchmark.JoinBenchmark --jars ./core/target/spark-core_2.11-3.0.0-SNAPSHOT-tests.jar ./sql/catalyst/target/spark-sql_2.11-3.0.0-SNAPSHOT-tests.jar
```
2. Generate benchmark result:
```console
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.JoinBenchmark"
```
## How was this patch tested?
manual tests
Closes#22661 from wangyum/SPARK-25664.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Yuming Wang <wgyumg@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PR addresses the exception raised on accessing chars out of delimiter string. In particular, the backward slash `\` as the CSV fields delimiter causes the following exception on reading `abc\1`:
```Scala
String index out of range: 1
java.lang.StringIndexOutOfBoundsException: String index out of range: 1
at java.lang.String.charAt(String.java:658)
```
because `str.charAt(1)` tries to access a char out of `str` in `CSVUtils.toChar`
## How was this patch tested?
Added tests for empty string and string containing the backward slash to `CSVUtilsSuite`. Besides of that I added an end-to-end test to check how the backward slash is handled in reading CSV string with it.
Closes#22654 from MaxGekk/csv-slash-delim.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
When we enable event log compression and compression codec as 'zstd', we are unable to open the webui of the running application from the history server page.
The reason is that, Replay listener was unable to read from the zstd compressed eventlog due to the zstd frame was not finished yet. This causes truncated error while reading the eventLog.
So, when we try to open the WebUI from the History server page, it throws "truncated error ", and we never able to open running application in the webui, when we enable zstd compression.
In this PR, when the IO excpetion happens, and if it is a running application, we log the error,
"Failed to read Spark event log: evetLogDirAppName.inprogress", instead of throwing exception.
## How was this patch tested?
Test steps:
1)spark.eventLog.compress = true
2)spark.io.compression.codec = zstd
3)restart history server
4) launch bin/spark-shell
5) run some queries
6) Open history server page
7) click on the application
**Before fix:**
**After fix:**
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22689 from shahidki31/SPARK-25697.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
