## What changes were proposed in this pull request?
Adds the recently added `summary` method to the python dataframe interface.
## How was this patch tested?
Additional inline doctests.
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#18762 from aray/summary-py.
## What changes were proposed in this pull request?
Fix typos
## How was this patch tested?
Existing tests
Author: Andrew Ash <andrew@andrewash.com>
Closes#18996 from ash211/patch-2.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/18955 , to fix a bug that we break whole stage codegen for `Limit`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18993 from cloud-fan/bug.
## What changes were proposed in this pull request?
Added support for ANALYZE TABLE [db_name].tablename PARTITION (partcol1[=val1], partcol2[=val2], ...) COMPUTE STATISTICS [NOSCAN] SQL command to calculate total number of rows and size in bytes for a subset of partitions. Calculated statistics are stored in Hive Metastore as user-defined properties attached to partition objects. Property names are the same as the ones used to store table-level statistics: spark.sql.statistics.totalSize and spark.sql.statistics.numRows.
When partition specification contains all partition columns with values, the command collects statistics for a single partition that matches the specification. When some partition columns are missing or listed without their values, the command collects statistics for all partitions which match a subset of partition column values specified.
For example, table t has 4 partitions with the following specs:
* Partition1: (ds='2008-04-08', hr=11)
* Partition2: (ds='2008-04-08', hr=12)
* Partition3: (ds='2008-04-09', hr=11)
* Partition4: (ds='2008-04-09', hr=12)
'ANALYZE TABLE t PARTITION (ds='2008-04-09', hr=11)' command will collect statistics only for partition 3.
'ANALYZE TABLE t PARTITION (ds='2008-04-09')' command will collect statistics for partitions 3 and 4.
'ANALYZE TABLE t PARTITION (ds, hr)' command will collect statistics for all four partitions.
When the optional parameter NOSCAN is specified, the command doesn't count number of rows and only gathers size in bytes.
The statistics gathered by ANALYZE TABLE command can be fetched using DESC EXTENDED [db_name.]tablename PARTITION command.
## How was this patch tested?
Added tests.
Author: Masha Basmanova <mbasmanova@fb.com>
Closes#18421 from mbasmanova/mbasmanova-analyze-partition.
## What changes were proposed in this pull request?
Dataset.sample requires a boolean flag withReplacement as the first argument. However, most of the time users simply want to sample some records without replacement. This ticket introduces a new sample function that simply takes in the fraction and seed.
## How was this patch tested?
Tested manually. Not sure yet if we should add a test case for just this wrapper ...
Author: Reynold Xin <rxin@databricks.com>
Closes#18988 from rxin/SPARK-21778.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/projects/SPARK/issues/SPARK-21739
This issue is caused by introducing TimeZoneAwareExpression.
When the **Cast** expression converts something into TimestampType, it should be resolved with setting `timezoneId`. In general, it is resolved in LogicalPlan phase.
However, there are still some places that use Cast expression statically to convert datatypes without setting `timezoneId`. In such cases, `NoSuchElementException: None.get` will be thrown for TimestampType.
This PR is proposed to fix the issue. We have checked the whole project and found two such usages(i.e., in`TableReader` and `HiveTableScanExec`).
## How was this patch tested?
unit test
Author: donnyzone <wellfengzhu@gmail.com>
Closes#18960 from DonnyZone/spark-21739.
## What changes were proposed in this pull request?
Decimal is a logical type of AVRO. We need to ensure the support of Hive's AVRO serde works well in Spark
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18977 from gatorsmile/addAvroTest.
## What changes were proposed in this pull request?
``` scala
scala> Seq(("""{"Hyukjin": 224, "John": 1225}""")).toDS.selectExpr("json_tuple(value, trim(null))").show()
...
java.lang.NullPointerException
at ...
```
Currently the `null` field name will throw NullPointException. As a given field name null can't be matched with any field names in json, we just output null as its column value. This PR achieves it by returning a very unlikely column name `__NullFieldName` in evaluation of the field names.
## How was this patch tested?
Added unit test.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#18930 from jmchung/SPARK-21677.
## What changes were proposed in this pull request?
Add Kerberos Support to Mesos. This includes kinit and --keytab support, but does not include delegation token renewal.
## How was this patch tested?
Manually against a Secure DC/OS Apache HDFS cluster.
Author: ArtRand <arand@soe.ucsc.edu>
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#18519 from mgummelt/SPARK-16742-kerberos.
## What changes were proposed in this pull request?
This pr sorted output attributes on their name and exprId in `AttributeSet.toSeq` to make the order consistent. If the order is different, spark possibly generates different code and then misses cache in `CodeGenerator`, e.g., `GenerateColumnAccessor` generates code depending on an input attribute order.
## How was this patch tested?
Added tests in `AttributeSetSuite` and manually checked if the cache worked well in the given query of the JIRA.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18959 from maropu/SPARK-18394.
## What changes were proposed in this pull request?
When running IntelliJ, we are unable to capture the exception of memory leak detection.
> org.apache.spark.executor.Executor: Managed memory leak detected
Explicitly setting `spark.unsafe.exceptionOnMemoryLeak` in SparkConf when building the SparkSession, instead of reading it from system properties.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18967 from gatorsmile/setExceptionOnMemoryLeak.
## What changes were proposed in this pull request?
Set isolated to false while using builtin hive jars and `SessionState.get` returns a `CliSessionState` instance.
## How was this patch tested?
1 Unit Tests
2 Manually verified: `hive.exec.strachdir` was only created once because of reusing cliSessionState
```java
➜ spark git:(SPARK-21428) ✗ bin/spark-sql --conf spark.sql.hive.metastore.jars=builtin
log4j:WARN No appenders could be found for logger (org.apache.hadoop.util.Shell).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17/07/16 23:59:27 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/07/16 23:59:27 INFO HiveMetaStore: 0: Opening raw store with implemenation class:org.apache.hadoop.hive.metastore.ObjectStore
17/07/16 23:59:27 INFO ObjectStore: ObjectStore, initialize called
17/07/16 23:59:28 INFO Persistence: Property hive.metastore.integral.jdo.pushdown unknown - will be ignored
17/07/16 23:59:28 INFO Persistence: Property datanucleus.cache.level2 unknown - will be ignored
17/07/16 23:59:29 INFO ObjectStore: Setting MetaStore object pin classes with hive.metastore.cache.pinobjtypes="Table,StorageDescriptor,SerDeInfo,Partition,Database,Type,FieldSchema,Order"
17/07/16 23:59:30 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:30 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO MetaStoreDirectSql: Using direct SQL, underlying DB is DERBY
17/07/16 23:59:31 INFO ObjectStore: Initialized ObjectStore
17/07/16 23:59:31 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
17/07/16 23:59:31 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
17/07/16 23:59:32 INFO HiveMetaStore: Added admin role in metastore
17/07/16 23:59:32 INFO HiveMetaStore: Added public role in metastore
17/07/16 23:59:32 INFO HiveMetaStore: No user is added in admin role, since config is empty
17/07/16 23:59:32 INFO HiveMetaStore: 0: get_all_databases
17/07/16 23:59:32 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_all_databases
17/07/16 23:59:32 INFO HiveMetaStore: 0: get_functions: db=default pat=*
17/07/16 23:59:32 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_functions: db=default pat=*
17/07/16 23:59:32 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MResourceUri" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:32 INFO SessionState: Created local directory: /var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/beea7261-221a-4711-89e8-8b12a9d37370_resources
17/07/16 23:59:32 INFO SessionState: Created HDFS directory: /tmp/hive/Kent/beea7261-221a-4711-89e8-8b12a9d37370
17/07/16 23:59:32 INFO SessionState: Created local directory: /var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/Kent/beea7261-221a-4711-89e8-8b12a9d37370
17/07/16 23:59:32 INFO SessionState: Created HDFS directory: /tmp/hive/Kent/beea7261-221a-4711-89e8-8b12a9d37370/_tmp_space.db
17/07/16 23:59:32 INFO SparkContext: Running Spark version 2.3.0-SNAPSHOT
17/07/16 23:59:32 INFO SparkContext: Submitted application: SparkSQL::10.0.0.8
17/07/16 23:59:32 INFO SecurityManager: Changing view acls to: Kent
17/07/16 23:59:32 INFO SecurityManager: Changing modify acls to: Kent
17/07/16 23:59:32 INFO SecurityManager: Changing view acls groups to:
17/07/16 23:59:32 INFO SecurityManager: Changing modify acls groups to:
17/07/16 23:59:32 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(Kent); groups with view permissions: Set(); users with modify permissions: Set(Kent); groups with modify permissions: Set()
17/07/16 23:59:33 INFO Utils: Successfully started service 'sparkDriver' on port 51889.
17/07/16 23:59:33 INFO SparkEnv: Registering MapOutputTracker
17/07/16 23:59:33 INFO SparkEnv: Registering BlockManagerMaster
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
17/07/16 23:59:33 INFO DiskBlockManager: Created local directory at /private/var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/blockmgr-9cfae28a-01e9-4c73-a1f1-f76fa52fc7a5
17/07/16 23:59:33 INFO MemoryStore: MemoryStore started with capacity 366.3 MB
17/07/16 23:59:33 INFO SparkEnv: Registering OutputCommitCoordinator
17/07/16 23:59:33 INFO Utils: Successfully started service 'SparkUI' on port 4040.
17/07/16 23:59:33 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://10.0.0.8:4040
17/07/16 23:59:33 INFO Executor: Starting executor ID driver on host localhost
17/07/16 23:59:33 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 51890.
17/07/16 23:59:33 INFO NettyBlockTransferService: Server created on 10.0.0.8:51890
17/07/16 23:59:33 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
17/07/16 23:59:33 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: Registering block manager 10.0.0.8:51890 with 366.3 MB RAM, BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:34 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('file:/Users/Kent/Documents/spark/spark-warehouse').
17/07/16 23:59:34 INFO SharedState: Warehouse path is 'file:/Users/Kent/Documents/spark/spark-warehouse'.
17/07/16 23:59:34 INFO HiveUtils: Initializing HiveMetastoreConnection version 1.2.1 using Spark classes.
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO HiveMetaStore: 0: get_database: default
17/07/16 23:59:34 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_database: default
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO HiveMetaStore: 0: get_database: global_temp
17/07/16 23:59:34 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_database: global_temp
17/07/16 23:59:34 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO StateStoreCoordinatorRef: Registered StateStoreCoordinator endpoint
spark-sql>
```
cc cloud-fan gatorsmile
Author: Kent Yao <yaooqinn@hotmail.com>
Author: hzyaoqin <hzyaoqin@corp.netease.com>
Closes#18648 from yaooqinn/SPARK-21428.
## What changes were proposed in this pull request?
The patch lets spark web ui use FQDN as its hostname instead of ip address.
In current implementation, ip address of a driver host is set to DRIVER_HOST_ADDRESS. This becomes a problem when we enable SSL using "spark.ssl.enabled", "spark.ssl.trustStore" and "spark.ssl.keyStore" properties. When we configure these properties, spark web ui is launched with SSL enabled and the HTTPS server is configured with the custom SSL certificate you configured in these properties.
In this case, client gets javax.net.ssl.SSLPeerUnverifiedException exception when the client accesses the spark web ui because the client fails to verify the SSL certificate (Common Name of the SSL cert does not match with DRIVER_HOST_ADDRESS).
To avoid the exception, we should use FQDN of the driver host for DRIVER_HOST_ADDRESS.
Error message that client gets when the client accesses spark web ui:
javax.net.ssl.SSLPeerUnverifiedException: Certificate for <10.102.138.239> doesn't match any of the subject alternative names: []
## How was this patch tested?
manual tests
Author: Hideaki Tanaka <tanakah@amazon.com>
Closes#18846 from thideeeee/SPARK-21642.
## What changes were proposed in this pull request?
For top-most limit, we will use a special operator to execute it: `CollectLimitExec`.
`CollectLimitExec` will retrieve `n`(which is the limit) rows from each partition of the child plan output, see https://github.com/apache/spark/blob/v2.2.0/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala#L311. It's very likely that we don't exhaust the child plan output.
This is fine when whole-stage-codegen is off, as child plan will release the resource via task completion listener. However, when whole-stage codegen is on, the resource can only be released if all output is consumed.
To fix this memory leak, one simple approach is, when `CollectLimitExec` retrieve `n` rows from child plan output, child plan output should only have `n` rows, then the output is exhausted and resource is released. This can be done by wrapping child plan with `LocalLimit`
## How was this patch tested?
a regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18955 from cloud-fan/leak.
## What changes were proposed in this pull request?
introduced `DiskBlockData`, a new implementation of `BlockData` representing a whole file.
this is somehow related to [SPARK-6236](https://issues.apache.org/jira/browse/SPARK-6236) as well
This class follows the implementation of `EncryptedBlockData` just without the encryption. hence:
* `toInputStream` is implemented using a `FileInputStream` (todo: encrypted version actually uses `Channels.newInputStream`, not sure if it's the right choice for this)
* `toNetty` is implemented in terms of `io.netty.channel.DefaultFileRegion`
* `toByteBuffer` fails for files larger than 2GB (same behavior of the original code, just postponed a bit), it also respects the same configuration keys defined by the original code to choose between memory mapping and simple file read.
## How was this patch tested?
added test to DiskStoreSuite and MemoryManagerSuite
Author: Eyal Farago <eyal@nrgene.com>
Closes#18855 from eyalfa/SPARK-3151.
## What changes were proposed in this pull request?
When use Vector.compressed to change a Vector to SparseVector, the performance is very low comparing with Vector.toSparse.
This is because you have to scan the value three times using Vector.compressed, but you just need two times when use Vector.toSparse.
When the length of the vector is large, there is significant performance difference between this two method.
## How was this patch tested?
The existing UT
Author: Peng Meng <peng.meng@intel.com>
Closes#18899 from mpjlu/optVectorCompress.
## What changes were proposed in this pull request?
When a session is closed the Thriftserver doesn't cancel the jobs which may still be running. This is a huge waste of resources.
This PR address the problem canceling the pending jobs when a session is closed.
## How was this patch tested?
The patch was tested manually.
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#18951 from mgaido91/SPARK-21738.
## What changes were proposed in this pull request?
Close the whole stage codegen when the function lines is longer than the maxlines which will be setted by
spark.sql.codegen.MaxFunctionLength parameter, because when the function is too long , it will not get the JIT optimizing.
A benchmark test result is 10x slower when the generated function is too long :
ignore("max function length of wholestagecodegen") {
val N = 20 << 15
val benchmark = new Benchmark("max function length of wholestagecodegen", N)
def f(): Unit = sparkSession.range(N)
.selectExpr(
"id",
"(id & 1023) as k1",
"cast(id & 1023 as double) as k2",
"cast(id & 1023 as int) as k3",
"case when id > 100 and id <= 200 then 1 else 0 end as v1",
"case when id > 200 and id <= 300 then 1 else 0 end as v2",
"case when id > 300 and id <= 400 then 1 else 0 end as v3",
"case when id > 400 and id <= 500 then 1 else 0 end as v4",
"case when id > 500 and id <= 600 then 1 else 0 end as v5",
"case when id > 600 and id <= 700 then 1 else 0 end as v6",
"case when id > 700 and id <= 800 then 1 else 0 end as v7",
"case when id > 800 and id <= 900 then 1 else 0 end as v8",
"case when id > 900 and id <= 1000 then 1 else 0 end as v9",
"case when id > 1000 and id <= 1100 then 1 else 0 end as v10",
"case when id > 1100 and id <= 1200 then 1 else 0 end as v11",
"case when id > 1200 and id <= 1300 then 1 else 0 end as v12",
"case when id > 1300 and id <= 1400 then 1 else 0 end as v13",
"case when id > 1400 and id <= 1500 then 1 else 0 end as v14",
"case when id > 1500 and id <= 1600 then 1 else 0 end as v15",
"case when id > 1600 and id <= 1700 then 1 else 0 end as v16",
"case when id > 1700 and id <= 1800 then 1 else 0 end as v17",
"case when id > 1800 and id <= 1900 then 1 else 0 end as v18")
.groupBy("k1", "k2", "k3")
.sum()
.collect()
benchmark.addCase(s"codegen = F") { iter =>
sparkSession.conf.set("spark.sql.codegen.wholeStage", "false")
f()
}
benchmark.addCase(s"codegen = T") { iter =>
sparkSession.conf.set("spark.sql.codegen.wholeStage", "true")
sparkSession.conf.set("spark.sql.codegen.MaxFunctionLength", "10000")
f()
}
benchmark.run()
/*
Java HotSpot(TM) 64-Bit Server VM 1.8.0_111-b14 on Windows 7 6.1
Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
max function length of wholestagecodegen: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
codegen = F 443 / 507 1.5 676.0 1.0X
codegen = T 3279 / 3283 0.2 5002.6 0.1X
*/
}
## How was this patch tested?
Run the unit test
Author: 10129659 <chen.yanshan@zte.com.cn>
Closes#18810 from eatoncys/codegen.
## What changes were proposed in this pull request?
Right now spark lets go of executors when they are idle for the 60s (or configurable time). I have seen spark let them go when they are idle but they were really needed. I have seen this issue when the scheduler was waiting to get node locality but that takes longer than the default idle timeout. In these jobs the number of executors goes down really small (less than 10) but there are still like 80,000 tasks to run.
We should consider not allowing executors to idle timeout if they are still needed according to the number of tasks to be run.
## How was this patch tested?
Tested by manually adding executors to `executorsIdsToBeRemoved` list and seeing if those executors were removed when there are a lot of tasks and a high `numExecutorsTarget` value.
Code used
In `ExecutorAllocationManager.start()`
```
start_time = clock.getTimeMillis()
```
In `ExecutorAllocationManager.schedule()`
```
val executorIdsToBeRemoved = ArrayBuffer[String]()
if ( now > start_time + 1000 * 60 * 2) {
logInfo("--- REMOVING 1/2 of the EXECUTORS ---")
start_time += 1000 * 60 * 100
var counter = 0
for (x <- executorIds) {
counter += 1
if (counter == 2) {
counter = 0
executorIdsToBeRemoved += x
}
}
}
Author: John Lee <jlee2@yahoo-inc.com>
Closes#18874 from yoonlee95/SPARK-21656.
This PR adds a `FeatureHasher` transformer, modeled on [scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html) and [Vowpal wabbit](https://github.com/JohnLangford/vowpal_wabbit/wiki/Feature-Hashing-and-Extraction).
The transformer operates on multiple input columns in one pass. Current behavior is:
* for numerical columns, the values are assumed to be real values and the feature index is `hash(columnName)` while feature value is `feature_value`
* for string columns, the values are assumed to be categorical and the feature index is `hash(column_name=feature_value)`, while feature value is `1.0`
* For hash collisions, feature values will be summed
* `null` (missing) values are ignored
The following dataframe illustrates the basic semantics:
```
+---+------+-----+---------+------+-----------------------------------------+
|int|double|float|stringNum|string|features |
+---+------+-----+---------+------+-----------------------------------------+
|3 |4.0 |5.0 |1 |foo |(16,[0,8,11,12,15],[5.0,3.0,1.0,4.0,1.0])|
|6 |7.0 |8.0 |2 |bar |(16,[0,8,11,12,15],[8.0,6.0,1.0,7.0,1.0])|
+---+------+-----+---------+------+-----------------------------------------+
```
## How was this patch tested?
New unit tests and manual experiments.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#18513 from MLnick/FeatureHasher.
## What changes were proposed in this pull request?
Check the option "numFeatures" only when reading LibSVM, not when writing. When writing, Spark was raising an exception. After the change it will ignore the option completely. liancheng HyukjinKwon
(Maybe the usage should be forbidden when writing, in a major version change?).
## How was this patch tested?
Manual test, that loading and writing LibSVM files work fine, both with and without the numFeatures option.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Jan Vrsovsky <jan.vrsovsky@firma.seznam.cz>
Closes#18872 from ProtD/master.
## What changes were proposed in this pull request?
Like Parquet, this PR aims to depend on the latest Apache ORC 1.4 for Apache Spark 2.3. There are key benefits for Apache ORC 1.4.
- Stability: Apache ORC 1.4.0 has many fixes and we can depend on ORC community more.
- Maintainability: Reduce the Hive dependency and can remove old legacy code later.
Later, we can get the following two key benefits by adding new ORCFileFormat in SPARK-20728 (#17980), too.
- Usability: User can use ORC data sources without hive module, i.e, -Phive.
- Speed: Use both Spark ColumnarBatch and ORC RowBatch together. This will be faster than the current implementation in Spark.
## How was this patch tested?
Pass the jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18640 from dongjoon-hyun/SPARK-21422.
## What changes were proposed in this pull request?
This patch adds the DataFrames API to the multivariate summarizer (mean, variance, etc.). In addition to all the features of MultivariateOnlineSummarizer, it also allows the user to select a subset of the metrics.
## How was this patch tested?
Testcases added.
## Performance
Resolve several performance issues in #17419, further optimization pending on SQL team's work. One of the SQL layer performance issue related to these feature has been resolved in #18712, thanks liancheng and cloud-fan
### Performance data
(test on my laptop, use 2 partitions. tries out = 20, warm up = 10)
The unit of test results is records/milliseconds (higher is better)
Vector size/records number | 1/10000000 | 10/1000000 | 100/1000000 | 1000/100000 | 10000/10000
----|------|----|---|----|----
Dataframe | 15149 | 7441 | 2118 | 224 | 21
RDD from Dataframe | 4992 | 4440 | 2328 | 320 | 33
raw RDD | 53931 | 20683 | 3966 | 528 | 53
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#18798 from WeichenXu123/SPARK-19634-dataframe-summarizer.
Proposed changes:
* Clarify the type error that `Column.substr()` gives.
Test plan:
* Tested this manually.
* Test code:
```python
from pyspark.sql.functions import col, lit
spark.createDataFrame([['nick']], schema=['name']).select(col('name').substr(0, lit(1)))
```
* Before:
```
TypeError: Can not mix the type
```
* After:
```
TypeError: startPos and length must be the same type. Got <class 'int'> and
<class 'pyspark.sql.column.Column'>, respectively.
```
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#18926 from nchammas/SPARK-21712-substr-type-error.
## What changes were proposed in this pull request?
The method name `asNonNullabe` should be `asNonNullable`.
## How was this patch tested?
N/A
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18952 from jiangxb1987/typo.
This version fixes a few issues in the import order checker; it provides
better error messages, and detects more improper ordering (thus the need
to change a lot of files in this patch). The main fix is that it correctly
complains about the order of packages vs. classes.
As part of the above, I moved some "SparkSession" import in ML examples
inside the "$example on$" blocks; that didn't seem consistent across
different source files to start with, and avoids having to add more on/off blocks
around specific imports.
The new scalastyle also seems to have a better header detector, so a few
license headers had to be updated to match the expected indentation.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18943 from vanzin/SPARK-21731.
Currently the launcher handle does not monitor the child spark-submit
process it launches; this means that if the child exits with an error,
the handle's state will never change, and an application will not know
that the application has failed.
This change adds code to monitor the child process, and changes the
handle state appropriately when the child process exits.
Tested with added unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18877 from vanzin/SPARK-17742.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/15900 , to fix one more bug:
When table schema is empty and need to be inferred at runtime, we should not resolve parent plans before the schema has been inferred, or the parent plans will be resolved against an empty schema and may get wrong result for something like `select *`
The fix logic is: introduce `UnresolvedCatalogRelation` as a placeholder. Then we replace it with `LogicalRelation` or `HiveTableRelation` during analysis, so that it's guaranteed that we won't resolve parent plans until the schema has been inferred.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18907 from cloud-fan/bug.
## What changes were proposed in this pull request?
This is a follow-up PR that moves the test case in PR-18920 (https://github.com/apache/spark/pull/18920) to DataFrameAggregateSuit.
## How was this patch tested?
unit test
Author: donnyzone <wellfengzhu@gmail.com>
Closes#18946 from DonnyZone/branch-19471-followingPR.
## What changes were proposed in this pull request?
This PR changes the codes to lazily init hive metastore client so that we can create SparkSession without talking to the hive metastore sever.
It's pretty helpful when you set a hive metastore server but it's down. You can still start the Spark shell to debug.
## How was this patch tested?
The new unit test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18944 from zsxwing/hive-lazy-init.
## What changes were proposed in this pull request?
This PR adds `since` annotation in documentation so that this can be rendered as below:
<img width="290" alt="2017-08-14 6 54 26" src="https://user-images.githubusercontent.com/6477701/29267050-034c1f64-8122-11e7-862b-7dfc38e292bf.png">
## How was this patch tested?
Manually checked the documentation by `cd sql && ./create-docs.sh`.
Also, Jenkins tests are required.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18939 from HyukjinKwon/add-sinces-date-functions.
## What changes were proposed in this pull request?
We put staging path to delete into the deleteOnExit cache of `FileSystem` in case of the path can't be successfully removed. But when we successfully remove the path, we don't remove it from the cache. We should do it to avoid continuing grow the cache size.
## How was this patch tested?
Added a test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18934 from viirya/SPARK-21721.
## What changes were proposed in this pull request?
Directly writing a snapshot file may generate a partial file. This PR changes it to write to a temp file then rename to the target file.
## How was this patch tested?
Jenkins.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18928 from zsxwing/SPARK-21696.
## What changes were proposed in this pull request?
Recently, we have also encountered such NPE issues in our production environment as described in:
https://issues.apache.org/jira/browse/SPARK-19471
This issue can be reproduced by the following examples:
` val df = spark.createDataFrame(Seq(("1", 1), ("1", 2), ("2", 3), ("2", 4))).toDF("x", "y")
//HashAggregate, SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key=false
df.groupBy("x").agg(rand(),sum("y")).show()
//ObjectHashAggregate, SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key=false
df.groupBy("x").agg(rand(),collect_list("y")).show()
//SortAggregate, SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key=false &&SQLConf.USE_OBJECT_HASH_AGG.key=false
df.groupBy("x").agg(rand(),collect_list("y")).show()`
`
This PR is based on PR-16820(https://github.com/apache/spark/pull/16820) with test cases for all aggregation paths. We want to push it forward.
> When AggregationIterator generates result projection, it does not call the initialize method of the Projection class. This will cause a runtime NullPointerException when the projection involves nondeterministic expressions.
## How was this patch tested?
unit test
verified in production environment
Author: donnyzone <wellfengzhu@gmail.com>
Closes#18920 from DonnyZone/Branch-spark-19471.
## What changes were proposed in this pull request?
At present, in test("broadcasted hash outer join operator selection") case, set the testData2 to _CACHE TABLE_, but no _uncache table_ testData2. It can make people confused.
In addition, in the joinsuite test cases, clear the cache table of work by SharedSQLContext _spark.sharedState.cacheManager.clearCache_ to do, so we do not need to uncache table
let's fix it. thanks.
## How was this patch tested?
Existing test cases.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#18914 from heary-cao/uncache_table.
## What changes were proposed in this pull request?
JIRA issue: https://issues.apache.org/jira/browse/SPARK-21658
Add default None for value in `na.replace` since `Dataframe.replace` and `DataframeNaFunctions.replace` are alias.
The default values are the same now.
```
>>> df = sqlContext.createDataFrame([('Alice', 10, 80.0)])
>>> df.replace({"Alice": "a"}).first()
Row(_1=u'a', _2=10, _3=80.0)
>>> df.na.replace({"Alice": "a"}).first()
Row(_1=u'a', _2=10, _3=80.0)
```
## How was this patch tested?
Existing tests.
cc viirya
Author: byakuinss <grace.chinhanyu@gmail.com>
Closes#18895 from byakuinss/SPARK-21658.
## What changes were proposed in this pull request?
Fix the race condition when serializing TaskDescriptions and adding jars by keeping the set of jars and files for a TaskSet constant across the lifetime of the TaskSet. Otherwise TaskDescription serialization can produce an invalid serialization when new file/jars are added concurrently as the TaskDescription is serialized.
## How was this patch tested?
Additional unit test ensures jars/files contained in the TaskDescription remain constant throughout the lifetime of the TaskSet.
Author: Andrew Ash <andrew@andrewash.com>
Closes#18913 from ash211/SPARK-21563.
## What changes were proposed in this pull request?
Several links on the worker page do not work correctly with the proxy because:
1) They don't acknowledge the proxy
2) They use relative paths (unlike the Application Page which uses full paths)
This patch fixes that. It also fixes a mistake in the proxy's Location header parsing which caused it to incorrectly handle redirects.
## How was this patch tested?
I checked the validity of every link with the proxy on and off.
Author: Anderson Osagie <osagie@gmail.com>
Closes#18915 from aosagie/fix/proxy-links.
## What changes were proposed in this pull request?
While discovering optimization rules and their test coverage, I did not find any tests for `CheckCartesianProducts` in the Catalyst folder. So, I decided to create a new test suite. Once I finished, I found a test in `JoinSuite` for this functionality so feel free to discard this change if it does not make much sense. The proposed test suite covers a few additional use cases.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#18909 from aokolnychyi/check-cartesian-join-tests.
## What changes were proposed in this pull request?
Update sbt version to 0.13.16. I think this is a useful stepping stone to getting to sbt 1.0.0.
## How was this patch tested?
Existing Build.
Author: pj.fanning <pj.fanning@workday.com>
Closes#18921 from pjfanning/SPARK-21709.
## What changes were proposed in this pull request?
Implemented a Python-only persistence framework for pipelines containing stages that cannot be saved using Java.
## How was this patch tested?
Created a custom Python-only UnaryTransformer, included it in a Pipeline, and saved/loaded the pipeline. The loaded pipeline was compared against the original using _compare_pipelines() in tests.py.
Author: Ajay Saini <ajays725@gmail.com>
Closes#18888 from ajaysaini725/PythonPipelines.
## What changes were proposed in this pull request?
This is trivial, but bugged me. We should download software over HTTPS.
And we can use RAT 0.12 while at it to pick up bug fixes.
## How was this patch tested?
N/A
Author: Sean Owen <sowen@cloudera.com>
Closes#18927 from srowen/Rat012.
Fixes --packages flag for the stand-alone case in cluster mode. Adds to the driver classpath the jars that are resolved via ivy along with any other jars passed to `spark.jars`. Jars not resolved by ivy are downloaded explicitly to a tmp folder on the driver node. Similar code is available in SparkSubmit so we refactored part of it to use it at the DriverWrapper class which is responsible for launching driver in standalone cluster mode.
Note: In stand-alone mode `spark.jars` contains the user jar so it can be fetched later on at the executor side.
Manually by submitting a driver in cluster mode within a standalone cluster and checking if dependencies were resolved at the driver side.
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Closes#18630 from skonto/fix_packages_stand_alone_cluster.
## What changes were proposed in this pull request?
Jira : https://issues.apache.org/jira/browse/SPARK-19122
`leftKeys` and `rightKeys` in `SortMergeJoinExec` are altered based on the ordering of join keys in the child's `outputPartitioning`. This is done everytime `requiredChildDistribution` is invoked during query planning.
## How was this patch tested?
- Added new test case
- Existing tests
Author: Tejas Patil <tejasp@fb.com>
Closes#16985 from tejasapatil/SPARK-19122_join_order_shuffle.
## What changes were proposed in this pull request?
[SPARK-21595](https://issues.apache.org/jira/browse/SPARK-21595) reported that there is excessive spilling to disk due to default spill threshold for `ExternalAppendOnlyUnsafeRowArray` being quite small for WINDOW operator. Old behaviour of WINDOW operator (pre https://github.com/apache/spark/pull/16909) would hold data in an array for first 4096 records post which it would switch to `UnsafeExternalSorter` and start spilling to disk after reaching `spark.shuffle.spill.numElementsForceSpillThreshold` (or earlier if there was paucity of memory due to excessive consumers).
Currently the (switch from in-memory to `UnsafeExternalSorter`) and (`UnsafeExternalSorter` spilling to disk) for `ExternalAppendOnlyUnsafeRowArray` is controlled by a single threshold. This PR aims to separate that to have more granular control.
## How was this patch tested?
Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#18843 from tejasapatil/SPARK-21595.
Add an option to the JDBC data source to initialize the environment of the remote database session
## What changes were proposed in this pull request?
This proposes an option to the JDBC datasource, tentatively called " sessionInitStatement" to implement the functionality of session initialization present for example in the Sqoop connector for Oracle (see https://sqoop.apache.org/docs/1.4.6/SqoopUserGuide.html#_oraoop_oracle_session_initialization_statements ) . After each database session is opened to the remote DB, and before starting to read data, this option executes a custom SQL statement (or a PL/SQL block in the case of Oracle).
See also https://issues.apache.org/jira/browse/SPARK-21519
## How was this patch tested?
Manually tested using Spark SQL data source and Oracle JDBC
Author: LucaCanali <luca.canali@cern.ch>
Closes#18724 from LucaCanali/JDBC_datasource_sessionInitStatement.
## What changes were proposed in this pull request?
1. In Spark Web UI, the Details for Stage Page don't have a navigation bar at the bottom. When we drop down to the bottom, it is better for us to see a navi bar right there to go wherever we what.
2. Executor ID is not equivalent to Host, it may be better to separate them, and then we can group the tasks by Hosts .
## How was this patch tested?
manually test
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#18893 from yaooqinn/SPARK-21675.
## What changes were proposed in this pull request?
This patch removes the unused SessionCatalog.getTableMetadataOption and ExternalCatalog. getTableOption.
## How was this patch tested?
Removed the test case.
Author: Reynold Xin <rxin@databricks.com>
Closes#18912 from rxin/remove-getTableOption.
## What changes were proposed in this pull request?
When train RF model, there are many warning messages like this:
> WARN RandomForest: Tree learning is using approximately 268492800 bytes per iteration, which exceeds requested limit maxMemoryUsage=268435456. This allows splitting 2622 nodes in this iteration.
This warning message is unnecessary and the data is not accurate.
Actually, if all the nodes cannot split in one iteration, it will show this warning. For most of the case, all the nodes cannot split just in one iteration, so for most of the case, it will show this warning for each iteration.
## How was this patch tested?
The existing UT
Author: Peng Meng <peng.meng@intel.com>
Closes#18868 from mpjlu/fixRFwarning.
## What changes were proposed in this pull request?
This patch introduces an internal interface for tracking metrics and/or statistics on data on the fly, as it is being written to disk during a `FileFormatWriter` job and partially reimplements SPARK-20703 in terms of it.
The interface basically consists of 3 traits:
- `WriteTaskStats`: just a tag for classes that represent statistics collected during a `WriteTask`
The only constraint it adds is that the class should be `Serializable`, as instances of it will be collected on the driver from all executors at the end of the `WriteJob`.
- `WriteTaskStatsTracker`: a trait for classes that can actually compute statistics based on tuples that are processed by a given `WriteTask` and eventually produce a `WriteTaskStats` instance.
- `WriteJobStatsTracker`: a trait for classes that act as containers of `Serializable` state that's necessary for instantiating `WriteTaskStatsTracker` on executors and finally process the resulting collection of `WriteTaskStats`, once they're gathered back on the driver.
Potential future use of this interface is e.g. CBO stats maintenance during `INSERT INTO table ... ` operations.
## How was this patch tested?
Existing tests for SPARK-20703 exercise the new code: `hive/SQLMetricsSuite`, `sql/JavaDataFrameReaderWriterSuite`, etc.
Author: Adrian Ionescu <adrian@databricks.com>
Closes#18884 from adrian-ionescu/write-stats-tracker-api.
