## What changes were proposed in this pull request?
Here are the major changes in this PR.
- Added the ability to recover `StreamingQuery.id` from checkpoint location, by writing the id to `checkpointLoc/metadata`.
- Added `StreamingQuery.runId` which is unique for every query started and does not persist across restarts. This is to identify each restart of a query separately (same as earlier behavior of `id`).
- Removed auto-generation of `StreamingQuery.name`. The purpose of name was to have the ability to define an identifier across restarts, but since id is precisely that, there is no need for a auto-generated name. This means name becomes purely cosmetic, and is null by default.
- Added `runId` to `StreamingQueryListener` events and `StreamingQueryProgress`.
Implementation details
- Renamed existing `StreamExecutionMetadata` to `OffsetSeqMetadata`, and moved it to the file `OffsetSeq.scala`, because that is what this metadata is tied to. Also did some refactoring to make the code cleaner (got rid of a lot of `.json` and `.getOrElse("{}")`).
- Added the `id` as the new `StreamMetadata`.
- When a StreamingQuery is created it gets or writes the `StreamMetadata` from `checkpointLoc/metadata`.
- All internal logging in `StreamExecution` uses `(name, id, runId)` instead of just `name`
TODO
- [x] Test handling of name=null in json generation of StreamingQueryProgress
- [x] Test handling of name=null in json generation of StreamingQueryListener events
- [x] Test python API of runId
## How was this patch tested?
Updated unit tests and new unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16113 from tdas/SPARK-18657.
## What changes were proposed in this pull request?
Move DataFrame.collect out of synchronized block so that we can query content in MemorySink when `DataFrame.collect` is running.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16162 from zsxwing/SPARK-18729.
## What changes were proposed in this pull request?
As reported in the Jira, there are some weird issues with exploding Python UDFs in SparkSQL.
The following test code can reproduce it. Notice: the following test code is reported to return wrong results in the Jira. However, as I tested on master branch, it causes exception and so can't return any result.
>>> from pyspark.sql.functions import *
>>> from pyspark.sql.types import *
>>>
>>> df = spark.range(10)
>>>
>>> def return_range(value):
... return [(i, str(i)) for i in range(value - 1, value + 1)]
...
>>> range_udf = udf(return_range, ArrayType(StructType([StructField("integer_val", IntegerType()),
... StructField("string_val", StringType())])))
>>>
>>> df.select("id", explode(range_udf(df.id))).show()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/spark/python/pyspark/sql/dataframe.py", line 318, in show
print(self._jdf.showString(n, 20))
File "/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
File "/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o126.showString.: java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:156)
at org.apache.spark.sql.execution.CodegenSupport$class.consume(WholeStageCodegenExec.scala:120)
at org.apache.spark.sql.execution.GenerateExec.consume(GenerateExec.scala:57)
The cause of this issue is, in `ExtractPythonUDFs` we insert `BatchEvalPythonExec` to run PythonUDFs in batch. `BatchEvalPythonExec` will add extra outputs (e.g., `pythonUDF0`) to original plan. In above case, the original `Range` only has one output `id`. After `ExtractPythonUDFs`, the added `BatchEvalPythonExec` has two outputs `id` and `pythonUDF0`.
Because the output of `GenerateExec` is given after analysis phase, in above case, it is the combination of `id`, i.e., the output of `Range`, and `col`. But in planning phase, we change `GenerateExec`'s child plan to `BatchEvalPythonExec` with additional output attributes.
It will cause no problem in non wholestage codegen. Because when evaluating the additional attributes are projected out the final output of `GenerateExec`.
However, as `GenerateExec` now supports wholestage codegen, the framework will input all the outputs of the child plan to `GenerateExec`. Then when consuming `GenerateExec`'s output data (i.e., calling `consume`), the number of output attributes is different to the output variables in wholestage codegen.
To solve this issue, this patch only gives the generator's output to `GenerateExec` after analysis phase. `GenerateExec`'s output is the combination of its child plan's output and the generator's output. So when we change `GenerateExec`'s child, its output is still correct.
## How was this patch tested?
Added test cases to PySpark.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16120 from viirya/fix-py-udf-with-generator.
This PR adds `spark.ui.showConsoleProgress` to the configuration docs.
I tested this PR by building the docs locally and confirming that this change shows up as expected.
Relates to #3029.
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#16151 from nchammas/ui-progressbar-doc.
Looking at the distributions provided on spark.apache.org, I see that the Spark YARN shuffle jar is under `yarn/` and not `lib/`.
This change is so minor I'm not sure it needs a JIRA. But let me know if so and I'll create one.
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#16130 from nchammas/yarn-doc-fix.
## What changes were proposed in this pull request?
This is kind of a long-standing bug, it's hidden until https://github.com/apache/spark/pull/15780 , which may add `AssertNotNull` on top of `LambdaVariable` and thus enables subexpression elimination.
However, subexpression elimination will evaluate the common expressions at the beginning, which is invalid for `LambdaVariable`. `LambdaVariable` usually represents loop variable, which can't be evaluated ahead of the loop.
This PR skips expressions containing `LambdaVariable` when doing subexpression elimination.
## How was this patch tested?
updated test in `DatasetAggregatorSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16143 from cloud-fan/aggregator.
## What changes were proposed in this pull request?
- Add StreamingQuery.explain and exception to Python.
- Fix StreamingQueryException to not expose `OffsetSeq`.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16125 from zsxwing/py-streaming-explain.
## What changes were proposed in this pull request?
Add R examples to ML programming guide for the following algorithms as POC:
* spark.glm
* spark.survreg
* spark.naiveBayes
* spark.kmeans
The four algorithms were added to SparkR since 2.0.0, more docs for algorithms added during 2.1 release cycle will be addressed in a separate follow-up PR.
## How was this patch tested?
This is the screenshots of generated ML programming guide for ```GeneralizedLinearRegression```:
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16136 from yanboliang/spark-18279.
## What changes were proposed in this pull request?
add `setFeaturesCol` and `setPredictionCol` for `OneVsRestModel`
## How was this patch tested?
added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16059 from zhengruifeng/ovrm_setCol.
## What changes were proposed in this pull request?
We currently have function input_file_name to get the path of the input file, but don't have functions to get the block start offset and length. This patch introduces two functions:
1. input_file_block_start: returns the file block start offset, or -1 if not available.
2. input_file_block_length: returns the file block length, or -1 if not available.
## How was this patch tested?
Updated existing test cases in ColumnExpressionSuite that covered input_file_name to also cover the two new functions.
Author: Reynold Xin <rxin@databricks.com>
Closes#16133 from rxin/SPARK-18702.
## What changes were proposed in this pull request?
If SparkR is running as a package and it has previously downloaded Spark Jar it should be able to run as before without having to set SPARK_HOME. Basically with this bug the auto install Spark will only work in the first session.
This seems to be a regression on the earlier behavior.
Fix is to always try to install or check for the cached Spark if running in an interactive session.
As discussed before, we should probably only install Spark iff running in an interactive session (R shell, RStudio etc)
## How was this patch tested?
Manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#16077 from felixcheung/rsessioninteractive.
## What changes were proposed in this pull request?
Even though in 2.1 creating a partitioned datasource table will not populate the partition data by default (until the user issues MSCK REPAIR TABLE), it seems we still scan the filesystem for no good reason.
We should avoid doing this when the user specifies a schema.
## How was this patch tested?
Perf stat tests.
Author: Eric Liang <ekl@databricks.com>
Closes#16090 from ericl/spark-18661.
## What changes were proposed in this pull request?
Fix for SPARK-18091 which is a bug related to large if expressions causing generated SpecificUnsafeProjection code to exceed JVM code size limit.
This PR changes if expression's code generation to place its predicate, true value and false value expressions' generated code in separate methods in context so as to never generate too long combined code.
## How was this patch tested?
Added a unit test and also tested manually with the application (having transformations similar to the unit test) which caused the issue to be identified in the first place.
Author: Kapil Singh <kapsingh@adobe.com>
Closes#15620 from kapilsingh5050/SPARK-18091-IfCodegenFix.
## What changes were proposed in this pull request?
The user guide for LSH is added to ml-features.md, with several scala/java examples in spark-examples.
## How was this patch tested?
Doc has been generated through Jekyll, and checked through manual inspection.
Author: Yunni <Euler57721@gmail.com>
Author: Yun Ni <yunn@uber.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Yun Ni <Euler57721@gmail.com>
Closes#15795 from Yunni/SPARK-18081-lsh-guide.
## What changes were proposed in this pull request?
This fix puts an explicit list of operators that Spark supports for correlated subqueries.
## How was this patch tested?
Run sql/test, catalyst/test and add a new test case on Generate.
Author: Nattavut Sutyanyong <nsy.can@gmail.com>
Closes#16046 from nsyca/spark18455.0.
## What changes were proposed in this pull request?
This PR is to upgrade:
```
sbt: 0.13.11 -> 0.13.13,
zinc: 0.3.9 -> 0.3.11,
maven-assembly-plugin: 2.6 -> 3.0.0
maven-compiler-plugin: 3.5.1 -> 3.6.
maven-jar-plugin: 2.6 -> 3.0.2
maven-javadoc-plugin: 2.10.3 -> 2.10.4
maven-source-plugin: 2.4 -> 3.0.1
org.codehaus.mojo:build-helper-maven-plugin: 1.10 -> 1.12
org.codehaus.mojo:exec-maven-plugin: 1.4.0 -> 1.5.0
```
The sbt release notes since the last version we used are: [v0.13.12](https://github.com/sbt/sbt/releases/tag/v0.13.12) and [v0.13.13 ](https://github.com/sbt/sbt/releases/tag/v0.13.13).
## How was this patch tested?
Pass build and the existing tests.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#16069 from weiqingy/SPARK-18638.
## What changes were proposed in this pull request?
This PR fixes two problems as below:
- Close `BufferedSource` after `Source.fromInputStream(...)` to release resource and make the tests pass on Windows in `ExecutorClassLoaderSuite`
```
[info] Exception encountered when attempting to run a suite with class name: org.apache.spark.repl.ExecutorClassLoaderSuite *** ABORTED *** (7 seconds, 333 milliseconds)
[info] java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-77b2f37b-6405-47c4-af1c-4a6a206511f2
[info] at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1010)
[info] at org.apache.spark.repl.ExecutorClassLoaderSuite.afterAll(ExecutorClassLoaderSuite.scala:76)
[info] at org.scalatest.BeforeAndAfterAll$class.afterAll(BeforeAndAfterAll.scala:213)
...
```
- Fix URI correctly so that related tests can be passed on Windows.
```
[info] - child first *** FAILED *** (78 milliseconds)
[info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b
[info] at java.net.URI$Parser.fail(URI.java:2848)
[info] at java.net.URI$Parser.parseAuthority(URI.java:3186)
...
[info] - parent first *** FAILED *** (15 milliseconds)
[info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b
[info] at java.net.URI$Parser.fail(URI.java:2848)
[info] at java.net.URI$Parser.parseAuthority(URI.java:3186)
...
[info] - child first can fall back *** FAILED *** (0 milliseconds)
[info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b
[info] at java.net.URI$Parser.fail(URI.java:2848)
[info] at java.net.URI$Parser.parseAuthority(URI.java:3186)
...
[info] - child first can fail *** FAILED *** (0 milliseconds)
[info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b
[info] at java.net.URI$Parser.fail(URI.java:2848)
[info] at java.net.URI$Parser.parseAuthority(URI.java:3186)
...
[info] - resource from parent *** FAILED *** (0 milliseconds)
[info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b
[info] at java.net.URI$Parser.fail(URI.java:2848)
[info] at java.net.URI$Parser.parseAuthority(URI.java:3186)
...
[info] - resources from parent *** FAILED *** (0 milliseconds)
[info] java.net.URISyntaxException: Illegal character in authority at index 7: file://C:\projects\spark\target\tmp\spark-00b66070-0548-463c-b6f3-8965d173da9b
[info] at java.net.URI$Parser.fail(URI.java:2848)
[info] at java.net.URI$Parser.parseAuthority(URI.java:3186)
```
## How was this patch tested?
Manually tested via AppVeyor.
**Before**
https://ci.appveyor.com/project/spark-test/spark/build/102-rpel-ExecutorClassLoaderSuite
**After**
https://ci.appveyor.com/project/spark-test/spark/build/108-rpel-ExecutorClassLoaderSuite
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16116 from HyukjinKwon/close-after-open.
## What changes were proposed in this pull request?
Force update to latest Netty 3.9.x, for dependencies like Flume, to resolve two CVEs. 3.9.2 is the first version that resolves both, and, this is the latest in the 3.9.x line.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#16102 from srowen/SPARK-18586.
## What changes were proposed in this pull request?
This patch significantly improves the IO / file listing performance of schema inference in Spark's built-in CSV data source.
Previously, this data source used the legacy `SparkContext.hadoopFile` and `SparkContext.hadoopRDD` methods to read files during its schema inference step, causing huge file-listing bottlenecks on the driver.
This patch refactors this logic to use Spark SQL's `text` data source to read files during this step. The text data source still performs some unnecessary file listing (since in theory we already have resolved the table prior to schema inference and therefore should be able to scan without performing _any_ extra listing), but that listing is much faster and takes place in parallel. In one production workload operating over tens of thousands of files, this change managed to reduce schema inference time from 7 minutes to 2 minutes.
A similar problem also affects the JSON file format and this patch originally fixed that as well, but I've decided to split that change into a separate patch so as not to conflict with changes in another JSON PR.
## How was this patch tested?
Existing unit tests, plus manual benchmarking on a production workload.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15813 from JoshRosen/use-text-data-source-in-csv-and-json.
## What changes were proposed in this pull request?
This patch bumps master branch version to 2.2.0-SNAPSHOT.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#16126 from rxin/SPARK-18695.
## What changes were proposed in this pull request?
Makes `Window.unboundedPreceding` and `Window.unboundedFollowing` backward compatible.
## How was this patch tested?
Pyspark SQL unittests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: zero323 <zero323@users.noreply.github.com>
Closes#16123 from zero323/SPARK-17845-follow-up.
## What changes were proposed in this pull request?
Update ML programming and migration guide for 2.1 release.
## How was this patch tested?
Doc change, no test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16076 from yanboliang/spark-18324.
## What changes were proposed in this pull request?
This PR adds a sql conf `spark.sql.streaming.noDataReportInterval` to control how long to wait before outputing the next StreamProgressEvent when there is no data.
## How was this patch tested?
The added unit test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16108 from zsxwing/SPARK-18670.
## What changes were proposed in this pull request?
It's better we can fix this issue by providing an option ```type``` for users to change the ```predict``` output schema, then they could output probabilities, log-space predictions, or original labels. In order to not involve breaking API change for 2.1, so revert this change firstly and will add it back after [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) resolved.
## How was this patch tested?
Existing unit tests.
This reverts commit daa975f4bf.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16118 from yanboliang/spark-18291-revert.
## What changes were proposed in this pull request?
This fixes the parser rule to match named expressions, which doesn't work for two reasons:
1. The name match is not coerced to a regular expression (missing .r)
2. The surrounding literals are incorrect and attempt to escape a single quote, which is unnecessary
## How was this patch tested?
This adds test cases for named expressions using the bracket syntax, including one with quoted spaces.
Author: Ryan Blue <blue@apache.org>
Closes#16107 from rdblue/SPARK-18677-fix-json-path.
### What changes were proposed in this pull request?
Added a test case for using joins with nested fields.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#16110 from gatorsmile/followup-18674.
## What changes were proposed in this pull request?
Two bugs are addressed here
1. INSERT OVERWRITE TABLE sometime crashed when catalog partition management was enabled. This was because when dropping partitions after an overwrite operation, the Hive client will attempt to delete the partition files. If the entire partition directory was dropped, this would fail. The PR fixes this by adding a flag to control whether the Hive client should attempt to delete files.
2. The static partition spec for OVERWRITE TABLE was not correctly resolved to the case-sensitive original partition names. This resulted in the entire table being overwritten if you did not correctly capitalize your partition names.
cc yhuai cloud-fan
## How was this patch tested?
Unit tests. Surprisingly, the existing overwrite table tests did not catch these edge cases.
Author: Eric Liang <ekl@databricks.com>
Closes#16088 from ericl/spark-18659.
## What changes were proposed in this pull request?
Currently, `JDBCRelation.insert` removes Spark options too early by mistakenly using `asConnectionProperties`. Spark options like `numPartitions` should be passed into `DataFrameWriter.jdbc` correctly. This bug have been **hidden** because `JDBCOptions.asConnectionProperties` fails to filter out the mixed-case options. This PR aims to fix both.
**JDBCRelation.insert**
```scala
override def insert(data: DataFrame, overwrite: Boolean): Unit = {
val url = jdbcOptions.url
val table = jdbcOptions.table
- val properties = jdbcOptions.asConnectionProperties
+ val properties = jdbcOptions.asProperties
data.write
.mode(if (overwrite) SaveMode.Overwrite else SaveMode.Append)
.jdbc(url, table, properties)
```
**JDBCOptions.asConnectionProperties**
```scala
scala> import org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions
scala> import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
scala> new JDBCOptions(Map("url" -> "jdbc:mysql://localhost:3306/temp", "dbtable" -> "t1", "numPartitions" -> "10")).asConnectionProperties
res0: java.util.Properties = {numpartitions=10}
scala> new JDBCOptions(new CaseInsensitiveMap(Map("url" -> "jdbc:mysql://localhost:3306/temp", "dbtable" -> "t1", "numPartitions" -> "10"))).asConnectionProperties
res1: java.util.Properties = {numpartitions=10}
```
## How was this patch tested?
Pass the Jenkins with a new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15863 from dongjoon-hyun/SPARK-18419.
## What changes were proposed in this pull request?
In Spark 2.1 ListingFileCatalog was significantly refactored (and renamed to InMemoryFileIndex). This introduced a regression where parallelism could only be introduced at the very top of the tree. However, in many cases (e.g. `spark.read.parquet(topLevelDir)`), the top of the tree is only a single directory.
This PR simplifies and fixes the parallel recursive listing code to allow parallelism to be introduced at any level during recursive descent (though note that once we decide to list a sub-tree in parallel, the sub-tree is listed in serial on executors).
cc mallman cloud-fan
## How was this patch tested?
Checked metrics in unit tests.
Author: Eric Liang <ekl@databricks.com>
Closes#16112 from ericl/spark-18679.
## What changes were proposed in this pull request?
Fix numPartition of JDBCSuite Testcase.
## How was this patch tested?
Before:
Run any one of the test cases in JDBCSuite, you will get the following warning.
```
10:34:26.389 WARN org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation: The number of partitions is reduced because the specified number of partitions is less than the difference between upper bound and lower bound. Updated number of partitions: 3; Input number of partitions: 4; Lower bound: 1; Upper bound: 4.
```
After: Pass tests without the warning.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#16062 from weiqingy/SPARK-18629.
This PR targets to both master and branch-2.1.
## What changes were proposed in this pull request?
Due to PARQUET-686, Parquet doesn't do string comparison correctly while doing filter push-down for string columns. This PR disables filter push-down for both string and binary columns to work around this issue. Binary columns are also affected because some Parquet data models (like Hive) may store string columns as a plain Parquet `binary` instead of a `binary (UTF8)`.
## How was this patch tested?
New test case added in `ParquetFilterSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#16106 from liancheng/spark-17213-bad-string-ppd.
## What changes were proposed in this pull request?
This replaces uses of `TextOutputFormat` with an `OutputStream`, which will either write directly to the filesystem or indirectly via a compressor (if so configured). This avoids intermediate buffering.
The inverse of this (reading directly from a stream) is necessary for streaming large JSON records (when `wholeFile` is enabled) so I wanted to keep the read and write paths symmetric.
## How was this patch tested?
Existing unit tests.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#16089 from NathanHowell/SPARK-18658.
## What changes were proposed in this pull request?
SPARK-18429 introduced count-min sketch aggregate function for SQL, but the implementation and testing is more complicated than needed. This simplifies the test cases and removes support for data types that don't have clear equality semantics:
1. Removed support for floating point and decimal types.
2. Removed the heavy randomized tests. The underlying CountMinSketch implementation already had pretty good test coverage through randomized tests, and the SPARK-18429 implementation is just to add an aggregate function wrapper around CountMinSketch. There is no need for randomized tests at three different levels of the implementations.
## How was this patch tested?
A lot of the change is to simplify test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#16093 from rxin/SPARK-18663.
## What changes were proposed in this pull request?
In Spark 2.1, we make Hive serde tables case-preserving by putting the table metadata in table properties, like what we did for data source table. However, we should not put table provider, as it will break forward compatibility. e.g. if we create a Hive serde table with Spark 2.1, using `sql("create table test stored as parquet as select 1")`, we will fail to read it with Spark 2.0, as Spark 2.0 mistakenly treat it as data source table because there is a `provider` entry in table properties.
Logically Hive serde table's provider is always hive, we don't need to store it in table properties, this PR removes it.
## How was this patch tested?
manually test the forward compatibility issue.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16080 from cloud-fan/hive.
## What changes were proposed in this pull request?
This PR makes `ExpressionEncoder.serializer.nullable` for flat encoder for a primitive type `false`. Since it is `true` for now, it is too conservative.
While `ExpressionEncoder.schema` has correct information (e.g. `<IntegerType, false>`), `serializer.head.nullable` of `ExpressionEncoder`, which got from `encoderFor[T]`, is always false. It is too conservative.
This is accomplished by checking whether a type is one of primitive types. If it is `true`, `nullable` should be `false`.
## How was this patch tested?
Added new tests for encoder and dataframe
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#15780 from kiszk/SPARK-18284.
## What changes were proposed in this pull request?
SQL query generated for the JDBC data source is not quoting columns in the predicate clause. When the source table has quoted column names, spark jdbc read fails with column not found error incorrectly.
Error:
org.h2.jdbc.JdbcSQLException: Column "ID" not found;
Source SQL statement:
SELECT "Name","Id" FROM TEST."mixedCaseCols" WHERE (Id < 1)
This PR fixes by quoting column names in the generated SQL for predicate clause when filters are pushed down to the data source.
Source SQL statement after the fix:
SELECT "Name","Id" FROM TEST."mixedCaseCols" WHERE ("Id" < 1)
## How was this patch tested?
Added new test case to the JdbcSuite
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#15662 from sureshthalamati/filter_quoted_cols-SPARK-18141.
## What changes were proposed in this pull request?
We current build 5 separate pip binary tar balls, doubling the release script runtime. It'd be better to build one, especially for use cases that are just using Spark locally. In the long run, it would make more sense to have Hadoop support be pluggable.
## How was this patch tested?
N/A - this is a release build script that doesn't have any automated test coverage. We will know if it goes wrong when we prepare releases.
Author: Reynold Xin <rxin@databricks.com>
Closes#16072 from rxin/SPARK-18639.
## What changes were proposed in this pull request?
Avoid to create multiple threads to stop StreamingContext. Otherwise, the latch added in #16091 can be passed too early.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16105 from zsxwing/SPARK-18617-2.
## What changes were proposed in this pull request?
In`JavaWrapper `'s destructor make Java Gateway dereference object in destructor, using `SparkContext._active_spark_context._gateway.detach`
Fixing the copying parameter bug, by moving the `copy` method from `JavaModel` to `JavaParams`
## How was this patch tested?
```scala
import random, string
from pyspark.ml.feature import StringIndexer
l = [(''.join(random.choice(string.ascii_uppercase) for _ in range(10)), ) for _ in range(int(7e5))] # 700000 random strings of 10 characters
df = spark.createDataFrame(l, ['string'])
for i in range(50):
indexer = StringIndexer(inputCol='string', outputCol='index')
indexer.fit(df)
```
* Before: would keep StringIndexer strong reference, causing GC issues and is halted midway
After: garbage collection works as the object is dereferenced, and computation completes
* Mem footprint tested using profiler
* Added a parameter copy related test which was failing before.
Author: Sandeep Singh <sandeep@techaddict.me>
Author: jkbradley <joseph.kurata.bradley@gmail.com>
Closes#15843 from techaddict/SPARK-18274.
## What changes were proposed in this pull request?
The current error message of USING join is quite confusing, for example:
```
scala> val df1 = List(1,2,3).toDS.withColumnRenamed("value", "c1")
df1: org.apache.spark.sql.DataFrame = [c1: int]
scala> val df2 = List(1,2,3).toDS.withColumnRenamed("value", "c2")
df2: org.apache.spark.sql.DataFrame = [c2: int]
scala> df1.join(df2, usingColumn = "c1")
org.apache.spark.sql.AnalysisException: using columns ['c1] can not be resolved given input columns: [c1, c2] ;;
'Join UsingJoin(Inner,List('c1))
:- Project [value#1 AS c1#3]
: +- LocalRelation [value#1]
+- Project [value#7 AS c2#9]
+- LocalRelation [value#7]
```
after this PR, it becomes:
```
scala> val df1 = List(1,2,3).toDS.withColumnRenamed("value", "c1")
df1: org.apache.spark.sql.DataFrame = [c1: int]
scala> val df2 = List(1,2,3).toDS.withColumnRenamed("value", "c2")
df2: org.apache.spark.sql.DataFrame = [c2: int]
scala> df1.join(df2, usingColumn = "c1")
org.apache.spark.sql.AnalysisException: USING column `c1` can not be resolved with the right join side, the right output is: [c2];
```
## How was this patch tested?
updated tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#16100 from cloud-fan/natural.
## What changes were proposed in this pull request?
spark-daemon.sh will lost single quotes around after #15338. as follows:
```
execute_command nice -n 0 bash /opt/cloudera/parcels/SPARK-2.1.0-cdh5.4.3.d20161129-21.04.38/lib/spark/bin/spark-submit --class org.apache.spark.sql.hive.thriftserver.HiveThriftServer2 --name Thrift JDBC/ODBC Server --conf spark.driver.extraJavaOptions=-XX:+UseG1GC -XX:-HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp
```
With this fix, as follows:
```
execute_command nice -n 0 bash /opt/cloudera/parcels/SPARK-2.1.0-cdh5.4.3.d20161129-21.04.38/lib/spark/bin/spark-submit --class org.apache.spark.sql.hive.thriftserver.HiveThriftServer2 --name 'Thrift JDBC/ODBC Server' --conf 'spark.driver.extraJavaOptions=-XX:+UseG1GC -XX:-HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp'
```
## How was this patch tested?
- Manual tests
- Build the package and start-thriftserver.sh with `--conf 'spark.driver.extraJavaOptions=-XX:+UseG1GC -XX:-HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp'`
Author: Yuming Wang <wgyumg@gmail.com>
Closes#16079 from wangyum/SPARK-18645.
## What changes were proposed in this pull request?
`spark.sql.unsafe.enabled` is deprecated since 1.6. There still are codes in UI to check it. We should remove it and clean the codes.
## How was this patch tested?
Changes to related existing unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16095 from viirya/remove-deprecated-config-code.
## What changes were proposed in this pull request?
Due to confusion between URI vs paths, in certain cases we escape partition values too many times, which causes some Hive client operations to fail or write data to the wrong location. This PR fixes at least some of these cases.
To my understanding this is how values, filesystem paths, and URIs interact.
- Hive stores raw (unescaped) partition values that are returned to you directly when you call listPartitions.
- Internally, we convert these raw values to filesystem paths via `ExternalCatalogUtils.[un]escapePathName`.
- In some circumstances we store URIs instead of filesystem paths. When a path is converted to a URI via `path.toURI`, the escaped partition values are further URI-encoded. This means that to get a path back from a URI, you must call `new Path(new URI(uriTxt))` in order to decode the URI-encoded string.
- In `CatalogStorageFormat` we store URIs as strings. This makes it easy to forget to URI-decode the value before converting it into a path.
- Finally, the Hive client itself uses mostly Paths for representing locations, and only URIs occasionally.
In the future we should probably clean this up, perhaps by dropping use of URIs when unnecessary. We should also try fixing escaping for partition names as well as values, though names are unlikely to contain special characters.
cc mallman cloud-fan yhuai
## How was this patch tested?
Unit tests.
Author: Eric Liang <ekl@databricks.com>
Closes#16071 from ericl/spark-18635.
### What changes were proposed in this pull request?
The following two `DataFrameReader` JDBC APIs ignore the user-specified parameters of parallelism degree.
```Scala
def jdbc(
url: String,
table: String,
columnName: String,
lowerBound: Long,
upperBound: Long,
numPartitions: Int,
connectionProperties: Properties): DataFrame
```
```Scala
def jdbc(
url: String,
table: String,
predicates: Array[String],
connectionProperties: Properties): DataFrame
```
This PR is to fix the issues. To verify the behavior correctness, we improve the plan output of `EXPLAIN` command by adding `numPartitions` in the `JDBCRelation` node.
Before the fix,
```
== Physical Plan ==
*Scan JDBCRelation(TEST.PEOPLE) [NAME#1896,THEID#1897] ReadSchema: struct<NAME:string,THEID:int>
```
After the fix,
```
== Physical Plan ==
*Scan JDBCRelation(TEST.PEOPLE) [numPartitions=3] [NAME#1896,THEID#1897] ReadSchema: struct<NAME:string,THEID:int>
```
### How was this patch tested?
Added the verification logics on all the test cases for JDBC concurrent fetching.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15975 from gatorsmile/jdbc.
## What changes were proposed in this pull request?
Similar to SPARK-18401, as a classification algorithm, logistic regression should support output original label instead of supporting index label.
In this PR, original label output is supported and test cases are modified and added. Document is also modified.
## How was this patch tested?
Unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15910 from wangmiao1981/audit.
## What changes were proposed in this pull request?
Fixed the potential SparkContext leak in `StreamingContextSuite.SPARK-18560 Receiver data should be deserialized properly` which was added in #16052. I also removed FakeByteArrayReceiver and used TestReceiver directly.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16091 from zsxwing/SPARK-18617-follow-up.
## What changes were proposed in this pull request?
As `queryStatus` in StreamingQueryListener events was removed in #15954, parsing 2.0.2 structured streaming logs will throw the following errror:
```
[info] com.fasterxml.jackson.databind.exc.UnrecognizedPropertyException: Unrecognized field "queryStatus" (class org.apache.spark.sql.streaming.StreamingQueryListener$QueryTerminatedEvent), not marked as ignorable (2 known properties: "id", "exception"])
[info] at [Source: {"Event":"org.apache.spark.sql.streaming.StreamingQueryListener$QueryTerminatedEvent","queryStatus":{"name":"query-1","id":1,"timestamp":1480491532753,"inputRate":0.0,"processingRate":0.0,"latency":null,"sourceStatuses":[{"description":"FileStreamSource[file:/Users/zsx/stream]","offsetDesc":"#0","inputRate":0.0,"processingRate":0.0,"triggerDetails":{"latency.getOffset.source":"1","triggerId":"1"}}],"sinkStatus":{"description":"FileSink[/Users/zsx/stream2]","offsetDesc":"[#0]"},"triggerDetails":{}},"exception":null}; line: 1, column: 521] (through reference chain: org.apache.spark.sql.streaming.QueryTerminatedEvent["queryStatus"])
[info] at com.fasterxml.jackson.databind.exc.UnrecognizedPropertyException.from(UnrecognizedPropertyException.java:51)
[info] at com.fasterxml.jackson.databind.DeserializationContext.reportUnknownProperty(DeserializationContext.java:839)
[info] at com.fasterxml.jackson.databind.deser.std.StdDeserializer.handleUnknownProperty(StdDeserializer.java:1045)
[info] at com.fasterxml.jackson.databind.deser.BeanDeserializerBase.handleUnknownProperty(BeanDeserializerBase.java:1352)
[info] at com.fasterxml.jackson.databind.deser.BeanDeserializerBase.handleUnknownProperties(BeanDeserializerBase.java:1306)
[info] at com.fasterxml.jackson.databind.deser.BeanDeserializer._deserializeUsingPropertyBased(BeanDeserializer.java:453)
[info] at com.fasterxml.jackson.databind.deser.BeanDeserializerBase.deserializeFromObjectUsingNonDefault(BeanDeserializerBase.java:1099)
...
```
This PR just ignores such errors and adds a test to make sure we can read 2.0.2 logs.
## How was this patch tested?
`query-event-logs-version-2.0.2.txt` has all types of events generated by Structured Streaming in Spark 2.0.2. `testQuietly("ReplayListenerBus should ignore broken event jsons generated in 2.0.2")` verified we can load them without any error.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16085 from zsxwing/SPARK-18655.
The problem exists because it's not possible to just concatenate encrypted
partition data from different spill files; currently each partition would
have its own initial vector to set up encryption, and the final merged file
should contain a single initial vector for each merged partiton, otherwise
iterating over each record becomes really hard.
To fix that, UnsafeShuffleWriter now decrypts the partitions when merging,
so that the merged file contains a single initial vector at the start of
the partition data.
Because it's not possible to do that using the fast transferTo path, when
encryption is enabled UnsafeShuffleWriter will revert back to using file
streams when merging. It may be possible to use a hybrid approach when
using encryption, using an intermediate direct buffer when reading from
files and encrypting the data, but that's better left for a separate patch.
As part of the change I made DiskBlockObjectWriter take a SerializerManager
instead of a "wrap stream" closure, since that makes it easier to test the
code without having to mock SerializerManager functionality.
Tested with newly added unit tests (UnsafeShuffleWriterSuite for the write
side and ExternalAppendOnlyMapSuite for integration), and by running some
apps that failed without the fix.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#15982 from vanzin/SPARK-18546.
