`HadoopFsRelation` subclasses, especially `ParquetRelation2` should set its own output format class, so that the default output committer can be setup correctly when doing appending (where we ignore user defined output committers).
Author: Cheng Lian <lian@databricks.com>
Closes#6998 from liancheng/spark-8604 and squashes the following commits:
9be51d1 [Cheng Lian] Adds more comments
6db1368 [Cheng Lian] HadoopFsRelation subclasses should set their output format class
a follow up of https://github.com/apache/spark/pull/6405.
Note: It's not a big change, a lot of changing is due to I swap some code in `aggregates.scala` to make aggregate functions right below its corresponding aggregate expressions.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#6723 from cloud-fan/type-check and squashes the following commits:
2124301 [Wenchen Fan] fix tests
5a658bb [Wenchen Fan] add tests
287d3bb [Wenchen Fan] apply type check interface to more expressions
This PR introduces `CatalystSchemaConverter` for converting Parquet schema to Spark SQL schema and vice versa. Original conversion code in `ParquetTypesConverter` is removed. Benefits of the new version are:
1. When converting Spark SQL schemas, it generates standard Parquet schemas conforming to [the most updated Parquet format spec] [1]. Converting to old style Parquet schemas is also supported via feature flag `spark.sql.parquet.followParquetFormatSpec` (which is set to `false` for now, and should be set to `true` after both read and write paths are fixed).
Note that although this version of Parquet format spec hasn't been officially release yet, Parquet MR 1.7.0 already sticks to it. So it should be safe to follow.
1. It implements backwards-compatibility rules described in the most updated Parquet format spec. Thus can recognize more schema patterns generated by other/legacy systems/tools.
1. Code organization follows convention used in [parquet-mr] [2], which is easier to follow. (Structure of `CatalystSchemaConverter` is similar to `AvroSchemaConverter`).
To fully implement backwards-compatibility rules in both read and write path, we also need to update `CatalystRowConverter` (which is responsible for converting Parquet records to `Row`s), `RowReadSupport`, and `RowWriteSupport`. These would be done in follow-up PRs.
TODO
- [x] More schema conversion test cases for legacy schema patterns.
[1]: ea09522659/LogicalTypes.md
[2]: https://github.com/apache/parquet-mr/
Author: Cheng Lian <lian@databricks.com>
Closes#6617 from liancheng/spark-6777 and squashes the following commits:
2a2062d [Cheng Lian] Don't convert decimals without precision information
b60979b [Cheng Lian] Adds a constructor which accepts a Configuration, and fixes default value of assumeBinaryIsString
743730f [Cheng Lian] Decimal scale shouldn't be larger than precision
a104a9e [Cheng Lian] Fixes Scala style issue
1f71d8d [Cheng Lian] Adds feature flag to allow falling back to old style Parquet schema conversion
ba84f4b [Cheng Lian] Fixes MapType schema conversion bug
13cb8d5 [Cheng Lian] Fixes MiMa failure
81de5b0 [Cheng Lian] Fixes UDT, workaround read path, and add tests
28ef95b [Cheng Lian] More AnalysisExceptions
b10c322 [Cheng Lian] Replaces require() with analysisRequire() which throws AnalysisException
cceaf3f [Cheng Lian] Implements backwards compatibility rules in CatalystSchemaConverter
make the `TakeOrdered` strategy and operator more general, such that it can optionally handle a projection when necessary
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#6780 from cloud-fan/limit and squashes the following commits:
34aa07b [Wenchen Fan] revert
07d5456 [Wenchen Fan] clean closure
20821ec [Wenchen Fan] fix
3676a82 [Wenchen Fan] address comments
b558549 [Wenchen Fan] address comments
214842b [Wenchen Fan] fix style
2d8be83 [Wenchen Fan] add LimitPushDown
948f740 [Wenchen Fan] fix existing
https://issues.apache.org/jira/browse/SPARK-8578
It is not very safe to use a custom output committer when append data to an existing dir. This changes adds the logic to check if we are appending data, and if so, we use the output committer associated with the file output format.
Author: Yin Huai <yhuai@databricks.com>
Closes#6964 from yhuai/SPARK-8578 and squashes the following commits:
43544c4 [Yin Huai] Do not use a custom output commiter when appendiing data.
Using similar approach used in `HiveThriftServer2Suite` to print stdout/stderr of the spawned process instead of logging them to see what happens on Jenkins. (This test suite only fails on Jenkins and doesn't spill out any log...)
cc yhuai
Author: Cheng Lian <lian@databricks.com>
Closes#6978 from liancheng/debug-hive-spark-submit-suite and squashes the following commits:
b031647 [Cheng Lian] Prints process stdout/stderr instead of logging them
This works around a bug in the underlying RetryingMetaStoreClient (HIVE-10384) by refreshing the metastore client on thrift exceptions. We attempt to emulate the proper hive behavior by retrying only as configured by hiveconf.
Author: Eric Liang <ekl@databricks.com>
Closes#6912 from ericl/spark-6749 and squashes the following commits:
2d54b55 [Eric Liang] use conf from state
0e3a74e [Eric Liang] use shim properly
980b3e5 [Eric Liang] Fix conf parsing hive 0.14 conf.
92459b6 [Eric Liang] Work around RetryingMetaStoreClient bug
To reproduce that:
```
JAVA_HOME=/home/hcheng/Java/jdk1.8.0_45 | build/sbt -Phadoop-2.3 -Phive 'test-only org.apache.spark.sql.hive.execution.HiveWindowFunctionQueryWithoutCodeGenSuite'
```
A simple workaround to fix that is update the original query, for getting the output size instead of the exact elements of the array (output by collect_set())
Author: Cheng Hao <hao.cheng@intel.com>
Closes#6402 from chenghao-intel/windowing and squashes the following commits:
99312ad [Cheng Hao] add order by for the select clause
edf8ce3 [Cheng Hao] update the code as suggested
7062da7 [Cheng Hao] fix the collect_set() behaviour differences under different versions of JDK
Currently we auto alias expression in parser. However, during parser phase we don't have enough information to do the right alias. For example, Generator that has more than 1 kind of element need MultiAlias, ExtractValue don't need Alias if it's in middle of a ExtractValue chain.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#6647 from cloud-fan/alias and squashes the following commits:
552eba4 [Wenchen Fan] fix python
5b5786d [Wenchen Fan] fix agg
73a90cb [Wenchen Fan] fix case-preserve of ExtractValue
4cfd23c [Wenchen Fan] fix order by
d18f401 [Wenchen Fan] refine
9f07359 [Wenchen Fan] address comments
39c1aef [Wenchen Fan] small fix
33640ec [Wenchen Fan] auto alias expressions in analyzer
This PR fixes a Parquet output file name collision bug which may cause data loss. Changes made:
1. Identify each write job issued by `InsertIntoHadoopFsRelation` with a UUID
All concrete data sources which extend `HadoopFsRelation` (Parquet and ORC for now) must use this UUID to generate task output file path to avoid name collision.
2. Make `TestHive` use a local mode `SparkContext` with 32 threads to increase parallelism
The major reason for this is that, the original parallelism of 2 is too low to reproduce the data loss issue. Also, higher concurrency may potentially caught more concurrency bugs during testing phase. (It did help us spotted SPARK-8501.)
3. `OrcSourceSuite` was updated to workaround SPARK-8501, which we detected along the way.
NOTE: This PR is made a little bit more complicated than expected because we hit two other bugs on the way and have to work them around. See [SPARK-8501] [1] and [SPARK-8513] [2].
[1]: https://github.com/liancheng/spark/tree/spark-8501
[2]: https://github.com/liancheng/spark/tree/spark-8513
----
Some background and a summary of offline discussion with yhuai about this issue for better understanding:
In 1.4.0, we added `HadoopFsRelation` to abstract partition support of all data sources that are based on Hadoop `FileSystem` interface. Specifically, this makes partition discovery, partition pruning, and writing dynamic partitions for data sources much easier.
To support appending, the Parquet data source tries to find out the max part number of part-files in the destination directory (i.e., `<id>` in output file name `part-r-<id>.gz.parquet`) at the beginning of the write job. In 1.3.0, this step happens on driver side before any files are written. However, in 1.4.0, this is moved to task side. Unfortunately, for tasks scheduled later, they may see wrong max part number generated of files newly written by other finished tasks within the same job. This actually causes a race condition. In most cases, this only causes nonconsecutive part numbers in output file names. But when the DataFrame contains thousands of RDD partitions, it's likely that two tasks may choose the same part number, then one of them gets overwritten by the other.
Before `HadoopFsRelation`, Spark SQL already supports appending data to Hive tables. From a user's perspective, these two look similar. However, they differ a lot internally. When data are inserted into Hive tables via Spark SQL, `InsertIntoHiveTable` simulates Hive's behaviors:
1. Write data to a temporary location
2. Move data in the temporary location to the final destination location using
- `Hive.loadTable()` for non-partitioned table
- `Hive.loadPartition()` for static partitions
- `Hive.loadDynamicPartitions()` for dynamic partitions
The important part is that, `Hive.copyFiles()` is invoked in step 2 to move the data to the destination directory (I found the name is kinda confusing since no "copying" occurs here, we are just moving and renaming stuff). If a file in the source directory and another file in the destination directory happen to have the same name, say `part-r-00001.parquet`, the former is moved to the destination directory and renamed with a `_copy_N` postfix (`part-r-00001_copy_1.parquet`). That's how Hive handles appending and avoids name collision between different write jobs.
Some alternatives fixes considered for this issue:
1. Use a similar approach as Hive
This approach is not preferred in Spark 1.4.0 mainly because file metadata operations in S3 tend to be slow, especially for tables with lots of file and/or partitions. That's why `InsertIntoHadoopFsRelation` just inserts to destination directory directly, and is often used together with `DirectParquetOutputCommitter` to reduce latency when working with S3. This means, we don't have the chance to do renaming, and must avoid name collision from the very beginning.
2. Same as 1.3, just move max part number detection back to driver side
This isn't doable because unlike 1.3, 1.4 also takes dynamic partitioning into account. When inserting into dynamic partitions, we don't know which partition directories will be touched on driver side before issuing the write job. Checking all partition directories is simply too expensive for tables with thousands of partitions.
3. Add extra component to output file names to avoid name collision
This seems to be the only reasonable solution for now. To be more specific, we need a JOB level unique identifier to identify all write jobs issued by `InsertIntoHadoopFile`. Notice that TASK level unique identifiers can NOT be used. Because in this way a speculative task will write to a different output file from the original task. If both tasks succeed, duplicate output will be left behind. Currently, the ORC data source adds `System.currentTimeMillis` to the output file name for uniqueness. This doesn't work because of exactly the same reason.
That's why this PR adds a job level random UUID in `BaseWriterContainer` (which is used by `InsertIntoHadoopFsRelation` to issue write jobs). The drawback is that record order is not preserved any more (output files of a later job may be listed before those of a earlier job). However, we never promise to preserve record order when writing data, and Hive doesn't promise this either because the `_copy_N` trick breaks the order.
Author: Cheng Lian <lian@databricks.com>
Closes#6864 from liancheng/spark-8406 and squashes the following commits:
db7a46a [Cheng Lian] More comments
f5c1133 [Cheng Lian] Addresses comments
85c478e [Cheng Lian] Workarounds SPARK-8513
088c76c [Cheng Lian] Adds comment about SPARK-8501
99a5e7e [Cheng Lian] Uses job level UUID in SimpleTextRelation and avoids double task abortion
4088226 [Cheng Lian] Works around SPARK-8501
1d7d206 [Cheng Lian] Adds more logs
8966bbb [Cheng Lian] Fixes Scala style issue
18b7003 [Cheng Lian] Uses job level UUID to take speculative tasks into account
3806190 [Cheng Lian] Lets TestHive use all cores by default
748dbd7 [Cheng Lian] Adding UUID to output file name to avoid accidental overwriting
Currently [the test case for SPARK-7862] [1] writes 100,000 lines of integer triples to stderr and makes Jenkins build output unnecessarily large and it's hard to debug other build errors. A proper fix is on the way in #6882. This PR ignores this test case temporarily until #6882 is merged.
[1]: https://github.com/apache/spark/pull/6404/files#diff-1ea02a6fab84e938582f7f87cc4d9ea1R641
Author: Cheng Lian <lian@databricks.com>
Closes#6925 from liancheng/spark-8508 and squashes the following commits:
41e5b47 [Cheng Lian] Ignores the test case until #6882 is merged
The issue link [SPARK-8379](https://issues.apache.org/jira/browse/SPARK-8379)
Currently,when we insert data to the dynamic partition with speculative tasks we will get the Exception
```
org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.hdfs.server.namenode.LeaseExpiredException):
Lease mismatch on /tmp/hive-jeanlyn/hive_2015-06-15_15-20-44_734_8801220787219172413-1/-ext-10000/ds=2015-06-15/type=2/part-00301.lzo
owned by DFSClient_attempt_201506031520_0011_m_000189_0_-1513487243_53
but is accessed by DFSClient_attempt_201506031520_0011_m_000042_0_-1275047721_57
```
This pr try to write the data to temporary dir when using dynamic parition avoid the speculative tasks writing the same file
Author: jeanlyn <jeanlyn92@gmail.com>
Closes#6833 from jeanlyn/speculation and squashes the following commits:
64bbfab [jeanlyn] use FileOutputFormat.getTaskOutputPath to get the path
8860af0 [jeanlyn] remove the never using code
e19a3bd [jeanlyn] avoid speculative tasks write same file
**Summary of the problem in SPARK-8470.** When using `HiveContext` to create a data frame of a user case class, Spark throws `scala.reflect.internal.MissingRequirementError` when it tries to infer the schema using reflection. This is caused by `HiveContext` silently overwriting the context class loader containing the user classes.
**What this issue is about.** This issue adds regression tests for SPARK-8470, which is already fixed in #6891. We closed SPARK-8470 as a duplicate because it is a different manifestation of the same problem in SPARK-8368. Due to the complexity of the reproduction, this requires us to pre-package a special test jar and include it in the Spark project itself.
I tested this with and without the fix in #6891 and verified that it passes only if the fix is present.
Author: Andrew Or <andrew@databricks.com>
Closes#6909 from andrewor14/SPARK-8498 and squashes the following commits:
5e9d688 [Andrew Or] Add regression test for SPARK-8470
https://issues.apache.org/jira/browse/SPARK-8368
Also, I add tests according https://issues.apache.org/jira/browse/SPARK-8058.
Author: Yin Huai <yhuai@databricks.com>
Closes#6891 from yhuai/SPARK-8368 and squashes the following commits:
37bb3db [Yin Huai] Update test timeout and comment.
8762eec [Yin Huai] Style.
695cd2d [Yin Huai] Correctly set the class loader in the conf of the state in client wrapper.
b3378fe [Yin Huai] Failed tests.
`Path.toUri.getPath` strips scheme part of output path (from `file:///foo` to `/foo`), which causes ORC data source only writes to the file system configured in Hadoop configuration. Should use `Path.toString` instead.
Author: Cheng Lian <lian@databricks.com>
Closes#6892 from liancheng/spark-8458 and squashes the following commits:
87f8199 [Cheng Lian] Don't strip scheme of output path when writing ORC files
Author: Sandy Ryza <sandy@cloudera.com>
Closes#6679 from sryza/sandy-spark-8135 and squashes the following commits:
c5554ff [Sandy Ryza] SPARK-8135. In SerializableWritable, don't load defaults when instantiating Configuration
This PR aimed to resolve udf_struct test failure in HiveCompatibilitySuite.
Currently, this is done by loosening CreateStruct's children type from NamedExpression to Expression and automatically generating StructField name for non-NamedExpression children.
The naming convention for unnamed children follows the udf's counterpart in Hive:
`col1, col2, col3, ...`
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#6828 from yijieshen/SPARK-8283 and squashes the following commits:
6052b73 [Yijie Shen] Doc fix
677e0b7 [Yijie Shen] Resolve udf_struct test failure by automatically generate structField name for non-NamedExpression children
1. Add `SQLConfEntry` to store the information about a configuration. For those configurations that cannot be found in `sql-programming-guide.md`, I left the doc as `<TODO>`.
2. Verify the value when setting a configuration if this is in SQLConf.
3. Use `SET -v` to display all public configurations.
Author: zsxwing <zsxwing@gmail.com>
Closes#6747 from zsxwing/sqlconf and squashes the following commits:
7d09bad [zsxwing] Use SQLConfEntry in HiveContext
49f6213 [zsxwing] Add getConf, setConf to SQLContext and HiveContext
e014f53 [zsxwing] Merge branch 'master' into sqlconf
93dad8e [zsxwing] Fix the unit tests
cf950c1 [zsxwing] Fix the code style and tests
3c5f03e [zsxwing] Add unsetConf(SQLConfEntry) and fix the code style
a2f4add [zsxwing] getConf will return the default value if a config is not set
037b1db [zsxwing] Add schema to SetCommand
0520c3c [zsxwing] Merge branch 'master' into sqlconf
7afb0ec [zsxwing] Fix the configurations about HiveThriftServer
7e728e3 [zsxwing] Add doc for SQLConfEntry and fix 'toString'
5e95b10 [zsxwing] Add enumConf
c6ba76d [zsxwing] setRawString => setConfString, getRawString => getConfString
4abd807 [zsxwing] Fix the test for 'set -v'
6e47e56 [zsxwing] Fix the compilation error
8973ced [zsxwing] Remove floatConf
1fc3a8b [zsxwing] Remove the 'conf' command and use 'set -v' instead
99c9c16 [zsxwing] Fix tests that use SQLConfEntry as a string
88a03cc [zsxwing] Add new lines between confs and return types
ce7c6c8 [zsxwing] Remove seqConf
f3c1b33 [zsxwing] Refactor SQLConf to display better error message
We encourage people to use TestHive in unit tests, because it's
impossible to create more than one HiveContext within one process. The
current implementation locks people into using a local[2] SparkContext
underlying their HiveContext. We should make it possible to override
this using a system property so that people can test against
local-cluster or remote spark clusters to make their tests more
realistic.
Author: Punya Biswal <pbiswal@palantir.com>
Closes#6844 from punya/feature/SPARK-8397 and squashes the following commits:
97ef394 [Punya Biswal] [SPARK-8397][SQL] Allow custom configuration for TestHive
https://issues.apache.org/jira/browse/SPARK-8306
I will try to add a test later.
marmbrus aarondav
Author: Yin Huai <yhuai@databricks.com>
Closes#6758 from yhuai/SPARK-8306 and squashes the following commits:
1292346 [Yin Huai] [SPARK-8306] AddJar command needs to set the new class loader to the HiveConf inside executionHive.state.
when i test the following code:
hiveContext.sql("""use testdb""")
val df = (1 to 3).map(i => (i, s"val_$i", i * 2)).toDF("a", "b", "c")
df.write
.format("parquet")
.mode(SaveMode.Overwrite)
.saveAsTable("ttt3")
hiveContext.sql("show TABLES in default")
found that the table ttt3 will be created under the database "default"
Author: baishuo <vc_java@hotmail.com>
Closes#6695 from baishuo/SPARK-8516-use-database and squashes the following commits:
9e155f9 [baishuo] remove no use comment
cb9f027 [baishuo] modify testcase
00a7a2d [baishuo] modify testcase
4df48c7 [baishuo] modify testcase
b742e69 [baishuo] modify testcase
3d19ad9 [baishuo] create table to specific database
In order to have better performance out of box, this PR turn on codegen by default, then codegen can be tested by sql/test and hive/test.
This PR also fix some corner cases for codegen.
Before 1.5 release, we should re-visit this, turn it off if it's not stable or causing regressions.
cc rxin JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#6726 from davies/enable_codegen and squashes the following commits:
f3b25a5 [Davies Liu] fix warning
73750ea [Davies Liu] fix long overflow when compare
3017a47 [Davies Liu] Merge branch 'master' of github.com:apache/spark into enable_codegen
a7d75da [Davies Liu] Merge branch 'master' of github.com:apache/spark into enable_codegen
ff5b75a [Davies Liu] Merge branch 'master' of github.com:apache/spark into enable_codegen
f4cf2c2 [Davies Liu] fix style
99fc139 [Davies Liu] Merge branch 'enable_codegen' of github.com:davies/spark into enable_codegen
91fc7a2 [Davies Liu] disable codegen for ScalaUDF
207e339 [Davies Liu] Update CodeGenerator.scala
44573a3 [Davies Liu] check thread safety of expression
f3886fa [Davies Liu] don't inline primitiveTerm for null literal
c8e7cd2 [Davies Liu] address comment
a8618c9 [Davies Liu] enable codegen by default
This change has two parts.
The first one gets rid of "ReflectionMagic". That worked well for the differences between 0.12 and
0.13, but breaks in 0.14, since some of the APIs that need to be used have primitive types. I could
not figure out a way to make that class work with primitive types. So instead I wrote some shims
(I can already hear the collective sigh) that find the appropriate methods via reflection. This should
be faster since the method instances are cached, and the code is not much uglier than before,
with the advantage that all the ugliness is local to one file (instead of multiple switch statements on
the version being used scattered in ClientWrapper).
The second part is simple: add code to handle Hive 0.14. A few new methods had to be added
to the new shims.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#6627 from vanzin/SPARK-8065 and squashes the following commits:
3fa4270 [Marcelo Vanzin] Indentation style.
4b8a3d4 [Marcelo Vanzin] Fix dep exclusion.
be3d0cc [Marcelo Vanzin] Merge branch 'master' into SPARK-8065
ca3fb1e [Marcelo Vanzin] Merge branch 'master' into SPARK-8065
b43f13e [Marcelo Vanzin] Since exclusions seem to work, clean up some of the code.
73bd161 [Marcelo Vanzin] Botched merge.
d2ddf01 [Marcelo Vanzin] Comment about excluded dep.
0c929d1 [Marcelo Vanzin] Merge branch 'master' into SPARK-8065
2c3c02e [Marcelo Vanzin] Try to fix tests by adding support for exclusions.
0a03470 [Marcelo Vanzin] Try to fix tests by upgrading calcite dependency.
13b2dfa [Marcelo Vanzin] Fix NPE.
6439d88 [Marcelo Vanzin] Minor style thing.
69b017b [Marcelo Vanzin] Style.
a21cad8 [Marcelo Vanzin] Part II: Add shims / version for Hive 0.14.
ae98c87 [Marcelo Vanzin] PART I: Get rid of reflection magic.
JIRA: https://issues.apache.org/jira/browse/SPARK-8052
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#6645 from viirya/cast_string_integraltype and squashes the following commits:
e19c6a3 [Liang-Chi Hsieh] For comment.
c3e472a [Liang-Chi Hsieh] Add test.
7ced9b0 [Liang-Chi Hsieh] Use java.math.BigDecimal for casting String to Decimal instead of using toDouble.
Currently, we use o.a.s.sql.Row both internally and externally. The external interface is wider than what the internal needs because it is designed to facilitate end-user programming. This design has proven to be very error prone and cumbersome for internal Row implementations.
As a first step, we create an InternalRow interface in the catalyst module, which is identical to the current Row interface. And we switch all internal operators/expressions to use this InternalRow instead. When we need to expose Row, we convert the InternalRow implementation into Row for users.
For all public API, we use Row (for example, data source APIs), which will be converted into/from InternalRow by CatalystTypeConverters.
For all internal data sources (Json, Parquet, JDBC, Hive), we use InternalRow for better performance, casted into Row in buildScan() (without change the public API). When create a PhysicalRDD, we cast them back to InternalRow.
cc rxin marmbrus JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#6792 from davies/internal_row and squashes the following commits:
f2abd13 [Davies Liu] fix scalastyle
a7e025c [Davies Liu] move InternalRow into catalyst
30db8ba [Davies Liu] Merge branch 'master' of github.com:apache/spark into internal_row
7cbced8 [Davies Liu] separate Row and InternalRow
[Related PR SPARK-7044] (https://github.com/apache/spark/pull/5671)
Author: zhichao.li <zhichao.li@intel.com>
Closes#6404 from zhichao-li/transform and squashes the following commits:
8418c97 [zhichao.li] add comments and remove useless failAfter logic
d9677e1 [zhichao.li] redirect the error desitination to be the same as the current process
Unit test is still in Scala.
Author: Reynold Xin <rxin@databricks.com>
Closes#6738 from rxin/utf8string-java and squashes the following commits:
562dc6e [Reynold Xin] Flag...
98e600b [Reynold Xin] Another try with encoding setting ..
cfa6bdf [Reynold Xin] Merge branch 'master' into utf8string-java
a3b124d [Reynold Xin] Try different UTF-8 encoded characters.
1ff7c82 [Reynold Xin] Enable UTF-8 encoding.
82d58cc [Reynold Xin] Reset run-tests.
2cb3c69 [Reynold Xin] Use utf-8 encoding in set bytes.
53f8ef4 [Reynold Xin] Hack Jenkins to run one test.
9a48e8d [Reynold Xin] Fixed runtime compilation error.
911c450 [Reynold Xin] Moved unit test also to Java.
4eff7bd [Reynold Xin] Improved unit test coverage.
8e89a3c [Reynold Xin] Fixed tests.
77c64bd [Reynold Xin] Fixed string type codegen.
ffedb62 [Reynold Xin] Code review feedback.
0967ce6 [Reynold Xin] Fixed import ordering.
45a123d [Reynold Xin] [SPARK-8286] Rewrite UTF8String in Java and move it into unsafe package.
```
create table t1 (a int, b string) as select key, value from src;
desc t1;
key int NULL
value string NULL
```
Thus Hive doesn't support specifying the column list for target table in CTAS, however, we should either throwing exception explicity, or supporting the this feature, we just pick up the later one, which seems useful and straightforward.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#6458 from chenghao-intel/ctas_column and squashes the following commits:
d1fa9b6 [Cheng Hao] bug in unittest
4e701aa [Cheng Hao] update as feedback
f305ec1 [Cheng Hao] support specifying the column list for target table in CTAS
This PR change to use Long as internal type for TimestampType for efficiency, which means it will the precision below 100ns.
Author: Davies Liu <davies@databricks.com>
Closes#6733 from davies/timestamp and squashes the following commits:
d9565fa [Davies Liu] remove print
65cf2f1 [Davies Liu] fix Timestamp in SparkR
86fecfb [Davies Liu] disable two timestamp tests
8f77ee0 [Davies Liu] fix scala style
246ee74 [Davies Liu] address comments
309d2e1 [Davies Liu] use Long for TimestampType in SQL
This is a followup to #6712.
Author: Reynold Xin <rxin@databricks.com>
Closes#6739 from rxin/6712-followup and squashes the following commits:
fd9acfb [Reynold Xin] [SPARK-7886] Added unit test for HAVING aggregate pushdown.
This builds on #6710 and also uses FunctionRegistry for function lookup in HiveContext.
Author: Reynold Xin <rxin@databricks.com>
Closes#6712 from rxin/udf-registry-hive and squashes the following commits:
f4c2df0 [Reynold Xin] Fixed style violation.
0bd4127 [Reynold Xin] Fixed Python UDFs.
f9a0378 [Reynold Xin] Disable one more test.
5609494 [Reynold Xin] Disable some failing tests.
4efea20 [Reynold Xin] Don't check children resolved for UDF resolution.
2ebe549 [Reynold Xin] Removed more hardcoded functions.
aadce78 [Reynold Xin] [SPARK-7886] Use FunctionRegistry for built-in expressions in HiveContext.
This patch switches to using FunctionRegistry for built-in expressions. It is based on #6463, but with some work to simplify it along with unit tests.
TODOs for future pull requests:
- Use static registration so we don't need to register all functions every time we start a new SQLContext
- Switch to using this in HiveContext
Author: Reynold Xin <rxin@databricks.com>
Author: Santiago M. Mola <santi@mola.io>
Closes#6710 from rxin/udf-registry and squashes the following commits:
6930822 [Reynold Xin] Fixed Python test.
b802c9a [Reynold Xin] Made UDF case insensitive.
e60d815 [Reynold Xin] Made UDF case insensitive.
852f9c0 [Reynold Xin] Fixed style violation.
e76a3c1 [Reynold Xin] Fixed parser.
52ddaba [Reynold Xin] Fixed compilation.
ee7854f [Reynold Xin] Improved error reporting.
ff906f2 [Reynold Xin] More robust constructor calling.
77b46f1 [Reynold Xin] Simplified the code.
2a2a149 [Reynold Xin] Merge pull request #6463 from smola/SPARK-7886
8616924 [Santiago M. Mola] [SPARK-7886] Add built-in expressions to FunctionRegistry.
1. explicitly import implicit conversion support.
2. use .nonEmpty instead of .size > 0
3. use val instead of var
4. comment indention
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#6700 from adrian-wang/shimsimprove and squashes the following commits:
d22e108 [Daoyuan Wang] several fix for HiveShim
As described in SPARK-8079, when writing a DataFrame to a `HadoopFsRelation`, if `HadoopFsRelation.prepareForWriteJob` throws exception, an unexpected NPE will be thrown during job abortion. (This issue doesn't bring much damage since the job is failing anyway.)
This PR makes the job/task abortion logic in `InsertIntoHadoopFsRelation` more robust to avoid such confusing exceptions.
Author: Cheng Lian <lian@databricks.com>
Closes#6612 from liancheng/spark-8079 and squashes the following commits:
87cd81e [Cheng Lian] Addresses @rxin's comment
1864c75 [Cheng Lian] Addresses review comments
9e6dbb3 [Cheng Lian] Makes InsertIntoHadoopFsRelation job/task abortion more robust
Author: Reynold Xin <rxin@databricks.com>
Closes#6677 from rxin/test-wildcard and squashes the following commits:
8a17b33 [Reynold Xin] Fixed line length.
6663813 [Reynold Xin] [SPARK-8114][SQL] Remove some wildcard import on TestSQLContext._ round 3.
Support runInBackground in SparkExecuteStatementOperation, and add cancellation
Author: Dong Wang <dong@databricks.com>
Closes#6207 from dongwang218/SPARK-6964-jdbc-cancel and squashes the following commits:
687c113 [Dong Wang] fix 100 characters
7bfa2a7 [Dong Wang] fix merge
380480f [Dong Wang] fix for liancheng's comments
eb3e385 [Dong Wang] small nit
341885b [Dong Wang] small fix
3d8ebf8 [Dong Wang] add spark.sql.hive.thriftServer.async flag
04142c3 [Dong Wang] set SQLSession for async execution
184ec35 [Dong Wang] keep hive conf
819ae03 [Dong Wang] [SPARK-6964][SQL][WIP] Support Cancellation in the Thrift Server
Fixed the following packages:
sql.columnar
sql.jdbc
sql.json
sql.parquet
Author: Reynold Xin <rxin@databricks.com>
Closes#6667 from rxin/testsqlcontext_wildcard and squashes the following commits:
134a776 [Reynold Xin] Fixed compilation break.
6da7b69 [Reynold Xin] [SPARK-8114][SQL] Remove some wildcard import on TestSQLContext._ cont'd.
This is a follow-up on #6393. I am removing the following files in this PR.
```
./sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala
./sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala
```
Basically, I re-factored the shim code as follows-
* Rewrote code directly with Hive 0.13 methods, or
* Converted code into private methods, or
* Extracted code into separate classes
But for leftover code that didn't fit in any of these cases, I created a HiveShim object. For eg, helper functions which wrap Hive 0.13 methods to work around Hive bugs are placed here.
Author: Cheolsoo Park <cheolsoop@netflix.com>
Closes#6604 from piaozhexiu/SPARK-6909 and squashes the following commits:
5dccc20 [Cheolsoo Park] Remove hive shim code
Author: Patrick Wendell <patrick@databricks.com>
Closes#6328 from pwendell/spark-1.5-update and squashes the following commits:
2f42d02 [Patrick Wendell] A few more excludes
4bebcf0 [Patrick Wendell] Update to RC4
61aaf46 [Patrick Wendell] Using new release candidate
55f1610 [Patrick Wendell] Another exclude
04b4f04 [Patrick Wendell] More issues with transient 1.4 changes
36f549b [Patrick Wendell] [SPARK-7801] [BUILD] Updating versions to SPARK 1.5.0
The current code references the schema of the DataFrame to be written before checking save mode. This triggers expensive metadata discovery prematurely. For save mode other than `Append`, this metadata discovery is useless since we either ignore the result (for `Ignore` and `ErrorIfExists`) or delete existing files (for `Overwrite`) later.
This PR fixes this issue by deferring metadata discovery after save mode checking.
Author: Cheng Lian <lian@databricks.com>
Closes#6583 from liancheng/spark-8014 and squashes the following commits:
1aafabd [Cheng Lian] Updates comments
088abaa [Cheng Lian] Avoids schema merging and partition discovery when data schema and partition schema are defined
8fbd93f [Cheng Lian] Fixes SPARK-8014
This closes#6570.
Author: Yin Huai <yhuai@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#6573 from rxin/deterministic and squashes the following commits:
356cd22 [Reynold Xin] Added unit test for the optimizer.
da3fde1 [Reynold Xin] Merge pull request #6570 from yhuai/SPARK-8023
da56200 [Yin Huai] Comments.
e38f264 [Yin Huai] Comment.
f9d6a73 [Yin Huai] Add a deterministic method to Expression.
https://issues.apache.org/jira/browse/SPARK-8020
Author: Yin Huai <yhuai@databricks.com>
Closes#6563 from yhuai/SPARK-8020 and squashes the following commits:
4e5addc [Yin Huai] style
bf766c6 [Yin Huai] Failed test.
0398f5b [Yin Huai] First populate the SQLConf and then construct executionHive and metadataHive.
Author: Reynold Xin <rxin@databricks.com>
Closes#6541 from rxin/trailing-whitespace-on and squashes the following commits:
f72ebe4 [Reynold Xin] [SPARK-3850] Turn style checker on for trailing whitespaces.
Author: Reynold Xin <rxin@databricks.com>
Closes#6535 from rxin/whitespace-sql and squashes the following commits:
de50316 [Reynold Xin] [SPARK-3850] Trim trailing spaces for SQL.
Author: Reynold Xin <rxin@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@databricks.com>
Closes#6527 from rxin/covariant-equals and squashes the following commits:
e7d7784 [Reynold Xin] [SPARK-7975] Enforce CovariantEqualsChecker
Author: Cheng Lian <lian@databricks.com>
Closes#6521 from liancheng/classloader-comment-fix and squashes the following commits:
fc09606 [Cheng Lian] Addresses @srowen's comment
59945c5 [Cheng Lian] Fixes a minor comment mistake in IsolatedClientLoader
I went through all the JavaDocs and tightened up visibility.
Author: Reynold Xin <rxin@databricks.com>
Closes#6526 from rxin/sql-1.4-visibility-for-docs and squashes the following commits:
bc37d1e [Reynold Xin] Tighten up visibility for JavaDoc.
Right now `unit-tests.log` are not of much value because we can't tell where the test boundaries are easily. This patch adds log statements before and after each test to outline the test boundaries, e.g.:
```
===== TEST OUTPUT FOR o.a.s.serializer.KryoSerializerSuite: 'kryo with parallelize for primitive arrays' =====
15/05/27 12:36:39.596 pool-1-thread-1-ScalaTest-running-KryoSerializerSuite INFO SparkContext: Starting job: count at KryoSerializerSuite.scala:230
15/05/27 12:36:39.596 dag-scheduler-event-loop INFO DAGScheduler: Got job 3 (count at KryoSerializerSuite.scala:230) with 4 output partitions (allowLocal=false)
15/05/27 12:36:39.596 dag-scheduler-event-loop INFO DAGScheduler: Final stage: ResultStage 3(count at KryoSerializerSuite.scala:230)
15/05/27 12:36:39.596 dag-scheduler-event-loop INFO DAGScheduler: Parents of final stage: List()
15/05/27 12:36:39.597 dag-scheduler-event-loop INFO DAGScheduler: Missing parents: List()
15/05/27 12:36:39.597 dag-scheduler-event-loop INFO DAGScheduler: Submitting ResultStage 3 (ParallelCollectionRDD[5] at parallelize at KryoSerializerSuite.scala:230), which has no missing parents
...
15/05/27 12:36:39.624 pool-1-thread-1-ScalaTest-running-KryoSerializerSuite INFO DAGScheduler: Job 3 finished: count at KryoSerializerSuite.scala:230, took 0.028563 s
15/05/27 12:36:39.625 pool-1-thread-1-ScalaTest-running-KryoSerializerSuite INFO KryoSerializerSuite:
***** FINISHED o.a.s.serializer.KryoSerializerSuite: 'kryo with parallelize for primitive arrays' *****
...
```
Author: Andrew Or <andrew@databricks.com>
Closes#6441 from andrewor14/demarcate-tests and squashes the following commits:
879b060 [Andrew Or] Fix compile after rebase
d622af7 [Andrew Or] Merge branch 'master' of github.com:apache/spark into demarcate-tests
017c8ba [Andrew Or] Merge branch 'master' of github.com:apache/spark into demarcate-tests
7790b6c [Andrew Or] Fix tests after logical merge conflict
c7460c0 [Andrew Or] Merge branch 'master' of github.com:apache/spark into demarcate-tests
c43ffc4 [Andrew Or] Fix tests?
8882581 [Andrew Or] Fix tests
ee22cda [Andrew Or] Fix log message
fa9450e [Andrew Or] Merge branch 'master' of github.com:apache/spark into demarcate-tests
12d1e1b [Andrew Or] Various whitespace changes (minor)
69cbb24 [Andrew Or] Make all test suites extend SparkFunSuite instead of FunSuite
bbce12e [Andrew Or] Fix manual things that cannot be covered through automation
da0b12f [Andrew Or] Add core tests as dependencies in all modules
f7d29ce [Andrew Or] Introduce base abstract class for all test suites
So we can enable a whitespace enforcement rule in the style checker to save code review time.
Author: Reynold Xin <rxin@databricks.com>
Closes#6478 from rxin/whitespace-hive and squashes the following commits:
e01b0e0 [Reynold Xin] Fixed tests.
a3bba22 [Reynold Xin] [SPARK-7927] whitespace fixes for Hive and ThriftServer.
https://issues.apache.org/jira/browse/SPARK-7853
This fixes the problem introduced by my change in https://github.com/apache/spark/pull/6435, which causes that Hive Context fails to create in spark shell because of the class loader issue.
Author: Yin Huai <yhuai@databricks.com>
Closes#6459 from yhuai/SPARK-7853 and squashes the following commits:
37ad33e [Yin Huai] Do not use hiveQlTable at all.
47cdb6d [Yin Huai] Move hiveconf.set to the end of setConf.
005649b [Yin Huai] Update comment.
35d86f3 [Yin Huai] Access TTable directly to make sure Hive will not internally use any metastore utility functions.
3737766 [Yin Huai] Recursively find all jars.
This PR is based on PR #6396 authored by chenghao-intel. Essentially, Spark SQL should use context classloader to load SerDe classes.
yhuai helped updating the test case, and I fixed a bug in the original `CliSuite`: while testing the CLI tool with `runCliWithin`, we don't append `\n` to the last query, thus the last query is never executed.
Original PR description is pasted below.
----
```
bin/spark-sql --jars ./sql/hive/src/test/resources/hive-hcatalog-core-0.13.1.jar
CREATE TABLE t1(a string, b string) ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe';
```
Throws exception like
```
15/05/26 00:16:33 ERROR SparkSQLDriver: Failed in [CREATE TABLE t1(a string, b string) ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe']
org.apache.spark.sql.execution.QueryExecutionException: FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. Cannot validate serde: org.apache.hive.hcatalog.data.JsonSerDe
at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$runHive$1.apply(ClientWrapper.scala:333)
at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$runHive$1.apply(ClientWrapper.scala:310)
at org.apache.spark.sql.hive.client.ClientWrapper.withHiveState(ClientWrapper.scala:139)
at org.apache.spark.sql.hive.client.ClientWrapper.runHive(ClientWrapper.scala:310)
at org.apache.spark.sql.hive.client.ClientWrapper.runSqlHive(ClientWrapper.scala:300)
at org.apache.spark.sql.hive.HiveContext.runSqlHive(HiveContext.scala:457)
at org.apache.spark.sql.hive.execution.HiveNativeCommand.run(HiveNativeCommand.scala:33)
at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:68)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:88)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:88)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:87)
at org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:922)
at org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:922)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:147)
at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:131)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:51)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:727)
at org.apache.spark.sql.hive.thriftserver.AbstractSparkSQLDriver.run(AbstractSparkSQLDriver.scala:57)
```
Author: Cheng Hao <hao.cheng@intel.com>
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#6435 from liancheng/classLoader and squashes the following commits:
d4c4845 [Cheng Lian] Fixes CliSuite
75e80e2 [Yin Huai] Update the fix.
fd26533 [Cheng Hao] scalastyle
dd78775 [Cheng Hao] workaround for classloader of IsolatedClientLoader
As stated in SPARK-7684, currently `TestHive.reset` has some execution order specific bug, which makes running specific test suites locally pretty frustrating. This PR refactors `MetastoreDataSourcesSuite` (which relies on `TestHive.reset` heavily) using various `withXxx` utility methods in `SQLTestUtils` to ask each test case to cleanup their own mess so that we can avoid calling `TestHive.reset`.
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#6353 from liancheng/workaround-spark-7684 and squashes the following commits:
26939aa [Yin Huai] Move the initialization of jsonFilePath to beforeAll.
a423d48 [Cheng Lian] Fixes Scala style issue
dfe45d0 [Cheng Lian] Refactors MetastoreDataSourcesSuite to workaround SPARK-7684
92a116d [Cheng Lian] Fixes minor styling issues
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#6318 from adrian-wang/dynpart and squashes the following commits:
ad73b61 [Daoyuan Wang] not use sqlTestUtils for try catch because dont have sqlcontext here
6c33b51 [Daoyuan Wang] fix according to liancheng
f0f8074 [Daoyuan Wang] some specific types as dynamic partition
This type is not really used. Might as well remove it.
Author: Reynold Xin <rxin@databricks.com>
Closes#6427 from rxin/evalutedType and squashes the following commits:
51a319a [Reynold Xin] [SPARK-7887][SQL] Remove EvaluatedType from SQL Expression.
I grep'ed hive-0.12.0 in the source code and removed all the profiles and doc references.
Author: Cheolsoo Park <cheolsoop@netflix.com>
Closes#6393 from piaozhexiu/SPARK-7850 and squashes the following commits:
fb429ce [Cheolsoo Park] Remove hive-0.13.1 profile
82bf09a [Cheolsoo Park] Remove hive 0.12.0 shim code
f3722da [Cheolsoo Park] Remove hive-0.12.0 profile and references from POM and build docs
So that potential partial/corrupted data files left by failed tasks/jobs won't affect normal data scan.
Author: Cheng Lian <lian@databricks.com>
Closes#6411 from liancheng/spark-7868 and squashes the following commits:
273ea36 [Cheng Lian] Ignores _temporary directories
In `DataSourceStrategy.createPhysicalRDD`, we use the relation schema as the target schema for converting incoming rows into Catalyst rows. However, we should be using the output schema instead, since our scan might return a subset of the relation's columns.
This patch incorporates #6414 by liancheng, which fixes an issue in `SimpleTestRelation` that prevented this bug from being caught by our old tests:
> In `SimpleTextRelation`, we specified `needsConversion` to `true`, indicating that values produced by this testing relation should be of Scala types, and need to be converted to Catalyst types when necessary. However, we also used `Cast` to convert strings to expected data types. And `Cast` always produces values of Catalyst types, thus no conversion is done at all. This PR makes `SimpleTextRelation` produce Scala values so that data conversion code paths can be properly tested.
Closes#5986.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Author: Cheng Lian <liancheng@users.noreply.github.com>
Closes#6400 from JoshRosen/SPARK-7858 and squashes the following commits:
e71c866 [Josh Rosen] Re-fix bug so that the tests pass again
56b13e5 [Josh Rosen] Add regression test to hadoopFsRelationSuites
2169a0f [Josh Rosen] Remove use of SpecificMutableRow and BufferedIterator
6cd7366 [Josh Rosen] Fix SPARK-7858 by using output types for conversion.
5a00e66 [Josh Rosen] Add assertions in order to reproduce SPARK-7858
8ba195c [Cheng Lian] Merge 9968fba9979287aaa1f141ba18bfb9d4c116a3b3 into 61664732b2
9968fba [Cheng Lian] Tests the data type conversion code paths
When committing/aborting a write task issued in `InsertIntoHadoopFsRelation`, if an exception is thrown from `OutputWriter.close()`, the committing/aborting process will be interrupted, and leaves messy stuff behind (e.g., the `_temporary` directory created by `FileOutputCommitter`).
This PR makes these two process more robust by catching potential exceptions and falling back to normal task committment/abort.
Author: Cheng Lian <lian@databricks.com>
Closes#6378 from liancheng/spark-7838 and squashes the following commits:
f18253a [Cheng Lian] Makes task committing/aborting in InsertIntoHadoopFsRelation more robust
The "Database does not exist" error reported in SPARK-7684 was caused by `HiveContext.newTemporaryConfiguration()`, which always creates a new temporary metastore directory and returns a metastore configuration pointing that directory. This makes `TestHive.reset()` always replaces old temporary metastore with an empty new one.
Author: Cheng Lian <lian@databricks.com>
Closes#6359 from liancheng/spark-7684 and squashes the following commits:
95d2eb8 [Cheng Lian] Addresses @marmbrust's comment
042769d [Cheng Lian] Don't create new temp directory in HiveContext.newTemporaryConfiguration()
This one continues the work of https://github.com/apache/spark/pull/6216.
Author: Yin Huai <yhuai@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#6366 from yhuai/insert and squashes the following commits:
3d717fb [Yin Huai] Use insertInto to handle the casue when table exists and Append is used for saveAsTable.
56d2540 [Yin Huai] Add PreWriteCheck to HiveContext's analyzer.
c636e35 [Yin Huai] Remove unnecessary empty lines.
cf83837 [Yin Huai] Move insertInto to write. Also, remove the partition columns from InsertIntoHadoopFsRelation.
0841a54 [Reynold Xin] Removed experimental tag for deprecated methods.
33ed8ef [Reynold Xin] [SPARK-7654][SQL] Move insertInto into reader/writer interface.
JIRA: https://issues.apache.org/jira/browse/SPARK-7270
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#5864 from viirya/dyn_partition_insert and squashes the following commits:
b5627df [Liang-Chi Hsieh] For comments.
3b21e4b [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into dyn_partition_insert
8a4352d [Liang-Chi Hsieh] Consider dynamic partition when inserting into hive table.
https://issues.apache.org/jira/browse/SPARK-7758
When initializing `executionHive`, we only masks
`javax.jdo.option.ConnectionURL` to override metastore location. However,
other properties that relates to the actual Hive metastore data source are not
masked. For example, when using Spark SQL with a PostgreSQL backed Hive
metastore, `executionHive` actually tries to use settings read from
`hive-site.xml`, which talks about PostgreSQL, to connect to the temporary
Derby metastore, thus causes error.
To fix this, we need to mask all metastore data source properties.
Specifically, according to the code of [Hive `ObjectStore.getDataSourceProps()`
method] [1], all properties whose name mentions "jdo" and "datanucleus" must be
included.
[1]: https://github.com/apache/hive/blob/release-0.13.1/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L288
Have tested using postgre sql as metastore, it worked fine.
Author: WangTaoTheTonic <wangtao111@huawei.com>
Closes#6314 from WangTaoTheTonic/SPARK-7758 and squashes the following commits:
ca7ae7c [WangTaoTheTonic] add comments
86caf2c [WangTaoTheTonic] delete unused import
e4f0feb [WangTaoTheTonic] block more data source related property
92a81fa [WangTaoTheTonic] fix style check
e3e683d [WangTaoTheTonic] override more configs to avoid failuer connecting to postgre sql
This closes#6104.
Author: Cheng Hao <hao.cheng@intel.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#6343 from rxin/window-df and squashes the following commits:
026d587 [Reynold Xin] Address code review feedback.
dc448fe [Reynold Xin] Fixed Hive tests.
9794d9d [Reynold Xin] Moved Java test package.
9331605 [Reynold Xin] Refactored API.
3313e2a [Reynold Xin] Merge pull request #6104 from chenghao-intel/df_window
d625a64 [Cheng Hao] Update the dataframe window API as suggsted
c141fb1 [Cheng Hao] hide all of properties of the WindowFunctionDefinition
3b1865f [Cheng Hao] scaladoc typos
f3fd2d0 [Cheng Hao] polish the unit test
6847825 [Cheng Hao] Add additional analystcs functions
57e3bc0 [Cheng Hao] typos
24a08ec [Cheng Hao] scaladoc
28222ed [Cheng Hao] fix bug of range/row Frame
1d91865 [Cheng Hao] style issue
53f89f2 [Cheng Hao] remove the over from the functions.scala
964c013 [Cheng Hao] add more unit tests and window functions
64e18a7 [Cheng Hao] Add Window Function support for DataFrame
Author: Yin Huai <yhuai@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#6285 from liancheng/spark-7763 and squashes the following commits:
bb2829d [Yin Huai] Fix hashCode.
d677f7d [Cheng Lian] Fixes Scala style issue
44b283f [Cheng Lian] Adds test case for SPARK-7616
6733276 [Yin Huai] Fix a bug that potentially causes https://issues.apache.org/jira/browse/SPARK-7616.
6cabf3c [Yin Huai] Update unit test.
7e02910 [Yin Huai] Use metastore partition columns and do not hijack maybePartitionSpec.
e9a03ec [Cheng Lian] Persists partition columns into metastore
java.lang.Math.exp(1.0) has different result between jdk versions. so do not use createQueryTest, write a separate test for it.
```
jdk version result
1.7.0_11 2.7182818284590455
1.7.0_05 2.7182818284590455
1.7.0_71 2.718281828459045
```
Author: scwf <wangfei1@huawei.com>
Closes#6274 from scwf/java_method and squashes the following commits:
3dd2516 [scwf] address comments
5fa1459 [scwf] style
df46445 [scwf] fix test error
fcb6d22 [scwf] fix udf_java_method
When no partition columns can be found, we should have an empty `PartitionSpec`, rather than a `PartitionSpec` with empty partition columns.
This PR together with #6285 should fix SPARK-7749.
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#6287 from liancheng/spark-7749 and squashes the following commits:
a799ff3 [Cheng Lian] Adds test cases for SPARK-7749
c4949be [Cheng Lian] Minor refactoring, and tolerant _TEMPORARY directory name
5aa87ea [Yin Huai] Make parsePartitions more robust.
fc56656 [Cheng Lian] Returns empty PartitionSpec if no partition columns can be inferred
19ae41e [Cheng Lian] Don't list base directory as leaf directory
Follow up of #6340, to avoid the test report missing once it fails.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#6312 from chenghao-intel/rollup_minor and squashes the following commits:
b03a25f [Cheng Hao] simplify the testData instantiation
09b7e8b [Cheng Hao] move the testData into beforeAll()
This is a follow up for #6257, which broke the maven test.
Add cube & rollup for DataFrame
For example:
```scala
testData.rollup($"a" + $"b", $"b").agg(sum($"a" - $"b"))
testData.cube($"a" + $"b", $"b").agg(sum($"a" - $"b"))
```
Author: Cheng Hao <hao.cheng@intel.com>
Closes#6304 from chenghao-intel/rollup and squashes the following commits:
04bb1de [Cheng Hao] move the table register/unregister into beforeAll/afterAll
a6069f1 [Cheng Hao] cancel the implicit keyword
ced4b8f [Cheng Hao] remove the unnecessary code changes
9959dfa [Cheng Hao] update the code as comments
e1d88aa [Cheng Hao] update the code as suggested
03bc3d9 [Cheng Hao] Remove the CubedData & RollupedData
5fd62d0 [Cheng Hao] hiden the CubedData & RollupedData
5ffb196 [Cheng Hao] Add Cube / Rollup for dataframe
follow up for #5806
Author: scwf <wangfei1@huawei.com>
Closes#6164 from scwf/FunctionRegistry and squashes the following commits:
15e6697 [scwf] use catalogconf in FunctionRegistry
```
select explode(map(value, key)) from src;
```
Throws exception
```
org.apache.spark.sql.AnalysisException: The number of aliases supplied in the AS clause does not match the number of columns output by the UDTF expected 2 aliases but got _c0 ;
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:38)
at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:43)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGenerate$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveGenerate$$makeGeneratorOutput(Analyzer.scala:605)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGenerate$$anonfun$apply$16$$anonfun$22.apply(Analyzer.scala:562)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGenerate$$anonfun$apply$16$$anonfun$22.apply(Analyzer.scala:548)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGenerate$$anonfun$apply$16.applyOrElse(Analyzer.scala:548)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveGenerate$$anonfun$apply$16.applyOrElse(Analyzer.scala:538)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:222)
```
Author: Cheng Hao <hao.cheng@intel.com>
Closes#6178 from chenghao-intel/explode and squashes the following commits:
916fbe9 [Cheng Hao] add more strict rules for TGF alias
5c3f2c5 [Cheng Hao] fix bug in unit test
e1d93ab [Cheng Hao] Add more unit test
19db09e [Cheng Hao] resolve names for generator in projection
A follow-up to #6244.
Author: Michael Armbrust <michael@databricks.com>
Closes#6247 from marmbrus/fixOrcTests and squashes the following commits:
e39ee1b [Michael Armbrust] [SQL] Fix serializability of ORC table scan
Fix break caused by merging #6225 and #6194.
Author: Michael Armbrust <michael@databricks.com>
Closes#6244 from marmbrus/fixOrcBuildBreak and squashes the following commits:
b10e47b [Michael Armbrust] [HOTFIX] Fix ORC Build break
This PR introduces several performance optimizations to `HadoopFsRelation` and `ParquetRelation2`:
1. Moving `FileStatus` listing from `DataSourceStrategy` into a cache within `HadoopFsRelation`.
This new cache generalizes and replaces the one used in `ParquetRelation2`.
This also introduces an interface change: to reuse cached `FileStatus` objects, `HadoopFsRelation.buildScan` methods now receive `Array[FileStatus]` instead of `Array[String]`.
1. When Parquet task side metadata reading is enabled, skip reading row group information when reading Parquet footers.
This is basically what PR #5334 does. Also, now we uses `ParquetFileReader.readAllFootersInParallel` to read footers in parallel.
Another optimization in question is, instead of asking `HadoopFsRelation.buildScan` to return an `RDD[Row]` for a single selected partition and then union them all, we ask it to return an `RDD[Row]` for all selected partitions. This optimization is based on the fact that Hadoop configuration broadcasting used in `NewHadoopRDD` takes 34% time in the following microbenchmark. However, this complicates data source user code because user code must merge partition values manually.
To check the cost of broadcasting in `NewHadoopRDD`, I also did microbenchmark after removing the `broadcast` call in `NewHadoopRDD`. All results are shown below.
### Microbenchmark
#### Preparation code
Generating a partitioned table with 50k partitions, 1k rows per partition:
```scala
import sqlContext._
import sqlContext.implicits._
for (n <- 0 until 500) {
val data = for {
p <- (n * 10) until ((n + 1) * 10)
i <- 0 until 1000
} yield (i, f"val_$i%04d", f"$p%04d")
data.
toDF("a", "b", "p").
write.
partitionBy("p").
mode("append").
parquet(path)
}
```
#### Benchmarking code
```scala
import sqlContext._
import sqlContext.implicits._
import org.apache.spark.sql.types._
import com.google.common.base.Stopwatch
val path = "hdfs://localhost:9000/user/lian/5k"
def benchmark(n: Int)(f: => Unit) {
val stopwatch = new Stopwatch()
def run() = {
stopwatch.reset()
stopwatch.start()
f
stopwatch.stop()
stopwatch.elapsedMillis()
}
val records = (0 until n).map(_ => run())
(0 until n).foreach(i => println(s"Round $i: ${records(i)} ms"))
println(s"Average: ${records.sum / n.toDouble} ms")
}
benchmark(3) { read.parquet(path).explain(extended = true) }
```
#### Results
Before:
```
Round 0: 72528 ms
Round 1: 68938 ms
Round 2: 65372 ms
Average: 68946.0 ms
```
After:
```
Round 0: 59499 ms
Round 1: 53645 ms
Round 2: 53844 ms
Round 3: 49093 ms
Round 4: 50555 ms
Average: 53327.2 ms
```
Also removing Hadoop configuration broadcasting:
(Note that I was testing on a local laptop, thus network cost is pretty low.)
```
Round 0: 15806 ms
Round 1: 14394 ms
Round 2: 14699 ms
Round 3: 15334 ms
Round 4: 14123 ms
Average: 14871.2 ms
```
Author: Cheng Lian <lian@databricks.com>
Closes#6225 from liancheng/spark-7673 and squashes the following commits:
2d58a2b [Cheng Lian] Skips reading row group information when using task side metadata reading
7aa3748 [Cheng Lian] Optimizes FileStatusCache by introducing a map from parent directories to child files
ba41250 [Cheng Lian] Reuses HadoopFsRelation FileStatusCache in ParquetRelation2
3d278f7 [Cheng Lian] Fixes a bug when reading a single Parquet data file
b84612a [Cheng Lian] Fixes Scala style issue
6a08b02 [Cheng Lian] WIP: Moves file status cache into HadoopFSRelation
A modified version of https://github.com/apache/spark/pull/6110, use `semanticEquals` to make it more efficient.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#6173 from cloud-fan/7269 and squashes the following commits:
e4a3cc7 [Wenchen Fan] address comments
cc02045 [Wenchen Fan] consider elements length equal
d7ff8f4 [Wenchen Fan] fix 7269
This PR updates PR #6135 authored by zhzhan from Hortonworks.
----
This PR implements a Spark SQL data source for accessing ORC files.
> **NOTE**
>
> Although ORC is now an Apache TLP, the codebase is still tightly coupled with Hive. That's why the new ORC data source is under `org.apache.spark.sql.hive` package, and must be used with `HiveContext`. However, it doesn't require existing Hive installation to access ORC files.
1. Saving/loading ORC files without contacting Hive metastore
1. Support for complex data types (i.e. array, map, and struct)
1. Aware of common optimizations provided by Spark SQL:
- Column pruning
- Partitioning pruning
- Filter push-down
1. Schema evolution support
1. Hive metastore table conversion
This PR also include initial work done by scwf from Huawei (PR #3753).
Author: Zhan Zhang <zhazhan@gmail.com>
Author: Cheng Lian <lian@databricks.com>
Closes#6194 from liancheng/polishing-orc and squashes the following commits:
55ecd96 [Cheng Lian] Reorganizes ORC test suites
d4afeed [Cheng Lian] Addresses comments
21ada22 [Cheng Lian] Adds @since and @Experimental annotations
128bd3b [Cheng Lian] ORC filter bug fix
d734496 [Cheng Lian] Polishes the ORC data source
2650a42 [Zhan Zhang] resolve review comments
3c9038e [Zhan Zhang] resolve review comments
7b3c7c5 [Zhan Zhang] save mode fix
f95abfd [Zhan Zhang] reuse test suite
7cc2c64 [Zhan Zhang] predicate fix
4e61c16 [Zhan Zhang] minor change
305418c [Zhan Zhang] orc data source support
Author: Michael Armbrust <michael@databricks.com>
Closes#6167 from marmbrus/configureIsolation and squashes the following commits:
6147cbe [Michael Armbrust] filter other conf
22cc3bc7 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into configureIsolation
07476ee [Michael Armbrust] filter empty prefixes
dfdf19c [Michael Armbrust] [SPARK-6906][SQL] Allow configuration of classloader isolation for hive
Also moved all the deprecated functions into one place for SQLContext and DataFrame, and updated tests to use the new API.
Author: Reynold Xin <rxin@databricks.com>
Closes#6210 from rxin/df-writer-reader-jdbc and squashes the following commits:
7465c2c [Reynold Xin] Fixed unit test.
118e609 [Reynold Xin] Updated tests.
3441b57 [Reynold Xin] Updated javadoc.
13cdd1c [Reynold Xin] [SPARK-7654][SQL] Move JDBC into DataFrame's reader/writer interface.
This patch introduces DataFrameWriter and DataFrameReader.
DataFrameReader interface, accessible through SQLContext.read, contains methods that create DataFrames. These methods used to reside in SQLContext. Example usage:
```scala
sqlContext.read.json("...")
sqlContext.read.parquet("...")
```
DataFrameWriter interface, accessible through DataFrame.write, implements a builder pattern to avoid the proliferation of options in writing DataFrame out. It currently implements:
- mode
- format (e.g. "parquet", "json")
- options (generic options passed down into data sources)
- partitionBy (partitioning columns)
Example usage:
```scala
df.write.mode("append").format("json").partitionBy("date").saveAsTable("myJsonTable")
```
TODO:
- [ ] Documentation update
- [ ] Move JDBC into reader / writer?
- [ ] Deprecate the old interfaces
- [ ] Move the generic load interface into reader.
- [ ] Update example code and documentation
Author: Reynold Xin <rxin@databricks.com>
Closes#6175 from rxin/reader-writer and squashes the following commits:
b146c95 [Reynold Xin] Deprecation of old APIs.
bd8abdf [Reynold Xin] Fixed merge conflict.
26abea2 [Reynold Xin] Added general load methods.
244fbec [Reynold Xin] Added equivalent to example.
4f15d92 [Reynold Xin] Added documentation for partitionBy.
7e91611 [Reynold Xin] [SPARK-7654][SQL] DataFrameReader and DataFrameWriter for input/output API.
for example:
table: src(key string, value string)
sql: with v1 as(select key, count(value) over (partition by key) cnt_val from src), v2 as(select v1.key, v1_lag.cnt_val from v1, v1 v1_lag where v1.key = v1_lag.key) select * from v2 limit 5;
then will analyze fail when resolving conflicting references in Join:
'Limit 5
'Project [*]
'Subquery v2
'Project ['v1.key,'v1_lag.cnt_val]
'Filter ('v1.key = 'v1_lag.key)
'Join Inner, None
Subquery v1
Project [key#95,cnt_val#94L]
Window [key#95,value#96], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFCount(value#96) WindowSpecDefinition [key#95], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS cnt_val#94L], WindowSpecDefinition [key#95], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Project [key#95,value#96]
MetastoreRelation default, src, None
Subquery v1_lag
Subquery v1
Project [key#97,cnt_val#94L]
Window [key#97,value#98], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFCount(value#98) WindowSpecDefinition [key#97], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS cnt_val#94L], WindowSpecDefinition [key#97], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Project [key#97,value#98]
MetastoreRelation default, src, None
Conflicting attributes: cnt_val#94L
Author: linweizhong <linweizhong@huawei.com>
Closes#6114 from Sephiroth-Lin/spark-7595 and squashes the following commits:
f8f2637 [linweizhong] Add unit test
dfe9169 [linweizhong] Handle windowExpression with self join
JavaTypeInference into catalyst
types.DateUtils into catalyst
CacheManager into execution
DefaultParserDialect into catalyst
Author: Reynold Xin <rxin@databricks.com>
Closes#6108 from rxin/sql-rename and squashes the following commits:
3fc9613 [Reynold Xin] Fixed import ordering.
83d9ff4 [Reynold Xin] Fixed codegen tests.
e271e86 [Reynold Xin] mima
f4e24a6 [Reynold Xin] [SQL] Move some classes into packages that are more appropriate.
This PR migrates Parquet data source to the newly introduced `FSBasedRelation`. `FSBasedParquetRelation` is created to replace `ParquetRelation2`. Major differences are:
1. Partition discovery code has been factored out to `FSBasedRelation`
1. `AppendingParquetOutputFormat` is not used now. Instead, an anonymous subclass of `ParquetOutputFormat` is used to handle appending and writing dynamic partitions
1. When scanning partitioned tables, `FSBasedParquetRelation.buildScan` only builds an `RDD[Row]` for a single selected partition
1. `FSBasedParquetRelation` doesn't rely on Catalyst expressions for filter push down, thus it doesn't extend `CatalystScan` anymore
After migrating `JSONRelation` (which extends `CatalystScan`), we can remove `CatalystScan`.
<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/6090)
<!-- Reviewable:end -->
Author: Cheng Lian <lian@databricks.com>
Closes#6090 from liancheng/parquet-migration and squashes the following commits:
6063f87 [Cheng Lian] Casts to OutputCommitter rather than FileOutputCommtter
bfd1cf0 [Cheng Lian] Fixes compilation error introduced while rebasing
f9ea56e [Cheng Lian] Adds ParquetRelation2 related classes to MiMa check whitelist
261d8c1 [Cheng Lian] Minor bug fix and more tests
db65660 [Cheng Lian] Migrates Parquet data source to FSBasedRelation
Some third-party UDTF extensions generate additional rows in the "GenericUDTF.close()" method, which is supported / documented by Hive.
https://cwiki.apache.org/confluence/display/Hive/DeveloperGuide+UDTF
However, Spark SQL ignores the "GenericUDTF.close()", and it causes bug while porting job from Hive to Spark SQL.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#5383 from chenghao-intel/udtf_close and squashes the following commits:
98b4e4b [Cheng Hao] Support UDTF.close
Author: Cheng Lian <lian@databricks.com>
Closes#6123 from liancheng/remove-println and squashes the following commits:
03356b6 [Cheng Lian] Removes debugging println
This makes HiveContext.analyzer overrideable.
Author: Santiago M. Mola <santi@mola.io>
Closes#6086 from smola/patch-3 and squashes the following commits:
8ece136 [Santiago M. Mola] [SPARK-7566][SQL] Add type to HiveContext.analyzer
This pull request adds since tag to all public methods/classes in SQL/DataFrame to indicate which version the methods/classes were first added.
Author: Reynold Xin <rxin@databricks.com>
Closes#6101 from rxin/tbc and squashes the following commits:
ed55e11 [Reynold Xin] Add since version to all DataFrame methods.
This PR adds partitioning support for the external data sources API. It aims to simplify development of file system based data sources, and provide first class partitioning support for both read path and write path. Existing data sources like JSON and Parquet can be simplified with this work.
## New features provided
1. Hive compatible partition discovery
This actually generalizes the partition discovery strategy used in Parquet data source in Spark 1.3.0.
1. Generalized partition pruning optimization
Now partition pruning is handled during physical planning phase. Specific data sources don't need to worry about this harness anymore.
(This also implies that we can remove `CatalystScan` after migrating the Parquet data source, since now we don't need to pass Catalyst expressions to data source implementations.)
1. Insertion with dynamic partitions
When inserting data to a `FSBasedRelation`, data can be partitioned dynamically by specified partition columns.
## New structures provided
### Developer API
1. `FSBasedRelation`
Base abstract class for file system based data sources.
1. `OutputWriter`
Base abstract class for output row writers, responsible for writing a single row object.
1. `FSBasedRelationProvider`
A new relation provider for `FSBasedRelation` subclasses. Note that data sources extending `FSBasedRelation` don't need to extend `RelationProvider` and `SchemaRelationProvider`.
### User API
New overloaded versions of
1. `DataFrame.save()`
1. `DataFrame.saveAsTable()`
1. `SQLContext.load()`
are provided to allow users to save/load DataFrames with user defined dynamic partition columns.
### Spark SQL query planning
1. `InsertIntoFSBasedRelation`
Used to implement write path for `FSBasedRelation`s.
1. New rules for `FSBasedRelation` in `DataSourceStrategy`
These are added to hook `FSBasedRelation` into physical query plan in read path, and perform partition pruning.
## TODO
- [ ] Use scratch directories when overwriting a table with data selected from itself.
Currently, this is not supported, because the table been overwritten is always deleted before writing any data to it.
- [ ] When inserting with dynamic partition columns, use external sorter to group the data first.
This ensures that we only need to open a single `OutputWriter` at a time. For data sources like Parquet, `OutputWriter`s can be quite memory consuming. One issue is that, this approach breaks the row distribution in the original DataFrame. However, we did't promise to preserve data distribution when writing a DataFrame.
- [x] More tests. Specifically, test cases for
- [x] Self-join
- [x] Loading partitioned relations with a subset of partition columns stored in data files.
- [x] `SQLContext.load()` with user defined dynamic partition columns.
## Parquet data source migration
Parquet data source migration is covered in PR https://github.com/liancheng/spark/pull/6, which is against this PR branch and for preview only. A formal PR need to be made after this one is merged.
Author: Cheng Lian <lian@databricks.com>
Closes#5526 from liancheng/partitioning-support and squashes the following commits:
5351a1b [Cheng Lian] Fixes compilation error introduced while rebasing
1f9b1a5 [Cheng Lian] Tweaks data schema passed to FSBasedRelations
43ba50e [Cheng Lian] Avoids serializing generated projection code
edf49e7 [Cheng Lian] Removed commented stale code block
348a922 [Cheng Lian] Adds projection in FSBasedRelation.buildScan(requiredColumns, inputPaths)
ad4d4de [Cheng Lian] Enables HDFS style globbing
8d12e69 [Cheng Lian] Fixes compilation error
c71ac6c [Cheng Lian] Addresses comments from @marmbrus
7552168 [Cheng Lian] Fixes typo in MimaExclude.scala
0349e09 [Cheng Lian] Fixes compilation error introduced while rebasing
52b0c9b [Cheng Lian] Adjusts project/MimaExclude.scala
c466de6 [Cheng Lian] Addresses comments
bc3f9b4 [Cheng Lian] Uses projection to separate partition columns and data columns while inserting rows
795920a [Cheng Lian] Fixes compilation error after rebasing
0b8cd70 [Cheng Lian] Adds Scala/Catalyst row conversion when writing non-partitioned tables
fa543f3 [Cheng Lian] Addresses comments
5849dd0 [Cheng Lian] Fixes doc typos. Fixes partition discovery refresh.
51be443 [Cheng Lian] Replaces FSBasedRelation.outputCommitterClass with FSBasedRelation.prepareForWrite
c4ed4fe [Cheng Lian] Bug fixes and a new test suite
a29e663 [Cheng Lian] Bug fix: should only pass actuall data files to FSBaseRelation.buildScan
5f423d3 [Cheng Lian] Bug fixes. Lets data source to customize OutputCommitter rather than OutputFormat
54c3d7b [Cheng Lian] Enforces that FileOutputFormat must be used
be0c268 [Cheng Lian] Uses TaskAttempContext rather than Configuration in OutputWriter.init
0bc6ad1 [Cheng Lian] Resorts to new Hadoop API, and now FSBasedRelation can customize output format class
f320766 [Cheng Lian] Adds prepareForWrite() hook, refactored writer containers
422ff4a [Cheng Lian] Fixes style issue
ce52353 [Cheng Lian] Adds new SQLContext.load() overload with user defined dynamic partition columns
8d2ff71 [Cheng Lian] Merges partition columns when reading partitioned relations
ca1805b [Cheng Lian] Removes duplicated partition discovery code in new Parquet
f18dec2 [Cheng Lian] More strict schema checking
b746ab5 [Cheng Lian] More tests
9b487bf [Cheng Lian] Fixes compilation errors introduced while rebasing
ea6c8dd [Cheng Lian] Removes remote debugging stuff
327bb1d [Cheng Lian] Implements partitioning support for data sources API
3c5073a [Cheng Lian] Fixes SaveModes used in test cases
fb5a607 [Cheng Lian] Fixes compilation error
9d17607 [Cheng Lian] Adds the contract that OutputWriter should have zero-arg constructor
5de194a [Cheng Lian] Forgot Apache licence header
95d0b4d [Cheng Lian] Renames PartitionedSchemaRelationProvider to FSBasedRelationProvider
770b5ba [Cheng Lian] Adds tests for FSBasedRelation
3ba9bbf [Cheng Lian] Adds DataFrame.saveAsTable() overrides which support partitioning
1b8231f [Cheng Lian] Renames FSBasedPrunedFilteredScan to FSBasedRelation
aa8ba9a [Cheng Lian] Javadoc fix
012ed2d [Cheng Lian] Adds PartitioningOptions
7dd8dd5 [Cheng Lian] Adds new interfaces and stub methods for data sources API partitioning support
Author: Reynold Xin <rxin@databricks.com>
Closes#6071 from rxin/parserdialect and squashes the following commits:
ca2eb31 [Reynold Xin] Rename Dialect -> ParserDialect.
This is a follow up of #5876 and should be merged after #5876.
Let's wait for unit testing result from Jenkins.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#5963 from chenghao-intel/useIsolatedClient and squashes the following commits:
f87ace6 [Cheng Hao] remove the TODO and add `resolved condition` for HiveTable
a8260e8 [Cheng Hao] Update code as feedback
f4e243f [Cheng Hao] remove the serde setting for SequenceFile
d166afa [Cheng Hao] style issue
d25a4aa [Cheng Hao] Add SerDe support for CTAS
The DAG visualization currently displays only low-level Spark primitives (e.g. `map`, `reduceByKey`, `filter` etc.). For SQL, these aren't particularly useful. Instead, we should display higher level physical operators (e.g. `Filter`, `Exchange`, `ShuffleHashJoin`). cc marmbrus
-----------------
**Before**
<img src="https://issues.apache.org/jira/secure/attachment/12731586/before.png" width="600px"/>
-----------------
**After** (Pay attention to the words)
<img src="https://issues.apache.org/jira/secure/attachment/12731587/after.png" width="600px"/>
-----------------
Author: Andrew Or <andrew@databricks.com>
Closes#5999 from andrewor14/dag-viz-sql and squashes the following commits:
0db23a4 [Andrew Or] Merge branch 'master' of github.com:apache/spark into dag-viz-sql
1e211db [Andrew Or] Update comment
0d49fd6 [Andrew Or] Merge branch 'master' of github.com:apache/spark into dag-viz-sql
ffd237a [Andrew Or] Fix style
202dac1 [Andrew Or] Make ignoreParent false by default
e61b1ab [Andrew Or] Visualize SQL operators, not low-level Spark primitives
569034a [Andrew Or] Add a flag to ignore parent settings and scopes
It's the first step: generalize UnresolvedGetField to support all map, struct, and array
TODO: add `apply` in Scala and `__getitem__` in Python, and unify the `getItem` and `getField` methods to one single API(or should we keep them for compatibility?).
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#5744 from cloud-fan/generalize and squashes the following commits:
715c589 [Wenchen Fan] address comments
7ea5b31 [Wenchen Fan] fix python test
4f0833a [Wenchen Fan] add python test
f515d69 [Wenchen Fan] add apply method and test cases
8df6199 [Wenchen Fan] fix python test
239730c [Wenchen Fan] fix test compile
2a70526 [Wenchen Fan] use _bin_op in dataframe.py
6bf72bc [Wenchen Fan] address comments
3f880c3 [Wenchen Fan] add java doc
ab35ab5 [Wenchen Fan] fix python test
b5961a9 [Wenchen Fan] fix style
c9d85f5 [Wenchen Fan] generalize UnresolvedGetField to support all map, struct, and array
This PR switches Spark SQL's Hive support to use the isolated hive client interface introduced by #5851, instead of directly interacting with the client. By using this isolated client we can now allow users to dynamically configure the version of Hive that they are connecting to by setting `spark.sql.hive.metastore.version` without the need recompile. This also greatly reduces the surface area for our interaction with the hive libraries, hopefully making it easier to support other versions in the future.
Jars for the desired hive version can be configured using `spark.sql.hive.metastore.jars`, which accepts the following options:
- a colon-separated list of jar files or directories for hive and hadoop.
- `builtin` - attempt to discover the jars that were used to load Spark SQL and use those. This
option is only valid when using the execution version of Hive.
- `maven` - download the correct version of hive on demand from maven.
By default, `builtin` is used for Hive 13.
This PR also removes the test step for building against Hive 12, as this will no longer be required to talk to Hive 12 metastores. However, the full removal of the Shim is deferred until a later PR.
Remaining TODOs:
- Remove the Hive Shims and inline code for Hive 13.
- Several HiveCompatibility tests are not yet passing.
- `nullformatCTAS` - As detailed below, we now are handling CTAS parsing ourselves instead of hacking into the Hive semantic analyzer. However, we currently only handle the common cases and not things like CTAS where the null format is specified.
- `combine1` now leaks state about compression somehow, breaking all subsequent tests. As such we currently add it to the blacklist
- `part_inherit_tbl_props` and `part_inherit_tbl_props_with_star` do not work anymore. We are correctly propagating the information
- "load_dyn_part14.*" - These tests pass when run on their own, but fail when run with all other tests. It seems our `RESET` mechanism may not be as robust as it used to be?
Other required changes:
- `CreateTableAsSelect` no longer carries parts of the HiveQL AST with it through the query execution pipeline. Instead, we parse CTAS during the HiveQL conversion and construct a `HiveTable`. The full parsing here is not yet complete as detailed above in the remaining TODOs. Since the operator is Hive specific, it is moved to the hive package.
- `Command` is simplified to be a trait that simply acts as a marker for a LogicalPlan that should be eagerly evaluated.
Author: Michael Armbrust <michael@databricks.com>
Closes#5876 from marmbrus/useIsolatedClient and squashes the following commits:
258d000 [Michael Armbrust] really really correct path handling
e56fd4a [Michael Armbrust] getAbsolutePath
5a259f5 [Michael Armbrust] fix typos
81bb366 [Michael Armbrust] comments from vanzin
5f3945e [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
4b5cd41 [Michael Armbrust] yin's comments
f5de7de [Michael Armbrust] cleanup
11e9c72 [Michael Armbrust] better coverage in versions suite
7e8f010 [Michael Armbrust] better error messages and jar handling
e7b3941 [Michael Armbrust] more permisive checking for function registration
da91ba7 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
5fe5894 [Michael Armbrust] fix serialization suite
81711c4 [Michael Armbrust] Initial support for running without maven
1d8ae44 [Michael Armbrust] fix final tests?
1c50813 [Michael Armbrust] more comments
a3bee70 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
a6f5df1 [Michael Armbrust] style
ab07f7e [Michael Armbrust] WIP
4d8bf02 [Michael Armbrust] Remove hive 12 compilation
8843a25 [Michael Armbrust] [SPARK-6908] [SQL] Use isolated Hive client
Avoid translating to CaseWhen and evaluate the key expression many times.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#5979 from cloud-fan/condition and squashes the following commits:
3ce54e1 [Wenchen Fan] add CaseKeyWhen
This is a follow up of #5827 to remove the additional `SparkSQLParser`
Author: Cheng Hao <hao.cheng@intel.com>
Closes#5965 from chenghao-intel/remove_sparksqlparser and squashes the following commits:
509a233 [Cheng Hao] Remove the HiveQlQueryExecution
a5f9e3b [Cheng Hao] Remove the duplicated SparkSQLParser
Author: Yin Huai <yhuai@databricks.com>
Closes#5951 from yhuai/fixBuildMaven and squashes the following commits:
fdde183 [Yin Huai] Move HiveWindowFunctionQuerySuite.scala to hive compatibility dir.
Adding more information about the implementation...
This PR is adding the support of window functions to Spark SQL (specifically OVER and WINDOW clause). For every expression having a OVER clause, we use a WindowExpression as the container of a WindowFunction and the corresponding WindowSpecDefinition (the definition of a window frame, i.e. partition specification, order specification, and frame specification appearing in a OVER clause).
# Implementation #
The high level work flow of the implementation is described as follows.
* Query parsing: In the query parse process, all WindowExpressions are originally placed in the projectList of a Project operator or the aggregateExpressions of an Aggregate operator. It makes our changes to simple and keep all of parsing rules for window functions at a single place (nodesToWindowSpecification). For the WINDOWclause in a query, we use a WithWindowDefinition as the container as the mapping from the name of a window specification to a WindowSpecDefinition. This changes is similar with our common table expression support.
* Analysis: The query analysis process has three steps for window functions.
* Resolve all WindowSpecReferences by replacing them with WindowSpecReferences according to the mapping table stored in the node of WithWindowDefinition.
* Resolve WindowFunctions in the projectList of a Project operator or the aggregateExpressions of an Aggregate operator. For this PR, we use Hive's functions for window functions because we will have a major refactoring of our internal UDAFs and it is better to switch our UDAFs after that refactoring work.
* Once we have resolved all WindowFunctions, we will use ResolveWindowFunction to extract WindowExpressions from projectList and aggregateExpressions and then create a Window operator for every distinct WindowSpecDefinition. With this choice, at the execution time, we can rely on the Exchange operator to do all of work on reorganizing the table and we do not need to worry about it in the physical Window operator. An example analyzed plan is shown as follows
```
sql("""
SELECT
year, country, product, sales,
avg(sales) over(partition by product) avg_product,
sum(sales) over(partition by country) sum_country
FROM sales
ORDER BY year, country, product
""").explain(true)
== Analyzed Logical Plan ==
Sort [year#34 ASC,country#35 ASC,product#36 ASC], true
Project [year#34,country#35,product#36,sales#37,avg_product#27,sum_country#28]
Window [year#34,country#35,product#36,sales#37,avg_product#27], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFSum(sales#37) WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS sum_country#28], WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Window [year#34,country#35,product#36,sales#37], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage(sales#37) WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS avg_product#27], WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Project [year#34,country#35,product#36,sales#37]
MetastoreRelation default, sales, None
```
* Query planning: In the process of query planning, we simple generate the physical Window operator based on the logical Window operator. Then, to prepare the executedPlan, the EnsureRequirements rule will add Exchange and Sort operators if necessary. The EnsureRequirements rule will analyze the data properties and try to not add unnecessary shuffle and sort. The physical plan for the above example query is shown below.
```
== Physical Plan ==
Sort [year#34 ASC,country#35 ASC,product#36 ASC], true
Exchange (RangePartitioning [year#34 ASC,country#35 ASC,product#36 ASC], 200), []
Window [year#34,country#35,product#36,sales#37,avg_product#27], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFSum(sales#37) WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS sum_country#28], WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Exchange (HashPartitioning [country#35], 200), [country#35 ASC]
Window [year#34,country#35,product#36,sales#37], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage(sales#37) WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS avg_product#27], WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Exchange (HashPartitioning [product#36], 200), [product#36 ASC]
HiveTableScan [year#34,country#35,product#36,sales#37], (MetastoreRelation default, sales, None), None
```
* Execution time: At execution time, a physical Window operator buffers all rows in a partition specified in the partition spec of a OVER clause. If necessary, it also maintains a sliding window frame. The current implementation tries to buffer the input parameters of a window function according to the window frame to avoid evaluating a row multiple times.
# Future work #
Here are three improvements that are not hard to add:
* Taking advantage of the window frame specification to reduce the number of rows buffered in the physical Window operator. For some cases, we only need to buffer the rows appearing in the sliding window. But for other cases, we will not be able to reduce the number of rows buffered (e.g. ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING).
* When aRAGEN frame is used, for <value> PRECEDING and <value> FOLLOWING, it will be great if the <value> part is an expression (we can start with Literal). So, when the data type of ORDER BY expression is a FractionalType, we can support FractionalType as the type <value> (<value> still needs to be evaluated as a positive value).
* When aRAGEN frame is used, we need to support DateType and TimestampType as the data type of the expression appearing in the order specification. Then, the <value> part of <value> PRECEDING and <value> FOLLOWING can support interval types (once we support them).
This is a joint work with guowei2 and yhuai
Thanks hbutani hvanhovell for his comments
Thanks scwf for his comments and unit tests
Author: Yin Huai <yhuai@databricks.com>
Closes#5604 from guowei2/windowImplement and squashes the following commits:
76fe1c8 [Yin Huai] Implementation.
aa2b0ae [Yin Huai] Tests.
See the comment in join function for more information.
Author: Reynold Xin <rxin@databricks.com>
Closes#5919 from rxin/self-join-resolve and squashes the following commits:
e2fb0da [Reynold Xin] Updated SQLConf comment.
7233a86 [Reynold Xin] Updated comment.
6be2b4d [Reynold Xin] Removed println
9f6b72f [Reynold Xin] [SPARK-6231][SQL/DF] Automatically resolve ambiguity in join condition for self-joins.
This PR adds initial support for loading multiple versions of Hive in a single JVM and provides a common interface for extracting metadata from the `HiveMetastoreClient` for a given version. This is accomplished by creating an isolated `ClassLoader` that operates according to the following rules:
- __Shared Classes__: Java, Scala, logging, and Spark classes are delegated to `baseClassLoader`
allowing the results of calls to the `ClientInterface` to be visible externally.
- __Hive Classes__: new instances are loaded from `execJars`. These classes are not
accessible externally due to their custom loading.
- __Barrier Classes__: Classes such as `ClientWrapper` are defined in Spark but must link to a specific version of Hive. As a result, the bytecode is acquired from the Spark `ClassLoader` but a new copy is created for each instance of `IsolatedClientLoader`.
This new instance is able to see a specific version of hive without using reflection where ever hive is consistent across versions. Since
this is a unique instance, it is not visible externally other than as a generic
`ClientInterface`, unless `isolationOn` is set to `false`.
In addition to the unit tests, I have also tested this locally against mysql instances of the Hive Metastore. I've also successfully ported Spark SQL to run with this client, but due to the size of the changes, that will come in a follow-up PR.
By default, Hive jars are currently downloaded from Maven automatically for a given version to ease packaging and testing. However, there is also support for specifying their location manually for deployments without internet.
Author: Michael Armbrust <michael@databricks.com>
Closes#5851 from marmbrus/isolatedClient and squashes the following commits:
c72f6ac [Michael Armbrust] rxins comments
1e271fa [Michael Armbrust] [SPARK-6907][SQL] Isolated client for HiveMetastore
based on #4015, we should not delete `sqlParser` from sqlcontext, that leads to mima failed. Users implement dialect to give a fallback for `sqlParser` and we should construct `sqlParser` in sqlcontext according to the dialect
`protected[sql] val sqlParser = new SparkSQLParser(getSQLDialect().parse(_))`
Author: Cheng Hao <hao.cheng@intel.com>
Author: scwf <wangfei1@huawei.com>
Closes#5827 from scwf/sqlparser1 and squashes the following commits:
81b9737 [scwf] comment fix
0878bd1 [scwf] remove comments
c19780b [scwf] fix mima tests
c2895cf [scwf] Merge branch 'master' of https://github.com/apache/spark into sqlparser1
493775c [Cheng Hao] update the code as feedback
81a731f [Cheng Hao] remove the unecessary comment
aab0b0b [Cheng Hao] polish the code a little bit
49b9d81 [Cheng Hao] shrink the comment for rebasing
At least in the version of Hive I tested on, the test was deleting
a temp directory generated by Hive instead of one containing partition
data. So fix the filter to only consider partition directories when
deciding what to delete.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#5854 from vanzin/hive-test-fix and squashes the following commits:
7594ae9 [Marcelo Vanzin] Fix typo.
729fa80 [Marcelo Vanzin] [minor] [hive] Fix QueryPartitionSuite.
Adds the functions `rand` (Uniform Dist) and `randn` (Normal Dist.) as expressions to DataFrames.
cc mengxr rxin
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#5819 from brkyvz/df-rng and squashes the following commits:
50d69d4 [Burak Yavuz] add seed for test that failed
4234c3a [Burak Yavuz] fix Rand expression
13cad5c [Burak Yavuz] couple fixes
7d53953 [Burak Yavuz] waiting for hive tests
b453716 [Burak Yavuz] move radn with seed down
03637f0 [Burak Yavuz] fix broken hive func
c5909eb [Burak Yavuz] deleted old implementation of Rand
6d43895 [Burak Yavuz] implemented random generators
This PR aims to make the SQL Parser Pluggable, and user can register it's own parser via Spark SQL CLI.
```
# add the jar into the classpath
$hchengmydesktop:spark>bin/spark-sql --jars sql99.jar
-- switch to "hiveql" dialect
spark-sql>SET spark.sql.dialect=hiveql;
spark-sql>SELECT * FROM src LIMIT 1;
-- switch to "sql" dialect
spark-sql>SET spark.sql.dialect=sql;
spark-sql>SELECT * FROM src LIMIT 1;
-- switch to a custom dialect
spark-sql>SET spark.sql.dialect=com.xxx.xxx.SQL99Dialect;
spark-sql>SELECT * FROM src LIMIT 1;
-- register the non-exist SQL dialect
spark-sql> SET spark.sql.dialect=NotExistedClass;
spark-sql> SELECT * FROM src LIMIT 1;
-- Exception will be thrown and switch to default sql dialect ("sql" for SQLContext and "hiveql" for HiveContext)
```
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4015 from chenghao-intel/sqlparser and squashes the following commits:
493775c [Cheng Hao] update the code as feedback
81a731f [Cheng Hao] remove the unecessary comment
aab0b0b [Cheng Hao] polish the code a little bit
49b9d81 [Cheng Hao] shrink the comment for rebasing
This is built on top of kaka1992 's PR #5711 using Logical plans.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#5761 from brkyvz/random-sample and squashes the following commits:
a1fb0aa [Burak Yavuz] remove unrelated file
69669c3 [Burak Yavuz] fix broken test
1ddb3da [Burak Yavuz] copy base
6000328 [Burak Yavuz] added python api and fixed test
3c11d1b [Burak Yavuz] fixed broken test
f400ade [Burak Yavuz] fix build errors
2384266 [Burak Yavuz] addressed comments v0.1
e98ebac [Burak Yavuz] [SPARK-7156][SQL] support RandomSplit in DataFrames
Coalesce and repartition now show up as part of the query plan, rather than resulting in a new `DataFrame`.
cc rxin
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#5762 from brkyvz/df-repartition and squashes the following commits:
b1e76dd [Burak Yavuz] added documentation on repartitions
5807e35 [Burak Yavuz] renamed coalescepartitions
fa4509f [Burak Yavuz] rename coalesce
2c349b5 [Burak Yavuz] address comments
f2e6af1 [Burak Yavuz] add ticks
686c90b [Burak Yavuz] made coalesce and repartition a part of the query plan
Update Maven build plugin versions and centralize plugin version management
Author: Sean Owen <sowen@cloudera.com>
Closes#5720 from srowen/SPARK-7168 and squashes the following commits:
98a8947 [Sean Owen] Make install, deploy plugin versions explicit
4ecf3b2 [Sean Owen] Update Maven build plugin versions and centralize plugin version management
Remove use of commons-lang in favor of commons-lang3 classes; remove commons-io use in favor of Guava
Author: Sean Owen <sowen@cloudera.com>
Closes#5703 from srowen/SPARK-7145 and squashes the following commits:
21fbe03 [Sean Owen] Remove use of commons-lang in favor of commons-lang3 classes; remove commons-io use in favor of Guava
according liancheng‘s comment in https://issues.apache.org/jira/browse/SPARK-6505, this patch remove the reflection call in HiveFunctionWrapper, and implement the functions named "deserializeObjectByKryo" and "serializeObjectByKryo" according the functions with the save name in
org.apache.hadoop.hive.ql.exec.Utilities.java
Author: baishuo <vc_java@hotmail.com>
Closes#5660 from baishuo/SPARK-6505-20150423 and squashes the following commits:
ae61ec4 [baishuo] modify code style
78d9fa3 [baishuo] modify code style
0b522a7 [baishuo] modify code style
a5ff9c7 [baishuo] Remove the reflection call in HiveFunctionWrapper
rename DataTypeParser.apply to DataTypeParser.parse to make it more clear and readable.
/cc rxin
Author: wangfei <wangfei1@huawei.com>
Closes#5710 from scwf/apply and squashes the following commits:
c319977 [wangfei] rename apply to parse
This is a reopening of #4867.
A short summary of the issues resolved from the previous PR:
1. HTTPClient version mismatch: Selenium (used for UI tests) requires version 4.3.x, and Tachyon included 4.2.5 through a transitive dependency of its shaded thrift jar. To address this, Tachyon 0.6.3 will promote the transitive dependencies of the shaded jar so they can be excluded in spark.
2. Jackson-Mapper-ASL version mismatch: In lower versions of hadoop-client (ie. 1.0.4), version 1.0.1 is included. The parquet library used in spark sql requires version 1.8+. Its unclear to me why upgrading tachyon-client would cause this dependency to break. The solution was to exclude jackson-mapper-asl from hadoop-client.
It seems that the dependency management in spark-parent will not work on transitive dependencies, one way to make sure jackson-mapper-asl is included with the correct version is to add it as a top level dependency. The best solution would be to exclude the dependency in the modules which require a higher version, but that did not fix the unit tests. Any suggestions on the best way to solve this would be appreciated!
Author: Calvin Jia <jia.calvin@gmail.com>
Closes#5354 from calvinjia/upgrade_tachyon_0.6.3 and squashes the following commits:
0eefe4d [Calvin Jia] Handle httpclient version in maven dependency management. Remove httpclient version setting from profiles.
7c00dfa [Calvin Jia] Set httpclient version to 4.3.2 for selenium. Specify version of httpclient for sql/hive (previously 4.2.5 transitive dependency of libthrift).
9263097 [Calvin Jia] Merge master to test latest changes
dbfc1bd [Calvin Jia] Use Tachyon 0.6.4 for cleaner dependencies.
e2ff80a [Calvin Jia] Exclude the jetty and curator promoted dependencies from tachyon-client.
a3a29da [Calvin Jia] Update tachyon-client exclusions.
0ae6c97 [Calvin Jia] Change tachyon version to 0.6.3
a204df9 [Calvin Jia] Update make distribution tachyon version.
a93c94f [Calvin Jia] Exclude jackson-mapper-asl from hadoop client since it has a lower version than spark's expected version.
a8a923c [Calvin Jia] Exclude httpcomponents from Tachyon
910fabd [Calvin Jia] Update to master
eed9230 [Calvin Jia] Update tachyon version to 0.6.1.
11907b3 [Calvin Jia] Use TachyonURI for tachyon paths instead of strings.
71bf441 [Calvin Jia] Upgrade Tachyon client version to 0.6.0.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#5625 from chenghao-intel/transform and squashes the following commits:
5ec1dd2 [Cheng Hao] fix the deadlock issue in ScriptTransform
I was looking at the code gen code and got confused by a few of use cases of apply, in particular apply on objects. So I went ahead and changed a few of them. Hopefully slightly more clear with a proper verb.
Author: Reynold Xin <rxin@databricks.com>
Closes#5624 from rxin/apply-rename and squashes the following commits:
ee45034 [Reynold Xin] [SQL] Rename some apply functions.
It's a bug while do query like:
```sql
select d from (select explode(array(1,1)) d from src limit 1) t
```
And it will throws exception like:
```
org.apache.spark.sql.AnalysisException: cannot resolve 'd' given input columns _c0; line 1 pos 7
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3$$anonfun$apply$1.applyOrElse(CheckAnalysis.scala:48)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3$$anonfun$apply$1.applyOrElse(CheckAnalysis.scala:45)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:250)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:250)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:50)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:249)
at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$transformExpressionUp$1(QueryPlan.scala:103)
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2$$anonfun$apply$2.apply(QueryPlan.scala:117)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:116)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
```
To solve the bug, it requires code refactoring for UDTF
The major changes are about:
* Simplifying the UDTF development, UDTF will manage the output attribute names any more, instead, the `logical.Generate` will handle that properly.
* UDTF will be asked for the output schema (data types) during the logical plan analyzing.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4602 from chenghao-intel/explode_bug and squashes the following commits:
c2a5132 [Cheng Hao] add back resolved for Alias
556e982 [Cheng Hao] revert the unncessary change
002c361 [Cheng Hao] change the rule of resolved for Generate
04ae500 [Cheng Hao] add qualifier only for generator output
5ee5d2c [Cheng Hao] prepend the new qualifier
d2e8b43 [Cheng Hao] Update the code as feedback
ca5e7f4 [Cheng Hao] shrink the commits
https://issues.apache.org/jira/browse/SPARK-6969
Author: Yin Huai <yhuai@databricks.com>
Closes#5583 from yhuai/refreshTableRefreshDataCache and squashes the following commits:
1e5142b [Yin Huai] Add todo.
92b2498 [Yin Huai] Minor updates.
367df92 [Yin Huai] Recache data in the command of REFRESH TABLE.
Thanks for the initial work from Ishiihara in #3173
This PR introduce a new join method of sort merge join, which firstly ensure that keys of same value are in the same partition, and inside each partition the Rows are sorted by key. Then we can run down both sides together, find matched rows using [sort merge join](http://en.wikipedia.org/wiki/Sort-merge_join). In this way, we don't have to store the whole hash table of one side as hash join, thus we have less memory usage. Also, this PR would benefit from #3438 , making the sorting phrase much more efficient.
We introduced a new configuration of "spark.sql.planner.sortMergeJoin" to switch between this(`true`) and ShuffledHashJoin(`false`), probably we want the default value of it be `false` at first.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: Michael Armbrust <michael@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#5208 from adrian-wang/smj and squashes the following commits:
2493b9f [Daoyuan Wang] fix style
5049d88 [Daoyuan Wang] propagate rowOrdering for RangePartitioning
f91a2ae [Daoyuan Wang] yin's comment: use external sort if option is enabled, add comments
f515cd2 [Daoyuan Wang] yin's comment: outputOrdering, join suite refine
ec8061b [Daoyuan Wang] minor change
413fd24 [Daoyuan Wang] Merge pull request #3 from marmbrus/pr/5208
952168a [Michael Armbrust] add type
5492884 [Michael Armbrust] copy when ordering
7ddd656 [Michael Armbrust] Cleanup addition of ordering requirements
b198278 [Daoyuan Wang] inherit ordering in project
c8e82a3 [Daoyuan Wang] fix style
6e897dd [Daoyuan Wang] hide boundReference from manually construct RowOrdering for key compare in smj
8681d73 [Daoyuan Wang] refactor Exchange and fix copy for sorting
2875ef2 [Daoyuan Wang] fix changed configuration
61d7f49 [Daoyuan Wang] add omitted comment
00a4430 [Daoyuan Wang] fix bug
078d69b [Daoyuan Wang] address comments: add comments, do sort in shuffle, and others
3af6ba5 [Daoyuan Wang] use buffer for only one side
171001f [Daoyuan Wang] change default outputordering
47455c9 [Daoyuan Wang] add apache license ...
a28277f [Daoyuan Wang] fix style
645c70b [Daoyuan Wang] address comments using sort
068c35d [Daoyuan Wang] fix new style and add some tests
925203b [Daoyuan Wang] address comments
07ce92f [Daoyuan Wang] fix ArrayIndexOutOfBound
42fca0e [Daoyuan Wang] code clean
e3ec096 [Daoyuan Wang] fix comment style..
2edd235 [Daoyuan Wang] fix outputpartitioning
57baa40 [Daoyuan Wang] fix sort eval bug
303b6da [Daoyuan Wang] fix several errors
95db7ad [Daoyuan Wang] fix brackets for if-statement
4464f16 [Daoyuan Wang] fix error
880d8e9 [Daoyuan Wang] sort merge join for spark sql
Even if we wrap column names in backticks like `` `a#$b.c` ``, we still handle the "." inside column name specially. I think it's fragile to use a special char to split name parts, why not put name parts in `UnresolvedAttribute` directly?
Author: Wenchen Fan <cloud0fan@outlook.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#5511 from cloud-fan/6898 and squashes the following commits:
48e3e57 [Wenchen Fan] more style fix
820dc45 [Wenchen Fan] do not ignore newName in UnresolvedAttribute
d81ad43 [Wenchen Fan] fix style
11699d6 [Wenchen Fan] completely support special chars in column names
This PR change the internal representation for StringType from java.lang.String to UTF8String, which is implemented use ArrayByte.
This PR should not break any public API, Row.getString() will still return java.lang.String.
This is the first step of improve the performance of String in SQL.
cc rxin
Author: Davies Liu <davies@databricks.com>
Closes#5350 from davies/string and squashes the following commits:
3b7bfa8 [Davies Liu] fix schema of AddJar
2772f0d [Davies Liu] fix new test failure
6d776a9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into string
59025c8 [Davies Liu] address comments from @marmbrus
341ec2c [Davies Liu] turn off scala style check in UTF8StringSuite
744788f [Davies Liu] Merge branch 'master' of github.com:apache/spark into string
b04a19c [Davies Liu] add comment for getString/setString
08d897b [Davies Liu] Merge branch 'master' of github.com:apache/spark into string
5116b43 [Davies Liu] rollback unrelated changes
1314a37 [Davies Liu] address comments from Yin
867bf50 [Davies Liu] fix String filter push down
13d9d42 [Davies Liu] Merge branch 'master' of github.com:apache/spark into string
2089d24 [Davies Liu] add hashcode check back
ac18ae6 [Davies Liu] address comment
fd11364 [Davies Liu] optimize UTF8String
8d17f21 [Davies Liu] fix hive compatibility tests
e5fa5b8 [Davies Liu] remove clone in UTF8String
28f3d81 [Davies Liu] Merge branch 'master' of github.com:apache/spark into string
28d6f32 [Davies Liu] refactor
537631c [Davies Liu] some comment about Date
9f4c194 [Davies Liu] convert data type for data source
956b0a4 [Davies Liu] fix hive tests
73e4363 [Davies Liu] Merge branch 'master' of github.com:apache/spark into string
9dc32d1 [Davies Liu] fix some hive tests
23a766c [Davies Liu] refactor
8b45864 [Davies Liu] fix codegen with UTF8String
bb52e44 [Davies Liu] fix scala style
c7dd4d2 [Davies Liu] fix some catalyst tests
38c303e [Davies Liu] fix python sql tests
5f9e120 [Davies Liu] fix sql tests
6b499ac [Davies Liu] fix style
a85fb27 [Davies Liu] refactor
d32abd1 [Davies Liu] fix utf8 for python api
4699c3a [Davies Liu] use Array[Byte] in UTF8String
21f67c6 [Davies Liu] cleanup
685fd07 [Davies Liu] use UTF8String instead of String for StringType
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4586 from adrian-wang/addjar and squashes the following commits:
efdd602 [Daoyuan Wang] move jar to another place
6c707e8 [Daoyuan Wang] restrict hive version for test
32c4fb8 [Daoyuan Wang] fix style and add a test
9957d87 [Daoyuan Wang] use sessionstate classloader in makeRDDforTable
0810e71 [Daoyuan Wang] remove variable substitution
1898309 [Daoyuan Wang] fix classnotfound
95a40da [Daoyuan Wang] support env argus in add jar, and set add jar ret to 0
In `leftsemijoin.q`, there is a data loading command for table `sales` already, but in `TestHive`, it also created the table `sales`, which causes duplicated records inserted into the `sales`.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4506 from chenghao-intel/df_table and squashes the following commits:
0be05f7 [Cheng Hao] Remove the table `sales` creating from TestHive
[SHOW PRINCIPALS role_name]
Lists all roles and users who belong to this role.
Only the admin role has privilege for this.
[SHOW COMPACTIONS]
It returns a list of all tables and partitions currently being compacted or scheduled for compaction when Hive transactions are being used.
[SHOW TRANSACTIONS]
It is for use by administrators when Hive transactions are being used. It returns a list of all currently open and aborted transactions in the system.
Author: DoingDone9 <799203320@qq.com>
Author: Zhongshuai Pei <799203320@qq.com>
Author: Xu Tingjun <xutingjun@huawei.com>
Closes#4902 from DoingDone9/SHOW_PRINCIPALS and squashes the following commits:
4add42f [Zhongshuai Pei] for test
311f806 [Zhongshuai Pei] for test
0c7550a [DoingDone9] Update HiveQl.scala
c8aeb1c [Xu Tingjun] aa
802261c [DoingDone9] Merge pull request #7 from apache/master
d00303b [DoingDone9] Merge pull request #6 from apache/master
98b134f [DoingDone9] Merge pull request #5 from apache/master
161cae3 [DoingDone9] Merge pull request #4 from apache/master
c87e8b6 [DoingDone9] Merge pull request #3 from apache/master
cb1852d [DoingDone9] Merge pull request #2 from apache/master
c3f046f [DoingDone9] Merge pull request #1 from apache/master
This PR follow up PR #3907 & #3891 & #4356.
According to marmbrus liancheng 's comments, I try to use fs.globStatus to retrieve all FileStatus objects under path(s), and then do the filtering locally.
[1]. get pathPattern by path, and put it into pathPatternSet. (hdfs://cluster/user/demo/2016/08/12 -> hdfs://cluster/user/demo/*/*/*)
[2]. retrieve all FileStatus objects ,and cache them by undating existPathSet.
[3]. do the filtering locally
[4]. if we have new pathPattern,do 1,2 step again. (external table maybe have more than one partition pathPattern)
chenghao-intel jeanlyn
Author: lazymam500 <lazyman500@gmail.com>
Author: lazyman <lazyman500@gmail.com>
Closes#5059 from lazyman500/SPARK-5068 and squashes the following commits:
5bfcbfd [lazyman] move spark.sql.hive.verifyPartitionPath to SQLConf,fix scala style
e1d6386 [lazymam500] fix scala style
f23133f [lazymam500] bug fix
47e0023 [lazymam500] fix scala style,add config flag,break the chaining
04c443c [lazyman] SPARK-5068: fix bug when partition path doesn't exists #2
41f60ce [lazymam500] Merge pull request #1 from apache/master
Author: haiyang <huhaiyang@huawei.com>
Closes#4929 from haiyangsea/cte and squashes the following commits:
220b67d [haiyang] add golden files for cte test
d3c7681 [haiyang] Merge branch 'master' into cte-repair
0ba2070 [haiyang] modify code style
9ce6b58 [haiyang] fix conflict
ff74741 [haiyang] add comment for With plan
0d56af4 [haiyang] code indention
776a440 [haiyang] add comments for resolve relation strategy
2fccd7e [haiyang] add comments for resolve relation strategy
241bbe2 [haiyang] fix cte problem of view
e9e1237 [haiyang] fix test case problem
614182f [haiyang] add test cases for CTE feature
32e415b [haiyang] add comment
1cc8c15 [haiyang] support with
03f1097 [haiyang] support with
e960099 [haiyang] support with
9aaa874 [haiyang] support with
0566978 [haiyang] support with
a99ecd2 [haiyang] support with
c3fa4c2 [haiyang] support with
3b6077f [haiyang] support with
5f8abe3 [haiyang] support with
4572b05 [haiyang] support with
f801f54 [haiyang] support with
```SQL
select key, v from src lateral view stack(3, 1+1, 2+2, 3) d as v;
```
Will cause exception
```
java.lang.ClassNotFoundException: stack
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308)
at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
at org.apache.spark.sql.hive.HiveFunctionWrapper.createFunction(Shim13.scala:148)
at org.apache.spark.sql.hive.HiveGenericUdtf.function$lzycompute(hiveUdfs.scala:274)
at org.apache.spark.sql.hive.HiveGenericUdtf.function(hiveUdfs.scala:274)
at org.apache.spark.sql.hive.HiveGenericUdtf.outputInspector$lzycompute(hiveUdfs.scala:280)
at org.apache.spark.sql.hive.HiveGenericUdtf.outputInspector(hiveUdfs.scala:280)
at org.apache.spark.sql.hive.HiveGenericUdtf.outputDataTypes$lzycompute(hiveUdfs.scala:285)
at org.apache.spark.sql.hive.HiveGenericUdtf.outputDataTypes(hiveUdfs.scala:285)
at org.apache.spark.sql.hive.HiveGenericUdtf.makeOutput(hiveUdfs.scala:291)
at org.apache.spark.sql.catalyst.expressions.Generator.output(generators.scala:60)
at org.apache.spark.sql.catalyst.plans.logical.Generate$$anonfun$2.apply(basicOperators.scala:60)
at org.apache.spark.sql.catalyst.plans.logical.Generate$$anonfun$2.apply(basicOperators.scala:60)
at scala.Option.map(Option.scala:145)
at org.apache.spark.sql.catalyst.plans.logical.Generate.generatorOutput(basicOperators.scala:60)
at org.apache.spark.sql.catalyst.plans.logical.Generate.output(basicOperators.scala:70)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveChildren$1.apply(LogicalPlan.scala:117)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveChildren$1.apply(LogicalPlan.scala:117)
```
Author: Cheng Hao <hao.cheng@intel.com>
Closes#5444 from chenghao-intel/hive_udtf and squashes the following commits:
065a98c [Cheng Hao] fix bug of Hive UDTF in Lateral View (ClassNotFound)
Otherwise we end up rewriting predicates to be trivially equal (i.e. `a#1 = a#2` -> `a#3 = a#3`), at which point the query is no longer valid.
Author: Michael Armbrust <michael@databricks.com>
Closes#5458 from marmbrus/selfJoinParquet and squashes the following commits:
22df77c [Michael Armbrust] [SPARK-6851][SQL] Create new instance for each converted parquet relation
'(' and ')' are special characters used in Parquet schema for type annotation. When we run an aggregation query, we will obtain attribute name such as "MAX(a)".
If we directly store the generated DataFrame as Parquet file, it causes failure when reading and parsing the stored schema string.
Several methods can be adopted to solve this. This pr uses a simplest one to just replace attribute names before generating Parquet schema based on these attributes.
Another possible method might be modifying all aggregation expression names from "func(column)" to "func[column]".
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#5263 from viirya/parquet_aggregation_name and squashes the following commits:
2d70542 [Liang-Chi Hsieh] Address comment.
463dff4 [Liang-Chi Hsieh] Instead of replacing special chars, showing error message to user to suggest using Alias.
1de001d [Liang-Chi Hsieh] Replace special characters '(' and ')' of Parquet schema.
When union non-decimal types with decimals, we use the following rules:
- FIRST `intTypeToFixed`, then fixed union decimals with precision/scale p1/s2 and p2/s2 will be promoted to
DecimalType(max(p1, p2), max(s1, s2))
- FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE (this is the same as Hive,
but note that unlimited decimals are considered bigger than doubles in WidenTypes)
Author: guowei2 <guowei2@asiainfo.com>
Closes#4004 from guowei2/SPARK-5203 and squashes the following commits:
ff50f5f [guowei2] fix code style
11df1bf [guowei2] fix decimal union with double, double->Decimal(15,15)
0f345f9 [guowei2] fix structType merge with decimal
101ed4d [guowei2] fix build error after rebase
0b196e4 [guowei2] code style
fe2c2ca [guowei2] handle union decimal precision in 'DecimalPrecision'
421d840 [guowei2] fix union types for decimal precision
ef2c661 [guowei2] fix union with different decimal type
Just fix a typo.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#5352 from viirya/fix_a_typo and squashes the following commits:
303b2d2 [Liang-Chi Hsieh] Fix typo.
This builds on my earlier pull requests and turns on the explicit type checking in scalastyle.
Author: Reynold Xin <rxin@databricks.com>
Closes#5342 from rxin/SPARK-6428 and squashes the following commits:
7b531ab [Reynold Xin] import ordering
2d9a8a5 [Reynold Xin] jl
e668b1c [Reynold Xin] override
9b9e119 [Reynold Xin] Parenthesis.
82e0cf5 [Reynold Xin] [SPARK-6428] Turn on explicit type checking for public methods.
https://issues.apache.org/jira/browse/SPARK-6575
Author: Yin Huai <yhuai@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Cheng Lian <lian@databricks.com>
Closes#5339 from yhuai/parquetRelationCache and squashes the following commits:
b0e1a42 [Yin Huai] Address comments.
83d9846 [Yin Huai] Remove unnecessary change.
c0dc7a4 [Yin Huai] Cache converted parquet relations.
NotImplementedError in scala 2.10 is a fatal exception, which is not very nice to throw when not actually fatal.
Author: Michael Armbrust <michael@databricks.com>
Closes#5315 from marmbrus/throwUnsupported and squashes the following commits:
c29e03b [Michael Armbrust] [SQL] Throw UnsupportedOperationException instead of NotImplementedError
052e05b [Michael Armbrust] [SQL] Throw UnsupportedOperationException instead of NotImplementedError
In order to do inbound checking and type conversion, we should use Literal.create() instead of constructor.
Author: Davies Liu <davies@databricks.com>
Closes#5320 from davies/literal and squashes the following commits:
1667604 [Davies Liu] fix style and add comment
5f8c0fd [Davies Liu] use Literal.create instread of constructor
