Resolve javac, scalac warnings of various types -- deprecations, Scala lang, unchecked cast, etc.
Author: Sean Owen <sowen@cloudera.com>
Closes#4950 from srowen/SPARK-6225 and squashes the following commits:
3080972 [Sean Owen] Ordered imports: Java, Scala, 3rd party, Spark
c67985b [Sean Owen] Resolve javac, scalac warnings of various types -- deprecations, Scala lang, unchecked cast, etc.
Author: Michael Armbrust <michael@databricks.com>
Closes#4920 from marmbrus/openStrategies and squashes the following commits:
cbc35c0 [Michael Armbrust] [SQL] Make Strategies a public developer API
jira: https://issues.apache.org/jira/browse/SPARK-6163
Author: Yin Huai <yhuai@databricks.com>
Closes#4896 from yhuai/SPARK-6163 and squashes the following commits:
45e023e [Yin Huai] Address @chenghao-intel's comment.
2e8734e [Yin Huai] Use JSON data source for jsonFile.
92a4a33 [Yin Huai] Test.
Based on #4904 with style errors fixed.
`LogicalPlan#resolve` will not only produce `Attribute`, but also "`GetField` chain".
So in `ResolveSortReferences`, after resolve the ordering expressions, we should not just collect the `Attribute` results, but also `Attribute` at the bottom of "`GetField` chain".
Author: Wenchen Fan <cloud0fan@outlook.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#4918 from marmbrus/pr/4904 and squashes the following commits:
997f84e [Michael Armbrust] fix style
3eedbfc [Wenchen Fan] fix 6145
Option 1 of 2: Convert spark-parent module name to spark-parent_2.10 / spark-parent_2.11
Author: Sean Owen <sowen@cloudera.com>
Closes#4912 from srowen/SPARK-6182.1 and squashes the following commits:
eff60de [Sean Owen] Convert spark-parent module name to spark-parent_2.10 / spark-parent_2.11
Integration test suites in the JDBC data source (`MySQLIntegration` and `PostgresIntegration`) depend on docker-client 2.7.5, which transitively depends on Guava 17.0. Unfortunately, Guava 17.0 is causing test runtime binary compatibility issues when Spark is compiled against Hive 0.12.0, or Hadoop 2.4.
Considering `MySQLIntegration` and `PostgresIntegration` are ignored right now, I'd suggest moving them from the Spark project to the [Spark integration tests] [1] project. This PR removes both the JDBC data source integration tests and the docker-client test dependency.
[1]: |https://github.com/databricks/spark-integration-tests
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Author: Cheng Lian <lian@databricks.com>
Closes#4872 from liancheng/remove-docker-client and squashes the following commits:
1f4169e [Cheng Lian] Removes DockerHacks
159b24a [Cheng Lian] Removed JDBC integration tests which depends on docker-client
- Various Fixes to docs
- Make data source traits actually interfaces
Based on #4862 but with fixed conflicts.
Author: Reynold Xin <rxin@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#4868 from marmbrus/pr/4862 and squashes the following commits:
fe091ea [Michael Armbrust] Merge remote-tracking branch 'origin/master' into pr/4862
0208497 [Reynold Xin] Test fixes.
34e0a28 [Reynold Xin] [SPARK-5310][SQL] Various fixes to Spark SQL docs.
This PR contains the following changes:
1. Add a new method, `DataType.equalsIgnoreCompatibleNullability`, which is the middle ground between DataType's equality check and `DataType.equalsIgnoreNullability`. For two data types `from` and `to`, it does `equalsIgnoreNullability` as well as if the nullability of `from` is compatible with that of `to`. For example, the nullability of `ArrayType(IntegerType, containsNull = false)` is compatible with that of `ArrayType(IntegerType, containsNull = true)` (for an array without null values, we can always say it may contain null values). However, the nullability of `ArrayType(IntegerType, containsNull = true)` is incompatible with that of `ArrayType(IntegerType, containsNull = false)` (for an array that may have null values, we cannot say it does not have null values).
2. For the `resolved` field of `InsertIntoTable`, use `equalsIgnoreCompatibleNullability` to replace the equality check of the data types.
3. For our data source write path, when appending data, we always use the schema of existing table to write the data. This is important for parquet, since nullability direct impacts the way to encode/decode values. If we do not do this, we may see corrupted values when reading values from a set of parquet files generated with different nullability settings.
4. When generating a new parquet table, we always set nullable/containsNull/valueContainsNull to true. So, we will not face situations that we cannot append data because containsNull/valueContainsNull in an Array/Map column of the existing table has already been set to `false`. This change makes the whole data pipeline more robust.
5. Update the equality check of JSON relation. Since JSON does not really cares nullability, `equalsIgnoreNullability` seems a better choice to compare schemata from to JSON tables.
JIRA: https://issues.apache.org/jira/browse/SPARK-5950
Thanks viirya for the initial work in #4729.
cc marmbrus liancheng
Author: Yin Huai <yhuai@databricks.com>
Closes#4826 from yhuai/insertNullabilityCheck and squashes the following commits:
3b61a04 [Yin Huai] Revert change on equals.
80e487e [Yin Huai] asNullable in UDT.
587d88b [Yin Huai] Make methods private.
0cb7ea2 [Yin Huai] marmbrus's comments.
3cec464 [Yin Huai] Cheng's comments.
486ed08 [Yin Huai] Merge remote-tracking branch 'upstream/master' into insertNullabilityCheck
d3747d1 [Yin Huai] Remove unnecessary change.
8360817 [Yin Huai] Merge remote-tracking branch 'upstream/master' into insertNullabilityCheck
8a3f237 [Yin Huai] Use equalsIgnoreNullability instead of equality check.
0eb5578 [Yin Huai] Fix tests.
f6ed813 [Yin Huai] Update old parquet path.
e4f397c [Yin Huai] Unit tests.
b2c06f8 [Yin Huai] Ignore nullability in JSON relation's equality check.
8bd008b [Yin Huai] nullable, containsNull, and valueContainsNull will be always true for parquet data.
bf50d73 [Yin Huai] When appending data, we use the schema of the existing table instead of the schema of the new data.
0a703e7 [Yin Huai] Test failed again since we cannot read correct content.
9a26611 [Yin Huai] Make InsertIntoTable happy.
8f19fe5 [Yin Huai] equalsIgnoreCompatibleNullability
4ec17fd [Yin Huai] Failed test.
Constructs like Hive `TRANSFORM` may generate malformed rows (via badly authored external scripts for example). I'm a bit hesitant to have this feature, since it introduces per-tuple cost when caching tables. However, considering caching tables is usually a one-time cost, this is probably worth having.
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Author: Cheng Lian <lian@databricks.com>
Closes#4842 from liancheng/spark-6082 and squashes the following commits:
b05dbff [Cheng Lian] Provides better error message for malformed rows when caching tables
The API signatire for join requires the JoinType to be the third parameter. The code examples provided for join show JoinType being provided as the 2nd parater resuling in errors (i.e. "df1.join(df2, "outer", $"df1Key" === $"df2Key") ). The correct sample code is df1.join(df2, $"df1Key" === $"df2Key", "outer")
Author: Paul Power <paul.power@peerside.com>
Closes#4847 from peerside/master and squashes the following commits:
ebc1efa [Paul Power] Merge pull request #1 from peerside/peerside-patch-1
e353340 [Paul Power] Updated comments use correct sample code for Dataframe joins
Always set `containsNull = true` when infer the schema of JSON datasets. If we set `containsNull` based on records we scanned, we may miss arrays with null values when we do sampling. Also, because future data can have arrays with null values, if we convert JSON data to parquet, always setting `containsNull = true` is a more robust way to go.
JIRA: https://issues.apache.org/jira/browse/SPARK-6052
Author: Yin Huai <yhuai@databricks.com>
Closes#4806 from yhuai/jsonArrayContainsNull and squashes the following commits:
05eab9d [Yin Huai] Change containsNull to true.
This is needed for the SQL bindings to work on Yarn.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#4822 from vanzin/SPARK-6074 and squashes the following commits:
fb52001 [Marcelo Vanzin] [SPARK-6074] [sql] Package pyspark sql bindings.
JIRA: https://issues.apache.org/jira/browse/SPARK-6024
Author: Yin Huai <yhuai@databricks.com>
Closes#4795 from yhuai/wideSchema and squashes the following commits:
4882e6f [Yin Huai] Address comments.
73e71b4 [Yin Huai] Address comments.
143927a [Yin Huai] Simplify code.
cc1d472 [Yin Huai] Make the schema wider.
12bacae [Yin Huai] If the JSON string of a schema is too large, split it before storing it in metastore.
e9b4f70 [Yin Huai] Failed test.
`FilteringParquetRowInputFormat` manually merges Parquet schemas before computing splits. However, it is duplicate because the schemas are already merged in `ParquetRelation2`. We don't need to re-merge them at `InputFormat`.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4786 from viirya/dup_parquet_schemas_merge and squashes the following commits:
ef78a5a [Liang-Chi Hsieh] Avoiding duplicate Parquet schema merging.
It is useful to let the user decide the number of rows to show in DataFrame.show
Author: Jacky Li <jacky.likun@huawei.com>
Closes#4767 from jackylk/show and squashes the following commits:
a0e0f4b [Jacky Li] fix testcase
7cdbe91 [Jacky Li] modify according to comment
bb54537 [Jacky Li] for Java compatibility
d7acc18 [Jacky Li] modify according to comments
981be52 [Jacky Li] add numRows param in DataFrame.show()
Please see JIRA (https://issues.apache.org/jira/browse/SPARK-6016) for details of the bug.
Author: Yin Huai <yhuai@databricks.com>
Closes#4775 from yhuai/parquetFooterCache and squashes the following commits:
78787b1 [Yin Huai] Remove footerCache in FilteringParquetRowInputFormat.
dff6fba [Yin Huai] Failed unit test.
DataFrame.explain return wrong result when the query is DDL command.
For example, the following two queries should print out the same execution plan, but it not.
sql("create table tb as select * from src where key > 490").explain(true)
sql("explain extended create table tb as select * from src where key > 490")
This is because DataFrame.explain leverage logicalPlan which had been forced executed, we should use the unexecuted plan queryExecution.logical.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#4707 from yanboliang/spark-5926 and squashes the following commits:
fa6db63 [Yanbo Liang] logicalPlan is not lazy
0e40a1b [Yanbo Liang] make DataFrame.explain leverage queryExecution.logical
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4760 from viirya/dup_literal and squashes the following commits:
06e7516 [Liang-Chi Hsieh] Remove duplicate Literal matching block.
`ReadContext.init` calls `InitContext.getMergedKeyValueMetadata`, which doesn't know how to merge conflicting user defined key-value metadata and throws exception. In our case, when dealing with different but compatible schemas, we have different Spark SQL schema JSON strings in different Parquet part-files, thus causes this problem. Reading similar Parquet files generated by Hive doesn't suffer from this issue.
In this PR, we manually merge the schemas before passing it to `ReadContext` to avoid the exception.
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Author: Cheng Lian <lian@databricks.com>
Closes#4768 from liancheng/spark-6010 and squashes the following commits:
9002f0a [Cheng Lian] Fixes SPARK-6010
Author: Michael Armbrust <michael@databricks.com>
Closes#4757 from marmbrus/udtConversions and squashes the following commits:
3714aad [Michael Armbrust] [SPARK-5996][SQL] Fix specialized outbound conversions
Also added desc/asc function for constructing sorting expressions more conveniently. And added a small fix to lift alias out of cast expression.
Author: Reynold Xin <rxin@databricks.com>
Closes#4752 from rxin/SPARK-5985 and squashes the following commits:
aeda5ae [Reynold Xin] Added Experimental flag to ColumnName.
047ad03 [Reynold Xin] Lift alias out of cast.
c9cf17c [Reynold Xin] [SPARK-5985][SQL] DataFrame sortBy -> orderBy in Python.
Added a new test suite to make sure Java DF programs can use varargs properly.
Also moved all suites into test.org.apache.spark package to make sure the suites also test for method visibility.
Author: Reynold Xin <rxin@databricks.com>
Closes#4751 from rxin/df-tests and squashes the following commits:
1e8b8e4 [Reynold Xin] Fixed imports and renamed JavaAPISuite.
a6ca53b [Reynold Xin] [SPARK-5904][SQL] DataFrame Java API test suites.
Author: Michael Armbrust <michael@databricks.com>
Closes#4738 from marmbrus/udtRepart and squashes the following commits:
c06d7b5 [Michael Armbrust] fix compilation
91c8829 [Michael Armbrust] [SQL][SPARK-5532] Repartition should not use external rdd representation
Author: Michael Armbrust <michael@databricks.com>
Closes#4736 from marmbrus/asExprs and squashes the following commits:
5ba97e4 [Michael Armbrust] [SPARK-5910][SQL] Support for as in selectExpr
1. Column is no longer a DataFrame to simplify class hierarchy.
2. Don't use varargs on abstract methods (see Scala compiler bug SI-9013).
Author: Reynold Xin <rxin@databricks.com>
Closes#4686 from rxin/SPARK-5904 and squashes the following commits:
fd9b199 [Reynold Xin] Fixed Python tests.
df25cef [Reynold Xin] Non final.
5221530 [Reynold Xin] [SPARK-5904][SQL] DataFrame API fixes.
The `int` is 64-bit on 64-bit machine (very common now), we should infer it as LongType for it in Spark SQL.
Also, LongType in SQL will come back as `int`.
Author: Davies Liu <davies@databricks.com>
Closes#4666 from davies/long and squashes the following commits:
6bc6cc4 [Davies Liu] infer int as LongType
Also added test cases for checking the serializability of HiveContext and SQLContext.
Author: Reynold Xin <rxin@databricks.com>
Closes#4628 from rxin/SPARK-5840 and squashes the following commits:
ecb3bcd [Reynold Xin] test cases and reviews.
55eb822 [Reynold Xin] [SPARK-5840][SQL] HiveContext cannot be serialized due to tuple extraction.
This pull request replaces calls to deprecated methods from `java.util.Date` with near-equivalents in `java.util.Calendar`.
Author: Tor Myklebust <tmyklebu@gmail.com>
Closes#4668 from tmyklebu/master and squashes the following commits:
66215b1 [Tor Myklebust] Use GregorianCalendar instead of Timestamp get methods.
Although we've migrated to the DataFrame API, lots of code still uses `rdd` or `srdd` as local variable names. This PR tries to address these naming inconsistencies and some other minor DataFrame related style issues.
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Author: Cheng Lian <lian@databricks.com>
Closes#4670 from liancheng/df-cleanup and squashes the following commits:
3e14448 [Cheng Lian] Cleans up DataFrame variable names and toDF() calls
The problem is that after we create an empty hive metastore parquet table (e.g. `CREATE TABLE test (a int) STORED AS PARQUET`), Hive will create an empty dir for us, which cause our data source `ParquetRelation2` fail to get the schema of the table. See JIRA for the case to reproduce the bug and the exception.
This PR is based on #4562 from chenghao-intel.
JIRA: https://issues.apache.org/jira/browse/SPARK-5852
Author: Yin Huai <yhuai@databricks.com>
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4655 from yhuai/CTASParquet and squashes the following commits:
b8b3450 [Yin Huai] Update tests.
2ac94f7 [Yin Huai] Update tests.
3db3d20 [Yin Huai] Minor update.
d7e2308 [Yin Huai] Revert changes in HiveMetastoreCatalog.scala.
36978d1 [Cheng Hao] Update the code as feedback
a04930b [Cheng Hao] fix bug of scan an empty parquet based table
442ffe0 [Cheng Hao] passdown the schema for Parquet File in HiveContext
Author: Michael Armbrust <michael@databricks.com>
Closes#4657 from marmbrus/pythonUdfs and squashes the following commits:
a7823a8 [Michael Armbrust] [SPARK-5868][SQL] Fix python UDFs in HiveContext and checks in SQLContext
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4649 from viirya/use_checkpath and squashes the following commits:
0f9a1a1 [Liang-Chi Hsieh] Use same function to check path parameter.
Author: Reynold Xin <rxin@databricks.com>
Closes#4640 from rxin/SPARK-5853 and squashes the following commits:
9c6f569 [Reynold Xin] [SPARK-5853][SQL] Schema support in Row.
Added a bunch of tags.
Also changed parquetFile to take varargs rather than a string followed by varargs.
Author: Reynold Xin <rxin@databricks.com>
Closes#4636 from rxin/df-doc and squashes the following commits:
651f80c [Reynold Xin] Fixed parquetFile in PySpark.
8dc3024 [Reynold Xin] [SQL] Various DataFrame doc changes.
This PR adds a `ShowTablesCommand` to support `SHOW TABLES [IN databaseName]` SQL command. The result of `SHOW TABLE` has two columns, `tableName` and `isTemporary`. For temporary tables, the value of `isTemporary` column will be `false`.
JIRA: https://issues.apache.org/jira/browse/SPARK-4865
Author: Yin Huai <yhuai@databricks.com>
Closes#4618 from yhuai/showTablesCommand and squashes the following commits:
0c09791 [Yin Huai] Use ShowTablesCommand.
85ee76d [Yin Huai] Since SHOW TABLES is not a Hive native command any more and we will not see "OK" (originally generated by Hive's driver), use SHOW DATABASES in the test.
94bacac [Yin Huai] Add SHOW TABLES to the list of noExplainCommands.
d71ed09 [Yin Huai] Fix test.
a4a6ec3 [Yin Huai] Add SHOW TABLE command.
Existing implementation of arithmetic operators and BinaryComparison operators have redundant type checking codes, e.g.:
Expression.n2 is used by Add/Subtract/Multiply.
(1) n2 always checks left.dataType == right.dataType. However, this checking should be done once when we resolve expression types;
(2) n2 requires dataType is a NumericType. This can be done once.
This PR optimizes arithmetic and predicate operators by removing such redundant type-checking codes.
Some preliminary benchmarking on 10G TPC-H data over 5 r3.2xlarge EC2 machines shows that this PR can reduce the query time by 5.5% to 11%.
The benchmark queries follow the template below, where OP is plus/minus/times/divide/remainder/bitwise and/bitwise or/bitwise xor.
SELECT l_returnflag, l_linestatus, SUM(l_quantity OP cnt1), SUM(l_quantity OP cnt2), ...., SUM(l_quantity OP cnt700)
FROM (
SELECT l_returnflag, l_linestatus, l_quantity, 1 AS cnt1, 2 AS cnt2, ..., 700 AS cnt700
FROM lineitem
WHERE l_shipdate <= '1998-09-01'
)
GROUP BY l_returnflag, l_linestatus;
Author: kai <kaizeng@eecs.berkeley.edu>
Closes#4472 from kai-zeng/arithmetic-optimize and squashes the following commits:
fef0cf1 [kai] Merge branch 'master' of github.com:apache/spark into arithmetic-optimize
4b3a1bb [kai] chmod a-x
5a41e49 [kai] chmod a-x Expression.scala
cb37c94 [kai] rebase onto spark master
7f6e968 [kai] chmod 100755 -> 100644
6cddb46 [kai] format
7490dbc [kai] fix unresolved-expression exception for EqualTo
9c40bc0 [kai] fix bitwisenot
3cbd363 [kai] clean up test code
ca47801 [kai] override evalInternal for bitwise ops
8fa84a1 [kai] add bitwise or and xor
6892fc4 [kai] revert override evalInternal
f8eba24 [kai] override evalInternal
31ccdd4 [kai] rewrite all bitwise op and remove evalInternal
86297e2 [kai] generalized
cb92ae1 [kai] bitwise-and: override eval
97a7d6c [kai] bitwise-and: override evalInternal using and func
0906c39 [kai] add bitwise test
62abbbc [kai] clean up predicate and arithmetic
b34d58d [kai] add caching and benmark option
12c5b32 [kai] override eval
1cd7571 [kai] fix sqrt and maxof
03fd0c3 [kai] fix predicate
16fd84c [kai] optimize + - * / % -(unary) abs < > <= >=
fd95823 [kai] remove unnecessary type checking
24d062f [kai] test suite
JIRA: https://issues.apache.org/jira/browse/SPARK-5839
Author: Yin Huai <yhuai@databricks.com>
Closes#4626 from yhuai/SPARK-5839 and squashes the following commits:
f779d85 [Yin Huai] Use subqeury to wrap replaced ParquetRelation.
2695f13 [Yin Huai] Merge remote-tracking branch 'upstream/master' into SPARK-5839
f1ba6ca [Yin Huai] Address comment.
2c7fa08 [Yin Huai] Use Subqueries to wrap a data source table.
JIRA: https://issues.apache.org/jira/browse/SPARK-5746
liancheng marmbrus
Author: Yin Huai <yhuai@databricks.com>
Closes#4617 from yhuai/insertOverwrite and squashes the following commits:
8e3019d [Yin Huai] Fix compilation error.
499e8e7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into insertOverwrite
e76e85a [Yin Huai] Address comments.
ac31b3c [Yin Huai] Merge remote-tracking branch 'upstream/master' into insertOverwrite
f30bdad [Yin Huai] Use toDF.
99da57e [Yin Huai] Merge remote-tracking branch 'upstream/master' into insertOverwrite
6b7545c [Yin Huai] Add a pre write check to the data source API.
a88c516 [Yin Huai] DDLParser will take a parsering function to take care CTAS statements.
Lifts `HiveMetastoreCatalog.refreshTable` to `Catalog`. Adds `RefreshTable` command to refresh (possibly cached) metadata in external data sources tables.
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Author: Cheng Lian <lian@databricks.com>
Closes#4624 from liancheng/refresh-table and squashes the following commits:
8d1aa4c [Cheng Lian] Adds REFRESH TABLE command
This PR adds the following filter types for data sources API:
- `IsNull`
- `IsNotNull`
- `Not`
- `And`
- `Or`
The code which converts Catalyst predicate expressions to data sources filters is very similar to filter conversion logics in `ParquetFilters` which converts Catalyst predicates to Parquet filter predicates. In this way we can support nested AND/OR/NOT predicates without changing current `BaseScan` type hierarchy.
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Author: Cheng Lian <lian@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#4623 from liancheng/more-fiters and squashes the following commits:
1b296f4 [Cheng Lian] Add more filter types for data sources API
This PR migrates the Parquet data source to the new data source write support API. Now users can also overwriting and appending to existing tables. Notice that inserting into partitioned tables is not supported yet.
When Parquet data source is enabled, insertion to Hive Metastore Parquet tables is also fullfilled by the Parquet data source. This is done by the newly introduced `HiveMetastoreCatalog.ParquetConversions` rule, which is a "proper" implementation of the original hacky `HiveStrategies.ParquetConversion`. The latter is still preserved, and can be removed together with the old Parquet support in the future.
TODO:
- [x] Update outdated comments in `newParquet.scala`.
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Author: Cheng Lian <lian@databricks.com>
Closes#4563 from liancheng/parquet-refining and squashes the following commits:
fa98d27 [Cheng Lian] Fixes test cases which should disable off Parquet data source
2476e82 [Cheng Lian] Fixes compilation error introduced during rebasing
a83d290 [Cheng Lian] Passes Hive Metastore partitioning information to ParquetRelation2
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Author: Cheng Lian <lian@databricks.com>
Closes#4613 from liancheng/df-implicit-rename and squashes the following commits:
db8bdd3 [Cheng Lian] Renames stringRddToDataFrame to stringRddToDataFrameHolder for consistency
If one tries an example by using copy&paste, throw an exception.
Author: Takeshi Yamamuro <linguin.m.s@gmail.com>
Closes#4615 from maropu/AddMissingImportInSqlContext and squashes the following commits:
ab21b66 [Takeshi Yamamuro] Add missing import in the example of SqlContext
Author: Yin Huai <yhuai@databricks.com>
Closes#4582 from yhuai/jsonErrorMessage and squashes the following commits:
152dbd4 [Yin Huai] Update error message.
1466256 [Yin Huai] Throw a better error message when a JSON object in the input dataset span multiple records (lines for files or strings for an RDD of strings).
Author: Yin Huai <yhuai@databricks.com>
Closes#4542 from yhuai/moveSaveMode and squashes the following commits:
65a4425 [Yin Huai] Move SaveMode to sql package.
Author: Yin Huai <yhuai@databricks.com>
Closes#4544 from yhuai/jsonUseLongTypeByDefault and squashes the following commits:
6e2ffc2 [Yin Huai] Use LongType as the default type for integers in JSON schema inference.
Author: Michael Armbrust <michael@databricks.com>
Author: wangfei <wangfei1@huawei.com>
Closes#4558 from marmbrus/errorMessages and squashes the following commits:
5e5ab50 [Michael Armbrust] Merge pull request #15 from scwf/errorMessages
fa38881 [wangfei] fix for grouping__id
f279a71 [wangfei] make right references for ScriptTransformation
d29fbde [Michael Armbrust] extra case
1a797b4 [Michael Armbrust] comments
d4e9015 [Michael Armbrust] add comment
af9e668 [Michael Armbrust] no braces
34eb3a4 [Michael Armbrust] more work
6197cd5 [Michael Armbrust] [SQL] Better error messages for analysis failures
Eases use in the spark-shell.
Author: Michael Armbrust <michael@databricks.com>
Closes#4545 from marmbrus/serialization and squashes the following commits:
04748e6 [Michael Armbrust] @scala.annotation.varargs
b36e219 [Michael Armbrust] moreFixes
- Removed DataFrame.apply for projection & filtering since they are extremely confusing.
- Added implicits for RDD[Int], RDD[Long], and RDD[String]
Author: Reynold Xin <rxin@databricks.com>
Closes#4543 from rxin/df-cleanup and squashes the following commits:
81ec915 [Reynold Xin] [SQL] More DataFrame fixes.
Also I fix a bunch of bad output in test cases.
Author: Michael Armbrust <michael@databricks.com>
Closes#4520 from marmbrus/selfJoin and squashes the following commits:
4f4a85c [Michael Armbrust] comments
49c8e26 [Michael Armbrust] fix tests
6fc38de [Michael Armbrust] fix style
55d64b3 [Michael Armbrust] fix dataframe selfjoins
1. DataFrame.renameColumn
2. DataFrame.show() and _repr_
3. Use simpleString() rather than jsonValue in DataFrame.dtypes
4. createDataFrame from local Python data, including pandas.DataFrame
Author: Davies Liu <davies@databricks.com>
Closes#4528 from davies/df3 and squashes the following commits:
014acea [Davies Liu] fix typo
6ba526e [Davies Liu] fix tests
46f5f95 [Davies Liu] address comments
6cbc154 [Davies Liu] dataframe.show() and improve dtypes
6f94f25 [Davies Liu] create DataFrame from local Python data
Also took the chance to fixed up some style ...
Author: Reynold Xin <rxin@databricks.com>
Closes#4489 from rxin/SPARK-5702 and squashes the following commits:
74f42e3 [Reynold Xin] [SPARK-5702][SQL] Allow short names for built-in data sources.
Do not recursively strip out projects. Only strip the first level project.
```scala
df("colA") + df("colB").as("colC")
```
Previously, the above would construct an invalid plan.
Author: Reynold Xin <rxin@databricks.com>
Closes#4519 from rxin/computability and squashes the following commits:
87ff763 [Reynold Xin] Code review feedback.
015c4fc [Reynold Xin] [SQL][DataFrame] Fix column computability.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4496 from chenghao-intel/df_explain and squashes the following commits:
552aa58 [Cheng Hao] Add explain support for DF
Deprecate inferSchema() and applySchema(), use createDataFrame() instead, which could take an optional `schema` to create an DataFrame from an RDD. The `schema` could be StructType or list of names of columns.
Author: Davies Liu <davies@databricks.com>
Closes#4498 from davies/create and squashes the following commits:
08469c1 [Davies Liu] remove Scala/Java API for now
c80a7a9 [Davies Liu] fix hive test
d1bd8f2 [Davies Liu] cleanup applySchema
9526e97 [Davies Liu] createDataFrame from RDD with columns
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4468 from chenghao-intel/json and squashes the following commits:
aeb7801 [Cheng Hao] avoid multiple json generator created
Also start from the bottom so we show the first error instead of the top error.
Author: Michael Armbrust <michael@databricks.com>
Closes#4439 from marmbrus/analysisException and squashes the following commits:
45862a0 [Michael Armbrust] fix hive test
a773bba [Michael Armbrust] Merge remote-tracking branch 'origin/master' into analysisException
f88079f [Michael Armbrust] update more cases
fede90a [Michael Armbrust] newline
fbf4bc3 [Michael Armbrust] move to sql
6235db4 [Michael Armbrust] [SQL] Add an exception for analysis errors.
Users will not need to put `Options()` in a CREATE TABLE statement when there is not option provided.
Author: Yin Huai <yhuai@databricks.com>
Closes#4515 from yhuai/makeOptionsOptional and squashes the following commits:
1a898d3 [Yin Huai] Make options optional.
Author: Sheng, Li <OopsOutOfMemory@users.noreply.github.com>
Author: OopsOutOfMemory <victorshengli@126.com>
Closes#4508 from OopsOutOfMemory/cmt and squashes the following commits:
d8a68c6 [Sheng, Li] Update ddl.scala
f24aeaf [OopsOutOfMemory] correct style
Author: Michael Armbrust <michael@databricks.com>
Closes#4436 from marmbrus/dfToString and squashes the following commits:
8a3c35f [Michael Armbrust] Merge remote-tracking branch 'origin/master' into dfToString
b72a81b [Michael Armbrust] add toString
This is a completion of https://github.com/apache/spark/pull/4033 which was withdrawn for some reason.
Author: Sean Owen <sowen@cloudera.com>
Closes#4470 from srowen/SPARK-5239.2 and squashes the following commits:
2398bde [Sean Owen] Avoid use of JDBC4-only isClosed()
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4494 from chenghao-intel/tiny_code_change and squashes the following commits:
450dfe7 [Cheng Hao] remove the duplicated code
~~The rule is simple: If you want `a.b` work, then `a` must be some level of nested array of struct(level 0 means just a StructType). And the result of `a.b` is same level of nested array of b-type.
An optimization is: the resolve chain looks like `Attribute -> GetItem -> GetField -> GetField ...`, so we could transmit the nested array information between `GetItem` and `GetField` to avoid repeated computation of `innerDataType` and `containsNullList` of that nested array.~~
marmbrus Could you take a look?
to evaluate `a.b`, if `a` is array of struct, then `a.b` means get field `b` on each element of `a`, and return a result of array.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#2405 from cloud-fan/nested-array-dot and squashes the following commits:
08a228a [Wenchen Fan] support dot notation on array of struct
- as with a `Symbol`
- distinct
- sqlContext.emptyDataFrame
- move add/remove col out of RDDApi section
Author: Michael Armbrust <michael@databricks.com>
Closes#4437 from marmbrus/dfMissingFuncs and squashes the following commits:
2004023 [Michael Armbrust] Add missing functions
Fix Scala code style.
Author: Hung Lin <hung@zoomdata.com>
Closes#4464 from hunglin/SPARK-5472 and squashes the following commits:
ef7a3b3 [Hung Lin] SPARK-5472: fix scala style
An example:
```
year month AVG('Adj Close) MAX('Adj Close)
1980 12 0.503218 0.595103
1981 01 0.523289 0.570307
1982 02 0.436504 0.475256
1983 03 0.410516 0.442194
1984 04 0.450090 0.483521
```
Author: Reynold Xin <rxin@databricks.com>
Closes#4416 from rxin/SPARK-5643 and squashes the following commits:
d0e0d6e [Reynold Xin] [SQL] Minor update to data source and statistics documentation.
269da83 [Reynold Xin] Updated isLocal comment.
2cf3c27 [Reynold Xin] Moved logic into optimizer.
1a04d8b [Reynold Xin] [SPARK-5643][SQL] Add a show method to print the content of a DataFrame in columnar format.
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Author: Cheng Lian <lian@databricks.com>
Closes#4440 from liancheng/parquet-oops and squashes the following commits:
f21ede4 [Cheng Lian] HiveParquetSuite was disabled by mistake, re-enable them.
Sometimes tests were failing due to the creation of multiple `SparkContext`s in a single JVM.
Author: Michael Armbrust <michael@databricks.com>
Closes#4441 from marmbrus/javaTests and squashes the following commits:
657b1e0 [Michael Armbrust] [SQL] Use TestSQLContext in Java tests
When the `GetField` chain(`a.b.c.d.....`) is interrupted by `GetItem` like `a.b[0].c.d....`, then the check of ambiguous reference to fields is broken.
The reason is that: for something like `a.b[0].c.d`, we first parse it to `GetField(GetField(GetItem(Unresolved("a.b"), 0), "c"), "d")`. Then in `LogicalPlan#resolve`, we resolve `"a.b"` and build a `GetField` chain from bottom(the relation). But for the 2 outer `GetFiled`, we have to resolve them in `Analyzer` or do it in `GetField` lazily, check data type of child, search needed field, etc. which is similar to what we have done in `LogicalPlan#resolve`.
So in this PR, the fix is just copy the same logic in `LogicalPlan#resolve` to `Analyzer`, which is simple and quick, but I do suggest introduce `UnresolvedGetFiled` like I explained in https://github.com/apache/spark/pull/2405.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#4068 from cloud-fan/simple and squashes the following commits:
a6857b5 [Wenchen Fan] fix import order
8411c40 [Wenchen Fan] use UnresolvedGetField
This PR adds a rule to Analyzer that will add preinsert data type casting and field renaming to the select clause in an `INSERT INTO/OVERWRITE` statement. Also, with the change of this PR, we always invalidate our in memory data cache after inserting into a BaseRelation.
cc marmbrus liancheng
Author: Yin Huai <yhuai@databricks.com>
Closes#4373 from yhuai/insertFollowUp and squashes the following commits:
08237a7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into insertFollowUp
316542e [Yin Huai] Doc update.
c9ccfeb [Yin Huai] Revert a unnecessary change.
84aecc4 [Yin Huai] Address comments.
1951fe1 [Yin Huai] Merge remote-tracking branch 'upstream/master'
c18da34 [Yin Huai] Invalidate cache after insert.
727f21a [Yin Huai] Preinsert casting and renaming.
Make below code works.
```
sql("DESCRIBE test").registerTempTable("describeTest")
sql("SELECT * FROM describeTest").collect()
```
Author: OopsOutOfMemory <victorshengli@126.com>
Author: Sheng, Li <OopsOutOfMemory@users.noreply.github.com>
Closes#4249 from OopsOutOfMemory/desc_query and squashes the following commits:
6fee13d [OopsOutOfMemory] up-to-date
e71430a [Sheng, Li] Update HiveOperatorQueryableSuite.scala
3ba1058 [OopsOutOfMemory] change to default argument
aac7226 [OopsOutOfMemory] Merge branch 'master' into desc_query
68eb6dd [OopsOutOfMemory] Merge branch 'desc_query' of github.com:OopsOutOfMemory/spark into desc_query
354ad71 [OopsOutOfMemory] query describe command
d541a35 [OopsOutOfMemory] refine test suite
e1da481 [OopsOutOfMemory] refine test suite
a780539 [OopsOutOfMemory] Merge branch 'desc_query' of github.com:OopsOutOfMemory/spark into desc_query
0015f82 [OopsOutOfMemory] code style
dd0aaef [OopsOutOfMemory] code style
c7d606d [OopsOutOfMemory] rename test suite
75f2342 [OopsOutOfMemory] refine code and test suite
f942c9b [OopsOutOfMemory] initial
11559ae [OopsOutOfMemory] code style
c5fdecf [OopsOutOfMemory] code style
aeaea5f [OopsOutOfMemory] rename test suite
ac2c3bb [OopsOutOfMemory] refine code and test suite
544573e [OopsOutOfMemory] initial
Author: Reynold Xin <rxin@databricks.com>
Closes#4410 from rxin/df-renameCol and squashes the following commits:
a6a796e [Reynold Xin] [SPARK-5639][SQL] Support DataFrame.renameColumn.
Author: Reynold Xin <rxin@databricks.com>
Closes#4408 from rxin/df-config-eager and squashes the following commits:
c0204cf [Reynold Xin] [SPARK-5638][SQL] Add a config flag to disable eager analysis of DataFrames.
This PR adds three major improvements to Parquet data source:
1. Partition discovery
While reading Parquet files resides in Hive style partition directories, `ParquetRelation2` automatically discovers partitioning information and infers partition column types.
This is also a partial work for [SPARK-5182] [1], which aims to provide first class partitioning support for the data source API. Related code in this PR can be easily extracted to the data source API level in future versions.
1. Schema merging
When enabled, Parquet data source collects schema information from all Parquet part-files and tries to merge them. Exceptions are thrown when incompatible schemas are detected. This feature is controlled by data source option `parquet.mergeSchema`, and is enabled by default.
1. Metastore Parquet table conversion moved to analysis phase
This greatly simplifies the conversion logic. `ParquetConversion` strategy can be removed once the old Parquet implementation is removed in the future.
This version of Parquet data source aims to entirely replace the old Parquet implementation. However, the old version hasn't been removed yet. Users can fall back to the old version by turning off SQL configuration `spark.sql.parquet.useDataSourceApi`.
Other JIRA tickets fixed as side effects in this PR:
- [SPARK-5509] [3]: `EqualTo` now uses a proper `Ordering` to compare binary types.
- [SPARK-3575] [4]: Metastore schema is now preserved and passed to `ParquetRelation2` via data source option `parquet.metastoreSchema`.
TODO:
- [ ] More test cases for partition discovery
- [x] Fix write path after data source write support (#4294) is merged
It turned out to be non-trivial to fall back to old Parquet implementation on the write path when Parquet data source is enabled. Since we're planning to include data source write support in 1.3.0, I simply ignored two test cases involving Parquet insertion for now.
- [ ] Fix outdated comments and documentations
PS: This PR looks big, but more than a half of the changed lines in this PR are trivial changes to test cases. To test Parquet with and without the new data source, almost all Parquet test cases are moved into wrapper driver functions. This introduces hundreds of lines of changes.
[1]: https://issues.apache.org/jira/browse/SPARK-5182
[2]: https://issues.apache.org/jira/browse/SPARK-5528
[3]: https://issues.apache.org/jira/browse/SPARK-5509
[4]: https://issues.apache.org/jira/browse/SPARK-3575
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Author: Cheng Lian <lian@databricks.com>
Closes#4308 from liancheng/parquet-partition-discovery and squashes the following commits:
b6946e6 [Cheng Lian] Fixes MiMA issues, addresses comments
8232e17 [Cheng Lian] Write support for Parquet data source
a49bd28 [Cheng Lian] Fixes spelling typo in trait name "CreateableRelationProvider"
808380f [Cheng Lian] Fixes issues introduced while rebasing
50dd8d1 [Cheng Lian] Addresses @rxin's comment, fixes UDT schema merging
adf2aae [Cheng Lian] Fixes compilation error introduced while rebasing
4e0175f [Cheng Lian] Fixes Python Parquet API, we need Py4J array to call varargs method
0d8ec1d [Cheng Lian] Adds more test cases
b35c8c6 [Cheng Lian] Fixes some typos and outdated comments
dd704fd [Cheng Lian] Fixes Python Parquet API
596c312 [Cheng Lian] Uses switch to control whether use Parquet data source or not
7d0f7a2 [Cheng Lian] Fixes Metastore Parquet table conversion
a1896c7 [Cheng Lian] Fixes all existing Parquet test suites except for ParquetMetastoreSuite
5654c9d [Cheng Lian] Draft version of Parquet partition discovery and schema merging
Hi, rxin marmbrus
I considered your suggestion (in #4127) and now re-write it. This is now up-to-date.
Could u please review it ?
Author: OopsOutOfMemory <victorshengli@126.com>
Closes#4227 from OopsOutOfMemory/describe and squashes the following commits:
053826f [OopsOutOfMemory] describe
SQLQuerySuite test failure:
[info] - simple select (22 milliseconds)
[info] - sorting (722 milliseconds)
[info] - external sorting (728 milliseconds)
[info] - limit (95 milliseconds)
[info] - date row *** FAILED *** (35 milliseconds)
[info] Results do not match for query:
[info] 'Limit 1
[info] 'Project [CAST(2015-01-28, DateType) AS c0#3630]
[info] 'UnresolvedRelation [testData], None
[info]
[info] == Analyzed Plan ==
[info] Limit 1
[info] Project [CAST(2015-01-28, DateType) AS c0#3630]
[info] LogicalRDD [key#0,value#1], MapPartitionsRDD[1] at mapPartitions at ExistingRDD.scala:35
[info]
[info] == Physical Plan ==
[info] Limit 1
[info] Project [16463 AS c0#3630]
[info] PhysicalRDD [key#0,value#1], MapPartitionsRDD[1] at mapPartitions at ExistingRDD.scala:35
[info]
[info] == Results ==
[info] !== Correct Answer - 1 == == Spark Answer - 1 ==
[info] ![2015-01-28] [2015-01-27] (QueryTest.scala:77)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:495)
[info] at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
[info] at org.scalatest.Assertions$class.fail(Assertions.scala:1328)
[info] at org.scalatest.FunSuite.fail(FunSuite.scala:1555)
[info] at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:77)
[info] at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:95)
[info] at org.apache.spark.sql.SQLQuerySuite$$anonfun$23.apply$mcV$sp(SQLQuerySuite.scala:300)
[info] at org.apache.spark.sql.SQLQuerySuite$$anonfun$23.apply(SQLQuerySuite.scala:300)
[info] at org.apache.spark.sql.SQLQuerySuite$$anonfun$23.apply(SQLQuerySuite.scala:300)
[info] at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
[info] at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
[info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info] at org.scalatest.Transformer.apply(Transformer.scala:22)
[info] at org.scalatest.Transformer.apply(Transformer.scala:20)
[info] at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
[info] at org.scalatest.Suite$class.withFixture(Suite.scala:1122)
[info] at org.scalatest.FunSuite.withFixture(FunSuite.scala:1555)
[info] at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
[info] at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
[info] at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
[info] at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
[info] at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
[info] at org.scalatest.FunSuite.runTest(FunSuite.scala:1555)
[info] at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
[info] at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
[info] at org.scalatest.SuperEngine$$anonfun$traverseSubNode
Author: wangfei <wangfei1@huawei.com>
Closes#4395 from scwf/SQLQuerySuite and squashes the following commits:
1431a2d [wangfei] fix conflicts
c35fe5e [wangfei] minor fix
01dab3a [wangfei] fix test failure of SQLQuerySuite
1. Added methods to create DataFrames from Seq[Product]
2. Added executeTake to avoid running a Spark job on LocalRelations.
Author: Reynold Xin <rxin@databricks.com>
Closes#4372 from rxin/localDataFrame and squashes the following commits:
f696858 [Reynold Xin] style checker.
839ef7f [Reynold Xin] [SPARK-5602][SQL] Better support for creating DataFrame from local data collection.
Author: Reynold Xin <rxin@databricks.com>
Closes#4379 from rxin/CachedTableSuite and squashes the following commits:
f2b44ce [Reynold Xin] [SQL] Fix flaky CachedTableSuite.
1. Removed LocalHiveContext in Python.
2. Reduced DSL UDF support from 22 arguments to 10 arguments so JavaDoc/ScalaDoc look nicer.
Author: Reynold Xin <rxin@databricks.com>
Closes#4374 from rxin/df-style and squashes the following commits:
e493342 [Reynold Xin] [SQL][DataFrame] Minor cleanup.
...aised in SPARK-4520.
The exception is thrown only for a thrift generated parquet file. The array element schema name is assumed as "array" as per ParquetAvro but for thrift generated parquet files, it is array_name + "_tuple". This leads to missing child of array group type and hence when the parquet rows are being materialized leads to the exception.
Author: Sadhan Sood <sadhan@tellapart.com>
Closes#4148 from sadhan/SPARK-4520 and squashes the following commits:
c5ccde8 [Sadhan Sood] [SPARK-4520] [SQL] This pr fixes the ArrayIndexOutOfBoundsException as raised in SPARK-4520.
Author: Reynold Xin <rxin@databricks.com>
Closes#4376 from rxin/SPARK-5605 and squashes the following commits:
c55f5fa [Reynold Xin] Added a Python test.
f4b8dbb [Reynold Xin] [SPARK-5605][SQL][DF] Allow using String to specify colum name in DSL aggregate functions.
Right now the PR adds few helper methods for java apis. But the issue was opened mainly to get rid of transformations in java api like `.rdd` and `.toJavaRDD` while working with `SQLContext` or `HiveContext`.
Author: kul <kuldeep.bora@gmail.com>
Closes#4243 from kul/master and squashes the following commits:
2390fba [kul] [SPARK-5426][SQL] Add SparkSQL Java API helper methods.
```scala
df.selectExpr("abs(colA)", "colB")
df.filter("age > 21")
```
Author: Reynold Xin <rxin@databricks.com>
Closes#4348 from rxin/SPARK-5579 and squashes the following commits:
2baeef2 [Reynold Xin] Fix Python.
b416372 [Reynold Xin] [SPARK-5579][SQL][DataFrame] Support for project/filter using SQL expressions.
The previous #3732 is reverted due to some test failure.
Have fixed that.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4325 from adrian-wang/datenative and squashes the following commits:
096e20d [Daoyuan Wang] fix for mixed timezone
0ed0fdc [Daoyuan Wang] fix test data
a2fdd4e [Daoyuan Wang] getDate
c37832b [Daoyuan Wang] row to catalyst
f0005b1 [Daoyuan Wang] add date in sql parser and java type conversion
024c9a6 [Daoyuan Wang] clean some import order
d6715fc [Daoyuan Wang] refactoring Date as Primitive Int internally
374abd5 [Daoyuan Wang] spark native date type support
SQL in HiveContext, should be case insensitive, however, the following query will fail.
```scala
udf.register("random0", () => { Math.random()})
assert(sql("SELECT RANDOM0() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
```
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4326 from chenghao-intel/udf_case_sensitive and squashes the following commits:
485cf66 [Cheng Hao] Support the case insensitive for UDF
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#3820 from adrian-wang/parquettimestamp and squashes the following commits:
b1e2a0d [Daoyuan Wang] fix for nanos
4dadef1 [Daoyuan Wang] fix wrong read
93f438d [Daoyuan Wang] parquet timestamp support
1. Added Java-friendly version of the expression operators (i.e. gt, geq)
2. Added JavaDoc for most operators
3. Simplified expression operators by having only one version of the function (that accepts Any). Previously we had two methods for each expression operator, one accepting Any and another accepting Column.
4. agg function now accepts varargs of (String, String).
Author: Reynold Xin <rxin@databricks.com>
Closes#4332 from rxin/df-update and squashes the following commits:
ab0aa69 [Reynold Xin] Added Java friendly expression methods. Added JavaDoc. For each expression operator, have only one version of the function (that accepts Any). Previously we had two methods for each expression operator, one accepting Any and another accepting Column.
576d07a [Reynold Xin] random commit.
Author: Reynold Xin <rxin@databricks.com>
Closes#4327 from rxin/schemarddTypeAlias and squashes the following commits:
e5a8ff3 [Reynold Xin] [SPARK-5551][SQL] Create type alias for SchemaRDD for source backward compatibility
They were there mostly for code review and easier check of the API. I don't think they need to be there anymore.
Author: Reynold Xin <rxin@databricks.com>
Closes#4328 from rxin/remove-df-api and squashes the following commits:
723d600 [Reynold Xin] [SQL][DataFrame] Remove DataFrameApi and ColumnApi.
This PR aims to support `INSERT INTO/OVERWRITE TABLE tableName` and `CREATE TABLE tableName AS SELECT` for the data source API (partitioned tables are not supported).
In this PR, I am also adding the support of `IF NOT EXISTS` for our ddl parser. The current semantic of `IF NOT EXISTS` is explained as follows.
* For a `CREATE TEMPORARY TABLE` statement, it does not `IF NOT EXISTS` for now.
* For a `CREATE TABLE` statement (we are creating a metastore table), if there is an existing table having the same name ...
* when `IF NOT EXISTS` clause is used, we will do nothing.
* when `IF NOT EXISTS` clause is not used, the user will see an exception saying the table already exists.
TODOs:
- [x] CTAS support
- [x] Programmatic APIs
- [ ] Python API (another PR)
- [x] More unit tests
- [ ] Documents (another PR)
marmbrus liancheng rxin
Author: Yin Huai <yhuai@databricks.com>
Closes#4294 from yhuai/writeSupport and squashes the following commits:
3db1539 [Yin Huai] save does not take overwrite.
1c98881 [Yin Huai] Fix test.
142372a [Yin Huai] Merge remote-tracking branch 'upstream/master' into writeSupport
34e1bfb [Yin Huai] Address comments.
1682ca6 [Yin Huai] Better support for CTAS statements.
e789d64 [Yin Huai] For the Scala API, let users to use tuples to provide options.
0128065 [Yin Huai] Short hand versions of save and load.
66ebd74 [Yin Huai] Formatting.
9203ec2 [Yin Huai] Merge remote-tracking branch 'upstream/master' into writeSupport
e5d29f2 [Yin Huai] Programmatic APIs.
1a719a5 [Yin Huai] CREATE TEMPORARY TABLE with IF NOT EXISTS is not allowed for now.
909924f [Yin Huai] Add saveAsTable for the data source API to DataFrame.
95a7c71 [Yin Huai] Fix bug when handling IF NOT EXISTS clause in a CREATE TEMPORARY TABLE statement.
d37b19c [Yin Huai] Cheng's comments.
fd6758c [Yin Huai] Use BeforeAndAfterAll.
7880891 [Yin Huai] Support CREATE TABLE AS SELECT STATEMENT and the IF NOT EXISTS clause.
cb85b05 [Yin Huai] Initial write support.
2f91354 [Yin Huai] Make INSERT OVERWRITE/INTO statements consistent between HiveQL and SqlParser.
This pull request contains a Spark SQL data source that can pull data from, and can put data into, a JDBC database.
I have tested both read and write support with H2, MySQL, and Postgres. It would surprise me if both read and write support worked flawlessly out-of-the-box for any other database; different databases have different names for different JDBC data types and different meanings for SQL types with the same name. However, this code is designed (see `DriverQuirks.scala`) to make it *relatively* painless to add support for another database by augmenting the type mapping contained in this PR.
Author: Tor Myklebust <tmyklebu@gmail.com>
Closes#4261 from tmyklebu/master and squashes the following commits:
cf167ce [Tor Myklebust] Work around other Java tests ruining TestSQLContext.
67893bf [Tor Myklebust] Move the jdbcRDD methods into SQLContext itself.
585f95b [Tor Myklebust] Dependencies go into the project's pom.xml.
829d5ba [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark
41647ef [Tor Myklebust] Hide a couple things that don't need to be public.
7318aea [Tor Myklebust] Fix scalastyle warnings.
a09eeac [Tor Myklebust] JDBC data source for Spark SQL.
176bb98 [Tor Myklebust] Add test deps for JDBC support.
1. Throw UnsupportedOperationException if a Column is not computable.
2. Perform eager analysis on DataFrame so we can catch errors when they happen (not when an action is run).
Author: Reynold Xin <rxin@databricks.com>
Author: Davies Liu <davies@databricks.com>
Closes#4296 from rxin/col-computability and squashes the following commits:
6527b86 [Reynold Xin] Merge pull request #8 from davies/col-computability
fd92bc7 [Reynold Xin] Merge branch 'master' into col-computability
f79034c [Davies Liu] fix python tests
5afe1ff [Reynold Xin] Fix scala test.
17f6bae [Reynold Xin] Various fixes.
b932e86 [Reynold Xin] Added eager analysis for error reporting.
e6f00b8 [Reynold Xin] [SQL][API] ComputableColumn vs IncomputableColumn
Author: Reynold Xin <rxin@databricks.com>
Closes#4313 from rxin/SPARK-5514 and squashes the following commits:
e34e91b [Reynold Xin] [SPARK-5514] DataFrame.collect should call executeCollect
Store daysSinceEpoch as an Int value(4 bytes) to represent DateType, instead of using java.sql.Date(8 bytes as Long) in catalyst row. This ensures the same comparison behavior of Hive and Catalyst.
Subsumes #3381
I thinks there are already some tests in JavaSQLSuite, and for python it will not affect python's datetime class.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#3732 from adrian-wang/datenative and squashes the following commits:
0ed0fdc [Daoyuan Wang] fix test data
a2fdd4e [Daoyuan Wang] getDate
c37832b [Daoyuan Wang] row to catalyst
f0005b1 [Daoyuan Wang] add date in sql parser and java type conversion
024c9a6 [Daoyuan Wang] clean some import order
d6715fc [Daoyuan Wang] refactoring Date as Primitive Int internally
374abd5 [Daoyuan Wang] spark native date type support
Not all Catalyst filter expressions can be converted to Parquet filter predicates. We should try to convert each individual predicate and then collect those convertible ones.
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Author: Cheng Lian <lian@databricks.com>
Closes#4255 from liancheng/spark-5465 and squashes the following commits:
14ccd37 [Cheng Lian] Fixes filter push-down for Parquet data source
I'll add test case in #4040
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4057 from adrian-wang/coal and squashes the following commits:
4d0111a [Daoyuan Wang] address Yin's comments
c393e18 [Daoyuan Wang] fix rebase conflicts
e47c03a [Daoyuan Wang] add coalesce in parser
c74828d [Daoyuan Wang] cast types for coalesce
Support `comment` in create a table field.
__CREATE TEMPORARY TABLE people(name string `comment` "the name of a person")__
Author: OopsOutOfMemory <victorshengli@126.com>
Closes#3999 from OopsOutOfMemory/meta_comment and squashes the following commits:
39150d4 [OopsOutOfMemory] add comment and refine test suite
Simplify some codes related to DataFrame.
* Calling `toAttributes` instead of a `map`.
* Original `createDataFrame` creates the `StructType` and its attributes in a redundant way. Refactored it to create `StructType` and call `toAttributes` on it directly.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4298 from viirya/refactor_df and squashes the following commits:
1d61c64 [Liang-Chi Hsieh] Revert it.
f36efb5 [Liang-Chi Hsieh] Relax the constraint of toDataFrame.
2c9f370 [Liang-Chi Hsieh] Just refactor DataFrame codes.
Author: kai <kaizeng@eecs.berkeley.edu>
Closes#4291 from kai-zeng/aggregate-fix and squashes the following commits:
78658ef [kai] remove redundant field "childOutput"
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#4250 from ueshin/issues/SPARK-5457 and squashes the following commits:
3c05e59 [Takuya UESHIN] Remove parameter to use default value of ApproxCountDistinct.
faea19d [Takuya UESHIN] Use overload instead of default value for Java support.
d1cca38 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-5457
663d43d [Takuya UESHIN] Add missing DSL for ApproxCountDistinct.
This PR makes Star a trait, and provides two implementations: UnresolvedStar (used for *, tblName.*) and ResolvedStar (used for df("*")).
Author: Reynold Xin <rxin@databricks.com>
Closes#4283 from rxin/df-star and squashes the following commits:
c9cba3e [Reynold Xin] Removed mapFunction in UnresolvedStar.
1a3a1d7 [Reynold Xin] [SQL] Support df("*") to select all columns in a data frame.
This patch changes DataFrame's `apply()` method to use an analyzed query plan when resolving column names. This fixes a bug where `apply` would throw "invalid call to qualifiers on unresolved object" errors when called on DataFrames constructed via `SQLContext.sql()`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#4282 from JoshRosen/SPARK-5462 and squashes the following commits:
b9e6da2 [Josh Rosen] [SPARK-5462] Use analyzed query plan in DataFrame.apply().
1. Added Dsl.column in case Dsl.col is shadowed.
2. Allow using String to specify the target data type in cast.
3. Support sorting on multiple columns using column names.
4. Added Java API test file.
Author: Reynold Xin <rxin@databricks.com>
Closes#4280 from rxin/dsl1 and squashes the following commits:
33ecb7a [Reynold Xin] Add the Java test.
d06540a [Reynold Xin] [SQL] DataFrame API improvements.
`select key, count( * ) from src group by key, 1` will get the wrong answer.
e.g. for this table
```
val testData2 =
TestSQLContext.sparkContext.parallelize(
TestData2(1, 1) ::
TestData2(1, 2) ::
TestData2(2, 1) ::
TestData2(2, 2) ::
TestData2(3, 1) ::
TestData2(3, 2) :: Nil, 2).toSchemaRDD
testData2.registerTempTable("testData2")
```
result of `SELECT a, count(1) FROM testData2 GROUP BY a, 1` is
```
[1,1]
[2,2]
[3,1]
```
Author: wangfei <wangfei1@huawei.com>
Closes#4169 from scwf/agg-bug and squashes the following commits:
05751db [wangfei] fix bugs when literal in agg grouping expressioons
Enable parquet filter pushdown of castable types like short, byte that can be cast to integer
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#4156 from saucam/filter_short and squashes the following commits:
a403979 [Yash Datta] SPARK-4786: Fix styling issues
d029866 [Yash Datta] SPARK-4786: Add test case
cb2e0d9 [Yash Datta] SPARK-4786: Parquet filter pushdown for castable types
...gs.
Parquet Converters allow developers to take advantage of dictionary encoding of column data to reduce Column Binary decoding.
The Spark PrimitiveConverter was not using that API and consequently for String columns that used dictionary compression repeated Binary to String conversions for the same String.
In measurements this could account for over 25% of entire query time.
For example a 500M row table split across 16 blocks was aggregated and summed in a litte under 30s before this change and a little under 20s after the change.
Author: Michael Davies <Michael.BellDavies@gmail.com>
Closes#4187 from MickDavies/SPARK-5309-2 and squashes the following commits:
327287e [Michael Davies] SPARK-5309: Add support for dictionaries in PrimitiveConverter for Strings.
33c002c [Michael Davies] SPARK-5309: Add support for dictionaries in PrimitiveConverter for Strings.
Turns out Scala does generate static methods for ones defined in a companion object. Finally no need to separate api.java.dsl and api.scala.dsl.
Author: Reynold Xin <rxin@databricks.com>
Closes#4276 from rxin/dsl and squashes the following commits:
30aa611 [Reynold Xin] Add all files.
1a9d215 [Reynold Xin] [SPARK-5445][SQL] Consolidate Java and Scala DSL static methods.
1. Added foreach, foreachPartition, flatMap to DataFrame.
2. Added col() in dsl.
3. Support renaming columns in toDataFrame.
4. Support type inference on arrays (in addition to Seq).
5. Updated mllib to use the new DSL.
Author: Reynold Xin <rxin@databricks.com>
Closes#4260 from rxin/sql-dsl-update and squashes the following commits:
73466c1 [Reynold Xin] Fixed LogisticRegression. Also added better error message for resolve.
fab3ccc [Reynold Xin] Bug fix.
d31fcd2 [Reynold Xin] Style fix.
62608c4 [Reynold Xin] [SQL] Various DataFrame DSL update.
Also removed the literal implicit transformation since it is pretty scary for API design. Instead, created a new lit method for creating literals. This doesn't break anything from a compatibility perspective because Literal was added two days ago.
Author: Reynold Xin <rxin@databricks.com>
Closes#4241 from rxin/df-docupdate and squashes the following commits:
c0f4810 [Reynold Xin] Fix Python merge conflict.
094c7d7 [Reynold Xin] Minor style fix. Reset Python tests.
3c89f4a [Reynold Xin] Package.
dfe6962 [Reynold Xin] Updated Python aggregate.
5dd4265 [Reynold Xin] Made dsl Java callable.
14b3c27 [Reynold Xin] Fix literal expression for symbols.
68b31cb [Reynold Xin] Literal.
4cfeb78 [Reynold Xin] [SPARK-5097][SQL] Address DataFrame code review feedback.
and
[SPARK-5448][SQL] Make CacheManager a concrete class and field in SQLContext
Author: Reynold Xin <rxin@databricks.com>
Closes#4242 from rxin/sqlCleanup and squashes the following commits:
e351cb2 [Reynold Xin] Fixed toDataFrame.
6545c42 [Reynold Xin] More changes.
728c017 [Reynold Xin] [SPARK-5447][SQL] Replaced reference to SchemaRDD with DataFrame.
Author: Reynold Xin <rxin@databricks.com>
Closes#4235 from rxin/df-tests1 and squashes the following commits:
f341db6 [Reynold Xin] [SPARK-5097][SQL] Test cases for DataFrame expressions.
This PR removes the deprecated `ParquetQuerySuite`, renamed `ParquetQuerySuite2` to `ParquetQuerySuite`, and refactored changes introduced in #4115 to `ParquetFilterSuite` . It is a follow-up of #3644.
Notice that test cases in the old `ParquetQuerySuite` have already been well covered by other test suites introduced in #3644.
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Author: Cheng Lian <lian@databricks.com>
Closes#4116 from liancheng/remove-deprecated-parquet-tests and squashes the following commits:
f73b8f9 [Cheng Lian] Removes deprecated Parquet test suite
* The `SqlLexical.allCaseVersions` will cause `StackOverflowException` if the key word is too long, the patch will fix that by normalizing all of the keywords in `SqlLexical`.
* And make a unified SparkSQLParser for sharing the common code.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3926 from chenghao-intel/long_keyword and squashes the following commits:
686660f [Cheng Hao] Support Long Keyword and Refactor the SQLParsers
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4040 from adrian-wang/coalesce and squashes the following commits:
0ac8e8f [Daoyuan Wang] add coalesce() in sql parser
JIRA: https://issues.apache.org/jira/browse/SPARK-5287
This PR only add `defaultSizeOf` to data types and make those internal type classes `protected[sql]`. I will use another PR to cleanup the type hierarchy of data types.
Author: Yin Huai <yhuai@databricks.com>
Closes#4081 from yhuai/SPARK-5287 and squashes the following commits:
90cec75 [Yin Huai] Update unit test.
e1c600c [Yin Huai] Make internal classes protected[sql].
7eaba68 [Yin Huai] Add `defaultSize` method to data types.
fd425e0 [Yin Huai] Add all native types to NativeType.defaultSizeOf.
Author: Jacky Li <jacky.likun@gmail.com>
Closes#4100 from jackylk/patch-9 and squashes the following commits:
b13b9d6 [Jacky Li] Update SQLConf.scala
4d3f83d [Jacky Li] Update SQLConf.scala
fcc8c85 [Jacky Li] [SQL] fix typo in class description
Author: Reynold Xin <rxin@databricks.com>
Closes#4092 from rxin/bigdecimal and squashes the following commits:
27b08c9 [Reynold Xin] Fixed test.
10cb496 [Reynold Xin] [SPARK-5279][SQL] Use java.math.BigDecimal as the exposed Decimal type.
As part of SPARK-5193:
1. Removed UDFRegistration as a mixin in SQLContext and made it a field ("udf").
2. For Java UDFs, renamed dataType to returnType.
3. For Scala UDFs, added type tags.
4. Added all Java UDF registration methods to Scala's UDFRegistration.
5. Documentation
Author: Reynold Xin <rxin@databricks.com>
Closes#4056 from rxin/udf-registration and squashes the following commits:
ae9c556 [Reynold Xin] Updated example.
675a3c9 [Reynold Xin] Style fix
47c24ff [Reynold Xin] Python fix.
5f00c45 [Reynold Xin] Restore data type position in java udf and added typetags.
032f006 [Reynold Xin] [SPARK-5193][SQL] Reconcile Java and Scala UDFRegistration.
1. Removed 2 implicits (logicalPlanToSparkQuery and baseRelationToSchemaRDD)
2. Moved extraStrategies into ExperimentalMethods.
3. Made private methods protected[sql] so they don't show up in javadocs.
4. Removed createParquetFile.
5. Added Java version of applySchema to SQLContext.
Author: Reynold Xin <rxin@databricks.com>
Closes#4049 from rxin/sqlContext-refactor and squashes the following commits:
a326a1a [Reynold Xin] Remove createParquetFile and add applySchema for Java to SQLContext.
ecd6685 [Reynold Xin] Added baseRelationToSchemaRDD back.
4a38c9b [Reynold Xin] [SPARK-5193][SQL] Tighten up SQLContext API
Declare SQLConf to be serializable to fix "Task not serializable" exceptions in SparkSQL
Author: Alex Baretta <alexbaretta@gmail.com>
Closes#4031 from alexbaretta/SPARK-5235-SQLConf and squashes the following commits:
c2103f5 [Alex Baretta] [SPARK-5235] Make SQLConf Serializable
`TaskContext.attemptId` is misleadingly-named, since it currently returns a taskId, which uniquely identifies a particular task attempt within a particular SparkContext, instead of an attempt number, which conveys how many times a task has been attempted.
This patch deprecates `TaskContext.attemptId` and add `TaskContext.taskId` and `TaskContext.attemptNumber` fields. Prior to this change, it was impossible to determine whether a task was being re-attempted (or was a speculative copy), which made it difficult to write unit tests for tasks that fail on early attempts or speculative tasks that complete faster than original tasks.
Earlier versions of the TaskContext docs suggest that `attemptId` behaves like `attemptNumber`, so there's an argument to be made in favor of changing this method's implementation. Since we've decided against making that change in maintenance branches, I think it's simpler to add better-named methods and retain the old behavior for `attemptId`; if `attemptId` behaved differently in different branches, then this would cause confusing build-breaks when backporting regression tests that rely on the new `attemptId` behavior.
Most of this patch is fairly straightforward, but there is a bit of trickiness related to Mesos tasks: since there's no field in MesosTaskInfo to encode the attemptId, I packed it into the `data` field alongside the task binary.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#3849 from JoshRosen/SPARK-4014 and squashes the following commits:
89d03e0 [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-4014
5cfff05 [Josh Rosen] Introduce wrapper for serializing Mesos task launch data.
38574d4 [Josh Rosen] attemptId -> taskAttemptId in PairRDDFunctions
a180b88 [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-4014
1d43aa6 [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-4014
eee6a45 [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-4014
0b10526 [Josh Rosen] Use putInt instead of putLong (silly mistake)
8c387ce [Josh Rosen] Use local with maxRetries instead of local-cluster.
cbe4d76 [Josh Rosen] Preserve attemptId behavior and deprecate it:
b2dffa3 [Josh Rosen] Address some of Reynold's minor comments
9d8d4d1 [Josh Rosen] Doc typo
1e7a933 [Josh Rosen] [SPARK-4014] Change TaskContext.attemptId to return attempt number instead of task ID.
fd515a5 [Josh Rosen] Add failing test for SPARK-4014
rxin follow up of #3732
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4041 from adrian-wang/decimal and squashes the following commits:
aa3d738 [Daoyuan Wang] fix auto refactor
7777a58 [Daoyuan Wang] move sql.types.decimal.Decimal to sql.types.Decimal
Mostly just moving stuff around. This should still be source compatible since we type aliased Row previously in org.apache.spark.sql.Row.
Added the following APIs to Row:
```scala
def getMap[K, V](i: Int): scala.collection.Map[K, V]
def getJavaMap[K, V](i: Int): java.util.Map[K, V]
def getSeq[T](i: Int): Seq[T]
def getList[T](i: Int): java.util.List[T]
def getStruct(i: Int): StructType
```
Author: Reynold Xin <rxin@databricks.com>
Closes#4030 from rxin/sql-row and squashes the following commits:
6c85c29 [Reynold Xin] Fixed style violation by adding a new line to Row.scala.
82b064a [Reynold Xin] [SPARK-5167][SQL] Move Row into sql package and make it usable for Java.
Having two versions of the data type APIs (one for Java, one for Scala) requires downstream libraries to also have two versions of the APIs if the library wants to support both Java and Scala. I took a look at the Scala version of the data type APIs - it can actually work out pretty well for Java out of the box.
As part of the PR, I created a sql.types package and moved all type definitions there. I then removed the Java specific data type API along with a lot of the conversion code.
This subsumes https://github.com/apache/spark/pull/3925
Author: Reynold Xin <rxin@databricks.com>
Closes#3958 from rxin/SPARK-5123-datatype-2 and squashes the following commits:
66505cc [Reynold Xin] [SPARK-5123] Expose only one version of the data type APIs (i.e. remove the Java-specific API).
This change should be binary and source backward compatible since we didn't change any user facing APIs.
Author: Reynold Xin <rxin@databricks.com>
Closes#3965 from rxin/SPARK-5168-sqlconf and squashes the following commits:
42eec09 [Reynold Xin] Fix default conf value.
0ef86cc [Reynold Xin] Fix constructor ordering.
4d7f910 [Reynold Xin] Properly override config.
ccc8e6a [Reynold Xin] [SPARK-5168] Make SQLConf a field rather than mixin in SQLContext
With changes in this PR, users can persist metadata of tables created based on the data source API in metastore through DDLs.
Author: Yin Huai <yhuai@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#3960 from yhuai/persistantTablesWithSchema2 and squashes the following commits:
069c235 [Yin Huai] Make exception messages user friendly.
c07cbc6 [Yin Huai] Get the location of test file in a correct way.
4456e98 [Yin Huai] Test data.
5315dfc [Yin Huai] rxin's comments.
7fc4b56 [Yin Huai] Add DDLStrategy and HiveDDLStrategy to plan DDLs based on the data source API.
aeaf4b3 [Yin Huai] Add comments.
06f9b0c [Yin Huai] Revert unnecessary changes.
feb88aa [Yin Huai] Merge remote-tracking branch 'apache/master' into persistantTablesWithSchema2
172db80 [Yin Huai] Fix unit test.
49bf1ac [Yin Huai] Unit tests.
8f8f1a1 [Yin Huai] [SPARK-4574][SQL] Adding support for defining schema in foreign DDL commands. #3431
f47fda1 [Yin Huai] Unit tests.
2b59723 [Michael Armbrust] Set external when creating tables
c00bb1b [Michael Armbrust] Don't use reflection to read options
1ea6e7b [Michael Armbrust] Don't fail when trying to uncache a table that doesn't exist
6edc710 [Michael Armbrust] Add tests.
d7da491 [Michael Armbrust] First draft of persistent tables.
Followup to #3870. Props to rahulaggarwalguavus for identifying the issue.
Author: Michael Armbrust <michael@databricks.com>
Closes#3990 from marmbrus/SPARK-5049 and squashes the following commits:
dd03e4e [Michael Armbrust] Fill in the partition values of parquet scans instead of using JoinedRow
Enable from follow multiple brackets:
```
select key from ((select * from testData limit 1) union all (select * from testData limit 1)) x limit 1
```
Author: scwf <wangfei1@huawei.com>
Closes#3853 from scwf/from and squashes the following commits:
14f110a [scwf] enable from follow multiple brackets
https://issues.apache.org/jira/browse/SPARK-4963
SchemaRDD.sample() return wrong results due to GapSamplingIterator operating on mutable row.
HiveTableScan make RDD with SpecificMutableRow and SchemaRDD.sample() will return GapSamplingIterator for iterating.
override def next(): T = {
val r = data.next()
advance
r
}
GapSamplingIterator.next() return the current underlying element and assigned it to r.
However if the underlying iterator is mutable row just like what HiveTableScan returned, underlying iterator and r will point to the same object.
After advance operation, we drop some underlying elments and it also changed r which is not expected. Then we return the wrong value different from initial r.
To fix this issue, the most direct way is to make HiveTableScan return mutable row with copy just like the initial commit that I have made. This solution will make HiveTableScan can not get the full advantage of reusable MutableRow, but it can make sample operation return correct result.
Further more, we need to investigate GapSamplingIterator.next() and make it can implement copy operation inside it. To achieve this, we should define every elements that RDD can store implement the function like cloneable and it will make huge change.
Author: Yanbo Liang <yanbohappy@gmail.com>
Closes#3827 from yanbohappy/spark-4963 and squashes the following commits:
0912ca0 [Yanbo Liang] code format keep
65c4e7c [Yanbo Liang] import file and clear annotation
55c7c56 [Yanbo Liang] better output of test case
cea7e2e [Yanbo Liang] SchemaRDD add copy operation before Sample operator
e840829 [Yanbo Liang] HiveTableScan return mutable row with copy
Follow up for #3712.
This PR finally remove ```CommandStrategy``` and make all commands follow ```RunnableCommand``` so they can go with ```case r: RunnableCommand => ExecutedCommand(r) :: Nil```.
One exception is the ```DescribeCommand``` of hive, which is a special case and need to distinguish hive table and temporary table, so still keep ```HiveCommandStrategy``` here.
Author: scwf <wangfei1@huawei.com>
Closes#3948 from scwf/followup-SPARK-4861 and squashes the following commits:
6b48e64 [scwf] minor style fix
2c62e9d [scwf] fix for hive module
5a7a819 [scwf] Refactory command in spark sql
Adding support for defining schema in foreign DDL commands. Now foreign DDL support commands like:
```
CREATE TEMPORARY TABLE avroTable
USING org.apache.spark.sql.avro
OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")
```
With this PR user can define schema instead of infer from file, so support ddl command as follows:
```
CREATE TEMPORARY TABLE avroTable(a int, b string)
USING org.apache.spark.sql.avro
OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")
```
Author: scwf <wangfei1@huawei.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Fei Wang <wangfei1@huawei.com>
Author: wangfei <wangfei1@huawei.com>
Closes#3431 from scwf/ddl and squashes the following commits:
7e79ce5 [Fei Wang] Merge pull request #22 from yhuai/pr3431yin
38f634e [Yin Huai] Remove Option from createRelation.
65e9c73 [Yin Huai] Revert all changes since applying a given schema has not been testd.
a852b10 [scwf] remove cleanIdentifier
f336a16 [Fei Wang] Merge pull request #21 from yhuai/pr3431yin
baf79b5 [Yin Huai] Test special characters quoted by backticks.
50a03b0 [Yin Huai] Use JsonRDD.nullTypeToStringType to convert NullType to StringType.
1eeb769 [Fei Wang] Merge pull request #20 from yhuai/pr3431yin
f5c22b0 [Yin Huai] Refactor code and update test cases.
f1cffe4 [Yin Huai] Revert "minor refactory"
b621c8f [scwf] minor refactory
d02547f [scwf] fix HiveCompatibilitySuite test failure
8dfbf7a [scwf] more tests for complex data type
ddab984 [Fei Wang] Merge pull request #19 from yhuai/pr3431yin
91ad91b [Yin Huai] Parse data types in DDLParser.
cf982d2 [scwf] fixed test failure
445b57b [scwf] address comments
02a662c [scwf] style issue
44eb70c [scwf] fix decimal parser issue
83b6fc3 [scwf] minor fix
9bf12f8 [wangfei] adding test case
7787ec7 [wangfei] added SchemaRelationProvider
0ba70df [wangfei] draft version
The pull only fixes the parsing error and changes API to use tableIdentifier. Joining different catalog datasource related change is not done in this pull.
Author: Alex Liu <alex_liu68@yahoo.com>
Closes#3941 from alexliu68/SPARK-SQL-4943-3 and squashes the following commits:
343ae27 [Alex Liu] [SPARK-4943][SQL] refactoring according to review
29e5e55 [Alex Liu] [SPARK-4943][SQL] fix failed Hive CTAS tests
6ae77ce [Alex Liu] [SPARK-4943][SQL] fix TestHive matching error
3652997 [Alex Liu] [SPARK-4943][SQL] Allow table name having dot to support db/catalog ...
CaseInsensitiveMap throws java.io.NotSerializableException.
Author: luogankun <luogankun@gmail.com>
Closes#3944 from luogankun/SPARK-5141 and squashes the following commits:
b6d63d5 [luogankun] [SPARK-5141]CaseInsensitiveMap throws java.io.NotSerializableException
This change does a few things to make the hadoop-provided profile more useful:
- Create new profiles for other libraries / services that might be provided by the infrastructure
- Simplify and fix the poms so that the profiles are only activated while building assemblies.
- Fix tests so that they're able to run when the profiles are activated
- Add a new env variable to be used by distributions that use these profiles to provide the runtime
classpath for Spark jobs and daemons.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#2982 from vanzin/SPARK-4048 and squashes the following commits:
82eb688 [Marcelo Vanzin] Add a comment.
eb228c0 [Marcelo Vanzin] Fix borked merge.
4e38f4e [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
9ef79a3 [Marcelo Vanzin] Alternative way to propagate test classpath to child processes.
371ebee [Marcelo Vanzin] Review feedback.
52f366d [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
83099fc [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
7377e7b [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
322f882 [Marcelo Vanzin] Fix merge fail.
f24e9e7 [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
8b00b6a [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
9640503 [Marcelo Vanzin] Cleanup child process log message.
115fde5 [Marcelo Vanzin] Simplify a comment (and make it consistent with another pom).
e3ab2da [Marcelo Vanzin] Fix hive-thriftserver profile.
7820d58 [Marcelo Vanzin] Fix CliSuite with provided profiles.
1be73d4 [Marcelo Vanzin] Restore flume-provided profile.
d1399ed [Marcelo Vanzin] Restore jetty dependency.
82a54b9 [Marcelo Vanzin] Remove unused profile.
5c54a25 [Marcelo Vanzin] Fix HiveThriftServer2Suite with *-provided profiles.
1fc4d0b [Marcelo Vanzin] Update dependencies for hive-thriftserver.
f7b3bbe [Marcelo Vanzin] Add snappy to hadoop-provided list.
9e4e001 [Marcelo Vanzin] Remove duplicate hive profile.
d928d62 [Marcelo Vanzin] Redirect child stderr to parent's log.
4d67469 [Marcelo Vanzin] Propagate SPARK_DIST_CLASSPATH on Yarn.
417d90e [Marcelo Vanzin] Introduce "SPARK_DIST_CLASSPATH".
2f95f0d [Marcelo Vanzin] Propagate classpath to child processes during testing.
1adf91c [Marcelo Vanzin] Re-enable maven-install-plugin for a few projects.
284dda6 [Marcelo Vanzin] Rework the "hadoop-provided" profile, add new ones.
This PR:
- Reenables `surefire`, and copies config from `scalatest` (which is itself an old fork of `surefire`, so similar)
- Tells `surefire` to test only Java tests
- Enables `surefire` and `scalatest` for all children, and in turn eliminates some duplication.
For me this causes the Scala and Java tests to be run once each, it seems, as desired. It doesn't affect the SBT build but works for Maven. I still need to verify that all of the Scala tests and Java tests are being run.
Author: Sean Owen <sowen@cloudera.com>
Closes#3651 from srowen/SPARK-4159 and squashes the following commits:
2e8a0af [Sean Owen] Remove specialized SPARK_HOME setting for REPL, YARN tests as it appears to be obsolete
12e4558 [Sean Owen] Append to unit-test.log instead of overwriting, so that both surefire and scalatest output is preserved. Also standardize/correct comments a bit.
e6f8601 [Sean Owen] Reenable Java tests by reenabling surefire with config cloned from scalatest; centralize test config in the parent
Author: Reynold Xin <rxin@databricks.com>
Closes#3862 from rxin/stringcontext-attr and squashes the following commits:
9b10f57 [Reynold Xin] Rename StrongToAttributeConversionHelper
72121af [Reynold Xin] [SPARK-5040][SQL] Support expressing unresolved attributes using $"attribute name" notation in SQL DSL.
As we learned in https://github.com/apache/spark/pull/3580, not explicitly typing implicit functions can lead to compiler bugs and potentially unexpected runtime behavior.
Author: Reynold Xin <rxin@databricks.com>
Closes#3859 from rxin/sql-implicits and squashes the following commits:
30c2c24 [Reynold Xin] [SPARK-5038] Add explicit return type for implicit functions in Spark SQL.
JIRA issue: [SPARK-4570](https://issues.apache.org/jira/browse/SPARK-4570)
We are planning to create a `BroadcastLeftSemiJoinHash` to implement the broadcast join for `left semijoin`
In left semijoin :
If the size of data from right side is smaller than the user-settable threshold `AUTO_BROADCASTJOIN_THRESHOLD`,
the planner would mark it as the `broadcast` relation and mark the other relation as the stream side. The broadcast table will be broadcasted to all of the executors involved in the join, as a `org.apache.spark.broadcast.Broadcast` object. It will use `joins.BroadcastLeftSemiJoinHash`.,else it will use `joins.LeftSemiJoinHash`.
The benchmark suggests these made the optimized version 4x faster when `left semijoin`
<pre><code>
Original:
left semi join : 9288 ms
Optimized:
left semi join : 1963 ms
</code></pre>
The micro benchmark load `data1/kv3.txt` into a normal Hive table.
Benchmark code:
<pre><code>
def benchmark(f: => Unit) = {
val begin = System.currentTimeMillis()
f
val end = System.currentTimeMillis()
end - begin
}
val sc = new SparkContext(
new SparkConf()
.setMaster("local")
.setAppName(getClass.getSimpleName.stripSuffix("$")))
val hiveContext = new HiveContext(sc)
import hiveContext._
sql("drop table if exists left_table")
sql("drop table if exists right_table")
sql( """create table left_table (key int, value string)
""".stripMargin)
sql( s"""load data local inpath "/data1/kv3.txt" into table left_table""")
sql( """create table right_table (key int, value string)
""".stripMargin)
sql(
"""
|from left_table
|insert overwrite table right_table
|select left_table.key, left_table.value
""".stripMargin)
val leftSimeJoin = sql(
"""select a.key from left_table a
|left semi join right_table b on a.key = b.key""".stripMargin)
val leftSemiJoinDuration = benchmark(leftSimeJoin.count())
println(s"left semi join : $leftSemiJoinDuration ms ")
</code></pre>
Author: wangxiaojing <u9jing@gmail.com>
Closes#3442 from wangxiaojing/SPARK-4570 and squashes the following commits:
a4a43c9 [wangxiaojing] rebase
f103983 [wangxiaojing] change style
fbe4887 [wangxiaojing] change style
ff2e618 [wangxiaojing] add testsuite
1a8da2a [wangxiaojing] add BroadcastLeftSemiJoinHash
Convert type of RowWriteSupport.attributes to Array.
Analysis of performance for writing very wide tables shows that time is spent predominantly in apply method on attributes var. Type of attributes previously was LinearSeqOptimized and apply is O(N) which made write O(N squared).
Measurements on 575 column table showed this change made a 6x improvement in write times.
Author: Michael Davies <Michael.BellDavies@gmail.com>
Closes#3843 from MickDavies/SPARK-4386 and squashes the following commits:
892519d [Michael Davies] [SPARK-4386] Improve performance when writing Parquet files
This PR is a simplified version of several filter optimization rules introduced in #3778 authored by scwf. Newly introduced optimizations include:
1. `a && a` => `a`
2. `a || a` => `a`
3. `(a || b || c || ...) && (a || b || d || ...)` => `a && b && (c || d || ...)`
The 3rd rule is particularly useful for optimizing the following query, which is planned into a cartesian product
```sql
SELECT *
FROM t1, t2
WHERE (t1.key = t2.key AND t1.value > 10)
OR (t1.key = t2.key AND t2.value < 20)
```
to the following one, which is planned into an equi-join:
```sql
SELECT *
FROM t1, t2
WHERE t1.key = t2.key
AND (t1.value > 10 OR t2.value < 20)
```
The example above is quite artificial, but common predicates are likely to appear in real life complex queries (like the one mentioned in #3778).
A difference between this PR and #3778 is that these optimizations are not limited to `Filter`, but are generalized to all logical plan nodes. Thanks to scwf for bringing up these optimizations, and chenghao-intel for the generalization suggestion.
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Author: Cheng Lian <lian@databricks.com>
Closes#3784 from liancheng/normalize-filters and squashes the following commits:
caca560 [Cheng Lian] Moves filter normalization into BooleanSimplification rule
4ab3a58 [Cheng Lian] Fixes test failure, adds more tests
5d54349 [Cheng Lian] Fixes typo in comment
2abbf8e [Cheng Lian] Forgot our sacred Apache licence header...
cf95639 [Cheng Lian] Adds an optimization rule for filter normalization
This is a follow-up of #3367 and #3644.
At the time #3644 was written, #3367 hadn't been merged yet, thus `IsNull` and `IsNotNull` filters are not covered in the first version of `ParquetFilterSuite`. This PR adds corresponding test cases.
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Author: Cheng Lian <lian@databricks.com>
Closes#3748 from liancheng/test-null-filters and squashes the following commits:
1ab943f [Cheng Lian] IsNull and IsNotNull Parquet filter test case for boolean type
bcd616b [Cheng Lian] Adds Parquet filter pushedown tests for IsNull and IsNotNull
It will cause exception while do query like:
SELECT key+key FROM src sort by value;
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3386 from chenghao-intel/sort and squashes the following commits:
38c78cc [Cheng Hao] revert the SortPartition in SparkStrategies
7e9dd15 [Cheng Hao] update the typo
fcd1d64 [Cheng Hao] rebase the latest master and update the SortBy unit test
spark sql does not support ```SELECT a, b FROM testData2 ORDER BY a desc, b```.
Author: wangfei <wangfei1@huawei.com>
Closes#3838 from scwf/orderby and squashes the following commits:
114b64a [wangfei] remove nouse methods
48145d3 [wangfei] fix order, using asc by default
There are a number of warnings generated in a normal, successful build right now. They're mostly Java unchecked cast warnings, which can be suppressed. But there's a grab bag of other Scala language warnings and so on that can all be easily fixed. The forthcoming PR fixes about 90% of the build warnings I see now.
Author: Sean Owen <sowen@cloudera.com>
Closes#3157 from srowen/SPARK-4297 and squashes the following commits:
8c9e469 [Sean Owen] Suppress unchecked cast warnings, and several other build warning fixes
This PR modifies the python `SchemaRDD` to use `sample()` and `takeSample()` from Scala instead of the slower python implementations from `rdd.py`. This is worthwhile because the `Row`'s are already serialized as Java objects.
In order to use the faster `takeSample()`, a `takeSampleToPython()` method was implemented in `SchemaRDD.scala` following the pattern of `collectToPython()`.
Author: jbencook <jbenjamincook@gmail.com>
Author: J. Benjamin Cook <jbenjamincook@gmail.com>
Closes#3764 from jbencook/master and squashes the following commits:
6fbc769 [J. Benjamin Cook] [SPARK-4860][pyspark][sql] fixing sloppy indentation for takeSampleToPython() arguments
5170da2 [J. Benjamin Cook] [SPARK-4860][pyspark][sql] fixing typo: from RDD to SchemaRDD
de22f70 [jbencook] [SPARK-4860][pyspark][sql] using sample() method from JavaSchemaRDD
b916442 [jbencook] [SPARK-4860][pyspark][sql] adding sample() to JavaSchemaRDD
020cbdf [jbencook] [SPARK-4860][pyspark][sql] using Scala implementations of `sample()` and `takeSample()`
...arquetFile accept hadoop glob pattern in path.
Author: Thu Kyaw <trk007@gmail.com>
Closes#3407 from tkyaw/master and squashes the following commits:
19115ad [Thu Kyaw] Merge https://github.com/apache/spark
ceded32 [Thu Kyaw] [SPARK-3928][SQL] Support wildcard matches on Parquet files.
d322c28 [Thu Kyaw] [SPARK-3928][SQL] Support wildcard matches on Parquet files.
ce677c6 [Thu Kyaw] [SPARK-3928][SQL] Support wildcard matches on Parquet files.
Add support for `GROUPING SETS`, `ROLLUP`, `CUBE` and the the virtual column `GROUPING__ID`.
More details on how to use the `GROUPING SETS" can be found at: https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation,+Cube,+Grouping+and+Rolluphttps://issues.apache.org/jira/secure/attachment/12676811/grouping_set.pdf
The generic idea of the implementations are :
1 Replace the `ROLLUP`, `CUBE` with `GROUPING SETS`
2 Explode each of the input row, and then feed them to `Aggregate`
* Each grouping set are represented as the bit mask for the `GroupBy Expression List`, for each bit, `1` means the expression is selected, otherwise `0` (left is the lower bit, and right is the higher bit in the `GroupBy Expression List`)
* Several of projections are constructed according to the grouping sets, and within each projection(Seq[Expression), we replace those expressions with `Literal(null)` if it's not selected in the grouping set (based on the bit mask)
* Output Schema of `Explode` is `child.output :+ grouping__id`
* GroupBy Expressions of `Aggregate` is `GroupBy Expression List :+ grouping__id`
* Keep the `Aggregation expressions` the same for the `Aggregate`
The expressions substitutions happen in Logic Plan analyzing, so we will benefit from the Logical Plan optimization (e.g. expression constant folding, and map side aggregation etc.), Only an `Explosive` operator added for Physical Plan, which will explode the rows according the pre-set projections.
A known issue will be done in the follow up PR:
* Optimization `ColumnPruning` is not supported yet for `Explosive` node.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#1567 from chenghao-intel/grouping_sets and squashes the following commits:
fe65fcc [Cheng Hao] Remove the extra space
3547056 [Cheng Hao] Add more doc and Simplify the Expand
a7c869d [Cheng Hao] update code as feedbacks
d23c672 [Cheng Hao] Add GroupingExpression to replace the Seq[Expression]
414b165 [Cheng Hao] revert the unnecessary changes
ec276c6 [Cheng Hao] Support Rollup/Cube/GroupingSets
```
TestSQLContext.sparkContext.parallelize(
"""{"ip":"27.31.100.29","headers":{"Host":"1.abc.com","Charset":"UTF-8"}}""" ::
"""{"ip":"27.31.100.29","headers":{}}""" ::
"""{"ip":"27.31.100.29","headers":""}""" :: Nil)
```
As empty string (the "headers") will be considered as String in the beginning (in line 2 and 3), it ignores the real nested data type (struct type "headers" in line 1), and also take the line 1 (the "headers") as String Type, which is not our expected.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3708 from chenghao-intel/json and squashes the following commits:
e7a72e9 [Cheng Hao] add more concise unit test
853de51 [Cheng Hao] NullType instead of StringType when sampling against empty string or null value
Author: Michael Armbrust <michael@databricks.com>
Closes#3727 from marmbrus/parquetNotEq and squashes the following commits:
2157bfc [Michael Armbrust] Fix parquet filter suite
Predicates like `a = NULL` and `a < NULL` can't be pushed down since Parquet `Lt`, `LtEq`, `Gt`, `GtEq` doesn't accept null value. Note that `Eq` and `NotEq` can only be used with `null` to represent predicates like `a IS NULL` and `a IS NOT NULL`.
However, normally this issue doesn't cause NPE because any value compared to `NULL` results `NULL`, and Spark SQL automatically optimizes out `NULL` predicate in the `SimplifyFilters` rule. Only testing code that intentionally disables the optimizer may trigger this issue. (That's why this issue is not marked as blocker and I do **NOT** think we need to backport this to branch-1.1
This PR restricts `Lt`, `LtEq`, `Gt` and `GtEq` to non-null values only, and only uses `Eq` with null value to pushdown `IsNull` and `IsNotNull`. Also, added support for Parquet `NotEq` filter for completeness and (tiny) performance gain, it's also used to pushdown `IsNotNull`.
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Author: Cheng Lian <lian@databricks.com>
Closes#3367 from liancheng/filters-with-null and squashes the following commits:
cc41281 [Cheng Lian] Fixes several styling issues
de7de28 [Cheng Lian] Adds stricter rules for Parquet filters with null
Add `sort by` support for both DSL & SqlParser.
This PR is relevant with #3386, either one merged, will cause the other rebased.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3481 from chenghao-intel/sortby and squashes the following commits:
041004f [Cheng Hao] Add sort by for DSL & SimpleSqlParser
Using lowercase for ```options``` key to make it case-insensitive, then we should use lower case to get value from parameters.
So flowing cmd work
```
create temporary table normal_parquet
USING org.apache.spark.sql.parquet
OPTIONS (
PATH '/xxx/data'
)
```
Author: scwf <wangfei1@huawei.com>
Author: wangfei <wangfei1@huawei.com>
Closes#3470 from scwf/ddl-ulcase and squashes the following commits:
ae78509 [scwf] address comments
8f4f585 [wangfei] address comments
3c132ef [scwf] minor fix
a0fc20b [scwf] Merge branch 'master' of https://github.com/apache/spark into ddl-ulcase
4f86401 [scwf] adding CaseInsensitiveMap
e244e8d [wangfei] using lower case in json
e0cb017 [wangfei] make options in-casesensitive
This PR brings support of using StructType(and other hashable types) as key in MapType.
Author: Davies Liu <davies@databricks.com>
Closes#3714 from davies/fix_struct_in_map and squashes the following commits:
68585d7 [Davies Liu] fix primitive types in MapType
9601534 [Davies Liu] support StructType as key in MapType
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3595 from chenghao-intel/udf0 and squashes the following commits:
a858973 [Cheng Hao] Add 0 arguments support for udf
This PR provides a set Parquet testing API (see trait `ParquetTest`) that enables developers to write more concise test cases. A new set of Parquet test suites built upon this API are added and aim to replace the old `ParquetQuerySuite`. To avoid potential merge conflicts, old testing code are not removed yet. The following classes can be safely removed after most Parquet related PRs are handled:
- `ParquetQuerySuite`
- `ParquetTestData`
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Author: Cheng Lian <lian@databricks.com>
Closes#3644 from liancheng/parquet-tests and squashes the following commits:
800e745 [Cheng Lian] Enforces ordering of test output
3bb8731 [Cheng Lian] Refactors HiveParquetSuite
aa2cb2e [Cheng Lian] Decouples ParquetTest and TestSQLContext
7b43a68 [Cheng Lian] Updates ParquetTest Scaladoc
7f07af0 [Cheng Lian] Adds a new set of Parquet test suites
In BroadcastHashJoin, currently it is using a hard coded value (5 minutes) to wait for the execution and broadcast of the small table.
In my opinion, it should be a configurable value since broadcast may exceed 5 minutes in some case, like in a busy/congested network environment.
Author: Jacky Li <jacky.likun@huawei.com>
Closes#3133 from jackylk/timeout-config and squashes the following commits:
733ac08 [Jacky Li] add spark.sql.broadcastTimeout in SQLConf.scala
557acd4 [Jacky Li] switch to sqlContext.getConf
81a5e20 [Jacky Li] make wait time configurable in BroadcastHashJoin
In `HashOuterJoin.scala`, spark read data from both side of join operation before zip them together. It is a waste for memory. We are trying to read data from only one side, put them into a hashmap, and then generate the `JoinedRow` with data from other side one by one.
Currently, we could only do this optimization for `left outer join` and `right outer join`. For `full outer join`, we will do something in another issue.
for
table test_csv contains 1 million records
table dim_csv contains 10 thousand records
SQL:
`select * from test_csv a left outer join dim_csv b on a.key = b.key`
the result is:
master:
```
CSV: 12671 ms
CSV: 9021 ms
CSV: 9200 ms
Current Mem Usage:787788984
```
after patch：
```
CSV: 10382 ms
CSV: 7543 ms
CSV: 7469 ms
Current Mem Usage:208145728
```
Author: tianyi <tianyi@asiainfo-linkage.com>
Author: tianyi <tianyi.asiainfo@gmail.com>
Closes#3375 from tianyi/SPARK-4483 and squashes the following commits:
72a8aec [tianyi] avoid having mutable state stored inside of the task
99c5c97 [tianyi] performance optimization
d2f94d7 [tianyi] fix bug: missing output when the join-key is null.
2be45d1 [tianyi] fix spell bug
1f2c6f1 [tianyi] remove commented codes
a676de6 [tianyi] optimize some codes
9e7d5b5 [tianyi] remove commented old codes
838707d [tianyi] Optimization about reduce memory costs during the HashOuterJoin
The problem is `codegenEnabled` is `val`, but it uses a `val` `sqlContext`, which can be override by subclasses. Here is a simple example to show this issue.
```Scala
scala> :paste
// Entering paste mode (ctrl-D to finish)
abstract class Foo {
protected val sqlContext = "Foo"
val codegenEnabled: Boolean = {
println(sqlContext) // it will call subclass's `sqlContext` which has not yet been initialized.
if (sqlContext != null) {
true
} else {
false
}
}
}
class Bar extends Foo {
override val sqlContext = "Bar"
}
println(new Bar().codegenEnabled)
// Exiting paste mode, now interpreting.
null
false
defined class Foo
defined class Bar
```
We should make `sqlContext` `final` to prevent subclasses from overriding it incorrectly.
Author: zsxwing <zsxwing@gmail.com>
Closes#3660 from zsxwing/SPARK-4812 and squashes the following commits:
1cbb623 [zsxwing] Make `sqlContext` final to prevent subclasses from overriding it incorrectly
Author: jerryshao <saisai.shao@intel.com>
Closes#3698 from jerryshao/SPARK-4847 and squashes the following commits:
4741130 [jerryshao] Make later added extraStrategies effect when calling strategies
When I use Parquet File as a output file using ParquetOutputFormat#getDefaultWorkFile, the file name is not zero padded while RDD#saveAsText does zero padding.
Author: Sasaki Toru <sasakitoa@nttdata.co.jp>
Closes#3602 from sasakitoa/parquet-zeroPadding and squashes the following commits:
6b0e58f [Sasaki Toru] Merge branch 'master' of git://github.com/apache/spark into parquet-zeroPadding
20dc79d [Sasaki Toru] Fixed the name of Parquet File generated by AppendingParquetOutputFormat
Unpersist a uncached RDD, will not raise exception, for example:
```
val data = Array(1, 2, 3, 4, 5)
val distData = sc.parallelize(data)
distData.unpersist(true)
```
But the `SchemaRDD` will raise exception if the `SchemaRDD` is not cached. Since `SchemaRDD` is the subclasses of the `RDD`, we should follow the same behavior.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3572 from chenghao-intel/try_uncache and squashes the following commits:
50a7a89 [Cheng Hao] SchemaRDD.unpersist() should not raise exception if it is not persisted
Author: Jacky Li <jacky.likun@huawei.com>
Closes#3630 from jackylk/remove and squashes the following commits:
150e7e0 [Jacky Li] remove unnecessary import
Author: Michael Armbrust <michael@databricks.com>
Closes#3613 from marmbrus/parquetPartitionPruning and squashes the following commits:
4f138f8 [Michael Armbrust] Use catalyst for partition pruning in newParquet.
Author: Jacky Li <jacky.likun@huawei.com>
Closes#3585 from jackylk/remove and squashes the following commits:
045423d [Jacky Li] remove unnecessary import
This is a very small fix that catches one specific exception and returns an empty table. #3441 will address this in a more principled way.
Author: Michael Armbrust <michael@databricks.com>
Closes#3586 from marmbrus/fixEmptyParquet and squashes the following commits:
2781d9f [Michael Armbrust] Handle empty lists for newParquet
04dd376 [Michael Armbrust] Avoid exception when reading empty parquet data through Hive
val jsc = new org.apache.spark.api.java.JavaSparkContext(sc)
val jhc = new org.apache.spark.sql.hive.api.java.JavaHiveContext(jsc)
val nrdd = jhc.hql("select null from spark_test.for_test")
println(nrdd.schema)
Then the error is thrown as follows:
scala.MatchError: NullType (of class org.apache.spark.sql.catalyst.types.NullType$)
at org.apache.spark.sql.types.util.DataTypeConversions$.asJavaDataType(DataTypeConversions.scala:43)
Author: YanTangZhai <hakeemzhai@tencent.com>
Author: yantangzhai <tyz0303@163.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#3538 from YanTangZhai/MatchNullType and squashes the following commits:
e052dff [yantangzhai] [SPARK-4676] [SQL] JavaSchemaRDD.schema may throw NullType MatchError if sql has null
4b4bb34 [yantangzhai] [SPARK-4676] [SQL] JavaSchemaRDD.schema may throw NullType MatchError if sql has null
896c7b7 [yantangzhai] fix NullType MatchError in JavaSchemaRDD when sql has null
6e643f8 [YanTangZhai] Merge pull request #11 from apache/master
e249846 [YanTangZhai] Merge pull request #10 from apache/master
d26d982 [YanTangZhai] Merge pull request #9 from apache/master
76d4027 [YanTangZhai] Merge pull request #8 from apache/master
03b62b0 [YanTangZhai] Merge pull request #7 from apache/master
8a00106 [YanTangZhai] Merge pull request #6 from apache/master
cbcba66 [YanTangZhai] Merge pull request #3 from apache/master
cdef539 [YanTangZhai] Merge pull request #1 from apache/master
Author: baishuo <vc_java@hotmail.com>
Closes#3526 from baishuo/master-trycatch and squashes the following commits:
d446e14 [baishuo] correct the code style
b36bf96 [baishuo] correct the code style
ae0e447 [baishuo] add finally to avoid resource leak
Spark SQL has embeded sqrt and abs but DSL doesn't support those functions.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#3401 from sarutak/dsl-missing-operator and squashes the following commits:
07700cf [Kousuke Saruta] Modified Literal(null, NullType) to Literal(null) in DslQuerySuite
8f366f8 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into dsl-missing-operator
1b88e2e [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into dsl-missing-operator
0396f89 [Kousuke Saruta] Added sqrt and abs to Spark SQL DSL
A very small nit update.
Author: Reynold Xin <rxin@databricks.com>
Closes#3552 from rxin/license-header and squashes the following commits:
df8d1a4 [Reynold Xin] Indent license header properly for interfaces.scala.
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3516 from ravipesala/ddl_doc and squashes the following commits:
d101fdf [ravipesala] Style issues fixed
d2238cd [ravipesala] Corrected documentation
Supporting multi column support in countDistinct function like count(distinct c1,c2..) in Spark SQL
Author: ravipesala <ravindra.pesala@huawei.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#3511 from ravipesala/countdistinct and squashes the following commits:
cc4dbb1 [ravipesala] style
070e12a [ravipesala] Supporting multi column support in count(distinct c1,c2..) in Spark SQL
group tab is missing for scaladoc
Author: Jacky Li <jacky.likun@gmail.com>
Closes#3458 from jackylk/patch-7 and squashes the following commits:
0121a70 [Jacky Li] add @group tab in limit() and count()
Re-implement the Python broadcast using file:
1) serialize the python object using cPickle, write into disks.
2) Create a wrapper in JVM (for the dumped file), it read data from during serialization
3) Using TorrentBroadcast or HttpBroadcast to transfer the data (compressed) into executors
4) During deserialization, writing the data into disk.
5) Passing the path into Python worker, read data from disk and unpickle it into python object, until the first access.
It fixes the performance regression introduced in #2659, has similar performance as 1.1, but support object larger than 2G, also improve the memory efficiency (only one compressed copy in driver and executor).
Testing with a 500M broadcast and 4 tasks (excluding the benefit from reused worker in 1.2):
name | 1.1 | 1.2 with this patch | improvement
---------|--------|---------|--------
python-broadcast-w-bytes | 25.20 | 9.33 | 170.13% |
python-broadcast-w-set | 4.13 | 4.50 | -8.35% |
Testing with 100 tasks (16 CPUs):
name | 1.1 | 1.2 with this patch | improvement
---------|--------|---------|--------
python-broadcast-w-bytes | 38.16 | 8.40 | 353.98%
python-broadcast-w-set | 23.29 | 9.59 | 142.80%
Author: Davies Liu <davies@databricks.com>
Closes#3417 from davies/pybroadcast and squashes the following commits:
50a58e0 [Davies Liu] address comments
b98de1d [Davies Liu] disable gc while unpickle
e5ee6b9 [Davies Liu] support large string
09303b8 [Davies Liu] read all data into memory
dde02dd [Davies Liu] improve performance of python broadcast
When we use ORDER BY clause, at first, attributes referenced by projection are resolved (1).
And then, attributes referenced at ORDER BY clause are resolved (2).
But when resolving attributes referenced at ORDER BY clause, the resolution result generated in (1) is discarded so for example, following query fails.
SELECT c1 + c2 FROM mytable ORDER BY c1;
The query above fails because when resolving the attribute reference 'c1', the resolution result of 'c2' is discarded.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#3363 from sarutak/SPARK-4487 and squashes the following commits:
fd314f3 [Kousuke Saruta] Fixed attribute resolution logic in Analyzer
6e60c20 [Kousuke Saruta] Fixed conflicts
cb5b7e9 [Kousuke Saruta] Added test case for SPARK-4487
282d529 [Kousuke Saruta] Fixed attributes reference resolution error
b6123e6 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into concat-feature
317b7fb [Kousuke Saruta] WIP
This PR is a workaround for SPARK-4479. Two changes are introduced: when merge sort is bypassed in `ExternalSorter`,
1. also bypass RDD elements buffering as buffering is the reason that `MutableRow` backed row objects must be copied, and
2. avoids defensive copies in `Exchange` operator
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Author: Cheng Lian <lian@databricks.com>
Closes#3422 from liancheng/avoids-defensive-copies and squashes the following commits:
591f2e9 [Cheng Lian] Passes all shuffle suites
0c3c91e [Cheng Lian] Fixes shuffle write metrics when merge sort is bypassed
ed5df3c [Cheng Lian] Fixes styling changes
f75089b [Cheng Lian] Avoids unnecessary defensive copies when sort based shuffle is on
Goals:
- Support for accessing parquet using SQL but not requiring Hive (thus allowing support of parquet tables with decimal columns)
- Support for folder based partitioning with automatic discovery of available partitions
- Caching of file metadata
See scaladoc of `ParquetRelation2` for more details.
Author: Michael Armbrust <michael@databricks.com>
Closes#3269 from marmbrus/newParquet and squashes the following commits:
1dd75f1 [Michael Armbrust] Pass all paths for FileInputFormat at once.
645768b [Michael Armbrust] Review comments.
abd8e2f [Michael Armbrust] Alternative implementation of parquet based on the datasources API.
938019e [Michael Armbrust] Add an experimental interface to data sources that exposes catalyst expressions.
e9d2641 [Michael Armbrust] logging / formatting improvements.
Sample code in the description of SchemaRDD.where is not correct
Author: Jacky Li <jacky.likun@gmail.com>
Closes#3344 from jackylk/patch-6 and squashes the following commits:
62cd126 [Jacky Li] [SQL] fix function description mistake
Executing sum distinct for empty table throws `java.lang.UnsupportedOperationException: empty.reduceLeft`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#3184 from ueshin/issues/SPARK-4318 and squashes the following commits:
8168c42 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-4318
66fdb0a [Takuya UESHIN] Re-refine aggregate functions.
6186eb4 [Takuya UESHIN] Fix Sum of GeneratedAggregate.
d2975f6 [Takuya UESHIN] Refine Sum and Average of GeneratedAggregate.
1bba675 [Takuya UESHIN] Refine Sum, SumDistinct and Average functions.
917e533 [Takuya UESHIN] Use aggregate instead of groupBy().
1a5f874 [Takuya UESHIN] Add tests to be executed as non-partial aggregation.
a5a57d2 [Takuya UESHIN] Fix empty Average.
22799dc [Takuya UESHIN] Fix empty Sum and SumDistinct.
65b7dd2 [Takuya UESHIN] Fix empty sum distinct.
The relational operator '<=>' is not working in Spark SQL. Same works in Spark HiveQL
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3387 from ravipesala/<=> and squashes the following commits:
7198e90 [ravipesala] Supporting relational operator '<=>' in Spark SQL
This PR enables the Web UI storage tab to show the in-memory table name instead of the mysterious query plan string as the name of the in-memory columnar RDD.
Note that after #2501, a single columnar RDD can be shared by multiple in-memory tables, as long as their query results are the same. In this case, only the first cached table name is shown. For example:
```sql
CACHE TABLE first AS SELECT * FROM src;
CACHE TABLE second AS SELECT * FROM src;
```
The Web UI only shows "In-memory table first".
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Author: Cheng Lian <lian@databricks.com>
Closes#3383 from liancheng/columnar-rdd-name and squashes the following commits:
071907f [Cheng Lian] Fixes tests
12ddfa6 [Cheng Lian] Names in-memory columnar RDD with corresponding table name
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#3277 from vanzin/version-1.3 and squashes the following commits:
7c3c396 [Marcelo Vanzin] Added temp repo to sbt build.
5f404ff [Marcelo Vanzin] Add another exclusion.
19457e7 [Marcelo Vanzin] Update old version to 1.2, add temporary 1.2 repo.
3c8d705 [Marcelo Vanzin] Workaround for MIMA checks.
e940810 [Marcelo Vanzin] Bumping version to 1.3.0-SNAPSHOT.
For expressions like `10 < someVar`, we should create an `Operators.Gt` filter, but right now an `Operators.Lt` is created. This issue affects all inequality predicates with literals on the left hand side.
(This bug existed before #3317 and affects branch-1.1. #3338 was opened to backport this to branch-1.1.)
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Author: Cheng Lian <lian@databricks.com>
Closes#3334 from liancheng/fix-parquet-comp-filter and squashes the following commits:
0130897 [Cheng Lian] Fixes Parquet comparison filter generation
This patch will bring support for broadcasting objects larger than 2G.
pickle, zlib, FrameSerializer and Array[Byte] all can not support objects larger than 2G, so this patch introduce LargeObjectSerializer to serialize broadcast objects, the object will be serialized and compressed into small chunks, it also change the type of Broadcast[Array[Byte]]] into Broadcast[Array[Array[Byte]]]].
Testing for support broadcast objects larger than 2G is slow and memory hungry, so this is tested manually, could be added into SparkPerf.
Author: Davies Liu <davies@databricks.com>
Author: Davies Liu <davies.liu@gmail.com>
Closes#2659 from davies/huge and squashes the following commits:
7b57a14 [Davies Liu] add more tests for broadcast
28acff9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
a2f6a02 [Davies Liu] bug fix
4820613 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
5875c73 [Davies Liu] address comments
10a349b [Davies Liu] address comments
0c33016 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
6182c8f [Davies Liu] Merge branch 'master' into huge
d94b68f [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
2514848 [Davies Liu] address comments
fda395b [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
1c2d928 [Davies Liu] fix scala style
091b107 [Davies Liu] broadcast objects larger than 2G
While reviewing PR #3083 and #3161, I noticed that Parquet record filter generation code can be simplified significantly according to the clue stated in [SPARK-4453](https://issues.apache.org/jira/browse/SPARK-4213). This PR addresses both SPARK-4453 and SPARK-4213 with this simplification.
While generating `ParquetTableScan` operator, we need to remove all Catalyst predicates that have already been pushed down to Parquet. Originally, we first generate the record filter, and then call `findExpression` to traverse the generated filter to find out all pushed down predicates [[1](64c6b9bad5/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala (L213-L228))]. In this way, we have to introduce the `CatalystFilter` class hierarchy to bind the Catalyst predicates together with their generated Parquet filter, and complicate the code base a lot.
The basic idea of this PR is that, we don't need `findExpression` after filter generation, because we already know a predicate can be pushed down if we can successfully generate its corresponding Parquet filter. SPARK-4213 is fixed by returning `None` for any unsupported predicate type.
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Author: Cheng Lian <lian@databricks.com>
Closes#3317 from liancheng/simplify-parquet-filters and squashes the following commits:
d6a9499 [Cheng Lian] Fixes import styling issue
43760e8 [Cheng Lian] Simplifies Parquet filter generation logic
Adds a new operator that uses Spark's `ExternalSort` class. It is off by default now, but we might consider making it the default if benchmarks show that it does not regress performance.
Author: Michael Armbrust <michael@databricks.com>
Closes#3268 from marmbrus/externalSort and squashes the following commits:
48b9726 [Michael Armbrust] comments
b98799d [Michael Armbrust] Add test
afd7562 [Michael Armbrust] Add support for external sort.
The Spark ParquetRelation.scala code makes the assumption that the parquet.Log class has already been loaded. If ParquetRelation.enableLogForwarding executes prior to the parquet.Log class being loaded then the code in enableLogForwarding has no affect.
ParquetRelation.scala attempts to override the parquet logger but, at least currently (and if your application simply reads a parquet file before it does anything else with Parquet), the parquet.Log class hasn't been loaded yet. Therefore the code in ParquetRelation.enableLogForwarding has no affect. If you look at the code in parquet.Log there's a static initializer that needs to be called prior to enableLogForwarding or whatever enableLogForwarding does gets undone by this static initializer.
The "fix" would be to force the static initializer to get called in parquet.Log as part of enableForwardLogging.
Author: Jim Carroll <jim@dontcallme.com>
Closes#3271 from jimfcarroll/parquet-logging and squashes the following commits:
37bdff7 [Jim Carroll] Fix Spark's control of Parquet logging.
Since parquet library has been updated , we no longer need to filter the records returned from parquet library for null records , as now the library skips those :
from parquet-hadoop/src/main/java/parquet/hadoop/InternalParquetRecordReader.java
public boolean nextKeyValue() throws IOException, InterruptedException {
boolean recordFound = false;
while (!recordFound) {
// no more records left
if (current >= total)
{ return false; }
try {
checkRead();
currentValue = recordReader.read();
current ++;
if (recordReader.shouldSkipCurrentRecord())
{
// this record is being filtered via the filter2 package
if (DEBUG) LOG.debug("skipping record");
continue;
}
if (currentValue == null)
{
// only happens with FilteredRecordReader at end of block current = totalCountLoadedSoFar;
if (DEBUG) LOG.debug("filtered record reader reached end of block");
continue;
}
recordFound = true;
if (DEBUG) LOG.debug("read value: " + currentValue);
} catch (RuntimeException e)
{ throw new ParquetDecodingException(format("Can not read value at %d in block %d in file %s", current, currentBlock, file), e); }
}
return true;
}
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#3229 from saucam/remove_filter and squashes the following commits:
8909ae9 [Yash Datta] SPARK-4365: Remove unnecessary filter call on records returned from parquet library
If you profile the writing of a Parquet file, the single worst time consuming call inside of org.apache.spark.sql.parquet.MutableRowWriteSupport.write is actually in the scala.collection.AbstractSequence.size call. This is because the size call actually ends up COUNTING the elements in a scala.collection.LinearSeqOptimized.length ("optimized?").
This doesn't need to be done. "size" is called repeatedly where needed rather than called once at the top of the method and stored in a 'val'.
Author: Jim Carroll <jim@dontcallme.com>
Closes#3254 from jimfcarroll/parquet-perf and squashes the following commits:
30cc0b5 [Jim Carroll] Improve performance when writing Parquet files.
While resolving struct fields, the resulted `GetField` expression is wrapped with an `Alias` to make it a named expression. Assume `a` is a struct instance with a field `b`, then `"a.b"` will be resolved as `Alias(GetField(a, "b"), "b")`. Thus, for this following SQL query:
```sql
SELECT a.b + 1 FROM t GROUP BY a.b + 1
```
the grouping expression is
```scala
Add(GetField(a, "b"), Literal(1, IntegerType))
```
while the aggregation expression is
```scala
Add(Alias(GetField(a, "b"), "b"), Literal(1, IntegerType))
```
This mismatch makes the above SQL query fail during the both analysis and execution phases. This PR fixes this issue by removing the alias when substituting aggregation expressions.
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Author: Cheng Lian <lian@databricks.com>
Closes#3248 from liancheng/spark-4322 and squashes the following commits:
23a46ea [Cheng Lian] Code simplification
dd20a79 [Cheng Lian] Should only trim aliases around `GetField`s
7f46532 [Cheng Lian] Enables struct fields as sub expressions of grouping fields
When sort based shuffle and code gen are on we were trying to ship the code generated rows during a shuffle. This doesn't work because the classes don't exist on the other side. Instead we now copy into a generic row before shipping.
Author: Michael Armbrust <michael@databricks.com>
Closes#3263 from marmbrus/aggCodeGen and squashes the following commits:
f6ba8cf [Michael Armbrust] fix and test
This is more uniform with the rest of SQL configuration and allows it to be turned on and off without restarting the SparkContext. In this PR I also turn off filter pushdown by default due to a number of outstanding issues (in particular SPARK-4258). When those are fixed we should turn it back on by default.
Author: Michael Armbrust <michael@databricks.com>
Closes#3258 from marmbrus/parquetFilters and squashes the following commits:
5655bfe [Michael Armbrust] Remove extra line.
15e9a98 [Michael Armbrust] Enable filters for tests
75afd39 [Michael Armbrust] Fix comments
78fa02d [Michael Armbrust] off by default
e7f9e16 [Michael Armbrust] First draft of correctly configuring parquet filter pushdown
This PR adds two features to the data sources API:
- Support for pushing down `IN` filters
- The ability for relations to optionally provide information about their `sizeInBytes`.
Author: Michael Armbrust <michael@databricks.com>
Closes#3260 from marmbrus/sourcesImprovements and squashes the following commits:
9a5e171 [Michael Armbrust] Use method instead of configuration directly
99c0e6b [Michael Armbrust] Add support for sizeInBytes.
416f167 [Michael Armbrust] Support for IN in data sources API.
2a04ab3 [Michael Armbrust] Simplify implementation of InSet.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#3139 from chenghao-intel/comparison_test and squashes the following commits:
f5d7146 [Cheng Hao] avoid exception in printing the codegen enabled
This implement the feature davies mentioned in https://github.com/apache/spark/pull/2901#discussion-diff-19313312
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#3012 from adrian-wang/iso8601 and squashes the following commits:
50df6e7 [Daoyuan Wang] json data timestamp ISO8601 support
it generates warnings at compile time marmbrus
Author: Xiangrui Meng <meng@databricks.com>
Closes#3192 from mengxr/dtc-decimal and squashes the following commits:
955e9fb [Xiangrui Meng] remove a decimal case branch that has no effect
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#3185 from ueshin/issues/SPARK-4319 and squashes the following commits:
a44a38e [Takuya UESHIN] Enable an ignored test "null count".
andrewor14 Another try at SPARK-1209, to address https://github.com/apache/spark/pull/2814#issuecomment-61197619
I successfully tested with `mvn -Dhadoop.version=1.0.4 -DskipTests clean package; mvn -Dhadoop.version=1.0.4 test` I assume that is what failed Jenkins last time. I also tried `-Dhadoop.version1.2.1` and `-Phadoop-2.4 -Pyarn -Phive` for more coverage.
So this is why the class was put in `org.apache.hadoop` to begin with, I assume. One option is to leave this as-is for now and move it only when Hadoop 1.0.x support goes away.
This is the other option, which adds a call to force the constructor to be public at run-time. It's probably less surprising than putting Spark code in `org.apache.hadoop`, but, does involve reflection. A `SecurityManager` might forbid this, but it would forbid a lot of stuff Spark does. This would also only affect Hadoop 1.0.x it seems.
Author: Sean Owen <sowen@cloudera.com>
Closes#3048 from srowen/SPARK-1209 and squashes the following commits:
0d48f4b [Sean Owen] For Hadoop 1.0.x, make certain constructors public, which were public in later versions
466e179 [Sean Owen] Disable MIMA warnings resulting from moving the class -- this was also part of the PairRDDFunctions type hierarchy though?
eb61820 [Sean Owen] Move SparkHadoopMapRedUtil / SparkHadoopMapReduceUtil from org.apache.hadoop to org.apache.spark
Following description is quoted from JIRA:
When I issue a hql query against a HiveContext where my predicate uses a column of string type with one of LT, LTE, GT, or GTE operator, I get the following error:
scala.MatchError: StringType (of class org.apache.spark.sql.catalyst.types.StringType$)
Looking at the code in org.apache.spark.sql.parquet.ParquetFilters, StringType is absent from the corresponding functions for creating these filters.
To reproduce, in a Hive 0.13.1 shell, I created the following table (at a specified DB):
create table sparkbug (
id int,
event string
) stored as parquet;
Insert some sample data:
insert into table sparkbug select 1, '2011-06-18' from <some table> limit 1;
insert into table sparkbug select 2, '2012-01-01' from <some table> limit 1;
Launch a spark shell and create a HiveContext to the metastore where the table above is located.
import org.apache.spark.sql._
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.hive.HiveContext
val hc = new HiveContext(sc)
hc.setConf("spark.sql.shuffle.partitions", "10")
hc.setConf("spark.sql.hive.convertMetastoreParquet", "true")
hc.setConf("spark.sql.parquet.compression.codec", "snappy")
import hc._
hc.hql("select * from <db>.sparkbug where event >= '2011-12-01'")
A scala.MatchError will appear in the output.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#3083 from sarutak/SPARK-4213 and squashes the following commits:
4ab6e56 [Kousuke Saruta] WIP
b6890c6 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-4213
9a1fae7 [Kousuke Saruta] Fixed ParquetFilters so that compare Strings
marmbrus
Author: Xiangrui Meng <meng@databricks.com>
Closes#3125 from mengxr/SPARK-4262 and squashes the following commits:
307695e [Xiangrui Meng] add .schemaRDD to JavaSchemaRDD
This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1.
Author: Davies Liu <davies@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Josh Rosen <joshrosen@databricks.com>
Closes#2920 from davies/fix_autobatch and squashes the following commits:
e544ef9 [Davies Liu] revert unrelated change
6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
1d557fc [Davies Liu] fix tests
8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
76abdce [Davies Liu] clean up
53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
b4292ce [Davies Liu] fix bug in master
d79744c [Davies Liu] recover hive tests
be37ece [Davies Liu] refactor
eb3938d [Davies Liu] refactor serializer in scala
8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.
Following #2919, this PR adds Python UDT (for internal use only) with tests under "pyspark.tests". Before `SQLContext.applySchema`, we check whether we need to convert user-type instances into SQL recognizable data. In the current implementation, a Python UDT must be paired with a Scala UDT for serialization on the JVM side. A following PR will add VectorUDT in MLlib for both Scala and Python.
marmbrus jkbradley davies
Author: Xiangrui Meng <meng@databricks.com>
Closes#3068 from mengxr/SPARK-4192-sql and squashes the following commits:
acff637 [Xiangrui Meng] merge master
dba5ea7 [Xiangrui Meng] only use pyClass for Python UDT output sqlType as well
2c9d7e4 [Xiangrui Meng] move import to global setup; update needsConversion
7c4a6a9 [Xiangrui Meng] address comments
75223db [Xiangrui Meng] minor update
f740379 [Xiangrui Meng] remove UDT from default imports
e98d9d0 [Xiangrui Meng] fix py style
4e84fce [Xiangrui Meng] remove local hive tests and add more tests
39f19e0 [Xiangrui Meng] add tests
b7f666d [Xiangrui Meng] add Python UDT
Author: Michael Armbrust <michael@databricks.com>
Closes#3077 from marmbrus/udfsWithUdts and squashes the following commits:
34b5f27 [Michael Armbrust] style
504adef [Michael Armbrust] Convert arguments to Scala UDFs
- Turns on compression for in-memory cached data by default
- Changes the default parquet compression format back to gzip (we have seen more OOMs with production workloads due to the way Snappy allocates memory)
- Ups the batch size to 10,000 rows
- Increases the broadcast threshold to 10mb.
- Uses our parquet implementation instead of the hive one by default.
- Cache parquet metadata by default.
Author: Michael Armbrust <michael@databricks.com>
Closes#3064 from marmbrus/fasterDefaults and squashes the following commits:
97ee9f8 [Michael Armbrust] parquet codec docs
e641694 [Michael Armbrust] Remote also
a12866a [Michael Armbrust] Cache metadata.
2d73acc [Michael Armbrust] Update docs defaults.
d63d2d5 [Michael Armbrust] document parquet option
da373f9 [Michael Armbrust] More aggressive defaults
This feature is based on an offline discussion with mengxr, hopefully can be useful for the new MLlib pipeline API.
For the following test snippet
```scala
case class KeyValue(key: Int, value: String)
val testData = sc.parallelize(1 to 10).map(i => KeyValue(i, i.toString)).toSchemaRDD
def foo(a: Int, b: String) => a.toString + b
```
the newly introduced DSL enables the following syntax
```scala
import org.apache.spark.sql.catalyst.dsl._
testData.select(Star(None), foo.call('key, 'value) as 'result)
```
which is equivalent to
```scala
testData.registerTempTable("testData")
sqlContext.registerFunction("foo", foo)
sql("SELECT *, foo(key, value) AS result FROM testData")
```
Author: Cheng Lian <lian@databricks.com>
Closes#3067 from liancheng/udf-dsl and squashes the following commits:
f132818 [Cheng Lian] Adds DSL support for Scala UDF
Queries which has 'not like' is not working spark sql.
sql("SELECT * FROM records where value not like 'val%'")
same query works in Spark HiveQL
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3075 from ravipesala/SPARK-4207 and squashes the following commits:
35c11e7 [ravipesala] Supported 'not like' syntax in sql
This PR adds User-Defined Types (UDTs) to SQL. It is a precursor to using SchemaRDD as a Dataset for the new MLlib API. Currently, the UDT API is private since there is incomplete support (e.g., no Java or Python support yet).
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#3063 from marmbrus/udts and squashes the following commits:
7ccfc0d [Michael Armbrust] remove println
46a3aee [Michael Armbrust] Slightly easier to read test output.
6cc434d [Michael Armbrust] Recursively convert rows.
e369b91 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udts
15c10a6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into sql-udt2
f3c72fe [Joseph K. Bradley] Fixing merge
e13cd8a [Joseph K. Bradley] Removed Vector UDTs
5817b2b [Joseph K. Bradley] style edits
30ce5b2 [Joseph K. Bradley] updates based on code review
d063380 [Joseph K. Bradley] Cleaned up Java UDT Suite, and added warning about element ordering when creating schema from Java Bean
a571bb6 [Joseph K. Bradley] Removed old UDT code (registry and Java UDTs). Cleaned up other code. Extended JavaUserDefinedTypeSuite
6fddc1c [Joseph K. Bradley] Made MyLabeledPoint into a Java Bean
20630bc [Joseph K. Bradley] fixed scalastyle
fa86b20 [Joseph K. Bradley] Removed Java UserDefinedType, and made UDTs private[spark] for now
8de957c [Joseph K. Bradley] Modified UserDefinedType to store Java class of user type so that registerUDT takes only the udt argument.
8b242ea [Joseph K. Bradley] Fixed merge error after last merge. Note: Last merge commit also removed SQL UDT examples from mllib.
7f29656 [Joseph K. Bradley] Moved udt case to top of all matches. Small cleanups
b028675 [Xiangrui Meng] allow any type in UDT
4500d8a [Xiangrui Meng] update example code
87264a5 [Xiangrui Meng] remove debug code
3143ac3 [Xiangrui Meng] remove unnecessary changes
cfbc321 [Xiangrui Meng] support UDT in parquet
db16139 [Joseph K. Bradley] Added more doc for UserDefinedType. Removed unused code in Suite
759af7a [Joseph K. Bradley] Added more doc to UserDefineType
63626a4 [Joseph K. Bradley] Updated ScalaReflectionsSuite per @marmbrus suggestions
51e5282 [Joseph K. Bradley] fixed 1 test
f025035 [Joseph K. Bradley] Cleanups before PR. Added new tests
85872f6 [Michael Armbrust] Allow schema calculation to be lazy, but ensure its available on executors.
dff99d6 [Joseph K. Bradley] Added UDTs for Vectors in MLlib, plus DatasetExample using the UDTs
cd60cb4 [Joseph K. Bradley] Trying to get other SQL tests to run
34a5831 [Joseph K. Bradley] Added MLlib dependency on SQL.
e1f7b9c [Joseph K. Bradley] blah
2f40c02 [Joseph K. Bradley] renamed UDT types
3579035 [Joseph K. Bradley] udt annotation now working
b226b9e [Joseph K. Bradley] Changing UDT to annotation
fea04af [Joseph K. Bradley] more cleanups
964b32e [Joseph K. Bradley] some cleanups
893ee4c [Joseph K. Bradley] udt finallly working
50f9726 [Joseph K. Bradley] udts
04303c9 [Joseph K. Bradley] udts
39f8707 [Joseph K. Bradley] removed old udt suite
273ac96 [Joseph K. Bradley] basic UDT is working, but deserialization has yet to be done
8bebf24 [Joseph K. Bradley] commented out convertRowToScala for debugging
53de70f [Joseph K. Bradley] more udts...
982c035 [Joseph K. Bradley] still working on UDTs
19b2f60 [Joseph K. Bradley] still working on UDTs
0eaeb81 [Joseph K. Bradley] Still working on UDTs
105c5a3 [Joseph K. Bradley] Adding UserDefinedType to SQL, not done yet.
This PR adds an API for unregistering temporary tables. If a temporary table has been cached before, it's unpersisted as well.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#3039 from liancheng/unregister-temp-table and squashes the following commits:
54ae99f [Cheng Lian] Fixes Scala styling issue
1948c14 [Cheng Lian] Removes the unpersist argument
aca41d3 [Cheng Lian] Ensures thread safety
7d4fb2b [Cheng Lian] Adds unregisterTempTable API
JIRA: https://issues.apache.org/jira/browse/SPARK-4185.
This PR also has the fix of #3052.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#3056 from yhuai/SPARK-4185 and squashes the following commits:
ed3a5a8 [Yin Huai] Correctly handle type conflicts between structs and primitive types in an array.
This PR overrides the `GetInfo` Hive Thrift API to provide correct version information. Another property `spark.sql.hive.version` is added to reveal the underlying Hive version. These are generally useful for Spark SQL ODBC driver providers. The Spark version information is extracted from the jar manifest. Also took the chance to remove the `SET -v` hack, which was a workaround for Simba ODBC driver connectivity.
TODO
- [x] Find a general way to figure out Hive (or even any dependency) version.
This [blog post](http://blog.soebes.de/blog/2014/01/02/version-information-into-your-appas-with-maven/) suggests several methods to inspect application version. In the case of Spark, this can be tricky because the chosen method:
1. must applies to both Maven build and SBT build
For Maven builds, we can retrieve the version information from the META-INF/maven directory within the assembly jar. But this doesn't work for SBT builds.
2. must not rely on the original jars of dependencies to extract specific dependency version, because Spark uses assembly jar.
This implies we can't read Hive version from Hive jar files since standard Spark distribution doesn't include them.
3. should play well with `SPARK_PREPEND_CLASSES` to ease local testing during development.
`SPARK_PREPEND_CLASSES` prevents classes to be loaded from the assembly jar, thus we can't locate the jar file and read its manifest.
Given these, maybe the only reliable method is to generate a source file containing version information at build time. pwendell Do you have any suggestions from the perspective of the build process?
**Update** Hive version is now retrieved from the newly introduced `HiveShim` object.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Author: Cheng Lian <lian@databricks.com>
Closes#2843 from liancheng/get-info and squashes the following commits:
a873d0f [Cheng Lian] Updates test case
53f43cd [Cheng Lian] Retrieves underlying Hive verson via HiveShim
1d282b8 [Cheng Lian] Removes the Simba ODBC "SET -v" hack
f857fce [Cheng Lian] Overrides Hive GetInfo Thrift API and adds Hive version property
`NoopColumnStats` was once used for binary, boolean and complex data types. This `ColumnStats` doesn't return properly shaped column statistics and causes caching failure if a table contains columns of the aforementioned types.
This PR adds `BooleanColumnStats`, `BinaryColumnStats` and `GenericColumnStats`, used for boolean, binary and all complex data types respectively. In addition, `NoopColumnStats` returns properly shaped column statistics containing null count and row count, but this class is now used for testing purpose only.
Author: Cheng Lian <lian@databricks.com>
Closes#3059 from liancheng/spark-4182 and squashes the following commits:
b398cfd [Cheng Lian] Fixes failed test case
fb3ee85 [Cheng Lian] Fixes SPARK-4182
This PR introduces a new set of APIs to Spark SQL to allow other developers to add support for reading data from new sources in `org.apache.spark.sql.sources`.
New sources must implement the interface `BaseRelation`, which is responsible for describing the schema of the data. BaseRelations have three `Scan` subclasses, which are responsible for producing an RDD containing row objects. The [various Scan interfaces](https://github.com/marmbrus/spark/blob/foreign/sql/core/src/main/scala/org/apache/spark/sql/sources/package.scala#L50) allow for optimizations such as column pruning and filter push down, when the underlying data source can handle these operations.
By implementing a class that inherits from RelationProvider these data sources can be accessed using using pure SQL. I've used the functionality to update the JSON support so it can now be used in this way as follows:
```sql
CREATE TEMPORARY TABLE jsonTableSQL
USING org.apache.spark.sql.json
OPTIONS (
path '/home/michael/data.json'
)
```
Further example usage can be found in the test cases: https://github.com/marmbrus/spark/tree/foreign/sql/core/src/test/scala/org/apache/spark/sql/sources
There is also a library that uses this new API to read avro data available here:
https://github.com/marmbrus/sql-avro
Author: Michael Armbrust <michael@databricks.com>
Closes#2475 from marmbrus/foreign and squashes the following commits:
1ed6010 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign
ab2c31f [Michael Armbrust] fix test
1d41bb5 [Michael Armbrust] unify argument names
5b47901 [Michael Armbrust] Remove sealed, more filter types
fab154a [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign
e3e690e [Michael Armbrust] Add hook for extraStrategies
a70d602 [Michael Armbrust] Fix style, more tests, FilteredSuite => PrunedFilteredSuite
70da6d9 [Michael Armbrust] Modify API to ease binary compatibility and interop with Java
7d948ae [Michael Armbrust] Fix equality of AttributeReference.
5545491 [Michael Armbrust] Address comments
5031ac3 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign
22963ef [Michael Armbrust] package objects compile wierdly...
b069146 [Michael Armbrust] traits => abstract classes
34f836a [Michael Armbrust] Make @DeveloperApi
0d74bcf [Michael Armbrust] Add documention on object life cycle
3e06776 [Michael Armbrust] remove line wraps
de3b68c [Michael Armbrust] Remove empty file
360cb30 [Michael Armbrust] style and java api
2957875 [Michael Armbrust] add override
0fd3a07 [Michael Armbrust] Draft of data sources API
- Adds optional precision and scale to Spark SQL's decimal type, which behave similarly to those in Hive 13 (https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf)
- Replaces our internal representation of decimals with a Decimal class that can store small values in a mutable Long, saving memory in this situation and letting some operations happen directly on Longs
This is still marked WIP because there are a few TODOs, but I'll remove that tag when done.
Author: Matei Zaharia <matei@databricks.com>
Closes#2983 from mateiz/decimal-1 and squashes the following commits:
35e6b02 [Matei Zaharia] Fix issues after merge
227f24a [Matei Zaharia] Review comments
31f915e [Matei Zaharia] Implement Davies's suggestions in Python
eb84820 [Matei Zaharia] Support reading/writing decimals as fixed-length binary in Parquet
4dc6bae [Matei Zaharia] Fix decimal support in PySpark
d1d9d68 [Matei Zaharia] Fix compile error and test issues after rebase
b28933d [Matei Zaharia] Support decimal precision/scale in Hive metastore
2118c0d [Matei Zaharia] Some test and bug fixes
81db9cb [Matei Zaharia] Added mutable Decimal that will be more efficient for small precisions
7af0c3b [Matei Zaharia] Add optional precision and scale to DecimalType, but use Unlimited for now
ec0a947 [Matei Zaharia] Make the result of AVG on Decimals be Decimal, not Double
Add `metadata: Metadata` to `StructField` to store extra information of columns. `Metadata` is a simple wrapper over `Map[String, Any]` with value types restricted to Boolean, Long, Double, String, Metadata, and arrays of those types. SerDe is via JSON.
Metadata is preserved through simple operations like `SELECT`.
marmbrus liancheng
Author: Xiangrui Meng <meng@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#2701 from mengxr/structfield-metadata and squashes the following commits:
dedda56 [Xiangrui Meng] merge remote
5ef930a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
c35203f [Xiangrui Meng] Merge pull request #1 from marmbrus/pr/2701
886b85c [Michael Armbrust] Expose Metadata and MetadataBuilder through the public scala and java packages.
589f314 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
1e2abcf [Xiangrui Meng] change default value of metadata to None in python
611d3c2 [Xiangrui Meng] move metadata from Expr to NamedExpr
ddfcfad [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
a438440 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
4266f4d [Xiangrui Meng] add StructField.toString back for backward compatibility
3f49aab [Xiangrui Meng] remove StructField.toString
24a9f80 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
473a7c5 [Xiangrui Meng] merge master
c9d7301 [Xiangrui Meng] organize imports
1fcbf13 [Xiangrui Meng] change metadata type in StructField for Scala/Java
60cc131 [Xiangrui Meng] add doc and header
60614c7 [Xiangrui Meng] add metadata
e42c452 [Xiangrui Meng] merge master
93518fb [Xiangrui Meng] support metadata in python
905bb89 [Xiangrui Meng] java conversions
618e349 [Xiangrui Meng] make tests work in scala
61b8e0f [Xiangrui Meng] merge master
7e5a322 [Xiangrui Meng] do not output metadata in StructField.toString
c41a664 [Xiangrui Meng] merge master
d8af0ed [Xiangrui Meng] move tests to SQLQuerySuite
67fdebb [Xiangrui Meng] add test on join
d65072e [Xiangrui Meng] remove Map.empty
367d237 [Xiangrui Meng] add test
c194d5e [Xiangrui Meng] add metadata field to StructField and Attribute
if the query contains "not between" does not work like.
SELECT * FROM src where key not between 10 and 20'
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#3017 from ravipesala/SPARK-4154 and squashes the following commits:
65fc89e [ravipesala] Handled admin comments
32e6d42 [ravipesala] 'not between' is not working
The parquet-mr project has introduced a new filter api (https://github.com/apache/incubator-parquet-mr/pull/4), along with several fixes . It can also eliminate entire RowGroups depending on certain statistics like min/max
We can leverage that to further improve performance of queries with filters.
Also filter2 api introduces ability to create custom filters. We can create a custom filter for the optimized In clause (InSet) , so that elimination happens in the ParquetRecordReader itself
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#2841 from saucam/master and squashes the following commits:
8282ba0 [Yash Datta] SPARK-3968: fix scala code style and add some more tests for filtering on optional columns
515df1c [Yash Datta] SPARK-3968: Add a test case for filter pushdown on optional column
5f4530e [Yash Datta] SPARK-3968: Fix scala code style
f304667 [Yash Datta] SPARK-3968: Using task metadata strategy for row group filtering
ec53e92 [Yash Datta] SPARK-3968: No push down should result in case we are unable to create a record filter
48163c3 [Yash Datta] SPARK-3968: Code cleanup
cc7b596 [Yash Datta] SPARK-3968: 1. Fix RowGroupFiltering not working 2. Use the serialization/deserialization from Parquet library for filter pushdown
caed851 [Yash Datta] Revert "SPARK-3968: Not pushing the filters in case of OPTIONAL columns" since filtering on optional columns is now supported in filter2 api
49703c9 [Yash Datta] SPARK-3968: Not pushing the filters in case of OPTIONAL columns
9d09741 [Yash Datta] SPARK-3968: Change parquet filter pushdown to use filter2 api of parquet-mr
Right now it works for only 2 tables like below query.
sql("SELECT * FROM records1 as a,records2 as b where a.key=b.key ")
But it does not work for more than 2 tables like below query
sql("SELECT * FROM records1 as a,records2 as b,records3 as c where a.key=b.key and a.key=c.key").
Author: ravipesala <ravindra.pesala@huawei.com>
Closes#2987 from ravipesala/multijoin and squashes the following commits:
429b005 [ravipesala] Support multiple joins
(This is just a look at what completely moving the classes would look like. I know Patrick flagged that as maybe not OK, although, it's private?)
Author: Sean Owen <sowen@cloudera.com>
Closes#2814 from srowen/SPARK-1209 and squashes the following commits:
ead1115 [Sean Owen] Disable MIMA warnings resulting from moving the class -- this was also part of the PairRDDFunctions type hierarchy though?
2d42c1d [Sean Owen] Move SparkHadoopMapRedUtil / SparkHadoopMapReduceUtil from org.apache.hadoop to org.apache.spark
