https://issues.apache.org/jira/browse/SPARK-7805
Because `sql/hive`'s tests depend on the test jar of `sql/core`, we do not need to store `SQLTestUtils` and `ParquetTest` in `src/main`. We should only add stuff that will be needed by `sql/console` or Python tests (for Python, we need it in `src/main`, right? davies).
Author: Yin Huai <yhuai@databricks.com>
Closes#6334 from yhuai/SPARK-7805 and squashes the following commits:
af6d0c9 [Yin Huai] mima
b86746a [Yin Huai] Move SQLTestUtils.scala and ParquetTest.scala to src/test.
(cherry picked from commit ed21476bc0)
Signed-off-by: Yin Huai <yhuai@databricks.com>
There excludes are unnecessary for 1.3 because the changes were made in 1.4.x.
Author: Xiangrui Meng <meng@databricks.com>
Closes#6254 from mengxr/SPARK-7681-mima and squashes the following commits:
7f0cea0 [Xiangrui Meng] remove mima excludes for 1.3
(cherry picked from commit 6845cb2ff4)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
JIRA: https://issues.apache.org/jira/browse/SPARK-7681
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#6209 from viirya/sparsevector_gemv and squashes the following commits:
ce0bb8b [Liang-Chi Hsieh] Still need to scal y when beta is 0.0 because it clears out y.
b890e63 [Liang-Chi Hsieh] Do not delete multiply for DenseVector.
57a8c1e [Liang-Chi Hsieh] Add MimaExcludes for v1.4.
458d1ae [Liang-Chi Hsieh] List DenseMatrix.multiply and SparseMatrix.multiply to MimaExcludes too.
054f05d [Liang-Chi Hsieh] Fix scala style.
410381a [Liang-Chi Hsieh] Address comments. Make Matrix.multiply more generalized.
4616696 [Liang-Chi Hsieh] Add support for SparseVector with SparseMatrix.
5d6d07a [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into sparsevector_gemv
c069507 [Liang-Chi Hsieh] Add SparseVector support for gemv with DenseMatrix.
(cherry picked from commit d03638cc2d)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
Replace the DriverQuirks with JdbcDialect(s) (and MySQLDialect/PostgresDialect)
and allow developers to change the dialects on the fly (for new JDBCRRDs only).
Some types (like an unsigned 64bit number) can be trivially mapped to java.
The status quo is that the RRD will fail to load.
This patch makes it possible to overwrite the type mapping to read e.g.
64Bit numbers as strings and handle them afterwards in software.
JDBCSuite has an example that maps all types to String, which should always
work (at the cost of extra code afterwards).
As a side effect it should now be possible to develop simple dialects
out-of-tree and even with spark-shell.
Author: Rene Treffer <treffer@measite.de>
Closes#5555 from rtreffer/jdbc-dialects and squashes the following commits:
3cbafd7 [Rene Treffer] [SPARK-6888] ignore classes belonging to changed API in MIMA report
fe7e2e8 [Rene Treffer] [SPARK-6888] Make the jdbc driver handling user-definable
This patch introduces a new shuffle manager that enhances the existing sort-based shuffle with a new cache-friendly sort algorithm that operates directly on binary data. The goals of this patch are to lower memory usage and Java object overheads during shuffle and to speed up sorting. It also lays groundwork for follow-up patches that will enable end-to-end processing of serialized records.
The new shuffle manager, `UnsafeShuffleManager`, can be enabled by setting `spark.shuffle.manager=tungsten-sort` in SparkConf.
The new shuffle manager uses directly-managed memory to implement several performance optimizations for certain types of shuffles. In cases where the new performance optimizations cannot be applied, the new shuffle manager delegates to SortShuffleManager to handle those shuffles.
UnsafeShuffleManager's optimizations will apply when _all_ of the following conditions hold:
- The shuffle dependency specifies no aggregation or output ordering.
- The shuffle serializer supports relocation of serialized values (this is currently supported
by KryoSerializer and Spark SQL's custom serializers).
- The shuffle produces fewer than 16777216 output partitions.
- No individual record is larger than 128 MB when serialized.
In addition, extra spill-merging optimizations are automatically applied when the shuffle compression codec supports concatenation of serialized streams. This is currently supported by Spark's LZF serializer.
At a high-level, UnsafeShuffleManager's design is similar to Spark's existing SortShuffleManager. In sort-based shuffle, incoming records are sorted according to their target partition ids, then written to a single map output file. Reducers fetch contiguous regions of this file in order to read their portion of the map output. In cases where the map output data is too large to fit in memory, sorted subsets of the output can are spilled to disk and those on-disk files are merged to produce the final output file.
UnsafeShuffleManager optimizes this process in several ways:
- Its sort operates on serialized binary data rather than Java objects, which reduces memory consumption and GC overheads. This optimization requires the record serializer to have certain properties to allow serialized records to be re-ordered without requiring deserialization. See SPARK-4550, where this optimization was first proposed and implemented, for more details.
- It uses a specialized cache-efficient sorter (UnsafeShuffleExternalSorter) that sorts arrays of compressed record pointers and partition ids. By using only 8 bytes of space per record in the sorting array, this fits more of the array into cache.
- The spill merging procedure operates on blocks of serialized records that belong to the same partition and does not need to deserialize records during the merge.
- When the spill compression codec supports concatenation of compressed data, the spill merge simply concatenates the serialized and compressed spill partitions to produce the final output partition. This allows efficient data copying methods, like NIO's `transferTo`, to be used and avoids the need to allocate decompression or copying buffers during the merge.
The shuffle read path is unchanged.
This patch is similar to [SPARK-4550](http://issues.apache.org/jira/browse/SPARK-4550) / #4450 but uses a slightly different implementation. The `unsafe`-based implementation featured in this patch lays the groundwork for followup patches that will enable sorting to operate on serialized data pages that will be prepared by Spark SQL's new `unsafe` operators (such as the new aggregation operator introduced in #5725).
### Future work
There are several tasks that build upon this patch, which will be left to future work:
- [SPARK-7271](https://issues.apache.org/jira/browse/SPARK-7271) Redesign / extend the shuffle interfaces to accept binary data as input. The goal here is to let us bypass serialization steps in cases where the sort input is produced by an operator that operates directly on binary data.
- Extension / redesign of the `Serializer` API. We can add new methods which allow serializers to determine the size requirements for serializing objects and for serializing objects directly to a specified memory address (similar to how `UnsafeRowConverter` works in Spark SQL).
<!-- Reviewable:start -->
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Author: Josh Rosen <joshrosen@databricks.com>
Closes#5868 from JoshRosen/unsafe-sort and squashes the following commits:
ef0a86e [Josh Rosen] Fix scalastyle errors
7610f2f [Josh Rosen] Add tests for proper cleanup of shuffle data.
d494ffe [Josh Rosen] Fix deserialization of JavaSerializer instances.
52a9981 [Josh Rosen] Fix some bugs in the address packing code.
51812a7 [Josh Rosen] Change shuffle manager sort name to tungsten-sort
4023fa4 [Josh Rosen] Add @Private annotation to some Java classes.
de40b9d [Josh Rosen] More comments to try to explain metrics code
df07699 [Josh Rosen] Attempt to clarify confusing metrics update code
5e189c6 [Josh Rosen] Track time spend closing / flushing files; split TimeTrackingOutputStream into separate file.
d5779c6 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-sort
c2ce78e [Josh Rosen] Fix a missed usage of MAX_PARTITION_ID
e3b8855 [Josh Rosen] Cleanup in UnsafeShuffleWriter
4a2c785 [Josh Rosen] rename 'sort buffer' to 'pointer array'
6276168 [Josh Rosen] Remove ability to disable spilling in UnsafeShuffleExternalSorter.
57312c9 [Josh Rosen] Clarify fileBufferSize units
2d4e4f4 [Josh Rosen] Address some minor comments in UnsafeShuffleExternalSorter.
fdcac08 [Josh Rosen] Guard against overflow when expanding sort buffer.
85da63f [Josh Rosen] Cleanup in UnsafeShuffleSorterIterator.
0ad34da [Josh Rosen] Fix off-by-one in nextInt() call
56781a1 [Josh Rosen] Rename UnsafeShuffleSorter to UnsafeShuffleInMemorySorter
e995d1a [Josh Rosen] Introduce MAX_SHUFFLE_OUTPUT_PARTITIONS.
e58a6b4 [Josh Rosen] Add more tests for PackedRecordPointer encoding.
4f0b770 [Josh Rosen] Attempt to implement proper shuffle write metrics.
d4e6d89 [Josh Rosen] Update to bit shifting constants
69d5899 [Josh Rosen] Remove some unnecessary override vals
8531286 [Josh Rosen] Add tests that automatically trigger spills.
7c953f9 [Josh Rosen] Add test that covers UnsafeShuffleSortDataFormat.swap().
e1855e5 [Josh Rosen] Fix a handful of misc. IntelliJ inspections
39434f9 [Josh Rosen] Avoid integer multiplication overflow in getMemoryUsage (thanks FindBugs!)
1e3ad52 [Josh Rosen] Delete unused ByteBufferOutputStream class.
ea4f85f [Josh Rosen] Roll back an unnecessary change in Spillable.
ae538dc [Josh Rosen] Document UnsafeShuffleManager.
ec6d626 [Josh Rosen] Add notes on maximum # of supported shuffle partitions.
0d4d199 [Josh Rosen] Bump up shuffle.memoryFraction to make tests pass.
b3b1924 [Josh Rosen] Properly implement close() and flush() in DummySerializerInstance.
1ef56c7 [Josh Rosen] Revise compression codec support in merger; test cross product of configurations.
b57c17f [Josh Rosen] Disable some overly-verbose logs that rendered DEBUG useless.
f780fb1 [Josh Rosen] Add test demonstrating which compression codecs support concatenation.
4a01c45 [Josh Rosen] Remove unnecessary log message
27b18b0 [Josh Rosen] That for inserting records AT the max record size.
fcd9a3c [Josh Rosen] Add notes + tests for maximum record / page sizes.
9d1ee7c [Josh Rosen] Fix MiMa excludes for ShuffleWriter change
fd4bb9e [Josh Rosen] Use own ByteBufferOutputStream rather than Kryo's
67d25ba [Josh Rosen] Update Exchange operator's copying logic to account for new shuffle manager
8f5061a [Josh Rosen] Strengthen assertion to check partitioning
01afc74 [Josh Rosen] Actually read data in UnsafeShuffleWriterSuite
1929a74 [Josh Rosen] Update to reflect upstream ShuffleBlockManager -> ShuffleBlockResolver rename.
e8718dd [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-sort
9b7ebed [Josh Rosen] More defensive programming RE: cleaning up spill files and memory after errors
7cd013b [Josh Rosen] Begin refactoring to enable proper tests for spilling.
722849b [Josh Rosen] Add workaround for transferTo() bug in merging code; refactor tests.
9883e30 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-sort
b95e642 [Josh Rosen] Refactor and document logic that decides when to spill.
1ce1300 [Josh Rosen] More minor cleanup
5e8cf75 [Josh Rosen] More minor cleanup
e67f1ea [Josh Rosen] Remove upper type bound in ShuffleWriter interface.
cfe0ec4 [Josh Rosen] Address a number of minor review comments:
8a6fe52 [Josh Rosen] Rename UnsafeShuffleSpillWriter to UnsafeShuffleExternalSorter
11feeb6 [Josh Rosen] Update TODOs related to shuffle write metrics.
b674412 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-sort
aaea17b [Josh Rosen] Add comments to UnsafeShuffleSpillWriter.
4f70141 [Josh Rosen] Fix merging; now passes UnsafeShuffleSuite tests.
133c8c9 [Josh Rosen] WIP towards testing UnsafeShuffleWriter.
f480fb2 [Josh Rosen] WIP in mega-refactoring towards shuffle-specific sort.
57f1ec0 [Josh Rosen] WIP towards packed record pointers for use in optimized shuffle sort.
69232fd [Josh Rosen] Enable compressible address encoding for off-heap mode.
7ee918e [Josh Rosen] Re-order imports in tests
3aeaff7 [Josh Rosen] More refactoring and cleanup; begin cleaning iterator interfaces
3490512 [Josh Rosen] Misc. cleanup
f156a8f [Josh Rosen] Hacky metrics integration; refactor some interfaces.
2776aca [Josh Rosen] First passing test for ExternalSorter.
5e100b2 [Josh Rosen] Super-messy WIP on external sort
595923a [Josh Rosen] Remove some unused variables.
8958584 [Josh Rosen] Fix bug in calculating free space in current page.
f17fa8f [Josh Rosen] Add missing newline
c2fca17 [Josh Rosen] Small refactoring of SerializerPropertiesSuite to enable test re-use:
b8a09fe [Josh Rosen] Back out accidental log4j.properties change
bfc12d3 [Josh Rosen] Add tests for serializer relocation property.
240864c [Josh Rosen] Remove PrefixComputer and require prefix to be specified as part of insert()
1433b42 [Josh Rosen] Store record length as int instead of long.
026b497 [Josh Rosen] Re-use a buffer in UnsafeShuffleWriter
0748458 [Josh Rosen] Port UnsafeShuffleWriter to Java.
87e721b [Josh Rosen] Renaming and comments
d3cc310 [Josh Rosen] Flag that SparkSqlSerializer2 supports relocation
e2d96ca [Josh Rosen] Expand serializer API and use new function to help control when new UnsafeShuffle path is used.
e267cee [Josh Rosen] Fix compilation of UnsafeSorterSuite
9c6cf58 [Josh Rosen] Refactor to use DiskBlockObjectWriter.
253f13e [Josh Rosen] More cleanup
8e3ec20 [Josh Rosen] Begin code cleanup.
4d2f5e1 [Josh Rosen] WIP
3db12de [Josh Rosen] Minor simplification and sanity checks in UnsafeSorter
767d3ca [Josh Rosen] Fix invalid range in UnsafeSorter.
e900152 [Josh Rosen] Add test for empty iterator in UnsafeSorter
57a4ea0 [Josh Rosen] Make initialSize configurable in UnsafeSorter
abf7bfe [Josh Rosen] Add basic test case.
81d52c5 [Josh Rosen] WIP on UnsafeSorter
(cherry picked from commit 73bed408fb)
Signed-off-by: Reynold Xin <rxin@databricks.com>
JavaTypeInference into catalyst
types.DateUtils into catalyst
CacheManager into execution
DefaultParserDialect into catalyst
Author: Reynold Xin <rxin@databricks.com>
Closes#6108 from rxin/sql-rename and squashes the following commits:
3fc9613 [Reynold Xin] Fixed import ordering.
83d9ff4 [Reynold Xin] Fixed codegen tests.
e271e86 [Reynold Xin] mima
f4e24a6 [Reynold Xin] [SQL] Move some classes into packages that are more appropriate.
(cherry picked from commit e683182c3e)
Signed-off-by: Michael Armbrust <michael@databricks.com>
This PR migrates Parquet data source to the newly introduced `FSBasedRelation`. `FSBasedParquetRelation` is created to replace `ParquetRelation2`. Major differences are:
1. Partition discovery code has been factored out to `FSBasedRelation`
1. `AppendingParquetOutputFormat` is not used now. Instead, an anonymous subclass of `ParquetOutputFormat` is used to handle appending and writing dynamic partitions
1. When scanning partitioned tables, `FSBasedParquetRelation.buildScan` only builds an `RDD[Row]` for a single selected partition
1. `FSBasedParquetRelation` doesn't rely on Catalyst expressions for filter push down, thus it doesn't extend `CatalystScan` anymore
After migrating `JSONRelation` (which extends `CatalystScan`), we can remove `CatalystScan`.
<!-- Reviewable:start -->
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Author: Cheng Lian <lian@databricks.com>
Closes#6090 from liancheng/parquet-migration and squashes the following commits:
6063f87 [Cheng Lian] Casts to OutputCommitter rather than FileOutputCommtter
bfd1cf0 [Cheng Lian] Fixes compilation error introduced while rebasing
f9ea56e [Cheng Lian] Adds ParquetRelation2 related classes to MiMa check whitelist
261d8c1 [Cheng Lian] Minor bug fix and more tests
db65660 [Cheng Lian] Migrates Parquet data source to FSBasedRelation
(cherry picked from commit 7ff16e8abe)
Signed-off-by: Michael Armbrust <michael@databricks.com>
This PR adds partitioning support for the external data sources API. It aims to simplify development of file system based data sources, and provide first class partitioning support for both read path and write path. Existing data sources like JSON and Parquet can be simplified with this work.
## New features provided
1. Hive compatible partition discovery
This actually generalizes the partition discovery strategy used in Parquet data source in Spark 1.3.0.
1. Generalized partition pruning optimization
Now partition pruning is handled during physical planning phase. Specific data sources don't need to worry about this harness anymore.
(This also implies that we can remove `CatalystScan` after migrating the Parquet data source, since now we don't need to pass Catalyst expressions to data source implementations.)
1. Insertion with dynamic partitions
When inserting data to a `FSBasedRelation`, data can be partitioned dynamically by specified partition columns.
## New structures provided
### Developer API
1. `FSBasedRelation`
Base abstract class for file system based data sources.
1. `OutputWriter`
Base abstract class for output row writers, responsible for writing a single row object.
1. `FSBasedRelationProvider`
A new relation provider for `FSBasedRelation` subclasses. Note that data sources extending `FSBasedRelation` don't need to extend `RelationProvider` and `SchemaRelationProvider`.
### User API
New overloaded versions of
1. `DataFrame.save()`
1. `DataFrame.saveAsTable()`
1. `SQLContext.load()`
are provided to allow users to save/load DataFrames with user defined dynamic partition columns.
### Spark SQL query planning
1. `InsertIntoFSBasedRelation`
Used to implement write path for `FSBasedRelation`s.
1. New rules for `FSBasedRelation` in `DataSourceStrategy`
These are added to hook `FSBasedRelation` into physical query plan in read path, and perform partition pruning.
## TODO
- [ ] Use scratch directories when overwriting a table with data selected from itself.
Currently, this is not supported, because the table been overwritten is always deleted before writing any data to it.
- [ ] When inserting with dynamic partition columns, use external sorter to group the data first.
This ensures that we only need to open a single `OutputWriter` at a time. For data sources like Parquet, `OutputWriter`s can be quite memory consuming. One issue is that, this approach breaks the row distribution in the original DataFrame. However, we did't promise to preserve data distribution when writing a DataFrame.
- [x] More tests. Specifically, test cases for
- [x] Self-join
- [x] Loading partitioned relations with a subset of partition columns stored in data files.
- [x] `SQLContext.load()` with user defined dynamic partition columns.
## Parquet data source migration
Parquet data source migration is covered in PR https://github.com/liancheng/spark/pull/6, which is against this PR branch and for preview only. A formal PR need to be made after this one is merged.
Author: Cheng Lian <lian@databricks.com>
Closes#5526 from liancheng/partitioning-support and squashes the following commits:
5351a1b [Cheng Lian] Fixes compilation error introduced while rebasing
1f9b1a5 [Cheng Lian] Tweaks data schema passed to FSBasedRelations
43ba50e [Cheng Lian] Avoids serializing generated projection code
edf49e7 [Cheng Lian] Removed commented stale code block
348a922 [Cheng Lian] Adds projection in FSBasedRelation.buildScan(requiredColumns, inputPaths)
ad4d4de [Cheng Lian] Enables HDFS style globbing
8d12e69 [Cheng Lian] Fixes compilation error
c71ac6c [Cheng Lian] Addresses comments from @marmbrus
7552168 [Cheng Lian] Fixes typo in MimaExclude.scala
0349e09 [Cheng Lian] Fixes compilation error introduced while rebasing
52b0c9b [Cheng Lian] Adjusts project/MimaExclude.scala
c466de6 [Cheng Lian] Addresses comments
bc3f9b4 [Cheng Lian] Uses projection to separate partition columns and data columns while inserting rows
795920a [Cheng Lian] Fixes compilation error after rebasing
0b8cd70 [Cheng Lian] Adds Scala/Catalyst row conversion when writing non-partitioned tables
fa543f3 [Cheng Lian] Addresses comments
5849dd0 [Cheng Lian] Fixes doc typos. Fixes partition discovery refresh.
51be443 [Cheng Lian] Replaces FSBasedRelation.outputCommitterClass with FSBasedRelation.prepareForWrite
c4ed4fe [Cheng Lian] Bug fixes and a new test suite
a29e663 [Cheng Lian] Bug fix: should only pass actuall data files to FSBaseRelation.buildScan
5f423d3 [Cheng Lian] Bug fixes. Lets data source to customize OutputCommitter rather than OutputFormat
54c3d7b [Cheng Lian] Enforces that FileOutputFormat must be used
be0c268 [Cheng Lian] Uses TaskAttempContext rather than Configuration in OutputWriter.init
0bc6ad1 [Cheng Lian] Resorts to new Hadoop API, and now FSBasedRelation can customize output format class
f320766 [Cheng Lian] Adds prepareForWrite() hook, refactored writer containers
422ff4a [Cheng Lian] Fixes style issue
ce52353 [Cheng Lian] Adds new SQLContext.load() overload with user defined dynamic partition columns
8d2ff71 [Cheng Lian] Merges partition columns when reading partitioned relations
ca1805b [Cheng Lian] Removes duplicated partition discovery code in new Parquet
f18dec2 [Cheng Lian] More strict schema checking
b746ab5 [Cheng Lian] More tests
9b487bf [Cheng Lian] Fixes compilation errors introduced while rebasing
ea6c8dd [Cheng Lian] Removes remote debugging stuff
327bb1d [Cheng Lian] Implements partitioning support for data sources API
3c5073a [Cheng Lian] Fixes SaveModes used in test cases
fb5a607 [Cheng Lian] Fixes compilation error
9d17607 [Cheng Lian] Adds the contract that OutputWriter should have zero-arg constructor
5de194a [Cheng Lian] Forgot Apache licence header
95d0b4d [Cheng Lian] Renames PartitionedSchemaRelationProvider to FSBasedRelationProvider
770b5ba [Cheng Lian] Adds tests for FSBasedRelation
3ba9bbf [Cheng Lian] Adds DataFrame.saveAsTable() overrides which support partitioning
1b8231f [Cheng Lian] Renames FSBasedPrunedFilteredScan to FSBasedRelation
aa8ba9a [Cheng Lian] Javadoc fix
012ed2d [Cheng Lian] Adds PartitioningOptions
7dd8dd5 [Cheng Lian] Adds new interfaces and stub methods for data sources API partitioning support
(cherry picked from commit 0595b6de8f)
Signed-off-by: Cheng Lian <lian@databricks.com>
This PR switches Spark SQL's Hive support to use the isolated hive client interface introduced by #5851, instead of directly interacting with the client. By using this isolated client we can now allow users to dynamically configure the version of Hive that they are connecting to by setting `spark.sql.hive.metastore.version` without the need recompile. This also greatly reduces the surface area for our interaction with the hive libraries, hopefully making it easier to support other versions in the future.
Jars for the desired hive version can be configured using `spark.sql.hive.metastore.jars`, which accepts the following options:
- a colon-separated list of jar files or directories for hive and hadoop.
- `builtin` - attempt to discover the jars that were used to load Spark SQL and use those. This
option is only valid when using the execution version of Hive.
- `maven` - download the correct version of hive on demand from maven.
By default, `builtin` is used for Hive 13.
This PR also removes the test step for building against Hive 12, as this will no longer be required to talk to Hive 12 metastores. However, the full removal of the Shim is deferred until a later PR.
Remaining TODOs:
- Remove the Hive Shims and inline code for Hive 13.
- Several HiveCompatibility tests are not yet passing.
- `nullformatCTAS` - As detailed below, we now are handling CTAS parsing ourselves instead of hacking into the Hive semantic analyzer. However, we currently only handle the common cases and not things like CTAS where the null format is specified.
- `combine1` now leaks state about compression somehow, breaking all subsequent tests. As such we currently add it to the blacklist
- `part_inherit_tbl_props` and `part_inherit_tbl_props_with_star` do not work anymore. We are correctly propagating the information
- "load_dyn_part14.*" - These tests pass when run on their own, but fail when run with all other tests. It seems our `RESET` mechanism may not be as robust as it used to be?
Other required changes:
- `CreateTableAsSelect` no longer carries parts of the HiveQL AST with it through the query execution pipeline. Instead, we parse CTAS during the HiveQL conversion and construct a `HiveTable`. The full parsing here is not yet complete as detailed above in the remaining TODOs. Since the operator is Hive specific, it is moved to the hive package.
- `Command` is simplified to be a trait that simply acts as a marker for a LogicalPlan that should be eagerly evaluated.
Author: Michael Armbrust <michael@databricks.com>
Closes#5876 from marmbrus/useIsolatedClient and squashes the following commits:
258d000 [Michael Armbrust] really really correct path handling
e56fd4a [Michael Armbrust] getAbsolutePath
5a259f5 [Michael Armbrust] fix typos
81bb366 [Michael Armbrust] comments from vanzin
5f3945e [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
4b5cd41 [Michael Armbrust] yin's comments
f5de7de [Michael Armbrust] cleanup
11e9c72 [Michael Armbrust] better coverage in versions suite
7e8f010 [Michael Armbrust] better error messages and jar handling
e7b3941 [Michael Armbrust] more permisive checking for function registration
da91ba7 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
5fe5894 [Michael Armbrust] fix serialization suite
81711c4 [Michael Armbrust] Initial support for running without maven
1d8ae44 [Michael Armbrust] fix final tests?
1c50813 [Michael Armbrust] more comments
a3bee70 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into useIsolatedClient
a6f5df1 [Michael Armbrust] style
ab07f7e [Michael Armbrust] WIP
4d8bf02 [Michael Armbrust] Remove hive 12 compilation
8843a25 [Michael Armbrust] [SPARK-6908] [SQL] Use isolated Hive client
(cherry picked from commit cd1d4110cf)
Signed-off-by: Yin Huai <yhuai@databricks.com>
Now that 1.3 has been released, we should enable MiMa checks for the `sql` subproject.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#5727 from JoshRosen/enable-more-mima-checks and squashes the following commits:
3ad302b [Josh Rosen] Merge remote-tracking branch 'origin/master' into enable-more-mima-checks
0c48e4d [Josh Rosen] Merge remote-tracking branch 'origin/master' into enable-more-mima-checks
e276cee [Josh Rosen] Fix SQL MiMa checks via excludes and private[sql]
44d0d01 [Josh Rosen] Add back 'launcher' exclude
1aae027 [Josh Rosen] Enable MiMa checks for launcher and sql projects.
Add `compressed` to `Vector` with some other methods: `numActives`, `numNonzeros`, `toSparse`, and `toDense`. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#5756 from mengxr/SPARK-6756 and squashes the following commits:
8d4ecbd [Xiangrui Meng] address comment and add mima excludes
da54179 [Xiangrui Meng] add toSparse, toDense, numActives, numNonzeros, and compressed to Vector
jira: https://issues.apache.org/jira/browse/SPARK-7090
LDA was implemented with extensibility in mind. And with the development of OnlineLDA and Gibbs Sampling, we are collecting more detailed requirements from different algorithms.
As Joseph Bradley jkbradley proposed in https://github.com/apache/spark/pull/4807 and with some further discussion, we'd like to adjust the code structure a little to present the common interface and extension point clearly.
Basically class LDA would be a common entrance for LDA computing. And each LDA object will refer to a LDAOptimizer for the concrete algorithm implementation. Users can customize LDAOptimizer with specific parameters and assign it to LDA.
Concrete changes:
1. Add a trait `LDAOptimizer`, which defines the common iterface for concrete implementations. Each subClass is a wrapper for a specific LDA algorithm.
2. Move EMOptimizer to file LDAOptimizer and inherits from LDAOptimizer, rename to EMLDAOptimizer. (in case a more generic EMOptimizer comes in the future)
-adjust the constructor of EMOptimizer, since all the parameters should be passed in through initialState method. This can avoid unwanted confusion or overwrite.
-move the code from LDA.initalState to initalState of EMLDAOptimizer
3. Add property ldaOptimizer to LDA and its getter/setter, and EMLDAOptimizer is the default Optimizer.
4. Change the return type of LDA.run from DistributedLDAModel to LDAModel.
Further work:
add OnlineLDAOptimizer and other possible Optimizers once ready.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#5661 from hhbyyh/ldaRefactor and squashes the following commits:
0e2e006 [Yuhao Yang] respond to review comments
08a45da [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaRefactor
e756ce4 [Yuhao Yang] solve mima exception
d74fd8f [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaRefactor
0bb8400 [Yuhao Yang] refactor LDA with Optimizer
ec2f857 [Yuhao Yang] protoptype for discussion
I've added a static getOrCreate method to the static SparkContext object that allows one to either retrieve a previously created SparkContext or to instantiate a new one with the provided config. The method accepts an optional SparkConf to make usage intuitive.
Still working on a test for this, basically want to create a new context from scratch, then ensure that subsequent calls don't overwrite that.
Author: Ilya Ganelin <ilya.ganelin@capitalone.com>
Closes#5501 from ilganeli/SPARK-6703 and squashes the following commits:
db9a963 [Ilya Ganelin] Closing second spark context
1dc0444 [Ilya Ganelin] Added ref equality check
8c884fa [Ilya Ganelin] Made getOrCreate synchronized
cb0c6b7 [Ilya Ganelin] Doc updates and code cleanup
270cfe3 [Ilya Ganelin] [SPARK-6703] Documentation fixes
15e8dea [Ilya Ganelin] Updated comments and added MiMa Exclude
0e1567c [Ilya Ganelin] Got rid of unecessary option for AtomicReference
dfec4da [Ilya Ganelin] Changed activeContext to AtomicReference
733ec9f [Ilya Ganelin] Fixed some bugs in test code
8be2f83 [Ilya Ganelin] Replaced match with if
e92caf7 [Ilya Ganelin] [SPARK-6703] Added test to ensure that getOrCreate both allows creation, retrieval, and a second context if desired
a99032f [Ilya Ganelin] Spacing fix
d7a06b8 [Ilya Ganelin] Updated SparkConf class to add getOrCreate method. Started test suite implementation
jira: https://issues.apache.org/jira/browse/SPARK-6693
It's kind of annoying when debugging and found you cannot print out the matrix as you want.
original toString of Matrix only print like following,
0.17810102596909183 0.5616906241468385 ... (10 total)
0.9692861997823815 0.015558159784155756 ...
0.8513015122819192 0.031523763918528847 ...
0.5396875653953941 0.3267864552779176 ...
The def toString(maxLines : Int, maxWidth : Int) is useful when debuging, logging and saving matrix to files.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#5344 from hhbyyh/addToString and squashes the following commits:
19a6836 [Yuhao Yang] remove extra line
6314b21 [Yuhao Yang] add exclude
736c324 [Yuhao Yang] add ut and exclude
420da39 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into addToString
c22f352 [Yuhao Yang] style change
64a9e0f [Yuhao Yang] add specific to string to matrix
I've added a timeout and retry loop around the SparkContext shutdown code that should fix this deadlock. If a SparkContext shutdown is in progress when another thread comes knocking, it will wait for 10 seconds for the lock, then fall through where the outer loop will re-submit the request.
Author: Ilya Ganelin <ilya.ganelin@capitalone.com>
Closes#5277 from ilganeli/SPARK-6492 and squashes the following commits:
8617a7e [Ilya Ganelin] Resolved merge conflict
2fbab66 [Ilya Ganelin] Added MIMA Exclude
a0e2c70 [Ilya Ganelin] Deleted stale imports
fa28ce7 [Ilya Ganelin] reverted to just having a single stopped
76fc825 [Ilya Ganelin] Updated to use atomic booleans instead of the synchronized vars
6e8a7f7 [Ilya Ganelin] Removing unecessary null check for now since i'm not fixing stop ordering yet
cdf7073 [Ilya Ganelin] [SPARK-6492] Moved stopped=true back to the start of the shutdown sequence so this can be addressed in a seperate PR
7fb795b [Ilya Ganelin] Spacing
b7a0c5c [Ilya Ganelin] Import ordering
df8224f [Ilya Ganelin] Added comment for added lock
343cb94 [Ilya Ganelin] [SPARK-6492] Added timeout/retry logic to fix a deadlock in SparkContext shutdown
Hi all - this patch changes the Stage class to an abstract class and introduces two new classes that extend it: ShuffleMapStage and ResultStage - with the goal of increasing readability of the DAGScheduler class. Their usage is updated within DAGScheduler.
Author: Ilya Ganelin <ilya.ganelin@capitalone.com>
Author: Ilya Ganelin <ilganeli@gmail.com>
Closes#4708 from ilganeli/SPARK-4655 and squashes the following commits:
c248924 [Ilya Ganelin] Merge branch 'SPARK-4655' of github.com:ilganeli/spark into SPARK-4655
d930385 [Ilya Ganelin] Fixed merge conflict from
a9a765f [Ilya Ganelin] Update DAGScheduler.scala
c03563c [Ilya Ganelin] Minor fixeS
c39e971 [Ilya Ganelin] Added return typing for public methods
845bc87 [Ilya Ganelin] Merge branch 'SPARK-4655' of github.com:ilganeli/spark into SPARK-4655
e8031d8 [Ilya Ganelin] Minor string fixes
4ec53ac [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-4655
c004f62 [Ilya Ganelin] Update DAGScheduler.scala
a2cb03f [Ilya Ganelin] [SPARK-4655] Replaced usages of Nil and eliminated some code reuse
3d5cf20 [Ilya Ganelin] [SPARK-4655] Moved mima exclude to 1.4
6912c55 [Ilya Ganelin] Resolved merge conflict
4bff208 [Ilya Ganelin] Minor stylistic fixes
c6fffbb [Ilya Ganelin] newline
41402ad [Ilya Ganelin] Style fixes
02c6981 [Ilya Ganelin] Merge branch 'SPARK-4655' of github.com:ilganeli/spark into SPARK-4655
c755a09 [Ilya Ganelin] Some more stylistic updates and minor refactoring
b6257a0 [Ilya Ganelin] Update MimaExcludes.scala
0f0c624 [Ilya Ganelin] Fixed merge conflict
2eba262 [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-4655
6b43d7b [Ilya Ganelin] Got rid of some spaces
6f1a5db [Ilya Ganelin] Revert "More minor formatting and refactoring"
1b3471b [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-4655
c9288e2 [Ilya Ganelin] More minor formatting and refactoring
d548caf [Ilya Ganelin] Formatting fix
c3ae5c2 [Ilya Ganelin] Explicit typing
0dacaf3 [Ilya Ganelin] Got rid of stale import
6da3a71 [Ilya Ganelin] Trailing whitespace
b85c5fe [Ilya Ganelin] Added minor fixes
a57dfcd [Ilya Ganelin] Added MiMA exclusion to get around binary compatibility check
83ed849 [Ilya Ganelin] moved braces for consistency
96dd161 [Ilya Ganelin] Fixed minor style error
cfd6f10 [Ilya Ganelin] Updated DAGScheduler to use new ResultStage and ShuffleMapStage classes
83494e9 [Ilya Ganelin] Added new Stage classes
This PR added a standard internal RPC interface for Spark and an Akka implementation. See [the design document](https://issues.apache.org/jira/secure/attachment/12698710/Pluggable%20RPC%20-%20draft%202.pdf) for more details.
I will split the whole work into multiple PRs to make it easier for code review. This is the first PR and avoid to touch too many files.
Author: zsxwing <zsxwing@gmail.com>
Closes#4588 from zsxwing/rpc-part1 and squashes the following commits:
fe3df4c [zsxwing] Move registerEndpoint and use actorSystem.dispatcher in asyncSetupEndpointRefByURI
f6f3287 [zsxwing] Remove RpcEndpointRef.toURI
8bd1097 [zsxwing] Fix docs and the code style
f459380 [zsxwing] Add RpcAddress.fromURI and rename urls to uris
b221398 [zsxwing] Move send methods above ask methods
15cfd7b [zsxwing] Merge branch 'master' into rpc-part1
9ffa997 [zsxwing] Fix MiMa tests
78a1733 [zsxwing] Merge remote-tracking branch 'origin/master' into rpc-part1
385b9c3 [zsxwing] Fix the code style and add docs
2cc3f78 [zsxwing] Add an asynchronous version of setupEndpointRefByUrl
e8dfec3 [zsxwing] Remove 'sendWithReply(message: Any, sender: RpcEndpointRef): Unit'
08564ae [zsxwing] Add RpcEnvFactory to create RpcEnv
e5df4ca [zsxwing] Handle AkkaFailure(e) in Actor
ec7c5b0 [zsxwing] Fix docs
7fc95e1 [zsxwing] Implement askWithReply in RpcEndpointRef
9288406 [zsxwing] Document thread-safety for setupThreadSafeEndpoint
3007c09 [zsxwing] Move setupDriverEndpointRef to RpcUtils and rename to makeDriverRef
c425022 [zsxwing] Fix the code style
5f87700 [zsxwing] Move the logical of processing message to a private function
3e56123 [zsxwing] Use lazy to eliminate CountDownLatch
07f128f [zsxwing] Remove ActionScheduler.scala
4d34191 [zsxwing] Remove scheduler from RpcEnv
7cdd95e [zsxwing] Add docs for RpcEnv
51e6667 [zsxwing] Add 'sender' to RpcCallContext and rename the parameter of receiveAndReply to 'context'
ffc1280 [zsxwing] Rename 'fail' to 'sendFailure' and other minor code style changes
28e6d0f [zsxwing] Add onXXX for network events and remove the companion objects of network events
3751c97 [zsxwing] Rename RpcResponse to RpcCallContext
fe7d1ff [zsxwing] Add explicit reply in rpc
7b9e0c9 [zsxwing] Fix the indentation
04a106e [zsxwing] Remove NopCancellable and add a const NOP in object SettableCancellable
2a579f4 [zsxwing] Remove RpcEnv.systemName
155b987 [zsxwing] Change newURI to uriOf and add some comments
45b2317 [zsxwing] A standard RPC interface and An Akka implementation
Adds a `Graph#minus` method which will return only unique `VertexId`'s from the calling `VertexRDD`.
To demonstrate a basic example with pseudocode:
```
Set((0L,0),(1L,1)).minus(Set((1L,1),(2L,2)))
> Set((0L,0))
```
Author: Brennon York <brennon.york@capitalone.com>
Closes#5175 from brennonyork/SPARK-6510 and squashes the following commits:
248d5c8 [Brennon York] added minus(VertexRDD[VD]) method to avoid createUsingIndex and updated the mask operations to simplify with andNot call
3fb7cce [Brennon York] updated graphx doc to reflect the addition of minus method
6575d92 [Brennon York] updated mima exclude
aaa030b [Brennon York] completed graph#minus functionality
7227c0f [Brennon York] beginning work on minus functionality
Changed method invocation of 'diff' to match that of 'innerJoin' and 'leftJoin' from VertexRDD[VD] to RDD[(VertexId, VD)]. This change maintains backwards compatibility and better unifies the VertexRDD methods to match each other.
Author: Brennon York <brennon.york@capitalone.com>
Closes#4733 from brennonyork/SPARK-5922 and squashes the following commits:
e800f08 [Brennon York] fixed merge conflicts
b9274af [Brennon York] fixed merge conflicts
f86375c [Brennon York] fixed minor include line
398ddb4 [Brennon York] fixed merge conflicts
aac1810 [Brennon York] updated to aggregateUsingIndex and added test to ensure that method works properly
2af0b88 [Brennon York] removed deprecation line
753c963 [Brennon York] fixed merge conflicts and set preference to use the diff(other: VertexRDD[VD]) method
2c678c6 [Brennon York] added mima exclude to exclude new public diff method from VertexRDD
93186f3 [Brennon York] added back the original diff method to sustain binary compatibility
f18356e [Brennon York] changed method invocation of 'diff' to match that of 'innerJoin' and 'leftJoin' from VertexRDD[VD] to RDD[(VertexId, VD)]
The issue is discussed in https://issues.apache.org/jira/browse/SPARK-5669. Replacing all JBLAS usage by netlib-java gives us a simpler dependency tree and less license issues to worry about. I didn't touch the test scope in this PR. The user guide is not modified to avoid merge conflicts with branch-1.3. srowen ankurdave pwendell
Author: Xiangrui Meng <meng@databricks.com>
Closes#4699 from mengxr/SPARK-5814 and squashes the following commits:
48635c6 [Xiangrui Meng] move netlib-java version to parent pom
ca21c74 [Xiangrui Meng] remove jblas from ml-guide
5f7767a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5814
c5c4183 [Xiangrui Meng] merge master
0f20cad [Xiangrui Meng] add mima excludes
e53e9f4 [Xiangrui Meng] remove jblas from mllib runtime
ceaa14d [Xiangrui Meng] replace jblas by netlib-java in graphx
fa7c2ca [Xiangrui Meng] move jblas to test scope
Another one from JoshRosen 's wish list. The first commit is much smaller and removes 2 of the 4 Clock classes. The second is much larger, necessary for consolidating the streaming one. I put together implementations in the way that seemed simplest. Almost all the change is standardizing class and method names.
Author: Sean Owen <sowen@cloudera.com>
Closes#4514 from srowen/SPARK-4682 and squashes the following commits:
5ed3a03 [Sean Owen] Javadoc Clock classes; make ManualClock private[spark]
169dd13 [Sean Owen] Add support for legacy org.apache.spark.streaming clock class names
277785a [Sean Owen] Reduce the net change in this patch by reversing some unnecessary syntax changes along the way
b5e53df [Sean Owen] FakeClock -> ManualClock; getTime() -> getTimeMillis()
160863a [Sean Owen] Consolidate Streaming Clock class into common util Clock
7c956b2 [Sean Owen] Consolidate Clocks except for Streaming Clock
This is part (1a) of the updates from the design doc in [https://docs.google.com/document/d/1BH9el33kBX8JiDdgUJXdLW14CA2qhTCWIG46eXZVoJs]
**UPDATE**: Most of the APIs are being kept private[spark] to allow further discussion. Here is a list of changes which are public:
* new output columns: rawPrediction, probabilities
* The “score” column is now called “rawPrediction”
* Classifiers now provide numClasses
* Params.get and .set are now protected instead of private[ml].
* ParamMap now has a size method.
* new classes: LinearRegression, LinearRegressionModel
* LogisticRegression now has an intercept.
### Sketch of APIs (most of which are private[spark] for now)
Abstract classes for learning algorithms (+ corresponding Model abstractions):
* Classifier (+ ClassificationModel)
* ProbabilisticClassifier (+ ProbabilisticClassificationModel)
* Regressor (+ RegressionModel)
* Predictor (+ PredictionModel)
* *For all of these*:
* There is no strongly typed training-time API.
* There is a strongly typed test-time (prediction) API which helps developers implement new algorithms.
Concrete classes: learning algorithms
* LinearRegression
* LogisticRegression (updated to use new abstract classes)
* Also, removed "score" in favor of "probability" output column. Changed BinaryClassificationEvaluator to match. (SPARK-5031)
Other updates:
* params.scala: Changed Params.set/get to be protected instead of private[ml]
* This was needed for the example of defining a class from outside of the MLlib namespace.
* VectorUDT: Will later change from private[spark] to public.
* This is needed for outside users to write their own validateAndTransformSchema() methods using vectors.
* Also, added equals() method.f
* SPARK-4942 : ML Transformers should allow output cols to be turned on,off
* Update validateAndTransformSchema
* Update transform
* (Updated examples, test suites according to other changes)
New examples:
* DeveloperApiExample.scala (example of defining algorithm from outside of the MLlib namespace)
* Added Java version too
Test Suites:
* LinearRegressionSuite
* LogisticRegressionSuite
* + Java versions of above suites
CC: mengxr etrain shivaram
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#3637 from jkbradley/ml-api-part1 and squashes the following commits:
405bfb8 [Joseph K. Bradley] Last edits based on code review. Small cleanups
fec348a [Joseph K. Bradley] Added JavaDeveloperApiExample.java and fixed other issues: Made developer API private[spark] for now. Added constructors Java can understand to specialized Param types.
8316d5e [Joseph K. Bradley] fixes after rebasing on master
fc62406 [Joseph K. Bradley] fixed test suites after last commit
bcb9549 [Joseph K. Bradley] Fixed issues after rebasing from master (after move from SchemaRDD to DataFrame)
9872424 [Joseph K. Bradley] fixed JavaLinearRegressionSuite.java Java sql api
f542997 [Joseph K. Bradley] Added MIMA excludes for VectorUDT (now public), and added DeveloperApi annotation to it
216d199 [Joseph K. Bradley] fixed after sql datatypes PR got merged
f549e34 [Joseph K. Bradley] Updates based on code review. Major ones are: * Created weakly typed Predictor.train() method which is called by fit() so that developers do not have to call schema validation or copy parameters. * Made Predictor.featuresDataType have a default value of VectorUDT. * NOTE: This could be dangerous since the FeaturesType type parameter cannot have a default value.
343e7bd [Joseph K. Bradley] added blanket mima exclude for ml package
82f340b [Joseph K. Bradley] Fixed bug in LogisticRegression (introduced in this PR). Fixed Java suites
0a16da9 [Joseph K. Bradley] Fixed Linear/Logistic RegressionSuites
c3c8da5 [Joseph K. Bradley] small cleanup
934f97b [Joseph K. Bradley] Fixed bugs from previous commit.
1c61723 [Joseph K. Bradley] * Made ProbabilisticClassificationModel into a subclass of ClassificationModel. Also introduced ProbabilisticClassifier. * This was to support output column “probabilityCol” in transform().
4e2f711 [Joseph K. Bradley] rat fix
bc654e1 [Joseph K. Bradley] Added spark.ml LinearRegressionSuite
8d13233 [Joseph K. Bradley] Added methods: * Classifier: batch predictRaw() * Predictor: train() without paramMap ProbabilisticClassificationModel.predictProbabilities() * Java versions of all above batch methods + others
1680905 [Joseph K. Bradley] Added JavaLabeledPointSuite.java for spark.ml, and added constructor to LabeledPoint which defaults weight to 1.0
adbe50a [Joseph K. Bradley] * fixed LinearRegression train() to use embedded paramMap * added Predictor.predict(RDD[Vector]) method * updated Linear/LogisticRegressionSuites
58802e3 [Joseph K. Bradley] added train() to Predictor subclasses which does not take a ParamMap.
57d54ab [Joseph K. Bradley] * Changed semantics of Predictor.train() to merge the given paramMap with the embedded paramMap. * remove threshold_internal from logreg * Added Predictor.copy() * Extended LogisticRegressionSuite
e433872 [Joseph K. Bradley] Updated docs. Added LabeledPointSuite to spark.ml
54b7b31 [Joseph K. Bradley] Fixed issue with logreg threshold being set correctly
0617d61 [Joseph K. Bradley] Fixed bug from last commit (sorting paramMap by parameter names in toString). Fixed bug in persisting logreg data. Added threshold_internal to logreg for faster test-time prediction (avoiding map lookup).
601e792 [Joseph K. Bradley] Modified ParamMap to sort parameters in toString. Cleaned up classes in class hierarchy, before implementing tests and examples.
d705e87 [Joseph K. Bradley] Added LinearRegression and Regressor back from ml-api branch
52f4fde [Joseph K. Bradley] removing everything except for simple class hierarchy for classification
d35bb5d [Joseph K. Bradley] fixed compilation issues, but have not added tests yet
bfade12 [Joseph K. Bradley] Added lots of classes for new ML API:
The only issue is that `analyzeBlock` is removed, which was marked as a developer API. I didn't change other tests in the ALSSuite under `spark.mllib` to ensure that the implementation is correct.
CC: srowen coderxiang
Author: Xiangrui Meng <meng@databricks.com>
Closes#4321 from mengxr/SPARK-5536 and squashes the following commits:
5a3cee8 [Xiangrui Meng] update python tests that are too strict
e840acf [Xiangrui Meng] ignore scala style check for ALS.train
e9a721c [Xiangrui Meng] update mima excludes
9ee6a36 [Xiangrui Meng] merge master
9a8aeac [Xiangrui Meng] update tests
d8c3271 [Xiangrui Meng] remove analyzeBlocks
d68eee7 [Xiangrui Meng] add checkpoint to new ALS
22a56f8 [Xiangrui Meng] wrap old ALS
c387dff [Xiangrui Meng] support random seed
3bdf24b [Xiangrui Meng] make storage level configurable in the new ALS
This method survived the code review and it has been there since v1.1.0. It exposes jblas types. Let's remove it from the public API. I think no one calls it directly.
Author: Xiangrui Meng <meng@databricks.com>
Closes#4318 from mengxr/SPARK-5540 and squashes the following commits:
586ade6 [Xiangrui Meng] hide ALS.solveLeastSquares
Added the 2 methods to Graph and GraphImpl. Both make calls to the underlying vertex and edge RDDs.
This is needed for another PR (for LDA): [https://github.com/apache/spark/pull/4047]
Notes:
* getCheckpointedFiles is plural and returns a Seq[String] instead of an Option[String].
* I attempted to test to make sure the methods returned the correct values after checkpointing. It did not work; I guess that checkpointing does not occur quickly enough? I noticed that there are not checkpointing tests for RDDs; is it just hard to test well?
CC: rxin
CC: mengxr (since related to LDA)
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#4253 from jkbradley/graphx-checkpoint and squashes the following commits:
b680148 [Joseph K. Bradley] added class tag to firstParent call in VertexRDDImpl.isCheckpointed, though not needed to compile
250810e [Joseph K. Bradley] In EdgeRDDImple, VertexRDDImpl, added transient back to partitionsRDD, and made isCheckpointed check firstParent instead of partitionsRDD
695b7a3 [Joseph K. Bradley] changed partitionsRDD in EdgeRDDImpl, VertexRDDImpl to be non-transient
cc00767 [Joseph K. Bradley] added overrides for isCheckpointed, getCheckpointFile in EdgeRDDImpl, VertexRDDImpl. The corresponding Graph methods now work.
188665f [Joseph K. Bradley] improved documentation
235738c [Joseph K. Bradley] Added isCheckpointed and getCheckpointFiles to Graph, GraphImpl
We have seen many use cases of `treeAggregate`/`treeReduce` outside the ML domain. Maybe it is time to move them to Core. pwendell
Author: Xiangrui Meng <meng@databricks.com>
Closes#4228 from mengxr/SPARK-5430 and squashes the following commits:
20ad40d [Xiangrui Meng] exclude tree* from mima
e89a43e [Xiangrui Meng] fix compile and update java doc
3ae1a4b [Xiangrui Meng] add treeReduce/treeAggregate to Python
6f948c5 [Xiangrui Meng] add treeReduce/treeAggregate to JavaRDDLike
d600b6c [Xiangrui Meng] move treeReduce and treeAggregate to core
Support for transposing local matrices added. The `.transpose` function creates a new object re-using the backing array(s) but switches `numRows` and `numCols`. Operations check the flag `.isTransposed` to see whether the indexing in `values` should be modified.
This PR will pave the way for transposing `BlockMatrix`.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#4109 from brkyvz/SPARK-5321 and squashes the following commits:
87ab83c [Burak Yavuz] fixed scalastyle
caf4438 [Burak Yavuz] addressed code review v3
c524770 [Burak Yavuz] address code review comments 2
77481e8 [Burak Yavuz] fixed MiMa
f1c1742 [Burak Yavuz] small refactoring
ccccdec [Burak Yavuz] fixed failed test
dd45c88 [Burak Yavuz] addressed code review
a01bd5f [Burak Yavuz] [SPARK-5321] Fixed MiMa issues
2a63593 [Burak Yavuz] [SPARK-5321] fixed bug causing failed gemm test
c55f29a [Burak Yavuz] [SPARK-5321] Support for transposing local matrices cleaned up
c408c05 [Burak Yavuz] [SPARK-5321] Support for transposing local matrices added
`reduceByWindow` for Java API is actually not Java compatible, change to make it Java compatible.
Current solution is to deprecate the old one and add a new API, but since old API actually is not correct, so is keeping the old one meaningful? just to keep the binary compatible? Also even adding new API still need to add to Mima exclusion, I'm not sure to change the API, or deprecate the old API and add a new one, which is the best solution?
Author: jerryshao <saisai.shao@intel.com>
Closes#4104 from jerryshao/SPARK-5315 and squashes the following commits:
5bc8987 [jerryshao] Address the comment
c7aa1b4 [jerryshao] Deprecate the old one to keep binary compatible
8e9dc67 [jerryshao] Fix JavaDStream reduceByWindow signature error
Current Java file stream doesn't support custom key/value type because of loss of type information, details can be seen in [SPARK-5297](https://issues.apache.org/jira/browse/SPARK-5297). Fix this problem by getting correct `ClassTag` from `Class[_]`.
Author: jerryshao <saisai.shao@intel.com>
Closes#4101 from jerryshao/SPARK-5297 and squashes the following commits:
e022ca3 [jerryshao] Add Mima exclusion
ecd61b8 [jerryshao] Fix Java fileInputStream type erasure problem
Pretty minor, but submitted for consideration -- this would at least help people make this check in the most efficient way I know.
Author: Sean Owen <sowen@cloudera.com>
Closes#4074 from srowen/SPARK-5270 and squashes the following commits:
66885b8 [Sean Owen] Add note that JavaRDDLike should not be implemented by user code
2e9b490 [Sean Owen] More tests, and Mima-exclude the new isEmpty method in JavaRDDLike
28395ff [Sean Owen] Add isEmpty to Java, Python
7dd04b7 [Sean Owen] Add efficient RDD.isEmpty()
`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
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).
Since GraphX is no longer alpha as of 1.2, MimaExcludes should not exclude GraphX for 1.3
Here are the individual excludes I had to add + the associated commits:
```
// SPARK-4444
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.graphx.EdgeRDD.fromEdges"),
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.EdgeRDD.filter"),
ProblemFilters.exclude[IncompatibleResultTypeProblem](
"org.apache.spark.graphx.impl.EdgeRDDImpl.filter"),
```
[9ac2bb18ed]
```
// SPARK-3623
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.Graph.checkpoint")
```
[e895e0cbec]
```
// SPARK-4620
ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.Graph.unpersist"),
```
[8817fc7fe8]
CC: rxin
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#3856 from jkbradley/graphx-mima and squashes the following commits:
1eea2f6 [Joseph K. Bradley] moved cleanup to run-tests
527ccd9 [Joseph K. Bradley] fixed jenkins script to remove ivy2 cache
802e252 [Joseph K. Bradley] Removed GraphX MIMA excludes and added line to clear spark from .m2 dir before Jenkins tests. This may not work yet...
30f8bb4 [Joseph K. Bradley] added individual mima excludes for graphx
a3fea42 [Joseph K. Bradley] removed graphx mima exclude for 1.3
This PR is a fixed version of the original PR #3237 by watermen and scwf.
This adds the ability to specify how many elements to print in `DStream.print`.
Author: Yadong Qi <qiyadong2010@gmail.com>
Author: q00251598 <qiyadong@huawei.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: wangfei <wangfei1@huawei.com>
Closes#3865 from tdas/print-num and squashes the following commits:
cd34e9e [Tathagata Das] Fix bug
7c09f16 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into HEAD
bb35d1a [Yadong Qi] Update MimaExcludes.scala
f8098ca [Yadong Qi] Update MimaExcludes.scala
f6ac3cb [Yadong Qi] Update MimaExcludes.scala
e4ed897 [Yadong Qi] Update MimaExcludes.scala
3b9d5cf [wangfei] fix conflicts
ec8a3af [q00251598] move to Spark 1.3
26a70c0 [q00251598] extend the Python DStream's print
b589a4b [q00251598] add another print function
Re-enable MiMa for Streaming Flume Sink module, now that 1.1.0 is released, per the JIRA TO-DO. That's pretty much all there is to this.
Author: Sean Owen <sowen@cloudera.com>
Closes#3842 from srowen/SPARK-2757 and squashes the following commits:
50ff80e [Sean Owen] Exclude apparent false positive turned up by re-enabling MiMa checks for Streaming Flume Sink
0e5ba5c [Sean Owen] Re-enable MiMa for Streaming Flume Sink module
Before we have a full picture of the operators we want to add, it might be safer to hide `Matrix.transposeMultiply` in 1.2.0. Another update we want to change is `Matrix.randn` and `Matrix.rand`, both of which should take a `Random` implementation. Otherwise, it is very likely to produce inconsistent RDDs. I also added some unit tests for matrix factory methods. All APIs are new in 1.2, so there is no incompatible changes.
brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes#3468 from mengxr/SPARK-4614 and squashes the following commits:
3b0e4e2 [Xiangrui Meng] add mima excludes
6bfd8a4 [Xiangrui Meng] hide transposeMultiply; add rng to rand and randn; add unit tests
This is blocking #3353 and other patches.
Author: Andrew Or <andrew@databricks.com>
Closes#3371 from andrewor14/mima-hot-fix and squashes the following commits:
842d059 [Andrew Or] Move excludes to the right section
c4d4f4e [Andrew Or] MIMA hot fix
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.
Add ReliableKafkaReceiver in Kafka connector to prevent data loss if WAL in Spark Streaming is enabled. Details and design doc can be seen in [SPARK-4062](https://issues.apache.org/jira/browse/SPARK-4062).
Author: jerryshao <saisai.shao@intel.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Saisai Shao <saisai.shao@intel.com>
Closes#2991 from jerryshao/kafka-refactor and squashes the following commits:
5461f1c [Saisai Shao] Merge pull request #8 from tdas/kafka-refactor3
eae4ad6 [Tathagata Das] Refectored KafkaStreamSuiteBased to eliminate KafkaTestUtils and made Java more robust.
fab14c7 [Tathagata Das] minor update.
149948b [Tathagata Das] Fixed mistake
14630aa [Tathagata Das] Minor updates.
d9a452c [Tathagata Das] Minor updates.
ec2e95e [Tathagata Das] Removed the receiver's locks and essentially reverted to Saisai's original design.
2a20a01 [jerryshao] Address some comments
9f636b3 [Saisai Shao] Merge pull request #5 from tdas/kafka-refactor
b2b2f84 [Tathagata Das] Refactored Kafka receiver logic and Kafka testsuites
e501b3c [jerryshao] Add Mima excludes
b798535 [jerryshao] Fix the missed issue
e5e21c1 [jerryshao] Change to while loop
ea873e4 [jerryshao] Further address the comments
98f3d07 [jerryshao] Fix comment style
4854ee9 [jerryshao] Address all the comments
96c7a1d [jerryshao] Update the ReliableKafkaReceiver unit test
8135d31 [jerryshao] Fix flaky test
a949741 [jerryshao] Address the comments
16bfe78 [jerryshao] Change the ordering of imports
0894aef [jerryshao] Add some comments
77c3e50 [jerryshao] Code refactor and add some unit tests
dd9aeeb [jerryshao] Initial commit for reliable Kafka receiver
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
(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
This is part of a broader effort to enable dynamic scaling of executors ([SPARK-3174](https://issues.apache.org/jira/browse/SPARK-3174)). This is intended to work alongside SPARK-3795 (#2746), SPARK-3796 and SPARK-3797, but is functionally independently of these other issues.
The logic is built on top of PraveenSeluka's changes at #2798. This is different from the changes there in a few major ways: (1) the mechanism is implemented within the existing scheduler backend framework rather than in new `Actor` classes. This also introduces a parent abstract class `YarnSchedulerBackend` to encapsulate common logic to communicate with the Yarn `ApplicationMaster`. (2) The interface of requesting executors exposed to the `SparkContext` is the same, but the communication between the scheduler backend and the AM uses total number executors desired instead of an incremental number. This is discussed in #2746 and explained in the comments in the code.
I have tested this significantly on a stable Yarn cluster.
------------
A remaining task for this issue is to tone down the error messages emitted when an executor is removed.
Currently, `SparkContext` and its components react as if the executor has failed, resulting in many scary error messages and eventual timeouts. While it's not strictly necessary to fix this as of the first-cut implementation of this mechanism, it would be good to add logic to distinguish this case. I prefer to address this in a separate PR. I have filed a separate JIRA for this task at SPARK-4134.
Author: Andrew Or <andrew@databricks.com>
Author: Andrew Or <andrewor14@gmail.com>
Closes#2840 from andrewor14/yarn-scaling-mechanism and squashes the following commits:
485863e [Andrew Or] Minor log message changes
4920be8 [Andrew Or] Clarify that public API is only for Yarn mode for now
1c57804 [Andrew Or] Reword a few comments + other review comments
6321140 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-scaling-mechanism
02836c0 [Andrew Or] Limit scope of synchronization
4e2ed7f [Andrew Or] Fix bug: keep track of removed executors properly
73ade46 [Andrew Or] Wording changes (minor)
2a7a6da [Andrew Or] Add `sc.killExecutor` as a shorthand (minor)
665f229 [Andrew Or] Mima excludes
79aa2df [Andrew Or] Simplify the request interface by asking for a total
04f625b [Andrew Or] Fix race condition that causes over-allocation of executors
f4783f8 [Andrew Or] Change the semantics of requesting executors
005a124 [Andrew Or] Fix tests
4628b16 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-scaling-mechanism
db4a679 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-scaling-mechanism
572f5c5 [Andrew Or] Unused import (minor)
f30261c [Andrew Or] Kill multiple executors rather than one at a time
de260d9 [Andrew Or] Simplify by skipping useless null check
9c52542 [Andrew Or] Simplify by skipping the TaskSchedulerImpl
97dd1a8 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-scaling-mechanism
d987b3e [Andrew Or] Move addWebUIFilters to Yarn scheduler backend
7b76d0a [Andrew Or] Expose mechanism in SparkContext as developer API
47466cd [Andrew Or] Refactor common Yarn scheduler backend logic
c4dfaac [Andrew Or] Avoid thrashing when removing executors
53e8145 [Andrew Or] Start yarn actor early to listen for AM registration message
bbee669 [Andrew Or] Add mechanism in yarn client mode
This PR encapsulates #2330, which is itself a continuation of #2240. The first goal of this PR is to provide an alternate, simpler implementation of the ConnectionManager which is based on Netty.
In addition to this goal, however, we want to resolve [SPARK-3796](https://issues.apache.org/jira/browse/SPARK-3796), which calls for a standalone shuffle service which can be integrated into the YARN NodeManager, Standalone Worker, or on its own. This PR makes the first step in this direction by ensuring that the actual Netty service is as small as possible and extracted from Spark core. Given this, we should be able to construct this standalone jar which can be included in other JVMs without incurring significant dependency or runtime issues. The actual work to ensure that such a standalone shuffle service would work in Spark will be left for a future PR, however.
In order to minimize dependencies and allow for the service to be long-running (possibly much longer-running than Spark, and possibly having to support multiple version of Spark simultaneously), the entire service has been ported to Java, where we have full control over the binary compatibility of the components and do not depend on the Scala runtime or version.
These issues: have been addressed by folding in #2330:
SPARK-3453: Refactor Netty module to use BlockTransferService interface
SPARK-3018: Release all buffers upon task completion/failure
SPARK-3002: Create a connection pool and reuse clients across different threads
SPARK-3017: Integration tests and unit tests for connection failures
SPARK-3049: Make sure client doesn't block when server/connection has error(s)
SPARK-3502: SO_RCVBUF and SO_SNDBUF should be bootstrap childOption, not option
SPARK-3503: Disable thread local cache in PooledByteBufAllocator
TODO before mergeable:
- [x] Implement uploadBlock()
- [x] Unit tests for RPC side of code
- [x] Performance testing (see comments [here](https://github.com/apache/spark/pull/2753#issuecomment-59475022))
- [x] Turn OFF by default (currently on for unit testing)
Author: Reynold Xin <rxin@apache.org>
Author: Aaron Davidson <aaron@databricks.com>
Author: cocoatomo <cocoatomo77@gmail.com>
Author: Patrick Wendell <pwendell@gmail.com>
Author: Prashant Sharma <prashant.s@imaginea.com>
Author: Davies Liu <davies.liu@gmail.com>
Author: Anand Avati <avati@redhat.com>
Closes#2753 from aarondav/netty and squashes the following commits:
cadfd28 [Aaron Davidson] Turn netty off by default
d7be11b [Aaron Davidson] Turn netty on by default
4a204b8 [Aaron Davidson] Fail block fetches if client connection fails
2b0d1c0 [Aaron Davidson] 100ch
0c5bca2 [Aaron Davidson] Merge branch 'master' of https://github.com/apache/spark into netty
14e37f7 [Aaron Davidson] Address Reynold's comments
8dfcceb [Aaron Davidson] Merge branch 'master' of https://github.com/apache/spark into netty
322dfc1 [Aaron Davidson] Address Reynold's comments, including major rename
e5675a4 [Aaron Davidson] Fail outstanding RPCs as well
ccd4959 [Aaron Davidson] Don't throw exception if client immediately fails
9da0bc1 [Aaron Davidson] Add RPC unit tests
d236dfd [Aaron Davidson] Remove no-op serializer :)
7b7a26c [Aaron Davidson] Fix Nio compile issue
dd420fd [Aaron Davidson] Merge branch 'master' of https://github.com/apache/spark into netty-test
939f276 [Aaron Davidson] Attempt to make comm. bidirectional
aa58f67 [cocoatomo] [SPARK-3909][PySpark][Doc] A corrupted format in Sphinx documents and building warnings
8dc1ded [cocoatomo] [SPARK-3867][PySpark] ./python/run-tests failed when it run with Python 2.6 and unittest2 is not installed
5b5dbe6 [Prashant Sharma] [SPARK-2924] Required by scala 2.11, only one fun/ctor amongst overriden alternatives, can have default argument(s).
2c5d9dc [Patrick Wendell] HOTFIX: Fix build issue with Akka 2.3.4 upgrade.
020691e [Davies Liu] [SPARK-3886] [PySpark] use AutoBatchedSerializer by default
ae4083a [Anand Avati] [SPARK-2805] Upgrade Akka to 2.3.4
29c6dcf [Aaron Davidson] [SPARK-3453] Netty-based BlockTransferService, extracted from Spark core
f7e7568 [Reynold Xin] Fixed spark.shuffle.io.receiveBuffer setting.
5d98ce3 [Reynold Xin] Flip buffer.
f6c220d [Reynold Xin] Merge with latest master.
407e59a [Reynold Xin] Fix style violation.
a0518c7 [Reynold Xin] Implemented block uploads.
4b18db2 [Reynold Xin] Copy the buffer in fetchBlockSync.
bec4ea2 [Reynold Xin] Removed OIO and added num threads settings.
1bdd7ee [Reynold Xin] Fixed tests.
d68f328 [Reynold Xin] Logging close() in case close() fails.
f63fb4c [Reynold Xin] Add more debug message.
6afc435 [Reynold Xin] Added logging.
c066309 [Reynold Xin] Implement java.io.Closeable interface.
519d64d [Reynold Xin] Mark private package visibility and MimaExcludes.
f0a16e9 [Reynold Xin] Fixed test hanging.
14323a5 [Reynold Xin] Removed BlockManager.getLocalShuffleFromDisk.
b2f3281 [Reynold Xin] Added connection pooling.
d23ed7b [Reynold Xin] Incorporated feedback from Norman: - use same pool for boss and worker - remove ioratio - disable caching of byte buf allocator - childoption sendbuf/receivebuf - fire exception through pipeline
9e0cb87 [Reynold Xin] Fixed BlockClientHandlerSuite
5cd33d7 [Reynold Xin] Fixed style violation.
cb589ec [Reynold Xin] Added more test cases covering cleanup when fault happens in ShuffleBlockFetcherIteratorSuite
1be4e8e [Reynold Xin] Shorten NioManagedBuffer and NettyManagedBuffer class names.
108c9ed [Reynold Xin] Forgot to add TestSerializer to the commit list.
b5c8d1f [Reynold Xin] Fixed ShuffleBlockFetcherIteratorSuite.
064747b [Reynold Xin] Reference count buffers and clean them up properly.
2b44cf1 [Reynold Xin] Added more documentation.
1760d32 [Reynold Xin] Use Epoll.isAvailable in BlockServer as well.
165eab1 [Reynold Xin] [SPARK-3453] Refactor Netty module to use BlockTransferService.
Sorter uses generic-typed key for sorting. When data is large, it creates lots of key objects, which is not efficient. We should reuse the key in Sorter for memory efficiency. This change is part of the petabyte sort implementation from rxin .
The `Sorter` class was written in Java and marked package private. So it is only available to `org.apache.spark.util.collection`. I renamed it to `TimSort` and add a simple wrapper of it, still called `Sorter`, in Scala, which is `private[spark]`.
The benchmark code is updated, which now resets the array before each run. Here is the result on sorting primitive Int arrays of size 25 million using Sorter:
~~~
[info] - Sorter benchmark for key-value pairs !!! IGNORED !!!
Java Arrays.sort() on non-primitive int array: Took 13237 ms
Java Arrays.sort() on non-primitive int array: Took 13320 ms
Java Arrays.sort() on non-primitive int array: Took 15718 ms
Java Arrays.sort() on non-primitive int array: Took 13283 ms
Java Arrays.sort() on non-primitive int array: Took 13267 ms
Java Arrays.sort() on non-primitive int array: Took 15122 ms
Java Arrays.sort() on non-primitive int array: Took 15495 ms
Java Arrays.sort() on non-primitive int array: Took 14877 ms
Java Arrays.sort() on non-primitive int array: Took 16429 ms
Java Arrays.sort() on non-primitive int array: Took 14250 ms
Java Arrays.sort() on non-primitive int array: (13878 ms first try, 14499 ms average)
Java Arrays.sort() on primitive int array: Took 2683 ms
Java Arrays.sort() on primitive int array: Took 2683 ms
Java Arrays.sort() on primitive int array: Took 2701 ms
Java Arrays.sort() on primitive int array: Took 2746 ms
Java Arrays.sort() on primitive int array: Took 2685 ms
Java Arrays.sort() on primitive int array: Took 2735 ms
Java Arrays.sort() on primitive int array: Took 2669 ms
Java Arrays.sort() on primitive int array: Took 2693 ms
Java Arrays.sort() on primitive int array: Took 2680 ms
Java Arrays.sort() on primitive int array: Took 2642 ms
Java Arrays.sort() on primitive int array: (2948 ms first try, 2691 ms average)
Sorter without key reuse on primitive int array: Took 10732 ms
Sorter without key reuse on primitive int array: Took 12482 ms
Sorter without key reuse on primitive int array: Took 10718 ms
Sorter without key reuse on primitive int array: Took 12650 ms
Sorter without key reuse on primitive int array: Took 10747 ms
Sorter without key reuse on primitive int array: Took 10783 ms
Sorter without key reuse on primitive int array: Took 12721 ms
Sorter without key reuse on primitive int array: Took 10604 ms
Sorter without key reuse on primitive int array: Took 10622 ms
Sorter without key reuse on primitive int array: Took 11843 ms
Sorter without key reuse on primitive int array: (11089 ms first try, 11390 ms average)
Sorter with key reuse on primitive int array: Took 5141 ms
Sorter with key reuse on primitive int array: Took 5298 ms
Sorter with key reuse on primitive int array: Took 5066 ms
Sorter with key reuse on primitive int array: Took 5164 ms
Sorter with key reuse on primitive int array: Took 5203 ms
Sorter with key reuse on primitive int array: Took 5274 ms
Sorter with key reuse on primitive int array: Took 5186 ms
Sorter with key reuse on primitive int array: Took 5159 ms
Sorter with key reuse on primitive int array: Took 5164 ms
Sorter with key reuse on primitive int array: Took 5078 ms
Sorter with key reuse on primitive int array: (5311 ms first try, 5173 ms average)
~~~
So with key reuse, it is faster and less likely to trigger GC.
Author: Xiangrui Meng <meng@databricks.com>
Author: Reynold Xin <rxin@apache.org>
Closes#2937 from mengxr/SPARK-4084 and squashes the following commits:
d73c3d0 [Xiangrui Meng] address comments
0b7b682 [Xiangrui Meng] fix mima
a72f53c [Xiangrui Meng] update timeIt
38ba50c [Xiangrui Meng] update timeIt
720f731 [Xiangrui Meng] add doc about JIT specialization
78f2879 [Xiangrui Meng] update tests
7de2efd [Xiangrui Meng] update the Sorter benchmark code to be correct
8626356 [Xiangrui Meng] add prepare to timeIt and update testsin SorterSuite
5f0d530 [Xiangrui Meng] update method modifiers of SortDataFormat
6ffbe66 [Xiangrui Meng] rename Sorter to TimSort and add a Scala wrapper that is private[spark]
b00db4d [Xiangrui Meng] doc and tests
cf94e8a [Xiangrui Meng] renaming
464ddce [Reynold Xin] cherry-pick rxin's commit
Yin Huai
Xiangrui Meng
Liang-Chi Hsieh
Rene Treffer
Josh Rosen
Reynold Xin
Cheng Lian
Tathagata Das
Michael Armbrust
Yuhao Yang
Ilya Ganelin
zsxwing
Brennon York
Marcelo Vanzin
Sean Owen
Joseph K. Bradley
Burak Yavuz
jerryshao
Yadong Qi
Andrew Or
Reynold Xin
