Author: David Arroyo Cazorla <darroyo@stratio.com>
Closes#7618 from darroyocazorla/master and squashes the following commits:
5f91379 [David Arroyo Cazorla] [SPARK-5447][SQL] Replace reference 'schema rdd' with DataFrame
We forgot to update doc. brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes#7608 from mengxr/SPARK-9243 and squashes the following commits:
0ea3236 [Xiangrui Meng] null -> zero in crosstab doc
Reverts ObjectPool. As it stands, it has a few problems:
1. ObjectPool doesn't work with spilling and memory accounting.
2. I don't think in the long run the idea of an object pool is what we want to support, since it essentially goes back to unmanaged memory, and creates pressure on GC, and is hard to account for the total in memory size.
3. The ObjectPool patch removed the specialized getters for strings and binary, and as a result, actually introduced branches when reading non primitive data types.
If we do want to support arbitrary user defined types in the future, I think we can just add an object array in UnsafeRow, rather than relying on indirect memory addressing through a pool. We also need to pick execution strategies that are optimized for those, rather than keeping a lot of unserialized JVM objects in memory during aggregation.
This is probably the hardest thing I had to revert in Spark, due to recent patches that also change the same part of the code. Would be great to get a careful look.
Author: Reynold Xin <rxin@databricks.com>
Closes#7591 from rxin/revert-object-pool and squashes the following commits:
01db0bc [Reynold Xin] Scala style.
eda89fc [Reynold Xin] Fixed describe.
2967118 [Reynold Xin] Fixed accessor for JoinedRow.
e3294eb [Reynold Xin] Merge branch 'master' into revert-object-pool
657855f [Reynold Xin] Temp commit.
c20f2c8 [Reynold Xin] Style fix.
fe37079 [Reynold Xin] Revert "[SPARK-8579] [SQL] support arbitrary object in UnsafeRow"
Spark has an option called spark.localExecution.enabled; according to the docs:
> Enables Spark to run certain jobs, such as first() or take() on the driver, without sending tasks to the cluster. This can make certain jobs execute very quickly, but may require shipping a whole partition of data to the driver.
This feature ends up adding quite a bit of complexity to DAGScheduler, especially in the runLocallyWithinThread method, but as far as I know nobody uses this feature (I searched the mailing list and haven't seen any recent mentions of the configuration nor stacktraces including the runLocally method). As a step towards scheduler complexity reduction, I propose that we remove this feature and all code related to it for Spark 1.5.
This pull request simply brings #7484 up to date.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#7585 from rxin/remove-local-exec and squashes the following commits:
84bd10e [Reynold Xin] Python fix.
1d9739a [Reynold Xin] Merge pull request #7484 from JoshRosen/remove-localexecution
eec39fa [Josh Rosen] Remove allowLocal(); deprecate user-facing uses of it.
b0835dc [Josh Rosen] Remove local execution code in DAGScheduler
8975d96 [Josh Rosen] Remove local execution tests.
ffa8c9b [Josh Rosen] Remove documentation for configuration
I've seen a few cases in the past few weeks that the compiler is throwing warnings that are caused by legitimate bugs. This patch upgrades warnings to errors, except deprecation warnings.
Note that ideally we should be able to mark deprecation warnings as errors as well. However, due to the lack of ability to suppress individual warning messages in the Scala compiler, we cannot do that (since we do need to access deprecated APIs in Hadoop).
Most of the work are done by ericl.
Author: Reynold Xin <rxin@databricks.com>
Author: Eric Liang <ekl@databricks.com>
Closes#7598 from rxin/warnings and squashes the following commits:
beb311b [Reynold Xin] Fixed tests.
542c031 [Reynold Xin] Fixed one more warning.
87c354a [Reynold Xin] Fixed all non-deprecation warnings.
78660ac [Eric Liang] first effort to fix warnings
This PR introduce unsafe version (using UnsafeRow) of HashJoin, HashOuterJoin and HashSemiJoin, including the broadcast one and shuffle one (except FullOuterJoin, which is better to be implemented using SortMergeJoin).
It use HashMap to store UnsafeRow right now, will change to use BytesToBytesMap for better performance (in another PR).
Author: Davies Liu <davies@databricks.com>
Closes#7480 from davies/unsafe_join and squashes the following commits:
6294b1e [Davies Liu] fix projection
10583f1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
dede020 [Davies Liu] fix test
84c9807 [Davies Liu] address comments
a05b4f6 [Davies Liu] support UnsafeRow in LeftSemiJoinBNL and BroadcastNestedLoopJoin
611d2ed [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
9481ae8 [Davies Liu] return UnsafeRow after join()
ca2b40f [Davies Liu] revert unrelated change
68f5cd9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
0f4380d [Davies Liu] ada a comment
69e38f5 [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
1a40f02 [Davies Liu] refactor
ab1690f [Davies Liu] address comments
60371f2 [Davies Liu] use UnsafeRow in SemiJoin
a6c0b7d [Davies Liu] Merge branch 'master' of github.com:apache/spark into unsafe_join
184b852 [Davies Liu] fix style
6acbb11 [Davies Liu] fix tests
95d0762 [Davies Liu] remove println
bea4a50 [Davies Liu] Unsafe HashJoin
This is the first PR for the aggregation improvement, which is tracked by https://issues.apache.org/jira/browse/SPARK-4366 (umbrella JIRA). This PR contains work for its subtasks, SPARK-3056, SPARK-3947, SPARK-4233, and SPARK-4367.
This PR introduces a new code path for evaluating aggregate functions. This code path is guarded by `spark.sql.useAggregate2` and by default the value of this flag is true.
This new code path contains:
* A new aggregate function interface (`AggregateFunction2`) and 7 built-int aggregate functions based on this new interface (`AVG`, `COUNT`, `FIRST`, `LAST`, `MAX`, `MIN`, `SUM`)
* A UDAF interface (`UserDefinedAggregateFunction`) based on the new code path and two example UDAFs (`MyDoubleAvg` and `MyDoubleSum`).
* A sort-based aggregate operator (`Aggregate2Sort`) for the new aggregate function interface .
* A sort-based aggregate operator (`FinalAndCompleteAggregate2Sort`) for distinct aggregations (for distinct aggregations the query plan will use `Aggregate2Sort` and `FinalAndCompleteAggregate2Sort` together).
With this change, `spark.sql.useAggregate2` is `true`, the flow of compiling an aggregation query is:
1. Our analyzer looks up functions and returns aggregate functions built based on the old aggregate function interface.
2. When our planner is compiling the physical plan, it tries try to convert all aggregate functions to the ones built based on the new interface. The planner will fallback to the old code path if any of the following two conditions is true:
* code-gen is disabled.
* there is any function that cannot be converted (right now, Hive UDAFs).
* the schema of grouping expressions contain any complex data type.
* There are multiple distinct columns.
Right now, the new code path handles a single distinct column in the query (you can have multiple aggregate functions using that distinct column). For a query having a aggregate function with DISTINCT and regular aggregate functions, the generated plan will do partial aggregations for those regular aggregate function.
Thanks chenghao-intel for his initial work on it.
Author: Yin Huai <yhuai@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#7458 from yhuai/UDAF and squashes the following commits:
7865f5e [Yin Huai] Put the catalyst expression in the comment of the generated code for it.
b04d6c8 [Yin Huai] Remove unnecessary change.
f1d5901 [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
35b0520 [Yin Huai] Use semanticEquals to replace grouping expressions in the output of the aggregate operator.
3b43b24 [Yin Huai] bug fix.
00eb298 [Yin Huai] Make it compile.
a3ca551 [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
e0afca3 [Yin Huai] Gracefully fallback to old aggregation code path.
8a8ac4a [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
88c7d4d [Yin Huai] Enable spark.sql.useAggregate2 by default for testing purpose.
dc96fd1 [Yin Huai] Many updates:
85c9c4b [Yin Huai] newline.
43de3de [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
c3614d7 [Yin Huai] Handle single distinct column.
68b8ee9 [Yin Huai] Support single distinct column set. WIP
3013579 [Yin Huai] Format.
d678aee [Yin Huai] Remove AggregateExpressionSuite.scala since our built-in aggregate functions will be based on AlgebraicAggregate and we need to have another way to test it.
e243ca6 [Yin Huai] Add aggregation iterators.
a101960 [Yin Huai] Change MyJavaUDAF to MyDoubleSum.
594cdf5 [Yin Huai] Change existing AggregateExpression to AggregateExpression1 and add an AggregateExpression as the common interface for both AggregateExpression1 and AggregateExpression2.
380880f [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
0a827b3 [Yin Huai] Add comments and doc. Move some classes to the right places.
a19fea6 [Yin Huai] Add UDAF interface.
262d4c4 [Yin Huai] Make it compile.
b2e358e [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
6edb5ac [Yin Huai] Format update.
70b169c [Yin Huai] Remove groupOrdering.
4721936 [Yin Huai] Add CheckAggregateFunction to extendedCheckRules.
d821a34 [Yin Huai] Cleanup.
32aea9c [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
5b46d41 [Yin Huai] Bug fix.
aff9534 [Yin Huai] Make Aggregate2Sort work with both algebraic AggregateFunctions and non-algebraic AggregateFunctions.
2857b55 [Yin Huai] Merge remote-tracking branch 'upstream/master' into UDAF
4435f20 [Yin Huai] Add ConvertAggregateFunction to HiveContext's analyzer.
1b490ed [Michael Armbrust] make hive test
8cfa6a9 [Michael Armbrust] add test
1b0bb3f [Yin Huai] Do not bind references in AlgebraicAggregate and use code gen for all places.
072209f [Yin Huai] Bug fix: Handle expressions in grouping columns that are not attribute references.
f7d9e54 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into UDAF
39ee975 [Yin Huai] Code cleanup: Remove unnecesary AttributeReferences.
b7720ba [Yin Huai] Add an analysis rule to convert aggregate function to the new version.
5c00f3f [Michael Armbrust] First draft of codegen
6bbc6ba [Michael Armbrust] now with correct answers\!
f7996d0 [Michael Armbrust] Add AlgebraicAggregate
dded1c5 [Yin Huai] wip
Author: Andrew Or <andrew@databricks.com>
Closes#7576 from andrewor14/clean-up-json-relation and squashes the following commits:
ea80803 [Andrew Or] Clean up duplicate code
Also make format_string the canonical form, rather than printf.
Author: Reynold Xin <rxin@databricks.com>
Closes#7579 from rxin/format_strings and squashes the following commits:
53ee54f [Reynold Xin] Fixed unit tests.
52357e1 [Reynold Xin] Add format_string alias.
b40a42a [Reynold Xin] [SPARK-9154][SQL] Rename formatString to format_string.
Jira: https://issues.apache.org/jira/browse/SPARK-9154
fixes bug of #7546
marmbrus I can't reopen the other PR, because I didn't closed it. Can you trigger Jenkins?
Author: Tarek Auel <tarek.auel@googlemail.com>
Closes#7571 from tarekauel/SPARK-9154 and squashes the following commits:
dcae272 [Tarek Auel] [SPARK-9154][SQL] build fix
1487602 [Tarek Auel] Merge remote-tracking branch 'upstream/master' into SPARK-9154
f512c5f [Tarek Auel] [SPARK-9154][SQL] build fix
a943d3e [Tarek Auel] [SPARK-9154] implicit input cast, added tests for null, support for null primitives
10b4de8 [Tarek Auel] [SPARK-9154][SQL] codegen removed fallback trait
cd8322b [Tarek Auel] [SPARK-9154][SQL] codegen string format
086caba [Tarek Auel] [SPARK-9154][SQL] codegen string format
This way, the sources package contains only public facing interfaces.
Author: Reynold Xin <rxin@databricks.com>
Closes#7565 from rxin/move-ds and squashes the following commits:
7661aff [Reynold Xin] Mima
9d5196a [Reynold Xin] Rearranged imports.
3dd7174 [Reynold Xin] [SPARK-8906][SQL] Move all internal data source classes into execution.datasources.
This patch fixes a managed memory leak in GeneratedAggregate. The leak occurs when the unsafe aggregation path is used to perform grouped aggregation on an empty input; in this case, GeneratedAggregate allocates an UnsafeFixedWidthAggregationMap that is never cleaned up because `next()` is never called on the aggregate result iterator.
This patch fixes this by short-circuiting on empty inputs.
This patch is an updated version of #6810.
Closes#6810.
Author: navis.ryu <navis@apache.org>
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7560 from JoshRosen/SPARK-8357 and squashes the following commits:
3486ce4 [Josh Rosen] Some minor cleanup
c649310 [Josh Rosen] Revert SparkPlan change:
3c7db0f [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-8357
adc8239 [Josh Rosen] Back out Projection changes.
c5419b3 [navis.ryu] addressed comments
143e1ef [navis.ryu] fixed format & added test for CCE case
735972f [navis.ryu] used new conf apis
1a02a55 [navis.ryu] Rolled-back test-conf cleanup & fixed possible CCE & added more tests
51178e8 [navis.ryu] addressed comments
4d326b9 [navis.ryu] fixed test fails
15c5afc [navis.ryu] added a test as suggested by JoshRosen
d396589 [navis.ryu] added comments
1b07556 [navis.ryu] [SPARK-8357] [SQL] Memory leakage on unsafe aggregation path with empty input
JIRA:
https://issues.apache.org/jira/browse/SPARK-9081https://issues.apache.org/jira/browse/SPARK-9168
This PR target at two modifications:
1. Change `isNaN` to return `false` on `null` input
2. Make `dropna` and `fillna` to fill/drop NaN values as well
3. Implement `nanvl`
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7523 from yjshen/fillna_dropna and squashes the following commits:
f0a51db [Yijie Shen] make coalesce untouched and implement nanvl
1d3e35f [Yijie Shen] make Coalesce aware of NaN in order to support fillna
2760cbc [Yijie Shen] change isNaN(null) to false as well as implement dropna
Pull Request for: https://issues.apache.org/jira/browse/SPARK-8230
Primary issue resolved is to implement array/map size for Spark SQL. Code is ready for review by a committer. Chen Hao is on the JIRA ticket, but I don't know his username on github, rxin is also on JIRA ticket.
Things to review:
1. Where to put added functions namespace wise, they seem to be part of a few operations on collections which includes `sort_array` and `array_contains`. Hence the name given `collectionOperations.scala` and `_collection_functions` in python.
2. In Python code, should it be in a `1.5.0` function array or in a collections array?
3. Are there any missing methods on the `Size` case class? Looks like many of these functions have generated Java code, is that also needed in this case?
4. Something else?
Author: Pedro Rodriguez <ski.rodriguez@gmail.com>
Author: Pedro Rodriguez <prodriguez@trulia.com>
Closes#7462 from EntilZha/SPARK-8230 and squashes the following commits:
9a442ae [Pedro Rodriguez] fixed functions and sorted __all__
9aea3bb [Pedro Rodriguez] removed imports from python docs
15d4bf1 [Pedro Rodriguez] Added null test case and changed to nullSafeCodeGen
d88247c [Pedro Rodriguez] removed python code
bd5f0e4 [Pedro Rodriguez] removed duplicate function from rebase/merge
59931b4 [Pedro Rodriguez] fixed compile bug instroduced when merging
c187175 [Pedro Rodriguez] updated code to add size to __all__ directly and removed redundent pretty print
130839f [Pedro Rodriguez] fixed failing test
aa9bade [Pedro Rodriguez] fix style
e093473 [Pedro Rodriguez] updated python code with docs, switched classes/traits implemented, added (failing) expression tests
0449377 [Pedro Rodriguez] refactored code to use better abstract classes/traits and implementations
9a1a2ff [Pedro Rodriguez] added unit tests for map size
2bfbcb6 [Pedro Rodriguez] added unit test for size
20df2b4 [Pedro Rodriguez] Finished working version of size function and added it to python
b503e75 [Pedro Rodriguez] First attempt at implementing size for maps and arrays
99a6a5c [Pedro Rodriguez] fixed failing test
cac75ac [Pedro Rodriguez] fix style
933d843 [Pedro Rodriguez] updated python code with docs, switched classes/traits implemented, added (failing) expression tests
42bb7d4 [Pedro Rodriguez] refactored code to use better abstract classes/traits and implementations
f9c3b8a [Pedro Rodriguez] added unit tests for map size
2515d9f [Pedro Rodriguez] added documentation
0e60541 [Pedro Rodriguez] added unit test for size
acf9853 [Pedro Rodriguez] Finished working version of size function and added it to python
84a5d38 [Pedro Rodriguez] First attempt at implementing size for maps and arrays
Add expressions `regex_extract` & `regex_replace`
Author: Cheng Hao <hao.cheng@intel.com>
Closes#7468 from chenghao-intel/regexp and squashes the following commits:
e5ea476 [Cheng Hao] minor update for documentation
ef96fd6 [Cheng Hao] update the code gen
72cf28f [Cheng Hao] Add more log for compilation error
4e11381 [Cheng Hao] Add regexp_replace / regexp_extract support
This PR adds DataFrame reader/writer shortcut methods for ORC in both Scala and Python.
Author: Cheng Lian <lian@databricks.com>
Closes#7444 from liancheng/spark-9100 and squashes the following commits:
284d043 [Cheng Lian] Fixes PySpark test cases and addresses PR comments
e0b09fb [Cheng Lian] Adds DataFrame reader/writer shortcut methods for ORC
This patch addresses code review feedback from #7456.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7551 from JoshRosen/unsafe-exchange-followup and squashes the following commits:
76dbdf8 [Josh Rosen] Add comments + more methods to UnsafeRowSerializer
3d7a1f2 [Josh Rosen] Add writeToStream() method to UnsafeRow
It can be ambiguous whether that is a string literal or a column name.
cc marmbrus
Author: Reynold Xin <rxin@databricks.com>
Closes#7556 from rxin/str-exprs and squashes the following commits:
92afa83 [Reynold Xin] [SPARK-9208][SQL] Remove variant of DataFrame string functions that accept column names.
This PR tries to accelerate Parquet schema discovery and `HadoopFsRelation` partition discovery. The acceleration is done by the following means:
- Turning off schema merging by default
Schema merging is not the most common case, but requires reading footers of all Parquet part-files and can be very slow.
- Avoiding `FileSystem.globStatus()` call when possible
`FileSystem.globStatus()` may issue multiple synchronous RPC calls, and can be very slow (esp. on S3). This PR adds `SparkHadoopUtil.globPathIfNecessary()`, which only issues RPC calls when the path contain glob-pattern specific character(s) (`{}[]*?\`).
This is especially useful when converting a metastore Parquet table with lots of partitions, since Spark SQL adds all partition directories as the input paths, and currently we do a `globStatus` call on each input path sequentially.
- Listing leaf files in parallel when the number of input paths exceeds a threshold
Listing leaf files is required by partition discovery. Currently it is done on driver side, and can be slow when there are lots of (nested) directories, since each `FileSystem.listStatus()` call issues an RPC. In this PR, we list leaf files in a BFS style, and resort to a Spark job once we found that the number of directories need to be listed exceed a threshold.
The threshold is controlled by `SQLConf` option `spark.sql.sources.parallelPartitionDiscovery.threshold`, which defaults to 32.
- Discovering Parquet schema in parallel
Currently, schema merging is also done on driver side, and needs to read footers of all part-files. This PR uses a Spark job to do schema merging. Together with task side metadata reading in Parquet 1.7.0, we never read any footers on driver side now.
Author: Cheng Lian <lian@databricks.com>
Closes#7396 from liancheng/accel-parquet and squashes the following commits:
5598efc [Cheng Lian] Uses ParquetInputFormat[InternalRow] instead of ParquetInputFormat[Row]
ff32cd0 [Cheng Lian] Excludes directories while listing leaf files
3c580f1 [Cheng Lian] Fixes test failure caused by making "mergeSchema" default to "false"
b1646aa [Cheng Lian] Should allow empty input paths
32e5f0d [Cheng Lian] Moves schema merging to executor side
This PR also remove the duplicated code between registerFunction and UserDefinedFunction.
cc JoshRosen
Author: Davies Liu <davies@databricks.com>
Closes#7450 from davies/fix_return_type and squashes the following commits:
e80bf9f [Davies Liu] remove debugging code
f94b1f6 [Davies Liu] fix mima
8f9c58b [Davies Liu] convert returned object from UDF into internal type
I don't think this function is useful at all in Scala/Java, since users can easily compute n * space easily.
Author: Reynold Xin <rxin@databricks.com>
Closes#7530 from rxin/remove-space and squashes the following commits:
c147873 [Reynold Xin] [SQL] Remove space from DataFrame Scala/Java API.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7525 from cloud-fan/deterministic and squashes the following commits:
4189bfa [Wenchen Fan] make deterministic describing the tree rather than the expression
This pull request aims to improve the performance of SQL's Exchange operator when shuffling UnsafeRows. It also makes several general efficiency improvements to Exchange.
Key changes:
- When performing hash partitioning, the old Exchange projected the partitioning columns into a new row then passed a `(partitioningColumRow: InternalRow, row: InternalRow)` pair into the shuffle. This is very inefficient because it ends up redundantly serializing the partitioning columns only to immediately discard them after the shuffle. After this patch's changes, Exchange now shuffles `(partitionId: Int, row: InternalRow)` pairs. This still isn't optimal, since we're still shuffling extra data that we don't need, but it's significantly more efficient than the old implementation; in the future, we may be able to further optimize this once we implement a new shuffle write interface that accepts non-key-value-pair inputs.
- Exchange's `compute()` method has been significantly simplified; the new code has less duplication and thus is easier to understand.
- When the Exchange's input operator produces UnsafeRows, Exchange will use a specialized `UnsafeRowSerializer` to serialize these rows. This serializer is significantly more efficient since it simply copies the UnsafeRow's underlying bytes. Note that this approach does not work for UnsafeRows that use the ObjectPool mechanism; I did not add support for this because we are planning to remove ObjectPool in the next few weeks.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7456 from JoshRosen/unsafe-exchange and squashes the following commits:
7e75259 [Josh Rosen] Fix cast in SparkSqlSerializer2Suite
0082515 [Josh Rosen] Some additional comments + small cleanup to remove an unused parameter
a27cfc1 [Josh Rosen] Add missing newline
741973c [Josh Rosen] Add simple test of UnsafeRow shuffling in Exchange.
359c6a4 [Josh Rosen] Remove println() and add comments
93904e7 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-exchange
8dd3ff2 [Josh Rosen] Exchange outputs UnsafeRows when its child outputs them
dd9c66d [Josh Rosen] Fix for copying logic
035af21 [Josh Rosen] Add logic for choosing when to use UnsafeRowSerializer
7876f31 [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-shuffle
cbea80b [Josh Rosen] Add UnsafeRowSerializer
0f2ac86 [Josh Rosen] Import ordering
3ca8515 [Josh Rosen] Big code simplification in Exchange
3526868 [Josh Rosen] Iniitial cut at removing shuffle on KV pairs
Catch this while reading the code
Author: Jacky Li <lee.unreal@gmail.com>
Author: Jacky Li <jackylk@users.noreply.github.com>
Closes#7524 from jackylk/patch-11 and squashes the following commits:
b679011 [Jacky Li] fix doc
e10e211 [Jacky Li] [SQL] Minor document fix in HadoopFsRelationProvider
Sometimes we need more than one step to initialize the mutable states in code gen like https://github.com/apache/spark/pull/7516
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7521 from cloud-fan/init and squashes the following commits:
2106445 [Wenchen Fan] improve code gen for mutable states
I also changed the semantics of concat w.r.t. null back to the same behavior as Hive.
That is to say, concat now returns null if any input is null.
Author: Reynold Xin <rxin@databricks.com>
Closes#7504 from rxin/concat_ws and squashes the following commits:
83fd950 [Reynold Xin] Fixed type casting.
3ae85f7 [Reynold Xin] Write null better.
cdc7be6 [Reynold Xin] Added code generation for pure string mode.
a61c4e4 [Reynold Xin] Updated comments.
2d51406 [Reynold Xin] [SPARK-8241][SQL] string function: concat_ws.
This PR contains a few clean-ups that are a part of SPARK-8638: a few style issues got fixed, and a few tests were moved.
Git commit message is wrong BTW :(...
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#7513 from hvanhovell/SPARK-8638-cleanup and squashes the following commits:
4e69d08 [Herman van Hovell] Fixed Perfomance Regression for Shrinking Window Frames (+Rebase)
This pull request fixes some of the problems in #6981.
- Added date functions to `__all__` so they get exposed
- Rename day_of_month -> dayofmonth
- Rename day_in_year -> dayofyear
- Rename week_of_year -> weekofyear
- Removed "day" from Scala/Python API since it is ambiguous. Only leaving the alias in SQL.
Author: Reynold Xin <rxin@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Reynold Xin <rxin@databricks.com>
Closes#7506 from rxin/datetime and squashes the following commits:
0cb24d9 [Reynold Xin] Export all functions in Python.
e44a4a0 [Reynold Xin] Removed day function from Scala and Python.
9c08fdc [Reynold Xin] [SQL] Make date/time functions more consistent with other database systems.
## Description
Performance improvements for Spark Window functions. This PR will also serve as the basis for moving away from Hive UDAFs to Spark UDAFs. See JIRA tickets SPARK-8638 and SPARK-7712 for more information.
## Improvements
* Much better performance (10x) in running cases (e.g. BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) and UNBOUDED FOLLOWING cases. The current implementation in spark uses a sliding window approach in these cases. This means that an aggregate is maintained for every row, so space usage is N (N being the number of rows). This also means that all these aggregates all need to be updated separately, this takes N*(N-1)/2 updates. The running case differs from the Sliding case because we are only adding data to an aggregate function (no reset is required), we only need to maintain one aggregate (like in the UNBOUNDED PRECEDING AND UNBOUNDED case), update the aggregate for each row, and get the aggregate value after each update. This is what the new implementation does. This approach only uses 1 buffer, and only requires N updates; I am currently working on data with window sizes of 500-1000 doing running sums and this saves a lot of time. The CURRENT ROW AND UNBOUNDED FOLLOWING case also uses this approach and the fact that aggregate operations are communitative, there is one twist though it will process the input buffer in reverse.
* Fewer comparisons in the sliding case. The current implementation determines frame boundaries for every input row. The new implementation makes more use of the fact that the window is sorted, maintains the boundaries, and only moves them when the current row order changes. This is a minor improvement.
* A single Window node is able to process all types of Frames for the same Partitioning/Ordering. This saves a little time/memory spent buffering and managing partitions. This will be enabled in a follow-up PR.
* A lot of the staging code is moved from the execution phase to the initialization phase. Minor performance improvement, and improves readability of the execution code.
## Benchmarking
I have done a small benchmark using [on time performance](http://www.transtats.bts.gov) data of the month april. I have used the origin as a partioning key, as a result there is quite some variation in window sizes. The code for the benchmark can be found in the JIRA ticket. These are the results per Frame type:
Frame | Master | SPARK-8638
----- | ------ | ----------
Entire Frame | 2 s | 1 s
Sliding | 18 s | 1 s
Growing | 14 s | 0.9 s
Shrinking | 13 s | 1 s
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#7057 from hvanhovell/SPARK-8638 and squashes the following commits:
3bfdc49 [Herman van Hovell] Fixed Perfomance Regression for Shrinking Window Frames (+Rebase)
2eb3b33 [Herman van Hovell] Corrected reverse range frame processing.
2cd2d5b [Herman van Hovell] Corrected reverse range frame processing.
b0654d7 [Herman van Hovell] Tests for exotic frame specifications.
e75b76e [Herman van Hovell] More docs, added support for reverse sliding range frames, and some reorganization of code.
1fdb558 [Herman van Hovell] Changed Data In HiveDataFrameWindowSuite.
ac2f682 [Herman van Hovell] Added a few more comments.
1938312 [Herman van Hovell] Added Documentation to the createBoundOrdering methods.
bb020e6 [Herman van Hovell] Major overhaul of Window operator.
It is very hard to track which expressions have code gen implemented or not. This patch removes the default fallback gencode implementation from Expression, and moves that into a new trait called CodegenFallback. Each concrete expression needs to either implement code generation, or mix in CodegenFallback. This makes it very easy to track which expressions have code generation implemented already.
Additionally, this patch creates an Unevaluable trait that can be used to track expressions that don't support evaluation (e.g. Star).
Author: Reynold Xin <rxin@databricks.com>
Closes#7487 from rxin/codegenfallback and squashes the following commits:
14ebf38 [Reynold Xin] Fixed Conv
6c1c882 [Reynold Xin] Fixed Alias.
b42611b [Reynold Xin] [SPARK-9150][SQL] Create a trait to track code generation for expressions.
cb5c066 [Reynold Xin] Removed extra import.
39cbe40 [Reynold Xin] [SPARK-8240][SQL] string function: concat
Author: Reynold Xin <rxin@databricks.com>
Closes#7500 from rxin/sqlconf and squashes the following commits:
a5726c8 [Reynold Xin] [SPARK-9174][SQL] Add documentation for all public SQLConfs.
a follow up of https://github.com/apache/spark/pull/7479.
The `TreeNode` is the root case of the requirement of `self: Product =>` stuff, so why not make `TreeNode` extend `Product`?
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7495 from cloud-fan/self-type and squashes the following commits:
8676af7 [Wenchen Fan] remove more self type
Now that we have two different internal row formats, UnsafeRow and the old Java-object-based row format, we end up having to perform conversions between these two formats. These conversions should not be performed by the operators themselves; instead, the planner should be responsible for inserting appropriate format conversions when they are needed.
This patch makes the following changes:
- Add two new physical operators for performing row format conversions, `ConvertToUnsafe` and `ConvertFromUnsafe`.
- Add new methods to `SparkPlan` to allow operators to express whether they output UnsafeRows and whether they can handle safe or unsafe rows as inputs.
- Implement an `EnsureRowFormats` rule to automatically insert converter operators where necessary.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7482 from JoshRosen/unsafe-converter-planning and squashes the following commits:
7450fa5 [Josh Rosen] Resolve conflicts in favor of choosing UnsafeRow
5220cce [Josh Rosen] Add roundtrip converter test
2bb8da8 [Josh Rosen] Add Union unsafe support + tests to bump up test coverage
6f79449 [Josh Rosen] Add even more assertions to execute()
08ce199 [Josh Rosen] Rename ConvertFromUnsafe -> ConvertToSafe
0e2d548 [Josh Rosen] Add assertion if operators' input rows are in different formats
cabb703 [Josh Rosen] Add tests for Filter
3b11ce3 [Josh Rosen] Add missing test file.
ae2195a [Josh Rosen] Fixes
0fef0f8 [Josh Rosen] Rename file.
d5f9005 [Josh Rosen] Finish writing EnsureRowFormats planner rule
b5df19b [Josh Rosen] Merge remote-tracking branch 'origin/master' into unsafe-converter-planning
9ba3038 [Josh Rosen] WIP
When the `condition` extracted by `ExtractEquiJoinKeys` contain join Predicate for left semi join, we can not plan it as semiJoin. Such as
SELECT * FROM testData2 x
LEFT SEMI JOIN testData2 y
ON x.b = y.b
AND x.a >= y.a + 2
Condition `x.a >= y.a + 2` can not evaluate on table `x`, so it throw errors
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#5643 from adrian-wang/spark7026 and squashes the following commits:
cc09809 [Daoyuan Wang] refactor semijoin and add plan test
575a7c8 [Daoyuan Wang] fix notserializable
27841de [Daoyuan Wang] fix rebase
10bf124 [Daoyuan Wang] fix style
72baa02 [Daoyuan Wang] fix style
8e0afca [Daoyuan Wang] merge commits for rebase
JIRA: https://issues.apache.org/jira/browse/SPARK-9080
cc rxin
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7464 from yijieshen/isNaN and squashes the following commits:
11ae039 [Yijie Shen] add isNaN in functions
666718e [Yijie Shen] add isNaN predicate expression
Just a small change to add Product type to the base expression/plan abstract classes, based on suggestions on #7434 and offline discussions.
Author: Reynold Xin <rxin@databricks.com>
Closes#7479 from rxin/remove-self-types and squashes the following commits:
e407ffd [Reynold Xin] [SPARK-9142][SQL] Removing unnecessary self types in Catalyst.
cc chenghao-intel adrian-wang
Author: zhichao.li <zhichao.li@intel.com>
Closes#6872 from zhichao-li/conv and squashes the following commits:
6ef3b37 [zhichao.li] add unittest and comments
78d9836 [zhichao.li] polish dataframe api and add unittest
e2bace3 [zhichao.li] update to use ImplicitCastInputTypes
cbcad3f [zhichao.li] add function conv
This builds on #7433 but also removes LeafNode/UnaryNode. These are slightly more complicated to remove. I had to change some abstract classes to traits in order for it to work.
The problem with LeafNode/UnaryNode is that they are often mixed in at the end of an Expression, and then the toString function actually gets resolved to the ones defined in TreeNode, rather than in Expression.
Author: Reynold Xin <rxin@databricks.com>
Closes#7434 from rxin/remove-binary-unary-leaf-node and squashes the following commits:
9e8a4de [Reynold Xin] Generator should not be foldable.
3135a8b [Reynold Xin] SortOrder should not be foldable.
9c589cf [Reynold Xin] Fixed one more test case...
2225331 [Reynold Xin] Aggregate expressions should not be foldable.
16b5c90 [Reynold Xin] [SPARK-9085][SQL] Remove LeafNode, UnaryNode, BinaryNode from TreeNode.
JIRA: https://issues.apache.org/jira/browse/SPARK-6941
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7342 from yijieshen/SPARK-6941 and squashes the following commits:
f82cbe7 [Yijie Shen] reorder import
dd67e40 [Yijie Shen] resolve comments
09518af [Yijie Shen] fix import order in DataframeSuite
0c635d4 [Yijie Shen] make match more specific
9df388d [Yijie Shen] move check into PreWriteCheck
847ab20 [Yijie Shen] Detect insertion error in DataSourceStrategy
- `BinaryType` for `Length`
- `FormatNumber`
Author: Cheng Hao <hao.cheng@intel.com>
Closes#7034 from chenghao-intel/expression and squashes the following commits:
e534b87 [Cheng Hao] python api style issue
601bbf5 [Cheng Hao] add python API support
3ebe288 [Cheng Hao] update as feedback
52274f7 [Cheng Hao] add support for udf_format_number and length for binary
These traits are not super useful, and yet cause problems with toString in expressions due to the orders they are mixed in.
Author: Reynold Xin <rxin@databricks.com>
Closes#7433 from rxin/remove-binary-node and squashes the following commits:
1881f78 [Reynold Xin] [SPARK-9086][SQL] Remove BinaryNode from TreeNode.
I also took the chance to more explicitly define the semantics of deterministic.
Author: Reynold Xin <rxin@databricks.com>
Closes#7428 from rxin/non-deterministic and squashes the following commits:
a760827 [Reynold Xin] [SPARK-9071][SQL] MonotonicallyIncreasingID and SparkPartitionID should be marked as nondeterministic.
https://issues.apache.org/jira/browse/SPARK-8221
One concern is the result would be negative if the divisor is not positive( i.e pmod(7, -3) ), but the behavior is the same as hive.
Author: zhichao.li <zhichao.li@intel.com>
Closes#6783 from zhichao-li/pmod2 and squashes the following commits:
7083eb9 [zhichao.li] update to the latest type checking
d26dba7 [zhichao.li] add pmod
We can keep expressions' mutable states in generated class(like `SpecificProjection`) as member variables, so that we can read and modify them inside codegened expressions.
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7392 from cloud-fan/mutable-state and squashes the following commits:
eb3a221 [Wenchen Fan] fix order
73144d8 [Wenchen Fan] naming improvement
318f41d [Wenchen Fan] address more comments
d43b65d [Wenchen Fan] address comments
fd45c7a [Wenchen Fan] Support mutable state in code gen expressions
JIRA: https://issues.apache.org/jira/browse/SPARK-8840
Currently the type coercion rules don't include float type. This PR simply adds it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#7280 from viirya/add_r_float_coercion and squashes the following commits:
c86dc0e [Liang-Chi Hsieh] For comments.
dbf0c1b [Liang-Chi Hsieh] Implicitly convert Double to Float based on provided schema.
733015a [Liang-Chi Hsieh] Add test case for DataFrame with float type.
30c2a40 [Liang-Chi Hsieh] Update test case.
52b5294 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into add_r_float_coercion
6f9159d [Liang-Chi Hsieh] Add another test case.
8db3244 [Liang-Chi Hsieh] schema also needs to support float. add test case.
0dcc992 [Liang-Chi Hsieh] Add float coercion on SparkR.
SPARK-8317 changed the SQL Exchange operator so that it no longer pushed sorting into Spark's shuffle layer, a change which allowed more efficient SQL-specific sorters to be used.
This patch performs some leftover cleanup based on those changes:
- Exchange's constructor should no longer accept a `newOrdering` since it's no longer used and no longer works as expected.
- `addOperatorsIfNecessary` looked at shuffle input's output ordering to decide whether to sort, but this is the wrong node to be examining: it needs to look at whether the post-shuffle node has the right ordering, since shuffling will not preserve row orderings. Thanks to davies for spotting this.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7407 from JoshRosen/SPARK-9050 and squashes the following commits:
e70be50 [Josh Rosen] No need to wrap line
e866494 [Josh Rosen] Refactor addOperatorsIfNecessary to make code clearer
2e467da [Josh Rosen] Remove `newOrdering` from Exchange.
