Rows with null values in partition column are not included in the results because none of the partition
where clause specify is null predicate on the partition column. This fix adds is null predicate on the partition column to the first JDBC partition where clause.
Example:
JDBCPartition(THEID < 1 or THEID is null, 0),JDBCPartition(THEID >= 1 AND THEID < 2,1),
JDBCPartition(THEID >= 2, 2)
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#11063 from sureshthalamati/nullable_jdbc_part_col_spark-13167.
## What changes were proposed in this pull request?
Broadcast left semi join without joining keys is already supported in BroadcastNestedLoopJoin, it has the same implementation as LeftSemiJoinBNL, we should remove that.
## How was this patch tested?
Updated unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11448 from davies/remove_bnl.
## What changes were proposed in this pull request?
This PR defer the resolution from a id of dictionary to value until the column is actually accessed (inside getInt/getLong), this is very useful for those columns and rows that are filtered out. It's also useful for binary type, we will not need to copy all the byte arrays.
This PR also change the underlying type for small decimal that could be fit within a Int, in order to use getInt() to lookup the value from IntDictionary.
## How was this patch tested?
Manually test TPCDS Q7 with scale factor 10, saw about 30% improvements (after PR #11274).
Author: Davies Liu <davies@databricks.com>
Closes#11437 from davies/decode_dict.
JIRA: https://issues.apache.org/jira/browse/SPARK-13511
## What changes were proposed in this pull request?
Current limit operator doesn't support wholestage codegen. This is open to add support for it.
In the `doConsume` of `GlobalLimit` and `LocalLimit`, we use a count term to count the processed rows. Once the row numbers catches the limit number, we set the variable `stopEarly` of `BufferedRowIterator` newly added in this pr to `true` that indicates we want to stop processing remaining rows. Then when the wholestage codegen framework checks `shouldStop()`, it will stop the processing of the row iterator.
Before this, the executed plan for a query `sqlContext.range(N).limit(100).groupBy().sum()` is:
TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Final,isDistinct=false)], output=[sum(id)#6L])
+- TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Partial,isDistinct=false)], output=[sum#9L])
+- GlobalLimit 100
+- Exchange SinglePartition, None
+- LocalLimit 100
+- Range 0, 1, 1, 524288000, [id#5L]
After add wholestage codegen support:
WholeStageCodegen
: +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Final,isDistinct=false)], output=[sum(id)#41L])
: +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Partial,isDistinct=false)], output=[sum#44L])
: +- GlobalLimit 100
: +- INPUT
+- Exchange SinglePartition, None
+- WholeStageCodegen
: +- LocalLimit 100
: +- Range 0, 1, 1, 524288000, [id#40L]
## How was this patch tested?
A test is added into BenchmarkWholeStageCodegen.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11391 from viirya/wholestage-limit.
## What changes were proposed in this pull request?
This PR adds support for implementing whole state codegen for sort. Builds heaving on nongli 's PR: https://github.com/apache/spark/pull/11008 (which actually implements the feature), and adds the following changes on top:
- [x] Generated code updates peak execution memory metrics
- [x] Unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`
## How was this patch tested?
New unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`. Further, all existing sort tests should pass.
Author: Sameer Agarwal <sameer@databricks.com>
Author: Nong Li <nong@databricks.com>
Closes#11359 from sameeragarwal/sort-codegen.
https://issues.apache.org/jira/browse/SPARK-13507https://issues.apache.org/jira/browse/SPARK-13509
## What changes were proposed in this pull request?
This PR adds the support to write CSV data directly by a single call to the given path.
Several unitests were added for each functionality.
## How was this patch tested?
This was tested with unittests and with `dev/run_tests` for coding style
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#11389 from HyukjinKwon/SPARK-13507-13509.
## What changes were proposed in this pull request?
Nested classes defined within Scala objects are translated into Java static nested classes. Unlike inner classes, they don't need outer scopes. But the analyzer still thinks that an outer scope is required.
This PR fixes this issue simply by checking whether a nested class is static before looking up its outer scope.
## How was this patch tested?
A test case is added to `DatasetSuite`. It checks contents of a Dataset whose element type is a nested class declared in a Scala object.
Author: Cheng Lian <lian@databricks.com>
Closes#11421 from liancheng/spark-13540-object-as-outer-scope.
JIRA: https://issues.apache.org/jira/browse/SPARK-13537
## What changes were proposed in this pull request?
In readBytes of VectorizedPlainValuesReader, we use buffer[offset] to access bytes in buffer. It is incorrect because offset is added with Platform.BYTE_ARRAY_OFFSET when initialization. We should fix it.
## How was this patch tested?
`ParquetHadoopFsRelationSuite` sometimes (depending on the randomly generated data) will be [failed](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52136/consoleFull) by this bug. After applying this, the test can be passed.
I added a test to `ParquetHadoopFsRelationSuite` with the data which will fail without this patch.
The error exception:
[info] ParquetHadoopFsRelationSuite:
[info] - test all data types - StringType (440 milliseconds)
[info] - test all data types - BinaryType (434 milliseconds)
[info] - test all data types - BooleanType (406 milliseconds)
20:59:38.618 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 2597.0 (TID 67966)
java.lang.ArrayIndexOutOfBoundsException: 46
at org.apache.spark.sql.execution.datasources.parquet.VectorizedPlainValuesReader.readBytes(VectorizedPlainValuesReader.java:88)
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11418 from viirya/fix-readbytes.
## What changes were proposed in this pull request?
This creates a `SessionState`, which groups a few fields that existed in `SQLContext`. Because `HiveContext` extends `SQLContext` we also need to make changes there. This is mainly a cleanup task that will soon pave the way for merging the two contexts.
## How was this patch tested?
Existing unit tests; this patch introduces no change in behavior.
Author: Andrew Or <andrew@databricks.com>
Closes#11405 from andrewor14/refactor-session.
## What changes were proposed in this pull request?
Fix readBytes in VectorizedPlainValuesReader. This fixes a copy and paste issue.
## How was this patch tested?
Ran ParquetHadoopFsRelationSuite which failed before this.
Author: Nong Li <nong@databricks.com>
Closes#11414 from nongli/spark-13533.
JIRA: https://issues.apache.org/jira/browse/SPARK-13530
## What changes were proposed in this pull request?
By enabling vectorized parquet scanner by default, the unit test `ParquetHadoopFsRelationSuite` based on `HadoopFsRelationTest` will be failed due to the lack of short type support in `UnsafeRowParquetRecordReader`. We should fix it.
The error exception:
[info] ParquetHadoopFsRelationSuite:
[info] - test all data types - StringType (499 milliseconds)
[info] - test all data types - BinaryType (447 milliseconds)
[info] - test all data types - BooleanType (520 milliseconds)
[info] - test all data types - ByteType (418 milliseconds)
00:22:58.920 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 124.0 (TID 1949)
org.apache.commons.lang.NotImplementedException: Unimplemented type: ShortType
at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.readIntBatch(UnsafeRowParquetRecordReader.java:769)
at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.readBatch(UnsafeRowParquetRecordReader.java:640)
at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.access$000(UnsafeRowParquetRecordReader.java:461)
at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader.nextBatch(UnsafeRowParquetRecordReader.java:224)
## How was this patch tested?
The unit test `ParquetHadoopFsRelationSuite` based on `HadoopFsRelationTest` will be [failed](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52110/consoleFull) due to the lack of short type support in UnsafeRowParquetRecordReader. By adding this support, the test can be passed.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11412 from viirya/add-shorttype-support.
## What changes were proposed in this pull request?
Change the default of the flag to enable this feature now that the implementation is complete.
## How was this patch tested?
The new parquet reader should be a drop in, so will be exercised by the existing tests.
Author: Nong Li <nong@databricks.com>
Closes#11397 from nongli/spark-13518.
## What changes were proposed in this pull request?
This patch includes these performance fixes:
- Remove unnecessary setNotNull() calls. The NULL bits are cleared already.
- Speed up RLE group decoding
- Speed up dictionary decoding by decoding NULLs directly into the result.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
In addition to the updated benchmarks, on TPCDS, the result of these changes
running Q55 (sf40) is:
```
TPCDS: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
---------------------------------------------------------------------------------
q55 (Before) 6398 / 6616 18.0 55.5
q55 (After) 4983 / 5189 23.1 43.3
```
Author: Nong Li <nong@databricks.com>
Closes#11375 from nongli/spark-13499.
## What changes were proposed in this pull request?
Currently, BroadcastNestedLoopJoin is implemented for worst case, it's too slow, very easy to hang forever. This PR will create fast path for some joinType and buildSide, also improve the worst case (will use much less memory than before).
Before this PR, one task requires O(N*K) + O(K) in worst cases, N is number of rows from one partition of streamed table, it could hang the job (because of GC).
In order to workaround this for InnerJoin, we have to disable auto-broadcast, switch to CartesianProduct: This could be workaround for InnerJoin, see https://forums.databricks.com/questions/6747/how-do-i-get-a-cartesian-product-of-a-huge-dataset.html
In this PR, we will have fast path for these joins :
InnerJoin with BuildLeft or BuildRight
LeftOuterJoin with BuildRight
RightOuterJoin with BuildLeft
LeftSemi with BuildRight
These fast paths are all stream based (take one pass on streamed table), required O(1) memory.
All other join types and build types will take two pass on streamed table, one pass to find the matched rows that includes streamed part, which require O(1) memory, another pass to find the rows from build table that does not have a matched row from streamed table, which required O(K) memory, K is the number rows from build side, one bit per row, should be much smaller than the memory for broadcast. The following join types work in this way:
LeftOuterJoin with BuildLeft
RightOuterJoin with BuildRight
FullOuterJoin with BuildLeft or BuildRight
LeftSemi with BuildLeft
This PR also added tests for all the join types for BroadcastNestedLoopJoin.
After this PR, for InnerJoin with one small table, BroadcastNestedLoopJoin should be faster than CartesianProduct, we don't need that workaround anymore.
## How was the this patch tested?
Added unit tests.
Author: Davies Liu <davies@databricks.com>
Closes#11328 from davies/nested_loop.
## What changes were proposed in this pull request?
This is another try of PR #11323.
This PR removes DataFrame RDD operations except for `foreach` and `foreachPartitions` (they are actions rather than transformations). Original calls are now replaced by calls to methods of `DataFrame.rdd`.
PR #11323 was reverted because it introduced a regression: both `DataFrame.foreach` and `DataFrame.foreachPartitions` wrap underlying RDD operations with `withNewExecutionId` to track Spark jobs. But they are removed in #11323.
## How was the this patch tested?
No extra tests are added. Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#11388 from liancheng/remove-df-rdd-ops.
## What changes were proposed in this pull request?
This patch creates the public API for runtime configuration and an implementation for it. The public runtime configuration includes configs for existing SQL, as well as Hadoop Configuration.
This new interface is currently dead code. It will be added to SQLContext and a session entry point to Spark when we add that.
## How was this patch tested?
a new unit test suite
Author: Reynold Xin <rxin@databricks.com>
Closes#11378 from rxin/SPARK-13487.
## What changes were proposed in this pull request?
This Pull request is used for the fix SPARK-12941, creating a data type mapping to Oracle for the corresponding data type"Stringtype" from dataframe. This PR is for the master branch fix, where as another PR is already tested with the branch 1.4
## How was the this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
This patch was tested using the Oracle docker .Created a new integration suite for the same.The oracle.jdbc jar was to be downloaded from the maven repository.Since there was no jdbc jar available in the maven repository, the jar was downloaded from oracle site manually and installed in the local; thus tested. So, for SparkQA test case run, the ojdbc jar might be manually placed in the local maven repository(com/oracle/ojdbc6/11.2.0.2.0) while Spark QA test run.
Author: thomastechs <thomas.sebastian@tcs.com>
Closes#11306 from thomastechs/master.
This pr added benchmark codes for Encoder#compress().
Also, it replaced the benchmark results with new ones because the output format of `Benchmark` changed.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#11236 from maropu/CompressionSpike.
## Motivation
As a pre-requisite to off-heap caching of blocks, we need a mechanism to prevent pages / blocks from being evicted while they are being read. With on-heap objects, evicting a block while it is being read merely leads to memory-accounting problems (because we assume that an evicted block is a candidate for garbage-collection, which will not be true during a read), but with off-heap memory this will lead to either data corruption or segmentation faults.
## Changes
### BlockInfoManager and reader/writer locks
This patch adds block-level read/write locks to the BlockManager. It introduces a new `BlockInfoManager` component, which is contained within the `BlockManager`, holds the `BlockInfo` objects that the `BlockManager` uses for tracking block metadata, and exposes APIs for locking blocks in either shared read or exclusive write modes.
`BlockManager`'s `get*()` and `put*()` methods now implicitly acquire the necessary locks. After a `get()` call successfully retrieves a block, that block is locked in a shared read mode. A `put()` call will block until it acquires an exclusive write lock. If the write succeeds, the write lock will be downgraded to a shared read lock before returning to the caller. This `put()` locking behavior allows us store a block and then immediately turn around and read it without having to worry about it having been evicted between the write and the read, which will allow us to significantly simplify `CacheManager` in the future (see #10748).
See `BlockInfoManagerSuite`'s test cases for a more detailed specification of the locking semantics.
### Auto-release of locks at the end of tasks
Our locking APIs support explicit release of locks (by calling `unlock()`), but it's not always possible to guarantee that locks will be released prior to the end of the task. One reason for this is our iterator interface: since our iterators don't support an explicit `close()` operator to signal that no more records will be consumed, operations like `take()` or `limit()` don't have a good means to release locks on their input iterators' blocks. Another example is broadcast variables, whose block locks can only be released at the end of the task.
To address this, `BlockInfoManager` uses a pair of maps to track the set of locks acquired by each task. Lock acquisitions automatically record the current task attempt id by obtaining it from `TaskContext`. When a task finishes, code in `Executor` calls `BlockInfoManager.unlockAllLocksForTask(taskAttemptId)` to free locks.
### Locking and the MemoryStore
In order to prevent in-memory blocks from being evicted while they are being read, the `MemoryStore`'s `evictBlocksToFreeSpace()` method acquires write locks on blocks which it is considering as candidates for eviction. These lock acquisitions are non-blocking, so a block which is being read will not be evicted. By holding write locks until the eviction is performed or skipped (in case evicting the blocks would not free enough memory), we avoid a race where a new reader starts to read a block after the block has been marked as an eviction candidate but before it has been removed.
### Locking and remote block transfer
This patch makes small changes to to block transfer and network layer code so that locks acquired by the BlockTransferService are released as soon as block transfer messages are consumed and released by Netty. This builds on top of #11193, a bug fix related to freeing of network layer ManagedBuffers.
## FAQ
- **Why not use Java's built-in [`ReadWriteLock`](https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/locks/ReadWriteLock.html)?**
Our locks operate on a per-task rather than per-thread level. Under certain circumstances a task may consist of multiple threads, so using `ReadWriteLock` would mean that we might call `unlock()` from a thread which didn't hold the lock in question, an operation which has undefined semantics. If we could rely on Java 8 classes, we might be able to use [`StampedLock`](https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/locks/StampedLock.html) to work around this issue.
- **Why not detect "leaked" locks in tests?**:
See above notes about `take()` and `limit`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10705 from JoshRosen/pin-pages.
## What changes were proposed in this pull request?
This PR removes DataFrame RDD operations. Original calls are now replaced by calls to methods of `DataFrame.rdd`.
## How was the this patch tested?
No extra tests are added. Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#11323 from liancheng/remove-df-rdd-ops.
## What changes were proposed in this pull request?
This patch moves SQLConf into org.apache.spark.sql.internal package to make it very explicit that it is internal. Soon I will also submit more API work that creates implementations of interfaces in this internal package.
## How was this patch tested?
If it compiles, then the refactoring should work.
Author: Reynold Xin <rxin@databricks.com>
Closes#11363 from rxin/SPARK-13486.
## What changes were proposed in this pull request?
This PR mostly rewrite the ColumnPruning rule to support most of the SQL logical plans (except those for Dataset).
This PR also fix a bug in Generate, it should always output UnsafeRow, added an regression test for that.
## How was this patch tested?
This is test by unit tests, also manually test with TPCDS Q78, which could prune all unused columns successfully, improved the performance by 78% (from 22s to 12s).
Author: Davies Liu <davies@databricks.com>
Closes#11354 from davies/fix_column_pruning.
## What changes were proposed in this pull request?
* Scala DataFrameStatFunctions: Added version of approxQuantile taking a List instead of an Array, for Python compatbility
* Python DataFrame and DataFrameStatFunctions: Added approxQuantile
## How was this patch tested?
* unit test in sql/tests.py
Documentation was copied from the existing approxQuantile exactly.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#11356 from jkbradley/approx-quantile-python.
This PR adds a new abstraction called an `ExpressionSet` which attempts to canonicalize expressions to remove cosmetic differences. Deterministic expressions that are in the set after canonicalization will always return the same answer given the same input (i.e. false positives should not be possible). However, it is possible that two canonical expressions that are not equal will in fact return the same answer given any input (i.e. false negatives are possible).
```scala
val set = AttributeSet('a + 1 :: 1 + 'a :: Nil)
set.iterator => Iterator('a + 1)
set.contains('a + 1) => true
set.contains(1 + 'a) => true
set.contains('a + 2) => false
```
Other relevant changes include:
- Since this concept overlaps with the existing `semanticEquals` and `semanticHash`, those functions are also ported to this new infrastructure.
- A memoized `canonicalized` version of the expression is added as a `lazy val` to `Expression` and is used by both `semanticEquals` and `ExpressionSet`.
- A set of unit tests for `ExpressionSet` are added
- Tests which expect `semanticEquals` to be less intelligent than it now is are updated.
As a followup, we should consider auditing the places where we do `O(n)` `semanticEquals` operations and replace them with `ExpressionSet`. We should also consider consolidating `AttributeSet` as a specialized factory for an `ExpressionSet.`
Author: Michael Armbrust <michael@databricks.com>
Closes#11338 from marmbrus/expressionSet.
Some parts of the engine rely on UnsafeRow which the vectorized parquet scanner does not want
to produce. This add a conversion in Physical RDD. In the case where codegen is used (and the
scan is the start of the pipeline), there is no requirement to use UnsafeRow. This patch adds
update PhysicallRDD to support codegen, which eliminates the need for the UnsafeRow conversion
in all cases.
The result of these changes for TPCDS-Q19 at the 10gb sf reduces the query time from 9.5 seconds
to 6.5 seconds.
Author: Nong Li <nong@databricks.com>
Closes#11141 from nongli/spark-13250.
## What changes were proposed in this pull request?
When we pass a Python function to JVM side, we also need to send its context, e.g. `envVars`, `pythonIncludes`, `pythonExec`, etc. However, it's annoying to pass around so many parameters at many places. This PR abstract python function along with its context, to simplify some pyspark code and make the logic more clear.
## How was the this patch tested?
by existing unit tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11342 from cloud-fan/python-clean.
JIRA: https://issues.apache.org/jira/browse/SPARK-13383
## What changes were proposed in this pull request?
When we do column pruning in Optimizer, we put additional Project on top of a logical plan. However, when we already wrap a BroadcastHint on a logical plan, the added Project will hide BroadcastHint after later execution.
We should take care of BroadcastHint when we do column pruning.
## How was the this patch tested?
Unit test is added.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#11260 from viirya/keep-broadcasthint.
## What changes were proposed in this pull request?
This PR mostly rewrite the ColumnPruning rule to support most of the SQL logical plans (except those for Dataset).
## How was the this patch tested?
This is test by unit tests, also manually test with TPCDS Q78, which could prune all unused columns successfully, improved the performance by 78% (from 22s to 12s).
Author: Davies Liu <davies@databricks.com>
Closes#11256 from davies/fix_column_pruning.
## What changes were proposed in this pull request?
This continues thunterdb 's work on `approxQuantile` API. It changes the signature of `approxQuantile` from `(col: String, quantile: Double, epsilon: Double): Double` to `(col: String, probabilities: Array[Double], relativeError: Double): Array[Double]` and update API doc. It also improves the error message in tests and simplifies the merge algorithm for summaries.
## How was the this patch tested?
Use the same unit tests as before.
Closes#11325
Author: Timothy Hunter <timhunter@databricks.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#11332 from mengxr/SPARK-6761.
## What changes were proposed in this pull request?
Generates code for SortMergeJoin.
## How was the this patch tested?
Unit tests and manually tested with TPCDS Q72, which showed 70% performance improvements (from 42s to 25s), but micro benchmark only show minor improvements, it may depends the distribution of data and number of columns.
Author: Davies Liu <davies@databricks.com>
Closes#11248 from davies/gen_smj.
The current implementation of statistics of UnaryNode does not considering output (for example, Project may product much less columns than it's child), we should considering it to have a better guess.
We usually only join with few columns from a parquet table, the size of projected plan could be much smaller than the original parquet files. Having a better guess of size help we choose between broadcast join or sort merge join.
After this PR, I saw a few queries choose broadcast join other than sort merge join without turning spark.sql.autoBroadcastJoinThreshold for every query, ended up with about 6-8X improvements on end-to-end time.
We use `defaultSize` of DataType to estimate the size of a column, currently For DecimalType/StringType/BinaryType and UDT, we are over-estimate too much (4096 Bytes), so this PR change them to some more reasonable values. Here are the new defaultSize for them:
DecimalType: 8 or 16 bytes, based on the precision
StringType: 20 bytes
BinaryType: 100 bytes
UDF: default size of SQL type
These numbers are not perfect (hard to have a perfect number for them), but should be better than 4096.
Author: Davies Liu <davies@databricks.com>
Closes#11210 from davies/statics.
The type checking functions of `If` and `UnwrapOption` are fixed to eliminate spurious failures. `UnwrapOption` was checking for an input of `ObjectType` but `ObjectType`'s accept function was hard coded to return `false`. `If`'s type check was returning a false negative in the case that the two options differed only by nullability.
Tests added:
- an end-to-end regression test is added to `DatasetSuite` for the reported failure.
- all the unit tests in `ExpressionEncoderSuite` are augmented to also confirm successful analysis. These tests are actually what pointed out the additional issues with `If` resolution.
Author: Michael Armbrust <michael@databricks.com>
Closes#11316 from marmbrus/datasetOptions.
JIRA: https://issues.apache.org/jira/browse/SPARK-6761
Compute approximate quantile based on the paper Greenwald, Michael and Khanna, Sanjeev, "Space-efficient Online Computation of Quantile Summaries," SIGMOD '01.
Author: Timothy Hunter <timhunter@databricks.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#6042 from viirya/approximate_quantile.
#### What changes were proposed in this pull request?
Ensure that all built-in expressions can be mapped to its SQL representation if there is one (e.g. ScalaUDF doesn't have a SQL representation). The function lists are from the expression list in `FunctionRegistry`.
window functions, grouping sets functions (`cube`, `rollup`, `grouping`, `grouping_id`), generator functions (`explode` and `json_tuple`) are covered by separate JIRA and PRs. Thus, this PR does not cover them. Except these functions, all the built-in expressions are covered. For details, see the list in `ExpressionToSQLSuite`.
Fixed a few issues. For example, the `prettyName` of `approx_count_distinct` is not right. The `sql` of `hash` function is not right, since the `hash` function does not accept `seed`.
Additionally, also correct the order of expressions in `FunctionRegistry` so that people are easier to find which functions are missing.
cc liancheng
#### How was the this patch tested?
Added two test cases in LogicalPlanToSQLSuite for covering `not like` and `not in`.
Added a new test suite `ExpressionToSQLSuite` to cover the functions:
1. misc non-aggregate functions + complex type creators + null expressions
2. math functions
3. aggregate functions
4. string functions
5. date time functions + calendar interval
6. collection functions
7. misc functions
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11314 from gatorsmile/expressionToSQL.
Use the HashedRelation which is a more optimized datastructure and reduce code complexity
Author: Xiu Guo <xguo27@gmail.com>
Closes#11291 from xguo27/SPARK-13422.
A common problem that users encounter with Spark 1.6.0 is that writing to a partitioned parquet table OOMs. The root cause is that parquet allocates a significant amount of memory that is not accounted for by our own mechanisms. As a workaround, we can ensure that only a single file is open per task unless the user explicitly asks for more.
Author: Michael Armbrust <michael@databricks.com>
Closes#11308 from marmbrus/parquetWriteOOM.
## What changes were proposed in this pull request?
This PR tries to fix all typos in all markdown files under `docs` module,
and fixes similar typos in other comments, too.
## How was the this patch tested?
manual tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11300 from dongjoon-hyun/minor_fix_typos.
https://issues.apache.org/jira/browse/SPARK-13381
This PR adds the support to load CSV data directly by a single call with given paths.
Also, I corrected this to refer all paths rather than the first path in schema inference, which JSON datasource dose.
Several unitests were added for each functionality.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11262 from HyukjinKwon/SPARK-13381.
## What changes were proposed in this pull request?
This PR adds equality operators to UDT classes so that they can be correctly tested for dataType equality during union operations.
This was previously causing `"AnalysisException: u"unresolved operator 'Union;""` when trying to unionAll two dataframes with UDT columns as below.
```
from pyspark.sql.tests import PythonOnlyPoint, PythonOnlyUDT
from pyspark.sql import types
schema = types.StructType([types.StructField("point", PythonOnlyUDT(), True)])
a = sqlCtx.createDataFrame([[PythonOnlyPoint(1.0, 2.0)]], schema)
b = sqlCtx.createDataFrame([[PythonOnlyPoint(3.0, 4.0)]], schema)
c = a.unionAll(b)
```
## How was the this patch tested?
Tested using two unit tests in sql/test.py and the DataFrameSuite.
Additional information here : https://issues.apache.org/jira/browse/SPARK-13410
Author: Franklyn D'souza <franklynd@gmail.com>
Closes#11279 from damnMeddlingKid/udt-union-all.
## What changes were proposed in this pull request?
Fixed the test failure `org.apache.spark.sql.util.ContinuousQueryListenerSuite.event ordering`: https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-maven-hadoop-2.6/202/testReport/junit/org.apache.spark.sql.util/ContinuousQueryListenerSuite/event_ordering/
```
org.scalatest.exceptions.TestFailedException:
Assert failed: : null equaled null onQueryTerminated called before onQueryStarted
org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
org.scalatest.Assertions$AssertionsHelper.macroAssert(Assertions.scala:466)
org.apache.spark.sql.util.ContinuousQueryListenerSuite$QueryStatusCollector$$anonfun$onQueryTerminated$1.apply$mcV$sp(ContinuousQueryListenerSuite.scala:204)
org.scalatest.concurrent.AsyncAssertions$Waiter.apply(AsyncAssertions.scala:349)
org.apache.spark.sql.util.ContinuousQueryListenerSuite$QueryStatusCollector.onQueryTerminated(ContinuousQueryListenerSuite.scala:203)
org.apache.spark.sql.execution.streaming.ContinuousQueryListenerBus.doPostEvent(ContinuousQueryListenerBus.scala:67)
org.apache.spark.sql.execution.streaming.ContinuousQueryListenerBus.doPostEvent(ContinuousQueryListenerBus.scala:32)
org.apache.spark.util.ListenerBus$class.postToAll(ListenerBus.scala:63)
org.apache.spark.sql.execution.streaming.ContinuousQueryListenerBus.postToAll(ContinuousQueryListenerBus.scala:32)
```
In the previous codes, when the test `adding and removing listener` finishes, there may be still some QueryTerminated events in the listener bus queue. Then when `event ordering` starts to run, it may see these events and throw the above exception.
This PR just added `waitUntilEmpty` in `after` to make sure all events be consumed after each test.
## How was the this patch tested?
Jenkins tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11275 from zsxwing/SPARK-13405.
https://issues.apache.org/jira/browse/SPARK-13137
This PR adds a filter in schema inference so that it does not emit NullPointException.
Also, I removed `MAX_COMMENT_LINES_IN_HEADER `but instead used a monad chaining with `filter()` and `first()`.
Lastly, I simply added a newline rather than adding a new file for this so that this is covered with the original tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11023 from HyukjinKwon/SPARK-13137.
Quite a few Spark SQL join operators broadcast one side of the join to all nodes. The are a few problems with this:
- This conflates broadcasting (a data exchange) with joining. Data exchanges should be managed by a different operator.
- All these nodes implement their own (duplicate) broadcasting logic.
- Re-use of indices is quite hard.
This PR defines both a ```BroadcastDistribution``` and ```BroadcastPartitioning```, these contain a `BroadcastMode`. The `BroadcastMode` defines the way in which we transform the Array of `InternalRow`'s into an index. We currently support the following `BroadcastMode`'s:
- IdentityBroadcastMode: This broadcasts the rows in their original form.
- HashSetBroadcastMode: This applies a projection to the input rows, deduplicates these rows and broadcasts the resulting `Set`.
- HashedRelationBroadcastMode: This transforms the input rows into a `HashedRelation`, and broadcasts this index.
To match this distribution we implement a ```BroadcastExchange``` operator which will perform the broadcast for us, and have ```EnsureRequirements``` plan this operator. The old Exchange operator has been renamed into ShuffleExchange in order to clearly separate between Shuffled and Broadcasted exchanges. Finally the classes in Exchange.scala have been moved to a dedicated package.
cc rxin davies
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11083 from hvanhovell/SPARK-13136.
