- Dec 28, 2016
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Kazuaki Ishizaki authored
[SPARK-16213][SQL] Reduce runtime overhead of a program that creates an primitive array in DataFrame ## What changes were proposed in this pull request? This PR reduces runtime overhead of a program the creates an primitive array in DataFrame by using the similar approach to #15044. Generated code performs boxing operation in an assignment from InternalRow to an `Object[]` temporary array (at Lines 051 and 061 in the generated code before without this PR). If we know that type of array elements is primitive, we apply the following optimizations: 1. Eliminate a pair of `isNullAt()` and a null assignment 2. Allocate an primitive array instead of `Object[]` (eliminate boxing operations) 3. Create `UnsafeArrayData` by using `UnsafeArrayWriter` to keep a primitive array in a row format instead of doing non-lightweight operations in constructor of `GenericArrayData` The PR also performs the same things for `CreateMap`. Here are performance results of [DataFrame programs](https://github.com/kiszk/spark/blob/6bf54ec5e227689d69f6db991e9ecbc54e153d0a/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/PrimitiveArrayBenchmark.scala#L83-L112) by up to 17.9x over without this PR. ``` Without SPARK-16043 OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Read a primitive array in DataFrame: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 3805 / 4150 0.0 507308.9 1.0X Double 3593 / 3852 0.0 479056.9 1.1X With SPARK-16043 Read a primitive array in DataFrame: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 213 / 271 0.0 28387.5 1.0X Double 204 / 223 0.0 27250.9 1.0X ``` Note : #15780 is enabled for these measurements An motivating example ``` java val df = sparkContext.parallelize(Seq(0.0d, 1.0d), 1).toDF df.selectExpr("Array(value + 1.1d, value + 2.2d)").show ``` Generated code without this PR ``` java /* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator { /* 006 */ private Object[] references; /* 007 */ private scala.collection.Iterator[] inputs; /* 008 */ private scala.collection.Iterator inputadapter_input; /* 009 */ private UnsafeRow serializefromobject_result; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder; /* 011 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter; /* 012 */ private Object[] project_values; /* 013 */ private UnsafeRow project_result; /* 014 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder project_holder; /* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter project_rowWriter; /* 016 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeArrayWriter project_arrayWriter; /* 017 */ /* 018 */ public GeneratedIterator(Object[] references) { /* 019 */ this.references = references; /* 020 */ } /* 021 */ /* 022 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 023 */ partitionIndex = index; /* 024 */ this.inputs = inputs; /* 025 */ inputadapter_input = inputs[0]; /* 026 */ serializefromobject_result = new UnsafeRow(1); /* 027 */ this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0); /* 028 */ this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1); /* 029 */ this.project_values = null; /* 030 */ project_result = new UnsafeRow(1); /* 031 */ this.project_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(project_result, 32); /* 032 */ this.project_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(project_holder, 1); /* 033 */ this.project_arrayWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeArrayWriter(); /* 034 */ /* 035 */ } /* 036 */ /* 037 */ protected void processNext() throws java.io.IOException { /* 038 */ while (inputadapter_input.hasNext()) { /* 039 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 040 */ double inputadapter_value = inputadapter_row.getDouble(0); /* 041 */ /* 042 */ final boolean project_isNull = false; /* 043 */ this.project_values = new Object[2]; /* 044 */ boolean project_isNull1 = false; /* 045 */ /* 046 */ double project_value1 = -1.0; /* 047 */ project_value1 = inputadapter_value + 1.1D; /* 048 */ if (false) { /* 049 */ project_values[0] = null; /* 050 */ } else { /* 051 */ project_values[0] = project_value1; /* 052 */ } /* 053 */ /* 054 */ boolean project_isNull4 = false; /* 055 */ /* 056 */ double project_value4 = -1.0; /* 057 */ project_value4 = inputadapter_value + 2.2D; /* 058 */ if (false) { /* 059 */ project_values[1] = null; /* 060 */ } else { /* 061 */ project_values[1] = project_value4; /* 062 */ } /* 063 */ /* 064 */ final ArrayData project_value = new org.apache.spark.sql.catalyst.util.GenericArrayData(project_values); /* 065 */ this.project_values = null; /* 066 */ project_holder.reset(); /* 067 */ /* 068 */ project_rowWriter.zeroOutNullBytes(); /* 069 */ /* 070 */ if (project_isNull) { /* 071 */ project_rowWriter.setNullAt(0); /* 072 */ } else { /* 073 */ // Remember the current cursor so that we can calculate how many bytes are /* 074 */ // written later. /* 075 */ final int project_tmpCursor = project_holder.cursor; /* 076 */ /* 077 */ if (project_value instanceof UnsafeArrayData) { /* 078 */ final int project_sizeInBytes = ((UnsafeArrayData) project_value).getSizeInBytes(); /* 079 */ // grow the global buffer before writing data. /* 080 */ project_holder.grow(project_sizeInBytes); /* 081 */ ((UnsafeArrayData) project_value).writeToMemory(project_holder.buffer, project_holder.cursor); /* 082 */ project_holder.cursor += project_sizeInBytes; /* 083 */ /* 084 */ } else { /* 085 */ final int project_numElements = project_value.numElements(); /* 086 */ project_arrayWriter.initialize(project_holder, project_numElements, 8); /* 087 */ /* 088 */ for (int project_index = 0; project_index < project_numElements; project_index++) { /* 089 */ if (project_value.isNullAt(project_index)) { /* 090 */ project_arrayWriter.setNullDouble(project_index); /* 091 */ } else { /* 092 */ final double project_element = project_value.getDouble(project_index); /* 093 */ project_arrayWriter.write(project_index, project_element); /* 094 */ } /* 095 */ } /* 096 */ } /* 097 */ /* 098 */ project_rowWriter.setOffsetAndSize(0, project_tmpCursor, project_holder.cursor - project_tmpCursor); /* 099 */ } /* 100 */ project_result.setTotalSize(project_holder.totalSize()); /* 101 */ append(project_result); /* 102 */ if (shouldStop()) return; /* 103 */ } /* 104 */ } /* 105 */ } ``` Generated code with this PR ``` java /* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator { /* 006 */ private Object[] references; /* 007 */ private scala.collection.Iterator[] inputs; /* 008 */ private scala.collection.Iterator inputadapter_input; /* 009 */ private UnsafeRow serializefromobject_result; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder; /* 011 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter; /* 012 */ private UnsafeArrayData project_arrayData; /* 013 */ private UnsafeRow project_result; /* 014 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder project_holder; /* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter project_rowWriter; /* 016 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeArrayWriter project_arrayWriter; /* 017 */ /* 018 */ public GeneratedIterator(Object[] references) { /* 019 */ this.references = references; /* 020 */ } /* 021 */ /* 022 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 023 */ partitionIndex = index; /* 024 */ this.inputs = inputs; /* 025 */ inputadapter_input = inputs[0]; /* 026 */ serializefromobject_result = new UnsafeRow(1); /* 027 */ this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0); /* 028 */ this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1); /* 029 */ /* 030 */ project_result = new UnsafeRow(1); /* 031 */ this.project_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(project_result, 32); /* 032 */ this.project_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(project_holder, 1); /* 033 */ this.project_arrayWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeArrayWriter(); /* 034 */ /* 035 */ } /* 036 */ /* 037 */ protected void processNext() throws java.io.IOException { /* 038 */ while (inputadapter_input.hasNext()) { /* 039 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 040 */ double inputadapter_value = inputadapter_row.getDouble(0); /* 041 */ /* 042 */ byte[] project_array = new byte[32]; /* 043 */ project_arrayData = new UnsafeArrayData(); /* 044 */ Platform.putLong(project_array, 16, 2); /* 045 */ project_arrayData.pointTo(project_array, 16, 32); /* 046 */ /* 047 */ boolean project_isNull1 = false; /* 048 */ /* 049 */ double project_value1 = -1.0; /* 050 */ project_value1 = inputadapter_value + 1.1D; /* 051 */ if (false) { /* 052 */ project_arrayData.setNullAt(0); /* 053 */ } else { /* 054 */ project_arrayData.setDouble(0, project_value1); /* 055 */ } /* 056 */ /* 057 */ boolean project_isNull4 = false; /* 058 */ /* 059 */ double project_value4 = -1.0; /* 060 */ project_value4 = inputadapter_value + 2.2D; /* 061 */ if (false) { /* 062 */ project_arrayData.setNullAt(1); /* 063 */ } else { /* 064 */ project_arrayData.setDouble(1, project_value4); /* 065 */ } /* 066 */ project_holder.reset(); /* 067 */ /* 068 */ // Remember the current cursor so that we can calculate how many bytes are /* 069 */ // written later. /* 070 */ final int project_tmpCursor = project_holder.cursor; /* 071 */ /* 072 */ if (project_arrayData instanceof UnsafeArrayData) { /* 073 */ final int project_sizeInBytes = ((UnsafeArrayData) project_arrayData).getSizeInBytes(); /* 074 */ // grow the global buffer before writing data. /* 075 */ project_holder.grow(project_sizeInBytes); /* 076 */ ((UnsafeArrayData) project_arrayData).writeToMemory(project_holder.buffer, project_holder.cursor); /* 077 */ project_holder.cursor += project_sizeInBytes; /* 078 */ /* 079 */ } else { /* 080 */ final int project_numElements = project_arrayData.numElements(); /* 081 */ project_arrayWriter.initialize(project_holder, project_numElements, 8); /* 082 */ /* 083 */ for (int project_index = 0; project_index < project_numElements; project_index++) { /* 084 */ if (project_arrayData.isNullAt(project_index)) { /* 085 */ project_arrayWriter.setNullDouble(project_index); /* 086 */ } else { /* 087 */ final double project_element = project_arrayData.getDouble(project_index); /* 088 */ project_arrayWriter.write(project_index, project_element); /* 089 */ } /* 090 */ } /* 091 */ } /* 092 */ /* 093 */ project_rowWriter.setOffsetAndSize(0, project_tmpCursor, project_holder.cursor - project_tmpCursor); /* 094 */ project_result.setTotalSize(project_holder.totalSize()); /* 095 */ append(project_result); /* 096 */ if (shouldStop()) return; /* 097 */ } /* 098 */ } /* 099 */ } ``` ## How was this patch tested? Added unit tests into `DataFrameComplexTypeSuite` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #13909 from kiszk/SPARK-16213.
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Tathagata Das authored
[SPARK-18669][SS][DOCS] Update Apache docs for Structured Streaming regarding watermarking and status ## What changes were proposed in this pull request? - Extended the Window operation section with code snippet and explanation of watermarking - Extended the Output Mode section with a table showing the compatibility between query type and output mode - Rewrote the Monitoring section with updated jsons generated by StreamingQuery.progress/status - Updated API changes in the StreamingQueryListener example TODO - [x] Figure showing the watermarking ## How was this patch tested? N/A ## Screenshots ### Section: Windowed Aggregation with Event Time <img width="927" alt="screen shot 2016-12-15 at 3 33 10 pm" src="https://cloud.githubusercontent.com/assets/663212/21246197/0e02cb1a-c2dc-11e6-8816-0cd28d8201d7.png">  <img width="929" alt="screen shot 2016-12-15 at 3 33 46 pm" src="https://cloud.githubusercontent.com/assets/663212/21246202/1652cefa-c2dc-11e6-8c64-3c05977fb3fc.png"> ---------------------------- ### Section: Output Modes  ---------------------------- ### Section: Monitoring   Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #16294 from tdas/SPARK-18669.
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sethah authored
## What changes were proposed in this pull request? More and more ML algos are accepting sample weights, and they have been tested rather heterogeneously and with code duplication. This patch adds extensible helper methods to `MLTestingUtils` that can be reused by various algorithms accepting sample weights. Up to now, there seems to be a few tests that have been implemented commonly: * Check that oversampling is the same as giving the instances sample weights proportional to the number of samples * Check that outliers with tiny sample weights do not affect the algorithm's performance This patch adds an additional test: * Check that algorithms are invariant to constant scaling of the sample weights. i.e. uniform sample weights with `w_i = 1.0` is effectively the same as uniform sample weights with `w_i = 10000` or `w_i = 0.0001` The instances of these tests occurred in LinearRegression, NaiveBayes, and LogisticRegression. Those tests have been removed/modified to use the new helper methods. These helper functions will be of use when [SPARK-9478](https://issues.apache.org/jira/browse/SPARK-9478) is implemented. ## How was this patch tested? This patch only involves modifying test suites. ## Other notes Both IsotonicRegression and GeneralizedLinearRegression also extend `HasWeightCol`. I did not modify these test suites because it will make this patch easier to review, and because they did not duplicate the same tests as the three suites that were modified. If we want to change them later, we can create a JIRA for it now, but it's open for debate. Author: sethah <seth.hendrickson16@gmail.com> Closes #15721 from sethah/SPARK-17772.
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Sean Owen authored
[SPARK-18993][BUILD] Unable to build/compile Spark in IntelliJ due to missing Scala deps in spark-tags ## What changes were proposed in this pull request? This adds back a direct dependency on Scala library classes from spark-tags because its Scala annotations need them. ## How was this patch tested? Existing tests Author: Sean Owen <sowen@cloudera.com> Closes #16418 from srowen/SPARK-18993.
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Carson Wang authored
## What changes were proposed in this pull request? Fix the document of `ForeachWriter` to use `writeStream` instead of `write` for a streaming dataset. ## How was this patch tested? Docs only. Author: Carson Wang <carson.wang@intel.com> Closes #16419 from carsonwang/FixDoc.
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uncleGen authored
## What changes were proposed in this pull request? In HDFS, when we copy a file into target directory, there will a temporary `._COPY_` file for a period of time. The duration depends on file size. If we do not skip this file, we will may read the same data for two times. ## How was this patch tested? update unit test Author: uncleGen <hustyugm@gmail.com> Closes #16370 from uncleGen/SPARK-18960.
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Sergei Lebedev authored
## What changes were proposed in this pull request? This is to workaround an implicit result of #4947 which suppressed the original Kryo exception if the overflow happened during serialization. ## How was this patch tested? `KryoSerializerSuite` was augmented to reflect this change. Author: Sergei Lebedev <superbobry@gmail.com> Closes #16416 from superbobry/patch-1.
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Yanbo Liang authored
## What changes were proposed in this pull request? Correct test cases of ```LogisticRegression``` raw2prediction & probability2prediction. ## How was this patch tested? Changed unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16407 from yanboliang/raw-probability.
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Peng authored
[SPARK-17645][MLLIB][ML] add feature selector method based on: False Discovery Rate (FDR) and Family wise error rate (FWE) ## What changes were proposed in this pull request? Univariate feature selection works by selecting the best features based on univariate statistical tests. FDR and FWE are a popular univariate statistical test for feature selection. In 2005, the Benjamini and Hochberg paper on FDR was identified as one of the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate. In statistics, FWE is the probability of making one or more false discoveries, or type I errors, among all the hypotheses when performing multiple hypotheses tests. https://en.wikipedia.org/wiki/Family-wise_error_rate We add FDR and FWE methods for ChiSqSelector in this PR, like it is implemented in scikit-learn. http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection ## How was this patch tested? ut will be added soon (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Peng <peng.meng@intel.com> Author: Peng, Meng <peng.meng@intel.com> Closes #15212 from mpjlu/fdr_fwe.
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Felix Cheung authored
## What changes were proposed in this pull request? add example with `--pip` and `--r` switch as it is actually done in create-release ## How was this patch tested? Doc only Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16364 from felixcheung/buildguide.
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- Dec 27, 2016
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gatorsmile authored
### What changes were proposed in this pull request? Since `spark.sql.hive.thriftServer.singleSession` is a configuration of SQL component, this conf can be moved from `SparkConf` to `StaticSQLConf`. When we introduced `spark.sql.hive.thriftServer.singleSession`, all the SQL configuration are session specific. They can be modified in different sessions. In Spark 2.1, static SQL configuration is added. It is a perfect fit for `spark.sql.hive.thriftServer.singleSession`. Previously, we did the same move for `spark.sql.warehouse.dir` from `SparkConf` to `StaticSQLConf` ### How was this patch tested? Added test cases in HiveThriftServer2Suites.scala Author: gatorsmile <gatorsmile@gmail.com> Closes #16392 from gatorsmile/hiveThriftServerSingleSession.
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Yuexin Zhang authored
## What changes were proposed in this pull request? On configuration doc page:https://spark.apache.org/docs/latest/configuration.html We mentioned spark.kryoserializer.buffer.max : Maximum allowable size of Kryo serialization buffer. This must be larger than any object you attempt to serialize. Increase this if you get a "buffer limit exceeded" exception inside Kryo. from source code, it has hard coded upper limit : ``` val maxBufferSizeMb = conf.getSizeAsMb("spark.kryoserializer.buffer.max", "64m").toInt if (maxBufferSizeMb >= ByteUnit.GiB.toMiB(2)) { throw new IllegalArgumentException("spark.kryoserializer.buffer.max must be less than " + s"2048 mb, got: + $maxBufferSizeMb mb.") } ``` We should mention "this value must be less than 2048 mb" on the configuration doc page as well. ## How was this patch tested? None. Since it's minor doc change. Author: Yuexin Zhang <yxzhang@cloudera.com> Closes #16412 from cnZach/SPARK-19006.
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hyukjinkwon authored
[SPARK-18842][TESTS] De-duplicate paths in classpaths in processes for local-cluster mode in ReplSuite to work around the length limitation on Windows ## What changes were proposed in this pull request? `ReplSuite`s hang due to the length limitation on Windows with the exception as below: ``` Spark context available as 'sc' (master = local-cluster[1,1,1024], app id = app-20161223114000-0000). Spark session available as 'spark'. Exception in thread "ExecutorRunner for app-20161223114000-0000/26995" java.lang.OutOfMemoryError: GC overhead limit exceeded at java.util.Arrays.copyOf(Arrays.java:3332) at java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:137) at java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:121) at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:622) at java.lang.StringBuilder.append(StringBuilder.java:202) at java.lang.ProcessImpl.createCommandLine(ProcessImpl.java:194) at java.lang.ProcessImpl.<init>(ProcessImpl.java:340) at java.lang.ProcessImpl.start(ProcessImpl.java:137) at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029) at org.apache.spark.deploy.worker.ExecutorRunner.org$apache$spark$deploy$worker$ExecutorRunner$$fetchAndRunExecutor(ExecutorRunner.scala:167) at org.apache.spark.deploy.worker.ExecutorRunner$$anon$1.run(ExecutorRunner.scala:73) ``` The reason is, it keeps failing and goes in an infinite loop. This fails because it uses the paths (via `getFile`) from URLs in the tests whereas some added afterward are normal local paths. (`url.getFile` gives `/C:/a/b/c` and some paths are added later as the format of `C:\a\b\c`. ) So, many classpaths are duplicated because normal local paths and paths from URLs are mixed. This length is up to 40K which hits the length limitation problem (32K) on Windows. The full command line built here is - https://gist.github.com/HyukjinKwon/46af7946c9a5fd4c6fc70a8a0aba1beb ## How was this patch tested? Manually via AppVeyor. **Before** https://ci.appveyor.com/project/spark-test/spark/build/395-find-path-issues **After** https://ci.appveyor.com/project/spark-test/spark/build/398-find-path-issues Author: hyukjinkwon <gurwls223@gmail.com> Closes #16398 from HyukjinKwon/SPARK-18842-more.
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Wenchen Fan authored
## What changes were proposed in this pull request? Currently `DatasetBenchmark` use `case class Data(l: Long, s: String)` as the record type of `RDD` and `Dataset`, which introduce serialization overhead only to `Dataset` and is unfair. This PR use `Long` as the record type, to be fairer for `Dataset` ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #16391 from cloud-fan/benchmark.
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Dongjoon Hyun authored
## What changes were proposed in this pull request? `JDBCSuite` and `JDBCWriterSuite` have their own `testH2Dialect`s for their testing purposes. This PR fixes `testH2Dialect` in `JDBCWriterSuite` by removing `getCatalystType` implementation in order to return correct types. Currently, it always returns `Some(StringType)` incorrectly. Note that, for the `testH2Dialect` in `JDBCSuite`, it's intentional because of the test case `Remap types via JdbcDialects`. ## How was this patch tested? This is a test only update. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #16409 from dongjoon-hyun/SPARK-H2-DIALECT.
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Wenchen Fan authored
## What changes were proposed in this pull request? `Literal` can use `CodegenContex.addReferenceObj` to implement codegen, instead of `CodegenFallback`. This can also simplify the generated code a little bit, before we will generate: `((Expression) references[1]).eval(null)`, now it's just `references[1]`. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #16402 from cloud-fan/minor.
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- Dec 26, 2016
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Wenchen Fan authored
## What changes were proposed in this pull request? When we describe a table, we only wanna see the information of this table, not read it, so it's ok even if the format class is not present at the classpath. ## How was this patch tested? new regression test Author: Wenchen Fan <wenchen@databricks.com> Closes #16388 from cloud-fan/hive.
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Wenchen Fan authored
## What changes were proposed in this pull request? Currently we implement `Aggregator` with `DeclarativeAggregate`, which will serialize/deserialize the buffer object every time we process an input. This PR implements `Aggregator` with `TypedImperativeAggregate` and avoids to serialize/deserialize buffer object many times. The benchmark shows we get about 2 times speed up. For simple buffer object that doesn't need serialization, we still go with `DeclarativeAggregate`, to avoid performance regression. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #16383 from cloud-fan/aggregator.
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Shixiong Zhu authored
## What changes were proposed in this pull request? The root cause of this issue is that RegisterWorkerResponse and LaunchExecutor are sent via two different channels (TCP connections) and their order is not guaranteed. This PR changes the master and worker codes to use `workerRef` to send RegisterWorkerResponse, so that RegisterWorkerResponse and LaunchExecutor are sent via the same connection. Hence `LaunchExecutor` will always be after `RegisterWorkerResponse` and never be ignored. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #16345 from zsxwing/SPARK-17755.
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- Dec 24, 2016
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hyukjinkwon authored
## What changes were proposed in this pull request? `CSVRelation.csvParser` does type dispatch for each value in each row. We can prevent this because the schema is already kept in `CSVRelation`. So, this PR proposes that converters are created first according to the schema, and then apply them to each. I just ran some small benchmarks as below after resembling the logics in https://github.com/apache/spark/blob/7c33b0fd050f3d2b08c1cfd7efbff8166832c1af/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVFileFormat.scala#L170-L178 to test the updated logics. ```scala test("Benchmark for CSV converter") { var numMalformedRecords = 0 val N = 500 << 12 val schema = StructType( StructField("a", StringType) :: StructField("b", StringType) :: StructField("c", StringType) :: StructField("d", StringType) :: Nil) val row = Array("1.0", "test", "2015-08-20 14:57:00", "FALSE") val data = spark.sparkContext.parallelize(List.fill(N)(row)) val parser = CSVRelation.csvParser(schema, schema.fieldNames, CSVOptions()) val benchmark = new Benchmark("CSV converter", N) benchmark.addCase("cast CSV string tokens", 10) { _ => data.flatMap { recordTokens => parser(recordTokens, numMalformedRecords) }.collect() } benchmark.run() } ``` **Before** ``` CSV converter: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ cast CSV string tokens 1061 / 1130 1.9 517.9 1.0X ``` **After** ``` CSV converter: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ cast CSV string tokens 940 / 1011 2.2 459.2 1.0X ``` ## How was this patch tested? Tests in `CSVTypeCastSuite` and `CSVRelation` Author: hyukjinkwon <gurwls223@gmail.com> Closes #16351 from HyukjinKwon/type-dispatch.
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Kousuke Saruta authored
## What changes were proposed in this pull request? This issue was reported by wangyum. In the AllJobsPage, JobPage and StagePage, the description length was limited before like as follows.  But recently, the limitation seems to have been accidentally removed.  The cause is that some tables are no longer `sortable` class although they were, and `sortable` class does not only mark tables as sortable but also limited the width of their child `td` elements. The reason why now some tables are not `sortable` class is because another sortable mechanism was introduced by #13620 and #13708 with pagination feature. To fix this issue, I've introduced new class `table-cell-width-limited` which limits the description cell width and the description is like what it was. <img width="1260" alt="2016-12-20 1 00 34" src="https://cloud.githubusercontent.com/assets/4736016/21320478/89141c7a-c654-11e6-8494-f8f91325980b.png"> ## How was this patch tested? Tested manually with my browser. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #16338 from sarutak/SPARK-18837.
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Liang-Chi Hsieh authored
## What changes were proposed in this pull request? `UnsafeKVExternalSorter` uses `UnsafeInMemorySorter` to sort the records of `BytesToBytesMap` if it is given a map. Currently we use the number of keys in `BytesToBytesMap` to determine if the array used for sort is enough or not. We has an assert that ensures the size of the array is enough: `map.numKeys() <= map.getArray().size() / 2`. However, each record in the map takes two entries in the array, one is record pointer, another is key prefix. So the correct assert should be `map.numKeys() * 2 <= map.getArray().size() / 2`. ## How was this patch tested? N/A Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #16232 from viirya/SPARK-18800-fix-UnsafeKVExternalSorter.
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wangzhenhua authored
[SPARK-18911][SQL] Define CatalogStatistics to interact with metastore and convert it to Statistics in relations ## What changes were proposed in this pull request? Statistics in LogicalPlan should use attributes to refer to columns rather than column names, because two columns from two relations can have the same column name. But CatalogTable doesn't have the concepts of attribute or broadcast hint in Statistics. Therefore, putting Statistics in CatalogTable is confusing. We define a different statistic structure in CatalogTable, which is only responsible for interacting with metastore, and is converted to statistics in LogicalPlan when it is used. ## How was this patch tested? add test cases Author: wangzhenhua <wangzhenhua@huawei.com> Author: Zhenhua Wang <wzh_zju@163.com> Closes #16323 from wzhfy/nameToAttr.
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- Dec 23, 2016
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Shixiong Zhu authored
[SPARK-18991][CORE] Change ContextCleaner.referenceBuffer to use ConcurrentHashMap to make it faster ## What changes were proposed in this pull request? The time complexity of ConcurrentHashMap's `remove` is O(1). Changing ContextCleaner.referenceBuffer's type from `ConcurrentLinkedQueue` to `ConcurrentHashMap's` will make the removal much faster. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #16390 from zsxwing/SPARK-18991.
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Pete Robbins authored
fails on big endian. Only change byte order on little endian ## What changes were proposed in this pull request? Fix test to only change byte order on LE platforms ## How was this patch tested? Test run on Big Endian and Little Endian platforms Author: Pete Robbins <robbinspg@gmail.com> Closes #16375 from robbinspg/SPARK-18963.
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- Dec 22, 2016
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Felix Cheung authored
## What changes were proposed in this pull request? It would make it easier to integrate with other component expecting row-based JSON format. This replaces the non-public toJSON RDD API. ## How was this patch tested? manual, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16368 from felixcheung/rJSON.
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Shixiong Zhu authored
## What changes were proposed in this pull request? Right now the name of threads created by Netty for Spark RPC are `shuffle-client-**` and `shuffle-server-**`. It's pretty confusing. This PR just uses the module name in TransportConf to set the thread name. In addition, it also includes the following minor fixes: - TransportChannelHandler.channelActive and channelInactive should call the corresponding super methods. - Make ShuffleBlockFetcherIterator throw NoSuchElementException if it has no more elements. Otherwise, if the caller calls `next` without `hasNext`, it will just hang. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #16380 from zsxwing/SPARK-18972.
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Shixiong Zhu authored
## What changes were proposed in this pull request? Add missing InterfaceStability.Evolving for Structured Streaming APIs ## How was this patch tested? Compiling the codes. Author: Shixiong Zhu <shixiong@databricks.com> Closes #16385 from zsxwing/SPARK-18985.
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saturday_s authored
## What changes were proposed in this pull request? This PR is an inheritance from #16000, and is a completion of #15904. **Description** - Augment the `org.apache.spark.status.api.v1` package for serving streaming information. - Retrieve the streaming information through StreamingJobProgressListener. > this api should cover exceptly the same amount of information as you can get from the web interface > the implementation is base on the current REST implementation of spark-core > and will be available for running applications only > > https://issues.apache.org/jira/browse/SPARK-18537 ## How was this patch tested? Local test. Author: saturday_s <shi.indetail@gmail.com> Author: Chan Chor Pang <ChorPang.Chan@access-company.com> Author: peterCPChan <universknight@gmail.com> Closes #16253 from saturday-shi/SPARK-18537.
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jerryshao authored
## What changes were proposed in this pull request? In current Spark we could add customized SparkListener through `SparkContext#addListener` API, but there's no equivalent API to remove the registered one. In our scenario SparkListener will be added repeatedly accordingly to the changed environment. If lacks the ability to remove listeners, there might be many registered listeners finally, this is unnecessary and potentially affects the performance. So here propose to add an API to remove registered listener. ## How was this patch tested? Add an unit test to verify it. Author: jerryshao <sshao@hortonworks.com> Closes #16382 from jerryshao/SPARK-18975.
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Reynold Xin authored
## What changes were proposed in this pull request? SortPartitions and RedistributeData logical operators are not actually used and can be removed. Note that we do have a Sort operator (with global flag false) that subsumed SortPartitions. ## How was this patch tested? Also updated test cases to reflect the removal. Author: Reynold Xin <rxin@databricks.com> Closes #16381 from rxin/SPARK-18973.
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hyukjinkwon authored
[SPARK-16975][SQL][FOLLOWUP] Do not duplicately check file paths in data sources implementing FileFormat ## What changes were proposed in this pull request? This PR cleans up duplicated checking for file paths in implemented data sources and prevent to attempt to list twice in ORC data source. https://github.com/apache/spark/pull/14585 handles a problem for the partition column name having `_` and the issue itself is resolved correctly. However, it seems the data sources implementing `FileFormat` are validating the paths duplicately. Assuming from the comment in `CSVFileFormat`, `// TODO: Move filtering.`, I guess we don't have to check this duplicately. Currently, this seems being filtered in `PartitioningAwareFileIndex.shouldFilterOut` and`PartitioningAwareFileIndex.isDataPath`. So, `FileFormat.inferSchema` will always receive leaf files. For example, running to codes below: ``` scala spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet") spark.read.parquet("/tmp/parquet") ``` gives the paths below without directories but just valid data files: ``` bash /tmp/parquet/_col=0/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet /tmp/parquet/_col=1/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet /tmp/parquet/_col=2/part-r-00000-25de2b50-225a-4bcf-a2bc-9eb9ed407ef6.snappy.parquet ... ``` to `FileFormat.inferSchema`. ## How was this patch tested? Unit test added in `HadoopFsRelationTest` and related existing tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14627 from HyukjinKwon/SPARK-16975.
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hyukjinkwon authored
[SPARK-18922][TESTS] Fix more resource-closing-related and path-related test failures in identified ones on Windows ## What changes were proposed in this pull request? There are several tests failing due to resource-closing-related and path-related problems on Windows as below. - `SQLQuerySuite`: ``` - specifying database name for a temporary table is not allowed *** FAILED *** (125 milliseconds) org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-1f4471ab-aac0-4239-ae35-833d54b37e52; at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:382) at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370) ``` - `JsonSuite`: ``` - Loading a JSON dataset from a text file with SQL *** FAILED *** (94 milliseconds) org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-c918a8b7-fc09-433c-b9d0-36c0f78ae918; at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:382) at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370) ``` - `StateStoreSuite`: ``` - SPARK-18342: commit fails when rename fails *** FAILED *** (16 milliseconds) java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: StateStoreSuite29777261fs://C:%5Cprojects%5Cspark%5Ctarget%5Ctmp%5Cspark-ef349862-7281-4963-aaf3-add0d670a4ad%5C?????-2218c2f8-2cf6-4f80-9cdf-96354e8246a77685899733421033312/0 at org.apache.hadoop.fs.Path.initialize(Path.java:206) at org.apache.hadoop.fs.Path.<init>(Path.java:116) at org.apache.hadoop.fs.Path.<init>(Path.java:89) ... Cause: java.net.URISyntaxException: Relative path in absolute URI: StateStoreSuite29777261fs://C:%5Cprojects%5Cspark%5Ctarget%5Ctmp%5Cspark-ef349862-7281-4963-aaf3-add0d670a4ad%5C?????-2218c2f8-2cf6-4f80-9cdf-96354e8246a77685899733421033312/0 at java.net.URI.checkPath(URI.java:1823) at java.net.URI.<init>(URI.java:745) at org.apache.hadoop.fs.Path.initialize(Path.java:203) ``` - `HDFSMetadataLogSuite`: ``` - FileManager: FileContextManager *** FAILED *** (94 milliseconds) java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-415bb0bd-396b-444d-be82-04599e025f21 at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1010) at org.apache.spark.sql.test.SQLTestUtils$class.withTempDir(SQLTestUtils.scala:127) at org.apache.spark.sql.execution.streaming.HDFSMetadataLogSuite.withTempDir(HDFSMetadataLogSuite.scala:38) - FileManager: FileSystemManager *** FAILED *** (78 milliseconds) java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-ef8222cd-85aa-47c0-a396-bc7979e15088 at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1010) at org.apache.spark.sql.test.SQLTestUtils$class.withTempDir(SQLTestUtils.scala:127) at org.apache.spark.sql.execution.streaming.HDFSMetadataLogSuite.withTempDir(HDFSMetadataLogSuite.scala:38) ``` And, there are some tests being failed due to the length limitation on cmd in Windows as below: - `LauncherBackendSuite`: ``` - local: launcher handle *** FAILED *** (30 seconds, 120 milliseconds) The code passed to eventually never returned normally. Attempted 283 times over 30.0960053 seconds. Last failure message: The reference was null. (LauncherBackendSuite.scala:56) org.scalatest.exceptions.TestFailedDueToTimeoutException: at org.scalatest.concurrent.Eventually$class.tryTryAgain$1(Eventually.scala:420) at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:438) - standalone/client: launcher handle *** FAILED *** (30 seconds, 47 milliseconds) The code passed to eventually never returned normally. Attempted 282 times over 30.037987100000002 seconds. Last failure message: The reference was null. (LauncherBackendSuite.scala:56) org.scalatest.exceptions.TestFailedDueToTimeoutException: at org.scalatest.concurrent.Eventually$class.tryTryAgain$1(Eventually.scala:420) at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:438) ``` The executed command is, https://gist.github.com/HyukjinKwon/d3fdd2e694e5c022992838a618a516bd, which is 16K length; however, the length limitation is 8K on Windows. So, it is being failed to launch. This PR proposes to fix the test failures on Windows and skip the tests failed due to the length limitation ## How was this patch tested? Manually tested via AppVeyor **Before** `SQLQuerySuite `: https://ci.appveyor.com/project/spark-test/spark/build/306-pr-references `JsonSuite`: https://ci.appveyor.com/project/spark-test/spark/build/307-pr-references `StateStoreSuite` : https://ci.appveyor.com/project/spark-test/spark/build/305-pr-references `HDFSMetadataLogSuite`: https://ci.appveyor.com/project/spark-test/spark/build/304-pr-references `LauncherBackendSuite`: https://ci.appveyor.com/project/spark-test/spark/build/303-pr-references **After** `SQLQuerySuite`: https://ci.appveyor.com/project/spark-test/spark/build/293-SQLQuerySuite `JsonSuite`: https://ci.appveyor.com/project/spark-test/spark/build/294-JsonSuite `StateStoreSuite`: https://ci.appveyor.com/project/spark-test/spark/build/297-StateStoreSuite `HDFSMetadataLogSuite`: https://ci.appveyor.com/project/spark-test/spark/build/319-pr-references `LauncherBackendSuite`: failed test skipped. Author: hyukjinkwon <gurwls223@gmail.com> Closes #16335 from HyukjinKwon/more-fixes-on-windows.
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Dongjoon Hyun authored
## What changes were proposed in this pull request? For a dead worker, we will not be able to see its worker page anyway. This PR removes the links to dead workers from the master page. ## How was this patch tested? Since this is UI change, please do the following steps manually. **1. Start a master and a slave** ``` sbin/start-master.sh sbin/start-slave.sh spark://10.22.16.140:7077 ```  **2. Stop the slave** ``` sbin/stop-slave.sh ```  **3. Start a slave** ``` sbin/start-slave.sh spark://10.22.16.140:7077 ```  **4. Stop the slave** ``` sbin/stop-slave.sh ```  **5. Driver list testing** Do the followings and stop the slave in a minute by `sbin/stop-slave.sh`. ``` sbin/start-master.sh sbin/start-slave.sh spark://10.22.16.140:7077 bin/spark-submit --master=spark://10.22.16.140:7077 --deploy-mode=cluster --class org.apache.spark.examples.SparkPi examples/target/scala-2.11/jars/spark-examples_2.11-2.2.0-SNAPSHOT.jar 10000 ```  Author: Dongjoon Hyun <dongjoon@apache.org> Closes #16366 from dongjoon-hyun/SPARK-18953.
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Reynold Xin authored
## What changes were proposed in this pull request? Starting Spark 2.1.0, bucketing feature is available for all file-based data sources. This patch fixes some function docs that haven't yet been updated to reflect that. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #16349 from rxin/ds-doc.
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Reynold Xin authored
## What changes were proposed in this pull request? This patch includes minor changes to improve readability for partition handling code. I'm in the middle of implementing some new feature and found some naming / implicit type inference not as intuitive. ## How was this patch tested? This patch should have no semantic change and the changes should be covered by existing test cases. Author: Reynold Xin <rxin@databricks.com> Closes #16378 from rxin/minor-fix.
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Shixiong Zhu authored
## What changes were proposed in this pull request? This PR audits places using `logicalPlan` in StreamExecution and ensures they all handles the case that `logicalPlan` cannot be created. In addition, this PR also fixes the following issues in `StreamingQueryException`: - `StreamingQueryException` and `StreamExecution` are cycle-dependent because in the `StreamingQueryException`'s constructor, it calls `StreamExecution`'s `toDebugString` which uses `StreamingQueryException`. Hence it will output `null` value in the error message. - Duplicated stack trace when calling Throwable.printStackTrace because StreamingQueryException's toString contains the stack trace. ## How was this patch tested? The updated `test("max files per trigger - incorrect values")`. I found this issue when I switched from `testStream` to the real codes to verify the failure in this test. Author: Shixiong Zhu <shixiong@databricks.com> Closes #16322 from zsxwing/SPARK-18907.
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- Dec 21, 2016
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Felix Cheung authored
## What changes were proposed in this pull request? make-distribution.sh should find JAVA_HOME for Ubuntu, Mac and other non-RHEL systems ## How was this patch tested? Manually Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16363 from felixcheung/buildjava.
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Burak Yavuz authored
## What changes were proposed in this pull request? https://spark-tests.appspot.com/test-details?suite_name=org.apache.spark.streaming.InputStreamsSuite&test_name=socket+input+stream ## How was this patch tested? Tested 2,000 times. Author: Burak Yavuz <brkyvz@gmail.com> Closes #16343 from brkyvz/sock.
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