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Posted to reviews@spark.apache.org by dongjoon-hyun <gi...@git.apache.org> on 2017/05/09 19:31:58 UTC
[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
GitHub user dongjoon-hyun opened a pull request:
https://github.com/apache/spark/pull/17924
[SPARK-20682][SQL] Support a new faster ORC data source based on Apache ORC
## What changes were proposed in this pull request?
Since [SPARK-2883](https://issues.apache.org/jira/browse/SPARK-2883), Apache Spark supports Apache ORC inside `sql/hive` module with Hive dependency. This issue aims to add a new and faster ORC data source inside `sql/core` and to replace the old ORC data source eventually. In this issue, the latest Apache ORC 1.4.0 (released yesterday) is used.
There are four key benefits.
- Speed: Use both Spark `ColumnarBatch` and ORC `RowBatch` together. This is faster than the current implementation in Spark.
- Stability: Apache ORC 1.4.0 has many fixes and we can depend on ORC community more.
- Usability: User can use `ORC` data sources without hive module, i.e, `-Phive`.
- Maintainability: Reduce the Hive dependency and can remove old legacy code later.
## How was this patch tested?
Pass the Jenkins tests with newly added test suites in `sql/core`.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/dongjoon-hyun/spark SPARK-20682
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/17924.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #17924
----
commit 8bfd4bb4aec6dc3a5e7f41ec8d5c2a25f14ab87b
Author: Dongjoon Hyun <do...@apache.org>
Date: 2017-04-25T19:30:24Z
[SPARK-20682][SQL] Implement new ORC data source based on Apache ORC
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76705 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76705/testReport)** for PR 17924 at commit [`85ef731`](https://github.com/apache/spark/commit/85ef73134b7b7450e0689e138339433a30b92dea).
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17924
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by cenyuhai <gi...@git.apache.org>.
Github user cenyuhai commented on the issue:
https://github.com/apache/spark/pull/17924
@dongjoon-hyun I have a question: does this orc data sources reader support a table contains multiple file format
for example:
table/
day=2017-09-01 RCFile
day=2017-09-02 ORCFile
ParquetFileFormat doesn't support this feature.
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[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
Posted by cloud-fan <gi...@git.apache.org>.
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/17924#discussion_r115646099
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcColumnarBatchReader.scala ---
@@ -0,0 +1,407 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.orc
+
+import org.apache.hadoop.mapreduce.{InputSplit, RecordReader, TaskAttemptContext}
+import org.apache.hadoop.mapreduce.lib.input.FileSplit
+import org.apache.orc._
+import org.apache.orc.mapred.OrcInputFormat
+import org.apache.orc.storage.ql.exec.vector._
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.memory.MemoryMode
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
+import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, ColumnVectorUtils}
+import org.apache.spark.sql.types._
+
+
+/**
+ * To support vectorization in WholeStageCodeGen, this reader returns ColumnarBatch.
+ */
+private[orc] class OrcColumnarBatchReader extends RecordReader[Void, ColumnarBatch] with Logging {
+ import OrcColumnarBatchReader._
+
+ /**
+ * ORC File Reader.
+ */
+ private var reader: Reader = _
+
+ /**
+ * ORC Data Schema.
+ */
+ private var schema: TypeDescription = _
+
+ /**
+ * Vectorized Row Batch.
+ */
+ private var batch: VectorizedRowBatch = _
+
+ /**
+ * Record reader from row batch.
+ */
+ private var rows: org.apache.orc.RecordReader = _
+
+ /**
+ * Spark Schema.
+ */
+ private var sparkSchema: StructType = _
+
+ /**
+ * Required Schema.
+ */
+ private var requiredSchema: StructType = _
+
+ /**
+ * Partition Column.
+ */
+ private var partitionColumns: StructType = _
+
+ private var useIndex: Boolean = false
+
+ /**
+ * Full Schema: requiredSchema + partition schema.
+ */
+ private var fullSchema: StructType = _
+
+ /**
+ * ColumnarBatch for vectorized execution by whole-stage codegen.
+ */
+ private var columnarBatch: ColumnarBatch = _
--- End diff --
IIRC, @viirya also has a PR for vectorized orc reader. In that PR, we simply wrap the orc column batch to expose spark column batch interfaces, instead of writing orc column batch to spark column batch. I think that approach is more efficient.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76705 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76705/testReport)** for PR 17924 at commit [`85ef731`](https://github.com/apache/spark/commit/85ef73134b7b7450e0689e138339433a30b92dea).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17924
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
Hi, @rxin , @marmbrus , @gatorsmile , @sameeragarwal .
Could you give us your opinions on this approach in Spark SQL part, too?
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17924
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76695 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76695/testReport)** for PR 17924 at commit [`4607e0e`](https://github.com/apache/spark/commit/4607e0e0e55abda3e9af8bc7812d2d1216a66076).
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by AmplabJenkins <gi...@git.apache.org>.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
Hi, I didn't try that, but that's not a concept of Spark data source table. Please don't expect that. :)
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[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun closed the pull request at:
https://github.com/apache/spark/pull/17924
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76920 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76920/testReport)** for PR 17924 at commit [`85ef731`](https://github.com/apache/spark/commit/85ef73134b7b7450e0689e138339433a30b92dea).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #80604 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/80604/testReport)** for PR 17924 at commit [`85ef731`](https://github.com/apache/spark/commit/85ef73134b7b7450e0689e138339433a30b92dea).
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76693 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76693/testReport)** for PR 17924 at commit [`8bfd4bb`](https://github.com/apache/spark/commit/8bfd4bb4aec6dc3a5e7f41ec8d5c2a25f14ab87b).
* This patch **fails to generate documentation**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `class OrcColumnarBatchReader extends RecordReader[Void, ColumnarBatch] with Logging `
* `class OrcRecordIterator extends Iterator[InternalRow] with Logging `
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17924
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76920 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76920/testReport)** for PR 17924 at commit [`85ef731`](https://github.com/apache/spark/commit/85ef73134b7b7450e0689e138339433a30b92dea).
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
Retest this please.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by viirya <gi...@git.apache.org>.
Github user viirya commented on the issue:
https://github.com/apache/spark/pull/17924
From the current benchmark, seems the performance has not obvious improvement, compared with the vectorized Hive ORC reader #13775.
Maybe with more efficient batch approach as @cloud-fan suggested, it can perform better.
Besides performance, getting rid of Hive dependency on ORC datasource is a great advantage for this.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17924
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[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
Posted by viirya <gi...@git.apache.org>.
Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/17924#discussion_r115646941
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcColumnarBatchReader.scala ---
@@ -0,0 +1,407 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.orc
+
+import org.apache.hadoop.mapreduce.{InputSplit, RecordReader, TaskAttemptContext}
+import org.apache.hadoop.mapreduce.lib.input.FileSplit
+import org.apache.orc._
+import org.apache.orc.mapred.OrcInputFormat
+import org.apache.orc.storage.ql.exec.vector._
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.memory.MemoryMode
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
+import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, ColumnVectorUtils}
+import org.apache.spark.sql.types._
+
+
+/**
+ * To support vectorization in WholeStageCodeGen, this reader returns ColumnarBatch.
+ */
+private[orc] class OrcColumnarBatchReader extends RecordReader[Void, ColumnarBatch] with Logging {
+ import OrcColumnarBatchReader._
+
+ /**
+ * ORC File Reader.
+ */
+ private var reader: Reader = _
+
+ /**
+ * ORC Data Schema.
+ */
+ private var schema: TypeDescription = _
+
+ /**
+ * Vectorized Row Batch.
+ */
+ private var batch: VectorizedRowBatch = _
+
+ /**
+ * Record reader from row batch.
+ */
+ private var rows: org.apache.orc.RecordReader = _
+
+ /**
+ * Spark Schema.
+ */
+ private var sparkSchema: StructType = _
+
+ /**
+ * Required Schema.
+ */
+ private var requiredSchema: StructType = _
+
+ /**
+ * Partition Column.
+ */
+ private var partitionColumns: StructType = _
+
+ private var useIndex: Boolean = false
+
+ /**
+ * Full Schema: requiredSchema + partition schema.
+ */
+ private var fullSchema: StructType = _
+
+ /**
+ * ColumnarBatch for vectorized execution by whole-stage codegen.
+ */
+ private var columnarBatch: ColumnarBatch = _
--- End diff --
Btw, the PR is at #13775.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76695 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76695/testReport)** for PR 17924 at commit [`4607e0e`](https://github.com/apache/spark/commit/4607e0e0e55abda3e9af8bc7812d2d1216a66076).
* This patch **fails to generate documentation**.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by viirya <gi...@git.apache.org>.
Github user viirya commented on the issue:
https://github.com/apache/spark/pull/17924
@dongjoon-hyun It is good for me. We can reduce the size of this PR too and mitigate review job.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
Retest this please.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
Please refer the superset in #17980 .
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
Hi, All.
For further discussion and easy comparision, I made another PR (#17943) except `ColumnarBatch`.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
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https://github.com/apache/spark/pull/17924
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
Yep. Since this is an approach adding new dependency on Apache ORC, the non-vectorized PR also will need more supports(or approval) from the committers. I'll wait for more opinions at the current status for a while.
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[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/17924#discussion_r115646321
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcColumnarBatchReader.scala ---
@@ -0,0 +1,407 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.orc
+
+import org.apache.hadoop.mapreduce.{InputSplit, RecordReader, TaskAttemptContext}
+import org.apache.hadoop.mapreduce.lib.input.FileSplit
+import org.apache.orc._
+import org.apache.orc.mapred.OrcInputFormat
+import org.apache.orc.storage.ql.exec.vector._
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.memory.MemoryMode
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
+import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, ColumnVectorUtils}
+import org.apache.spark.sql.types._
+
+
+/**
+ * To support vectorization in WholeStageCodeGen, this reader returns ColumnarBatch.
+ */
+private[orc] class OrcColumnarBatchReader extends RecordReader[Void, ColumnarBatch] with Logging {
+ import OrcColumnarBatchReader._
+
+ /**
+ * ORC File Reader.
+ */
+ private var reader: Reader = _
+
+ /**
+ * ORC Data Schema.
+ */
+ private var schema: TypeDescription = _
+
+ /**
+ * Vectorized Row Batch.
+ */
+ private var batch: VectorizedRowBatch = _
+
+ /**
+ * Record reader from row batch.
+ */
+ private var rows: org.apache.orc.RecordReader = _
+
+ /**
+ * Spark Schema.
+ */
+ private var sparkSchema: StructType = _
+
+ /**
+ * Required Schema.
+ */
+ private var requiredSchema: StructType = _
+
+ /**
+ * Partition Column.
+ */
+ private var partitionColumns: StructType = _
+
+ private var useIndex: Boolean = false
+
+ /**
+ * Full Schema: requiredSchema + partition schema.
+ */
+ private var fullSchema: StructType = _
+
+ /**
+ * ColumnarBatch for vectorized execution by whole-stage codegen.
+ */
+ private var columnarBatch: ColumnarBatch = _
--- End diff --
Oh, thank you for the comment. It sounds efficient. I'll take a look.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
BTW, the latest version is maintained in #17980.
Recently, Spark Vector format is changed.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76699 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76699/testReport)** for PR 17924 at commit [`4607e0e`](https://github.com/apache/spark/commit/4607e0e0e55abda3e9af8bc7812d2d1216a66076).
* This patch **fails to generate documentation**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #76693 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/76693/testReport)** for PR 17924 at commit [`8bfd4bb`](https://github.com/apache/spark/commit/8bfd4bb4aec6dc3a5e7f41ec8d5c2a25f14ab87b).
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[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
Posted by viirya <gi...@git.apache.org>.
Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/17924#discussion_r115646778
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcColumnarBatchReader.scala ---
@@ -0,0 +1,407 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.orc
+
+import org.apache.hadoop.mapreduce.{InputSplit, RecordReader, TaskAttemptContext}
+import org.apache.hadoop.mapreduce.lib.input.FileSplit
+import org.apache.orc._
+import org.apache.orc.mapred.OrcInputFormat
+import org.apache.orc.storage.ql.exec.vector._
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.memory.MemoryMode
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
+import org.apache.spark.sql.execution.vectorized.{ColumnarBatch, ColumnVectorUtils}
+import org.apache.spark.sql.types._
+
+
+/**
+ * To support vectorization in WholeStageCodeGen, this reader returns ColumnarBatch.
+ */
+private[orc] class OrcColumnarBatchReader extends RecordReader[Void, ColumnarBatch] with Logging {
+ import OrcColumnarBatchReader._
+
+ /**
+ * ORC File Reader.
+ */
+ private var reader: Reader = _
+
+ /**
+ * ORC Data Schema.
+ */
+ private var schema: TypeDescription = _
+
+ /**
+ * Vectorized Row Batch.
+ */
+ private var batch: VectorizedRowBatch = _
+
+ /**
+ * Record reader from row batch.
+ */
+ private var rows: org.apache.orc.RecordReader = _
+
+ /**
+ * Spark Schema.
+ */
+ private var sparkSchema: StructType = _
+
+ /**
+ * Required Schema.
+ */
+ private var requiredSchema: StructType = _
+
+ /**
+ * Partition Column.
+ */
+ private var partitionColumns: StructType = _
+
+ private var useIndex: Boolean = false
+
+ /**
+ * Full Schema: requiredSchema + partition schema.
+ */
+ private var fullSchema: StructType = _
+
+ /**
+ * ColumnarBatch for vectorized execution by whole-stage codegen.
+ */
+ private var columnarBatch: ColumnarBatch = _
--- End diff --
More specially, we wrap Hive's `ColumnVector` to expose spark `ColumnVector` for constructing Spark `ColumnarBatch`. So we don't need to move data from one vector format to another vector format.
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17924
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
@cloud-fan and @viirya .
Shall we remove the vectorized part from this PR?
- The non-vectorized ORCFileFormat is mandatory and also the performance is better than the current one.
- After merging `sql/core` ORCFileFormat, many people (including @viirya and me) can work together in parallel.
How do you think about that?
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
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[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/17924#discussion_r115650375
--- Diff: sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala ---
@@ -0,0 +1,415 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.orc
+
+import java.io.File
+
+import scala.util.Try
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.io.IntWritable
+import org.apache.hadoop.mapreduce.{JobID, TaskAttemptID, TaskID, TaskType}
+import org.apache.hadoop.mapreduce.lib.input.FileSplit
+import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
+import org.apache.orc.OrcFile
+import org.apache.orc.mapred.OrcStruct
+import org.apache.orc.mapreduce.OrcInputFormat
+import org.apache.orc.storage.ql.exec.vector.{BytesColumnVector, LongColumnVector}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase
+import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.util.{Benchmark, Utils}
+
+
+/**
+ * Benchmark to measure orc read performance.
+ *
+ * This is in `sql/hive` module in order to compare `sql/core` and `sql/hive` ORC data sources.
+ * After removing `sql/hive` ORC data sources, we need to move this into `sql/core` module
+ * like the other ORC test suites.
+ */
+object OrcReadBenchmark {
+ val conf = new SparkConf()
+ conf.set("orc.compression", "snappy")
+
+ private val spark = SparkSession.builder()
+ .master("local[1]")
+ .appName("OrcReadBenchmark")
+ .config(conf)
+ .getOrCreate()
+
+ // Set default configs. Individual cases will change them if necessary.
+ spark.conf.set(SQLConf.ORC_VECTORIZED_READER_ENABLED.key, "true")
+ spark.conf.set(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key, "true")
+ spark.conf.set(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, "true")
+
+ def withTempPath(f: File => Unit): Unit = {
+ val path = Utils.createTempDir()
+ path.delete()
+ try f(path) finally Utils.deleteRecursively(path)
+ }
+
+ def withTempTable(tableNames: String*)(f: => Unit): Unit = {
+ try f finally tableNames.foreach(spark.catalog.dropTempView)
+ }
+
+ def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
+ val (keys, values) = pairs.unzip
+ val currentValues = keys.map(key => Try(spark.conf.get(key)).toOption)
+ (keys, values).zipped.foreach(spark.conf.set)
+ try f finally {
+ keys.zip(currentValues).foreach {
+ case (key, Some(value)) => spark.conf.set(key, value)
+ case (key, None) => spark.conf.unset(key)
+ }
+ }
+ }
+
+ private val SQL_ORC_FILE_FORMAT = "org.apache.spark.sql.execution.datasources.orc.OrcFileFormat"
+ private val HIVE_ORC_FILE_FORMAT = "org.apache.spark.sql.hive.orc.OrcFileFormat"
--- End diff --
We need to keep both versions before complete transition and for safety. Instead, we can make configurable which file format is used for `orc` data source string, e.g, `USING ORC`.
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[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
Posted by viirya <gi...@git.apache.org>.
Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/17924#discussion_r115649175
--- Diff: sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala ---
@@ -0,0 +1,415 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.orc
+
+import java.io.File
+
+import scala.util.Try
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.io.IntWritable
+import org.apache.hadoop.mapreduce.{JobID, TaskAttemptID, TaskID, TaskType}
+import org.apache.hadoop.mapreduce.lib.input.FileSplit
+import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
+import org.apache.orc.OrcFile
+import org.apache.orc.mapred.OrcStruct
+import org.apache.orc.mapreduce.OrcInputFormat
+import org.apache.orc.storage.ql.exec.vector.{BytesColumnVector, LongColumnVector}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase
+import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.util.{Benchmark, Utils}
+
+
+/**
+ * Benchmark to measure orc read performance.
+ *
+ * This is in `sql/hive` module in order to compare `sql/core` and `sql/hive` ORC data sources.
+ * After removing `sql/hive` ORC data sources, we need to move this into `sql/core` module
+ * like the other ORC test suites.
+ */
+object OrcReadBenchmark {
+ val conf = new SparkConf()
+ conf.set("orc.compression", "snappy")
+
+ private val spark = SparkSession.builder()
+ .master("local[1]")
+ .appName("OrcReadBenchmark")
+ .config(conf)
+ .getOrCreate()
+
+ // Set default configs. Individual cases will change them if necessary.
+ spark.conf.set(SQLConf.ORC_VECTORIZED_READER_ENABLED.key, "true")
+ spark.conf.set(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key, "true")
+ spark.conf.set(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, "true")
+
+ def withTempPath(f: File => Unit): Unit = {
+ val path = Utils.createTempDir()
+ path.delete()
+ try f(path) finally Utils.deleteRecursively(path)
+ }
+
+ def withTempTable(tableNames: String*)(f: => Unit): Unit = {
+ try f finally tableNames.foreach(spark.catalog.dropTempView)
+ }
+
+ def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
+ val (keys, values) = pairs.unzip
+ val currentValues = keys.map(key => Try(spark.conf.get(key)).toOption)
+ (keys, values).zipped.foreach(spark.conf.set)
+ try f finally {
+ keys.zip(currentValues).foreach {
+ case (key, Some(value)) => spark.conf.set(key, value)
+ case (key, None) => spark.conf.unset(key)
+ }
+ }
+ }
+
+ private val SQL_ORC_FILE_FORMAT = "org.apache.spark.sql.execution.datasources.orc.OrcFileFormat"
+ private val HIVE_ORC_FILE_FORMAT = "org.apache.spark.sql.hive.orc.OrcFileFormat"
--- End diff --
Will we keep current Hive ORC datasource even this is in Spark?
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17924
**[Test build #80604 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/80604/testReport)** for PR 17924 at commit [`85ef731`](https://github.com/apache/spark/commit/85ef73134b7b7450e0689e138339433a30b92dea).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request #17924: [SPARK-20682][SQL] Support a new faster ORC data ...
Posted by viirya <gi...@git.apache.org>.
Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/17924#discussion_r115650693
--- Diff: sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala ---
@@ -0,0 +1,415 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.orc
+
+import java.io.File
+
+import scala.util.Try
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.io.IntWritable
+import org.apache.hadoop.mapreduce.{JobID, TaskAttemptID, TaskID, TaskType}
+import org.apache.hadoop.mapreduce.lib.input.FileSplit
+import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
+import org.apache.orc.OrcFile
+import org.apache.orc.mapred.OrcStruct
+import org.apache.orc.mapreduce.OrcInputFormat
+import org.apache.orc.storage.ql.exec.vector.{BytesColumnVector, LongColumnVector}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase
+import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.util.{Benchmark, Utils}
+
+
+/**
+ * Benchmark to measure orc read performance.
+ *
+ * This is in `sql/hive` module in order to compare `sql/core` and `sql/hive` ORC data sources.
+ * After removing `sql/hive` ORC data sources, we need to move this into `sql/core` module
+ * like the other ORC test suites.
+ */
+object OrcReadBenchmark {
+ val conf = new SparkConf()
+ conf.set("orc.compression", "snappy")
+
+ private val spark = SparkSession.builder()
+ .master("local[1]")
+ .appName("OrcReadBenchmark")
+ .config(conf)
+ .getOrCreate()
+
+ // Set default configs. Individual cases will change them if necessary.
+ spark.conf.set(SQLConf.ORC_VECTORIZED_READER_ENABLED.key, "true")
+ spark.conf.set(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key, "true")
+ spark.conf.set(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, "true")
+
+ def withTempPath(f: File => Unit): Unit = {
+ val path = Utils.createTempDir()
+ path.delete()
+ try f(path) finally Utils.deleteRecursively(path)
+ }
+
+ def withTempTable(tableNames: String*)(f: => Unit): Unit = {
+ try f finally tableNames.foreach(spark.catalog.dropTempView)
+ }
+
+ def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
+ val (keys, values) = pairs.unzip
+ val currentValues = keys.map(key => Try(spark.conf.get(key)).toOption)
+ (keys, values).zipped.foreach(spark.conf.set)
+ try f finally {
+ keys.zip(currentValues).foreach {
+ case (key, Some(value)) => spark.conf.set(key, value)
+ case (key, None) => spark.conf.unset(key)
+ }
+ }
+ }
+
+ private val SQL_ORC_FILE_FORMAT = "org.apache.spark.sql.execution.datasources.orc.OrcFileFormat"
+ private val HIVE_ORC_FILE_FORMAT = "org.apache.spark.sql.hive.orc.OrcFileFormat"
--- End diff --
So to avoid datasource name conflict, we may change Hive ORC datasource's shortName to other than "orc".
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[GitHub] spark issue #17924: [SPARK-20682][SQL] Support a new faster ORC data source ...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on the issue:
https://github.com/apache/spark/pull/17924
Retest this please.
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