[SPARK-13537][SQL] Fix readBytes in VectorizedPlainValuesReader
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.
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@ -85,7 +85,7 @@ public class VectorizedPlainValuesReader extends ValuesReader implements Vectori
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for (int i = 0; i < total; i++) {
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// Bytes are stored as a 4-byte little endian int. Just read the first byte.
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// TODO: consider pushing this in ColumnVector by adding a readBytes with a stride.
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c.putByte(rowId + i, buffer[offset]);
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c.putByte(rowId + i, Platform.getByte(buffer, offset));
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offset += 4;
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}
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}
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@ -175,4 +175,37 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
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}
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}
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}
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test(s"SPARK-13537: Fix readBytes in VectorizedPlainValuesReader") {
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withTempPath { file =>
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val path = file.getCanonicalPath
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val schema = new StructType()
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.add("index", IntegerType, nullable = false)
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.add("col", ByteType, nullable = true)
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val data = Seq(Row(1, -33.toByte), Row(2, 0.toByte), Row(3, -55.toByte), Row(4, 56.toByte),
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Row(5, 127.toByte), Row(6, -44.toByte), Row(7, 23.toByte), Row(8, -95.toByte),
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Row(9, 127.toByte), Row(10, 13.toByte))
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val rdd = sqlContext.sparkContext.parallelize(data)
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val df = sqlContext.createDataFrame(rdd, schema).orderBy("index").coalesce(1)
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df.write
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.mode("overwrite")
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.format(dataSourceName)
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.option("dataSchema", df.schema.json)
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.save(path)
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val loadedDF = sqlContext
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.read
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.format(dataSourceName)
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.option("dataSchema", df.schema.json)
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.schema(df.schema)
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.load(path)
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.orderBy("index")
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checkAnswer(loadedDF, df)
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}
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}
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}
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