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[HUDI-3206] Unify Hive's MOR implementations to avoid duplication (#4559)

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Unify Hive's MOR implementations to avoid duplication to avoid duplication across implementations for different file-formats (Parquet, HFile, etc)

- Extracted HoodieRealtimeFileInputFormatBase (extending COW HoodieFileInputFormatBase base)
- Rebased Parquet, HFile implementations onto HoodieRealtimeFileInputFormatBase
- Tidying up
This commit is contained in:
Alexey Kudinkin
2022-02-07 11:06:28 -08:00
committed by GitHub
parent 773b317983
commit 3f263b82ce
10 changed files with 460 additions and 415 deletions
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16
hudi-spark-datasource/hudi-spark/src/test/resources/index/zorder/z-index-table-merged.json
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@@ -1,8 +1,8 @@
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8
hudi-spark-datasource/hudi-spark/src/test/resources/index/zorder/z-index-table.json
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@@ -1,4 +1,4 @@
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21
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestColumnStatsIndex.scala
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@@ -20,23 +20,20 @@ package org.apache.hudi.functional
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileSystem, LocatedFileStatus, Path}
import org.apache.hudi.common.fs.FSUtils
import org.apache.hudi.common.model.HoodieColumnRangeMetadata
import org.apache.hudi.common.util.ParquetUtils
import org.apache.hudi.index.columnstats.ColumnStatsIndexHelper
import org.apache.hudi.testutils.HoodieClientTestBase
import org.apache.spark.sql._
import org.apache.spark.sql.expressions.UserDefinedFunction
import org.apache.spark.sql.functions.typedLit
import org.apache.spark.sql.types._
import org.apache.spark.sql.{Column, DataFrame, Dataset, Row, RowFactory, SaveMode, SparkSession, functions}
import org.junit.jupiter.api.Assertions.{assertEquals, assertNotNull, assertTrue}
import org.junit.jupiter.api.{AfterEach, BeforeEach, Disabled, Test}
import org.junit.jupiter.api.{AfterEach, BeforeEach, Test}
import java.math.BigInteger
import java.nio.charset.StandardCharsets
import java.sql.{Date, Timestamp}
import scala.collection.JavaConverters._
import scala.util.{Random, Success}
import scala.util.Random
class TestColumnStatsIndex extends HoodieClientTestBase {
var spark: SparkSession = _
@@ -354,11 +351,10 @@ class TestColumnStatsIndex extends HoodieClientTestBase {
.distinct()
.collect()
.map(_.getString(0))
.sorted
val uuidToIdx: UserDefinedFunction = functions.udf((fileName: String) => {
val (uuid, idx) = uuids.zipWithIndex.find { case (uuid, _) => fileName.contains(uuid) }.get
fileName.replace(uuid, idx.toString)
val uuid = uuids.find(uuid => fileName.contains(uuid)).get
fileName.replace(uuid, "xxx")
})
ds.withColumn("file", uuidToIdx(ds("file")))
@@ -409,12 +405,11 @@ class TestColumnStatsIndex extends HoodieClientTestBase {
.mkString("\n")
private def sort(df: DataFrame): DataFrame = {
// Since upon parsing JSON, Spark re-order columns in lexicographical order
// of their names, we have to shuffle new Z-index table columns order to match
// Rows are sorted by filename as well to avoid
val sortedCols = df.columns.sorted
// Sort dataset by the first 2 columns (to minimize non-determinism in case multiple files have the same
// value of the first column)
df.select(sortedCols.head, sortedCols.tail: _*)
.sort("file")
.sort("c1_maxValue", "c1_minValue")
}
}