leesf commented on a change in pull request #3330: URL: https://github.com/apache/hudi/pull/3330#discussion_r675936657
########## File path: hudi-client/hudi-spark-client/src/main/scala/org/apache/spark/sql/Zoptimize.scala ########## @@ -0,0 +1,750 @@ +/* + * 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 + +import java.sql.Date +import java.util.concurrent.{Executors, ThreadPoolExecutor} + +import com.google.common.util.concurrent.ThreadFactoryBuilder +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileStatus, Path} +import org.apache.parquet.hadoop.ParquetFileReader + +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute +import org.apache.spark.sql.catalyst.expressions.{Alias, And, Ascending, Attribute, AttributeReference, BoundReference, EqualNullSafe, EqualTo, Expression, ExtractValue, GetStructField, GreaterThan, GreaterThanOrEqual, In, IsNotNull, IsNull, LessThan, LessThanOrEqual, Literal, Not, Or, SortOrder, StartsWith, UnsafeProjection} +import org.apache.spark.sql.catalyst.expressions.codegen.LazilyGeneratedOrdering +import org.apache.spark.sql.execution.datasources.PartitionedFile +import org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.hudi.ZOrderingUtil +import org.apache.spark.sql.hudi.execution._ +import org.apache.spark.sql.sources.Filter +import org.apache.spark.sql.types._ +import org.apache.spark.sql.vectorized.ColumnarBatch +import org.apache.spark.unsafe.types.UTF8String +import org.apache.spark.util.{MutablePair, SerializableConfiguration} + +import scala.collection.JavaConverters._ +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer +import scala.concurrent.duration._ +import scala.concurrent.{ExecutionContext, Future} + +object Zoptimize { + + case class FileStats(val minVal: String, val maxVal: String, val num_nulls: Int = 0) + case class ColumnFileStats(val fileName: String, val colName: String, val minVal: String, val maxVal: String, val num_nulls: Int = 0) + + def createZIndexedDataFrameByRange(df: DataFrame, zCols: String, fileNum: Int): DataFrame = { + createZIndexedDataFrameByRange(df, zCols.split(",").map(_.trim), fileNum) + } + + def createZIndexDataFrameBySample(df: DataFrame, zCols: String, fileNum: Int): DataFrame = { + createZIndexDataFrameBySample(df, zCols.split(",").map(_.trim), fileNum) + } + + /** + * create z-order DataFrame by sample + * first, sample origin data to get z-cols bounds, then create z-order DataFrame + * support all type data. + * this method need more resource and cost more time than createZIndexedDataFrameByMapValue + */ + def createZIndexDataFrameBySample(df: DataFrame, zCols: Seq[String], fileNum: Int): DataFrame = { + val spark = df.sparkSession + val columnsMap = df.schema.fields.map(item => (item.name, item)).toMap + val fieldNum = df.schema.fields.length + val checkCols = zCols.filter(col => columnsMap(col) != null) + + if (zCols.isEmpty || checkCols.isEmpty) { + df + } else { + val zFields = zCols.map { col => + val newCol = columnsMap(col) + if (newCol == null) { + (-1, null) + } else { + newCol.dataType match { + case LongType | DoubleType | FloatType | StringType | IntegerType | DateType | TimestampType | ShortType | ByteType => + (df.schema.fields.indexOf(newCol), newCol) + case d: DecimalType => + (df.schema.fields.indexOf(newCol), newCol) + case _ => + (-1, null) + } + } + }.filter(_._1 != -1) + // Complex type found, use createZIndexedDataFrameByRange + if (zFields.length != zCols.length) { + return createZIndexedDataFrameByRange(df, zCols, fieldNum) + } + + val rawRdd = df.rdd + val sampleRdd = rawRdd.map { row => + val values = zFields.map { case (index, field) => + field.dataType match { + case LongType => + if (row.isNullAt(index)) Long.MaxValue else row.getLong(index) + case DoubleType => + if (row.isNullAt(index)) Long.MaxValue else java.lang.Double.doubleToLongBits(row.getDouble(index)) + case IntegerType => + if (row.isNullAt(index)) Long.MaxValue else row.getInt(index).toLong + case FloatType => + if (row.isNullAt(index)) Long.MaxValue else java.lang.Double.doubleToLongBits(row.getFloat(index).toDouble) + case StringType => + if (row.isNullAt(index)) "" else row.getString(index) + case DateType => + if (row.isNullAt(index)) Long.MaxValue else row.getDate(index).getTime + case TimestampType => + if (row.isNullAt(index)) Long.MaxValue else row.getTimestamp(index).getTime + case ByteType => + if (row.isNullAt(index)) Long.MaxValue else row.getByte(index).toLong + case ShortType => + if (row.isNullAt(index)) Long.MaxValue else row.getShort(index).toLong + case d: DecimalType => + if (row.isNullAt(index)) Long.MaxValue else row.getDecimal(index).longValue() + case _ => + null + } + }.filter(v => v != null).toArray + (values, null) + } + val zOrderBounds = df.sparkSession.sessionState.conf.getConfString("spark.zorder.bounds.number", "200000").toInt Review comment: remove hardcode `20000`? -- This is an automated message from the Apache Git Service. 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