[HUDI-4584] Cleaning up Spark utilities

hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/keygen/BuiltinKeyGenerator.java

@@ -452,7 +452,7 @@ protected static class SparkRowConverter {
     private final SparkRowSerDe rowSerDe;
 
     SparkRowConverter(StructType schema) {
-      this.rowSerDe = HoodieSparkUtils.getDeserializer(schema);
+      this.rowSerDe = HoodieSparkUtils.getCatalystRowSerDe(schema);
       this.avroConverter = AvroConversionUtils.createConverterToAvro(schema, STRUCT_NAME, NAMESPACE);
     }
 

hudi-client/hudi-spark-client/src/main/java/org/apache/hudi/table/upgrade/SparkUpgradeDowngradeHelper.java

@@ -19,11 +19,11 @@
 
 package org.apache.hudi.table.upgrade;
 
-import org.apache.hudi.HoodieSparkUtils;
 import org.apache.hudi.common.engine.HoodieEngineContext;
 import org.apache.hudi.config.HoodieWriteConfig;
 import org.apache.hudi.table.HoodieSparkTable;
 import org.apache.hudi.table.HoodieTable;
+import org.apache.hudi.util.SparkKeyGenUtils;
 
 /**
  * Spark upgrade and downgrade helper.
@@ -47,6 +47,6 @@ public HoodieTable getTable(HoodieWriteConfig config, HoodieEngineContext contex
 
   @Override
   public String getPartitionColumns(HoodieWriteConfig config) {
-    return HoodieSparkUtils.getPartitionColumns(config.getProps());
+    return SparkKeyGenUtils.getPartitionColumns(config.getProps());
   }
 }

hudi-client/hudi-spark-client/src/main/scala/org/apache/hudi/AvroConversionUtils.scala

@@ -19,13 +19,12 @@
 package org.apache.hudi
 
 import org.apache.avro.Schema.Type
-import org.apache.avro.generic.{GenericRecord, GenericRecordBuilder, IndexedRecord}
+import org.apache.avro.generic.GenericRecord
 import org.apache.avro.{AvroRuntimeException, JsonProperties, Schema}
 import org.apache.hudi.HoodieSparkUtils.sparkAdapter
 import org.apache.hudi.avro.HoodieAvroUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.encoders.RowEncoder
 import org.apache.spark.sql.types.{ArrayType, DataType, MapType, StructType}
 import org.apache.spark.sql.{Dataset, Row, SparkSession}
 
@@ -90,8 +89,7 @@ object AvroConversionUtils {
   @Deprecated
   def createConverterToRow(sourceAvroSchema: Schema,
                            targetSqlType: StructType): GenericRecord => Row = {
-    val encoder = RowEncoder.apply(targetSqlType).resolveAndBind()
-    val serde = sparkAdapter.createSparkRowSerDe(encoder)
+    val serde = sparkAdapter.createSparkRowSerDe(targetSqlType)
     val converter = AvroConversionUtils.createAvroToInternalRowConverter(sourceAvroSchema, targetSqlType)
 
     avro => converter.apply(avro).map(serde.deserializeRow).get
@@ -104,8 +102,7 @@ object AvroConversionUtils {
   def createConverterToAvro(sourceSqlType: StructType,
                             structName: String,
                             recordNamespace: String): Row => GenericRecord = {
-    val encoder = RowEncoder.apply(sourceSqlType).resolveAndBind()
-    val serde = sparkAdapter.createSparkRowSerDe(encoder)
+    val serde = sparkAdapter.createSparkRowSerDe(sourceSqlType)
     val avroSchema = AvroConversionUtils.convertStructTypeToAvroSchema(sourceSqlType, structName, recordNamespace)
     val (nullable, _) = resolveAvroTypeNullability(avroSchema)
 

hudi-client/hudi-spark-client/src/main/scala/org/apache/hudi/HoodieSparkUtils.scala

@@ -20,27 +20,13 @@ package org.apache.hudi
 
 import org.apache.avro.Schema
 import org.apache.avro.generic.GenericRecord
-import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hudi.avro.HoodieAvroUtils
 import org.apache.hudi.client.utils.SparkRowSerDe
-import org.apache.hudi.common.config.TypedProperties
 import org.apache.hudi.common.model.HoodieRecord
-import org.apache.hudi.common.table.HoodieTableMetaClient
-import org.apache.hudi.internal.schema.InternalSchema
-import org.apache.hudi.internal.schema.convert.AvroInternalSchemaConverter
-import org.apache.hudi.internal.schema.utils.InternalSchemaUtils
-import org.apache.hudi.keygen.constant.KeyGeneratorOptions
-import org.apache.hudi.keygen.factory.HoodieSparkKeyGeneratorFactory
-import org.apache.hudi.keygen.{BaseKeyGenerator, CustomAvroKeyGenerator, CustomKeyGenerator, KeyGenerator}
 import org.apache.spark.SPARK_VERSION
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
-import org.apache.spark.sql.catalyst.encoders.RowEncoder
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression, Literal}
-import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
-import java.util.Properties
-
-import org.apache.hudi.avro.HoodieAvroUtils
 
 import scala.collection.JavaConverters._
 
@@ -72,63 +58,6 @@ object HoodieSparkUtils extends SparkAdapterSupport with SparkVersionsSupport {
     }))
   }
 
-  /**
-   * This method copied from [[org.apache.spark.deploy.SparkHadoopUtil]].
-   * [[org.apache.spark.deploy.SparkHadoopUtil]] becomes private since Spark 3.0.0 and hence we had to copy it locally.
-   */
-  def isGlobPath(pattern: Path): Boolean = {
-    pattern.toString.exists("{}[]*?\\".toSet.contains)
-  }
-
-  /**
-   * This method is inspired from [[org.apache.spark.deploy.SparkHadoopUtil]] with some modifications like
-   * skipping meta paths.
-   */
-  def globPath(fs: FileSystem, pattern: Path): Seq[Path] = {
-    // find base path to assist in skipping meta paths
-    var basePath = pattern.getParent
-    while (basePath.getName.equals("*")) {
-      basePath = basePath.getParent
-    }
-
-    Option(fs.globStatus(pattern)).map { statuses => {
-      val nonMetaStatuses = statuses.filterNot(entry => {
-        // skip all entries in meta path
-        var leafPath = entry.getPath
-        // walk through every parent until we reach base path. if .hoodie is found anywhere, path needs to be skipped
-        while (!leafPath.equals(basePath) && !leafPath.getName.equals(HoodieTableMetaClient.METAFOLDER_NAME)) {
-            leafPath = leafPath.getParent
-        }
-        leafPath.getName.equals(HoodieTableMetaClient.METAFOLDER_NAME)
-      })
-      nonMetaStatuses.map(_.getPath.makeQualified(fs.getUri, fs.getWorkingDirectory)).toSeq
-    }
-    }.getOrElse(Seq.empty[Path])
-  }
-
-  /**
-   * This method copied from [[org.apache.spark.deploy.SparkHadoopUtil]].
-   * [[org.apache.spark.deploy.SparkHadoopUtil]] becomes private since Spark 3.0.0 and hence we had to copy it locally.
-   */
-  def globPathIfNecessary(fs: FileSystem, pattern: Path): Seq[Path] = {
-    if (isGlobPath(pattern)) globPath(fs, pattern) else Seq(pattern)
-  }
-
-  /**
-   * Checks to see whether input path contains a glob pattern and if yes, maps it to a list of absolute paths
-   * which match the glob pattern. Otherwise, returns original path
-   *
-   * @param paths List of absolute or globbed paths
-   * @param fs    File system
-   * @return list of absolute file paths
-   */
-  def checkAndGlobPathIfNecessary(paths: Seq[String], fs: FileSystem): Seq[Path] = {
-    paths.flatMap(path => {
-      val qualified = new Path(path).makeQualified(fs.getUri, fs.getWorkingDirectory)
-      globPathIfNecessary(fs, qualified)
-    })
-  }
-
   /**
    * @deprecated please use other overload [[createRdd]]
    */
@@ -182,142 +111,7 @@ object HoodieSparkUtils extends SparkAdapterSupport with SparkVersionsSupport {
     }
   }
 
-  def getDeserializer(structType: StructType) : SparkRowSerDe = {
-    val encoder = RowEncoder.apply(structType).resolveAndBind()
-    sparkAdapter.createSparkRowSerDe(encoder)
-  }
-
-  /**
-   * Convert Filters to Catalyst Expressions and joined by And. If convert success return an
-   * Non-Empty Option[Expression],or else return None.
-   */
-  def convertToCatalystExpressions(filters: Seq[Filter],
-                                   tableSchema: StructType): Seq[Option[Expression]] = {
-    filters.map(convertToCatalystExpression(_, tableSchema))
-  }
-
-
-  /**
-   * Convert Filters to Catalyst Expressions and joined by And. If convert success return an
-   * Non-Empty Option[Expression],or else return None.
-   */
-  def convertToCatalystExpression(filters: Array[Filter],
-                                  tableSchema: StructType): Option[Expression] = {
-    val expressions = convertToCatalystExpressions(filters, tableSchema)
-    if (expressions.forall(p => p.isDefined)) {
-      if (expressions.isEmpty) {
-        None
-      } else if (expressions.length == 1) {
-        expressions.head
-      } else {
-        Some(expressions.map(_.get).reduce(org.apache.spark.sql.catalyst.expressions.And))
-      }
-    } else {
-      None
-    }
-  }
-
-  /**
-   * Convert Filter to Catalyst Expression. If convert success return an Non-Empty
-   * Option[Expression],or else return None.
-   */
-  def convertToCatalystExpression(filter: Filter, tableSchema: StructType): Option[Expression] = {
-    Option(
-      filter match {
-        case EqualTo(attribute, value) =>
-          org.apache.spark.sql.catalyst.expressions.EqualTo(toAttribute(attribute, tableSchema), Literal.create(value))
-        case EqualNullSafe(attribute, value) =>
-          org.apache.spark.sql.catalyst.expressions.EqualNullSafe(toAttribute(attribute, tableSchema), Literal.create(value))
-        case GreaterThan(attribute, value) =>
-          org.apache.spark.sql.catalyst.expressions.GreaterThan(toAttribute(attribute, tableSchema), Literal.create(value))
-        case GreaterThanOrEqual(attribute, value) =>
-          org.apache.spark.sql.catalyst.expressions.GreaterThanOrEqual(toAttribute(attribute, tableSchema), Literal.create(value))
-        case LessThan(attribute, value) =>
-          org.apache.spark.sql.catalyst.expressions.LessThan(toAttribute(attribute, tableSchema), Literal.create(value))
-        case LessThanOrEqual(attribute, value) =>
-          org.apache.spark.sql.catalyst.expressions.LessThanOrEqual(toAttribute(attribute, tableSchema), Literal.create(value))
-        case In(attribute, values) =>
-          val attrExp = toAttribute(attribute, tableSchema)
-          val valuesExp = values.map(v => Literal.create(v))
-          org.apache.spark.sql.catalyst.expressions.In(attrExp, valuesExp)
-        case IsNull(attribute) =>
-          org.apache.spark.sql.catalyst.expressions.IsNull(toAttribute(attribute, tableSchema))
-        case IsNotNull(attribute) =>
-          org.apache.spark.sql.catalyst.expressions.IsNotNull(toAttribute(attribute, tableSchema))
-        case And(left, right) =>
-          val leftExp = convertToCatalystExpression(left, tableSchema)
-          val rightExp = convertToCatalystExpression(right, tableSchema)
-          if (leftExp.isEmpty || rightExp.isEmpty) {
-            null
-          } else {
-            org.apache.spark.sql.catalyst.expressions.And(leftExp.get, rightExp.get)
-          }
-        case Or(left, right) =>
-          val leftExp = convertToCatalystExpression(left, tableSchema)
-          val rightExp = convertToCatalystExpression(right, tableSchema)
-          if (leftExp.isEmpty || rightExp.isEmpty) {
-            null
-          } else {
-            org.apache.spark.sql.catalyst.expressions.Or(leftExp.get, rightExp.get)
-          }
-        case Not(child) =>
-          val childExp = convertToCatalystExpression(child, tableSchema)
-          if (childExp.isEmpty) {
-            null
-          } else {
-            org.apache.spark.sql.catalyst.expressions.Not(childExp.get)
-          }
-        case StringStartsWith(attribute, value) =>
-          val leftExp = toAttribute(attribute, tableSchema)
-          val rightExp = Literal.create(s"$value%")
-          sparkAdapter.getCatalystPlanUtils.createLike(leftExp, rightExp)
-        case StringEndsWith(attribute, value) =>
-          val leftExp = toAttribute(attribute, tableSchema)
-          val rightExp = Literal.create(s"%$value")
-          sparkAdapter.getCatalystPlanUtils.createLike(leftExp, rightExp)
-        case StringContains(attribute, value) =>
-          val leftExp = toAttribute(attribute, tableSchema)
-          val rightExp = Literal.create(s"%$value%")
-          sparkAdapter.getCatalystPlanUtils.createLike(leftExp, rightExp)
-        case _ => null
-      }
-    )
-  }
-
-  /**
-   * @param properties config properties
-   * @return partition columns
-   */
-  def getPartitionColumns(properties: Properties): String = {
-    val props = new TypedProperties(properties)
-    val keyGenerator = HoodieSparkKeyGeneratorFactory.createKeyGenerator(props)
-    getPartitionColumns(keyGenerator, props)
-  }
-
-  /**
-   * @param keyGen key generator
-   * @return partition columns
-   */
-  def getPartitionColumns(keyGen: KeyGenerator, typedProperties: TypedProperties): String = {
-    keyGen match {
-      // For CustomKeyGenerator and CustomAvroKeyGenerator, the partition path filed format
-      // is: "field_name: field_type", we extract the field_name from the partition path field.
-      case c: BaseKeyGenerator
-        if c.isInstanceOf[CustomKeyGenerator] || c.isInstanceOf[CustomAvroKeyGenerator] =>
-        c.getPartitionPathFields.asScala.map(pathField =>
-          pathField.split(CustomAvroKeyGenerator.SPLIT_REGEX)
-            .headOption.getOrElse(s"Illegal partition path field format: '$pathField' for ${c.getClass.getSimpleName}"))
-          .mkString(",")
-
-      case b: BaseKeyGenerator => b.getPartitionPathFields.asScala.mkString(",")
-      case _ => typedProperties.getString(KeyGeneratorOptions.PARTITIONPATH_FIELD_NAME.key())
-    }
-  }
-
-  private def toAttribute(columnName: String, tableSchema: StructType): AttributeReference = {
-    val field = tableSchema.find(p => p.name == columnName)
-    assert(field.isDefined, s"Cannot find column: $columnName, Table Columns are: " +
-      s"${tableSchema.fieldNames.mkString(",")}")
-    AttributeReference(columnName, field.get.dataType, field.get.nullable)()
+  def getCatalystRowSerDe(structType: StructType) : SparkRowSerDe = {
+    sparkAdapter.createSparkRowSerDe(structType)
   }
 }

hudi-client/hudi-spark-client/src/main/scala/org/apache/hudi/util/PathUtils.scala

@@ -0,0 +1,84 @@
+/*
+ * 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.hudi.util
+
+import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hudi.common.table.HoodieTableMetaClient
+
+/**
+ * TODO convert to Java, move to hudi-common
+ */
+object PathUtils {
+
+  /**
+   * This method copied from [[org.apache.spark.deploy.SparkHadoopUtil]].
+   * [[org.apache.spark.deploy.SparkHadoopUtil]] becomes private since Spark 3.0.0 and hence we had to copy it locally.
+   */
+  def isGlobPath(pattern: Path): Boolean = {
+    pattern.toString.exists("{}[]*?\\".toSet.contains)
+  }
+
+  /**
+   * This method is inspired from [[org.apache.spark.deploy.SparkHadoopUtil]] with some modifications like
+   * skipping meta paths.
+   */
+  def globPath(fs: FileSystem, pattern: Path): Seq[Path] = {
+    // find base path to assist in skipping meta paths
+    var basePath = pattern.getParent
+    while (basePath.getName.equals("*")) {
+      basePath = basePath.getParent
+    }
+
+    Option(fs.globStatus(pattern)).map { statuses => {
+      val nonMetaStatuses = statuses.filterNot(entry => {
+        // skip all entries in meta path
+        var leafPath = entry.getPath
+        // walk through every parent until we reach base path. if .hoodie is found anywhere, path needs to be skipped
+        while (!leafPath.equals(basePath) && !leafPath.getName.equals(HoodieTableMetaClient.METAFOLDER_NAME)) {
+          leafPath = leafPath.getParent
+        }
+        leafPath.getName.equals(HoodieTableMetaClient.METAFOLDER_NAME)
+      })
+      nonMetaStatuses.map(_.getPath.makeQualified(fs.getUri, fs.getWorkingDirectory)).toSeq
+    }
+    }.getOrElse(Seq.empty[Path])
+  }
+
+  /**
+   * This method copied from [[org.apache.spark.deploy.SparkHadoopUtil]].
+   * [[org.apache.spark.deploy.SparkHadoopUtil]] becomes private since Spark 3.0.0 and hence we had to copy it locally.
+   */
+  def globPathIfNecessary(fs: FileSystem, pattern: Path): Seq[Path] = {
+    if (isGlobPath(pattern)) globPath(fs, pattern) else Seq(pattern)
+  }
+
+  /**
+   * Checks to see whether input path contains a glob pattern and if yes, maps it to a list of absolute paths
+   * which match the glob pattern. Otherwise, returns original path
+   *
+   * @param paths List of absolute or globbed paths
+   * @param fs    File system
+   * @return list of absolute file paths
+   */
+  def checkAndGlobPathIfNecessary(paths: Seq[String], fs: FileSystem): Seq[Path] = {
+    paths.flatMap(path => {
+      val qualified = new Path(path).makeQualified(fs.getUri, fs.getWorkingDirectory)
+      globPathIfNecessary(fs, qualified)
+    })
+  }
+}