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/*
* 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.text.SimpleDateFormat
import java.util.{Date, Locale, Properties, UUID}
import scala.collection.JavaConverters._
import org.apache.spark.annotation.InterfaceStability
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.analysis.{EliminateSubqueryAliases, UnresolvedRelation}
import org.apache.spark.sql.catalyst.catalog._
import org.apache.spark.sql.catalyst.plans.logical.{AnalysisBarrier, InsertIntoTable, LogicalPlan}
import org.apache.spark.sql.execution.SQLExecution
import org.apache.spark.sql.execution.command.DDLUtils
import org.apache.spark.sql.execution.datasources.{CreateTable, DataSource, LogicalRelation}
import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Utils
import org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2
import org.apache.spark.sql.sources.BaseRelation
import org.apache.spark.sql.sources.v2._
import org.apache.spark.sql.types.StructType
/**
* Interface used to write a [[Dataset]] to external storage systems (e.g. file systems,
* key-value stores, etc). Use `Dataset.write` to access this.
*
* @since 1.4.0
*/
@InterfaceStability.Stable
final class DataFrameWriter[T] private[sql](ds: Dataset[T]) {
private val df = ds.toDF()
/**
* Specifies the behavior when data or table already exists. Options include:
* - `SaveMode.Overwrite`: overwrite the existing data.
* - `SaveMode.Append`: append the data.
* - `SaveMode.Ignore`: ignore the operation (i.e. no-op).
* - `SaveMode.ErrorIfExists`: default option, throw an exception at runtime.
*
* @since 1.4.0
*/
def mode(saveMode: SaveMode): DataFrameWriter[T] = {
this.mode = saveMode
this
}
/**
* Specifies the behavior when data or table already exists. Options include:
* - `overwrite`: overwrite the existing data.
* - `append`: append the data.
* - `ignore`: ignore the operation (i.e. no-op).
* - `error` or `errorifexists`: default option, throw an exception at runtime.
*
* @since 1.4.0
*/
def mode(saveMode: String): DataFrameWriter[T] = {
this.mode = saveMode.toLowerCase(Locale.ROOT) match {
case "overwrite" => SaveMode.Overwrite
case "append" => SaveMode.Append
case "ignore" => SaveMode.Ignore
case "error" | "errorifexists" | "default" => SaveMode.ErrorIfExists
case _ => throw new IllegalArgumentException(s"Unknown save mode: $saveMode. " +
"Accepted save modes are 'overwrite', 'append', 'ignore', 'error', 'errorifexists'.")
}
this
}
/**
* Specifies the underlying output data source. Built-in options include "parquet", "json", etc.
*
* @since 1.4.0
*/
def format(source: String): DataFrameWriter[T] = {
this.source = source
this
}
/**
* Adds an output option for the underlying data source.
*
* You can set the following option(s):
*
*
`timeZone` (default session local timezone): sets the string that indicates a timezone
* to be used to format timestamps in the JSON/CSV datasources or partition values.
*
*
* @since 1.4.0
*/
def option(key: String, value: String): DataFrameWriter[T] = {
this.extraOptions += (key -> value)
this
}
/**
* Adds an output option for the underlying data source.
*
* @since 2.0.0
*/
def option(key: String, value: Boolean): DataFrameWriter[T] = option(key, value.toString)
/**
* Adds an output option for the underlying data source.
*
* @since 2.0.0
*/
def option(key: String, value: Long): DataFrameWriter[T] = option(key, value.toString)
/**
* Adds an output option for the underlying data source.
*
* @since 2.0.0
*/
def option(key: String, value: Double): DataFrameWriter[T] = option(key, value.toString)
/**
* (Scala-specific) Adds output options for the underlying data source.
*
* You can set the following option(s):
*
*
`timeZone` (default session local timezone): sets the string that indicates a timezone
* to be used to format timestamps in the JSON/CSV datasources or partition values.
*
*
* @since 1.4.0
*/
def options(options: scala.collection.Map[String, String]): DataFrameWriter[T] = {
this.extraOptions ++= options
this
}
/**
* Adds output options for the underlying data source.
*
* You can set the following option(s):
*
*
`timeZone` (default session local timezone): sets the string that indicates a timezone
* to be used to format timestamps in the JSON/CSV datasources or partition values.
*
*
* @since 1.4.0
*/
def options(options: java.util.Map[String, String]): DataFrameWriter[T] = {
this.options(options.asScala)
this
}
/**
* Partitions the output by the given columns on the file system. If specified, the output is
* laid out on the file system similar to Hive's partitioning scheme. As an example, when we
* partition a dataset by year and then month, the directory layout would look like:
*
* - year=2016/month=01/
* - year=2016/month=02/
*
* Partitioning is one of the most widely used techniques to optimize physical data layout.
* It provides a coarse-grained index for skipping unnecessary data reads when queries have
* predicates on the partitioned columns. In order for partitioning to work well, the number
* of distinct values in each column should typically be less than tens of thousands.
*
* This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark
* 2.1.0.
*
* @since 1.4.0
*/
@scala.annotation.varargs
def partitionBy(colNames: String*): DataFrameWriter[T] = {
this.partitioningColumns = Option(colNames)
this
}
/**
* Buckets the output by the given columns. If specified, the output is laid out on the file
* system similar to Hive's bucketing scheme.
*
* This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark
* 2.1.0.
*
* @since 2.0
*/
@scala.annotation.varargs
def bucketBy(numBuckets: Int, colName: String, colNames: String*): DataFrameWriter[T] = {
this.numBuckets = Option(numBuckets)
this.bucketColumnNames = Option(colName +: colNames)
this
}
/**
* Sorts the output in each bucket by the given columns.
*
* This is applicable for all file-based data sources (e.g. Parquet, JSON) starting with Spark
* 2.1.0.
*
* @since 2.0
*/
@scala.annotation.varargs
def sortBy(colName: String, colNames: String*): DataFrameWriter[T] = {
this.sortColumnNames = Option(colName +: colNames)
this
}
/**
* Saves the content of the `DataFrame` at the specified path.
*
* @since 1.4.0
*/
def save(path: String): Unit = {
this.extraOptions += ("path" -> path)
save()
}
/**
* Saves the content of the `DataFrame` as the specified table.
*
* @since 1.4.0
*/
def save(): Unit = {
if (source.toLowerCase(Locale.ROOT) == DDLUtils.HIVE_PROVIDER) {
throw new AnalysisException("Hive data source can only be used with tables, you can not " +
"write files of Hive data source directly.")
}
assertNotBucketed("save")
val cls = DataSource.lookupDataSource(source, df.sparkSession.sessionState.conf)
if (classOf[DataSourceV2].isAssignableFrom(cls)) {
val ds = cls.newInstance()
ds match {
case ws: WriteSupport =>
val options = new DataSourceOptions((extraOptions ++
DataSourceV2Utils.extractSessionConfigs(
ds = ds.asInstanceOf[DataSourceV2],
conf = df.sparkSession.sessionState.conf)).asJava)
// Using a timestamp and a random UUID to distinguish different writing jobs. This is good
// enough as there won't be tons of writing jobs created at the same second.
val jobId = new SimpleDateFormat("yyyyMMddHHmmss", Locale.US)
.format(new Date()) + "-" + UUID.randomUUID()
val writer = ws.createWriter(jobId, df.logicalPlan.schema, mode, options)
if (writer.isPresent) {
runCommand(df.sparkSession, "save") {
WriteToDataSourceV2(writer.get(), df.planWithBarrier)
}
}
// Streaming also uses the data source V2 API. So it may be that the data source implements
// v2, but has no v2 implementation for batch writes. In that case, we fall back to saving
// as though it's a V1 source.
case _ => saveToV1Source()
}
} else {
saveToV1Source()
}
}
private def saveToV1Source(): Unit = {
// Code path for data source v1.
runCommand(df.sparkSession, "save") {
DataSource(
sparkSession = df.sparkSession,
className = source,
partitionColumns = partitioningColumns.getOrElse(Nil),
options = extraOptions.toMap).planForWriting(mode, df.planWithBarrier)
}
}
/**
* Inserts the content of the `DataFrame` to the specified table. It requires that
* the schema of the `DataFrame` is the same as the schema of the table.
*
* @note Unlike `saveAsTable`, `insertInto` ignores the column names and just uses position-based
* resolution. For example:
*
* {{{
* scala> Seq((1, 2)).toDF("i", "j").write.mode("overwrite").saveAsTable("t1")
* scala> Seq((3, 4)).toDF("j", "i").write.insertInto("t1")
* scala> Seq((5, 6)).toDF("a", "b").write.insertInto("t1")
* scala> sql("select * from t1").show
* +---+---+
* | i| j|
* +---+---+
* | 5| 6|
* | 3| 4|
* | 1| 2|
* +---+---+
* }}}
*
* Because it inserts data to an existing table, format or options will be ignored.
*
* @since 1.4.0
*/
def insertInto(tableName: String): Unit = {
insertInto(df.sparkSession.sessionState.sqlParser.parseTableIdentifier(tableName))
}
private def insertInto(tableIdent: TableIdentifier): Unit = {
assertNotBucketed("insertInto")
if (partitioningColumns.isDefined) {
throw new AnalysisException(
"insertInto() can't be used together with partitionBy(). " +
"Partition columns have already been defined for the table. " +
"It is not necessary to use partitionBy()."
)
}
runCommand(df.sparkSession, "insertInto") {
InsertIntoTable(
table = UnresolvedRelation(tableIdent),
partition = Map.empty[String, Option[String]],
query = df.planWithBarrier,
overwrite = mode == SaveMode.Overwrite,
ifPartitionNotExists = false)
}
}
private def getBucketSpec: Option[BucketSpec] = {
if (sortColumnNames.isDefined) {
require(numBuckets.isDefined, "sortBy must be used together with bucketBy")
}
numBuckets.map { n =>
BucketSpec(n, bucketColumnNames.get, sortColumnNames.getOrElse(Nil))
}
}
private def assertNotBucketed(operation: String): Unit = {
if (numBuckets.isDefined || sortColumnNames.isDefined) {
throw new AnalysisException(s"'$operation' does not support bucketing right now")
}
}
private def assertNotPartitioned(operation: String): Unit = {
if (partitioningColumns.isDefined) {
throw new AnalysisException( s"'$operation' does not support partitioning")
}
}
/**
* Saves the content of the `DataFrame` as the specified table.
*
* In the case the table already exists, behavior of this function depends on the
* save mode, specified by the `mode` function (default to throwing an exception).
* When `mode` is `Overwrite`, the schema of the `DataFrame` does not need to be
* the same as that of the existing table.
*
* When `mode` is `Append`, if there is an existing table, we will use the format and options of
* the existing table. The column order in the schema of the `DataFrame` doesn't need to be same
* as that of the existing table. Unlike `insertInto`, `saveAsTable` will use the column names to
* find the correct column positions. For example:
*
* {{{
* scala> Seq((1, 2)).toDF("i", "j").write.mode("overwrite").saveAsTable("t1")
* scala> Seq((3, 4)).toDF("j", "i").write.mode("append").saveAsTable("t1")
* scala> sql("select * from t1").show
* +---+---+
* | i| j|
* +---+---+
* | 1| 2|
* | 4| 3|
* +---+---+
* }}}
*
* In this method, save mode is used to determine the behavior if the data source table exists in
* Spark catalog. We will always overwrite the underlying data of data source (e.g. a table in
* JDBC data source) if the table doesn't exist in Spark catalog, and will always append to the
* underlying data of data source if the table already exists.
*
* When the DataFrame is created from a non-partitioned `HadoopFsRelation` with a single input
* path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
* and Parquet), the table is persisted in a Hive compatible format, which means other systems
* like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
* specific format.
*
* @since 1.4.0
*/
def saveAsTable(tableName: String): Unit = {
saveAsTable(df.sparkSession.sessionState.sqlParser.parseTableIdentifier(tableName))
}
private def saveAsTable(tableIdent: TableIdentifier): Unit = {
val catalog = df.sparkSession.sessionState.catalog
val tableExists = catalog.tableExists(tableIdent)
val db = tableIdent.database.getOrElse(catalog.getCurrentDatabase)
val tableIdentWithDB = tableIdent.copy(database = Some(db))
val tableName = tableIdentWithDB.unquotedString
(tableExists, mode) match {
case (true, SaveMode.Ignore) =>
// Do nothing
case (true, SaveMode.ErrorIfExists) =>
throw new AnalysisException(s"Table $tableIdent already exists.")
case (true, SaveMode.Overwrite) =>
// Get all input data source or hive relations of the query.
val srcRelations = df.logicalPlan.collect {
case LogicalRelation(src: BaseRelation, _, _, _) => src
case relation: HiveTableRelation => relation.tableMeta.identifier
}
val tableRelation = df.sparkSession.table(tableIdentWithDB).queryExecution.analyzed
EliminateSubqueryAliases(tableRelation) match {
// check if the table is a data source table (the relation is a BaseRelation).
case LogicalRelation(dest: BaseRelation, _, _, _) if srcRelations.contains(dest) =>
throw new AnalysisException(
s"Cannot overwrite table $tableName that is also being read from")
// check hive table relation when overwrite mode
case relation: HiveTableRelation
if srcRelations.contains(relation.tableMeta.identifier) =>
throw new AnalysisException(
s"Cannot overwrite table $tableName that is also being read from")
case _ => // OK
}
// Drop the existing table
catalog.dropTable(tableIdentWithDB, ignoreIfNotExists = true, purge = false)
createTable(tableIdentWithDB)
// Refresh the cache of the table in the catalog.
catalog.refreshTable(tableIdentWithDB)
case _ => createTable(tableIdent)
}
}
private def createTable(tableIdent: TableIdentifier): Unit = {
val storage = DataSource.buildStorageFormatFromOptions(extraOptions.toMap)
val tableType = if (storage.locationUri.isDefined) {
CatalogTableType.EXTERNAL
} else {
CatalogTableType.MANAGED
}
val tableDesc = CatalogTable(
identifier = tableIdent,
tableType = tableType,
storage = storage,
schema = new StructType,
provider = Some(source),
partitionColumnNames = partitioningColumns.getOrElse(Nil),
bucketSpec = getBucketSpec)
runCommand(df.sparkSession, "saveAsTable") {
CreateTable(tableDesc, mode, Some(df.planWithBarrier))
}
}
/**
* Saves the content of the `DataFrame` to an external database table via JDBC. In the case the
* table already exists in the external database, behavior of this function depends on the
* save mode, specified by the `mode` function (default to throwing an exception).
*
* Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash
* your external database systems.
*
* You can set the following JDBC-specific option(s) for storing JDBC:
*
*
`truncate` (default `false`): use `TRUNCATE TABLE` instead of `DROP TABLE`.
*
*
* In case of failures, users should turn off `truncate` option to use `DROP TABLE` again. Also,
* due to the different behavior of `TRUNCATE TABLE` among DBMS, it's not always safe to use this.
* MySQLDialect, DB2Dialect, MsSqlServerDialect, DerbyDialect, and OracleDialect supports this
* while PostgresDialect and default JDBCDirect doesn't. For unknown and unsupported JDBCDirect,
* the user option `truncate` is ignored.
*
* @param url JDBC database url of the form `jdbc:subprotocol:subname`
* @param table Name of the table in the external database.
* @param connectionProperties JDBC database connection arguments, a list of arbitrary string
* tag/value. Normally at least a "user" and "password" property
* should be included. "batchsize" can be used to control the
* number of rows per insert. "isolationLevel" can be one of
* "NONE", "READ_COMMITTED", "READ_UNCOMMITTED", "REPEATABLE_READ",
* or "SERIALIZABLE", corresponding to standard transaction
* isolation levels defined by JDBC's Connection object, with default
* of "READ_UNCOMMITTED".
* @since 1.4.0
*/
def jdbc(url: String, table: String, connectionProperties: Properties): Unit = {
assertNotPartitioned("jdbc")
assertNotBucketed("jdbc")
// connectionProperties should override settings in extraOptions.
this.extraOptions ++= connectionProperties.asScala
// explicit url and dbtable should override all
this.extraOptions += ("url" -> url, "dbtable" -> table)
format("jdbc").save()
}
/**
* Saves the content of the `DataFrame` in JSON format (
* JSON Lines text format or newline-delimited JSON) at the specified path.
* This is equivalent to:
* {{{
* format("json").save(path)
* }}}
*
* You can set the following JSON-specific option(s) for writing JSON files:
*
*
`compression` (default `null`): compression codec to use when saving to file. This can be
* one of the known case-insensitive shorten names (`none`, `bzip2`, `gzip`, `lz4`,
* `snappy` and `deflate`).
*
`dateFormat` (default `yyyy-MM-dd`): sets the string that indicates a date format.
* Custom date formats follow the formats at `java.text.SimpleDateFormat`. This applies to
* date type.
*
`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss.SSSXXX`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* `java.text.SimpleDateFormat`. This applies to timestamp type.
*
*
* @since 1.4.0
*/
def json(path: String): Unit = {
format("json").save(path)
}
/**
* Saves the content of the `DataFrame` in Parquet format at the specified path.
* This is equivalent to:
* {{{
* format("parquet").save(path)
* }}}
*
* You can set the following Parquet-specific option(s) for writing Parquet files:
*
*
`compression` (default is the value specified in `spark.sql.parquet.compression.codec`):
* compression codec to use when saving to file. This can be one of the known case-insensitive
* shorten names(`none`, `snappy`, `gzip`, and `lzo`). This will override
* `spark.sql.parquet.compression.codec`.
*
*
* @since 1.4.0
*/
def parquet(path: String): Unit = {
format("parquet").save(path)
}
/**
* Saves the content of the `DataFrame` in ORC format at the specified path.
* This is equivalent to:
* {{{
* format("orc").save(path)
* }}}
*
* You can set the following ORC-specific option(s) for writing ORC files:
*
*
`compression` (default is the value specified in `spark.sql.orc.compression.codec`):
* compression codec to use when saving to file. This can be one of the known case-insensitive
* shorten names(`none`, `snappy`, `zlib`, and `lzo`). This will override
* `orc.compress` and `spark.sql.orc.compression.codec`. If `orc.compress` is given,
* it overrides `spark.sql.orc.compression.codec`.
*
*
* @since 1.5.0
* @note Currently, this method can only be used after enabling Hive support
*/
def orc(path: String): Unit = {
format("orc").save(path)
}
/**
* Saves the content of the `DataFrame` in a text file at the specified path.
* The DataFrame must have only one column that is of string type.
* Each row becomes a new line in the output file. For example:
* {{{
* // Scala:
* df.write.text("/path/to/output")
*
* // Java:
* df.write().text("/path/to/output")
* }}}
*
* You can set the following option(s) for writing text files:
*
*
`compression` (default `null`): compression codec to use when saving to file. This can be
* one of the known case-insensitive shorten names (`none`, `bzip2`, `gzip`, `lz4`,
* `snappy` and `deflate`).
*
*
* @since 1.6.0
*/
def text(path: String): Unit = {
format("text").save(path)
}
/**
* Saves the content of the `DataFrame` in CSV format at the specified path.
* This is equivalent to:
* {{{
* format("csv").save(path)
* }}}
*
* You can set the following CSV-specific option(s) for writing CSV files:
*
*
`sep` (default `,`): sets a single character as a separator for each
* field and value.
*
`quote` (default `"`): sets a single character used for escaping quoted values where
* the separator can be part of the value. If an empty string is set, it uses `u0000`
* (null character).
*
`escape` (default `\`): sets a single character used for escaping quotes inside
* an already quoted value.
*
`charToEscapeQuoteEscaping` (default `escape` or `\0`): sets a single character used for
* escaping the escape for the quote character. The default value is escape character when escape
* and quote characters are different, `\0` otherwise.
*
`escapeQuotes` (default `true`): a flag indicating whether values containing
* quotes should always be enclosed in quotes. Default is to escape all values containing
* a quote character.
*
`quoteAll` (default `false`): a flag indicating whether all values should always be
* enclosed in quotes. Default is to only escape values containing a quote character.
*
`header` (default `false`): writes the names of columns as the first line.
*
`nullValue` (default empty string): sets the string representation of a null value.
*
`compression` (default `null`): compression codec to use when saving to file. This can be
* one of the known case-insensitive shorten names (`none`, `bzip2`, `gzip`, `lz4`,
* `snappy` and `deflate`).
*
`dateFormat` (default `yyyy-MM-dd`): sets the string that indicates a date format.
* Custom date formats follow the formats at `java.text.SimpleDateFormat`. This applies to
* date type.
*
`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss.SSSXXX`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* `java.text.SimpleDateFormat`. This applies to timestamp type.
*
`ignoreLeadingWhiteSpace` (default `true`): a flag indicating whether or not leading
* whitespaces from values being written should be skipped.
*
`ignoreTrailingWhiteSpace` (default `true`): a flag indicating defines whether or not
* trailing whitespaces from values being written should be skipped.
*
*
* @since 2.0.0
*/
def csv(path: String): Unit = {
format("csv").save(path)
}
/**
* Wrap a DataFrameWriter action to track the QueryExecution and time cost, then report to the
* user-registered callback functions.
*/
private def runCommand(session: SparkSession, name: String)(command: LogicalPlan): Unit = {
val qe = session.sessionState.executePlan(command)
try {
val start = System.nanoTime()
// call `QueryExecution.toRDD` to trigger the execution of commands.
SQLExecution.withNewExecutionId(session, qe)(qe.toRdd)
val end = System.nanoTime()
session.listenerManager.onSuccess(name, qe, end - start)
} catch {
case e: Exception =>
session.listenerManager.onFailure(name, qe, e)
throw e
}
}
///////////////////////////////////////////////////////////////////////////////////////
// Builder pattern config options
///////////////////////////////////////////////////////////////////////////////////////
private var source: String = df.sparkSession.sessionState.conf.defaultDataSourceName
private var mode: SaveMode = SaveMode.ErrorIfExists
private val extraOptions = new scala.collection.mutable.HashMap[String, String]
private var partitioningColumns: Option[Seq[String]] = None
private var bucketColumnNames: Option[Seq[String]] = None
private var numBuckets: Option[Int] = None
private var sortColumnNames: Option[Seq[String]] = None
}
