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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.util.Properties
import scala.collection.JavaConverters._
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoTable, Project}
import org.apache.spark.sql.execution.datasources.{BucketSpec, CreateTableUsingAsSelect, DataSource, HadoopFsRelation}
import org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils
/**
* 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
*/
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`: default option, throw an exception at runtime.
*
* @since 1.4.0
*/
def mode(saveMode: String): DataFrameWriter[T] = {
this.mode = saveMode.toLowerCase match {
case "overwrite" => SaveMode.Overwrite
case "append" => SaveMode.Append
case "ignore" => SaveMode.Ignore
case "error" | "default" => SaveMode.ErrorIfExists
case _ => throw new IllegalArgumentException(s"Unknown save mode: $saveMode. " +
"Accepted save modes are 'overwrite', 'append', 'ignore', 'error'.")
}
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.
*
* @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.
*
* @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.
*
* @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 was initially applicable for Parquet but in 1.5+ covers JSON, text, ORC and avro as well.
*
* @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 Parquet, JSON and ORC.
*
* @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 Parquet, JSON and ORC.
*
* @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 = {
assertNotBucketed("save")
val dataSource = DataSource(
df.sparkSession,
className = source,
partitionColumns = partitioningColumns.getOrElse(Nil),
bucketSpec = getBucketSpec,
options = extraOptions.toMap)
dataSource.write(mode, df)
}
/**
* 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 be defined for the table. " +
"It is not necessary to use partitionBy()."
)
}
df.sparkSession.sessionState.executePlan(
InsertIntoTable(
table = UnresolvedRelation(tableIdent),
partition = Map.empty[String, Option[String]],
child = df.logicalPlan,
overwrite = mode == SaveMode.Overwrite,
ifNotExists = false)).toRdd
}
private def normalizedParCols: Option[Seq[String]] = partitioningColumns.map { cols =>
cols.map(normalize(_, "Partition"))
}
private def normalizedBucketColNames: Option[Seq[String]] = bucketColumnNames.map { cols =>
cols.map(normalize(_, "Bucketing"))
}
private def normalizedSortColNames: Option[Seq[String]] = sortColumnNames.map { cols =>
cols.map(normalize(_, "Sorting"))
}
private def getBucketSpec: Option[BucketSpec] = {
if (sortColumnNames.isDefined) {
require(numBuckets.isDefined, "sortBy must be used together with bucketBy")
}
for {
n <- numBuckets
} yield {
require(n > 0 && n < 100000, "Bucket number must be greater than 0 and less than 100000.")
// partitionBy columns cannot be used in bucketBy
if (normalizedParCols.nonEmpty &&
normalizedBucketColNames.get.toSet.intersect(normalizedParCols.get.toSet).nonEmpty) {
throw new AnalysisException(
s"bucketBy columns '${bucketColumnNames.get.mkString(", ")}' should not be part of " +
s"partitionBy columns '${partitioningColumns.get.mkString(", ")}'")
}
BucketSpec(n, normalizedBucketColNames.get, normalizedSortColNames.getOrElse(Nil))
}
}
/**
* The given column name may not be equal to any of the existing column names if we were in
* case-insensitive context. Normalize the given column name to the real one so that we don't
* need to care about case sensitivity afterwards.
*/
private def normalize(columnName: String, columnType: String): String = {
val validColumnNames = df.logicalPlan.output.map(_.name)
validColumnNames.find(df.sparkSession.sessionState.analyzer.resolver(_, columnName))
.getOrElse(throw new AnalysisException(s"$columnType column $columnName not found in " +
s"existing columns (${validColumnNames.mkString(", ")})"))
}
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|
* +---+---+
* }}}
*
* 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 tableExists = df.sparkSession.sessionState.catalog.tableExists(tableIdent)
(tableExists, mode) match {
case (true, SaveMode.Ignore) =>
// Do nothing
case (true, SaveMode.ErrorIfExists) =>
throw new AnalysisException(s"Table $tableIdent already exists.")
case _ =>
val cmd =
CreateTableUsingAsSelect(
tableIdent,
source,
partitioningColumns.map(_.toArray).getOrElse(Array.empty[String]),
getBucketSpec,
mode,
extraOptions.toMap,
df.logicalPlan)
df.sparkSession.sessionState.executePlan(cmd).toRdd
}
}
/**
* 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.
*
* @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.
* @since 1.4.0
*/
def jdbc(url: String, table: String, connectionProperties: Properties): Unit = {
assertNotPartitioned("jdbc")
assertNotBucketed("jdbc")
val props = new Properties()
extraOptions.foreach { case (key, value) =>
props.put(key, value)
}
// connectionProperties should override settings in extraOptions
props.putAll(connectionProperties)
val conn = JdbcUtils.createConnectionFactory(url, props)()
try {
var tableExists = JdbcUtils.tableExists(conn, url, table)
if (mode == SaveMode.Ignore && tableExists) {
return
}
if (mode == SaveMode.ErrorIfExists && tableExists) {
sys.error(s"Table $table already exists.")
}
if (mode == SaveMode.Overwrite && tableExists) {
JdbcUtils.dropTable(conn, table)
tableExists = false
}
// Create the table if the table didn't exist.
if (!tableExists) {
val schema = JdbcUtils.schemaString(df, url)
val sql = s"CREATE TABLE $table ($schema)"
val statement = conn.createStatement
try {
statement.executeUpdate(sql)
} finally {
statement.close()
}
}
} finally {
conn.close()
}
JdbcUtils.saveTable(df, url, table, props)
}
/**
* Saves the content of the [[DataFrame]] in JSON format 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.SSSZZ`): 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 `snappy`): 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`.
*
*
* @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 the single character as a separator for each
* field and value.
*
`quote` (default `"`): sets the single character used for escaping quoted values where
* the separator can be part of the value.
*
`escape` (default `\`): sets the single character used for escaping quotes inside
* an already quoted value.
*
`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.SSSZZ`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* `java.text.SimpleDateFormat`. This applies to timestamp type.
*
*
* @since 2.0.0
*/
def csv(path: String): Unit = {
format("csv").save(path)
}
///////////////////////////////////////////////////////////////////////////////////////
// Builder pattern config options
///////////////////////////////////////////////////////////////////////////////////////
private var source: String = df.sparkSession.sessionState.conf.defaultDataSourceName
private var mode: SaveMode = SaveMode.ErrorIfExists
private var 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
}
