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/*
* Copyright 2015 TouchType Ltd
*
* Licensed 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 com.databricks.spark.redshift
import java.net.URI
import java.sql.{Connection, Date, SQLException, Timestamp}
import com.amazonaws.auth.AWSCredentials
import com.amazonaws.services.s3.AmazonS3Client
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.TaskContext
import org.slf4j.LoggerFactory
import scala.collection.mutable
import scala.util.control.NonFatal
import com.databricks.spark.redshift.Parameters.MergedParameters
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode}
import org.apache.spark.sql.types._
/**
* Functions to write data to Redshift.
*
* At a high level, writing data back to Redshift involves the following steps:
*
* - Use the spark-avro library to save the DataFrame to S3 using Avro serialization. Prior to
* saving the data, certain data type conversions are applied in order to work around
* limitations in Avro's data type support and Redshift's case-insensitive identifier handling.
*
* While writing the Avro files, we use accumulators to keep track of which partitions were
* non-empty. After the write operation completes, we use this to construct a list of non-empty
* Avro partition files.
*
* - If there is data to be written (i.e. not all partitions were empty), then use the list of
* non-empty Avro files to construct a JSON manifest file to tell Redshift to load those files.
* This manifest is written to S3 alongside the Avro files themselves. We need to use an
* explicit manifest, as opposed to simply passing the name of the directory containing the
* Avro files, in order to work around a bug related to parsing of empty Avro files (see #96).
*
* - Start a new JDBC transaction and disable auto-commit. Depending on the SaveMode, issue
* DELETE TABLE or CREATE TABLE commands, then use the COPY command to instruct Redshift to load
* the Avro data into the appropriate table.
*/
private[redshift] class RedshiftWriter(
jdbcWrapper: JDBCWrapper,
s3ClientFactory: AWSCredentials => AmazonS3Client) {
private val log = LoggerFactory.getLogger(getClass)
/**
* Generate CREATE TABLE statement for Redshift
*/
// Visible for testing.
private[redshift] def createTableSql(data: DataFrame, params: MergedParameters): String = {
val schemaSql = jdbcWrapper.schemaString(data.schema)
val distStyleDef = params.distStyle match {
case Some(style) => s"DISTSTYLE $style"
case None => ""
}
val distKeyDef = params.distKey match {
case Some(key) => s"DISTKEY ($key)"
case None => ""
}
val sortKeyDef = params.sortKeySpec.getOrElse("")
val table = params.table.get
s"CREATE TABLE IF NOT EXISTS $table ($schemaSql) $distStyleDef $distKeyDef $sortKeyDef"
}
/**
* Generate the COPY SQL command
*/
private def copySql(
sqlContext: SQLContext,
params: MergedParameters,
creds: AWSCredentials,
manifestUrl: String): String = {
val credsString: String = AWSCredentialsUtils.getRedshiftCredentialsString(params, creds)
val fixedUrl = Utils.fixS3Url(manifestUrl)
s"COPY ${params.table.get} FROM '$fixedUrl' CREDENTIALS '$credsString' FORMAT AS " +
s"AVRO 'auto' manifest ${params.extraCopyOptions}"
}
/**
* Generate COMMENT SQL statements for the table and columns.
*/
private[redshift] def commentActions(tableComment: Option[String], schema: StructType):
List[String] = {
tableComment.toList.map(desc => s"COMMENT ON TABLE %s IS '${desc.replace("'", "''")}'") ++
schema.fields
.withFilter(f => f.metadata.contains("description"))
.map(f => s"""COMMENT ON COLUMN %s."${f.name.replace("\"", "\\\"")}""""
+ s" IS '${f.metadata.getString("description").replace("'", "''")}'")
}
/**
* Perform the Redshift load by issuing a COPY statement.
*/
private def doRedshiftLoad(
conn: Connection,
data: DataFrame,
params: MergedParameters,
creds: AWSCredentials,
manifestUrl: Option[String]): Unit = {
// If the table doesn't exist, we need to create it first, using JDBC to infer column types
val createStatement = createTableSql(data, params)
log.info(createStatement)
jdbcWrapper.executeInterruptibly(conn.prepareStatement(createStatement))
val preActions = commentActions(params.description, data.schema) ++ params.preActions
// Execute preActions
preActions.foreach { action =>
val actionSql = if (action.contains("%s")) action.format(params.table.get) else action
log.info("Executing preAction: " + actionSql)
jdbcWrapper.executeInterruptibly(conn.prepareStatement(actionSql))
}
manifestUrl.foreach { manifestUrl =>
// Load the temporary data into the new file
val copyStatement = copySql(data.sqlContext, params, creds, manifestUrl)
log.info(copyStatement)
try {
jdbcWrapper.executeInterruptibly(conn.prepareStatement(copyStatement))
} catch {
case e: SQLException =>
log.error("SQLException thrown while running COPY query; will attempt to retrieve " +
"more information by querying the STL_LOAD_ERRORS table", e)
// Try to query Redshift's STL_LOAD_ERRORS table to figure out why the load failed.
// See http://docs.aws.amazon.com/redshift/latest/dg/r_STL_LOAD_ERRORS.html for details.
conn.rollback()
val errorLookupQuery =
"""
| SELECT *
| FROM stl_load_errors
| WHERE query = pg_last_query_id()
""".stripMargin
val detailedException: Option[SQLException] = try {
val results =
jdbcWrapper.executeQueryInterruptibly(conn.prepareStatement(errorLookupQuery))
if (results.next()) {
val errCode = results.getInt("err_code")
val errReason = results.getString("err_reason").trim
val columnLength: String =
Option(results.getString("col_length"))
.map(_.trim)
.filter(_.nonEmpty)
.map(n => s"($n)")
.getOrElse("")
val exceptionMessage =
s"""
|Error (code $errCode) while loading data into Redshift: "$errReason"
|Table name: ${params.table.get}
|Column name: ${results.getString("colname").trim}
|Column type: ${results.getString("type").trim}$columnLength
|Raw line: ${results.getString("raw_line")}
|Raw field value: ${results.getString("raw_field_value")}
""".stripMargin
Some(new SQLException(exceptionMessage, e))
} else {
None
}
} catch {
case NonFatal(e2) =>
log.error("Error occurred while querying STL_LOAD_ERRORS", e2)
None
}
throw detailedException.getOrElse(e)
}
}
// Execute postActions
params.postActions.foreach { action =>
val actionSql = if (action.contains("%s")) action.format(params.table.get) else action
log.info("Executing postAction: " + actionSql)
jdbcWrapper.executeInterruptibly(conn.prepareStatement(actionSql))
}
}
/**
* Serialize temporary data to S3, ready for Redshift COPY, and create a manifest file which can
* be used to instruct Redshift to load the non-empty temporary data partitions.
*
* @return the URL of the manifest file in S3, in `s3://path/to/file/manifest.json` format, if
* at least one record was written, and None otherwise.
*/
private def unloadData(
sqlContext: SQLContext,
data: DataFrame,
tempDir: String): Option[String] = {
// spark-avro does not support Date types. In addition, it converts Timestamps into longs
// (milliseconds since the Unix epoch). Redshift is capable of loading timestamps in
// 'epochmillisecs' format but there's no equivalent format for dates. To work around this, we
// choose to write out both dates and timestamps as strings.
// For additional background and discussion, see #39.
// Convert the rows so that timestamps and dates become formatted strings.
// Formatters are not thread-safe, and thus these functions are not thread-safe.
// However, each task gets its own deserialized copy, making this safe.
val conversionFunctions: Array[Any => Any] = data.schema.fields.map { field =>
field.dataType match {
case DateType =>
val dateFormat = Conversions.createRedshiftDateFormat()
(v: Any) => {
if (v == null) null else dateFormat.format(v.asInstanceOf[Date])
}
case TimestampType =>
val timestampFormat = Conversions.createRedshiftTimestampFormat()
(v: Any) => {
if (v == null) null else timestampFormat.format(v.asInstanceOf[Timestamp])
}
case _ => (v: Any) => v
}
}
// Use Spark accumulators to determine which partitions were non-empty.
val nonEmptyPartitions =
sqlContext.sparkContext.accumulableCollection(mutable.HashSet.empty[Int])
val convertedRows: RDD[Row] = data.rdd.mapPartitions { iter: Iterator[Row] =>
if (iter.hasNext) {
nonEmptyPartitions += TaskContext.get.partitionId()
}
iter.map { row =>
val convertedValues: Array[Any] = new Array(conversionFunctions.length)
var i = 0
while (i < conversionFunctions.length) {
convertedValues(i) = conversionFunctions(i)(row(i))
i += 1
}
Row.fromSeq(convertedValues)
}
}
// Convert all column names to lowercase, which is necessary for Redshift to be able to load
// those columns (see #51).
val schemaWithLowercaseColumnNames: StructType =
StructType(data.schema.map(f => f.copy(name = f.name.toLowerCase)))
if (schemaWithLowercaseColumnNames.map(_.name).toSet.size != data.schema.size) {
throw new IllegalArgumentException(
"Cannot save table to Redshift because two or more column names would be identical" +
" after conversion to lowercase: " + data.schema.map(_.name).mkString(", "))
}
// Update the schema so that Avro writes date and timestamp columns as formatted timestamp
// strings. This is necessary for Redshift to be able to load these columns (see #39).
val convertedSchema: StructType = StructType(
schemaWithLowercaseColumnNames.map {
case StructField(name, DateType, nullable, meta) =>
StructField(name, StringType, nullable, meta)
case StructField(name, TimestampType, nullable, meta) =>
StructField(name, StringType, nullable, meta)
case other => other
}
)
sqlContext.createDataFrame(convertedRows, convertedSchema)
.write
.format("com.databricks.spark.avro")
.save(tempDir)
if (nonEmptyPartitions.value.isEmpty) {
None
} else {
// See https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html
// for a description of the manifest file format. The URLs in this manifest must be absolute
// and complete.
// The saved filenames depend on the spark-avro version. In spark-avro 1.0.0, the write
// path uses SparkContext.saveAsHadoopFile(), which produces filenames of the form
// part-XXXXX.avro. In spark-avro 2.0.0+, the partition filenames are of the form
// part-r-XXXXX-UUID.avro.
val fs = FileSystem.get(URI.create(tempDir), sqlContext.sparkContext.hadoopConfiguration)
val partitionIdRegex = "^part-(?:r-)?(\\d+)[^\\d+].*$".r
val filesToLoad: Seq[String] = {
val nonEmptyPartitionIds = nonEmptyPartitions.value.toSet
fs.listStatus(new Path(tempDir)).map(_.getPath.getName).collect {
case file @ partitionIdRegex(id) if nonEmptyPartitionIds.contains(id.toInt) => file
}
}
// It's possible that tempDir contains AWS access keys. We shouldn't save those credentials to
// S3, so let's first sanitize `tempdir` and make sure that it uses the s3:// scheme:
val sanitizedTempDir = Utils.fixS3Url(
Utils.removeCredentialsFromURI(URI.create(tempDir)).toString).stripSuffix("/")
val manifestEntries = filesToLoad.map { file =>
s"""{"url":"$sanitizedTempDir/$file", "mandatory":true}"""
}
val manifest = s"""{"entries": [${manifestEntries.mkString(",\n")}]}"""
val manifestPath = sanitizedTempDir + "/manifest.json"
val fsDataOut = fs.create(new Path(manifestPath))
try {
fsDataOut.write(manifest.getBytes("utf-8"))
} finally {
fsDataOut.close()
}
Some(manifestPath)
}
}
/**
* Write a DataFrame to a Redshift table, using S3 and Avro serialization
*/
def saveToRedshift(
sqlContext: SQLContext,
data: DataFrame,
saveMode: SaveMode,
params: MergedParameters) : Unit = {
if (params.table.isEmpty) {
throw new IllegalArgumentException(
"For save operations you must specify a Redshift table name with the 'dbtable' parameter")
}
if (!params.useStagingTable) {
log.warn("Setting useStagingTable=false is deprecated; instead, we recommend that you " +
"drop the target table yourself. For more details on this deprecation, see" +
"https://github.com/databricks/spark-redshift/pull/157")
}
val creds: AWSCredentials =
AWSCredentialsUtils.load(params, sqlContext.sparkContext.hadoopConfiguration)
Utils.assertThatFileSystemIsNotS3BlockFileSystem(
new URI(params.rootTempDir), sqlContext.sparkContext.hadoopConfiguration)
Utils.checkThatBucketHasObjectLifecycleConfiguration(params.rootTempDir, s3ClientFactory(creds))
// Save the table's rows to S3:
val manifestUrl = unloadData(sqlContext, data, params.createPerQueryTempDir())
val conn = jdbcWrapper.getConnector(params.jdbcDriver, params.jdbcUrl, params.credentials)
conn.setAutoCommit(false)
try {
val table: TableName = params.table.get
if (saveMode == SaveMode.Overwrite) {
// Overwrites must drop the table in case there has been a schema update
jdbcWrapper.executeInterruptibly(conn.prepareStatement(s"DROP TABLE IF EXISTS $table;"))
if (!params.useStagingTable) {
// If we're not using a staging table, commit now so that Redshift doesn't have to
// maintain a snapshot of the old table during the COPY; this sacrifices atomicity for
// performance.
conn.commit()
}
}
log.info(s"Loading new Redshift data to: $table")
doRedshiftLoad(conn, data, params, creds, manifestUrl)
conn.commit()
} catch {
case NonFatal(e) =>
try {
log.error("Exception thrown during Redshift load; will roll back transaction", e)
conn.rollback()
} catch {
case NonFatal(e2) =>
log.error("Exception while rolling back transaction", e2)
}
throw e
} finally {
conn.close()
}
}
}
object DefaultRedshiftWriter extends RedshiftWriter(
DefaultJDBCWrapper,
awsCredentials => new AmazonS3Client(awsCredentials))
