Redshift Data Source for Spark

A library to load data into Spark SQL DataFrames from Amazon Redshift, and write them back to Redshift tables. Amazon S3 is used to efficiently transfer data in and out of Redshift, and JDBC is used to automatically trigger the appropriate COPY and UNLOAD commands on Redshift.

This library is more suited to ETL than interactive queries, since large amounts of data could be extracted to S3 for each query execution. If you plan to perform many queries against the same Redshift tables then we recommend saving the extracted data in a format such as Parquet.

Installation
Usage:
- Data sources API: Scala, Python, SQL
- Hadoop InputFormat
Configuration
Transactional Guarantees
Migration Guide

Installation

This library requires Apache Spark 1.4+ and Amazon Redshift 1.0.963+.

You may use this library in your applications with the following dependency information:

Scala 2.10

groupId: com.databricks
artifactId: spark-redshift_2.10
version: 0.6.0

Scala 2.11

groupId: com.databricks
artifactId: spark-redshift_2.11
version: 0.6.0

You will also need to provide a JDBC driver that is compatible with Redshift. Amazon recommend that you use their driver, which is distributed as a JAR that is hosted on Amazon's website. This library has also been successfully tested using the Postgres JDBC driver.

Note on Hadoop versions: This library depends on spark-avro, which should automatically be downloaded because it is declared as a dependency. However, you may need to provide the corresponding avro-mapred dependency which matches your Hadoop distribution. In most deployments, however, this dependency will be automatically provided by your cluster's Spark assemblies and no additional action will be required.

Note on Amazon SDK dependency: This library declares a provided dependency on components of the AWS Java SDK. In most cases, these libraries will be provided by your deployment environment. However, if you get ClassNotFoundExceptions for Amazon SDK classes then you will need to add explicit dependencies on com.amazonaws.aws-java-sdk-core and com.amazonaws.aws-java-sdk-s3 as part of your build / runtime configuration. See the comments in project/SparkRedshiftBuild.scala for more details.

Usage

Data Sources API

Once you have configured your AWS credentials, you can use this library via the Data Sources API in Scala, Python or SQL, as follows:

Scala

import org.apache.spark.sql._

val sc = // existing SparkContext
val sqlContext = new SQLContext(sc)

// Get some data from a Redshift table
val df: DataFrame = sqlContext.read
    .format("com.databricks.spark.redshift")
    .option("url", "jdbc:redshift://redshifthost:5439/database?user=username&password=pass")
    .option("dbtable", "my_table")
    .option("tempdir", "s3n://path/for/temp/data")
    .load()

// Can also load data from a Redshift query
val df: DataFrame = sqlContext.read
    .format("com.databricks.spark.redshift")
    .option("url", "jdbc:redshift://redshifthost:5439/database?user=username&password=pass")
    .option("query", "select x, count(*) my_table group by x")
    .option("tempdir", "s3n://path/for/temp/data")
    .load()

// Apply some transformations to the data as per normal, then you can use the
// Data Source API to write the data back to another table

df.write
  .format("com.databricks.spark.redshift")
    .option("url", "jdbc:redshift://redshifthost:5439/database?user=username&password=pass")
    .option("dbtable", "my_table_copy")
    .option("tempdir", "s3n://path/for/temp/data")
  .mode("error")
  .save()

Python

from pyspark.sql import SQLContext

sc = # existing SparkContext
sql_context = SQLContext(sc)

# Read data from a table
df = sql_context.read \
    .format("com.databricks.spark.redshift") \
    .option("url", "jdbc:redshift://redshifthost:5439/database?user=username&password=pass") \
    .option("dbtable", "my_table") \
    .option("tempdir", "s3n://path/for/temp/data") \
    .load()

# Read data from a query
df = sql_context.read \
    .format("com.databricks.spark.redshift") \
    .option("url", "jdbc:redshift://redshifthost:5439/database?user=username&password=pass") \
    .option("query", "select x, count(*) my_table group by x") \
    .option("tempdir", "s3n://path/for/temp/data") \
    .load()

# Write back to a table
df.write \
  .format("com.databricks.spark.redshift") \
  .option("url", "jdbc:redshift://redshifthost:5439/database?user=username&password=pass") \
  .option("dbtable", "my_table_copy") \
  .option("tempdir", "s3n://path/for/temp/data") \
  .mode("error") \
  .save()

SQL

Reading data using SQL:

CREATE TABLE my_table
USING com.databricks.spark.redshift
OPTIONS (
  dbtable 'my_table',
  tempdir 's3n://path/for/temp/data',
  url 'jdbc:redshift://redshifthost:5439/database?user=username&password=pass'
);

Writing data using SQL:

-- Create a new table, throwing an error if a table with the same name already exists:
CREATE TABLE my_table
USING com.databricks.spark.redshift
OPTIONS (
  dbtable 'my_table',
  tempdir 's3n://path/for/temp/data'
  url 'jdbc:redshift://redshifthost:5439/database?user=username&password=pass'
)
AS SELECT * FROM tabletosave;

Note that the SQL API only supports the creation of new tables and not overwriting or appending; this corresponds to the default save mode of the other language APIs.

Hadoop InputFormat

The library contains a Hadoop input format for Redshift tables unloaded with the ESCAPE option, which you may make direct use of as follows:

import com.databricks.spark.redshift.RedshiftInputFormat

val records = sc.newAPIHadoopFile(
  path,
  classOf[RedshiftInputFormat],
  classOf[java.lang.Long],
  classOf[Array[String]])

Configuration

AWS Credentials

This library reads and writes data to S3 when transferring data to/from Redshift. As a result, it requires AWS credentials with read and write access to a S3 bucket (specified using the tempdir configuration parameter). Assuming that Spark has been configured to access S3, it should automatically discover the proper credentials to pass to Redshift.

There are three ways of configuring AWS credentials for use by this library:

Set keys in Hadoop conf (best option for most users): You can specify AWS keys via Hadoop configuration properties. For example, if your tempdir configuration points to a s3n:// filesystem then you can set the fs.s3n.awsAccessKeyId and fs.s3n.awsSecretAccessKey properties in a Hadoop XML configuration file or call sc.hadoopConfiguration.set() to mutate Spark's global Hadoop configuration.

For example, if you are using the s3n filesystem then add

sc.hadoopConfiguration.set("fs.s3n.awsAccessKeyId", "YOUR_KEY_ID")
sc.hadoopConfiguration.set("fs.s3n.awsSecretAccessKey", "YOUR_SECRET_ACCESS_KEY")

and for the s3a filesystem add

sc.hadoopConfiguration.set("fs.s3a.access.key", "YOUR_KEY_ID")
sc.hadoopConfiguration.set("fs.s3a.secret.key", "YOUR_SECRET_ACCESS_KEY")

Python users will have to use a slightly different method to modify the hadoopConfiguration, since this field is not exposed in all versions of PySpark. Although the following command relies on some Spark internals, it should work with all PySpark versions and is unlikely to break or change in the future:

sc._jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", "YOUR_KEY_ID")
sc._jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", "YOUR_SECRET_ACCESS_KEY")

Encode keys in tempdir URI: For example, the URI s3n://ACCESSKEY:SECRETKEY@bucket/path/to/temp/dir encodes the key pair (ACCESSKEY, SECRETKEY). Due to Hadoop limitations, this approach will not work for secret keys which contain forward slash (/) characters.
IAM instance profiles: If you are running on EC2 and authenticate to S3 using IAM and instance profiles, then you must must configure the temporary_aws_access_key_id, temporary_aws_secret_access_key, and temporary_aws_session_token configuration properties to point to temporary keys created via the AWS Security Token Service. These temporary keys will then be passed to Redshift via LOAD and UNLOAD commands.

⚠️ Note: This library does not clean up the temporary files that it creates in S3. As a result, we recommend that you use a dedicated temporary S3 bucket with an object lifecycle configuration to ensure that temporary files are automatically deleted after a specified expiration period.

Parameters

The parameter map or OPTIONS provided in Spark SQL supports the following settings.

Parameter	Required	Default	Notes
`dbtable`	Yes, unless `query` is specified	No default	The table to create or read from in Redshift. This parameter is required when saving data back to Redshift.
`query`	Yes, unless `dbtable` is specified	No default	The query to read from in Redshift
`user`	No	No default	The Redshift username. Must be used in tandem with `password` option. May only be used if the user and password are not passed in the URL, passing both will result in an error.
`password`	No	No default	The Redshift password. Must be used in tandem with `user` option. May only be used if the user and password are not passed in the URL; passing both will result in an error.
`url`	Yes	No default	A JDBC URL, of the format, `jdbc:subprotocol://host:port/database?user=username&password=password` `subprotocol` can be `postgresql` or `redshift`, depending on which JDBC driver you have loaded. Note however that one Redshift-compatible driver must be on the classpath and match this URL. `host` and `port` should point to the Redshift master node, so security groups and/or VPC will need to be configured to allow access from your driver application. `database` identifies a Redshift database name `user` and `password` are credentials to access the database, which must be embedded in this URL for JDBC, and your user account should have necessary privileges for the table being referenced.
`temporary_aws_access_key_id`	No, unless using EC2 instance profile authentication	No default	AWS access key, must have write permissions to the S3 bucket.
`temporary_aws_secret_access_key`	No, unless using EC2 instance profile authentication	No default	AWS secret access key corresponding to provided access key.
`temporary_aws_session_token`	No, unless using EC2 instance profile authentication	No default	AWS session token corresponding to provided access key.
`tempdir`	Yes	No default	A writeable location in Amazon S3, to be used for unloaded data when reading and Avro data to be loaded into Redshift when writing. If you're using Redshift data source for Spark as part of a regular ETL pipeline, it can be useful to set a Lifecycle Policy on a bucket and use that as a temp location for this data.
`jdbcdriver`	No	Determined by the JDBC URL's subprotocol	The class name of the JDBC driver to use. This class must be on the classpath. In most cases, it should not be necessary to specify this option, as the appropriate driver classname should automatically be determined by the JDBC URL's subprotocol.
`diststyle`	No	`EVEN`	The Redshift Distribution Style to be used when creating a table. Can be one of `EVEN`, `KEY` or `ALL` (see Redshift docs). When using `KEY`, you must also set a distribution key with the `distkey` option.
`distkey`	No, unless using `DISTSTYLE KEY`	No default	The name of a column in the table to use as the distribution key when creating a table.
`sortkeyspec`	No	No default	A full Redshift Sort Key definition. Examples include: `SORTKEY(my_sort_column)` `COMPOUND SORTKEY(sort_col_1, sort_col_2)` `INTERLEAVED SORTKEY(sort_col_1, sort_col_2)`
`usestagingtable`	No	`true`	When performing an overwrite of existing data, this setting can be used to stage the new data in a temporary table, such that we make sure the `COPY` finishes successfully before making any changes to the existing table. This means that we minimize the amount of time that the target table will be unavailable and restore the old data should the `COPY` fail. You may wish to disable this by setting the parameter to `false` if you can't spare the disk space in your Redshift cluster and/or don't have requirements to keep the table availability high.
`preactions`	No	No default	This can be a `;` separated list of SQL commands to be executed before loading `COPY` command. It may be useful to have some `DELETE` commands or similar run here before loading new data. If the command contains `%s`, the table name will be formatted in before execution (in case you're using a staging table). Be warned that if this commands fail, it is treated as an error and you'll get an exception. If using a staging table, the changes will be reverted and the backup table restored if pre actions fail.
`postactions`	No	No default	This can be a `;` separated list of SQL commands to be executed after a successful `COPY` when loading data. It may be useful to have some `GRANT` commands or similar run here when loading new data. If the command contains `%s`, the table name will be formatted in before execution (in case you're using a staging table). Be warned that if this commands fail, it is treated as an error and you'll get an exception. If using a staging table, the changes will be reverted and the backup table restored if post actions fail.
`extracopyoptions`	No	No default	A list extra options to append to the Redshift `COPY` command when loading data, e.g. `TRUNCATECOLUMNS` or `MAXERROR n` (see the Redshift docs for other options). Note that since these options are appended to the end of the `COPY` command, only options that make sense at the end of the command can be used, but that should cover most possible use cases.

Additional configuration options

Configuring the maximum size of string columns

When creating Redshift tables, this library's default behavior is to create TEXT columns for string columns. Redshift stores TEXT columns as VARCHAR(256), so these columns have a maximum size of 256 characters (source).

To support larger columns, you can use the maxlength column metadata field to specify the maximum length of individual string columns. This can also be done as a space-savings performance optimization in order to declare columns with a smaller maximum length than the default.

⚠️ Note: Due to limitations in Spark, metadata modification is unsupported in the Python, SQL, and R language APIs.

Here is an example of updating multiple columns' metadata fields using Spark's Scala API:

import org.apache.spark.sql.types.MetadataBuilder

// Specify the custom width of each column
val columnLengthMap = Map(
  "language_code" -> 2,
  "country_code" -> 2,
  "url" -> 2083
)

var df = ... // the dataframe you'll want to write to Redshift

// Apply each column metadata customization
columnLengthMap.foreach { case (colName, length) =>
  val metadata = new MetadataBuilder().putLong("maxlength", length).build()
  df = df.withColumn(colName, df(colName).as(colName, metadata))
}

df.write
  .format("com.databricks.spark.redshift")
  .option("url", jdbcURL)
  .option("tempdir", s3TempDirectory)
  .option("dbtable", sessionTable)
  .save()

Transactional Guarantees

This section describes the transactional guarantees of the Redshift data source for Spark

General background on Redshift and S3's properties

For general information on Redshift's transactional guarantees, see the Managing Concurrent Write Operations chapter in the Redshift documentation. In a nutshell, Redshift provides serializable isolation (according to the documentation for Redshift's BEGIN command, "[although] you can use any of the four transaction isolation levels, Amazon Redshift processes all isolation levels as serializable"). According to its documentation, "Amazon Redshift supports a default automatic commit behavior in which each separately-executed SQL command commits individually." Thus, individual commands like COPY and UNLOAD are atomic and transactional, while explicit BEGIN and END should only be necessary to enforce the atomicity of multiple commands / queries.

When reading from / writing to Redshift, this library reads and writes data in S3. Both Spark and Redshift produce partitioned output which is stored in multiple files in S3. According to the Amazon S3 Data Consistency Model documentation, S3 bucket listing operations are eventually-consistent, so the files must to go to special lengths to avoid missing / incomplete data due to this source of eventual-consistency.

Guarantees of the Redshift data source for Spark

Creating a new table: Creating a new table is a two-step process, consisting of a CREATE TABLE command followed by a COPY command to append the initial set of rows. Currently, these two steps are performed in separate transactions, so their effects may become visible at different times to readers. The COPY itself is atomic, so the table will never be visible in a state where it contains a non-empty subset of the saved rows. In a future release, this will be changed so that the CREATE TABLE and COPY statements are issued as part of the same transaction.

Appending to an existing table: In the COPY command, this library uses manifests to guard against certain eventually-consistent S3 operations. As a result, it appends to existing tables have the same atomic and transactional properties as regular Redshift COPY commands.

Overwriting an existing table: By default, this library uses transactions to perform overwrites. Outside of a transaction, it will create an empty temporary table and append the new rows using a COPY statement. If the COPY succeeds, it will use a transaction to atomically delete the overwritten table and rename the temporary table to destination table.

In a future release, this will be changed so that the temporary table is created in the same transaction as the COPY.

This use of a staging table can be disabled by setting usestagingtable to false, in which case the destination table will be deleted before the COPY, sacrificing the atomicity of the overwrite operation.

Querying Redshift tables: Queries use Redshift's UNLOAD command to execute a query and save its results to S3 and use manifests to guard against certain eventually-consistent S3 operations. As a result, queries from Redshift data source for Spark should have the same consistency properties as regular Redshift queries.

Migration Guide

Some breaking changes were made in version 0.3 of the Hadoop InputFormat. Users should make the following changes in their code if they would like to use the 0.3+ versions, when using the input format directly:

com.databricks.examples.redshift.input -> com.databricks.spark.redshift
SchemaRDD -> DataFrame
import com.databricks.examples.redshift.input.RedshiftInputFormat._ -> import com.databricks.spark.redshift._

Version 0.4+ adds the DataSource API and JDBC, which is an entirely new API, so although this won't break code using the InputFormat directly, you may wish to make use of the new functionality to avoid performing UNLOAD queries manually.

h5chauhan/spark-redshift