To ensure the best experience for our customers, we have decided to inline this connector directly in Databricks Runtime. The latest version of Databricks Runtime (3.0+) includes an advanced version of the RedShift connector for Spark that features both performance improvements (full query pushdown) as well as security improvements (automatic encryption). For more information, refer to the Databricks documentation. As a result, we will no longer be making releases separately from Databricks Runtime.

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
  • Snapshot builds
• Usage:
  • Data sources API: Scala, Python, SQL, R
  • Hadoop InputFormat
• Configuration
  • Authenticating to S3 and Redshift
  • Encryption
  • Parameters
• Additional configuration options
  • Configuring the maximum size of string columns
  • Setting a custom column type
  • Configuring column encoding
  • Setting descriptions on columns
• Transactional Guarantees
• Common problems and solutions
• S3 bucket and Redshift cluster are in different AWS regions
• Migration Guide

This library requires Apache Spark 2.0+ and Amazon Redshift 1.0.963+.

For version that works with Spark 1.x, please check for the 1.x branch.

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: 3.0.0-preview1

Scala 2.11

groupId: com.databricks
artifactId: spark-redshift_2.11
version: 3.0.0-preview1

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.

Master snapshot builds of this library are built using jitpack.io. In order to use these snapshots in your build, you'll need to add the JitPack repository to your build file.

• In Maven:

  <repositories>
     <repository>
       <id>jitpack.io</id>
       <url>https://jitpack.io</url>
     </repository>
  </repositories>
  

  then

  <dependency>
    <groupId>com.github.databricks</groupId>
    <artifactId>spark-redshift_2.10</artifactId>  <!-- For Scala 2.11, use spark-redshift_2.11 instead -->
    <version>master-SNAPSHOT</version>
  </dependency>
  

• In SBT:

  resolvers += "jitpack" at "https://jitpack.io"
  

  then

  libraryDependencies += "com.github.databricks" %% "spark-redshift" % "master-SNAPSHOT"
  

• In Databricks: use the "Advanced Options" toggle in the "Create Library" screen to specify a custom Maven repository:

  

  Use https://jitpack.io as the repository.

  • For Scala 2.10: use the coordinate com.github.databricks:spark-redshift_2.10:master-SNAPSHOT
  • For Scala 2.11: use the coordinate com.github.databricks:spark-redshift_2.11:master-SNAPSHOT

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

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()

// Using IAM Role based authentication
df.write
  .format("com.databricks.spark.redshift")
  .option("url", "jdbc:redshift://redshifthost:5439/database?user=username&password=pass")
  .option("dbtable", "my_table_copy")
  .option("aws_iam_role", "arn:aws:iam::123456789000:role/redshift_iam_role")
  .option("tempdir", "s3n://path/for/temp/data")
  .mode("error")
  .save()

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()

# Using IAM Role based authentication
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") \
  .option("aws_iam_role", "arn:aws:iam::123456789000:role/redshift_iam_role") \
  .mode("error") \
  .save()

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.

Reading data using R:

df <- read.df(
   NULL,
   "com.databricks.spark.redshift",
   tempdir = "s3n://path/for/temp/data",
   dbtable = "my_table",
   url = "jdbc:redshift://redshifthost:5439/database?user=username&password=pass")

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]])

The use of this library involves several connections which must be authenticated / secured, all of which are illustrated in the following diagram:

                            ┌───────┐
       ┌───────────────────▶│  S3   │◀─────────────────┐
       │    IAM or keys     └───────┘    IAM or keys   │
       │                        ▲                      │
       │                        │ IAM or keys          │
       ▼                        ▼               ┌──────▼────┐
┌────────────┐            ┌───────────┐         │┌──────────┴┐
│  Redshift  │            │   Spark   │         ││   Spark   │
│            │◀──────────▶│  Driver   │◀────────▶┤ Executors │
└────────────┘            └───────────┘          └───────────┘
               JDBC with                  Configured
               username /                     in
                password                    Spark
            (can enable SSL)

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).

⚠️ 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. See the Encryption section of this document for a discussion of how these files may be encrypted.

The following describes how each connection can be authenticated:

• Spark driver to Redshift: The Spark driver connects to Redshift via JDBC using a username and password. Redshift does not support the use of IAM roles to authenticate this connection. This connection can be secured using SSL; for more details, see the Encryption section below.

• Spark to S3: S3 acts as a middleman to store bulk data when reading from or writing to Redshift. Spark connects to S3 using both the Hadoop FileSystem interfaces and directly using the Amazon Java SDK's S3 client.

  This connection can be authenticated using either AWS keys or IAM roles (DBFS mountpoints are not currently supported, so Databricks users who do not want to rely on AWS keys should use cluster IAM roles instead).

  There are multiple ways of providing these credentials:

  1. Default Credential Provider Chain (best option for most users): AWS credentials will automatically be retrieved through the DefaultAWSCredentialsProviderChain.

     If you use IAM instance roles to authenticate to S3 (e.g. on Databricks, EMR, or EC2), then you should probably use this method.

     If another method of providing credentials is used (methods 2 or 3), then that will take precedence over this default.

  2. Set keys in Hadoop conf: 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")

  3. 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](https://issues.apache.org/jira/browse/HADOOP-3733), this
  approach will not work for secret keys which contain forward slash (`/`) characters, even if
  those characters are urlencoded.
  

• Redshift to S3: Redshift also connects to S3 during COPY and UNLOAD queries. There are three methods of authenticating this connection:

  1. Have Redshift assume an IAM role (most secure): You can grant Redshift permission to assume an IAM role during COPY or UNLOAD operations and then configure this library to instruct Redshift to use that role:

     1. Create an IAM role granting appropriate S3 permissions to your bucket.
     2. Follow the guide Authorizing Amazon Redshift to Access Other AWS Services On Your Behalf to configure this role's trust policy in order to allow Redshift to assume this role.
     3. Follow the steps in the Authorizing COPY and UNLOAD Operations Using IAM Roles guide to associate that IAM role with your Redshift cluster.
     4. Set this library's aws_iam_role option to the role's ARN.

  2. Forward Spark's S3 credentials to Redshift: if the forward_spark_s3_credentials option is set to true then this library will automatically discover the credentials that Spark is using to connect to S3 and will forward those credentials to Redshift over JDBC. If Spark is authenticating to S3 using an IAM instance role then a set of temporary STS credentials will be passed to Redshift; otherwise, AWS keys will be passed. These credentials are sent as part of the JDBC query, so therefore it is strongly recommended to enable SSL encryption of the JDBC connection when using this authentication method.

  3. Use Security Token Service (STS) credentials: You may 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 credentials are sent as part of the JDBC query, so therefore it is strongly recommended to enable SSL encryption of the JDBC connection when using this authentication method. If you choose this option then please be aware of the risk that the credentials expire before the read / write operation succeeds.

  These three options are mutually-exclusive and you must explicitly choose which one to use.

• Securing JDBC: The Redshift and Postgres JDBC drivers both support SSL. To enable SSL support, first configure Java to add the required certificates by following the Using SSL and Server Certificates in Java instructions in the Redshift documentation. Then, follow the instructions in JDBC Driver Configuration Options to add the appropriate SSL options to the JDBC url used with this library.

• Encrypting UNLOAD data stored in S3 (data stored when reading from Redshift): According to the Redshift documentation on Unloading Data to S3, "UNLOAD automatically encrypts data files using Amazon S3 server-side encryption (SSE-S3)."

  Redshift also supports client-side encryption with a custom key (see: Unloading Encrypted Data Files) but this library currently lacks the capability to specify the required symmetric key.

• Encrypting COPY data stored in S3 (data stored when writing to Redshift): According to the Redshift documentation on Loading Encrypted Data Files from Amazon S3:

  You can use the COPY command to load data files that were uploaded to Amazon S3 using server-side encryption with AWS-managed encryption keys (SSE-S3 or SSE-KMS), client-side encryption, or both. COPY does not support Amazon S3 server-side encryption with a customer-supplied key (SSE-C)

  To use this capability, you should configure your Hadoop S3 FileSystem to use encryption by setting the appropriate configuration properties (which will vary depending on whether you are using s3a, s3n, EMRFS, etc.). Note that the MANIFEST file (a list of all files written) will not be encrypted.

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

<p>Since setting <tt>usestagingtable=false</tt> operation risks data loss / unavailability, we have chosen to deprecate it in favor of requiring users to manually drop the destination table themselves.</p>
</td>

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()

If you need to manually set a column type, you can use the redshift_type column metadata. For example, if you desire to override the Spark SQL Schema -> Redshift SQL type matcher to assign a user-defined column type, you can do the following:

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

// Specify the custom width of each column
val columnTypeMap = Map(
  "language_code" -> "CHAR(2)",
  "country_code" -> "CHAR(2)",
  "url" -> "BPCHAR(111)"
)

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

// Apply each column metadata customization
columnTypeMap.foreach { case (colName, colType) =>
  val metadata = new MetadataBuilder().putString("redshift_type", colType).build()
  df = df.withColumn(colName, df(colName).as(colName, metadata))
}

When creating a table, this library can be configured to use a specific compression encoding on individual columns. You can use the encoding column metadata field to specify a compression encoding for each column (see Amazon docs for available encodings).

Redshift allows columns to have descriptions attached that should show up in most query tools (using the COMMENT command). You can set the description column metadata field to specify a description for individual columns.

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

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.

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.

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

Creating a new table (SaveMode.CreateIfNotExists): 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. Both of these operations are performed in a single transaction.

Overwriting an existing table: By default, this library uses transactions to perform overwrites, which are implemented by deleting the destination table, creating a new empty table, and appending rows to it.

If the deprecated usestagingtable setting is set to false then this library will commit the DELETE TABLE command before appending rows to the new table, sacrificing the atomicity of the overwrite operation but reducing the amount of staging space that Redshift needs during the overwrite.

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.

By default, S3 <-> Redshift copies will not work if the S3 bucket and Redshift cluster are in different AWS regions.

If you attempt to perform a read of a Redshift table and the regions are mismatched then you may see a confusing error, such as

java.sql.SQLException: [Amazon](500310) Invalid operation: S3ServiceException:The bucket you are attempting to access must be addressed using the specified endpoint. Please send all future requests to this endpoint.

Similarly, attempting to write to Redshift using a S3 bucket in a different region may cause the following error:

error:  Problem reading manifest file - S3ServiceException:The bucket you are attempting to access must be addressed using the specified endpoint. Please send all future requests to this endpoint.,Status 301,Error PermanentRedirect

For writes: Redshift's COPY command allows the S3 bucket's region to be explicitly specified, so you can make writes to Redshift work properly in these cases by adding

region 'the-region-name'

to the extracopyoptions setting. For example, with a bucket in the US East (Virginia) region and the Scala API, use

.option("extracopyoptions", "region 'us-east-1'")

For reads: According to its documentation, the Redshift UNLOAD command does not support writing to a bucket in a different region:

Important

The Amazon S3 bucket where Amazon Redshift will write the output files must reside in the same region as your cluster.

As a result, this use-case is not supported by this library. The only workaround is to use a new bucket in the same region as your Redshift cluster.

• Version 3.0 now requires forward_spark_s3_credentials to be explicitly set before Spark S3 credentials will be forwarded to Redshift. Users who use the aws_iam_role or temporary_aws_* authentication mechanisms will be unaffected by this change. Users who relied on the old default behavior will now need to explicitly set forward_spark_s3_credentials to true to continue using their previous Redshift to S3 authentication mechanism. For a discussion of the three authentication mechanisms and their security trade-offs, see the Authenticating to S3 and Redshift section of this README.