Cloud Storage connector

The Cloud Storage connector is an open source Java library that lets you run Apache Hadoop or Apache Spark jobs directly on data in Cloud Storage, and offers a number of benefits over choosing the Hadoop Distributed File System (HDFS).

Connector Support. The Cloud Storage connector is supported by Google Cloud Platform for use with GCP products and use cases, and when used with Cloud Dataproc is supported at the same level as Cloud Dataproc (see Getting support).

Benefits of the Cloud Storage connector

• Direct data access – Store your data in Cloud Storage and access it directly, with no need to transfer it into HDFS first.
• HDFS compatibility – You can easily access your data in Cloud Storage using the gs:// prefix instead of hdfs://.
• Interoperability – Storing data in Cloud Storage enables seamless interoperability between Spark, Hadoop, and Google services.
• Data accessibility – When you shut down a Hadoop cluster, you still have access to your data in Cloud Storage, unlike HDFS.
• High data availability – Data stored in Cloud Storage is highly available and globally replicated without a loss of performance.
• No storage management overhead – Unlike HDFS, Cloud Storage requires no routine maintenance such as checking the file system, upgrading or rolling back to a previous version of the file system, etc.
• Quick startup – In HDFS, a MapReduce job can't start until the NameNode is out of safe mode—a process that can take from a few seconds to many minutes depending on the size and state of your data. With Cloud Storage, you can start your job as soon as the task nodes start, leading to significant cost savings over time.

Getting the connector

Cloud Dataproc clusters

The Cloud Storage connector is installed by default on all Cloud Dataproc cluster nodes under /usr/lib/hadoop/lib/. It's available in both Spark and PySpark environments.

If your application uses a connector version that is different from the connector version deployed on your Cloud Dataproc cluster, you must either:

1. create a new cluster with an initialization action that installs the connector version used by your application, or
2. include and relocate the connector classes and connector dependencies for the version you are using into your application's jar so that the connector version you are using will not conflict with the connector version deployed on your Cloud Dataproc cluster (see this example of dependencies relocation in Maven).

Other Spark/Hadoop clusters

You can download the Cloud Storage connector for Hadoop 1.x or the Cloud Storage connector for Hadoop 2.x. See Installing the Cloud Storage connector to install and configure the connector.

Using the connector

There are multiple ways to access data stored in Cloud Storage:

• In a Spark (or PySpark) or Hadoop application using the gs:// prefix.
• The hadoop shell: hadoop fs -ls gs://CONFIGBUCKET/dir/file.
• The GCP Console Cloud Storage browser.
• Using the gsutil cp or gsutil rsync commands.

Resources

• Use the Cloud Storage connector with Apache Spark
• Apache Hadoop FileSystem API

Java version

The Cloud Storage connector requires Java 8.

Apache Maven Dependency Information

<dependency>
    <groupId>com.google.cloud.bigdataoss</groupId>
    <artifactId>gcs-connector</artifactId>
    <version>insert "hadoopX-X.X.X" connector version number here</version>
    <scope>provided</scope>
</dependency>

For more detailed information, see the Cloud Storage connector release notes and Javadoc reference.

What's next

• Learn more about Cloud Storage