External Apache Hive metastore (legacy)

This article describes how to set up Databricks clusters to connect to existing external Apache Hive metastores. It provides information about metastore deployment modes, recommended network setup, and cluster configuration requirements, followed by instructions for configuring clusters to connect to an external metastore. For Hive library versions included in Databricks Runtime, see the relevant Databricks Runtime version release notes.

Important

  • SQL Server does not work as the underlying metastore database for Hive 2.0 and above.

  • If you use Azure Database for MySQL as an external metastore, you must change the value of the lower_case_table_names property from 1 (the default) to 2 in the server-side database configuration. For details, see Identifier Case Sensitivity.

Note

Using external metastores is a legacy data governance model. Databricks recommends that you upgrade to Unity Catalog. Unity Catalog simplifies security and governance of your data by providing a central place to administer and audit data access across multiple workspaces in your account. See What is Unity Catalog?.

Hive metastore deployment modes

In a production environment, you can deploy a Hive metastore in two modes: local and remote.

Local mode

The metastore client running inside a cluster connects to the underlying metastore database directly via JDBC.

Remote mode

Instead of connecting to the underlying database directly, the metastore client connects to a separate metastore service via the Thrift protocol. The metastore service connects to the underlying database. When running a metastore in remote mode, DBFS is not supported.

For more details about these deployment modes, see the Hive documentation.

Note

The examples in this document use MySQL as the underlying metastore database.

Cluster configurations

You must set three sets of configuration options to connect a cluster to an external metastore:

  • Spark options configure Spark with the Hive metastore version and the JARs for the metastore client.

  • Hive options configure the metastore client to connect to the external metastore.

Spark configuration options

Set spark.sql.hive.metastore.version to the version of your Hive metastore and spark.sql.hive.metastore.jars as follows:

  • Hive 0.13: do not set spark.sql.hive.metastore.jars.

    Note

    Hive 1.2.0 and 1.2.1 are not the built-in metastore on Databricks Runtime 7.0 and above. If you want to use Hive 1.2.0 or 1.2.1 with Databricks Runtime 7.0 and above, follow the procedure described in Download the metastore jars and point to them.

  • Hive 2.3.7 (Databricks Runtime 7.0 - 9.x) or Hive 2.3.9 (Databricks Runtime 10.0 and above): set spark.sql.hive.metastore.jars to builtin.

  • For all other Hive versions, Databricks recommends that you download the metastore JARs and set the configuration spark.sql.hive.metastore.jars to point to the downloaded JARs using the procedure described in Download the metastore jars and point to them.

Download the metastore jars and point to them

  1. Create a cluster with spark.sql.hive.metastore.jars set to maven and spark.sql.hive.metastore.version to match the version of your metastore.

  2. When the cluster is running, search the driver log and find a line like the following:

    17/11/18 22:41:19 INFO IsolatedClientLoader: Downloaded metastore jars to <path>

    The directory <path> is the location of downloaded JARs in the driver node of the cluster.

    Alternatively you can run the following code in a Scala notebook to print the location of the JARs:

    import com.typesafe.config.ConfigFactory
val path = ConfigFactory.load().getString("java.io.tmpdir")

println(s"\nHive JARs are downloaded to the path: $path \n")

  3. Run %sh cp -r <path> /dbfs/hive_metastore_jar (replacing <path> with your cluster’s info) to copy this directory to a directory in DBFS root called hive_metastore_jar through the DBFS client in the driver node.

  4. Create an init script that copies /dbfs/hive_metastore_jar to the local filesystem of the node, making sure to make the init script sleep a few seconds before it accesses the DBFS client. This ensures that the client is ready.

  5. Set spark.sql.hive.metastore.jars to use this directory. If your init script copies /dbfs/hive_metastore_jar to /databricks/hive_metastore_jars/, set spark.sql.hive.metastore.jars to /databricks/hive_metastore_jars/*. The location must include the trailing /*.

  6. Restart the cluster.

Hive configuration options

This section describes options specific to Hive.

Set up an external metastore using the UI

To set up an external metastore using the Databricks UI:

  1. Click the Clusters button on the sidebar.

  2. Click Create Cluster.

  3. Enter the following Spark configuration options:

    Local mode

    # Hive specific configuration options.
# spark.hadoop prefix is added to make sure these Hive specific options will propagate to the metastore client.
spark.hadoop.javax.jdo.option.ConnectionURL jdbc:mysql://<mysql-host>:<mysql-port>/<metastore-db>

# Driver class name for a JDBC metastore (Runtime 3.4 and later)
spark.hadoop.javax.jdo.option.ConnectionDriverName org.mariadb.jdbc.Driver

# Driver class name for a JDBC metastore (prior to Runtime 3.4)
# spark.hadoop.javax.jdo.option.ConnectionDriverName com.mysql.jdbc.Driver

spark.hadoop.javax.jdo.option.ConnectionUserName <mysql-username>
spark.hadoop.javax.jdo.option.ConnectionPassword <mysql-password>

# Spark specific configuration options
spark.sql.hive.metastore.version <hive-version>
# Skip this one if <hive-version> is 0.13.x.
spark.sql.hive.metastore.jars <hive-jar-source>

    Remote mode

    # Hive specific configuration option
# spark.hadoop prefix is added to make sure these Hive specific options will propagate to the metastore client.
spark.hadoop.hive.metastore.uris thrift://<metastore-host>:<metastore-port>

# Spark specific configuration options
spark.sql.hive.metastore.version <hive-version>
# Skip this one if <hive-version> is 0.13.x.
spark.sql.hive.metastore.jars <hive-jar-source>

  4. Continue your cluster configuration, following the instructions in Compute configuration reference.

  5. Click Create Cluster to create the cluster.

Set up an external metastore using an init script

Init scripts let you connect to an existing Hive metastore without manually setting required configurations.

Local mode

  1. Create the base directory you want to store the init script in if it does not exist. The following example uses dbfs:/databricks/scripts.

  2. Run the following snippet in a notebook. The snippet creates the init script /databricks/scripts/external-metastore.sh in Databricks File System (DBFS). This init script writes required configuration options to a configuration file named 00-custom-spark.conf in a JSON-like format under /databricks/driver/conf/ inside every node of the cluster. Databricks provides default Spark configurations in the /databricks/driver/conf/spark-branch.conf file. Configuration files in the /databricks/driver/conf directory apply in reverse alphabetical order. If you want to change the name of the 00-custom-spark.conf file, make sure that it continues to apply before the spark-branch.conf file.

    dbutils.fs.put(
    "/databricks/scripts/external-metastore.sh",
    """#!/bin/sh
      |# Loads environment variables to determine the correct JDBC driver to use.
      |source /etc/environment
      |# Quoting the label (i.e. EOF) with single quotes to disable variable interpolation.
      |cat << 'EOF' > /databricks/driver/conf/00-custom-spark.conf
      |[driver] {
      |    # Hive specific configuration options for metastores in local mode.
      |    # spark.hadoop prefix is added to make sure these Hive specific options will propagate to the metastore client.
      |    "spark.hadoop.javax.jdo.option.ConnectionURL" = "jdbc:mysql://<mysql-host>:<mysql-port>/<metastore-db>"
      |    "spark.hadoop.javax.jdo.option.ConnectionUserName" = "<mysql-username>"
      |    "spark.hadoop.javax.jdo.option.ConnectionPassword" = "<mysql-password>"
      |
      |    # Spark specific configuration options
      |    "spark.sql.hive.metastore.version" = "<hive-version>"
      |    # Skip this one if <hive-version> is 0.13.x.
      |    "spark.sql.hive.metastore.jars" = "<hive-jar-source>"
      |
      |EOF
      |
      |case "$DATABRICKS_RUNTIME_VERSION" in
      |  "")
      |     DRIVER="com.mysql.jdbc.Driver"
      |     ;;
      |  *)
      |     DRIVER="org.mariadb.jdbc.Driver"
      |     ;;
      |esac
      |# Add the JDBC driver separately since must use variable expansion to choose the correct
      |# driver version.
      |cat << EOF >> /databricks/driver/conf/00-custom-spark.conf
      |    "spark.hadoop.javax.jdo.option.ConnectionDriverName" = "$DRIVER"
      |}
      |EOF
      |""".stripMargin,
    overwrite = true
)

  3. Configure your cluster with the init script.

  4. Restart the cluster.

Remote mode

  1. Create the base directory you want to store the init script in if it does not exist. The following example uses dbfs:/databricks/scripts.

  2. Run the following snippet in a notebook:

    dbutils.fs.put(
    "/databricks/scripts/external-metastore.sh",
    """#!/bin/sh
      |
      |# Quoting the label (i.e. EOF) with single quotes to disable variable interpolation.
      |cat << 'EOF' > /databricks/driver/conf/00-custom-spark.conf
      |[driver] {
      |    # Hive specific configuration options for metastores in remote mode.
      |    # spark.hadoop prefix is added to make sure these Hive specific options will propagate to the metastore client.
      |    "spark.hadoop.hive.metastore.uris" = "thrift://<metastore-host>:<metastore-port>"
      |
      |    # Spark specific configuration options
      |    "spark.sql.hive.metastore.version" = "<hive-version>"
      |    # Skip this one if <hive-version> is 0.13.x.
      |    "spark.sql.hive.metastore.jars" = "<hive-jar-source>"
      |
      |    # If you need to use AssumeRole, uncomment the following settings.
      |    # "spark.hadoop.fs.s3a.credentialsType" = "AssumeRole"
      |    # "spark.hadoop.fs.s3a.stsAssumeRole.arn" = "<sts-arn>"
      |}
      |EOF
      |""".stripMargin,
    overwrite = true
)

  3. Configure your cluster with the init script.

  4. Restart the cluster.

Troubleshooting

Clusters do not start (due to incorrect init script settings)

If an init script for setting up the external metastore causes cluster creation to fail, configure the init script to log, and debug the init script using the logs.

Error in SQL statement: InvocationTargetException

  • Error message pattern in the full exception stack trace:

    Caused by: javax.jdo.JDOFatalDataStoreException: Unable to open a test connection to the given database. JDBC url = [...]

    External metastore JDBC connection information is misconfigured. Verify the configured hostname, port, username, password, and JDBC driver class name. Also, make sure that the username has the right privilege to access the metastore database.

  • Error message pattern in the full exception stack trace:

    Required table missing : "`DBS`" in Catalog "" Schema "". DataNucleus requires this table to perform its persistence operations. [...]

    External metastore database not properly initialized. Verify that you created the metastore database and put the correct database name in the JDBC connection string. Then, start a new cluster with the following two Spark configuration options:

    datanucleus.schema.autoCreateTables true
datanucleus.fixedDatastore false

    In this way, the Hive client library will try to create and initialize tables in the metastore database automatically when it tries to access them but finds them absent.

Error in SQL statement: AnalysisException: Unable to instantiate org.apache.hadoop.hive.metastore.HiveMetastoreClient

Error message in the full exception stacktrace:

The specified datastore driver (driver name) was not found in the CLASSPATH

The cluster is configured to use an incorrect JDBC driver.

Setting datanucleus.autoCreateSchema to true doesn’t work as expected

By default, Databricks also sets datanucleus.fixedDatastore to true, which prevents any accidental structural changes to the metastore databases. Therefore, the Hive client library cannot create metastore tables even if you set datanucleus.autoCreateSchema to true. This strategy is, in general, safer for production environments since it prevents the metastore database to be accidentally upgraded.

If you do want to use datanucleus.autoCreateSchema to help initialize the metastore database, make sure you set datanucleus.fixedDatastore to false. Also, you may want to flip both flags after initializing the metastore database to provide better protection to your production environment.