Deploy ScalarDB Analytics in Public Cloud Environments

This guide explains how to deploy ScalarDB Analytics in a public cloud environment. ScalarDB Analytics currently uses Apache Spark as an execution engine and supports managed Spark services provided by public cloud providers, such as Amazon EMR and Databricks.

Supported managed Spark services and their application types

ScalarDB Analytics supports the following managed Spark services and application types.

Public Cloud Service	Spark Driver	Spark Connect	JDBC
Amazon EMR (EMR on EC2)	✅	✅	❌
Databricks	✅	❌	✅

Configure and deploy

Select your public cloud environment, and follow the instructions to set up and deploy ScalarDB Analytics.

Amazon EMR

Use Amazon EMR

You can use Amazon EMR (EMR on EC2) to run analytical queries through ScalarDB Analytics. For the basics to launch an EMR cluster, please refer to the AWS EMR on EC2 documentation.

ScalarDB Analytics configuration

To enable ScalarDB Analytics, you need to add the following configuration to the Software setting when you launch an EMR cluster. Be sure to replace the content in the angle brackets:

[
  {
    "Classification": "spark-defaults",
    "Properties": {
      "spark.jars.packages": "com.scalar-labs:scalardb-analytics-spark-all-<SPARK_VERSION>_<SCALA_VERSION>:<SCALARDB_ANALYTICS_VERSION>",
      "spark.sql.catalog.<CATALOG_NAME>": "com.scalar.db.analytics.spark.ScalarDbAnalyticsCatalog",
      "spark.sql.extensions": "com.scalar.db.analytics.spark.extension.ScalarDbAnalyticsExtensions",
      "spark.sql.catalog.<CATALOG_NAME>.license.cert_pem": "<YOUR_LICENSE_CERT_PEM>",
      "spark.sql.catalog.<CATALOG_NAME>.license.key": "<YOUR_LICENSE_KEY>",

      // Add your data source configuration below
    }
  }
]

The following describes what you should change the content in the angle brackets to:

• <SPARK_VERSION>: The version of Spark (3.5 or 3.4).
• <SCALA_VERSION>: The version of Scala used to build Spark (2.13 or 2.12).
• <SCALARDB_ANALYTICS_VERSION>: The version of ScalarDB Analytics.
• <CATALOG_NAME>: The name of the catalog.
• <YOUR_LICENSE_CERT_PEM>: The PEM encoded license certificate.
• <YOUR_LICENSE_KEY>: The license key.

For more details, refer to Set up ScalarDB Analytics in the Spark configuration.

Run analytical queries via the Spark driver

After the EMR Spark cluster has launched, you can use ssh to connect to the primary node of the EMR cluster and run your Spark application. For details on how to create a Spark Driver application, refer to Spark Driver application.

Run analytical queries via Spark Connect

You can use Spark Connect to run your Spark application remotely by using the EMR cluster that you launched.

You first need to configure the Software setting in the same way as the Spark Driver application. You also need to set the following configuration to enable Spark Connect.

Allow inbound traffic for a Spark Connect server

1. Create a security group to allow inbound traffic for a Spark Connect server. (Port 15001 is the default).
2. Allow the role of "Amazon EMR service role" to attach the security group to the primary node of the EMR cluster.
3. Add the security group to the primary node of the EMR cluster as "Additional security groups" when you launch the EMR cluster.

Launch the Spark Connect server via a bootstrap action

1. Create a script file to launch the Spark Connect server as follows:

   #!/usr/bin/env bash
   
   set -eu -o pipefail
   cd /var/lib/spark
   sudo -u spark /usr/lib/spark/sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_<SCALA_VERSION>:<SPARK_FULL_VERSION>,com.scalar-labs:scalardb-analytics-spark-all-<SPARK_VERSION>_<SCALA_VERSION>:<SCALARDB_ANALYTICS_VERSION>

   The following describes what you should change the content in the angle brackets to:

   • <SCALA_VERSION>: The major and minor version of Scala that matches your Spark installation (2.12 or 2.13).
   • <SPARK_FULL_VERSION>: The full version of Spark you are using (such as 3.5.3).
   • <SPARK_VERSION>: The major and minor version of Spark you are using (3.4 or 3.5).
   • <SCALARDB_ANALYTICS_VERSION>: The version of ScalarDB Analytics.

2. Upload the script file to S3.

3. Allow the role of "EC2 instance profile for Amazon EMR" to access the uploaded script file in S3.

4. Add the uploaded script file to "Bootstrap actions" when you launch the EMR cluster.

Run analytical queries

You can run your Spark application via Spark Connect from anywhere by using the remote URL of the Spark Connect server, which is sc://<PRIMARY_NODE_PUBLIC_HOSTNAME>:15001.

For details on how to create a Spark application by using Spark Connect, refer to Spark Connect application.

Databricks

Use Databricks

You can use Databricks to run analytical queries through ScalarDB Analytics.

note

Note that Databricks provides a modified version of Apache Spark, which works differently from the original Apache Spark.

Prepare the secret values for the license certificate and license key

Store the license certificate and license key in the cluster by using the Databricks CLI.

databricks secrets create-scope scalardb-analytics-secret # you can use any secret scope name
cat license_key.json | databricks secrets put-secret scalardb-analytics-secret license-key
cat license_cert.pem | databricks secrets put-secret scalardb-analytics-secret license-cert

note

For details on how to install and use the Databricks CLI, refer to the Databricks CLI documentation.

Prepare an init script for loading the ScalarDB Analytics library JAR

1. Download the ScalarDB Analytics library JAR file from the Maven repository. Choose the appropriate JAR file based on your Spark, Scala, and ScalarDB versions:

   • scalardb-analytics-spark-all-3.4_2.13 (Spark v3.4, Scala v2.13)
   • scalardb-analytics-spark-all-3.5_2.13 (Spark v3.5, Scala v2.13)
   • scalardb-analytics-spark-all-3.4_2.12 (Spark v3.4, Scala v2.12)
   • scalardb-analytics-spark-all-3.5_2.12 (Spark v3.5, Scala v2.12)

2. Upload the JAR file to the Databricks workspace.

3. Create an init script as follows, replacing <PATH_TO_YOUR_JAR_FILE_IN_WORKSPACE> with the path to your JAR file in the Databricks workspace:

   #!/bin/bash
   
   # Target directories
   TARGET_DIRECTORIES=("/databricks/jars" "/databricks/hive_metastore_jars")
   JAR_PATH="<PATH_TO_YOUR_JAR_FILE_IN_WORKSPACE>"
   
   # Copy the JAR file to the target directories
   for TARGET_DIR in "${TARGET_DIRECTORIES[@]}"; do
    mkdir -p "$TARGET_DIR"
    cp "$JAR_PATH" "$TARGET_DIR/"
   done

4. Upload the init script to the Databricks workspace.

Launch Databricks compute

ScalarDB Analytics works with all-purpose compute on Databricks. When you launch compute, you need to configure the compute to enable ScalarDB Analytics as follows:

1. Select Create compute in the Compute menu.

2. Select Unrestricted from the Policy dropdown menu.

3. Select an appropriate Databricks runtime version that supports Spark 3.4 or 3.5.

4. Go to the Advanced section. In the Access mode tab, select Manual as the access mode, and choose No isolation shared.

5. In the Advanced section, select the Spark tab, and enter the following configurations in Spark config:

   spark.sql.extensions com.scalar.db.analytics.spark.extension.ScalarDbAnalyticsExtensions
   spark.sql.catalog.<CATALOG_NAME> com.scalar.db.analytics.spark.ScalarDbAnalyticsCatalog
   spark.sql.catalog.<CATALOG_NAME>.license.cert_pem <YOUR_LICENSE_CERT_PEM>
   spark.sql.catalog.<CATALOG_NAME>.license.key <YOUR_LICENSE_KEY>

   Replace the placeholders:

   • <CATALOG_NAME>: The name of the catalog. This must match a catalog created on the ScalarDB Analytics server.
   • <YOUR_LICENSE_CERT_PEM>: The PEM encoded license certificate.
   • <YOUR_LICENSE_KEY>: The license key.

6. In the Advanced section, select the init scripts tab, and specify the path to the init script in the workspace you uploaded.

7. Select Create.

Run analytical queries via the Spark Driver

You can run your Spark application on the properly configured Databricks compute with Databricks Notebook or Databricks Jobs to access the tables in ScalarDB Analytics. To run the Spark application, you can migrate your Pyspark, Scala, or Spark SQL application to Databricks Notebook, or use Databricks Jobs to run your Spark application. ScalarDB Analytics works with task types for Notebook, Python, JAR, and SQL.

For more details on how to use Databricks Jobs, refer to the Databricks Jobs documentation.

Run analytical queries via the JDBC driver

Databricks supports JDBC to run SQL jobs on compute. After compute is launched, you can get the JDBC URL of the compute in the Advanced > JDBC/ODBC tab. To connect to the compute by using JDBC, you need to add the Databricks JDBC driver to your application dependencies. For example, if you are using Gradle, you can add the following dependency to your build.gradle file after replacing <DRIVER_VERSION> with the version of the Databricks JDBC driver you want to use:

implementation("com.databricks:databricks-jdbc:<DRIVER_VERSION>")

Then, you can connect to the compute by using JDBC with the JDBC URL (<YOUR_COMPUTE_JDBC_URL>), as is common with JDBC applications.

Class.forName("com.databricks.client.jdbc.Driver");
String url = "<YOUR_COMPUTE_JDBC_URL>";
Connection conn = DriverManager.getConnection(url);

For more details on how to use JDBC with Databricks, refer to the Databricks JDBC Driver documentation.