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Migrate to Azure Managed Instance for Apache Cassandra using Apache Spark


This resource is based on an article originally published here.

Where possible, we recommend using Apache Cassandra native replication to migrate data from your existing cluster into Azure Managed Instance for Apache Cassandra by configuring a hybrid cluster. This approach will use Apache Cassandra's gossip protocol to replicate data from your source data-center into your new managed instance datacenter. However, there may be some scenarios where your source database version isn't compatible, or a hybrid cluster setup is otherwise not feasible.

This tutorial describes how to migrate data to Migrate to Azure Managed Instance for Apache Cassandra in an offline fashion using the Cassandra Spark Connector, and Azure Databricks for Apache Spark.


Provision an Azure Databricks cluster

We recommend selecting Databricks runtime version 7.5, which supports Spark 3.0.

Screenshot that shows finding the Databricks runtime version.

Add dependencies

Add the Apache Spark Cassandra Connector library to your cluster to connect to both native and Azure Cosmos DB Cassandra endpoints. In your cluster, select Libraries > Install New > Maven, and then add com.datastax.spark:spark-cassandra-connector-assembly_2.12:3.0.0 in Maven coordinates.

Screenshot that shows searching for Maven packages in Databricks.

Select Install, and then restart the cluster when installation is complete.


Make sure that you restart the Databricks cluster after the Cassandra Connector library has been installed.

Create Scala Notebook for migration

Create a Scala Notebook in Databricks. Replace your source and target Cassandra configurations with the corresponding credentials, and source and target keyspaces and tables. Then run the following code:

import com.datastax.spark.connector._
import com.datastax.spark.connector.cql._
import org.apache.spark.SparkContext
// source cassandra configs
val sourceCassandra = Map( 
    "" -> "<Source Cassandra Host>",
    "spark.cassandra.connection.port" -> "9042",
    "spark.cassandra.auth.username" -> "<USERNAME>",
    "spark.cassandra.auth.password" -> "<PASSWORD>",
    "spark.cassandra.connection.ssl.enabled" -> "false",
    "keyspace" -> "<KEYSPACE>",
    "table" -> "<TABLE>"
//target cassandra configs
val targetCassandra = Map( 
    "" -> "<Source Cassandra Host>",
    "spark.cassandra.connection.port" -> "9042",
    "spark.cassandra.auth.username" -> "<USERNAME>",
    "spark.cassandra.auth.password" -> "<PASSWORD>",
    "spark.cassandra.connection.ssl.enabled" -> "true",
    "keyspace" -> "<KEYSPACE>",
    "table" -> "<TABLE>",
    //throughput related settings below - tweak these depending on data volumes. 
    "spark.cassandra.output.batch.size.rows"-> "1",
    "spark.cassandra.output.concurrent.writes" -> "1000",
    "spark.cassandra.connection.remoteConnectionsPerExecutor" -> "10",
    "spark.cassandra.concurrent.reads" -> "512",
    "spark.cassandra.output.batch.grouping.buffer.size" -> "1000",
    "spark.cassandra.connection.keep_alive_ms" -> "600000000"
//Read from source Cassandra
val DFfromSourceCassandra = sqlContext
//Write to target Cassandra
  .mode(SaveMode.Append) // only required for Spark 3.x


If you have a need to preserve the original writetime of each row, refer to the cassandra migrator sample.

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