Build a Scala application using Apache Spark and YugabyteDB

The following tutorial describes how to use Scala's Spark API spark-shell with YugabyteDB, and perform YSQL queries.


This tutorial assumes that you have:

  • YugabyteDB running. If you are new to YugabyteDB, follow the steps in Quick start.
  • Java Development Kit (JDK) 1.8. JDK installers for Linux and macOS can be downloaded from OpenJDK, AdoptOpenJDK, or Azul Systems. Homebrew users on macOS can install using brew install AdoptOpenJDK/openjdk/adoptopenjdk8.
  • Apache Spark 3.3.0.

Start Scala Spark shell with YugabyteDB driver

From your Spark installation directory, use the following command to start spark-shell, and pass the YugabyteDB driver package with the --packages parameter. The command fetches the YugabyteDB driver from local cache (if present), or installs the driver from maven central.

./bin/spark-shell --packages com.yugabyte:jdbc-yugabytedb:42.3.0

The Scala prompt should be available as follows:

Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 3.3.0

Using Scala version 2.12.15 (OpenJDK 64-Bit Server VM, Java 1.8.0_292)
Type in expressions to have them evaluated.
Type :help for more information.

Set up the database

  1. From your YugabyteDB installation directory, use ysqlsh shell to read and write directly to the database as follows:

  2. Create a database ysql_spark_shell and connect to it using the following:

    yugabyte=# CREATE DATABASE ysql_spark_shell;
    yugabyte=# \c ysql_spark_shell;
    You are now connected to database "ysql_spark_shell" as user "yugabyte".
  3. Create a table in the ysql_spark_shell database to read and write data through the JDBC connector as follows:

    ysql_spark_shell=# CREATE TABLE test AS SELECT generate_series(1,100000) AS id, random(), ceil(random() * 20);

Set up connectivity with YugabyteDB

From your Scala prompt, set up the connection URL and properties to read and write data through the JDBC connector to YugabyteDB.

scala> val jdbcUrl = s"jdbc:yugabytedb://localhost:5433/ysql_spark_shell"
scala> import java.util.Properties
scala> val connectionProperties = new Properties()
scala> connectionProperties.put("user", s"yugabyte")
scala> connectionProperties.put("password", s"yugabyte")
scala> connectionProperties.setProperty("Driver", "com.yugabyte.Driver")
jdbcUrl: String = jdbc:yugabytedb://localhost:5433/ysql_spark_shell
import java.util.Properties
connectionProperties: java.util.Properties = {user=yugabyte, password=yugabyte, Driver=com.yugabyte.Driver}
res60: Object = null

Store and retrieve data

To store and retrieve data using the JDBC connector, create a DataFrame in one of the following ways:

Use DataFrame API

Create a DataFrame for the test table to read data via the JDBC connector using the following:

scala> val test_Df =, "test", connectionProperties)

Use SQL queries

A;ternatively, you can use SQL queries to create a DataFrame which pushes down the queries to YugabyteDB through the JDBC connector to fetch the rows, and create a DataFrame for that result.

scala la> val test_Df =, table="(select * from test) test_alias", connectionProperties)

Output the schema of the DataFrame created as follows:

scala> test_Df.printSchema
|-- id: integer (nullable = true)
|-- random: double (nullable = true)
|-- ceil: double (nullable = true)

Read some data from the table using the DataFrame APIs:

|ceil    |  sum(id)|
|     8.0|248688663|
|     7.0|254438906|
|    18.0|253717793|
|     1.0|253651826|
|     4.0|251144069|
|    11.0|252091080|
|    14.0|244487874|
|    19.0|256220339|
|     3.0|247630466|
|     2.0|249126085|

Use spark.sql() API

Another alternative is to use the spark.sql() API to directly execute SQL queries from Scala shell using the following code:

scala>spark.sql("select ceil, sum(id) from test group by ceil limit 10").show

The output will be similar to SQL queries.

The following spark query renames the column of the table test from ceil to round_off in the DataFrame, then creates a new table with the schema of the changed DataFrame, inserts all its data in the new table, and names it as test_copy using the JDBC connector.

scala> spark.table("test").withColumnRenamed("ceil", "round_off").write.jdbc(jdbcUrl, "test_copy"connectionProperties)

Verify that the new table test_copy is created with the changed schema, and all the data from test is copied to it using the following commands from your ysqlsh terminal:

ysql_spark_shell=# \dt
           List of relations
 Schema |   Name    | Type  |  Owner
 public | test_copy | table | yugabyte
 public | test      | table | yugabyte
(2 rows)
ysql_spark_shell=# \d test_copy
                   Table "public.test_copy"
  Column   |       Type       | Collation | Nullable | Default
 id        | integer          |           |          |
 random    | double precision |           |          |
 round_off | double precision |           |          |
ysql_spark_shell=# SELECT COUNT(*) FROM test_copy;
(1 row)

Use the append SaveMode to append data from test_copy to the test table as follows:

scala> import org.apache.spark.sql.SaveMode
scala> val test_copy_Df =, table="(select * from test_copy) test_copy_alias"connectionProperties)
scala> spark.table("test_copy").write.mode(SaveMode.Append).jdbc(jdbcUrl, "test", connectionProperties)

Verify the changes using ysqlsh:

ysql_spark_shell=# SELECT COUNT(*) FROM test;
(1 row)


To maintain parallelism while fetching the table content, create a DataFrame for the table test with some specific options as follows:

scala> val new_test_df ="jdbc")
              .option("url", "jdbc:yugabytedb://")
              .option("dbtable", "test")
              .option("user", "yugabyte")
              .option("password", "yugabyte")
              .option("driver", "com.yugabyte.Driver")
              .option("load-balance", "true")
              .option("numPartitions", 5)
              .option("partitionColumn", "ceil")
              .option("lowerBound", 0)
              .option("upperBound", 20)
              .option("pushDownPredicate", true)
              .option("pushDownAggregate", true).load
scala> new_test_df.createOrReplaceTempView("test")
scala> spark.sql("select sum(ceil) from test where id > 50000").show

The options help in breaking down the whole task into numPartitions parallel tasks on the basis of the partitionColumn, with the help of minimum and maximum value of the column; where,

  • numPartitions - divides the whole task to numPartitions parallel tasks.
  • lowerBound - minimum value of the partitionColumn in a table.
  • upperBound - maximum value of the partitionColumn in a table.
  • partitionColumn - the column on the basis of which a partition occurs.

Additionally, the following two options help in optimizing the SQL queries executing on this DataFrame if those SQL queries consist of some filters or aggregate functions by pushing down those filters or aggregates to YugabyteDB using the JDBC connector.

  • pushDownPredicate - optimizes the query by pushing down the filters to YugabyteDB using the JDBC connector.
  • pushDownAggregate - optimizes the query by pushing down the aggregates to YugabyteDB using the JDBC connector.

Verify parallelism

To verify that the spark job is created,

  1. Navigate to the Spark UI using https://localhost:4040. If your port 4040 is in use, then change the port to the one mentioned when you started the spark-sql shell.

  2. From the SQL/DataFrame tab, click the last executed SQL statement to see if numPartitions=5 is displayed as shown in the following image: