In Apache Cassandra Lunch #49: Spark SQL for Cassandra Data Operations, we discuss how we can use Spark SQL for Cassandra data operations. The live recording of Cassandra Lunch, which includes a more in-depth discussion and a demo, is embedded below in case you were not able to attend live. If you would like to attend Apache Cassandra Lunch live, it is hosted every Wednesday at 12 PM EST. Register here now!

In the walkthrough below, we use the Catalog method from DataStax’s Spark Cassandra Connector. We could use Spark’s SQL shell; however, there are more logs, and using the Spark Scala Shell is more succinct for our purposes.

In the walkthrough below, we cover a few different items. We cover basic Cassandra schema commands, basic Cassandra data operations (Cassandra to Cassandra), and basic Cassandra data operations (source file to Cassandra). There are some limitations with what we can do with Spark SQL for Cassandra data operations, and you can find out more in the live recording of Cassandra Lunch #49 embedded below.

Walkthrough

You can either follow along below, or using this GitHub repository’s README.md

Prerequisites

• Docker
• Spark 3.0.X

1. Setup Dockerized Apache Cassandra

1.1 – Clone repo and cd into it

git clone https://github.com/Anant/example-cassandra-spark-sql.git

cd example-cassandra-spark-sql

1.2 – Start Apache Cassandra Container and Mount Directory

docker run --name cassandra -p 9042:9042 -d -v "$(pwd)":/example-cassandra-spark-sql cassandra:latest

1.3 – Run cqlsh

docker exec -it cassandra cqlsh

1.4 – Run setup.cql

source '/example-cassandra-spark-sql/setup.cql'

2. Start Spark Shell

2.1 – Navigate to Spark directory and start in standalone cluster mode

./sbin/start-master.sh

2.2 – Start worker and point it at the master

You can find your Spark master URL at localhost:8080

./sbin/start-slave.sh <master-url>

2.3 – Start Spark Shell

./bin/spark-shell --packages com.datastax.spark:spark-cassandra-connector_2.12:3.0.0 \
--master <spark-master-url> \
--conf spark.cassandra.connection.host=127.0.0.1 \
--conf spark.cassandra.connection.port=9042 \
--conf spark.sql.extensions=com.datastax.spark.connector.CassandraSparkExtensions \
--conf spark.sql.catalog.cassandra=com.datastax.spark.connector.datasource.CassandraCatalog

3. Basic Cassandra Schema Commands

We will cover some basic Cassandra Schema commands we can do with Spark SQL. More can this can be found here

3.1 – Create Table

spark.sql("CREATE TABLE cassandra.demo.testTable (key_1 Int, key_2 Int, key_3 Int, cc1 STRING, cc2 String, cc3 String, value String) USING cassandra PARTITIONED BY (key_1, key_2, key_3) TBLPROPERTIES (clustering_key='cc1.asc, cc2.desc, cc3.asc', compaction='{class=SizeTieredCompactionStrategy,bucket_high=1001}')")

3.2 – Alter Table

spark.sql("ALTER TABLE cassandra.demo.testTable ADD COLUMNS (newCol INT)")

spark.sql("describe table cassandra.demo.testTable").show

3.3 – Drop Table

spark.sql("DROP TABLE cassandra.demo.testTable")

spark.sql("SHOW TABLES from cassandra.demo").show

4. Basic Data Operations (Cassandra to Cassandra)

4.1 – Read

Perform a basic read

spark.sql("SELECT * from cassandra.demo.previous_employees_by_job_title").show

4.2 – Write

Write data to a table from another table and use SQL functions

spark.sql("INSERT INTO cassandra.demo.days_worked_by_previous_employees_by_job_title SELECT job_title, employee_id, employee_name, abs(datediff(last_day, first_day)) as number_of_days_worked from cassandra.demo.previous_employees_by_job_title")

4.3 – Joins

Join data from two tables together

spark.sql("""
SELECT cassandra.demo.previous_employees_by_job_title.job_title, cassandra.demo.previous_employees_by_job_title.employee_name, cassandra.demo.previous_employees_by_job_title.first_day, cassandra.demo.previous_employees_by_job_title.last_day, cassandra.demo.days_worked_by_previous_employees_by_job_title.number_of_days_worked 
FROM cassandra.demo.previous_employees_by_job_title 
LEFT JOIN cassandra.demo.days_worked_by_previous_employees_by_job_title ON cassandra.demo.previous_employees_by_job_title.employee_id=cassandra.demo.days_worked_by_previous_employees_by_job_title.employee_id 
WHERE cassandra.demo.days_worked_by_previous_employees_by_job_title.job_title='Dentist'
""").show

5. Truncate tables with CQLSH

TRUNCATE TABLE demo.previous_employees_by_job_title ; 

TRUNCATE TABLE demo.days_worked_by_previous_employees_by_job_title ; 

6. Basic Data Operations (Source File to Cassandra)

6.1 – Restart Spark Shell

./bin/spark-shell --packages com.datastax.spark:spark-cassandra-connector_2.12:3.0.0 \
--master spark://arpans-mbp.lan:7077 \
--conf spark.cassandra.connection.host=127.0.0.1 \
--conf spark.cassandra.connection.port=9042 \
--conf spark.sql.extensions=com.datastax.spark.connector.CassandraSparkExtensions \
--conf spark.sql.catalog.cassandra=com.datastax.spark.connector.datasource.CassandraCatalog \
--files /path/to/example-cassandra-spark-sql/previous_employees_by_job_title.csv 

6.2 – Load CSV data to df

val csv_df = spark.read.format("csv").option("header", "true").load("/path/to/example-cassandra-spark-sql/previous_employees_by_job_title.csv")

6.3 – Create temp view to use Spark SQL

csv_df.createOrReplaceTempView("source")

6.4 – Write into Cassandra table using Spark SQL

spark.sql("INSERT INTO cassandra.demo.previous_employees_by_job_title SELECT * from source")

And that will wrap up our basic walkthrough on Spark SQL for Cassandra data operations. Again, if you want to watch this demo live, be sure to check out the embedded live recording below! Also, if you missed last week’s Apache Cassandra Lunch #48: Airflow and Cassandra, be sure to check that out as well!

Resources

• https://github.com/datastax/spark-cassandra-connector
• https://docs.datastax.com/en/dse/6.8/dse-dev/datastax_enterprise/spark/sparkSqlSupportedSyntax.html

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