How to work with Elasticsearch Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Elasticsearch, Spark can work with live Elasticsearch data. This article describes how to connect to and query Elasticsearch data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Elasticsearch data due to optimized data processing built into the driver. When you issue complex SQL queries to Elasticsearch, the driver pushes supported SQL operations, like filters and aggregations, directly to Elasticsearch and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Elasticsearch data using native data types.

Getting Started

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Install the CData JDBC Driver for Elasticsearch

Download the CData JDBC Driver for Elasticsearch installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Elasticsearch Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Elasticsearch JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Elasticsearch/lib/cdata.jdbc.elasticsearch.jar
   

2. With the shell running, you can connect to Elasticsearch with a JDBC URL and use the SQL Context load() function to read a table.

   Set the Server and Port connection properties to connect. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.

   The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.

   Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Elasticsearch JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.elasticsearch.jar
   

   Fill in the connection properties and copy the connection string to the clipboard.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Elasticsearch, using the connection string generated above.

   scala> val elasticsearch_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:elasticsearch:Server=127.0.0.1;Port=9200;User=admin;Password=123456;").option("dbtable","Orders").option("driver","cdata.jdbc.elasticsearch.ElasticsearchDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Elasticsearch data as a temporary table:

   scala> elasticsearch_df.registerTable("orders")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> elasticsearch_df.sqlContext.sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = New York").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Elasticsearch in Apache Spark, you are able to perform fast and complex analytics on Elasticsearch data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.