How to work with Apify 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 Apify, Spark can work with live Apify data. This article describes how to connect to and query Apify data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Apify data due to optimized data processing built into the driver. When you issue complex SQL queries to Apify, the driver pushes supported SQL operations, like filters and aggregations, directly to Apify 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 Apify data using native data types.

Install the CData JDBC Driver for Apify

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

Start a Spark Shell and Connect to Apify Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Apify/lib/cdata.jdbc.api.jar
   

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

   Start by setting the Profile connection property to the location of the Apify Profile on disk (e.g. C:\profiles\Apify.apip). Next, set the ProfileSettings connection property to the connection string for Apify (see below).

   Apify API Profile Settings

   Obtain your API token from the Apify Console by navigating to Settings and generating a new API token.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.api.jar
   

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

   

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Apify.apip;ProfileSettings='APIKey=your_api_token';").option("dbtable","ActorBuilds").option("driver","cdata.jdbc.api.APIDriver").load()
   

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

4. Register the Apify data as a temporary table:

   scala> api_df.registerTable("actorbuilds")

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

   scala> api_df.sqlContext.sql("SELECT Id, ActorId FROM ActorBuilds WHERE Status = SUCCEEDED").collect.foreach(println)

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

   

Using the CData JDBC Driver for Apify in Apache Spark, you are able to perform fast and complex analytics on Apify 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.