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Work with Evernote Data in Apache Spark Using SQL

Access and process Evernote Data in Apache Spark using the CData JDBC Driver.

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

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

Install the CData JDBC Driver for Evernote

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

Start a Spark Shell and Connect to Evernote Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for Evernote JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Evernote/lib/cdata.jdbc.evernote.jar
  2. With the shell running, you can connect to Evernote with a JDBC URL and use the SQL Context load() function to read a table.

    Evernote uses the OAuth authentication standard. You can use the embedded OAuth application to connect without setting any connection properties. Alternatively, you can create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.evernote.jar

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

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

    scala> val evernote_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:evernote:OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;").option("dbtable","Notes").option("driver","cdata.jdbc.evernote.EvernoteDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Evernote data as a temporary table:

    scala> evernote_df.registerTable("notes")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> evernote_df.sqlContext.sql("SELECT Title, Author FROM Notes WHERE Guid = ab26f704-5edf-4a9f-9e4c-8da893a4acd8").collect.foreach(println)

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

Using the CData JDBC Driver for Evernote in Apache Spark, you are able to perform fast and complex analytics on Evernote data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.