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

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

Install the CData JDBC Driver for Google Translate

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

Start a Spark Shell and Connect to Google Translate Data

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

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

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

   Authentication

   Google Cloud Translation API requires OAuth 2.0 authentication to ensure secure access to translation services, datasets, glossaries, and adaptive MT resources. This authentication method allows you to securely connect to your Google Cloud project and manage translation resources with proper authorization.

   OAuth 2.0 Setup and Configuration

   Step 1: Create Google Cloud Project and Enable API

   To set up OAuth authentication:

   1. Visit the Google Cloud Console
   2. Create a new project or select an existing project
   3. Note down your Project ID (required for all API calls)
   4. Navigate to "APIs & Services" > "Library"
   5. Search for and enable the "Cloud Translation API"
   6. Go to "APIs & Services" > "Credentials"
   7. Click "Create Credentials" and select "OAuth Client ID"
   8. Configure the OAuth consent screen if prompted
   9. Select "Desktop application" or "Web application" as appropriate
   10. Set the authorized redirect URI (CallbackURL)
   11. Copy the Client ID and Client Secret for use in your connection

   Required Connection Properties

   • AuthScheme: Set this to OAuth (required)
   • OAuthClientId: Client ID from Google Cloud Console (required)
   • OAuthClientSecret: Client secret from Google Cloud Console (required)
   • CallbackURL: Redirect URI specified in your OAuth application (required)
   • InitiateOAuth: Set to GETANDREFRESH for automatic token management (recommended)
   • ProjectId: Your Google Cloud project ID or project number (required for queries)

   Required OAuth Scopes

   The Google Cloud Translation API Profile requires the following OAuth scope:

   • https://www.googleapis.com/auth/cloud-translation - Full access to Cloud Translation API resources including translation, datasets, glossaries, and adaptive MT

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Google Translate 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 Google Translate, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\GoogleTranslate.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;").option("dbtable","SupportedLanguages").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 Google Translate data as a temporary table:

   scala> api_df.registerTable("supportedlanguages")

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

   scala> api_df.sqlContext.sql("SELECT LanguageCode, DisplayName FROM SupportedLanguages WHERE ProjectId = my-project-12345").collect.foreach(println)

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

   

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