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

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

Install the CData JDBC Driver for Miro

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

Start a Spark Shell and Connect to Miro Data

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

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

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

   Using API Key Authentication

   Miro uses API Key authentication with an access token. To generate an access token:

   1. Log in to your Miro account
   2. Navigate to Settings > Your apps
   3. Click "Create new app" or select an existing app
   4. Configure the required permissions (e.g., boards:read, teams:read)
   5. Install the app and generate an access token
   6. Copy the generated access token (it will only be shown once)

   After obtaining your access token, set the following connection properties:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your access token.

   Connecting to Miro

   Once the authentication is configured, you can connect to Miro and query data from any of the available tables such as Boards, Items, Teams, Organizations, and more.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Miro 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.

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

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Miro.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_access_token';").option("dbtable","Boards").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 Miro data as a temporary table:

   scala> api_df.registerTable("boards")

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

   scala> api_df.sqlContext.sql("SELECT , FROM Boards WHERE BoardId = 3074457361234567890").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 Miro in Apache Spark, you are able to perform fast and complex analytics on Miro 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.