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

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

Install the CData JDBC Driver for Telegram

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

Start a Spark Shell and Connect to Telegram Data

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

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

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

   Using API Key Authentication

   Telegram uses Bot Tokens to authenticate API requests. You can obtain a Bot Token by creating a bot via BotFather on Telegram (https://t.me/BotFather). Once created, BotFather will provide a token in the format

   123456789:ABCdefGhIJKlmNoPQRsTUVwxyZ

   .

   After setting the following connection properties, you are ready to connect:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Telegram Bot Token obtained from BotFather.

   Example connection string

   Profile=C:\profiles\Telegram.apip;ProfileSettings='APIKey=your_bot_token';
   

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("availablegifts")

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

   scala> api_df.sqlContext.sql("SELECT ,  FROM AvailableGifts WHERE  = ").collect.foreach(println)

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

   

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