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

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

Install the CData JDBC Driver for Alchemy

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

Start a Spark Shell and Connect to Alchemy Data

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

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

2. With the shell running, you can connect to Alchemy 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 Alchemy Profile on disk (e.g. C:\profiles\Alchemy.apip). Next, set the ProfileSettings connection property to the connection string for Alchemy (see below).

   Alchemy API Profile Settings

   Alchemy uses API key authentication. The API key is supplied in the request URL path. To obtain an API key:

   1. Sign in to your Alchemy account at https://dashboard.alchemy.com.
   2. Open an existing app or create a new one.
   3. Copy the app's API key from the API Key dialog.

   After obtaining your API key, set the following connection properties:

   • AuthScheme: Set this to APIKey.
   • Network: Optional. The blockchain network slug for NFT API requests (for example, eth-mainnet, base-mainnet, polygon-mainnet). Defaults to eth-mainnet.

   Set the following in the ProfileSettings connection property:

   • APIKey: Set this to your Alchemy app API key.

   Built-in Connection String Designer

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

   
   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Alchemy.apip;AuthScheme=APIKey;Network=eth-mainnet;ProfileSettings='APIKey=your_api_key';").option("dbtable","ContractsForOwner").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 Alchemy data as a temporary table:

   scala> api_df.registerTable("contractsforowner")

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

   scala> api_df.sqlContext.sql("SELECT Address, Name FROM ContractsForOwner WHERE Owner = 0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045").collect.foreach(println)

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

   

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