What is spark-redis-connector?

Spark-redis-connector is a tool that lets Apache Spark, a system for processing large amounts of data across many machines, read from and write to Redis, a fast in-memory database often used for caching and quick lookups. It plugs into Spark using Spark's modern SQL and DataFrame interfaces, which let you query data using SQL-like statements or table-style code, rather than the older, lower-level programming interfaces that earlier Redis connectors relied on. The connector supports Redis's five core native data structures: plain strings, hashes (key-value groupings), lists, sets, and sorted sets. Each one is exposed to Spark as a table with a predictable set of columns, so you can read Redis data into Spark and write Spark data back out to Redis using ordinary SQL queries or DataFrame code, without needing to understand Redis's internal commands. It finds matching Redis keys using Redis's SCAN feature, which lets you search by a pattern like "user:*" instead of listing every key by hand. The project is built for specific, tested combinations of Spark version 3.3 through 3.5, Scala 2.12, and Java 8 or newer. To use it, you download a released jar file, either the full version bundled with its dependencies or a smaller one expecting those dependencies to already be present, and load it into Spark using the standard "--jars" option. From there, you can create a temporary view backed by Redis in Spark SQL, or use Spark's DataFrame reader and writer directly in Scala code, specifying options like the Redis host, port, data type, and key pattern. Support for Redis Streams, a different Redis feature used for streaming data, is intentionally left out of this project for now, since it would need separate handling. Detailed guides for each supported Redis data type, along with a full list of configuration options covering connection pooling, SSL, and key or column naming, are included in the project's documentation folder.