Скачать или смотреть Spark Streaming Too Slow? Fix State & GC Issues with RocksDB

𝗦𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗯𝗿𝗲𝗮𝗸𝘀 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 𝘄𝗵𝗲𝗻 𝗝𝗩𝗠 𝗺𝗲𝗺𝗼𝗿𝘆 𝗮𝗻𝗱 𝗚𝗖 𝗸𝗶𝗰𝗸 𝗶𝗻.
In this video, we go deep into optimizing Spark Streaming state using 𝗥𝗼𝗰𝗸𝘀𝗗𝗕 𝗻𝗮𝘁𝗶𝘃𝗲 𝘀𝘁𝗼𝗿𝗮𝗴𝗲.

You’ll learn:

  • Why Spark’s micro-batching model struggles with large state

  • How RocksDB off-heap state removes 𝗝𝗩𝗠 𝗚𝗖 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀

  • LSM-tree internals and why they matter for streaming workloads

  • Incremental checkpointing and fault tolerance at scale

  • When 𝗦𝗽𝗮𝗿𝗸 + 𝗥𝗼𝗰𝗸𝘀𝗗𝗕 makes sense vs Apache Flink

This is practical, architecture-level guidance for real-time analytics, fraud detection, and AI streaming pipelines handling terabyte-scale state.

If you’re running Spark Streaming in production and hitting latency or memory walls — this is for you.

𝗟𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 𝗮𝗱𝘃𝗮𝗻𝗰𝗲 𝘁𝗼𝗽𝗶𝗰𝘀 𝗼𝗻 𝗦𝗽𝗮𝗿𝗸 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝘀𝗸𝘀 𝘄𝗶𝘁𝗵 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗥𝗼𝗼𝘁:
  1. https://quantumroot.in/courses/big-da...
  2. https://quantumroot.in/courses/apache...
  3. https://quantumroot.in/courses/data-e...