能否将Elasticsearch用作Spark Streaming的状态维护数据存储系统？

Absolutely, you can use Elasticsearch as a state storage system for Spark Streaming (D-Streams) — and your specific scenario (small state volume + low access frequency) makes this even more viable than it would be for high-throughput workloads.

Let’s break this down:

Why It’s a Great Fit for Your Case #

• Small state size: Elasticsearch handles small to medium datasets smoothly. Since your state isn’t massive, you won’t run into major storage or retrieval bottlenecks that might plague larger workloads.
• Low access frequency: The slight latency overhead of Elasticsearch compared to in-memory stores or HDFS checkpoints won’t be a dealbreaker here. You’re not hitting the state store constantly, so occasional round-trips to Elasticsearch won’t disrupt your streaming job.

Key Considerations to Avoid Pitfalls #

While it works, there are a few things to keep in mind to ensure your setup is reliable:

• Failure recovery vs. native checkpointing: Spark’s built-in checkpointing to HDFS/S3 is designed to restore the streaming job’s driver state and metadata. If you use Elasticsearch for state, you’ll need to build your own logic for syncing state changes and recovering from failures. Make sure to use idempotent writes to prevent duplicate state entries if your job restarts.
• Performance tradeoffs: Elasticsearch is a search engine first, not a dedicated low-latency state store. For high-frequency state reads/writes, tools like Redis or even HDFS checkpoints will be faster. But again, your low-access scenario minimizes this concern.
• Data consistency: Use reliable write semantics (like enabling acknowledged writes in your Elasticsearch config) to ensure state updates aren’t lost. Since D-Streams operate on micro-batches, you can batch state updates and commit them atomically where possible to maintain consistency.
• Simplify your index design: Don’t overcomplicate the Elasticsearch index for state storage. Use a straightforward mapping that matches your state schema, skip unnecessary text analyzers, and stick to a single index (or a small set of indices) to reduce overhead.

Quick Code Snippet to Get Started #

Here’s a simplified example of how you might write updated state from a DStream to Elasticsearch using the Elasticsearch-Spark connector:

import org.elasticsearch.spark._

// Inside your streaming job's processing logic
yourDStream.foreachRDD { rdd =>
  // Calculate your updated state (replace with your actual logic)
  val updatedStateRDD = rdd.map { record =>
    val key = record._1
    val newState = /* compute new state based on existing state and record */
    (key, newState)
  }
  
  // Write the updated state to Elasticsearch
  updatedStateRDD.saveToEs("spark_streaming_state/state_records")
}

内容的提问来源于stack exchange，提问作者Pranjut