Elasticsearch 5.1: What's New, Release History & Support Lifecycle

What Is New in Elasticsearch 5.1

Elasticsearch 5.1 delivers significant enhancements to the core engine, scripting, and data handling capabilities. This release focuses on performance, stability, and providing more powerful tools for developers. The key updates are summarized below.

How does the new Painless scripting language improve performance?

Painless is a new secure, high-performance scripting language designed specifically for Elasticsearch. It executes significantly faster than previous options like Groovy, especially for complex scripts. This speed boost comes from its sandboxed design and just-in-time (JIT) compilation.

In practice, this means your aggregations and update-by-query operations that rely on inline scripting will run much faster. The syntax is also straightforward for Java developers, making it easier to write and maintain.

What is reindexing from a remote cluster and why is it useful?

The reindex-from-remote feature allows you to pull data from an older Elasticsearch cluster directly into a 5.1 cluster. This is a game-changer for migrations, as you no longer need to rely on external ETL tools to move your data between major versions.

You can use it by specifying a remote host in the reindex request body. This simplifies the upgrade path from versions like 2.x to 5.x dramatically, though network speed between clusters will be the limiting factor.

POST _reindex
{
  "source": {
    "remote": {
      "host": "http://old-cluster:9200"
    },
    "index": "old_index"
  },
  "dest": {
    "index": "new_index"
  }
}

How do the disk-based shard allocation controls help?

Elasticsearch 5.1 introduces finer-grained control over shard allocation based on disk usage. You can now set a high and low watermark for disk usage as a percentage of total disk space, not just as a fixed byte size.

This matters because it prevents nodes from running out of disk space, which can cause shard failures. The new percentage-based defaults are more adaptive to nodes with different disk sizes in the same cluster. You can configure this in your elasticsearch.yml.

What optimizations were made to the internal engine?

The internal engine saw major optimizations for indexing and recovery operations. Changes include better management of the version map and more efficient segment merging strategies.

These low-level improvements reduce heap memory usage during heavy indexing and speed up node recovery times. You'll likely see less garbage collection pressure and more stable performance during data ingestion spikes.

What's new with the Date Histogram aggregation?

The Date Histogram aggregation now supports returning empty buckets for intervals where no data exists. Previously, these gaps would be omitted from the response entirely.

This is crucial for generating complete time series charts in Kibana, as it ensures your graphs have a consistent timeline even with missing data points. You enable this by setting "min_doc_count": 0 in your aggregation query.