Elasticsearch 1.2: What's New, Release History & EOL Status

What Is New in Elasticsearch 1.2

Elasticsearch 1.2 delivers significant enhancements in aggregation performance, introduces powerful new scripting capabilities, and adds critical features for production environments. This release focuses on making complex data analysis faster and more flexible while hardening the system's reliability.

How did aggregations get faster in 1.2?

The aggregation framework received major optimizations, making it significantly faster and more memory-efficient. These improvements are most noticeable when running complex nested aggregations on large datasets.

Internally, the execution model was refined to reduce overhead. This means you can now run the same aggregation queries as before but with lower latency and less heap memory consumption, which directly impacts cluster stability at scale.

What new scripting languages are supported?

Elasticsearch 1.2 expanded its scripting support beyond the native MVEL to include JavaScript and Python. This gives developers more flexibility to write custom scripts for scoring, filtering, or updating documents using a language they're already comfortable with.

In practice, you can now use expressions like lang: 'js' or lang: 'python' directly in your queries. The scripting module is pluggable, so you can even register your own scripting engines if the built-in options don't meet your needs.

What are pipeline aggregations?

Pipeline aggregations are a powerful new feature that allow you to perform operations on the output of other aggregations. Instead of just calculating metrics on raw document data, you can now chain aggregations together to create complex data processing pipelines.

For example, you can calculate the derivative of a time series or a moving average. This eliminates the need to post-process aggregation results in your application, keeping the entire analytical workflow within Elasticsearch where it's more efficient.

How does the new snapshot/restore API work?

The snapshot and restore functionality provides a native way to backup and recover your indices. You can now take snapshots of your cluster's state and data, storing them in shared repositories like AWS S3 or HDFS.

This matters because it gives you a production-grade disaster recovery solution without relying on external tools. You can perform incremental snapshots and restore to different cluster configurations, which is essential for any serious deployment.

What compression improvements were made?

Elasticsearch 1.2 introduced the DEFLATE compression algorithm as an alternative to LZO for compressing stored fields. DEFLATE typically provides better compression ratios, which can significantly reduce disk usage for indices with large stored fields.

You can configure this per-index by setting index.codec to deflate in your index settings. The trade-off is slightly higher CPU usage during indexing, but the storage savings are often worth it for read-heavy workloads.