Elasticsearch Elasticsearch Version History: Key Changes and Improvements

By Opster Team

Updated: Jan 28, 2024

| 2 min read


Elasticsearch, a popular open-source search and analytics engine, has undergone numerous changes and improvements since its initial release in 2010. In this article, we will discuss the key changes and improvements in Elasticsearch’s version history, focusing on major releases and their impact on the platform’s performance, scalability, and usability. If you want to learn about Elasticsearch version, check out this guide. You should also take a look at this guide, which contains a detailed explanation on how to upgrade versions in Elasticsearch.

1. Elasticsearch 1.x (2014)

The 1.x series introduced several important features and improvements, including:

– Aggregations: A significant enhancement to the existing facets API, aggregations provided a more flexible and powerful way to perform complex data analysis and visualization.

Snapshot and Restore: This feature allowed users to create snapshots of their indices and restore them to a different cluster or a later point in time, improving data recovery and migration capabilities.

Circuit Breakers: To prevent out-of-memory errors, circuit breakers were added to monitor and limit memory usage for various operations, such as query execution and field data loading.

2. Elasticsearch 2.x (2015)

The 2.x series focused on improving performance, stability, and security, with key changes including:

– Doc Values: By default, doc values were enabled for all fields except analyzed strings, reducing heap usage and improving query performance.

– Query Refactoring: The query DSL was refactored to simplify and standardize the syntax, making it easier to build and maintain complex queries.

Delete-By-Query and Update-By-Query: These APIs were reintroduced as plugins, allowing users to perform bulk updates and deletions based on query conditions.

3. Elasticsearch 5.x (2016)

The 5.x series brought significant improvements in performance, scalability, and ease of use, with notable features including:

– Ingest Node: This new node type allowed users to preprocess documents before indexing, simplifying data transformation and enrichment workflows.

– Painless Scripting: A new scripting language called Painless was introduced, offering better performance and security compared to the existing scripting languages.

– Cross-Cluster Search: This feature enabled users to search and aggregate data across multiple Elasticsearch clusters, improving scalability and data management.

4. Elasticsearch 6.x (2017)

The 6.x series focused on further improving performance, stability, and usability, with key changes including:

– Sequence IDs: This feature introduced a new method for tracking changes to documents, enabling faster and more accurate replication and recovery.

– Index Sorting: Users could now sort indices on one or more fields during indexing, improving search performance for sorted queries.

– Sparse Fields: The introduction of sparse fields reduced storage and memory usage for fields with a low percentage of non-null values.

5. Elasticsearch 7.x (2019)

The 7.x series introduced several major features and improvements, such as:

– Cluster Coordination: A new coordination algorithm called Zen2 was introduced, improving cluster stability and resilience.

– Adaptive Replica Selection: This feature allowed Elasticsearch to intelligently route search requests to the most appropriate replica, reducing search latency and improving load balancing.

– Faster Top-K Retrieval: The introduction of the Block-Max WAND algorithm improved the performance of top-K queries, reducing query execution time and resource usage.

6. Elasticsearch 8.x (2022)

The current 8.x series focuses on improved security and 

– Security enabled by default: Security is now automatically configured by default when spinning up a new cluster, ensuring that your cluster doesn’t stay unprotected.

– Full speed on Machine Learning: New AI models have been created to enable compelling use cases for NLP, Generative AI, kNN search and many more.

– Archived indexes: This feature introduces the ability to read indices created in older releases without having to reindex them, which makes upgrades much smoother.


In conclusion, Elasticsearch’s version history showcases a continuous effort to enhance the platform’s performance, scalability, and usability. As Elasticsearch evolves, users can expect further improvements and features that cater to their data search and analytics needs.