RedisJSON Beats MongoDB and ElasticSearch: 200× Faster Writes and 500× Faster Reads

Recent official benchmark reports demonstrate that RedisJSON (combined with RediSearch) overwhelmingly outperforms other NoSQL databases.

Key conclusions:

For isolated writes, RedisJSON is 5.4× faster than MongoDB and more than 200× faster than ElasticSearch.

For isolated reads, RedisJSON is 12.7× faster than MongoDB and over 500× faster than ElasticSearch.

In mixed‑workload scenarios, real‑time updates do not degrade RedisJSON’s search or read performance, whereas ElasticSearch’s performance drops.

Query Engine

The development of reresearch and RedisJSON places a strong emphasis on performance, providing analysis tools and detectors for developers.

Version 2.2 improves loading and query performance by 1.7× compared with 2.0, while also enhancing throughput and data‑load latency.

Loading Optimization

The following NYC taxi benchmark charts illustrate the performance gains of each new reresearch version.

Each new version delivers substantial performance improvements.

Full‑Text Search Optimization

We indexed 5.9 million Wikipedia abstracts and ran a full‑text search panel. The charts show that moving from v2.0 to v2.2 yields large gains in write, read, and search latency, increasing overall Search and JSON throughput.

Comparison with Other Frameworks

We compared RedisJSON against MongoDB and ElasticSearch using the YCSB benchmark suite, covering document storage, availability, cloud support, professional support, scalability, and performance.

Test environment:

MongoDB v5.0.3

ElasticSearch 7.15

RedisJSON (RediSearch 2.2 + RedisJSON 2.0) on OSS Redis Cluster v6.2.6 with 27 shards across three nodes.

All tests ran on identical AWS m5d.8xlarge VMs (four VMs per setup: one client and three database servers) with local SSDs, tightly packed in a single availability zone to ensure low‑latency networking.

100% Write Benchmark

RedisJSON’s ingest speed is 8.8× faster than ElasticSearch and 1.8× faster than MongoDB, while maintaining sub‑millisecond latency for each operation; 99 % of Redis requests complete in under 1.5 ms.

RedisJSON uniquely updates its index automatically on every write, unlike ElasticSearch which batches updates in a near‑real‑time queue.

Overall, RedisJSON is >200× faster than ElasticSearch and >5.4× faster than MongoDB for isolated writes.

100% Read Benchmark

RedisJSON delivers 15.8× higher read throughput than ElasticSearch and 2.8× higher than MongoDB, while keeping sub‑millisecond latency across the entire latency range.

Overall, RedisJSON is >500× faster than ElasticSearch and >12.7× faster than MongoDB for isolated reads.

Mixed Read/Write/Search Benchmark

Real‑world workloads combine reads, writes, and searches. Using a 65 % search / 35 % read mix, RedisJSON and ElasticSearch achieve similar throughput, but RedisJSON maintains stable latency while ElasticSearch degrades as write ratio increases.

Increasing write proportion does not affect RedisJSON’s read or search latency, but it significantly slows ElasticSearch.

Full Latency Analysis

Comprehensive latency measurements under sustainable loads show RedisJSON consistently stays in the sub‑millisecond range across all operation types, whereas MongoDB and ElasticSearch exhibit higher tail latencies.

At the p99 percentile, RedisJSON records 0.23 ms latency, compared with 5.01 ms for MongoDB and 10.49 ms for ElasticSearch.

During writes, both MongoDB and RedisJSON keep sub‑millisecond p99 latency, while ElasticSearch shows >10 ms tail latency due to GC and cache misses.

Getting Started

To start using RedisJSON, create a free Redis Cloud database in any region or run the RedisJSON Docker container.

Documentation has been updated to help developers quickly enable query and search features.

Client libraries for popular programming languages are also available to accelerate adoption.