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Search Benchmarking: RediSearch vs. Elasticsearch

Click to learn more about RediSearch: RediSearch: A High Performance Search Engine as a Redis Module white paper


RediSearch is a distributed full-text search and aggregation engine built as a module on top of Redis. It enables  users to execute complex search queries on their Redis dataset in an extremely fast manner. The unique architecture of RediSearch, which was written in C and built from the ground up on optimized data structures, makes it a true alternative to other search engines in the market. It works great as a standalone search engine for indexing and for retrieval of searchable data.

When we first launched RediSearch, we benchmarked it against popular search engines like Elasticsearch and Solr to test how powerful the engine is. This time, we decided to try a slightly different benchmark in order to (a) give you a clear, reproducible setup, with all search engines optimized to provide their best performance and (b) simulate multiple real life scenarios based on what we see from our RediSearch users.

The Benchmark

In this Search benchmark, we compared RediSearch to Elasticsearch over two use cases:

  1. Index and query the wikipedia dataset
  2. Fast indexing in a multi-tenant environment

Wikipedia benchmark

We first indexed 5.6 million docs (5.3GB) from Wikipedia and then performed two-word search queries over the indexed dataset.

Indexing results

As you can see in the figure below, RediSearch built its index in 221 seconds versus 349 seconds for Elasticsearch, or 58% faster.

Querying results

Once the dataset was indexed, we launched two-word search queries using 32 clients running on a dedicated load-generator server. As you can see in the figure below, RediSearch throughput reached 12.5K ops/sec compared to 3.1K ops/sec with Elasticsearch, or x4 faster. Furthermore, RediSearch  latency was slightly better, at 8msec on average compared to 10msec with Elasticsearch.

Multi-tenant indexing benchmark

Here, we simulated a multi-tenant e-commerce application where each tenant represented a product category and maintained its own index. For this benchmark, we built 50K indices (or products), which each stored up to 500 documents (or items), for a total of 25 million docs. RediSearch built the indices in just 201 seconds, while running an average of 125K indices/sec. However, Elasticsearch crashed after 921 indices and clearly was not designed to cope with this load.

Benchmark setup


Cloud Instance TypevCPUMem (GiB)Network
One AWS c4.8xlarge: One for the load-generator and one for the search engine366010 Gigabit

Dataset source

NameDescription and Source#docssize
wikidumpDate: Feb 7, 20195.6M5.3 GB

RediSearch configuration

Number of shards
  • 5 for the Wikipedia benchmark
  • 20 for the multi-tenant benchmark
Doc table size10M

Elasticsearch configuration

Number of shards5
JVM settings (Xms and Xmx)25GB
Indices.queries.cache.size and index.queries.cache.enabledLike mentioned here


RediSearchVersion 1.4.3
ElasticsearchVersion 6.6.0 with Lucene version 7.6.0
RediSearchBenchmarkBenchmark code here


We benchmarked RediSearch and Elasticsearch for the following use cases:

  • A simple Wikipedia use case – We found RediSearch faster by 58% on indexing and x4 faster when performing two-word searches on the indexed dataset.
  • A more advanced multi-tenant use case – RediSearch created 50k indices in just 201 seconds while Elasticsearch crashed after 921 indices were created.

Elasticsearch is a great feature-rich search product created by the great people at, but when it comes to performance, it has inherent architecture deficiencies, as summarized by the table below:

Search engineDedicated engine based on modern and optimized data-structuresbased on Lucene engine
Programming languageC-based, extremely optimizedJava
Memory technologyRuns natively on DRAM and Persistent MemoryDisk-based with a caching option
ProtocolThe optimized RESP (REdis Serialization Protocol)HTTP

Read more about RediSearch here and the technology behind it. To get started with RediSearch – try our Redis Cloud Pro here or download Redis Enterprise Software here.


Following feedback from readers we updated the reference to the wikipedia dataset and added a link to the benchmark source code for reproduction purposes. We would be happy to get more feedback if any.