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Milvus Performance Evaluation 2023

Milvus Performance Evaluation 2023

Mar 17, 20232 min read

At Zilliz, developers often ask us: "How does Milvus compare to previous versions for embedding workloads?" There are two main reasons for this. Firstly, developers starting a new project are eager to use the latest software versions to access the latest features but may be hesitant due to concerns about performance degradation. Secondly, current users may also have performance concerns and will only upgrade if the new capabilities are appealing.

To alleviate these concerns, we would like to share the latest benchmarks conducted on Milvus v2.2.3 vs. Milvus 2.2.0 and Milvus v2.0.0, comparing the search latencies and throughput across four well-known datasets (DEPP, GIST, GloVe, and SIFT). The benchmarks showed that Milvus v2.2.3 outperforms v2.2.0 and v2.0.0, significantly improving search and indexing speeds. Specifically, in Milvus 2.2.3, we achieved a 2.5x reduction in search latency compared to the original Milvus 2.0.0 release. In addition, our testing under an identical environment showed a 4.5x increase in QPS with Milvus 2.2.3 compared to 2.0.0.

We felt that this data would prove valuable to engineers evaluating the suitability of the latest version of Milvus for their similarity search use cases; Download the "Milvus Performance Evaluation 2023" technical paper to read the Methodology and Evaluation Results.

Our overriding goal was to create a consistent, up-to-date comparison that reflects the latest Milvus developments. We will periodically re-run these and other vector database benchmarks and update our detailed technical paper with our findings. All of the code for these benchmarks is available on Github. Feel free to open up issues or pull requests on that repository if you have any questions, comments, or suggestions.

Versions tested

Milvus v2.2.3 | release date: February 10, 2023
Milvus v2.2.2 | release date: November 18, 2022
Milvus v2.0.0 | release date: January 25, 2022

We identified the most commonly evaluated characteristics for working with embeddings in building a representative benchmark suite. We looked at performance across:

Query latency – measured in milliseconds
QOS – queries per second
Latency vs. Throughput
Scalability at the Billion-scale
Scalability on Multi-replica

About the dataset

In addition, there are six datasets used for the benchmarks:

Four (4) datasets from ann-benchmarks were used to evaluate the different Milvus versions' search performance.
Two (2) billion-scale datasets (BIGANN and LAION's LAION-5B) were used to evaluate the scalability of Milvus 2.2.3.

Results

ANN Search Latency

Milvus 2.2.3 we achieved a 2.5x reduction in search latency compared to the original Milvus 2.0.0 release. Search Latency of Milvus 2.0.0, 2.2.0 and 2.2.3.png

ANN Search Throughput

We saw a 4.5x increase in QPS with Milvus 2.2.3 compared to 2.0.0. QPS of Milvus 2.0.0, 2.2.9 and 2.2.3.png

Billion Scale

A scaled-out Milvus 2.2.3 cluster showed little performance degradation in both search latency and QPS. Latency and QPS of differnt cluster sizes | Milvus.png

Multi-replicat (Linear scalability)

Milvus 2.2.3 showed linear scalability when using multiple replicas. Latency and QPS at replica number doubles | Milvus.png

Summary

We highly encourage developers to run these benchmarks to independently verify the results on the environment and data sets of their choice.

What's next?

Download the detailed technical paper: "Milvus Performance Evaluation 2023."
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Chris Churilo
Chris Churilo is the VP of Marketing (or ALT) at Zilliz where she leads all community, developer relations, and marketing efforts. Prior to Zilliz, Chris was a founding member of the InfluxData’s go to market efforts and helped propel the time series database platform to dominance in the market. In earlier roles she defined and designed a SaaS monitoring solution at Centroid, and prior to that she was the VP of product management at iPass and was the LOB for several cloud services that required her to track the business and operational metrics and analytics to help identify and resolve issues.