Comparison of performance test results between an Elasticsearch V8.13 cluster and an Elasticsearch V8.9 cluster
Alibaba Cloud Elasticsearch V8.13 is released. This version supports hardware acceleration and provides new open source features and enhancements for vector search scenarios. Alibaba Cloud Elasticsearch clusters of this version provide better performance than clusters of the previous version. This topic describes the comparison of the performance test results of an Alibaba Cloud Elasticsearch V8.13 cluster and an Alibaba Cloud Elasticsearch V8.9 cluster.
Configurations
Cluster configurations
Versions: 8.13 and 8.9
Data node specifications: 16 vCPUs and 32 GiB of memory
Storage type of data nodes: Enterprise SSD (ESSD)
Storage performance level of data nodes: PL1
Storage space per data node: 500 GiB
Number of data nodes: 1
NoteTo reduce variables and highlight cluster performance, clusters with a single data node are used in this test. Alibaba Cloud Elasticsearch does not allow you to create a cluster with a single data node. The performance of a cluster is positively correlated with the number of data nodes.
One primary shard is configured for the index that stores the test dataset, and no replica shard is configured for the primary shard.
Mappings and settings of the index:
{ "vectors_so": { "aliases": {}, "mappings": { "properties": { "acceptedAnswerId": { "type": "keyword" }, "body": { "type": "text" }, "creationDate": { "type": "date" }, "questionId": { "type": "keyword" }, "tags": { "type": "keyword" }, "title": { "type": "text" }, "titleVector": { "type": "dense_vector", "dims": 768, "index": true, "similarity": "dot_product" }, "type": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "user": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "userId": { "type": "keyword" } } }, "settings": { "index": { "number_of_shards": "1", "number_of_replicas": "0" } } } }
Test dataset
In this test, a dataset with 2 million 768-dimensional vector data records from Stack Overflow is used. You can obtain the test dataset from Elastic_vector.
Sample data of the test dataset:
{"user":"209706","tags":["java","svn","git"],"questionId":"3329041","creationDate":"2010-07-25T11:43:57.960","title":"Best practice to store .jar files in VCS (SVN, Git, 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know, in the time of Maven it is not recommended to store libraries in VCS, but sometimes it makes sense, though. My question is how to best store them - compressed or uncompressed? Uncompressed they are larger, but if they are replaced a couple of times with newer ones, then maybe the stored difference between two uncompressed .jar files might be much smaller than the difference of compressed ones. Did someone make some tests? "}Sample query for the test dataset:
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Test results
The test results are for reference only. We recommend that you perform stress testing by using your actual business data.
The test results are returned for the query that is performed only after data is written. If you perform forced merging on the data before you perform a query, the query results can be significantly improved.
Query performance
Cases for k-nearest neighbors (k-NN) queries:
knn-search-100-100: k=100 and num_candidates=100
knn-search-1000-1000: k=1000 and num_candidates=1000
20 threads used at a time
Elasticsearch version | Query case | Queries per second (QPS) | 50th percentile latency (ms) | 99th percentile latency (ms) | CPU utilization (%) |
8.13 | knn-search-100-100 | 813.16 | 22.2122 | 45.159 | 92% |
knn-search-1000-1000 | 157.58 | 123.515 | 205.025 | 100% | |
8.9 | knn-search-100-100 | 191.32 | 97.4562 | 159.974 | 99% |
knn-search-1000-1000 | 32.97 | 588.647 | 839.913 | 100% |
Single thread used at a time
Elasticsearch version | Query case | Queries per second (QPS) | 50th percentile latency (ms) | 99th percentile latency (ms) | CPU utilization (%) |
8.13 | knn-search-100-100 | 168.79 | 4.47948 | 6.00996 | 22% |
knn-search-1000-1000 | 69.18 | 13.2466 | 15.2825 | 42% | |
8.9 | knn-search-100-100 | 16.48 | 59.4866 | 65.296 | 7% |
knn-search-1000-1000 | 2.98 | 336.262 | 363.806 | 8% |
Write performance
Elasticsearch version | Write TPS (documents/s) | Latency (ms) (bulk size = 1000) | CPU utilization (%) |
8.13 | 8201.66 | 1105.91 | 100 |
8.9 | 4711.86 | 2263.13 | 100 |
Storage performance
Engine version | Storage capacity |
8.13 | 30.8 GB |
8.9 | 30.8 GB |
Test result summary
Query performance: The query performance of the Elasticsearch V8.13 cluster is 4 to 5 times higher than that of the Elasticsearch V8.9 cluster. In scenarios where a single thread is used to process queries at a time, the query performance of the Elasticsearch V8.13 cluster is up to 10 times higher than that of the Elasticsearch V8.9 cluster.
Write performance: The write performance of the Elasticsearch V8.13 cluster is 1.74 times higher than that of the Elasticsearch V8.9 cluster.