https://github.com/lyst/elasticsearch-ranking-benchmarks
Comparing Ranking Performance Across Elasticsearch Configurations
https://github.com/lyst/elasticsearch-ranking-benchmarks
Last synced: 4 months ago
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Comparing Ranking Performance Across Elasticsearch Configurations
- Host: GitHub
- URL: https://github.com/lyst/elasticsearch-ranking-benchmarks
- Owner: lyst
- Created: 2020-04-08T13:46:09.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-03-20T15:46:52.000Z (about 2 years ago)
- Last Synced: 2025-04-30T12:31:51.753Z (about 1 year ago)
- Language: Python
- Size: 291 KB
- Stars: 2
- Watchers: 3
- Forks: 3
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# Elasticsearch Ranking Benchmarks
A comparison of ranking performance using different index configurations and sorting approaches.
## _Note on metrics_
Even with caching off (request cache and query cache off) a lot of the performance comes from the filesystem cache used by Elasticsearch.
It's difficult to reliably test absolute performance differences offline.
These benchmarks are _indicative_ of relative performance between configurations.
Suggesting directions to explore in a production setting.
These benchmarks run as a single-node cluster on a laptop with no other traffic.
The shape and relative differences between configurations is more important than the absolute values in the timing measurements.
The values on the _yaxis_, query + fetch time in milliseconds, gives some idea of the scale.
## Running benchmarks
### Run benchmarks for a config
See configuration.py for benchmark configurations.
Benchmark needs to be run at least twice to warm-up the filesystem cache.
```sh
docker-compose run --rm benchmarks bash
> python run.py --benchmark-name opendistro-versions
```
## Plotting
## Benchmarks
### Open Distro Versions
Comparing different versions of [Open Distro](https://opendistro.github.io/) ranking performance.
Sorting by a `float` field.
Changing the `fetch_size`.
### Rank Features
Comparing [rank feature queries](https://www.elastic.co/guide/en/elasticsearch/reference/7.4/query-dsl-rank-feature-query.html).
Rank feature fields populated with random data from a lognormal distribution.
Changing the `fetch_size`.
`_doc` sort is the fastest (baseline).
Sorting by one float field is approximately the same as using a single rank feature.
Each additional rank_feature added to the should clause adds additional time.
