This repository is forked from https://github.com/quickwit-oss/search-benchmark-game.
It aims to achieve a close comparison between Tantivy and Lucene using the same search workload from luceneutil.
The latest results can be found here.
Want to run the benchmark or make changes? Here is the development guide.
The corpus used in the benchmark is a snapshot of the English Wikipedia text.
wget http://home.apache.org/~mikemccand/enwiki-20120502-lines-1k-fixed-utf8-with-random-label.txt.lzma
lzma -d http://home.apache.org/~mikemccand/enwiki-20120502-lines-1k-fixed-utf8-with-random-label.txt.lzma
This file is required in the corpus directory. If you have many repositories and want to save on space, you can save the corpus to another directory and use symlinks.
ln -s ~/bench_corpus/enwiki-20120502-lines-1k-fixed-utf8-with-random-label.txt ./corpus/enwiki-20120502-lines-1k-fixed-utf8-with-random-label.txt
The search tasks are from here. This repository has a copy, too.
As of now, this benchmark uses only basic text queries which includes:
TermQuery
BooleanQuery
PhraseQuery (with slop)
- Text analysis: Both engine use a simple whitespace tokenizer.
- Both indices are force merged down to a single segment.
- A single search thread executes each task TK times, discards the first TK (warmup), and records the minimum time of the remaining 8.
- The search thread is a simple Rust client, spwaning a sub-process running either Tantivy or Lucene and communicating over a local Unix pipe.
- Version: 0.21
- Rust version: 1.71.1
Features: All default
- Version: 9.8.0
- JDK version/flags: 20/???
- Disabled query cache.
The benchmark uses a client that simulates a closed-loop system, where a new query is sent only after the completion of the previous one. This is to measure the lowest latency from each engine.
The workload is run against both engines in multiple iterations, including a warmup run at the beginning.
makeis used to manage tasks.- You need rust. The recommended way to install it via rustup
- JDK 17+. The are plenty of ways to install it.
- Gradle - 8.1 or up.
- Python3.6+.
- Node.js - 18/19.
make clean
# build the engines, also make indices using the dev corpus
# the dev corpus is a down-sampled version of the larger corpus
# this is useful for fast development/iteration
make dev-index
# run the benchmark
make bench
# serve the results
make serve
# This downloads the 33M entries of wiki text
make corpus
make index
make bench
make serve
# Note: make can take one liner like this
make index bench serve
cd ./web
yarn build
Will rebuild the UI. Then, in the top level directory, you can run the normal commands to see your changes:
make serve
This repo is still work-in-progress of building the trust of the results. It tries to make an apple-to-apple comparison as much as possible.
It is totally possible that your run of the benchmark turns out to be much different that mine. Make sure you take into account of your hardware environment when interpreting the results.