Go to file
tamo 06c414a753
move the benchmarks to another crate so we can download the datasets automatically without adding overhead to the build of milli
2021-06-02 11:11:50 +02:00
.github remove tests on main 2021-05-03 15:21:20 +02:00
benchmarks move the benchmarks to another crate so we can download the datasets automatically without adding overhead to the build of milli 2021-06-02 11:11:50 +02:00
helpers Update version for the next release (v0.2.1) 2021-05-05 14:57:34 +02:00
http-ui fix http-ui 2021-06-01 16:24:46 +02:00
infos Fix PR comments 2021-06-01 18:06:46 +02:00
milli move the benchmarks to another crate so we can download the datasets automatically without adding overhead to the build of milli 2021-06-02 11:11:50 +02:00
search Update version for the next release (v0.2.1) 2021-05-05 14:57:34 +02:00
.gitignore Change the project to become a workspace with milli as a default-member 2021-02-12 16:15:09 +01:00
Cargo.lock Update version for the next release (v0.2.1) 2021-05-05 14:57:34 +02:00
Cargo.toml move the benchmarks to another crate so we can download the datasets automatically without adding overhead to the build of milli 2021-06-02 11:11:50 +02:00
LICENSE Update LICENSE 2021-03-15 16:15:14 +01:00
README.md do not use echo that espaces newline 2021-04-29 09:25:35 +02:00
bors.toml Add bors 2021-05-03 12:29:30 +02:00
qc_loop.sh Initial commit 2020-05-31 14:22:06 +02:00

README.md

the milli logo

a concurrent indexer combined with fast and relevant search algorithms

Introduction

This engine is a prototype, do not use it in production. This is one of the most advanced search engine I have worked on. It currently only supports the proximity criterion.

Compile and Run the server

You can specify the number of threads to use to index documents and many other settings too.

cd http-ui
cargo run --release -- --db my-database.mdb -vvv --indexing-jobs 8

Index your documents

It can index a massive amount of documents in not much time, I already achieved to index:

  • 115m songs (song and artist name) in ~1h and take 107GB on disk.
  • 12m cities (name, timezone and country ID) in 15min and take 10GB on disk.

All of that on a 39$/month machine with 4cores.

You can feed the engine with your CSV (comma-seperated, yes) data like this:

printf "name,age\nhello,32\nkiki,24\n" | http POST 127.0.0.1:9700/documents content-type:text/csv

Here ids will be automatically generated as UUID v4 if they doesn't exist in some or every documents.

Note that it also support JSON and JSON streaming, you can send them to the engine by using the content-type:application/json and content-type:application/x-ndjson headers respectively.

Querying the engine via the website

You can query the engine by going to the HTML page itself.