mirror of
https://github.com/meilisearch/MeiliSearch
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62 lines
2.1 KiB
Markdown
62 lines
2.1 KiB
Markdown
# mega-mini-indexer
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A prototype of concurrent indexing, only contains postings ids
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## Introduction
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This engine is a prototype, do not use it in production.
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This is one of the most advanced search engine I have worked on.
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It currently only supports the proximity criterion.
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### Compile all the binaries
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```bash
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cargo build --release --bins
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```
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## Indexing
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It can index mass documents in no much time, I already achieved to index:
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- 109m songs (song and artist name) in 21min and take 29GB on disk.
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- 12m cities (name, timezone and country ID) in 3min13s and take 3.3GB on disk.
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All of that on a 39$/month machine with 4cores.
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### Index your documents
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You first need to split your csv yourself, the engine is currently not able to split it itself.
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The bigger the split size is the faster the engine will index your documents but the higher the RAM usage will be too.
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Here we use [the awesome xsv tool](https://github.com/BurntSushi/xsv) to split our big dataset.
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```bash
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cat my-data.csv | xsv split -s 2000000 my-data-split/
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```
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Once your data is ready you can feed the engine with it, it will spawn one thread by CSV part up to one by number of core.
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```bash
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./target/release/indexer --db my-data.mmdb ../my-data-split/*
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```
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## Querying
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The engine is designed to handle very frequent words like any other word frequency.
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This is why you can search for "asia dubai" (the most common timezone) in the countries datasets in no time (59ms) even with 12m documents.
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We haven't modified the algorithm to handle queries that are scattered over multiple attributes, this is an open issue (#4).
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### Exposing a website to request the database
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Once you've indexed the dataset you will be able to access it with your brwoser.
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```bash
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./target/release/serve -l 0.0.0.0:8700 --db my-data.mmdb
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```
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## Gaps
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There is many ways to make the engine search for too long and consume too much CPU.
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This can for example be achieved by querying the engine for "the best of the do" on the songs and subreddits datasets.
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There is plenty of way to improve the algorithms and there is and will be new issues explaining potential improvements.
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