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https://github.com/meilisearch/MeiliSearch
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Introduce an infos binary to fetch stats
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112
src/bin/infos.rs
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112
src/bin/infos.rs
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use std::path::PathBuf;
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use std::{str, io};
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use heed::EnvOpenOptions;
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use milli::Index;
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use structopt::StructOpt;
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#[cfg(target_os = "linux")]
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#[global_allocator]
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static ALLOC: jemallocator::Jemalloc = jemallocator::Jemalloc;
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#[derive(Debug, StructOpt)]
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#[structopt(name = "milli-info", about = "A stats crawler for milli.")]
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struct Opt {
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/// The database path where the database is located.
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/// It is created if it doesn't already exist.
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#[structopt(long = "db", parse(from_os_str))]
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database: PathBuf,
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/// The maximum size the database can take on disk. It is recommended to specify
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/// the whole disk space (value must be a multiple of a page size).
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#[structopt(long = "db-size", default_value = "107374182400")] // 100 GB
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database_size: usize,
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/// Verbose mode (-v, -vv, -vvv, etc.)
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#[structopt(short, long, parse(from_occurrences))]
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verbose: usize,
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#[structopt(subcommand)]
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command: Command,
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}
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#[derive(Debug, StructOpt)]
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enum Command {
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/// Outputs a CSV of the most frequent words of this index.
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///
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/// `word` are displayed and ordered by frequency.
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/// `document_frequency` defines the number of documents which contains the word.
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/// `frequency` defines the number times the word appears in all the documents.
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MostCommonWords {
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/// The maximum number of frequencies to return.
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#[structopt(default_value = "10")]
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limit: usize,
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}
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}
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fn main() -> anyhow::Result<()> {
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let opt = Opt::from_args();
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stderrlog::new()
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.verbosity(opt.verbose)
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.show_level(false)
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.timestamp(stderrlog::Timestamp::Off)
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.init()?;
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let env = EnvOpenOptions::new()
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.map_size(opt.database_size)
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.max_dbs(10)
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.open(&opt.database)?;
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// Open the LMDB database.
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let index = Index::new(&env, opt.database)?;
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let rtxn = env.read_txn()?;
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match opt.command {
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Command::MostCommonWords { limit } => most_common_words(&index, &rtxn, limit),
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}
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}
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fn most_common_words(index: &Index, rtxn: &heed::RoTxn, limit: usize) -> anyhow::Result<()> {
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use std::collections::BinaryHeap;
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use std::cmp::Reverse;
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use roaring::RoaringBitmap;
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let mut heap = BinaryHeap::with_capacity(limit + 1);
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let mut prev = None as Option<(String, u64, RoaringBitmap)>;
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for result in index.word_position_docids.iter(rtxn)? {
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if limit == 0 { break }
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let (bytes, postings) = result?;
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let (word, _position) = bytes.split_at(bytes.len() - 4);
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let word = str::from_utf8(word)?;
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match prev.as_mut() {
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Some((prev_word, freq, docids)) if prev_word == word => {
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*freq += docids.len();
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docids.union_with(&postings);
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},
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Some((prev_word, freq, docids)) => {
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heap.push(Reverse((docids.len(), *freq, prev_word.to_string())));
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if heap.len() > limit { heap.pop(); }
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prev = Some((word.to_string(), postings.len(), postings))
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},
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None => prev = Some((word.to_string(), postings.len(), postings)),
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}
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}
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if let Some((prev_word, freq, docids)) = prev {
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heap.push(Reverse((docids.len(), freq, prev_word.to_string())));
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if heap.len() > limit { heap.pop(); }
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}
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let stdout = io::stdout();
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let mut wtr = csv::Writer::from_writer(stdout.lock());
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wtr.write_record(&["word", "document_frequency", "frequency"])?;
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for Reverse((document_frequency, frequency, word)) in heap.into_sorted_vec() {
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wtr.write_record(&[word, document_frequency.to_string(), frequency.to_string()])?;
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}
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Ok(wtr.flush()?)
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}
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