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https://github.com/meilisearch/meilisearch.git
synced 2024-11-22 18:17:39 +08:00
Implement a memory dumper
It moves the in memory HashMaps used when indexing to a disk based MTBL file
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b12bfcb03b
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2
Cargo.lock
generated
2
Cargo.lock
generated
@ -1483,7 +1483,7 @@ dependencies = [
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[[package]]
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name = "roaring"
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version = "0.6.0"
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source = "git+https://github.com/Kerollmops/roaring-rs.git?branch=deserialize-from-slice#24420bb9f980749476cec860ea8dd3c1683c0cd1"
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source = "git+https://github.com/Kerollmops/roaring-rs.git?branch=mem-usage#a71692552902019751ef5b0e57336f030045a76a"
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dependencies = [
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"byteorder",
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]
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@ -20,7 +20,7 @@ memmap = "0.7.0"
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once_cell = "1.4.0"
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oxidized-mtbl = { git = "https://github.com/Kerollmops/oxidized-mtbl.git", rev = "9451be8" }
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rayon = "1.3.1"
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roaring = { git = "https://github.com/Kerollmops/roaring-rs.git", branch = "deserialize-from-slice" }
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roaring = { git = "https://github.com/Kerollmops/roaring-rs.git", branch = "mem-usage" }
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slice-group-by = "0.2.6"
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smallstr = "0.2.0"
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smallvec = "1.4.0"
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@ -2,6 +2,7 @@ use std::collections::hash_map::Entry;
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use std::collections::{HashMap, BTreeSet};
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use std::convert::{TryFrom, TryInto};
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use std::fs::File;
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use std::mem;
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use std::path::PathBuf;
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use std::time::Instant;
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@ -45,6 +46,14 @@ struct Opt {
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#[structopt(short, long)]
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jobs: Option<usize>,
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/// Maximum number of bytes to allocate, will be divided by the number of
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/// cores used. It is recommended to set a maximum of half of the available memory
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/// as the current measurement method is really bad.
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///
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/// The minumum amount of memory used will be 50MB anyway.
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#[structopt(long, default_value = "4294967296")]
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max_memory_usage: usize,
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/// CSV file to index, if unspecified the CSV is read from standard input.
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csv_file: Option<PathBuf>,
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}
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@ -57,6 +66,21 @@ struct Indexed {
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documents: Vec<(DocumentId, Vec<u8>)>,
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}
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impl Indexed {
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fn new(
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word_positions: FastMap4<SmallVec32<u8>, RoaringBitmap>,
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word_position_docids: FastMap4<(SmallVec32<u8>, Position), RoaringBitmap>,
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headers: Vec<u8>,
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documents: Vec<(DocumentId, Vec<u8>)>,
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) -> anyhow::Result<Indexed>
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{
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// We store the words from the postings.
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let new_words: BTreeSet<_> = word_position_docids.iter().map(|((w, _), _)| w).collect();
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let fst = fst::Set::from_iter(new_words)?;
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Ok(Indexed { fst, headers, word_positions, word_position_docids, documents })
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}
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}
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#[derive(Default)]
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struct MtblKvStore(Option<File>);
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@ -175,6 +199,7 @@ impl MtblKvStore {
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where F: FnMut(&[u8], &[u8]) -> anyhow::Result<()>
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{
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eprintln!("Merging {} MTBL stores...", stores.len());
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let before = Instant::now();
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let mmaps: Vec<_> = stores.iter().flat_map(|m| {
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m.0.as_ref().map(|f| unsafe { memmap::Mmap::map(f).unwrap() })
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@ -192,20 +217,49 @@ impl MtblKvStore {
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(f)(k, v)?;
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}
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eprintln!("MTBL stores merged!");
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eprintln!("MTBL stores merged in {:.02?}!", before.elapsed());
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Ok(())
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}
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}
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fn mem_usage(
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word_positions: &FastMap4<SmallVec32<u8>, RoaringBitmap>,
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word_position_docids: &FastMap4<(SmallVec32<u8>, Position), RoaringBitmap>,
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documents: &Vec<(u32, Vec<u8>)>,
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) -> usize
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{
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use std::mem::size_of;
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let documents =
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documents.iter().map(|(_, d)| d.capacity()).sum::<usize>()
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+ documents.capacity() * size_of::<(Position, Vec<u8>)>();
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let word_positions =
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word_positions.iter().map(|(k, r)| {
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(if k.spilled() { k.capacity() } else { 0 }) + r.mem_usage()
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}).sum::<usize>()
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+ word_positions.capacity() * size_of::<(SmallVec32<u8>, RoaringBitmap)>();
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let word_position_docids =
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word_position_docids.iter().map(|((k, _), r)| {
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(if k.spilled() { k.capacity() } else { 0 }) + r.mem_usage()
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}).sum::<usize>()
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+ word_position_docids.capacity() * size_of::<((SmallVec32<u8>, Position), RoaringBitmap)>();
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documents + word_positions + word_position_docids
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}
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fn index_csv(
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mut rdr: csv::Reader<File>,
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thread_index: usize,
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num_threads: usize,
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max_mem_usage: usize,
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) -> anyhow::Result<Vec<MtblKvStore>>
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{
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eprintln!("{:?}: Indexing into an Indexed...", thread_index);
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let mut document = csv::StringRecord::new();
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let mut stores = Vec::new();
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let mut word_positions = FastMap4::default();
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let mut word_position_docids = FastMap4::default();
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let mut documents = Vec::new();
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@ -217,6 +271,7 @@ fn index_csv(
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let headers = writer.into_inner()?;
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let mut document_id: usize = 0;
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let mut document = csv::StringRecord::new();
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while rdr.read_record(&mut document)? {
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document_id = document_id + 1;
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@ -251,16 +306,28 @@ fn index_csv(
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writer.write_byte_record(document.as_byte_record())?;
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let document = writer.into_inner()?;
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documents.push((document_id, document));
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if documents.len() % 100_000 == 0 {
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let usage = mem_usage(&word_positions, &word_position_docids, &documents);
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if usage > max_mem_usage {
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eprintln!("Whoops too much memory used ({}B).", usage);
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let word_positions = mem::take(&mut word_positions);
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let word_position_docids = mem::take(&mut word_position_docids);
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let documents = mem::take(&mut documents);
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let indexed = Indexed::new(word_positions, word_position_docids, headers.clone(), documents)?;
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eprintln!("{:?}: Indexed created!", thread_index);
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stores.push(MtblKvStore::from_indexed(indexed)?);
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}
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}
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}
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// We store the words from the postings.
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let new_words: BTreeSet<_> = word_position_docids.iter().map(|((w, _), _)| w).collect();
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let fst = fst::Set::from_iter(new_words)?;
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let indexed = Indexed { fst, headers, word_positions, word_position_docids, documents };
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let indexed = Indexed::new(word_positions, word_position_docids, headers, documents)?;
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eprintln!("{:?}: Indexed created!", thread_index);
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stores.push(MtblKvStore::from_indexed(indexed)?);
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MtblKvStore::from_indexed(indexed).map(|x| vec![x])
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Ok(stores)
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}
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// TODO merge with the previous values
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@ -362,15 +429,17 @@ fn main() -> anyhow::Result<()> {
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let index = Index::new(&env)?;
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// We duplicate the file # CPU times
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let num_threads = rayon::current_num_threads();
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let max_memory_usage = (opt.max_memory_usage / num_threads).max(50 * 1024 * 1024); // 50MB
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// We duplicate the file # jobs times.
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let file = opt.csv_file.unwrap();
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let csv_readers: Vec<_> = (0..num_threads).map(|_| csv::Reader::from_path(&file)).collect::<Result<_, _>>()?;
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let stores: Vec<_> = csv_readers
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.into_par_iter()
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.enumerate()
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.map(|(i, rdr)| index_csv(rdr, i, num_threads))
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.map(|(i, rdr)| index_csv(rdr, i, num_threads, max_memory_usage))
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.collect::<Result<_, _>>()?;
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let stores: Vec<_> = stores.into_iter().flatten().collect();
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