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Export the indexing part into a module

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This commit is contained in:
Clément Renault 2020-10-20 14:20:17 +02:00
parent eb92e72e6c
commit a122d3d466
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6 changed files with 861 additions and 824 deletions
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68
src/indexing/merge_function.rs Normal file
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use bstr::ByteSlice as _;
use fst::IntoStreamer;
use roaring::RoaringBitmap;
use crate::heed_codec::CboRoaringBitmapCodec;
const WORDS_FST_KEY: &[u8] = crate::WORDS_FST_KEY.as_bytes();
const HEADERS_KEY: &[u8] = crate::HEADERS_KEY.as_bytes();
const DOCUMENTS_IDS_KEY: &[u8] = crate::DOCUMENTS_IDS_KEY.as_bytes();
pub fn main_merge(key: &[u8], values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
match key {
WORDS_FST_KEY => {
let fsts: Vec<_> = values.iter().map(|v| fst::Set::new(v).unwrap()).collect();
// Union of the FSTs
let mut op = fst::set::OpBuilder::new();
fsts.iter().for_each(|fst| op.push(fst.into_stream()));
let op = op.r#union();
let mut build = fst::SetBuilder::memory();
build.extend_stream(op.into_stream()).unwrap();
Ok(build.into_inner().unwrap())
},
HEADERS_KEY => {
assert!(values.windows(2).all(|vs| vs[0] == vs[1]));
Ok(values[0].to_vec())
},
DOCUMENTS_IDS_KEY => word_docids_merge(&[], values),
otherwise => panic!("wut {:?}", otherwise),
}
}
pub fn word_docids_merge(_key: &[u8], values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
let (head, tail) = values.split_first().unwrap();
let mut head = RoaringBitmap::deserialize_from(head.as_slice()).unwrap();
for value in tail {
let bitmap = RoaringBitmap::deserialize_from(value.as_slice()).unwrap();
head.union_with(&bitmap);
}
let mut vec = Vec::with_capacity(head.serialized_size());
head.serialize_into(&mut vec).unwrap();
Ok(vec)
}
pub fn docid_word_positions_merge(key: &[u8], _values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
panic!("merging docid word positions is an error ({:?})", key.as_bstr())
}
pub fn words_pairs_proximities_docids_merge(_key: &[u8], values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
let (head, tail) = values.split_first().unwrap();
let mut head = CboRoaringBitmapCodec::deserialize_from(head.as_slice()).unwrap();
for value in tail {
let bitmap = CboRoaringBitmapCodec::deserialize_from(value.as_slice()).unwrap();
head.union_with(&bitmap);
}
let mut vec = Vec::new();
CboRoaringBitmapCodec::serialize_into(&head, &mut vec).unwrap();
Ok(vec)
}
pub fn documents_merge(key: &[u8], _values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
panic!("merging documents is an error ({:?})", key.as_bstr())
}

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src/indexing/mod.rs Normal file
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use std::fs::File;
use std::io::{self, Read, Seek, SeekFrom};
use std::sync::mpsc::sync_channel;
use std::time::Instant;
use anyhow::Context;
use bstr::ByteSlice as _;
use flate2::read::GzDecoder;
use grenad::{Writer, Sorter, Merger, Reader, FileFuse, CompressionType};
use heed::types::ByteSlice;
use log::{debug, info};
use rayon::prelude::*;
use structopt::StructOpt;
use tempfile::tempfile;
use crate::Index;
use self::store::Store;
use self::merge_function::{
main_merge, word_docids_merge, words_pairs_proximities_docids_merge,
docid_word_positions_merge, documents_merge,
};
mod store;
mod merge_function;
#[derive(Debug, StructOpt)]
pub struct IndexerOpt {
/// The amount of documents to skip before printing
/// a log regarding the indexing advancement.
#[structopt(long, default_value = "1000000")] // 1m
log_every_n: usize,
/// MTBL max number of chunks in bytes.
#[structopt(long)]
max_nb_chunks: Option<usize>,
/// The maximum amount of memory to use for the MTBL buffer. It is recommended
/// to use something like 80%-90% of the available memory.
///
/// It is automatically split by the number of jobs e.g. if you use 7 jobs
/// and 7 GB of max memory, each thread will use a maximum of 1 GB.
#[structopt(long, default_value = "7516192768")] // 7 GB
max_memory: usize,
/// Size of the linked hash map cache when indexing.
/// The bigger it is, the faster the indexing is but the more memory it takes.
#[structopt(long, default_value = "500")]
linked_hash_map_size: usize,
/// The name of the compression algorithm to use when compressing intermediate
/// chunks during indexing documents.
///
/// Choosing a fast algorithm will make the indexing faster but may consume more memory.
#[structopt(long, default_value = "snappy", possible_values = &["snappy", "zlib", "lz4", "lz4hc", "zstd"])]
chunk_compression_type: CompressionType,
/// The level of compression of the chosen algorithm.
#[structopt(long, requires = "chunk-compression-type")]
chunk_compression_level: Option<u32>,
/// The number of bytes to remove from the begining of the chunks while reading/sorting
/// or merging them.
///
/// File fusing must only be enable on file systems that support the `FALLOC_FL_COLLAPSE_RANGE`,
/// (i.e. ext4 and XFS). File fusing will only work if the `enable-chunk-fusing` is set.
#[structopt(long, default_value = "4294967296")] // 4 GB
chunk_fusing_shrink_size: u64,
/// Enable the chunk fusing or not, this reduces the amount of disk used by a factor of 2.
#[structopt(long)]
enable_chunk_fusing: bool,
/// Number of parallel jobs for indexing, defaults to # of CPUs.
#[structopt(long)]
indexing_jobs: Option<usize>,
}
type MergeFn = fn(&[u8], &[Vec<u8>]) -> Result<Vec<u8>, ()>;
fn create_writer(typ: CompressionType, level: Option<u32>, file: File) -> io::Result<Writer<File>> {
let mut builder = Writer::builder();
builder.compression_type(typ);
if let Some(level) = level {
builder.compression_level(level);
}
builder.build(file)
}
fn create_sorter(
merge: MergeFn,
chunk_compression_type: CompressionType,
chunk_compression_level: Option<u32>,
chunk_fusing_shrink_size: Option<u64>,
max_nb_chunks: Option<usize>,
max_memory: Option<usize>,
) -> Sorter<MergeFn>
{
let mut builder = Sorter::builder(merge);
if let Some(shrink_size) = chunk_fusing_shrink_size {
builder.file_fusing_shrink_size(shrink_size);
}
builder.chunk_compression_type(chunk_compression_type);
if let Some(level) = chunk_compression_level {
builder.chunk_compression_level(level);
}
if let Some(nb_chunks) = max_nb_chunks {
builder.max_nb_chunks(nb_chunks);
}
if let Some(memory) = max_memory {
builder.max_memory(memory);
}
builder.build()
}
fn writer_into_reader(writer: Writer<File>, shrink_size: Option<u64>) -> anyhow::Result<Reader<FileFuse>> {
let mut file = writer.into_inner()?;
file.seek(SeekFrom::Start(0))?;
let file = if let Some(shrink_size) = shrink_size {
FileFuse::builder().shrink_size(shrink_size).build(file)
} else {
FileFuse::new(file)
};
Reader::new(file).map_err(Into::into)
}
fn merge_readers(sources: Vec<Reader<FileFuse>>, merge: MergeFn) -> Merger<FileFuse, MergeFn> {
let mut builder = Merger::builder(merge);
builder.extend(sources);
builder.build()
}
fn merge_into_lmdb_database(
wtxn: &mut heed::RwTxn,
database: heed::PolyDatabase,
sources: Vec<Reader<FileFuse>>,
merge: MergeFn,
) -> anyhow::Result<()> {
debug!("Merging {} MTBL stores...", sources.len());
let before = Instant::now();
let merger = merge_readers(sources, merge);
let mut in_iter = merger.into_merge_iter()?;
let mut out_iter = database.iter_mut::<_, ByteSlice, ByteSlice>(wtxn)?;
while let Some((k, v)) = in_iter.next()? {
out_iter.append(k, v).with_context(|| format!("writing {:?} into LMDB", k.as_bstr()))?;
}
debug!("MTBL stores merged in {:.02?}!", before.elapsed());
Ok(())
}
fn write_into_lmdb_database(
wtxn: &mut heed::RwTxn,
database: heed::PolyDatabase,
mut reader: Reader<FileFuse>,
) -> anyhow::Result<()> {
debug!("Writing MTBL stores...");
let before = Instant::now();
let mut out_iter = database.iter_mut::<_, ByteSlice, ByteSlice>(wtxn)?;
while let Some((k, v)) = reader.next()? {
out_iter.append(k, v).with_context(|| format!("writing {:?} into LMDB", k.as_bstr()))?;
}
debug!("MTBL stores merged in {:.02?}!", before.elapsed());
Ok(())
}
fn csv_bytes_readers<'a>(
content: &'a [u8],
gzipped: bool,
count: usize,
) -> Vec<csv::Reader<Box<dyn Read + Send + 'a>>>
{
let mut readers = Vec::new();
for _ in 0..count {
let content = if gzipped {
Box::new(GzDecoder::new(content)) as Box<dyn Read + Send>
} else {
Box::new(content) as Box<dyn Read + Send>
};
let reader = csv::Reader::from_reader(content);
readers.push(reader);
}
readers
}
pub fn run<'a>(
env: &heed::Env,
index: &Index,
opt: IndexerOpt,
content: &'a [u8],
gzipped: bool,
) -> anyhow::Result<()>
{
let jobs = opt.indexing_jobs.unwrap_or(0);
let pool = rayon::ThreadPoolBuilder::new().num_threads(jobs).build()?;
pool.install(|| run_intern(env, index, opt, content, gzipped))
}
fn run_intern<'a>(
env: &heed::Env,
index: &Index,
opt: IndexerOpt,
content: &'a [u8],
gzipped: bool,
) -> anyhow::Result<()>
{
let before_indexing = Instant::now();
let num_threads = rayon::current_num_threads();
let linked_hash_map_size = opt.linked_hash_map_size;
let max_nb_chunks = opt.max_nb_chunks;
let max_memory_by_job = opt.max_memory / num_threads;
let chunk_compression_type = opt.chunk_compression_type;
let chunk_compression_level = opt.chunk_compression_level;
let log_every_n = opt.log_every_n;
let chunk_fusing_shrink_size = if opt.enable_chunk_fusing {
Some(opt.chunk_fusing_shrink_size)
} else {
None
};
let readers = csv_bytes_readers(content, gzipped, num_threads)
.into_par_iter()
.enumerate()
.map(|(i, rdr)| {
let store = Store::new(
linked_hash_map_size,
max_nb_chunks,
Some(max_memory_by_job),
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
)?;
store.index_csv(rdr, i, num_threads, log_every_n)
})
.collect::<Result<Vec<_>, _>>()?;
let mut main_readers = Vec::with_capacity(readers.len());
let mut word_docids_readers = Vec::with_capacity(readers.len());
let mut docid_word_positions_readers = Vec::with_capacity(readers.len());
let mut words_pairs_proximities_docids_readers = Vec::with_capacity(readers.len());
let mut documents_readers = Vec::with_capacity(readers.len());
readers.into_iter().for_each(|readers| {
main_readers.push(readers.main);
word_docids_readers.push(readers.word_docids);
docid_word_positions_readers.push(readers.docid_word_positions);
words_pairs_proximities_docids_readers.push(readers.words_pairs_proximities_docids);
documents_readers.push(readers.documents);
});
// This is the function that merge the readers
// by using the given merge function.
let merge_readers = move |readers, merge| {
let mut writer = tempfile().and_then(|f| {
create_writer(chunk_compression_type, chunk_compression_level, f)
})?;
let merger = merge_readers(readers, merge);
merger.write_into(&mut writer)?;
writer_into_reader(writer, chunk_fusing_shrink_size)
};
// The enum and the channel which is used to transfert
// the readers merges potentially done on another thread.
enum DatabaseType { Main, WordDocids, WordsPairsProximitiesDocids };
let (sender, receiver) = sync_channel(3);
debug!("Merging the main, word docids and words pairs proximity docids in parallel...");
rayon::spawn(move || {
vec![
(DatabaseType::Main, main_readers, main_merge as MergeFn),
(DatabaseType::WordDocids, word_docids_readers, word_docids_merge),
(
DatabaseType::WordsPairsProximitiesDocids,
words_pairs_proximities_docids_readers,
words_pairs_proximities_docids_merge,
),
]
.into_par_iter()
.for_each(|(dbtype, readers, merge)| {
let result = merge_readers(readers, merge);
sender.send((dbtype, result)).unwrap();
});
});
let mut wtxn = env.write_txn()?;
debug!("Writing the docid word positions into LMDB on disk...");
merge_into_lmdb_database(
&mut wtxn,
*index.docid_word_positions.as_polymorph(),
docid_word_positions_readers,
docid_word_positions_merge,
)?;
debug!("Writing the documents into LMDB on disk...");
merge_into_lmdb_database(
&mut wtxn,
*index.documents.as_polymorph(),
documents_readers,
documents_merge,
)?;
for (db_type, result) in receiver {
let content = result?;
match db_type {
DatabaseType::Main => {
debug!("Writing the main elements into LMDB on disk...");
write_into_lmdb_database(&mut wtxn, index.main, content)?;
},
DatabaseType::WordDocids => {
debug!("Writing the words docids into LMDB on disk...");
let db = *index.word_docids.as_polymorph();
write_into_lmdb_database(&mut wtxn, db, content)?;
},
DatabaseType::WordsPairsProximitiesDocids => {
debug!("Writing the words pairs proximities docids into LMDB on disk...");
let db = *index.word_pair_proximity_docids.as_polymorph();
write_into_lmdb_database(&mut wtxn, db, content)?;
},
}
}
debug!("Retrieving the number of documents...");
let count = index.number_of_documents(&wtxn)?;
wtxn.commit()?;
info!("Wrote {} documents in {:.02?}", count, before_indexing.elapsed());
Ok(())
}

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use std::collections::{BTreeMap, HashMap};
use std::convert::TryFrom;
use std::fs::File;
use std::io::Read;
use std::iter::FromIterator;
use std::time::Instant;
use std::{cmp, iter};
use anyhow::Context;
use bstr::ByteSlice as _;
use csv::StringRecord;
use heed::BytesEncode;
use linked_hash_map::LinkedHashMap;
use log::{debug, info};
use grenad::{Reader, FileFuse, Writer, Sorter, CompressionType};
use roaring::RoaringBitmap;
use tempfile::tempfile;
use crate::heed_codec::{CsvStringRecordCodec, BoRoaringBitmapCodec, CboRoaringBitmapCodec};
use crate::tokenizer::{simple_tokenizer, only_token};
use crate::{SmallVec32, Position, DocumentId};
use super::{MergeFn, create_writer, create_sorter, writer_into_reader};
use super::merge_function::{main_merge, word_docids_merge, words_pairs_proximities_docids_merge};
const LMDB_MAX_KEY_LENGTH: usize = 511;
const ONE_KILOBYTE: usize = 1024 * 1024;
const MAX_POSITION: usize = 1000;
const MAX_ATTRIBUTES: usize = u32::max_value() as usize / MAX_POSITION;
const WORDS_FST_KEY: &[u8] = crate::WORDS_FST_KEY.as_bytes();
const HEADERS_KEY: &[u8] = crate::HEADERS_KEY.as_bytes();
const DOCUMENTS_IDS_KEY: &[u8] = crate::DOCUMENTS_IDS_KEY.as_bytes();
pub struct Readers {
pub main: Reader<FileFuse>,
pub word_docids: Reader<FileFuse>,
pub docid_word_positions: Reader<FileFuse>,
pub words_pairs_proximities_docids: Reader<FileFuse>,
pub documents: Reader<FileFuse>,
}
pub struct Store {
word_docids: LinkedHashMap<SmallVec32<u8>, RoaringBitmap>,
word_docids_limit: usize,
words_pairs_proximities_docids: LinkedHashMap<(SmallVec32<u8>, SmallVec32<u8>, u8), RoaringBitmap>,
words_pairs_proximities_docids_limit: usize,
documents_ids: RoaringBitmap,
// MTBL parameters
chunk_compression_type: CompressionType,
chunk_compression_level: Option<u32>,
chunk_fusing_shrink_size: Option<u64>,
// MTBL sorters
main_sorter: Sorter<MergeFn>,
word_docids_sorter: Sorter<MergeFn>,
words_pairs_proximities_docids_sorter: Sorter<MergeFn>,
// MTBL writers
docid_word_positions_writer: Writer<File>,
documents_writer: Writer<File>,
}
impl Store {
pub fn new(
linked_hash_map_size: usize,
max_nb_chunks: Option<usize>,
max_memory: Option<usize>,
chunk_compression_type: CompressionType,
chunk_compression_level: Option<u32>,
chunk_fusing_shrink_size: Option<u64>,
) -> anyhow::Result<Store>
{
// We divide the max memory by the number of sorter the Store have.
let max_memory = max_memory.map(|mm| cmp::max(ONE_KILOBYTE, mm / 3));
let main_sorter = create_sorter(
main_merge,
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
max_nb_chunks,
max_memory,
);
let word_docids_sorter = create_sorter(
word_docids_merge,
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
max_nb_chunks,
max_memory,
);
let words_pairs_proximities_docids_sorter = create_sorter(
words_pairs_proximities_docids_merge,
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
max_nb_chunks,
max_memory,
);
let documents_writer = tempfile().and_then(|f| {
create_writer(chunk_compression_type, chunk_compression_level, f)
})?;
let docid_word_positions_writer = tempfile().and_then(|f| {
create_writer(chunk_compression_type, chunk_compression_level, f)
})?;
Ok(Store {
word_docids: LinkedHashMap::with_capacity(linked_hash_map_size),
word_docids_limit: linked_hash_map_size,
words_pairs_proximities_docids: LinkedHashMap::with_capacity(linked_hash_map_size),
words_pairs_proximities_docids_limit: linked_hash_map_size,
documents_ids: RoaringBitmap::new(),
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
main_sorter,
word_docids_sorter,
words_pairs_proximities_docids_sorter,
docid_word_positions_writer,
documents_writer,
})
}
// Save the documents ids under the position and word we have seen it.
fn insert_word_docid(&mut self, word: &str, id: DocumentId) -> anyhow::Result<()> {
// if get_refresh finds the element it is assured to be at the end of the linked hash map.
match self.word_docids.get_refresh(word.as_bytes()) {
Some(old) => { old.insert(id); },
None => {
let word_vec = SmallVec32::from(word.as_bytes());
// A newly inserted element is append at the end of the linked hash map.
self.word_docids.insert(word_vec, RoaringBitmap::from_iter(Some(id)));
// If the word docids just reached it's capacity we must make sure to remove
// one element, this way next time we insert we doesn't grow the capacity.
if self.word_docids.len() == self.word_docids_limit {
// Removing the front element is equivalent to removing the LRU element.
let lru = self.word_docids.pop_front();
Self::write_word_docids(&mut self.word_docids_sorter, lru)?;
}
}
}
Ok(())
}
// Save the documents ids under the words pairs proximities that it contains.
fn insert_words_pairs_proximities_docids<'a>(
&mut self,
words_pairs_proximities: impl IntoIterator<Item=((&'a str, &'a str), u8)>,
id: DocumentId,
) -> anyhow::Result<()>
{
for ((w1, w2), prox) in words_pairs_proximities {
let w1 = SmallVec32::from(w1.as_bytes());
let w2 = SmallVec32::from(w2.as_bytes());
let key = (w1, w2, prox);
// if get_refresh finds the element it is assured
// to be at the end of the linked hash map.
match self.words_pairs_proximities_docids.get_refresh(&key) {
Some(old) => { old.insert(id); },
None => {
// A newly inserted element is append at the end of the linked hash map.
let ids = RoaringBitmap::from_iter(Some(id));
self.words_pairs_proximities_docids.insert(key, ids);
}
}
}
// If the linked hashmap is over capacity we must remove the overflowing elements.
let len = self.words_pairs_proximities_docids.len();
let overflow = len.checked_sub(self.words_pairs_proximities_docids_limit);
if let Some(overflow) = overflow {
let mut lrus = Vec::with_capacity(overflow);
// Removing front elements is equivalent to removing the LRUs.
let iter = iter::from_fn(|| self.words_pairs_proximities_docids.pop_front());
iter.take(overflow).for_each(|x| lrus.push(x));
Self::write_words_pairs_proximities(&mut self.words_pairs_proximities_docids_sorter, lrus)?;
}
Ok(())
}
fn write_headers(&mut self, headers: &StringRecord) -> anyhow::Result<()> {
let headers = CsvStringRecordCodec::bytes_encode(headers)
.with_context(|| format!("could not encode csv record"))?;
Ok(self.main_sorter.insert(HEADERS_KEY, headers)?)
}
fn write_document(
&mut self,
document_id: DocumentId,
words_positions: &HashMap<String, SmallVec32<Position>>,
record: &StringRecord,
) -> anyhow::Result<()>
{
// We compute the list of words pairs proximities (self-join) and write it directly to disk.
let words_pair_proximities = compute_words_pair_proximities(&words_positions);
self.insert_words_pairs_proximities_docids(words_pair_proximities, document_id)?;
// We store document_id associated with all the words the record contains.
for (word, _) in words_positions {
self.insert_word_docid(word, document_id)?;
}
let record = CsvStringRecordCodec::bytes_encode(record)
.with_context(|| format!("could not encode CSV record"))?;
self.documents_ids.insert(document_id);
self.documents_writer.insert(document_id.to_be_bytes(), record)?;
Self::write_docid_word_positions(&mut self.docid_word_positions_writer, document_id, words_positions)?;
Ok(())
}
fn write_words_pairs_proximities(
sorter: &mut Sorter<MergeFn>,
iter: impl IntoIterator<Item=((SmallVec32<u8>, SmallVec32<u8>, u8), RoaringBitmap)>,
) -> anyhow::Result<()>
{
let mut key = Vec::new();
let mut buffer = Vec::new();
for ((w1, w2, min_prox), docids) in iter {
key.clear();
key.extend_from_slice(w1.as_bytes());
key.push(0);
key.extend_from_slice(w2.as_bytes());
// Storing the minimun proximity found between those words
key.push(min_prox);
// We serialize the document ids into a buffer
buffer.clear();
buffer.reserve(CboRoaringBitmapCodec::serialized_size(&docids));
CboRoaringBitmapCodec::serialize_into(&docids, &mut buffer)?;
// that we write under the generated key into MTBL
if lmdb_key_valid_size(&key) {
sorter.insert(&key, &buffer)?;
}
}
Ok(())
}
fn write_docid_word_positions(
writer: &mut Writer<File>,
id: DocumentId,
words_positions: &HashMap<String, SmallVec32<Position>>,
) -> anyhow::Result<()>
{
// We prefix the words by the document id.
let mut key = id.to_be_bytes().to_vec();
let base_size = key.len();
// We order the words lexicographically, this way we avoid passing by a sorter.
let words_positions = BTreeMap::from_iter(words_positions);
for (word, positions) in words_positions {
key.truncate(base_size);
key.extend_from_slice(word.as_bytes());
// We serialize the positions into a buffer.
let positions = RoaringBitmap::from_iter(positions.iter().cloned());
let bytes = BoRoaringBitmapCodec::bytes_encode(&positions)
.with_context(|| "could not serialize positions")?;
// that we write under the generated key into MTBL
if lmdb_key_valid_size(&key) {
writer.insert(&key, &bytes)?;
}
}
Ok(())
}
fn write_word_docids<I>(sorter: &mut Sorter<MergeFn>, iter: I) -> anyhow::Result<()>
where I: IntoIterator<Item=(SmallVec32<u8>, RoaringBitmap)>
{
let mut key = Vec::new();
let mut buffer = Vec::new();
for (word, ids) in iter {
key.clear();
key.extend_from_slice(&word);
// We serialize the document ids into a buffer
buffer.clear();
let ids = RoaringBitmap::from_iter(ids);
buffer.reserve(ids.serialized_size());
ids.serialize_into(&mut buffer)?;
// that we write under the generated key into MTBL
if lmdb_key_valid_size(&key) {
sorter.insert(&key, &buffer)?;
}
}
Ok(())
}
fn write_documents_ids(sorter: &mut Sorter<MergeFn>, ids: RoaringBitmap) -> anyhow::Result<()> {
let mut buffer = Vec::with_capacity(ids.serialized_size());
ids.serialize_into(&mut buffer)?;
sorter.insert(DOCUMENTS_IDS_KEY, &buffer)?;
Ok(())
}
pub fn index_csv<'a>(
mut self,
mut rdr: csv::Reader<Box<dyn Read + Send + 'a>>,
thread_index: usize,
num_threads: usize,
log_every_n: usize,
) -> anyhow::Result<Readers>
{
debug!("{:?}: Indexing in a Store...", thread_index);
// Write the headers into the store.
let headers = rdr.headers()?;
self.write_headers(&headers)?;
let mut before = Instant::now();
let mut document_id: usize = 0;
let mut document = csv::StringRecord::new();
let mut words_positions = HashMap::new();
while rdr.read_record(&mut document)? {
// We skip documents that must not be indexed by this thread.
if document_id % num_threads == thread_index {
// This is a log routine that we do every `log_every_n` documents.
if document_id % log_every_n == 0 {
let count = format_count(document_id);
info!("We have seen {} documents so far ({:.02?}).", count, before.elapsed());
before = Instant::now();
}
let document_id = DocumentId::try_from(document_id).context("generated id is too big")?;
for (attr, content) in document.iter().enumerate().take(MAX_ATTRIBUTES) {
for (pos, token) in simple_tokenizer(&content).filter_map(only_token).enumerate().take(MAX_POSITION) {
let word = token.to_lowercase();
let position = (attr * MAX_POSITION + pos) as u32;
words_positions.entry(word).or_insert_with(SmallVec32::new).push(position);
}
}
// We write the document in the documents store.
self.write_document(document_id, &words_positions, &document)?;
words_positions.clear();
}
// Compute the document id of the next document.
document_id = document_id + 1;
}
let readers = self.finish()?;
debug!("{:?}: Store created!", thread_index);
Ok(readers)
}
fn finish(mut self) -> anyhow::Result<Readers> {
let comp_type = self.chunk_compression_type;
let comp_level = self.chunk_compression_level;
let shrink_size = self.chunk_fusing_shrink_size;
Self::write_word_docids(&mut self.word_docids_sorter, self.word_docids)?;
Self::write_documents_ids(&mut self.main_sorter, self.documents_ids)?;
Self::write_words_pairs_proximities(
&mut self.words_pairs_proximities_docids_sorter,
self.words_pairs_proximities_docids,
)?;
let mut word_docids_wtr = tempfile().and_then(|f| create_writer(comp_type, comp_level, f))?;
let mut builder = fst::SetBuilder::memory();
let mut iter = self.word_docids_sorter.into_iter()?;
while let Some((word, val)) = iter.next()? {
// This is a lexicographically ordered word position
// we use the key to construct the words fst.
builder.insert(word)?;
word_docids_wtr.insert(word, val)?;
}
let fst = builder.into_set();
self.main_sorter.insert(WORDS_FST_KEY, fst.as_fst().as_bytes())?;
let mut main_wtr = tempfile().and_then(|f| create_writer(comp_type, comp_level, f))?;
self.main_sorter.write_into(&mut main_wtr)?;
let mut words_pairs_proximities_docids_wtr = tempfile().and_then(|f| create_writer(comp_type, comp_level, f))?;
self.words_pairs_proximities_docids_sorter.write_into(&mut words_pairs_proximities_docids_wtr)?;
let main = writer_into_reader(main_wtr, shrink_size)?;
let word_docids = writer_into_reader(word_docids_wtr, shrink_size)?;
let words_pairs_proximities_docids = writer_into_reader(words_pairs_proximities_docids_wtr, shrink_size)?;
let docid_word_positions = writer_into_reader(self.docid_word_positions_writer, shrink_size)?;
let documents = writer_into_reader(self.documents_writer, shrink_size)?;
Ok(Readers {
main,
word_docids,
docid_word_positions,
words_pairs_proximities_docids,
documents,
})
}
}
/// Outputs a list of all pairs of words with the shortest proximity between 1 and 7 inclusive.
///
/// This list is used by the engine to calculate the documents containing words that are
/// close to each other.
fn compute_words_pair_proximities(
word_positions: &HashMap<String, SmallVec32<Position>>,
) -> HashMap<(&str, &str), u8>
{
use itertools::Itertools;
let mut words_pair_proximities = HashMap::new();
for ((w1, ps1), (w2, ps2)) in word_positions.iter().cartesian_product(word_positions) {
let mut min_prox = None;
for (ps1, ps2) in ps1.iter().cartesian_product(ps2) {
let prox = crate::proximity::positions_proximity(*ps1, *ps2);
let prox = u8::try_from(prox).unwrap();
// We don't care about a word that appear at the
// same position or too far from the other.
if prox >= 1 && prox <= 7 {
if min_prox.map_or(true, |mp| prox < mp) {
min_prox = Some(prox)
}
}
}
if let Some(min_prox) = min_prox {
words_pair_proximities.insert((w1.as_str(), w2.as_str()), min_prox);
}
}
words_pair_proximities
}
fn format_count(n: usize) -> String {
human_format::Formatter::new().with_decimals(1).with_separator("").format(n as f64)
}
fn lmdb_key_valid_size(key: &[u8]) -> bool {
!key.is_empty() && key.len() <= LMDB_MAX_KEY_LENGTH
}

1
src/lib.rs
View File

@ -1,4 +1,5 @@
mod criterion;
mod indexing;
mod mdfs;
mod query_tokens;
mod search;

833
src/subcommand/indexer.rs
View File

@ -1,40 +1,12 @@
use std::collections::{BTreeMap, HashMap};
use std::convert::TryFrom;
use std::fs::File;
use std::io::{self, Read, Write, Seek, SeekFrom};
use std::iter::FromIterator;
use std::path::PathBuf;
use std::sync::mpsc::sync_channel;
use std::time::Instant;
use std::{cmp, iter, thread};
use anyhow::{Context, bail};
use bstr::ByteSlice as _;
use csv::StringRecord;
use flate2::read::GzDecoder;
use fst::IntoStreamer;
use heed::{EnvOpenOptions, BytesEncode, types::ByteSlice};
use linked_hash_map::LinkedHashMap;
use log::{debug, info};
use grenad::{Reader, FileFuse, Writer, Merger, Sorter, CompressionType};
use rayon::prelude::*;
use roaring::RoaringBitmap;
use anyhow::bail;
use heed::EnvOpenOptions;
use structopt::StructOpt;
use tempfile::tempfile;
use crate::heed_codec::{CsvStringRecordCodec, BoRoaringBitmapCodec, CboRoaringBitmapCodec};
use crate::tokenizer::{simple_tokenizer, only_token};
use crate::{SmallVec32, Index, Position, DocumentId};
const LMDB_MAX_KEY_LENGTH: usize = 511;
const ONE_KILOBYTE: usize = 1024 * 1024;
const MAX_POSITION: usize = 1000;
const MAX_ATTRIBUTES: usize = u32::max_value() as usize / MAX_POSITION;
const WORDS_FST_KEY: &[u8] = crate::WORDS_FST_KEY.as_bytes();
const HEADERS_KEY: &[u8] = crate::HEADERS_KEY.as_bytes();
const DOCUMENTS_IDS_KEY: &[u8] = crate::DOCUMENTS_IDS_KEY.as_bytes();
use crate::indexing::{self, IndexerOpt};
use crate::Index;
#[derive(Debug, StructOpt)]
#[structopt(name = "milli-indexer")]
@ -50,10 +22,6 @@ pub struct Opt {
#[structopt(long = "db-size", default_value = "107374182400")] // 100 GB
database_size: usize,
/// Number of parallel jobs, defaults to # of CPUs.
#[structopt(short, long)]
jobs: Option<usize>,
#[structopt(flatten)]
indexer: IndexerOpt,
@ -71,667 +39,6 @@ pub struct Opt {
csv_file: Option<PathBuf>,
}
#[derive(Debug, StructOpt)]
struct IndexerOpt {
/// The amount of documents to skip before printing
/// a log regarding the indexing advancement.
#[structopt(long, default_value = "1000000")] // 1m
log_every_n: usize,
/// MTBL max number of chunks in bytes.
#[structopt(long)]
max_nb_chunks: Option<usize>,
/// The maximum amount of memory to use for the MTBL buffer. It is recommended
/// to use something like 80%-90% of the available memory.
///
/// It is automatically split by the number of jobs e.g. if you use 7 jobs
/// and 7 GB of max memory, each thread will use a maximum of 1 GB.
#[structopt(long, default_value = "7516192768")] // 7 GB
max_memory: usize,
/// Size of the linked hash map cache when indexing.
/// The bigger it is, the faster the indexing is but the more memory it takes.
#[structopt(long, default_value = "500")]
linked_hash_map_size: usize,
/// The name of the compression algorithm to use when compressing intermediate
/// chunks during indexing documents.
///
/// Choosing a fast algorithm will make the indexing faster but may consume more memory.
#[structopt(long, default_value = "snappy", possible_values = &["snappy", "zlib", "lz4", "lz4hc", "zstd"])]
chunk_compression_type: CompressionType,
/// The level of compression of the chosen algorithm.
#[structopt(long, requires = "chunk-compression-type")]
chunk_compression_level: Option<u32>,
/// The number of bytes to remove from the begining of the chunks while reading/sorting
/// or merging them.
///
/// File fusing must only be enable on file systems that support the `FALLOC_FL_COLLAPSE_RANGE`,
/// (i.e. ext4 and XFS). File fusing will only work if the `enable-chunk-fusing` is set.
#[structopt(long, default_value = "4294967296")] // 4 GB
chunk_fusing_shrink_size: u64,
/// Enable the chunk fusing or not, this reduces the amount of disk used by a factor of 2.
#[structopt(long)]
enable_chunk_fusing: bool,
}
fn format_count(n: usize) -> String {
human_format::Formatter::new().with_decimals(1).with_separator("").format(n as f64)
}
fn lmdb_key_valid_size(key: &[u8]) -> bool {
!key.is_empty() && key.len() <= LMDB_MAX_KEY_LENGTH
}
fn create_writer(typ: CompressionType, level: Option<u32>, file: File) -> io::Result<Writer<File>> {
let mut builder = Writer::builder();
builder.compression_type(typ);
if let Some(level) = level {
builder.compression_level(level);
}
builder.build(file)
}
fn writer_into_reader(writer: Writer<File>, shrink_size: Option<u64>) -> anyhow::Result<Reader<FileFuse>> {
let mut file = writer.into_inner()?;
file.seek(SeekFrom::Start(0))?;
let file = if let Some(shrink_size) = shrink_size {
FileFuse::builder().shrink_size(shrink_size).build(file)
} else {
FileFuse::new(file)
};
Reader::new(file).map_err(Into::into)
}
fn create_sorter(
merge: MergeFn,
chunk_compression_type: CompressionType,
chunk_compression_level: Option<u32>,
chunk_fusing_shrink_size: Option<u64>,
max_nb_chunks: Option<usize>,
max_memory: Option<usize>,
) -> Sorter<MergeFn>
{
let mut builder = Sorter::builder(merge);
if let Some(shrink_size) = chunk_fusing_shrink_size {
builder.file_fusing_shrink_size(shrink_size);
}
builder.chunk_compression_type(chunk_compression_type);
if let Some(level) = chunk_compression_level {
builder.chunk_compression_level(level);
}
if let Some(nb_chunks) = max_nb_chunks {
builder.max_nb_chunks(nb_chunks);
}
if let Some(memory) = max_memory {
builder.max_memory(memory);
}
builder.build()
}
/// Outputs a list of all pairs of words with the shortest proximity between 1 and 7 inclusive.
///
/// This list is used by the engine to calculate the documents containing words that are
/// close to each other.
fn compute_words_pair_proximities(
word_positions: &HashMap<String, SmallVec32<Position>>,
) -> HashMap<(&str, &str), u8>
{
use itertools::Itertools;
let mut words_pair_proximities = HashMap::new();
for ((w1, ps1), (w2, ps2)) in word_positions.iter().cartesian_product(word_positions) {
let mut min_prox = None;
for (ps1, ps2) in ps1.iter().cartesian_product(ps2) {
let prox = crate::proximity::positions_proximity(*ps1, *ps2);
let prox = u8::try_from(prox).unwrap();
// We don't care about a word that appear at the
// same position or too far from the other.
if prox >= 1 && prox <= 7 {
if min_prox.map_or(true, |mp| prox < mp) {
min_prox = Some(prox)
}
}
}
if let Some(min_prox) = min_prox {
words_pair_proximities.insert((w1.as_str(), w2.as_str()), min_prox);
}
}
words_pair_proximities
}
type MergeFn = fn(&[u8], &[Vec<u8>]) -> Result<Vec<u8>, ()>;
struct Readers {
main: Reader<FileFuse>,
word_docids: Reader<FileFuse>,
docid_word_positions: Reader<FileFuse>,
words_pairs_proximities_docids: Reader<FileFuse>,
documents: Reader<FileFuse>,
}
struct Store {
word_docids: LinkedHashMap<SmallVec32<u8>, RoaringBitmap>,
word_docids_limit: usize,
words_pairs_proximities_docids: LinkedHashMap<(SmallVec32<u8>, SmallVec32<u8>, u8), RoaringBitmap>,
words_pairs_proximities_docids_limit: usize,
documents_ids: RoaringBitmap,
// MTBL parameters
chunk_compression_type: CompressionType,
chunk_compression_level: Option<u32>,
chunk_fusing_shrink_size: Option<u64>,
// MTBL sorters
main_sorter: Sorter<MergeFn>,
word_docids_sorter: Sorter<MergeFn>,
words_pairs_proximities_docids_sorter: Sorter<MergeFn>,
// MTBL writers
docid_word_positions_writer: Writer<File>,
documents_writer: Writer<File>,
}
impl Store {
pub fn new(
linked_hash_map_size: usize,
max_nb_chunks: Option<usize>,
max_memory: Option<usize>,
chunk_compression_type: CompressionType,
chunk_compression_level: Option<u32>,
chunk_fusing_shrink_size: Option<u64>,
) -> anyhow::Result<Store>
{
// We divide the max memory by the number of sorter the Store have.
let max_memory = max_memory.map(|mm| cmp::max(ONE_KILOBYTE, mm / 3));
let main_sorter = create_sorter(
main_merge,
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
max_nb_chunks,
max_memory,
);
let word_docids_sorter = create_sorter(
word_docids_merge,
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
max_nb_chunks,
max_memory,
);
let words_pairs_proximities_docids_sorter = create_sorter(
words_pairs_proximities_docids_merge,
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
max_nb_chunks,
max_memory,
);
let documents_writer = tempfile().and_then(|f| {
create_writer(chunk_compression_type, chunk_compression_level, f)
})?;
let docid_word_positions_writer = tempfile().and_then(|f| {
create_writer(chunk_compression_type, chunk_compression_level, f)
})?;
Ok(Store {
word_docids: LinkedHashMap::with_capacity(linked_hash_map_size),
word_docids_limit: linked_hash_map_size,
words_pairs_proximities_docids: LinkedHashMap::with_capacity(linked_hash_map_size),
words_pairs_proximities_docids_limit: linked_hash_map_size,
documents_ids: RoaringBitmap::new(),
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
main_sorter,
word_docids_sorter,
words_pairs_proximities_docids_sorter,
docid_word_positions_writer,
documents_writer,
})
}
// Save the documents ids under the position and word we have seen it.
fn insert_word_docid(&mut self, word: &str, id: DocumentId) -> anyhow::Result<()> {
// if get_refresh finds the element it is assured to be at the end of the linked hash map.
match self.word_docids.get_refresh(word.as_bytes()) {
Some(old) => { old.insert(id); },
None => {
let word_vec = SmallVec32::from(word.as_bytes());
// A newly inserted element is append at the end of the linked hash map.
self.word_docids.insert(word_vec, RoaringBitmap::from_iter(Some(id)));
// If the word docids just reached it's capacity we must make sure to remove
// one element, this way next time we insert we doesn't grow the capacity.
if self.word_docids.len() == self.word_docids_limit {
// Removing the front element is equivalent to removing the LRU element.
let lru = self.word_docids.pop_front();
Self::write_word_docids(&mut self.word_docids_sorter, lru)?;
}
}
}
Ok(())
}
// Save the documents ids under the words pairs proximities that it contains.
fn insert_words_pairs_proximities_docids<'a>(
&mut self,
words_pairs_proximities: impl IntoIterator<Item=((&'a str, &'a str), u8)>,
id: DocumentId,
) -> anyhow::Result<()>
{
for ((w1, w2), prox) in words_pairs_proximities {
let w1 = SmallVec32::from(w1.as_bytes());
let w2 = SmallVec32::from(w2.as_bytes());
let key = (w1, w2, prox);
// if get_refresh finds the element it is assured
// to be at the end of the linked hash map.
match self.words_pairs_proximities_docids.get_refresh(&key) {
Some(old) => { old.insert(id); },
None => {
// A newly inserted element is append at the end of the linked hash map.
let ids = RoaringBitmap::from_iter(Some(id));
self.words_pairs_proximities_docids.insert(key, ids);
}
}
}
// If the linked hashmap is over capacity we must remove the overflowing elements.
let len = self.words_pairs_proximities_docids.len();
let overflow = len.checked_sub(self.words_pairs_proximities_docids_limit);
if let Some(overflow) = overflow {
let mut lrus = Vec::with_capacity(overflow);
// Removing front elements is equivalent to removing the LRUs.
let iter = iter::from_fn(|| self.words_pairs_proximities_docids.pop_front());
iter.take(overflow).for_each(|x| lrus.push(x));
Self::write_words_pairs_proximities(&mut self.words_pairs_proximities_docids_sorter, lrus)?;
}
Ok(())
}
fn write_headers(&mut self, headers: &StringRecord) -> anyhow::Result<()> {
let headers = CsvStringRecordCodec::bytes_encode(headers)
.with_context(|| format!("could not encode csv record"))?;
Ok(self.main_sorter.insert(HEADERS_KEY, headers)?)
}
fn write_document(
&mut self,
document_id: DocumentId,
words_positions: &HashMap<String, SmallVec32<Position>>,
record: &StringRecord,
) -> anyhow::Result<()>
{
// We compute the list of words pairs proximities (self-join) and write it directly to disk.
let words_pair_proximities = compute_words_pair_proximities(&words_positions);
self.insert_words_pairs_proximities_docids(words_pair_proximities, document_id)?;
// We store document_id associated with all the words the record contains.
for (word, _) in words_positions {
self.insert_word_docid(word, document_id)?;
}
let record = CsvStringRecordCodec::bytes_encode(record)
.with_context(|| format!("could not encode CSV record"))?;
self.documents_ids.insert(document_id);
self.documents_writer.insert(document_id.to_be_bytes(), record)?;
Self::write_docid_word_positions(&mut self.docid_word_positions_writer, document_id, words_positions)?;
Ok(())
}
fn write_words_pairs_proximities(
sorter: &mut Sorter<MergeFn>,
iter: impl IntoIterator<Item=((SmallVec32<u8>, SmallVec32<u8>, u8), RoaringBitmap)>,
) -> anyhow::Result<()>
{
let mut key = Vec::new();
let mut buffer = Vec::new();
for ((w1, w2, min_prox), docids) in iter {
key.clear();
key.extend_from_slice(w1.as_bytes());
key.push(0);
key.extend_from_slice(w2.as_bytes());
// Storing the minimun proximity found between those words
key.push(min_prox);
// We serialize the document ids into a buffer
buffer.clear();
buffer.reserve(CboRoaringBitmapCodec::serialized_size(&docids));
CboRoaringBitmapCodec::serialize_into(&docids, &mut buffer)?;
// that we write under the generated key into MTBL
if lmdb_key_valid_size(&key) {
sorter.insert(&key, &buffer)?;
}
}
Ok(())
}
fn write_docid_word_positions(
writer: &mut Writer<File>,
id: DocumentId,
words_positions: &HashMap<String, SmallVec32<Position>>,
) -> anyhow::Result<()>
{
// We prefix the words by the document id.
let mut key = id.to_be_bytes().to_vec();
let base_size = key.len();
// We order the words lexicographically, this way we avoid passing by a sorter.
let words_positions = BTreeMap::from_iter(words_positions);
for (word, positions) in words_positions {
key.truncate(base_size);
key.extend_from_slice(word.as_bytes());
// We serialize the positions into a buffer.
let positions = RoaringBitmap::from_iter(positions.iter().cloned());
let bytes = BoRoaringBitmapCodec::bytes_encode(&positions)
.with_context(|| "could not serialize positions")?;
// that we write under the generated key into MTBL
if lmdb_key_valid_size(&key) {
writer.insert(&key, &bytes)?;
}
}
Ok(())
}
fn write_word_docids<I>(sorter: &mut Sorter<MergeFn>, iter: I) -> anyhow::Result<()>
where I: IntoIterator<Item=(SmallVec32<u8>, RoaringBitmap)>
{
let mut key = Vec::new();
let mut buffer = Vec::new();
for (word, ids) in iter {
key.clear();
key.extend_from_slice(&word);
// We serialize the document ids into a buffer
buffer.clear();
let ids = RoaringBitmap::from_iter(ids);
buffer.reserve(ids.serialized_size());
ids.serialize_into(&mut buffer)?;
// that we write under the generated key into MTBL
if lmdb_key_valid_size(&key) {
sorter.insert(&key, &buffer)?;
}
}
Ok(())
}
fn write_documents_ids(sorter: &mut Sorter<MergeFn>, ids: RoaringBitmap) -> anyhow::Result<()> {
let mut buffer = Vec::with_capacity(ids.serialized_size());
ids.serialize_into(&mut buffer)?;
sorter.insert(DOCUMENTS_IDS_KEY, &buffer)?;
Ok(())
}
pub fn index_csv(
mut self,
mut rdr: csv::Reader<Box<dyn Read + Send>>,
thread_index: usize,
num_threads: usize,
log_every_n: usize,
) -> anyhow::Result<Readers>
{
debug!("{:?}: Indexing in a Store...", thread_index);
// Write the headers into the store.
let headers = rdr.headers()?;
self.write_headers(&headers)?;
let mut before = Instant::now();
let mut document_id: usize = 0;
let mut document = csv::StringRecord::new();
let mut words_positions = HashMap::new();
while rdr.read_record(&mut document)? {
// We skip documents that must not be indexed by this thread.
if document_id % num_threads == thread_index {
// This is a log routine that we do every `log_every_n` documents.
if document_id % log_every_n == 0 {
let count = format_count(document_id);
info!("We have seen {} documents so far ({:.02?}).", count, before.elapsed());
before = Instant::now();
}
let document_id = DocumentId::try_from(document_id).context("generated id is too big")?;
for (attr, content) in document.iter().enumerate().take(MAX_ATTRIBUTES) {
for (pos, token) in simple_tokenizer(&content).filter_map(only_token).enumerate().take(MAX_POSITION) {
let word = token.to_lowercase();
let position = (attr * MAX_POSITION + pos) as u32;
words_positions.entry(word).or_insert_with(SmallVec32::new).push(position);
}
}
// We write the document in the documents store.
self.write_document(document_id, &words_positions, &document)?;
words_positions.clear();
}
// Compute the document id of the next document.
document_id = document_id + 1;
}
let readers = self.finish()?;
debug!("{:?}: Store created!", thread_index);
Ok(readers)
}
fn finish(mut self) -> anyhow::Result<Readers> {
let comp_type = self.chunk_compression_type;
let comp_level = self.chunk_compression_level;
let shrink_size = self.chunk_fusing_shrink_size;
Self::write_word_docids(&mut self.word_docids_sorter, self.word_docids)?;
Self::write_documents_ids(&mut self.main_sorter, self.documents_ids)?;
Self::write_words_pairs_proximities(
&mut self.words_pairs_proximities_docids_sorter,
self.words_pairs_proximities_docids,
)?;
let mut word_docids_wtr = tempfile().and_then(|f| create_writer(comp_type, comp_level, f))?;
let mut builder = fst::SetBuilder::memory();
let mut iter = self.word_docids_sorter.into_iter()?;
while let Some((word, val)) = iter.next()? {
// This is a lexicographically ordered word position
// we use the key to construct the words fst.
builder.insert(word)?;
word_docids_wtr.insert(word, val)?;
}
let fst = builder.into_set();
self.main_sorter.insert(WORDS_FST_KEY, fst.as_fst().as_bytes())?;
let mut main_wtr = tempfile().and_then(|f| create_writer(comp_type, comp_level, f))?;
self.main_sorter.write_into(&mut main_wtr)?;
let mut words_pairs_proximities_docids_wtr = tempfile().and_then(|f| create_writer(comp_type, comp_level, f))?;
self.words_pairs_proximities_docids_sorter.write_into(&mut words_pairs_proximities_docids_wtr)?;
let main = writer_into_reader(main_wtr, shrink_size)?;
let word_docids = writer_into_reader(word_docids_wtr, shrink_size)?;
let words_pairs_proximities_docids = writer_into_reader(words_pairs_proximities_docids_wtr, shrink_size)?;
let docid_word_positions = writer_into_reader(self.docid_word_positions_writer, shrink_size)?;
let documents = writer_into_reader(self.documents_writer, shrink_size)?;
Ok(Readers {
main,
word_docids,
docid_word_positions,
words_pairs_proximities_docids,
documents,
})
}
}
fn main_merge(key: &[u8], values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
match key {
WORDS_FST_KEY => {
let fsts: Vec<_> = values.iter().map(|v| fst::Set::new(v).unwrap()).collect();
// Union of the FSTs
let mut op = fst::set::OpBuilder::new();
fsts.iter().for_each(|fst| op.push(fst.into_stream()));
let op = op.r#union();
let mut build = fst::SetBuilder::memory();
build.extend_stream(op.into_stream()).unwrap();
Ok(build.into_inner().unwrap())
},
HEADERS_KEY => {
assert!(values.windows(2).all(|vs| vs[0] == vs[1]));
Ok(values[0].to_vec())
},
DOCUMENTS_IDS_KEY => word_docids_merge(&[], values),
otherwise => panic!("wut {:?}", otherwise),
}
}
fn word_docids_merge(_key: &[u8], values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
let (head, tail) = values.split_first().unwrap();
let mut head = RoaringBitmap::deserialize_from(head.as_slice()).unwrap();
for value in tail {
let bitmap = RoaringBitmap::deserialize_from(value.as_slice()).unwrap();
head.union_with(&bitmap);
}
let mut vec = Vec::with_capacity(head.serialized_size());
head.serialize_into(&mut vec).unwrap();
Ok(vec)
}
fn docid_word_positions_merge(key: &[u8], _values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
panic!("merging docid word positions is an error ({:?})", key.as_bstr())
}
fn words_pairs_proximities_docids_merge(_key: &[u8], values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
let (head, tail) = values.split_first().unwrap();
let mut head = CboRoaringBitmapCodec::deserialize_from(head.as_slice()).unwrap();
for value in tail {
let bitmap = CboRoaringBitmapCodec::deserialize_from(value.as_slice()).unwrap();
head.union_with(&bitmap);
}
let mut vec = Vec::new();
CboRoaringBitmapCodec::serialize_into(&head, &mut vec).unwrap();
Ok(vec)
}
fn documents_merge(key: &[u8], _values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
panic!("merging documents is an error ({:?})", key.as_bstr())
}
fn merge_readers(sources: Vec<Reader<FileFuse>>, merge: MergeFn) -> Merger<FileFuse, MergeFn> {
let mut builder = Merger::builder(merge);
builder.extend(sources);
builder.build()
}
fn merge_into_lmdb_database(
wtxn: &mut heed::RwTxn,
database: heed::PolyDatabase,
sources: Vec<Reader<FileFuse>>,
merge: MergeFn,
) -> anyhow::Result<()> {
debug!("Merging {} MTBL stores...", sources.len());
let before = Instant::now();
let merger = merge_readers(sources, merge);
let mut in_iter = merger.into_merge_iter()?;
let mut out_iter = database.iter_mut::<_, ByteSlice, ByteSlice>(wtxn)?;
while let Some((k, v)) = in_iter.next()? {
out_iter.append(k, v).with_context(|| format!("writing {:?} into LMDB", k.as_bstr()))?;
}
debug!("MTBL stores merged in {:.02?}!", before.elapsed());
Ok(())
}
fn write_into_lmdb_database(
wtxn: &mut heed::RwTxn,
database: heed::PolyDatabase,
mut reader: Reader<FileFuse>,
) -> anyhow::Result<()> {
debug!("Writing MTBL stores...");
let before = Instant::now();
let mut out_iter = database.iter_mut::<_, ByteSlice, ByteSlice>(wtxn)?;
while let Some((k, v)) = reader.next()? {
out_iter.append(k, v).with_context(|| format!("writing {:?} into LMDB", k.as_bstr()))?;
}
debug!("MTBL stores merged in {:.02?}!", before.elapsed());
Ok(())
}
/// Returns the list of CSV sources that the indexer must read.
///
/// There is `num_threads` sources. If the file is not specified, the standard input is used.
fn csv_readers(
csv_file_path: Option<PathBuf>,
num_threads: usize,
) -> anyhow::Result<Vec<csv::Reader<Box<dyn Read + Send>>>>
{
match csv_file_path {
Some(file_path) => {
// We open the file # jobs times.
iter::repeat_with(|| {
let file = File::open(&file_path)
.with_context(|| format!("Failed to read CSV file {}", file_path.display()))?;
// if the file extension is "gz" or "gzip" we can decode and read it.
let r = if file_path.extension().map_or(false, |e| e == "gz" || e == "gzip") {
Box::new(GzDecoder::new(file)) as Box<dyn Read + Send>
} else {
Box::new(file) as Box<dyn Read + Send>
};
Ok(csv::Reader::from_reader(r)) as anyhow::Result<_>
})
.take(num_threads)
.collect()
},
None => {
let mut csv_readers = Vec::new();
let mut writers = Vec::new();
for (r, w) in iter::repeat_with(ringtail::io::pipe).take(num_threads) {
let r = Box::new(r) as Box<dyn Read + Send>;
csv_readers.push(csv::Reader::from_reader(r));
writers.push(w);
}
thread::spawn(move || {
let stdin = std::io::stdin();
let mut stdin = stdin.lock();
let mut buffer = [0u8; 4096];
loop {
match stdin.read(&mut buffer)? {
0 => return Ok(()) as io::Result<()>,
size => for w in &mut writers {
w.write_all(&buffer[..size])?;
}
}
}
});
Ok(csv_readers)
},
}
}
pub fn run(opt: Opt) -> anyhow::Result<()> {
stderrlog::new()
.verbosity(opt.verbose)
@ -739,10 +46,6 @@ pub fn run(opt: Opt) -> anyhow::Result<()> {
.timestamp(stderrlog::Timestamp::Off)
.init()?;
if let Some(jobs) = opt.jobs {
rayon::ThreadPoolBuilder::new().num_threads(jobs).build_global()?;
}
if opt.database.exists() {
bail!("Database ({}) already exists, delete it to continue.", opt.database.display());
}
@ -753,130 +56,12 @@ pub fn run(opt: Opt) -> anyhow::Result<()> {
.max_dbs(10)
.open(&opt.database)?;
let before_indexing = Instant::now();
let index = Index::new(&env)?;
let num_threads = rayon::current_num_threads();
let linked_hash_map_size = opt.indexer.linked_hash_map_size;
let max_nb_chunks = opt.indexer.max_nb_chunks;
let max_memory_by_job = opt.indexer.max_memory / num_threads;
let chunk_compression_type = opt.indexer.chunk_compression_type;
let chunk_compression_level = opt.indexer.chunk_compression_level;
let log_every_n = opt.indexer.log_every_n;
let file_path = opt.csv_file.unwrap();
let gzipped = file_path.extension().map_or(false, |e| e == "gz" || e == "gzip");
let file = File::open(file_path)?;
let content = unsafe { memmap::Mmap::map(&file)? };
let chunk_fusing_shrink_size = if opt.indexer.enable_chunk_fusing {
Some(opt.indexer.chunk_fusing_shrink_size)
} else {
None
};
let readers = csv_readers(opt.csv_file, num_threads)?
.into_par_iter()
.enumerate()
.map(|(i, rdr)| {
let store = Store::new(
linked_hash_map_size,
max_nb_chunks,
Some(max_memory_by_job),
chunk_compression_type,
chunk_compression_level,
chunk_fusing_shrink_size,
)?;
store.index_csv(rdr, i, num_threads, log_every_n)
})
.collect::<Result<Vec<_>, _>>()?;
let mut main_readers = Vec::with_capacity(readers.len());
let mut word_docids_readers = Vec::with_capacity(readers.len());
let mut docid_word_positions_readers = Vec::with_capacity(readers.len());
let mut words_pairs_proximities_docids_readers = Vec::with_capacity(readers.len());
let mut documents_readers = Vec::with_capacity(readers.len());
readers.into_iter().for_each(|readers| {
main_readers.push(readers.main);
word_docids_readers.push(readers.word_docids);
docid_word_positions_readers.push(readers.docid_word_positions);
words_pairs_proximities_docids_readers.push(readers.words_pairs_proximities_docids);
documents_readers.push(readers.documents);
});
// This is the function that merge the readers
// by using the given merge function.
let merge_readers = move |readers, merge| {
let mut writer = tempfile().and_then(|f| {
create_writer(chunk_compression_type, chunk_compression_level, f)
})?;
let merger = merge_readers(readers, merge);
merger.write_into(&mut writer)?;
writer_into_reader(writer, chunk_fusing_shrink_size)
};
// The enum and the channel which is used to transfert
// the readers merges potentially done on another thread.
enum DatabaseType { Main, WordDocids, WordsPairsProximitiesDocids };
let (sender, receiver) = sync_channel(3);
debug!("Merging the main, word docids and words pairs proximity docids in parallel...");
rayon::spawn(move || {
vec![
(DatabaseType::Main, main_readers, main_merge as MergeFn),
(DatabaseType::WordDocids, word_docids_readers, word_docids_merge),
(
DatabaseType::WordsPairsProximitiesDocids,
words_pairs_proximities_docids_readers,
words_pairs_proximities_docids_merge,
),
]
.into_par_iter()
.for_each(|(dbtype, readers, merge)| {
let result = merge_readers(readers, merge);
sender.send((dbtype, result)).unwrap();
});
});
let mut wtxn = env.write_txn()?;
debug!("Writing the docid word positions into LMDB on disk...");
merge_into_lmdb_database(
&mut wtxn,
*index.docid_word_positions.as_polymorph(),
docid_word_positions_readers,
docid_word_positions_merge,
)?;
debug!("Writing the documents into LMDB on disk...");
merge_into_lmdb_database(
&mut wtxn,
*index.documents.as_polymorph(),
documents_readers,
documents_merge,
)?;
for (db_type, result) in receiver {
let content = result?;
match db_type {
DatabaseType::Main => {
debug!("Writing the main elements into LMDB on disk...");
write_into_lmdb_database(&mut wtxn, index.main, content)?;
},
DatabaseType::WordDocids => {
debug!("Writing the words docids into LMDB on disk...");
let db = *index.word_docids.as_polymorph();
write_into_lmdb_database(&mut wtxn, db, content)?;
},
DatabaseType::WordsPairsProximitiesDocids => {
debug!("Writing the words pairs proximities docids into LMDB on disk...");
let db = *index.word_pair_proximity_docids.as_polymorph();
write_into_lmdb_database(&mut wtxn, db, content)?;
},
}
}
debug!("Retrieving the number of documents...");
let count = index.number_of_documents(&wtxn)?;
wtxn.commit()?;
info!("Wrote {} documents in {:.02?}", count, before_indexing.elapsed());
Ok(())
indexing::run(&env, &index, opt.indexer, &content, gzipped)
}

4
src/subcommand/serve.rs
View File

@ -19,6 +19,7 @@ use tokio::sync::broadcast;
use warp::filters::ws::Message;
use warp::{Filter, http::Response};
use crate::indexing::IndexerOpt;
use crate::tokenizer::{simple_tokenizer, TokenType};
use crate::{Index, UpdateStore, SearchResult};
@ -51,6 +52,9 @@ pub struct Opt {
/// The ip and port on which the database will listen for HTTP requests.
#[structopt(short = "l", long, default_value = "127.0.0.1:9700")]
http_listen_addr: String,
#[structopt(flatten)]
indexer: IndexerOpt,
}
fn highlight_record(record: &csv::StringRecord, words: &HashSet<String>) -> csv::StringRecord {