meilisearch/src/bin/indexer.rs

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use std::convert::TryInto;
use std::convert::TryFrom;
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use std::fs::File;
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use std::io::{self, Read, Write};
use std::iter::FromIterator;
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use std::path::PathBuf;
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use std::thread;
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use std::time::Instant;
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use anyhow::Context;
use arc_cache::ArcCache;
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use cow_utils::CowUtils;
use fst::IntoStreamer;
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use heed::EnvOpenOptions;
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use heed::types::*;
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use log::debug;
use memmap::Mmap;
use oxidized_mtbl::{Reader, Writer, Merger, Sorter, CompressionType};
use rayon::prelude::*;
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use roaring::RoaringBitmap;
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use slice_group_by::StrGroupBy;
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use structopt::StructOpt;
use milli::{SmallVec32, Index, DocumentId, Position, Attribute};
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const LMDB_MAX_KEY_LENGTH: usize = 512;
const ONE_MILLION: usize = 1_000_000;
const MAX_POSITION: usize = 1000;
const MAX_ATTRIBUTES: usize = u32::max_value() as usize / MAX_POSITION;
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const HEADERS_KEY: &[u8] = b"\0headers";
const WORDS_FST_KEY: &[u8] = b"\x05words-fst";
const DOCUMENTS_KEY: &[u8] = b"\x06documents";
const WORD_POSITIONS_BYTE: u8 = 1;
const WORD_POSITION_DOCIDS_BYTE: u8 = 2;
const WORD_ATTRIBUTE_DOCIDS_BYTE: u8 = 3;
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#[cfg(target_os = "linux")]
#[global_allocator]
static ALLOC: jemallocator::Jemalloc = jemallocator::Jemalloc;
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pub fn simple_alphanumeric_tokens(string: &str) -> impl Iterator<Item = &str> {
let is_alphanumeric = |s: &&str| s.chars().next().map_or(false, char::is_alphanumeric);
string.linear_group_by_key(|c| c.is_alphanumeric()).filter(is_alphanumeric)
}
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#[derive(Debug, StructOpt)]
#[structopt(name = "milli-indexer", about = "The indexer binary of the milli project.")]
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struct Opt {
/// The database path where the database is located.
/// It is created if it doesn't already exist.
#[structopt(long = "db", parse(from_os_str))]
database: PathBuf,
/// Number of parallel jobs, defaults to # of CPUs.
#[structopt(short, long)]
jobs: Option<usize>,
/// MTBL max number of chunks in bytes.
#[structopt(long)]
max_nb_chunks: Option<usize>,
/// MTBL max memory in bytes.
#[structopt(long)]
max_memory: Option<usize>,
/// Size of the ARC cache when indexing.
#[structopt(long)]
arc_cache_size: Option<usize>,
/// Verbose mode (-v, -vv, -vvv, etc.)
#[structopt(short, long, parse(from_occurrences))]
verbose: usize,
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/// CSV file to index, if unspecified the CSV is read from standard input.
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/// Note that it is much faster to index from a file.
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csv_file: Option<PathBuf>,
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}
fn lmdb_key_valid_size(key: &[u8]) -> bool {
!key.is_empty() && key.len() <= LMDB_MAX_KEY_LENGTH
}
type MergeFn = fn(&[u8], &[Vec<u8>]) -> Result<Vec<u8>, ()>;
struct Store {
word_positions: ArcCache<SmallVec32<u8>, RoaringBitmap>,
word_position_docids: ArcCache<(SmallVec32<u8>, Position), RoaringBitmap>,
word_attribute_docids: ArcCache<(SmallVec32<u8>, Attribute), RoaringBitmap>,
sorter: Sorter<MergeFn>,
documents_sorter: Sorter<MergeFn>,
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}
impl Store {
fn new(arc_cache_size: Option<usize>, max_nb_chunks: Option<usize>, max_memory: Option<usize>) -> Store {
let mut builder = Sorter::builder(merge as MergeFn);
builder.chunk_compression_type(CompressionType::Snappy);
if let Some(nb_chunks) = max_nb_chunks {
builder.max_nb_chunks(nb_chunks);
}
if let Some(memory) = max_memory {
builder.max_memory(memory);
}
let mut documents_builder = Sorter::builder(docs_merge as MergeFn);
documents_builder.chunk_compression_type(CompressionType::Snappy);
let arc_cache_size = arc_cache_size.unwrap_or(65_535);
Store {
word_positions: ArcCache::new(arc_cache_size),
word_position_docids: ArcCache::new(arc_cache_size),
word_attribute_docids: ArcCache::new(arc_cache_size),
sorter: builder.build(),
documents_sorter: documents_builder.build(),
}
}
// Save the positions where this word has been seen.
pub fn insert_word_position(&mut self, word: &str, position: Position) -> anyhow::Result<()> {
let word = SmallVec32::from(word.as_bytes());
let position = RoaringBitmap::from_iter(Some(position));
let (_, lrus) = self.word_positions.insert(word, position, |old, new| old.union_with(&new));
Self::write_word_positions(&mut self.sorter, lrus)
}
// Save the documents ids under the position and word we have seen it.
pub fn insert_word_position_docid(&mut self, word: &str, position: Position, id: DocumentId) -> anyhow::Result<()> {
let word_vec = SmallVec32::from(word.as_bytes());
let ids = RoaringBitmap::from_iter(Some(id));
let (_, lrus) = self.word_position_docids.insert((word_vec, position), ids, |old, new| old.union_with(&new));
Self::write_word_position_docids(&mut self.sorter, lrus)?;
self.insert_word_attribute_docid(word, position / MAX_POSITION as u32, id)
}
// Save the documents ids under the attribute and word we have seen it.
fn insert_word_attribute_docid(&mut self, word: &str, attribute: Attribute, id: DocumentId) -> anyhow::Result<()> {
let word = SmallVec32::from(word.as_bytes());
let ids = RoaringBitmap::from_iter(Some(id));
let (_, lrus) = self.word_attribute_docids.insert((word, attribute), ids, |old, new| old.union_with(&new));
Self::write_word_attribute_docids(&mut self.sorter, lrus)
}
pub fn write_headers(&mut self, headers: &[u8]) -> anyhow::Result<()> {
Ok(self.sorter.insert(HEADERS_KEY, headers)?)
}
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pub fn write_document(&mut self, id: DocumentId, content: &[u8]) -> anyhow::Result<()> {
Ok(self.documents_sorter.insert(id.to_be_bytes(), content)?)
}
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fn write_word_positions<I>(sorter: &mut Sorter<MergeFn>, iter: I) -> anyhow::Result<()>
where I: IntoIterator<Item=(SmallVec32<u8>, RoaringBitmap)>
{
// postings ids keys are all prefixed
let mut key = vec![WORD_POSITIONS_BYTE];
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let mut buffer = Vec::new();
for (word, positions) in iter {
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key.truncate(1);
key.extend_from_slice(&word);
// We serialize the positions into a buffer
buffer.clear();
positions.serialize_into(&mut buffer)?;
// that we write under the generated key into MTBL
if lmdb_key_valid_size(&key) {
sorter.insert(&key, &buffer)?;
}
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}
Ok(())
}
fn write_word_position_docids<I>(sorter: &mut Sorter<MergeFn>, iter: I) -> anyhow::Result<()>
where I: IntoIterator<Item=((SmallVec32<u8>, Position), RoaringBitmap)>
{
// postings positions ids keys are all prefixed
let mut key = vec![WORD_POSITION_DOCIDS_BYTE];
let mut buffer = Vec::new();
for ((word, pos), ids) in iter {
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key.truncate(1);
key.extend_from_slice(&word);
// we postfix the word by the positions it appears in
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key.extend_from_slice(&pos.to_be_bytes());
// We serialize the document ids into a buffer
buffer.clear();
ids.serialize_into(&mut buffer)?;
// that we write under the generated key into MTBL
if lmdb_key_valid_size(&key) {
sorter.insert(&key, &buffer)?;
}
}
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Ok(())
}
fn write_word_attribute_docids<I>(sorter: &mut Sorter<MergeFn>, iter: I) -> anyhow::Result<()>
where I: IntoIterator<Item=((SmallVec32<u8>, Attribute), RoaringBitmap)>
{
// postings attributes keys are all prefixed
let mut key = vec![WORD_ATTRIBUTE_DOCIDS_BYTE];
let mut buffer = Vec::new();
for ((word, attr), ids) in iter {
key.truncate(1);
key.extend_from_slice(&word);
// we postfix the word by the positions it appears in
key.extend_from_slice(&attr.to_be_bytes());
// We serialize the document ids into a buffer
buffer.clear();
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(())
}
pub fn finish(mut self) -> anyhow::Result<Reader<Mmap>> {
Self::write_word_positions(&mut self.sorter, self.word_positions)?;
Self::write_word_position_docids(&mut self.sorter, self.word_position_docids)?;
Self::write_word_attribute_docids(&mut self.sorter, self.word_attribute_docids)?;
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let mut wtr = tempfile::tempfile().map(Writer::new)?;
let mut builder = fst::SetBuilder::memory();
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let mut iter = self.sorter.into_iter()?;
while let Some(result) = iter.next() {
let (key, val) = result?;
if let Some((&1, word)) = key.split_first() {
// This is a lexicographically ordered word position
// we use the key to construct the words fst.
builder.insert(word)?;
}
wtr.insert(key, val)?;
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}
let fst = builder.into_set();
wtr.insert(WORDS_FST_KEY, fst.as_fst().as_bytes())?;
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let mut docs_wtr = tempfile::tempfile().map(Writer::new)?;
self.documents_sorter.write_into(&mut docs_wtr)?;
let docs_file = docs_wtr.into_inner()?;
let docs_mmap = unsafe { Mmap::map(&docs_file)? };
wtr.insert(DOCUMENTS_KEY, docs_mmap)?;
let file = wtr.into_inner()?;
let mmap = unsafe { Mmap::map(&file)? };
let reader = Reader::new(mmap)?;
Ok(reader)
}
}
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fn docs_merge(key: &[u8], values: &[Vec<u8>]) -> Result<Vec<u8>, ()> {
let key = key.try_into().unwrap();
let id = u32::from_be_bytes(key);
panic!("documents must not conflict ({} with {} values)!", id, values.len())
}
fn 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 two 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_KEY => {
let sources: Vec<_> = values.iter().map(Reader::new).collect::<Result<_, _>>().unwrap();
let mut builder = Merger::builder(docs_merge);
builder.extend(sources);
let merger = builder.build();
let mut builder = Writer::builder();
builder.compression_type(CompressionType::Snappy);
let mut wtr = builder.memory();
merger.write_into(&mut wtr).unwrap();
Ok(wtr.into_inner().unwrap())
},
key => match key[0] {
WORD_POSITIONS_BYTE | WORD_POSITION_DOCIDS_BYTE | WORD_ATTRIBUTE_DOCIDS_BYTE => {
let mut first = RoaringBitmap::deserialize_from(values[0].as_slice()).unwrap();
for value in &values[1..] {
let bitmap = RoaringBitmap::deserialize_from(value.as_slice()).unwrap();
first.union_with(&bitmap);
}
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let mut vec = Vec::new();
first.serialize_into(&mut vec).unwrap();
Ok(vec)
},
otherwise => panic!("wut {:?}", otherwise),
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}
}
}
// TODO merge with the previous values
// TODO store the documents in a compressed MTBL
fn lmdb_writer(wtxn: &mut heed::RwTxn, index: &Index, key: &[u8], val: &[u8]) -> anyhow::Result<()> {
if key == WORDS_FST_KEY {
// Write the words fst
index.main.put::<_, Str, ByteSlice>(wtxn, "words-fst", val)?;
}
else if key == HEADERS_KEY {
// Write the headers
index.main.put::<_, Str, ByteSlice>(wtxn, "headers", val)?;
}
else if key == DOCUMENTS_KEY {
// Write the documents
index.main.put::<_, Str, ByteSlice>(wtxn, "documents", val)?;
}
else if key.starts_with(&[WORD_POSITIONS_BYTE]) {
// Write the postings lists
index.word_positions.as_polymorph()
.put::<_, ByteSlice, ByteSlice>(wtxn, &key[1..], val)?;
}
else if key.starts_with(&[WORD_POSITION_DOCIDS_BYTE]) {
// Write the postings lists
index.word_position_docids.as_polymorph()
.put::<_, ByteSlice, ByteSlice>(wtxn, &key[1..], val)?;
}
else if key.starts_with(&[WORD_ATTRIBUTE_DOCIDS_BYTE]) {
// Write the attribute postings lists
index.word_attribute_docids.as_polymorph()
.put::<_, ByteSlice, ByteSlice>(wtxn, &key[1..], val)?;
}
Ok(())
}
fn merge_into_lmdb<F>(sources: Vec<Reader<Mmap>>, mut f: F) -> anyhow::Result<()>
where F: FnMut(&[u8], &[u8]) -> anyhow::Result<()>
{
debug!("Merging {} MTBL stores...", sources.len());
let before = Instant::now();
let mut builder = Merger::builder(merge);
builder.extend(sources);
let merger = builder.build();
let mut iter = merger.into_merge_iter()?;
while let Some(result) = iter.next() {
let (k, v) = result?;
(f)(&k, &v)?;
}
debug!("MTBL stores merged in {:.02?}!", before.elapsed());
Ok(())
}
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fn index_csv(
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mut rdr: csv::Reader<Box<dyn Read + Send>>,
thread_index: usize,
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num_threads: usize,
arc_cache_size: Option<usize>,
max_nb_chunks: Option<usize>,
max_memory: Option<usize>,
) -> anyhow::Result<Reader<Mmap>>
{
debug!("{:?}: Indexing into a Store...", thread_index);
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let mut store = Store::new(arc_cache_size, max_nb_chunks, max_memory);
// Write the headers into a Vec of bytes and then into the store.
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let headers = rdr.headers()?;
let mut writer = csv::WriterBuilder::new().has_headers(false).from_writer(Vec::new());
writer.write_byte_record(headers.as_byte_record())?;
let headers = writer.into_inner()?;
store.write_headers(&headers)?;
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let mut before = Instant::now();
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let mut document_id: usize = 0;
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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// We skip documents that must not be indexed by this thread
if document_id % num_threads != thread_index { continue }
let document_id = DocumentId::try_from(document_id).context("generated id is too big")?;
if document_id % (ONE_MILLION as u32) == 0 {
debug!("We have seen {}m documents so far ({:.02?}).",
document_id / ONE_MILLION as u32, before.elapsed());
before = Instant::now();
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}
for (attr, content) in document.iter().enumerate().take(MAX_ATTRIBUTES) {
for (pos, word) in simple_alphanumeric_tokens(&content).enumerate().take(MAX_POSITION) {
let word = word.cow_to_lowercase();
let position = (attr * MAX_POSITION + pos) as u32;
store.insert_word_position(&word, position)?;
store.insert_word_position_docid(&word, position, document_id)?;
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}
}
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// We write the document in the database.
let mut writer = csv::WriterBuilder::new().has_headers(false).from_writer(Vec::new());
writer.write_byte_record(document.as_byte_record())?;
let document = writer.into_inner()?;
store.write_document(document_id, &document)?;
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}
let reader = store.finish()?;
debug!("{:?}: Store created!", thread_index);
Ok(reader)
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}
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fn main() -> anyhow::Result<()> {
let opt = Opt::from_args();
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stderrlog::new()
.verbosity(opt.verbose)
.show_level(false)
.timestamp(stderrlog::Timestamp::Off)
.init()?;
if let Some(jobs) = opt.jobs {
rayon::ThreadPoolBuilder::new().num_threads(jobs).build_global()?;
}
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std::fs::create_dir_all(&opt.database)?;
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let env = EnvOpenOptions::new()
.map_size(100 * 1024 * 1024 * 1024) // 100 GB
.max_readers(10)
.max_dbs(10)
.open(opt.database)?;
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let index = Index::new(&env)?;
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let num_threads = rayon::current_num_threads();
let arc_cache_size = opt.arc_cache_size;
let max_nb_chunks = opt.max_nb_chunks;
let max_memory = opt.max_memory;
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let csv_readers = match opt.csv_file {
Some(file_path) => {
// We open the file # jobs times.
(0..num_threads)
.map(|_| {
let file = File::open(&file_path)?;
let r = Box::new(file) as Box<dyn Read + Send>;
Ok(csv::Reader::from_reader(r)) as io::Result<_>
})
.collect::<Result<Vec<_>, _>>()?
},
None => {
let mut csv_readers = Vec::new();
let mut writers = Vec::new();
for (r, w) in (0..num_threads).map(|_| pipe::pipe()) {
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])?;
}
}
}
});
csv_readers
},
};
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let stores = csv_readers
.into_par_iter()
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.enumerate()
.map(|(i, rdr)| index_csv(rdr, i, num_threads, arc_cache_size, max_nb_chunks, max_memory))
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.collect::<Result<_, _>>()?;
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debug!("We are writing into LMDB...");
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let mut wtxn = env.write_txn()?;
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merge_into_lmdb(stores, |k, v| lmdb_writer(&mut wtxn, &index, k, v))?;
let count = index.documents(&wtxn)?.unwrap().metadata().count_entries;
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wtxn.commit()?;
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debug!("Wrote {} documents into LMDB", count);
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Ok(())
}