snowykami/meilisearch
1
0
Fork 0
mirror of https://github.com/meilisearch/meilisearch.git synced 2024-11-27 04:25:06 +08:00
Code Issues Actions 4 Packages Projects Releases Wiki Activity

Introduce the database-stats infos subcommand

Browse Source
This commit is contained in:
Kerollmops 2020-10-01 11:39:30 +02:00 committed by Clément Renault
parent 079742b4d3
commit 891e0188dd
No known key found for this signature in database
GPG Key ID: 92ADA4E935E71FA4
1 changed files with 79 additions and 48 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

127
src/bin/infos.rs
View File

@ -18,7 +18,7 @@ const DOCID_WORD_POSITIONS_DB_NAME: &str = "docid-word-positions";
const WORD_PAIR_PROXIMITY_DOCIDS_DB_NAME: &str = "word-pair-proximity-docids";
const DOCUMENTS_DB_NAME: &str = "documents";
const DATABASE_NAMES: &[&str] = &[
const ALL_DATABASE_NAMES: &[&str] = &[
MAIN_DB_NAME,
WORD_DOCIDS_DB_NAME,
DOCID_WORD_POSITIONS_DB_NAME,
@ -26,6 +26,12 @@ const DATABASE_NAMES: &[&str] = &[
DOCUMENTS_DB_NAME,
];
const POSTINGS_DATABASE_NAMES: &[&str] = &[
WORD_DOCIDS_DB_NAME,
DOCID_WORD_POSITIONS_DB_NAME,
WORD_PAIR_PROXIMITY_DOCIDS_DB_NAME,
];
#[derive(Debug, StructOpt)]
#[structopt(name = "milli-info", about = "A stats crawler for milli.")]
struct Opt {
@ -85,13 +91,16 @@ enum Command {
/// Outputs the average number of positions for each document words.
AverageNumberOfPositionsByWord,
/// Outputs some statistics about the words pairs proximities
/// (median, quartiles, percentiles, minimum, maximum, averge).
WordPairProximityStats,
/// Outputs some statistics about the given database (e.g. median, quartiles,
/// percentiles, minimum, maximum, averge, key size, value size).
DatabaseStats {
#[structopt(possible_values = POSTINGS_DATABASE_NAMES)]
database: String,
},
/// Outputs the size in bytes of the specified database.
SizeOfDatabase {
#[structopt(possible_values = DATABASE_NAMES)]
#[structopt(possible_values = ALL_DATABASE_NAMES)]
database: String,
},
@ -152,7 +161,7 @@ fn main() -> anyhow::Result<()> {
average_number_of_positions_by_word(&index, &rtxn)
},
SizeOfDatabase { database } => size_of_database(&index, &rtxn, &database),
WordPairProximityStats => word_pair_proximity_stats(&index, &rtxn),
DatabaseStats { database } => database_stats(&index, &rtxn, &database),
WordPairProximitiesDocids { full_display, word1, word2 } => {
word_pair_proximities_docids(&index, &rtxn, !full_display, word1, word2)
},
@ -384,54 +393,76 @@ fn size_of_database(index: &Index, rtxn: &heed::RoTxn, name: &str) -> anyhow::Re
Ok(())
}
fn word_pair_proximity_stats(index: &Index, rtxn: &heed::RoTxn) -> anyhow::Result<()> {
fn database_stats(index: &Index, rtxn: &heed::RoTxn, name: &str) -> anyhow::Result<()> {
use heed::types::ByteSlice;
use heed::{Error, BytesDecode};
use milli::CboRoaringBitmapCodec;
use roaring::RoaringBitmap;
use milli::{BoRoaringBitmapCodec, CboRoaringBitmapCodec, RoaringBitmapCodec};
let mut key_size = 0u64;
let mut val_size = 0u64;
let mut values_length = Vec::new();
fn compute_stats<'a, DC: BytesDecode<'a, DItem = RoaringBitmap>>(
db: heed::PolyDatabase,
rtxn: &'a heed::RoTxn,
name: &str,
) -> anyhow::Result<()>
{
let mut key_size = 0u64;
let mut val_size = 0u64;
let mut values_length = Vec::new();
let db = index.word_pair_proximity_docids.as_polymorph();
for result in db.iter::<_, ByteSlice, ByteSlice>(rtxn)? {
let (key, val) = result?;
key_size += key.len() as u64;
val_size += val.len() as u64;
let val = CboRoaringBitmapCodec::bytes_decode(val).ok_or(Error::Decoding)?;
values_length.push(val.len() as u32);
for result in db.iter::<_, ByteSlice, ByteSlice>(rtxn)? {
let (key, val) = result?;
key_size += key.len() as u64;
val_size += val.len() as u64;
let val = DC::bytes_decode(val).ok_or(Error::Decoding)?;
values_length.push(val.len() as u32);
}
values_length.sort_unstable();
let median = values_length.get(values_length.len() / 2).unwrap_or(&0);
let first_quartile = values_length.get(values_length.len() / 4).unwrap_or(&0);
let third_quartile = values_length.get(values_length.len() / 4 * 3).unwrap_or(&0);
let ninety_percentile = values_length.get(values_length.len() / 100 * 90).unwrap_or(&0);
let ninety_five_percentile = values_length.get(values_length.len() / 100 * 95).unwrap_or(&0);
let ninety_nine_percentile = values_length.get(values_length.len() / 100 * 99).unwrap_or(&0);
let minimum = values_length.first().unwrap_or(&0);
let maximum = values_length.last().unwrap_or(&0);
let count = values_length.len();
let sum = values_length.iter().map(|l| *l as u64).sum::<u64>();
println!("The {} database stats on the lengths", name);
println!("\tnumber of proximity pairs: {}", count);
println!("\tfirst quartile: {}", first_quartile);
println!("\tmedian: {}", median);
println!("\tthird quartile: {}", third_quartile);
println!("\t90th percentile: {}", ninety_percentile);
println!("\t95th percentile: {}", ninety_five_percentile);
println!("\t99th percentile: {}", ninety_nine_percentile);
println!("\tminimum: {}", minimum);
println!("\tmaximum: {}", maximum);
println!("\taverage: {}", sum as f64 / count as f64);
println!("\ttotal key size: {} bytes", key_size);
println!("\ttotal val size: {} bytes", val_size);
println!("\ttotal size: {} bytes", key_size + val_size);
Ok(())
}
values_length.sort_unstable();
let median = values_length.get(values_length.len() / 2).unwrap_or(&0);
let first_quartile = values_length.get(values_length.len() / 4).unwrap_or(&0);
let third_quartile = values_length.get(values_length.len() / 4 * 3).unwrap_or(&0);
let ninety_percentile = values_length.get(values_length.len() / 100 * 90).unwrap_or(&0);
let ninety_five_percentile = values_length.get(values_length.len() / 100 * 95).unwrap_or(&0);
let ninety_nine_percentile = values_length.get(values_length.len() / 100 * 99).unwrap_or(&0);
let minimum = values_length.first().unwrap_or(&0);
let maximum = values_length.last().unwrap_or(&0);
let count = values_length.len();
let sum = values_length.iter().map(|l| *l as u64).sum::<u64>();
println!("word-pair-proximity-docids stats on the lengths");
println!("\tnumber of proximity pairs: {}", count);
println!("\tfirst quartile: {}", first_quartile);
println!("\tmedian: {}", median);
println!("\tthird quartile: {}", third_quartile);
println!("\t90th percentile: {}", ninety_percentile);
println!("\t95th percentile: {}", ninety_five_percentile);
println!("\t99th percentile: {}", ninety_nine_percentile);
println!("\tminimum: {}", minimum);
println!("\tmaximum: {}", maximum);
println!("\taverage: {}", sum as f64 / count as f64);
println!();
println!("\ttotal key size: {} bytes", key_size);
println!("\ttotal val size: {} bytes", val_size);
println!("\ttotal size: {} bytes", key_size + val_size);
Ok(())
match name {
WORD_DOCIDS_DB_NAME => {
let db = index.word_docids.as_polymorph();
compute_stats::<RoaringBitmapCodec>(*db, rtxn, name)
},
DOCID_WORD_POSITIONS_DB_NAME => {
let db = index.docid_word_positions.as_polymorph();
compute_stats::<BoRoaringBitmapCodec>(*db, rtxn, name)
},
WORD_PAIR_PROXIMITY_DOCIDS_DB_NAME => {
let db = index.word_pair_proximity_docids.as_polymorph();
compute_stats::<CboRoaringBitmapCodec>(*db, rtxn, name)
},
unknown => anyhow::bail!("unknown database {:?}", unknown),
}
}
fn word_pair_proximities_docids(