MeiliSearch/meilisearch/src/analytics/segment_analytics.rs
2024-07-25 10:52:56 +02:00

2010 lines
72 KiB
Rust

use std::collections::{BTreeSet, BinaryHeap, HashMap, HashSet};
use std::fs;
use std::mem::take;
use std::path::{Path, PathBuf};
use std::sync::Arc;
use std::time::{Duration, Instant};
use actix_web::http::header::{CONTENT_TYPE, USER_AGENT};
use actix_web::HttpRequest;
use byte_unit::Byte;
use index_scheduler::IndexScheduler;
use meilisearch_auth::{AuthController, AuthFilter};
use meilisearch_types::locales::Locale;
use meilisearch_types::InstanceUid;
use once_cell::sync::Lazy;
use regex::Regex;
use segment::message::{Identify, Track, User};
use segment::{AutoBatcher, Batcher, HttpClient};
use serde::Serialize;
use serde_json::{json, Value};
use sysinfo::{Disks, System};
use time::OffsetDateTime;
use tokio::select;
use tokio::sync::mpsc::{self, Receiver, Sender};
use uuid::Uuid;
use super::{
config_user_id_path, DocumentDeletionKind, DocumentFetchKind, MEILISEARCH_CONFIG_PATH,
};
use crate::analytics::Analytics;
use crate::option::{
default_http_addr, IndexerOpts, LogMode, MaxMemory, MaxThreads, ScheduleSnapshot,
};
use crate::routes::indexes::documents::{DocumentEditionByFunction, UpdateDocumentsQuery};
use crate::routes::indexes::facet_search::FacetSearchQuery;
use crate::routes::{create_all_stats, Stats};
use crate::search::{
FacetSearchResult, FederatedSearch, MatchingStrategy, SearchQuery, SearchQueryWithIndex,
SearchResult, SimilarQuery, SimilarResult, DEFAULT_CROP_LENGTH, DEFAULT_CROP_MARKER,
DEFAULT_HIGHLIGHT_POST_TAG, DEFAULT_HIGHLIGHT_PRE_TAG, DEFAULT_SEARCH_LIMIT,
DEFAULT_SEMANTIC_RATIO,
};
use crate::Opt;
const ANALYTICS_HEADER: &str = "X-Meilisearch-Client";
/// Write the instance-uid in the `data.ms` and in `~/.config/MeiliSearch/path-to-db-instance-uid`. Ignore the errors.
fn write_user_id(db_path: &Path, user_id: &InstanceUid) {
let _ = fs::write(db_path.join("instance-uid"), user_id.to_string());
if let Some((meilisearch_config_path, user_id_path)) =
MEILISEARCH_CONFIG_PATH.as_ref().zip(config_user_id_path(db_path))
{
let _ = fs::create_dir_all(meilisearch_config_path);
let _ = fs::write(user_id_path, user_id.to_string());
}
}
const SEGMENT_API_KEY: &str = "P3FWhhEsJiEDCuEHpmcN9DHcK4hVfBvb";
pub fn extract_user_agents(request: &HttpRequest) -> Vec<String> {
request
.headers()
.get(ANALYTICS_HEADER)
.or_else(|| request.headers().get(USER_AGENT))
.and_then(|header| header.to_str().ok())
.unwrap_or("unknown")
.split(';')
.map(str::trim)
.map(ToString::to_string)
.collect()
}
pub enum AnalyticsMsg {
BatchMessage(Track),
AggregateGetSearch(SearchAggregator),
AggregatePostSearch(SearchAggregator),
AggregateGetSimilar(SimilarAggregator),
AggregatePostSimilar(SimilarAggregator),
AggregatePostMultiSearch(MultiSearchAggregator),
AggregatePostFacetSearch(FacetSearchAggregator),
AggregateAddDocuments(DocumentsAggregator),
AggregateDeleteDocuments(DocumentsDeletionAggregator),
AggregateUpdateDocuments(DocumentsAggregator),
AggregateEditDocumentsByFunction(EditDocumentsByFunctionAggregator),
AggregateGetFetchDocuments(DocumentsFetchAggregator),
AggregatePostFetchDocuments(DocumentsFetchAggregator),
}
pub struct SegmentAnalytics {
instance_uid: InstanceUid,
sender: Sender<AnalyticsMsg>,
user: User,
}
impl SegmentAnalytics {
#[allow(clippy::new_ret_no_self)]
pub async fn new(
opt: &Opt,
index_scheduler: Arc<IndexScheduler>,
auth_controller: Arc<AuthController>,
) -> Arc<dyn Analytics> {
let instance_uid = super::find_user_id(&opt.db_path);
let first_time_run = instance_uid.is_none();
let instance_uid = instance_uid.unwrap_or_else(Uuid::new_v4);
write_user_id(&opt.db_path, &instance_uid);
let client = reqwest::Client::builder().connect_timeout(Duration::from_secs(10)).build();
// if reqwest throws an error we won't be able to send analytics
if client.is_err() {
return super::MockAnalytics::new(opt);
}
let client =
HttpClient::new(client.unwrap(), "https://telemetry.meilisearch.com".to_string());
let user = User::UserId { user_id: instance_uid.to_string() };
let mut batcher = AutoBatcher::new(client, Batcher::new(None), SEGMENT_API_KEY.to_string());
// If Meilisearch is Launched for the first time:
// 1. Send an event Launched associated to the user `total_launch`.
// 2. Batch an event Launched with the real instance-id and send it in one hour.
if first_time_run {
let _ = batcher
.push(Track {
user: User::UserId { user_id: "total_launch".to_string() },
event: "Launched".to_string(),
..Default::default()
})
.await;
let _ = batcher.flush().await;
let _ = batcher
.push(Track {
user: user.clone(),
event: "Launched".to_string(),
..Default::default()
})
.await;
}
let (sender, inbox) = mpsc::channel(100); // How many analytics can we bufferize
let segment = Box::new(Segment {
inbox,
user: user.clone(),
opt: opt.clone(),
batcher,
post_search_aggregator: SearchAggregator::default(),
post_multi_search_aggregator: MultiSearchAggregator::default(),
post_facet_search_aggregator: FacetSearchAggregator::default(),
get_search_aggregator: SearchAggregator::default(),
add_documents_aggregator: DocumentsAggregator::default(),
delete_documents_aggregator: DocumentsDeletionAggregator::default(),
update_documents_aggregator: DocumentsAggregator::default(),
edit_documents_by_function_aggregator: EditDocumentsByFunctionAggregator::default(),
get_fetch_documents_aggregator: DocumentsFetchAggregator::default(),
post_fetch_documents_aggregator: DocumentsFetchAggregator::default(),
get_similar_aggregator: SimilarAggregator::default(),
post_similar_aggregator: SimilarAggregator::default(),
});
tokio::spawn(segment.run(index_scheduler.clone(), auth_controller.clone()));
let this = Self { instance_uid, sender, user: user.clone() };
Arc::new(this)
}
}
impl super::Analytics for SegmentAnalytics {
fn instance_uid(&self) -> Option<&InstanceUid> {
Some(&self.instance_uid)
}
fn publish(&self, event_name: String, mut send: Value, request: Option<&HttpRequest>) {
let user_agent = request.map(extract_user_agents);
send["user-agent"] = json!(user_agent);
let event = Track {
user: self.user.clone(),
event: event_name.clone(),
properties: send,
..Default::default()
};
let _ = self.sender.try_send(AnalyticsMsg::BatchMessage(event));
}
fn get_search(&self, aggregate: SearchAggregator) {
let _ = self.sender.try_send(AnalyticsMsg::AggregateGetSearch(aggregate));
}
fn post_search(&self, aggregate: SearchAggregator) {
let _ = self.sender.try_send(AnalyticsMsg::AggregatePostSearch(aggregate));
}
fn get_similar(&self, aggregate: SimilarAggregator) {
let _ = self.sender.try_send(AnalyticsMsg::AggregateGetSimilar(aggregate));
}
fn post_similar(&self, aggregate: SimilarAggregator) {
let _ = self.sender.try_send(AnalyticsMsg::AggregatePostSimilar(aggregate));
}
fn post_facet_search(&self, aggregate: FacetSearchAggregator) {
let _ = self.sender.try_send(AnalyticsMsg::AggregatePostFacetSearch(aggregate));
}
fn post_multi_search(&self, aggregate: MultiSearchAggregator) {
let _ = self.sender.try_send(AnalyticsMsg::AggregatePostMultiSearch(aggregate));
}
fn add_documents(
&self,
documents_query: &UpdateDocumentsQuery,
index_creation: bool,
request: &HttpRequest,
) {
let aggregate = DocumentsAggregator::from_query(documents_query, index_creation, request);
let _ = self.sender.try_send(AnalyticsMsg::AggregateAddDocuments(aggregate));
}
fn delete_documents(&self, kind: DocumentDeletionKind, request: &HttpRequest) {
let aggregate = DocumentsDeletionAggregator::from_query(kind, request);
let _ = self.sender.try_send(AnalyticsMsg::AggregateDeleteDocuments(aggregate));
}
fn update_documents(
&self,
documents_query: &UpdateDocumentsQuery,
index_creation: bool,
request: &HttpRequest,
) {
let aggregate = DocumentsAggregator::from_query(documents_query, index_creation, request);
let _ = self.sender.try_send(AnalyticsMsg::AggregateUpdateDocuments(aggregate));
}
fn update_documents_by_function(
&self,
documents_query: &DocumentEditionByFunction,
index_creation: bool,
request: &HttpRequest,
) {
let aggregate =
EditDocumentsByFunctionAggregator::from_query(documents_query, index_creation, request);
let _ = self.sender.try_send(AnalyticsMsg::AggregateEditDocumentsByFunction(aggregate));
}
fn get_fetch_documents(&self, documents_query: &DocumentFetchKind, request: &HttpRequest) {
let aggregate = DocumentsFetchAggregator::from_query(documents_query, request);
let _ = self.sender.try_send(AnalyticsMsg::AggregateGetFetchDocuments(aggregate));
}
fn post_fetch_documents(&self, documents_query: &DocumentFetchKind, request: &HttpRequest) {
let aggregate = DocumentsFetchAggregator::from_query(documents_query, request);
let _ = self.sender.try_send(AnalyticsMsg::AggregatePostFetchDocuments(aggregate));
}
}
/// This structure represent the `infos` field we send in the analytics.
/// It's quite close to the `Opt` structure except all sensitive informations
/// have been simplified to a boolean.
/// It's send as-is in amplitude thus you should never update a name of the
/// struct without the approval of the PM.
#[derive(Debug, Clone, Serialize)]
struct Infos {
env: String,
experimental_contains_filter: bool,
experimental_enable_metrics: bool,
experimental_search_queue_size: usize,
experimental_logs_mode: LogMode,
experimental_replication_parameters: bool,
experimental_enable_logs_route: bool,
experimental_reduce_indexing_memory_usage: bool,
experimental_max_number_of_batched_tasks: usize,
gpu_enabled: bool,
db_path: bool,
import_dump: bool,
dump_dir: bool,
ignore_missing_dump: bool,
ignore_dump_if_db_exists: bool,
import_snapshot: bool,
schedule_snapshot: Option<u64>,
snapshot_dir: bool,
ignore_missing_snapshot: bool,
ignore_snapshot_if_db_exists: bool,
http_addr: bool,
http_payload_size_limit: Byte,
task_queue_webhook: bool,
task_webhook_authorization_header: bool,
log_level: String,
max_indexing_memory: MaxMemory,
max_indexing_threads: MaxThreads,
with_configuration_file: bool,
ssl_auth_path: bool,
ssl_cert_path: bool,
ssl_key_path: bool,
ssl_ocsp_path: bool,
ssl_require_auth: bool,
ssl_resumption: bool,
ssl_tickets: bool,
}
impl From<Opt> for Infos {
fn from(options: Opt) -> Self {
// We wants to decompose this whole struct by hand to be sure we don't forget
// to add analytics when we add a field in the Opt.
// Thus we must not insert `..` at the end.
let Opt {
db_path,
experimental_contains_filter,
experimental_enable_metrics,
experimental_search_queue_size,
experimental_logs_mode,
experimental_replication_parameters,
experimental_enable_logs_route,
experimental_reduce_indexing_memory_usage,
experimental_max_number_of_batched_tasks,
http_addr,
master_key: _,
env,
task_webhook_url,
task_webhook_authorization_header,
max_index_size: _,
max_task_db_size: _,
http_payload_size_limit,
ssl_cert_path,
ssl_key_path,
ssl_auth_path,
ssl_ocsp_path,
ssl_require_auth,
ssl_resumption,
ssl_tickets,
import_snapshot,
ignore_missing_snapshot,
ignore_snapshot_if_db_exists,
snapshot_dir,
schedule_snapshot,
import_dump,
ignore_missing_dump,
ignore_dump_if_db_exists,
dump_dir,
log_level,
indexer_options,
config_file_path,
#[cfg(feature = "analytics")]
no_analytics: _,
} = options;
let schedule_snapshot = match schedule_snapshot {
ScheduleSnapshot::Disabled => None,
ScheduleSnapshot::Enabled(interval) => Some(interval),
};
let IndexerOpts { max_indexing_memory, max_indexing_threads, skip_index_budget: _ } =
indexer_options;
// We're going to override every sensible information.
// We consider information sensible if it contains a path, an address, or a key.
Self {
env,
experimental_contains_filter,
experimental_enable_metrics,
experimental_search_queue_size,
experimental_logs_mode,
experimental_replication_parameters,
experimental_enable_logs_route,
experimental_reduce_indexing_memory_usage,
gpu_enabled: meilisearch_types::milli::vector::is_cuda_enabled(),
db_path: db_path != PathBuf::from("./data.ms"),
import_dump: import_dump.is_some(),
dump_dir: dump_dir != PathBuf::from("dumps/"),
ignore_missing_dump,
ignore_dump_if_db_exists,
import_snapshot: import_snapshot.is_some(),
schedule_snapshot,
snapshot_dir: snapshot_dir != PathBuf::from("snapshots/"),
ignore_missing_snapshot,
ignore_snapshot_if_db_exists,
http_addr: http_addr != default_http_addr(),
http_payload_size_limit,
experimental_max_number_of_batched_tasks,
task_queue_webhook: task_webhook_url.is_some(),
task_webhook_authorization_header: task_webhook_authorization_header.is_some(),
log_level: log_level.to_string(),
max_indexing_memory,
max_indexing_threads,
with_configuration_file: config_file_path.is_some(),
ssl_auth_path: ssl_auth_path.is_some(),
ssl_cert_path: ssl_cert_path.is_some(),
ssl_key_path: ssl_key_path.is_some(),
ssl_ocsp_path: ssl_ocsp_path.is_some(),
ssl_require_auth,
ssl_resumption,
ssl_tickets,
}
}
}
pub struct Segment {
inbox: Receiver<AnalyticsMsg>,
user: User,
opt: Opt,
batcher: AutoBatcher,
get_search_aggregator: SearchAggregator,
post_search_aggregator: SearchAggregator,
post_multi_search_aggregator: MultiSearchAggregator,
post_facet_search_aggregator: FacetSearchAggregator,
add_documents_aggregator: DocumentsAggregator,
delete_documents_aggregator: DocumentsDeletionAggregator,
update_documents_aggregator: DocumentsAggregator,
edit_documents_by_function_aggregator: EditDocumentsByFunctionAggregator,
get_fetch_documents_aggregator: DocumentsFetchAggregator,
post_fetch_documents_aggregator: DocumentsFetchAggregator,
get_similar_aggregator: SimilarAggregator,
post_similar_aggregator: SimilarAggregator,
}
impl Segment {
fn compute_traits(opt: &Opt, stats: Stats) -> Value {
static FIRST_START_TIMESTAMP: Lazy<Instant> = Lazy::new(Instant::now);
static SYSTEM: Lazy<Value> = Lazy::new(|| {
let disks = Disks::new_with_refreshed_list();
let mut sys = System::new_all();
sys.refresh_all();
let kernel_version = System::kernel_version()
.and_then(|k| k.split_once('-').map(|(k, _)| k.to_string()));
json!({
"distribution": System::name(),
"kernel_version": kernel_version,
"cores": sys.cpus().len(),
"ram_size": sys.total_memory(),
"disk_size": disks.iter().map(|disk| disk.total_space()).max(),
"server_provider": std::env::var("MEILI_SERVER_PROVIDER").ok(),
})
});
let number_of_documents =
stats.indexes.values().map(|index| index.number_of_documents).collect::<Vec<u64>>();
json!({
"start_since_days": FIRST_START_TIMESTAMP.elapsed().as_secs() / (60 * 60 * 24), // one day
"system": *SYSTEM,
"stats": {
"database_size": stats.database_size,
"indexes_number": stats.indexes.len(),
"documents_number": number_of_documents,
},
"infos": Infos::from(opt.clone()),
})
}
async fn run(
mut self,
index_scheduler: Arc<IndexScheduler>,
auth_controller: Arc<AuthController>,
) {
const INTERVAL: Duration = Duration::from_secs(60 * 60); // one hour
// The first batch must be sent after one hour.
let mut interval =
tokio::time::interval_at(tokio::time::Instant::now() + INTERVAL, INTERVAL);
loop {
select! {
_ = interval.tick() => {
self.tick(index_scheduler.clone(), auth_controller.clone()).await;
},
msg = self.inbox.recv() => {
match msg {
Some(AnalyticsMsg::BatchMessage(msg)) => drop(self.batcher.push(msg).await),
Some(AnalyticsMsg::AggregateGetSearch(agreg)) => self.get_search_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregatePostSearch(agreg)) => self.post_search_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregatePostMultiSearch(agreg)) => self.post_multi_search_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregatePostFacetSearch(agreg)) => self.post_facet_search_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregateAddDocuments(agreg)) => self.add_documents_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregateDeleteDocuments(agreg)) => self.delete_documents_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregateUpdateDocuments(agreg)) => self.update_documents_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregateEditDocumentsByFunction(agreg)) => self.edit_documents_by_function_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregateGetFetchDocuments(agreg)) => self.get_fetch_documents_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregatePostFetchDocuments(agreg)) => self.post_fetch_documents_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregateGetSimilar(agreg)) => self.get_similar_aggregator.aggregate(agreg),
Some(AnalyticsMsg::AggregatePostSimilar(agreg)) => self.post_similar_aggregator.aggregate(agreg),
None => (),
}
}
}
}
}
async fn tick(
&mut self,
index_scheduler: Arc<IndexScheduler>,
auth_controller: Arc<AuthController>,
) {
if let Ok(stats) =
create_all_stats(index_scheduler.into(), auth_controller.into(), &AuthFilter::default())
{
// Replace the version number with the prototype name if any.
let version = if let Some(prototype) = build_info::DescribeResult::from_build()
.and_then(|describe| describe.as_prototype())
{
prototype
} else {
env!("CARGO_PKG_VERSION")
};
let _ = self
.batcher
.push(Identify {
context: Some(json!({
"app": {
"version": version.to_string(),
},
})),
user: self.user.clone(),
traits: Self::compute_traits(&self.opt, stats),
..Default::default()
})
.await;
}
let Segment {
inbox: _,
opt: _,
batcher: _,
user,
get_search_aggregator,
post_search_aggregator,
post_multi_search_aggregator,
post_facet_search_aggregator,
add_documents_aggregator,
delete_documents_aggregator,
update_documents_aggregator,
edit_documents_by_function_aggregator,
get_fetch_documents_aggregator,
post_fetch_documents_aggregator,
get_similar_aggregator,
post_similar_aggregator,
} = self;
if let Some(get_search) =
take(get_search_aggregator).into_event(user, "Documents Searched GET")
{
let _ = self.batcher.push(get_search).await;
}
if let Some(post_search) =
take(post_search_aggregator).into_event(user, "Documents Searched POST")
{
let _ = self.batcher.push(post_search).await;
}
if let Some(post_multi_search) = take(post_multi_search_aggregator)
.into_event(user, "Documents Searched by Multi-Search POST")
{
let _ = self.batcher.push(post_multi_search).await;
}
if let Some(post_facet_search) =
take(post_facet_search_aggregator).into_event(user, "Facet Searched POST")
{
let _ = self.batcher.push(post_facet_search).await;
}
if let Some(add_documents) =
take(add_documents_aggregator).into_event(user, "Documents Added")
{
let _ = self.batcher.push(add_documents).await;
}
if let Some(delete_documents) =
take(delete_documents_aggregator).into_event(user, "Documents Deleted")
{
let _ = self.batcher.push(delete_documents).await;
}
if let Some(update_documents) =
take(update_documents_aggregator).into_event(user, "Documents Updated")
{
let _ = self.batcher.push(update_documents).await;
}
if let Some(edit_documents_by_function) = take(edit_documents_by_function_aggregator)
.into_event(user, "Documents Edited By Function")
{
let _ = self.batcher.push(edit_documents_by_function).await;
}
if let Some(get_fetch_documents) =
take(get_fetch_documents_aggregator).into_event(user, "Documents Fetched GET")
{
let _ = self.batcher.push(get_fetch_documents).await;
}
if let Some(post_fetch_documents) =
take(post_fetch_documents_aggregator).into_event(user, "Documents Fetched POST")
{
let _ = self.batcher.push(post_fetch_documents).await;
}
if let Some(get_similar_documents) =
take(get_similar_aggregator).into_event(user, "Similar GET")
{
let _ = self.batcher.push(get_similar_documents).await;
}
if let Some(post_similar_documents) =
take(post_similar_aggregator).into_event(user, "Similar POST")
{
let _ = self.batcher.push(post_similar_documents).await;
}
let _ = self.batcher.flush().await;
}
}
#[derive(Default)]
pub struct SearchAggregator {
timestamp: Option<OffsetDateTime>,
// context
user_agents: HashSet<String>,
// requests
total_received: usize,
total_succeeded: usize,
total_degraded: usize,
total_used_negative_operator: usize,
time_spent: BinaryHeap<usize>,
// sort
sort_with_geo_point: bool,
// every time a request has a filter, this field must be incremented by the number of terms it contains
sort_sum_of_criteria_terms: usize,
// every time a request has a filter, this field must be incremented by one
sort_total_number_of_criteria: usize,
// distinct
distinct: bool,
// filter
filter_with_geo_radius: bool,
filter_with_geo_bounding_box: bool,
// every time a request has a filter, this field must be incremented by the number of terms it contains
filter_sum_of_criteria_terms: usize,
// every time a request has a filter, this field must be incremented by one
filter_total_number_of_criteria: usize,
used_syntax: HashMap<String, usize>,
// attributes_to_search_on
// every time a search is done using attributes_to_search_on
attributes_to_search_on_total_number_of_uses: usize,
// q
// The maximum number of terms in a q request
max_terms_number: usize,
// vector
// The maximum number of floats in a vector request
max_vector_size: usize,
// Whether the semantic ratio passed to a hybrid search equals the default ratio.
semantic_ratio: bool,
// Whether a non-default embedder was specified
embedder: bool,
hybrid: bool,
retrieve_vectors: bool,
// every time a search is done, we increment the counter linked to the used settings
matching_strategy: HashMap<String, usize>,
// List of the unique Locales passed as parameter
locales: BTreeSet<Locale>,
// pagination
max_limit: usize,
max_offset: usize,
finite_pagination: usize,
// formatting
max_attributes_to_retrieve: usize,
max_attributes_to_highlight: usize,
highlight_pre_tag: bool,
highlight_post_tag: bool,
max_attributes_to_crop: usize,
crop_marker: bool,
show_matches_position: bool,
crop_length: bool,
// facets
facets_sum_of_terms: usize,
facets_total_number_of_facets: usize,
// scoring
show_ranking_score: bool,
show_ranking_score_details: bool,
ranking_score_threshold: bool,
}
impl SearchAggregator {
#[allow(clippy::field_reassign_with_default)]
pub fn from_query(query: &SearchQuery, request: &HttpRequest) -> Self {
let SearchQuery {
q,
vector,
offset,
limit,
page,
hits_per_page,
attributes_to_retrieve: _,
retrieve_vectors,
attributes_to_crop: _,
crop_length,
attributes_to_highlight: _,
show_matches_position,
show_ranking_score,
show_ranking_score_details,
filter,
sort,
distinct,
facets: _,
highlight_pre_tag,
highlight_post_tag,
crop_marker,
matching_strategy,
attributes_to_search_on,
hybrid,
ranking_score_threshold,
locales,
} = query;
let mut ret = Self::default();
ret.timestamp = Some(OffsetDateTime::now_utc());
ret.total_received = 1;
ret.user_agents = extract_user_agents(request).into_iter().collect();
if let Some(ref sort) = sort {
ret.sort_total_number_of_criteria = 1;
ret.sort_with_geo_point = sort.iter().any(|s| s.contains("_geoPoint("));
ret.sort_sum_of_criteria_terms = sort.len();
}
ret.distinct = distinct.is_some();
if let Some(ref filter) = filter {
static RE: Lazy<Regex> = Lazy::new(|| Regex::new("AND | OR").unwrap());
ret.filter_total_number_of_criteria = 1;
let syntax = match filter {
Value::String(_) => "string".to_string(),
Value::Array(values) => {
if values.iter().map(|v| v.to_string()).any(|s| RE.is_match(&s)) {
"mixed".to_string()
} else {
"array".to_string()
}
}
_ => "none".to_string(),
};
// convert the string to a HashMap
ret.used_syntax.insert(syntax, 1);
let stringified_filters = filter.to_string();
ret.filter_with_geo_radius = stringified_filters.contains("_geoRadius(");
ret.filter_with_geo_bounding_box = stringified_filters.contains("_geoBoundingBox(");
ret.filter_sum_of_criteria_terms = RE.split(&stringified_filters).count();
}
// attributes_to_search_on
if attributes_to_search_on.is_some() {
ret.attributes_to_search_on_total_number_of_uses = 1;
}
if let Some(ref q) = q {
ret.max_terms_number = q.split_whitespace().count();
}
if let Some(ref vector) = vector {
ret.max_vector_size = vector.len();
}
ret.retrieve_vectors |= retrieve_vectors;
if query.is_finite_pagination() {
let limit = hits_per_page.unwrap_or_else(DEFAULT_SEARCH_LIMIT);
ret.max_limit = limit;
ret.max_offset = page.unwrap_or(1).saturating_sub(1) * limit;
ret.finite_pagination = 1;
} else {
ret.max_limit = *limit;
ret.max_offset = *offset;
ret.finite_pagination = 0;
}
ret.matching_strategy.insert(format!("{:?}", matching_strategy), 1);
if let Some(locales) = locales {
ret.locales = locales.iter().copied().collect();
}
ret.highlight_pre_tag = *highlight_pre_tag != DEFAULT_HIGHLIGHT_PRE_TAG();
ret.highlight_post_tag = *highlight_post_tag != DEFAULT_HIGHLIGHT_POST_TAG();
ret.crop_marker = *crop_marker != DEFAULT_CROP_MARKER();
ret.crop_length = *crop_length != DEFAULT_CROP_LENGTH();
ret.show_matches_position = *show_matches_position;
ret.show_ranking_score = *show_ranking_score;
ret.show_ranking_score_details = *show_ranking_score_details;
ret.ranking_score_threshold = ranking_score_threshold.is_some();
if let Some(hybrid) = hybrid {
ret.semantic_ratio = hybrid.semantic_ratio != DEFAULT_SEMANTIC_RATIO();
ret.embedder = hybrid.embedder.is_some();
ret.hybrid = true;
}
ret
}
pub fn succeed(&mut self, result: &SearchResult) {
let SearchResult {
hits: _,
query: _,
processing_time_ms,
hits_info: _,
semantic_hit_count: _,
facet_distribution: _,
facet_stats: _,
degraded,
used_negative_operator,
} = result;
self.total_succeeded = self.total_succeeded.saturating_add(1);
if *degraded {
self.total_degraded = self.total_degraded.saturating_add(1);
}
if *used_negative_operator {
self.total_used_negative_operator = self.total_used_negative_operator.saturating_add(1);
}
self.time_spent.push(*processing_time_ms as usize);
}
/// Aggregate one [SearchAggregator] into another.
pub fn aggregate(&mut self, mut other: Self) {
let Self {
timestamp,
user_agents,
total_received,
total_succeeded,
ref mut time_spent,
sort_with_geo_point,
sort_sum_of_criteria_terms,
sort_total_number_of_criteria,
distinct,
filter_with_geo_radius,
filter_with_geo_bounding_box,
filter_sum_of_criteria_terms,
filter_total_number_of_criteria,
used_syntax,
attributes_to_search_on_total_number_of_uses,
max_terms_number,
max_vector_size,
retrieve_vectors,
matching_strategy,
max_limit,
max_offset,
finite_pagination,
max_attributes_to_retrieve,
max_attributes_to_highlight,
highlight_pre_tag,
highlight_post_tag,
max_attributes_to_crop,
crop_marker,
show_matches_position,
crop_length,
facets_sum_of_terms,
facets_total_number_of_facets,
show_ranking_score,
show_ranking_score_details,
semantic_ratio,
embedder,
hybrid,
total_degraded,
total_used_negative_operator,
ranking_score_threshold,
ref mut locales,
} = other;
if self.timestamp.is_none() {
self.timestamp = timestamp;
}
// context
for user_agent in user_agents.into_iter() {
self.user_agents.insert(user_agent);
}
// request
self.total_received = self.total_received.saturating_add(total_received);
self.total_succeeded = self.total_succeeded.saturating_add(total_succeeded);
self.total_degraded = self.total_degraded.saturating_add(total_degraded);
self.total_used_negative_operator =
self.total_used_negative_operator.saturating_add(total_used_negative_operator);
self.time_spent.append(time_spent);
// sort
self.sort_with_geo_point |= sort_with_geo_point;
self.sort_sum_of_criteria_terms =
self.sort_sum_of_criteria_terms.saturating_add(sort_sum_of_criteria_terms);
self.sort_total_number_of_criteria =
self.sort_total_number_of_criteria.saturating_add(sort_total_number_of_criteria);
// distinct
self.distinct |= distinct;
// filter
self.filter_with_geo_radius |= filter_with_geo_radius;
self.filter_with_geo_bounding_box |= filter_with_geo_bounding_box;
self.filter_sum_of_criteria_terms =
self.filter_sum_of_criteria_terms.saturating_add(filter_sum_of_criteria_terms);
self.filter_total_number_of_criteria =
self.filter_total_number_of_criteria.saturating_add(filter_total_number_of_criteria);
for (key, value) in used_syntax.into_iter() {
let used_syntax = self.used_syntax.entry(key).or_insert(0);
*used_syntax = used_syntax.saturating_add(value);
}
// attributes_to_search_on
self.attributes_to_search_on_total_number_of_uses = self
.attributes_to_search_on_total_number_of_uses
.saturating_add(attributes_to_search_on_total_number_of_uses);
// q
self.max_terms_number = self.max_terms_number.max(max_terms_number);
// vector
self.max_vector_size = self.max_vector_size.max(max_vector_size);
self.retrieve_vectors |= retrieve_vectors;
self.semantic_ratio |= semantic_ratio;
self.hybrid |= hybrid;
self.embedder |= embedder;
// pagination
self.max_limit = self.max_limit.max(max_limit);
self.max_offset = self.max_offset.max(max_offset);
self.finite_pagination += finite_pagination;
// formatting
self.max_attributes_to_retrieve =
self.max_attributes_to_retrieve.max(max_attributes_to_retrieve);
self.max_attributes_to_highlight =
self.max_attributes_to_highlight.max(max_attributes_to_highlight);
self.highlight_pre_tag |= highlight_pre_tag;
self.highlight_post_tag |= highlight_post_tag;
self.max_attributes_to_crop = self.max_attributes_to_crop.max(max_attributes_to_crop);
self.crop_marker |= crop_marker;
self.show_matches_position |= show_matches_position;
self.crop_length |= crop_length;
// facets
self.facets_sum_of_terms = self.facets_sum_of_terms.saturating_add(facets_sum_of_terms);
self.facets_total_number_of_facets =
self.facets_total_number_of_facets.saturating_add(facets_total_number_of_facets);
// matching strategy
for (key, value) in matching_strategy.into_iter() {
let matching_strategy = self.matching_strategy.entry(key).or_insert(0);
*matching_strategy = matching_strategy.saturating_add(value);
}
// scoring
self.show_ranking_score |= show_ranking_score;
self.show_ranking_score_details |= show_ranking_score_details;
self.ranking_score_threshold |= ranking_score_threshold;
// locales
self.locales.append(locales);
}
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
let Self {
timestamp,
user_agents,
total_received,
total_succeeded,
time_spent,
sort_with_geo_point,
sort_sum_of_criteria_terms,
sort_total_number_of_criteria,
distinct,
filter_with_geo_radius,
filter_with_geo_bounding_box,
filter_sum_of_criteria_terms,
filter_total_number_of_criteria,
used_syntax,
attributes_to_search_on_total_number_of_uses,
max_terms_number,
max_vector_size,
retrieve_vectors,
matching_strategy,
max_limit,
max_offset,
finite_pagination,
max_attributes_to_retrieve,
max_attributes_to_highlight,
highlight_pre_tag,
highlight_post_tag,
max_attributes_to_crop,
crop_marker,
show_matches_position,
crop_length,
facets_sum_of_terms,
facets_total_number_of_facets,
show_ranking_score,
show_ranking_score_details,
semantic_ratio,
embedder,
hybrid,
total_degraded,
total_used_negative_operator,
ranking_score_threshold,
locales,
} = self;
if total_received == 0 {
None
} else {
// we get all the values in a sorted manner
let time_spent = time_spent.into_sorted_vec();
// the index of the 99th percentage of value
let percentile_99th = time_spent.len() * 99 / 100;
// We are only interested by the slowest value of the 99th fastest results
let time_spent = time_spent.get(percentile_99th);
let properties = json!({
"user-agent": user_agents,
"requests": {
"99th_response_time": time_spent.map(|t| format!("{:.2}", t)),
"total_succeeded": total_succeeded,
"total_failed": total_received.saturating_sub(total_succeeded), // just to be sure we never panics
"total_received": total_received,
"total_degraded": total_degraded,
"total_used_negative_operator": total_used_negative_operator,
},
"sort": {
"with_geoPoint": sort_with_geo_point,
"avg_criteria_number": format!("{:.2}", sort_sum_of_criteria_terms as f64 / sort_total_number_of_criteria as f64),
},
"distinct": distinct,
"filter": {
"with_geoRadius": filter_with_geo_radius,
"with_geoBoundingBox": filter_with_geo_bounding_box,
"avg_criteria_number": format!("{:.2}", filter_sum_of_criteria_terms as f64 / filter_total_number_of_criteria as f64),
"most_used_syntax": used_syntax.iter().max_by_key(|(_, v)| *v).map(|(k, _)| json!(k)).unwrap_or_else(|| json!(null)),
},
"attributes_to_search_on": {
"total_number_of_uses": attributes_to_search_on_total_number_of_uses,
},
"q": {
"max_terms_number": max_terms_number,
},
"vector": {
"max_vector_size": max_vector_size,
"retrieve_vectors": retrieve_vectors,
},
"hybrid": {
"enabled": hybrid,
"semantic_ratio": semantic_ratio,
"embedder": embedder,
},
"pagination": {
"max_limit": max_limit,
"max_offset": max_offset,
"most_used_navigation": if finite_pagination > (total_received / 2) { "exhaustive" } else { "estimated" },
},
"formatting": {
"max_attributes_to_retrieve": max_attributes_to_retrieve,
"max_attributes_to_highlight": max_attributes_to_highlight,
"highlight_pre_tag": highlight_pre_tag,
"highlight_post_tag": highlight_post_tag,
"max_attributes_to_crop": max_attributes_to_crop,
"crop_marker": crop_marker,
"show_matches_position": show_matches_position,
"crop_length": crop_length,
},
"facets": {
"avg_facets_number": format!("{:.2}", facets_sum_of_terms as f64 / facets_total_number_of_facets as f64),
},
"matching_strategy": {
"most_used_strategy": matching_strategy.iter().max_by_key(|(_, v)| *v).map(|(k, _)| json!(k)).unwrap_or_else(|| json!(null)),
},
"locales": locales,
"scoring": {
"show_ranking_score": show_ranking_score,
"show_ranking_score_details": show_ranking_score_details,
"ranking_score_threshold": ranking_score_threshold,
},
});
Some(Track {
timestamp,
user: user.clone(),
event: event_name.to_string(),
properties,
..Default::default()
})
}
}
}
#[derive(Default)]
pub struct MultiSearchAggregator {
timestamp: Option<OffsetDateTime>,
// requests
total_received: usize,
total_succeeded: usize,
// sum of the number of distinct indexes in each single request, use with total_received to compute an avg
total_distinct_index_count: usize,
// number of queries with a single index, use with total_received to compute a proportion
total_single_index: usize,
// sum of the number of search queries in the requests, use with total_received to compute an average
total_search_count: usize,
// scoring
show_ranking_score: bool,
show_ranking_score_details: bool,
// federation
use_federation: bool,
// context
user_agents: HashSet<String>,
}
impl MultiSearchAggregator {
pub fn from_federated_search(
federated_search: &FederatedSearch,
request: &HttpRequest,
) -> Self {
let timestamp = Some(OffsetDateTime::now_utc());
let user_agents = extract_user_agents(request).into_iter().collect();
let use_federation = federated_search.federation.is_some();
let distinct_indexes: HashSet<_> = federated_search
.queries
.iter()
.map(|query| {
let query = &query;
// make sure we get a compilation error if a field gets added to / removed from SearchQueryWithIndex
let SearchQueryWithIndex {
index_uid,
federation_options: _,
q: _,
vector: _,
offset: _,
limit: _,
page: _,
hits_per_page: _,
attributes_to_retrieve: _,
retrieve_vectors: _,
attributes_to_crop: _,
crop_length: _,
attributes_to_highlight: _,
show_ranking_score: _,
show_ranking_score_details: _,
show_matches_position: _,
filter: _,
sort: _,
distinct: _,
facets: _,
highlight_pre_tag: _,
highlight_post_tag: _,
crop_marker: _,
matching_strategy: _,
attributes_to_search_on: _,
hybrid: _,
ranking_score_threshold: _,
locales: _,
} = query;
index_uid.as_str()
})
.collect();
let show_ranking_score =
federated_search.queries.iter().any(|query| query.show_ranking_score);
let show_ranking_score_details =
federated_search.queries.iter().any(|query| query.show_ranking_score_details);
Self {
timestamp,
total_received: 1,
total_succeeded: 0,
total_distinct_index_count: distinct_indexes.len(),
total_single_index: if distinct_indexes.len() == 1 { 1 } else { 0 },
total_search_count: federated_search.queries.len(),
show_ranking_score,
show_ranking_score_details,
user_agents,
use_federation,
}
}
pub fn succeed(&mut self) {
self.total_succeeded = self.total_succeeded.saturating_add(1);
}
/// Aggregate one [MultiSearchAggregator] into another.
pub fn aggregate(&mut self, other: Self) {
// write the aggregate in a way that will cause a compilation error if a field is added.
// get ownership of self, replacing it by a default value.
let this = std::mem::take(self);
let timestamp = this.timestamp.or(other.timestamp);
let total_received = this.total_received.saturating_add(other.total_received);
let total_succeeded = this.total_succeeded.saturating_add(other.total_succeeded);
let total_distinct_index_count =
this.total_distinct_index_count.saturating_add(other.total_distinct_index_count);
let total_single_index = this.total_single_index.saturating_add(other.total_single_index);
let total_search_count = this.total_search_count.saturating_add(other.total_search_count);
let show_ranking_score = this.show_ranking_score || other.show_ranking_score;
let show_ranking_score_details =
this.show_ranking_score_details || other.show_ranking_score_details;
let mut user_agents = this.user_agents;
let use_federation = this.use_federation || other.use_federation;
for user_agent in other.user_agents.into_iter() {
user_agents.insert(user_agent);
}
// need all fields or compile error
let mut aggregated = Self {
timestamp,
total_received,
total_succeeded,
total_distinct_index_count,
total_single_index,
total_search_count,
user_agents,
show_ranking_score,
show_ranking_score_details,
use_federation,
// do not add _ or ..Default::default() here
};
// replace the default self with the aggregated value
std::mem::swap(self, &mut aggregated);
}
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
let Self {
timestamp,
total_received,
total_succeeded,
total_distinct_index_count,
total_single_index,
total_search_count,
user_agents,
show_ranking_score,
show_ranking_score_details,
use_federation,
} = self;
if total_received == 0 {
None
} else {
let properties = json!({
"user-agent": user_agents,
"requests": {
"total_succeeded": total_succeeded,
"total_failed": total_received.saturating_sub(total_succeeded), // just to be sure we never panics
"total_received": total_received,
},
"indexes": {
"total_single_index": total_single_index,
"total_distinct_index_count": total_distinct_index_count,
"avg_distinct_index_count": (total_distinct_index_count as f64) / (total_received as f64), // not 0 else returned early
},
"searches": {
"total_search_count": total_search_count,
"avg_search_count": (total_search_count as f64) / (total_received as f64),
},
"scoring": {
"show_ranking_score": show_ranking_score,
"show_ranking_score_details": show_ranking_score_details,
},
"federation": {
"use_federation": use_federation,
}
});
Some(Track {
timestamp,
user: user.clone(),
event: event_name.to_string(),
properties,
..Default::default()
})
}
}
}
#[derive(Default)]
pub struct FacetSearchAggregator {
timestamp: Option<OffsetDateTime>,
// context
user_agents: HashSet<String>,
// requests
total_received: usize,
total_succeeded: usize,
time_spent: BinaryHeap<usize>,
// The set of all facetNames that were used
facet_names: HashSet<String>,
// As there been any other parameter than the facetName or facetQuery ones?
additional_search_parameters_provided: bool,
}
impl FacetSearchAggregator {
#[allow(clippy::field_reassign_with_default)]
pub fn from_query(query: &FacetSearchQuery, request: &HttpRequest) -> Self {
let FacetSearchQuery {
facet_query: _,
facet_name,
vector,
q,
filter,
matching_strategy,
attributes_to_search_on,
hybrid,
ranking_score_threshold,
locales,
} = query;
let mut ret = Self::default();
ret.timestamp = Some(OffsetDateTime::now_utc());
ret.total_received = 1;
ret.user_agents = extract_user_agents(request).into_iter().collect();
ret.facet_names = Some(facet_name.clone()).into_iter().collect();
ret.additional_search_parameters_provided = q.is_some()
|| vector.is_some()
|| filter.is_some()
|| *matching_strategy != MatchingStrategy::default()
|| attributes_to_search_on.is_some()
|| hybrid.is_some()
|| ranking_score_threshold.is_some()
|| locales.is_some();
ret
}
pub fn succeed(&mut self, result: &FacetSearchResult) {
let FacetSearchResult { facet_hits: _, facet_query: _, processing_time_ms } = result;
self.total_succeeded = self.total_succeeded.saturating_add(1);
self.time_spent.push(*processing_time_ms as usize);
}
/// Aggregate one [FacetSearchAggregator] into another.
pub fn aggregate(&mut self, mut other: Self) {
let Self {
timestamp,
user_agents,
total_received,
total_succeeded,
ref mut time_spent,
facet_names,
additional_search_parameters_provided,
} = other;
if self.timestamp.is_none() {
self.timestamp = timestamp;
}
// context
for user_agent in user_agents.into_iter() {
self.user_agents.insert(user_agent);
}
// request
self.total_received = self.total_received.saturating_add(total_received);
self.total_succeeded = self.total_succeeded.saturating_add(total_succeeded);
self.time_spent.append(time_spent);
// facet_names
for facet_name in facet_names.into_iter() {
self.facet_names.insert(facet_name);
}
// additional_search_parameters_provided
self.additional_search_parameters_provided |= additional_search_parameters_provided;
}
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
let Self {
timestamp,
user_agents,
total_received,
total_succeeded,
time_spent,
facet_names,
additional_search_parameters_provided,
} = self;
if total_received == 0 {
None
} else {
// the index of the 99th percentage of value
let percentile_99th = 0.99 * (total_succeeded as f64 - 1.) + 1.;
// we get all the values in a sorted manner
let time_spent = time_spent.into_sorted_vec();
// We are only interested by the slowest value of the 99th fastest results
let time_spent = time_spent.get(percentile_99th as usize);
let properties = json!({
"user-agent": user_agents,
"requests": {
"99th_response_time": time_spent.map(|t| format!("{:.2}", t)),
"total_succeeded": total_succeeded,
"total_failed": total_received.saturating_sub(total_succeeded), // just to be sure we never panics
"total_received": total_received,
},
"facets": {
"total_distinct_facet_count": facet_names.len(),
"additional_search_parameters_provided": additional_search_parameters_provided,
},
});
Some(Track {
timestamp,
user: user.clone(),
event: event_name.to_string(),
properties,
..Default::default()
})
}
}
}
#[derive(Default)]
pub struct DocumentsAggregator {
timestamp: Option<OffsetDateTime>,
// set to true when at least one request was received
updated: bool,
// context
user_agents: HashSet<String>,
content_types: HashSet<String>,
primary_keys: HashSet<String>,
index_creation: bool,
}
impl DocumentsAggregator {
pub fn from_query(
documents_query: &UpdateDocumentsQuery,
index_creation: bool,
request: &HttpRequest,
) -> Self {
let UpdateDocumentsQuery { primary_key, csv_delimiter: _ } = documents_query;
let mut primary_keys = HashSet::new();
if let Some(primary_key) = primary_key.clone() {
primary_keys.insert(primary_key);
}
let mut content_types = HashSet::new();
let content_type = request
.headers()
.get(CONTENT_TYPE)
.and_then(|s| s.to_str().ok())
.unwrap_or("unknown")
.to_string();
content_types.insert(content_type);
Self {
timestamp: Some(OffsetDateTime::now_utc()),
updated: true,
user_agents: extract_user_agents(request).into_iter().collect(),
content_types,
primary_keys,
index_creation,
}
}
/// Aggregate one [DocumentsAggregator] into another.
pub fn aggregate(&mut self, other: Self) {
let Self { timestamp, user_agents, primary_keys, content_types, index_creation, updated } =
other;
if self.timestamp.is_none() {
self.timestamp = timestamp;
}
self.updated |= updated;
// we can't create a union because there is no `into_union` method
for user_agent in user_agents {
self.user_agents.insert(user_agent);
}
for primary_key in primary_keys {
self.primary_keys.insert(primary_key);
}
for content_type in content_types {
self.content_types.insert(content_type);
}
self.index_creation |= index_creation;
}
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
let Self { timestamp, user_agents, primary_keys, content_types, index_creation, updated } =
self;
if !updated {
None
} else {
let properties = json!({
"user-agent": user_agents,
"payload_type": content_types,
"primary_key": primary_keys,
"index_creation": index_creation,
});
Some(Track {
timestamp,
user: user.clone(),
event: event_name.to_string(),
properties,
..Default::default()
})
}
}
}
#[derive(Default)]
pub struct EditDocumentsByFunctionAggregator {
timestamp: Option<OffsetDateTime>,
// Set to true if at least one request was filtered
filtered: bool,
// Set to true if at least one request contained a context
with_context: bool,
// context
user_agents: HashSet<String>,
index_creation: bool,
}
impl EditDocumentsByFunctionAggregator {
pub fn from_query(
documents_query: &DocumentEditionByFunction,
index_creation: bool,
request: &HttpRequest,
) -> Self {
let DocumentEditionByFunction { filter, context, function: _ } = documents_query;
Self {
timestamp: Some(OffsetDateTime::now_utc()),
user_agents: extract_user_agents(request).into_iter().collect(),
filtered: filter.is_some(),
with_context: context.is_some(),
index_creation,
}
}
/// Aggregate one [DocumentsAggregator] into another.
pub fn aggregate(&mut self, other: Self) {
let Self { timestamp, user_agents, index_creation, filtered, with_context } = other;
if self.timestamp.is_none() {
self.timestamp = timestamp;
}
// we can't create a union because there is no `into_union` method
for user_agent in user_agents {
self.user_agents.insert(user_agent);
}
self.index_creation |= index_creation;
self.filtered |= filtered;
self.with_context |= with_context;
}
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
let Self { timestamp, user_agents, index_creation, filtered, with_context } = self;
let properties = json!({
"user-agent": user_agents,
"filtered": filtered,
"with_context": with_context,
"index_creation": index_creation,
});
Some(Track {
timestamp,
user: user.clone(),
event: event_name.to_string(),
properties,
..Default::default()
})
}
}
#[derive(Default, Serialize)]
pub struct DocumentsDeletionAggregator {
#[serde(skip)]
timestamp: Option<OffsetDateTime>,
// context
#[serde(rename = "user-agent")]
user_agents: HashSet<String>,
#[serde(rename = "requests.total_received")]
total_received: usize,
per_document_id: bool,
clear_all: bool,
per_batch: bool,
per_filter: bool,
}
impl DocumentsDeletionAggregator {
pub fn from_query(kind: DocumentDeletionKind, request: &HttpRequest) -> Self {
Self {
timestamp: Some(OffsetDateTime::now_utc()),
user_agents: extract_user_agents(request).into_iter().collect(),
total_received: 1,
per_document_id: matches!(kind, DocumentDeletionKind::PerDocumentId),
clear_all: matches!(kind, DocumentDeletionKind::ClearAll),
per_batch: matches!(kind, DocumentDeletionKind::PerBatch),
per_filter: matches!(kind, DocumentDeletionKind::PerFilter),
}
}
/// Aggregate one [DocumentsAggregator] into another.
pub fn aggregate(&mut self, other: Self) {
let Self {
timestamp,
user_agents,
total_received,
per_document_id,
clear_all,
per_batch,
per_filter,
} = other;
if self.timestamp.is_none() {
self.timestamp = timestamp;
}
// we can't create a union because there is no `into_union` method
for user_agent in user_agents {
self.user_agents.insert(user_agent);
}
self.total_received = self.total_received.saturating_add(total_received);
self.per_document_id |= per_document_id;
self.clear_all |= clear_all;
self.per_batch |= per_batch;
self.per_filter |= per_filter;
}
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
// if we had no timestamp it means we never encountered any events and
// thus we don't need to send this event.
let timestamp = self.timestamp?;
Some(Track {
timestamp: Some(timestamp),
user: user.clone(),
event: event_name.to_string(),
properties: serde_json::to_value(self).ok()?,
..Default::default()
})
}
}
#[derive(Default, Serialize)]
pub struct DocumentsFetchAggregator {
#[serde(skip)]
timestamp: Option<OffsetDateTime>,
// context
#[serde(rename = "user-agent")]
user_agents: HashSet<String>,
#[serde(rename = "requests.total_received")]
total_received: usize,
// a call on ../documents/:doc_id
per_document_id: bool,
// if a filter was used
per_filter: bool,
#[serde(rename = "vector.retrieve_vectors")]
retrieve_vectors: bool,
// pagination
#[serde(rename = "pagination.max_limit")]
max_limit: usize,
#[serde(rename = "pagination.max_offset")]
max_offset: usize,
}
impl DocumentsFetchAggregator {
pub fn from_query(query: &DocumentFetchKind, request: &HttpRequest) -> Self {
let (limit, offset, retrieve_vectors) = match query {
DocumentFetchKind::PerDocumentId { retrieve_vectors } => (1, 0, *retrieve_vectors),
DocumentFetchKind::Normal { limit, offset, retrieve_vectors, .. } => {
(*limit, *offset, *retrieve_vectors)
}
};
Self {
timestamp: Some(OffsetDateTime::now_utc()),
user_agents: extract_user_agents(request).into_iter().collect(),
total_received: 1,
per_document_id: matches!(query, DocumentFetchKind::PerDocumentId { .. }),
per_filter: matches!(query, DocumentFetchKind::Normal { with_filter, .. } if *with_filter),
max_limit: limit,
max_offset: offset,
retrieve_vectors,
}
}
/// Aggregate one [DocumentsFetchAggregator] into another.
pub fn aggregate(&mut self, other: Self) {
let Self {
timestamp,
user_agents,
total_received,
per_document_id,
per_filter,
max_limit,
max_offset,
retrieve_vectors,
} = other;
if self.timestamp.is_none() {
self.timestamp = timestamp;
}
for user_agent in user_agents {
self.user_agents.insert(user_agent);
}
self.total_received = self.total_received.saturating_add(total_received);
self.per_document_id |= per_document_id;
self.per_filter |= per_filter;
self.max_limit = self.max_limit.max(max_limit);
self.max_offset = self.max_offset.max(max_offset);
self.retrieve_vectors |= retrieve_vectors;
}
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
// if we had no timestamp it means we never encountered any events and
// thus we don't need to send this event.
let timestamp = self.timestamp?;
Some(Track {
timestamp: Some(timestamp),
user: user.clone(),
event: event_name.to_string(),
properties: serde_json::to_value(self).ok()?,
..Default::default()
})
}
}
#[derive(Default)]
pub struct SimilarAggregator {
timestamp: Option<OffsetDateTime>,
// context
user_agents: HashSet<String>,
// requests
total_received: usize,
total_succeeded: usize,
time_spent: BinaryHeap<usize>,
// filter
filter_with_geo_radius: bool,
filter_with_geo_bounding_box: bool,
// every time a request has a filter, this field must be incremented by the number of terms it contains
filter_sum_of_criteria_terms: usize,
// every time a request has a filter, this field must be incremented by one
filter_total_number_of_criteria: usize,
used_syntax: HashMap<String, usize>,
// Whether a non-default embedder was specified
embedder: bool,
retrieve_vectors: bool,
// pagination
max_limit: usize,
max_offset: usize,
// formatting
max_attributes_to_retrieve: usize,
// scoring
show_ranking_score: bool,
show_ranking_score_details: bool,
ranking_score_threshold: bool,
}
impl SimilarAggregator {
#[allow(clippy::field_reassign_with_default)]
pub fn from_query(query: &SimilarQuery, request: &HttpRequest) -> Self {
let SimilarQuery {
id: _,
embedder,
offset,
limit,
attributes_to_retrieve: _,
retrieve_vectors,
show_ranking_score,
show_ranking_score_details,
filter,
ranking_score_threshold,
} = query;
let mut ret = Self::default();
ret.timestamp = Some(OffsetDateTime::now_utc());
ret.total_received = 1;
ret.user_agents = extract_user_agents(request).into_iter().collect();
if let Some(ref filter) = filter {
static RE: Lazy<Regex> = Lazy::new(|| Regex::new("AND | OR").unwrap());
ret.filter_total_number_of_criteria = 1;
let syntax = match filter {
Value::String(_) => "string".to_string(),
Value::Array(values) => {
if values.iter().map(|v| v.to_string()).any(|s| RE.is_match(&s)) {
"mixed".to_string()
} else {
"array".to_string()
}
}
_ => "none".to_string(),
};
// convert the string to a HashMap
ret.used_syntax.insert(syntax, 1);
let stringified_filters = filter.to_string();
ret.filter_with_geo_radius = stringified_filters.contains("_geoRadius(");
ret.filter_with_geo_bounding_box = stringified_filters.contains("_geoBoundingBox(");
ret.filter_sum_of_criteria_terms = RE.split(&stringified_filters).count();
}
ret.max_limit = *limit;
ret.max_offset = *offset;
ret.show_ranking_score = *show_ranking_score;
ret.show_ranking_score_details = *show_ranking_score_details;
ret.ranking_score_threshold = ranking_score_threshold.is_some();
ret.embedder = embedder.is_some();
ret.retrieve_vectors = *retrieve_vectors;
ret
}
pub fn succeed(&mut self, result: &SimilarResult) {
let SimilarResult { id: _, hits: _, processing_time_ms, hits_info: _ } = result;
self.total_succeeded = self.total_succeeded.saturating_add(1);
self.time_spent.push(*processing_time_ms as usize);
}
/// Aggregate one [SimilarAggregator] into another.
pub fn aggregate(&mut self, mut other: Self) {
let Self {
timestamp,
user_agents,
total_received,
total_succeeded,
ref mut time_spent,
filter_with_geo_radius,
filter_with_geo_bounding_box,
filter_sum_of_criteria_terms,
filter_total_number_of_criteria,
used_syntax,
max_limit,
max_offset,
max_attributes_to_retrieve,
show_ranking_score,
show_ranking_score_details,
embedder,
ranking_score_threshold,
retrieve_vectors,
} = other;
if self.timestamp.is_none() {
self.timestamp = timestamp;
}
// context
for user_agent in user_agents.into_iter() {
self.user_agents.insert(user_agent);
}
// request
self.total_received = self.total_received.saturating_add(total_received);
self.total_succeeded = self.total_succeeded.saturating_add(total_succeeded);
self.time_spent.append(time_spent);
// filter
self.filter_with_geo_radius |= filter_with_geo_radius;
self.filter_with_geo_bounding_box |= filter_with_geo_bounding_box;
self.filter_sum_of_criteria_terms =
self.filter_sum_of_criteria_terms.saturating_add(filter_sum_of_criteria_terms);
self.filter_total_number_of_criteria =
self.filter_total_number_of_criteria.saturating_add(filter_total_number_of_criteria);
for (key, value) in used_syntax.into_iter() {
let used_syntax = self.used_syntax.entry(key).or_insert(0);
*used_syntax = used_syntax.saturating_add(value);
}
self.embedder |= embedder;
self.retrieve_vectors |= retrieve_vectors;
// pagination
self.max_limit = self.max_limit.max(max_limit);
self.max_offset = self.max_offset.max(max_offset);
// formatting
self.max_attributes_to_retrieve =
self.max_attributes_to_retrieve.max(max_attributes_to_retrieve);
// scoring
self.show_ranking_score |= show_ranking_score;
self.show_ranking_score_details |= show_ranking_score_details;
self.ranking_score_threshold |= ranking_score_threshold;
}
pub fn into_event(self, user: &User, event_name: &str) -> Option<Track> {
let Self {
timestamp,
user_agents,
total_received,
total_succeeded,
time_spent,
filter_with_geo_radius,
filter_with_geo_bounding_box,
filter_sum_of_criteria_terms,
filter_total_number_of_criteria,
used_syntax,
max_limit,
max_offset,
max_attributes_to_retrieve,
show_ranking_score,
show_ranking_score_details,
embedder,
ranking_score_threshold,
retrieve_vectors,
} = self;
if total_received == 0 {
None
} else {
// we get all the values in a sorted manner
let time_spent = time_spent.into_sorted_vec();
// the index of the 99th percentage of value
let percentile_99th = time_spent.len() * 99 / 100;
// We are only interested by the slowest value of the 99th fastest results
let time_spent = time_spent.get(percentile_99th);
let properties = json!({
"user-agent": user_agents,
"requests": {
"99th_response_time": time_spent.map(|t| format!("{:.2}", t)),
"total_succeeded": total_succeeded,
"total_failed": total_received.saturating_sub(total_succeeded), // just to be sure we never panics
"total_received": total_received,
},
"filter": {
"with_geoRadius": filter_with_geo_radius,
"with_geoBoundingBox": filter_with_geo_bounding_box,
"avg_criteria_number": format!("{:.2}", filter_sum_of_criteria_terms as f64 / filter_total_number_of_criteria as f64),
"most_used_syntax": used_syntax.iter().max_by_key(|(_, v)| *v).map(|(k, _)| json!(k)).unwrap_or_else(|| json!(null)),
},
"vector": {
"retrieve_vectors": retrieve_vectors,
},
"hybrid": {
"embedder": embedder,
},
"pagination": {
"max_limit": max_limit,
"max_offset": max_offset,
},
"formatting": {
"max_attributes_to_retrieve": max_attributes_to_retrieve,
},
"scoring": {
"show_ranking_score": show_ranking_score,
"show_ranking_score_details": show_ranking_score_details,
"ranking_score_threshold": ranking_score_threshold,
},
});
Some(Track {
timestamp,
user: user.clone(),
event: event_name.to_string(),
properties,
..Default::default()
})
}
}
}