2153 lines
67 KiB
Rust
2153 lines
67 KiB
Rust
//! PromQL query engine
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//!
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//! Implements a subset of PromQL for querying time-series data.
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//! Provides instant and range query execution with basic aggregations.
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use axum::{
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extract::{Path, Query, State},
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http::StatusCode,
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response::{IntoResponse, Json},
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routing::get,
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Router,
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};
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use nightlight_types::{Error, Label, Result, Sample, SeriesId, TimeSeries};
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use parking_lot::Mutex;
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use promql_parser::{
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label::{MatchOp, Matchers},
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parser::{
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AggregateExpr, BinModifier, BinaryExpr, Call, Expr, LabelModifier, MatrixSelector,
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NumberLiteral, UnaryExpr, VectorMatchCardinality, VectorSelector,
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},
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};
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use serde::{Deserialize, Serialize};
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use std::collections::{BTreeMap, HashMap, VecDeque};
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use std::sync::atomic::{AtomicU64, Ordering};
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use std::sync::Arc;
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use std::time::Instant;
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use tokio::sync::RwLock;
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#[cfg(test)]
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use tracing::info;
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use tracing::{debug, error};
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const QUERY_DURATION_HISTORY_LIMIT: usize = 512;
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/// Query service state
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#[derive(Clone)]
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pub struct QueryService {
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// Reference to queryable storage (shared with ingestion)
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storage: Arc<RwLock<QueryableStorage>>,
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metrics: Arc<QueryMetrics>,
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}
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/// In-memory queryable storage (reads from ingestion buffer)
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#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
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pub struct QueryableStorage {
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// Series metadata indexed by SeriesId
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pub series: HashMap<SeriesId, TimeSeries>,
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// Inverted index: label name -> label value -> [SeriesId]
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pub label_index: HashMap<String, HashMap<String, Vec<SeriesId>>>,
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}
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#[derive(Debug)]
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pub struct QueryMetrics {
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queries_total: AtomicU64,
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queries_failed: AtomicU64,
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queries_active: AtomicU64,
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durations_ms: Mutex<VecDeque<u64>>,
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}
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#[derive(Debug, Clone, Copy, Default)]
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pub struct QueryMetricsSnapshot {
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pub queries_total: u64,
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pub queries_failed: u64,
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pub queries_active: u64,
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pub query_duration_p50: f64,
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pub query_duration_p95: f64,
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pub query_duration_p99: f64,
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}
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#[derive(Debug, Clone)]
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enum EvalValue {
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Vector(Vec<TimeSeries>),
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Scalar(f64),
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}
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impl QueryService {
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pub fn new() -> Self {
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Self {
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storage: Arc::new(RwLock::new(QueryableStorage {
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series: HashMap::new(),
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label_index: HashMap::new(),
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})),
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metrics: Arc::new(QueryMetrics::new()),
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}
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}
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/// Create QueryService from existing shared storage
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pub fn from_storage(storage: Arc<RwLock<QueryableStorage>>) -> Self {
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Self {
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storage,
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metrics: Arc::new(QueryMetrics::new()),
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}
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}
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/// Create QueryService and load persistent state from disk if it exists
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#[cfg(test)]
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pub fn new_with_persistence(data_path: &std::path::Path) -> Result<Self> {
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let storage = QueryableStorage::load_from_file(data_path)?;
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info!(
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"Loaded {} series from persistent storage",
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storage.series.len()
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);
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Ok(Self {
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storage: Arc::new(RwLock::new(storage)),
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metrics: Arc::new(QueryMetrics::new()),
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})
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}
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/// Save current storage state to disk
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#[cfg(test)]
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pub async fn save_to_disk(&self, data_path: &std::path::Path) -> Result<()> {
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let storage = self.storage.read().await;
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storage.save_to_file(data_path)?;
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debug!(
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"Saved {} series to persistent storage",
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storage.series.len()
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);
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Ok(())
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}
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/// Create Axum router for query endpoints
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pub fn router(self) -> Router {
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Router::new()
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.route("/api/v1/query", get(handle_instant_query))
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.route("/api/v1/query_range", get(handle_range_query))
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.route("/api/v1/label/:label_name/values", get(handle_label_values))
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.route("/api/v1/series", get(handle_series))
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.with_state(self)
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}
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pub fn metrics(&self) -> Arc<QueryMetrics> {
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Arc::clone(&self.metrics)
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}
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/// Execute an instant query at a specific timestamp
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pub async fn execute_instant_query(&self, query: &str, time: i64) -> Result<QueryResult> {
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debug!("Executing instant query: {} at time {}", query, time);
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let started = self.metrics.begin_query();
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// Parse PromQL expression
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let expr = promql_parser::parser::parse(query)
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.map_err(|e| Error::Query(format!("Parse error: {:?}", e)));
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let expr = match expr {
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Ok(expr) => expr,
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Err(error) => {
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self.metrics.finish_query(started, false);
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return Err(error);
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}
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};
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// Execute the expression
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let storage = self.storage.read().await;
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let result = self.evaluate_value(&expr, time, time, 0, &storage).await;
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let success = result.is_ok();
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self.metrics.finish_query(started, success);
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let result = result?;
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Ok(instant_query_from_eval_value(result, time))
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}
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/// Execute a range query over a time range with step
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pub async fn execute_range_query(
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&self,
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query: &str,
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start: i64,
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end: i64,
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step: i64,
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) -> Result<RangeQueryResult> {
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debug!(
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"Executing range query: {} from {} to {} step {}",
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query, start, end, step
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);
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let started = self.metrics.begin_query();
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if step <= 0 {
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self.metrics.finish_query(started, false);
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return Err(Error::InvalidTimeRange(
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"range query step must be greater than zero".to_string(),
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));
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}
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if end < start {
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self.metrics.finish_query(started, false);
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return Err(Error::InvalidTimeRange(
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"range query end must be greater than or equal to start".to_string(),
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));
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}
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// Parse PromQL expression
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let expr = promql_parser::parser::parse(query)
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.map_err(|e| Error::Query(format!("Parse error: {:?}", e)));
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let expr = match expr {
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Ok(expr) => expr,
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Err(error) => {
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self.metrics.finish_query(started, false);
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return Err(error);
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}
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};
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let storage = self.storage.read().await;
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let mut results: HashMap<String, RangeResult> = HashMap::new();
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// Evaluate at each step
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let mut current_time = start;
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while current_time <= end {
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let step_result = self
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.evaluate_value(&expr, current_time, end, step, &storage)
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.await;
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let step_result = match step_result {
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Ok(step_result) => step_result,
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Err(error) => {
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self.metrics.finish_query(started, false);
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return Err(error);
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}
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};
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append_range_step_result(&mut results, step_result, current_time);
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current_time += step;
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}
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let result = RangeQueryResult {
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result_type: "matrix".to_string(),
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result: results.into_values().collect(),
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};
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self.metrics.finish_query(started, true);
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Ok(result)
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}
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/// Evaluate a PromQL expression (recursive with boxing for async)
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fn evaluate_value<'a>(
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&'a self,
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expr: &'a Expr,
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time: i64,
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end_time: i64,
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step: i64,
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storage: &'a QueryableStorage,
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) -> std::pin::Pin<Box<dyn std::future::Future<Output = Result<EvalValue>> + Send + 'a>> {
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Box::pin(async move {
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match expr {
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Expr::VectorSelector(selector) => {
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// Vector selector: metric_name{label="value"}
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self.evaluate_vector_selector(selector, time, storage)
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.map(EvalValue::Vector)
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}
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Expr::MatrixSelector(selector) => {
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// Range selector: metric_name[5m]
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self.evaluate_matrix_selector(selector, time, storage)
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.map(EvalValue::Vector)
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}
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Expr::Aggregate(agg) => {
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// Aggregation: sum(metric), avg(metric), etc.
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self.evaluate_aggregation(agg, time, end_time, step, storage)
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.await
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.map(EvalValue::Vector)
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}
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Expr::Call(call) => {
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// Function call: rate(metric[5m]), etc.
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self.evaluate_function(call, time, end_time, step, storage)
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.await
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}
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Expr::Binary(binary) => {
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// Binary operation: metric1 + metric2
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self.evaluate_binary(binary, time, end_time, step, storage)
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.await
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}
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Expr::NumberLiteral(NumberLiteral { val }) => Ok(EvalValue::Scalar(*val)),
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Expr::Paren(paren) => {
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self.evaluate_value(&paren.expr, time, end_time, step, storage)
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.await
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}
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Expr::Unary(unary) => {
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self.evaluate_unary(unary, time, end_time, step, storage)
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.await
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}
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_ => Err(Error::Query(format!(
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"Unsupported expression type: {:?}",
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expr
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))),
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}
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})
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}
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/// Evaluate vector selector
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fn evaluate_vector_selector(
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&self,
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selector: &VectorSelector,
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time: i64,
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storage: &QueryableStorage,
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) -> Result<Vec<TimeSeries>> {
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// Find all series matching the label matchers
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let matching_series: Vec<TimeSeries> = storage
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.series
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.values()
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.filter(|ts| self.matches_selector(ts, selector))
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.cloned()
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.map(|mut ts| {
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// Filter to get sample closest to query time
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ts.samples.retain(|s| s.timestamp <= time);
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// Keep only the most recent sample
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if let Some(last) = ts.samples.last().cloned() {
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ts.samples = vec![last];
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}
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ts
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})
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.filter(|ts| !ts.samples.is_empty())
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.collect();
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Ok(matching_series)
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}
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/// Evaluate matrix selector (range selector)
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fn evaluate_matrix_selector(
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&self,
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selector: &MatrixSelector,
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time: i64,
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storage: &QueryableStorage,
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) -> Result<Vec<TimeSeries>> {
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// Get the time range in milliseconds
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let range_ms = selector.range.as_millis() as i64;
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let start_time = time - range_ms;
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// Evaluate underlying vector selector
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let mut series = storage
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.series
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.values()
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.filter(|ts| self.matches_selector(ts, &selector.vs))
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.cloned()
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.collect::<Vec<_>>();
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// Filter samples to the time range
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for ts in &mut series {
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ts.samples
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.retain(|s| s.timestamp >= start_time && s.timestamp <= time);
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}
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// Remove empty series
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series.retain(|ts| !ts.samples.is_empty());
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Ok(series)
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}
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/// Evaluate aggregation (sum, avg, min, max, count)
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async fn evaluate_aggregation(
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&self,
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agg: &AggregateExpr,
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time: i64,
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end_time: i64,
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step: i64,
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storage: &QueryableStorage,
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) -> Result<Vec<TimeSeries>> {
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let input_series = expect_vector(
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self.evaluate_value(&agg.expr, time, end_time, step, storage)
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.await?,
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"aggregation input must be a vector",
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)?;
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if input_series.is_empty() {
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return Ok(vec![]);
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}
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let mut groups: BTreeMap<String, (Vec<Label>, Vec<f64>)> = BTreeMap::new();
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for series in input_series {
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let Some(sample) = series.samples.last() else {
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continue;
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};
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let labels = aggregation_output_labels(&series.labels, agg.modifier.as_ref());
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let key = labels_key(&labels);
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groups
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.entry(key)
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.or_insert_with(|| (labels, Vec::new()))
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.1
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.push(sample.value);
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}
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let op = agg.op.to_string().to_lowercase();
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let mut results = Vec::new();
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for (labels, values) in groups.into_values() {
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if values.is_empty() {
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continue;
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}
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let aggregated_value = aggregate_values(&op, &values)?;
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results.push(TimeSeries {
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id: SeriesId(results.len() as u64),
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labels,
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samples: vec![Sample::new(time, aggregated_value)],
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});
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}
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Ok(results)
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}
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/// Evaluate function call (rate, irate, increase, histogram_quantile)
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async fn evaluate_function(
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&self,
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call: &Call,
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time: i64,
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end_time: i64,
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step: i64,
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storage: &QueryableStorage,
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) -> Result<EvalValue> {
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let func_name = &call.func.name;
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match func_name.to_string().as_str() {
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"rate" => self
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.evaluate_rate(call, time, end_time, step, storage)
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.await
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.map(EvalValue::Vector),
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"irate" => self
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.evaluate_irate(call, time, end_time, step, storage)
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.await
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.map(EvalValue::Vector),
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"increase" => self
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.evaluate_increase(call, time, end_time, step, storage)
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.await
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.map(EvalValue::Vector),
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"histogram_quantile" => self
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.evaluate_histogram_quantile(call, time, end_time, step, storage)
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.await
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.map(EvalValue::Vector),
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_ => Err(Error::Query(format!("Unsupported function: {}", func_name))),
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}
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}
|
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|
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/// Evaluate rate() function - calculates per-second average rate of increase
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async fn evaluate_rate(
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&self,
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call: &Call,
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time: i64,
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end_time: i64,
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step: i64,
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storage: &QueryableStorage,
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) -> Result<Vec<TimeSeries>> {
|
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// rate() expects a range vector (MatrixSelector) as argument
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if call.args.args.is_empty() {
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return Err(Error::Query(
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"rate() requires a range vector argument".into(),
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));
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}
|
|
|
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let arg = &call.args.args[0];
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let series_list = expect_vector(
|
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self.evaluate_value(arg, time, end_time, step, storage)
|
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.await?,
|
|
"rate() requires a range vector argument",
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)?;
|
|
|
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// Apply rate calculation to each series
|
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let mut result = Vec::new();
|
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for series in series_list {
|
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if series.samples.len() < 2 {
|
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continue; // Need at least 2 samples for rate calculation
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}
|
|
|
|
// Get first and last samples
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let first = &series.samples[0];
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let last = &series.samples[series.samples.len() - 1];
|
|
|
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// Calculate time range in seconds
|
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let duration_seconds = (last.timestamp - first.timestamp) as f64 / 1000.0;
|
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if duration_seconds <= 0.0 {
|
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continue;
|
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}
|
|
|
|
// Calculate rate (per-second average)
|
|
// For counter metrics, we should handle resets, but simplified here
|
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let value_diff = last.value - first.value;
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let rate = value_diff / duration_seconds;
|
|
|
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// Create result series with single sample at query time
|
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result.push(TimeSeries {
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id: series.id,
|
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labels: series.labels.clone(),
|
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samples: vec![Sample {
|
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timestamp: time,
|
|
value: rate.max(0.0), // Rates can't be negative for counters
|
|
}],
|
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});
|
|
}
|
|
|
|
Ok(result)
|
|
}
|
|
|
|
/// Evaluate irate() function - calculates instant rate using last two samples
|
|
async fn evaluate_irate(
|
|
&self,
|
|
call: &Call,
|
|
time: i64,
|
|
end_time: i64,
|
|
step: i64,
|
|
storage: &QueryableStorage,
|
|
) -> Result<Vec<TimeSeries>> {
|
|
// irate() expects a range vector (MatrixSelector) as argument
|
|
if call.args.args.is_empty() {
|
|
return Err(Error::Query(
|
|
"irate() requires a range vector argument".into(),
|
|
));
|
|
}
|
|
|
|
let arg = &call.args.args[0];
|
|
let series_list = expect_vector(
|
|
self.evaluate_value(arg, time, end_time, step, storage)
|
|
.await?,
|
|
"irate() requires a range vector argument",
|
|
)?;
|
|
|
|
// Apply irate calculation to each series
|
|
let mut result = Vec::new();
|
|
for series in series_list {
|
|
if series.samples.len() < 2 {
|
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continue; // Need at least 2 samples for irate calculation
|
|
}
|
|
|
|
// Get last two samples
|
|
let second_last = &series.samples[series.samples.len() - 2];
|
|
let last = &series.samples[series.samples.len() - 1];
|
|
|
|
// Calculate time difference in seconds
|
|
let duration_seconds = (last.timestamp - second_last.timestamp) as f64 / 1000.0;
|
|
if duration_seconds <= 0.0 {
|
|
continue;
|
|
}
|
|
|
|
// Calculate instant rate
|
|
let value_diff = last.value - second_last.value;
|
|
let rate = value_diff / duration_seconds;
|
|
|
|
// Create result series with single sample at query time
|
|
result.push(TimeSeries {
|
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id: series.id,
|
|
labels: series.labels.clone(),
|
|
samples: vec![Sample {
|
|
timestamp: time,
|
|
value: rate.max(0.0), // Rates can't be negative for counters
|
|
}],
|
|
});
|
|
}
|
|
|
|
Ok(result)
|
|
}
|
|
|
|
/// Evaluate increase() function - calculates total increase over time range
|
|
async fn evaluate_increase(
|
|
&self,
|
|
call: &Call,
|
|
time: i64,
|
|
end_time: i64,
|
|
step: i64,
|
|
storage: &QueryableStorage,
|
|
) -> Result<Vec<TimeSeries>> {
|
|
// increase() expects a range vector (MatrixSelector) as argument
|
|
if call.args.args.is_empty() {
|
|
return Err(Error::Query(
|
|
"increase() requires a range vector argument".into(),
|
|
));
|
|
}
|
|
|
|
let arg = &call.args.args[0];
|
|
let series_list = expect_vector(
|
|
self.evaluate_value(arg, time, end_time, step, storage)
|
|
.await?,
|
|
"increase() requires a range vector argument",
|
|
)?;
|
|
|
|
// Apply increase calculation to each series
|
|
let mut result = Vec::new();
|
|
for series in series_list {
|
|
if series.samples.len() < 2 {
|
|
continue; // Need at least 2 samples for increase calculation
|
|
}
|
|
|
|
// Get first and last samples
|
|
let first = &series.samples[0];
|
|
let last = &series.samples[series.samples.len() - 1];
|
|
|
|
// Calculate total increase
|
|
// For counter metrics, we should handle resets, but simplified here
|
|
let increase = last.value - first.value;
|
|
|
|
// Create result series with single sample at query time
|
|
result.push(TimeSeries {
|
|
id: series.id,
|
|
labels: series.labels.clone(),
|
|
samples: vec![Sample {
|
|
timestamp: time,
|
|
value: increase.max(0.0), // Increase can't be negative for counters
|
|
}],
|
|
});
|
|
}
|
|
|
|
Ok(result)
|
|
}
|
|
|
|
/// Evaluate histogram_quantile() from Prometheus-style cumulative buckets.
|
|
async fn evaluate_histogram_quantile(
|
|
&self,
|
|
call: &Call,
|
|
time: i64,
|
|
end_time: i64,
|
|
step: i64,
|
|
storage: &QueryableStorage,
|
|
) -> Result<Vec<TimeSeries>> {
|
|
if call.args.args.len() != 2 {
|
|
return Err(Error::Query(
|
|
"histogram_quantile() requires a scalar quantile and a bucket vector".into(),
|
|
));
|
|
}
|
|
|
|
let quantile = expect_scalar(
|
|
self.evaluate_value(&call.args.args[0], time, end_time, step, storage)
|
|
.await?,
|
|
"histogram_quantile() requires a scalar quantile",
|
|
)?;
|
|
if !(0.0..=1.0).contains(&quantile) {
|
|
return Err(Error::Query(format!(
|
|
"histogram_quantile() quantile must be within [0, 1], got {quantile}"
|
|
)));
|
|
}
|
|
|
|
let buckets = expect_vector(
|
|
self.evaluate_value(&call.args.args[1], time, end_time, step, storage)
|
|
.await?,
|
|
"histogram_quantile() requires a bucket vector",
|
|
)?;
|
|
|
|
let mut grouped: BTreeMap<String, (Vec<Label>, Vec<(f64, f64)>)> = BTreeMap::new();
|
|
for series in buckets {
|
|
let Some(sample) = series.samples.last() else {
|
|
continue;
|
|
};
|
|
let Some(le) = series.get_label("le") else {
|
|
continue;
|
|
};
|
|
let upper_bound = parse_histogram_bound(le)?;
|
|
let labels = histogram_output_labels(&series.labels);
|
|
let key = labels_key(&labels);
|
|
grouped
|
|
.entry(key)
|
|
.or_insert_with(|| (labels, Vec::new()))
|
|
.1
|
|
.push((upper_bound, sample.value));
|
|
}
|
|
|
|
let mut results = Vec::new();
|
|
for (labels, mut buckets) in grouped.into_values() {
|
|
buckets.sort_by(|(lhs, _), (rhs, _)| lhs.total_cmp(rhs));
|
|
let value = histogram_quantile_value(quantile, &buckets)?;
|
|
results.push(TimeSeries {
|
|
id: SeriesId(results.len() as u64),
|
|
labels,
|
|
samples: vec![Sample::new(time, value)],
|
|
});
|
|
}
|
|
|
|
Ok(results)
|
|
}
|
|
|
|
/// Evaluate binary operations for scalar/vector arithmetic and one-to-one vector matching.
|
|
async fn evaluate_binary(
|
|
&self,
|
|
binary: &BinaryExpr,
|
|
time: i64,
|
|
end_time: i64,
|
|
step: i64,
|
|
storage: &QueryableStorage,
|
|
) -> Result<EvalValue> {
|
|
if let Some(modifier) = &binary.modifier {
|
|
if !matches!(modifier.card, VectorMatchCardinality::OneToOne) {
|
|
return Err(Error::Query(
|
|
"many-to-one and many-to-many vector matching are not supported yet".into(),
|
|
));
|
|
}
|
|
}
|
|
|
|
let lhs = self
|
|
.evaluate_value(&binary.lhs, time, end_time, step, storage)
|
|
.await?;
|
|
let rhs = self
|
|
.evaluate_value(&binary.rhs, time, end_time, step, storage)
|
|
.await?;
|
|
|
|
match (lhs, rhs) {
|
|
(EvalValue::Scalar(lhs), EvalValue::Scalar(rhs)) => {
|
|
evaluate_scalar_binary(binary, lhs, rhs).map(EvalValue::Scalar)
|
|
}
|
|
(EvalValue::Vector(lhs), EvalValue::Scalar(rhs)) => {
|
|
evaluate_vector_scalar_binary(binary, lhs, rhs, true).map(EvalValue::Vector)
|
|
}
|
|
(EvalValue::Scalar(lhs), EvalValue::Vector(rhs)) => {
|
|
evaluate_vector_scalar_binary(binary, rhs, lhs, false).map(EvalValue::Vector)
|
|
}
|
|
(EvalValue::Vector(lhs), EvalValue::Vector(rhs)) => {
|
|
evaluate_vector_vector_binary(binary, lhs, rhs).map(EvalValue::Vector)
|
|
}
|
|
}
|
|
}
|
|
|
|
async fn evaluate_unary(
|
|
&self,
|
|
unary: &UnaryExpr,
|
|
time: i64,
|
|
end_time: i64,
|
|
step: i64,
|
|
storage: &QueryableStorage,
|
|
) -> Result<EvalValue> {
|
|
match self
|
|
.evaluate_value(&unary.expr, time, end_time, step, storage)
|
|
.await?
|
|
{
|
|
EvalValue::Scalar(value) => Ok(EvalValue::Scalar(-value)),
|
|
EvalValue::Vector(mut series) => {
|
|
for entry in &mut series {
|
|
for sample in &mut entry.samples {
|
|
sample.value = -sample.value;
|
|
}
|
|
}
|
|
Ok(EvalValue::Vector(series))
|
|
}
|
|
}
|
|
}
|
|
|
|
fn matches_selector(&self, ts: &TimeSeries, selector: &VectorSelector) -> bool {
|
|
if let Some(name) = selector.name.as_deref() {
|
|
if ts.name() != Some(name) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
self.matches_matchers(ts, &selector.matchers)
|
|
}
|
|
|
|
fn matches_matchers(&self, ts: &TimeSeries, matchers: &Matchers) -> bool {
|
|
if !matchers
|
|
.matchers
|
|
.iter()
|
|
.all(|matcher| self.matcher_matches(ts, matcher))
|
|
{
|
|
return false;
|
|
}
|
|
|
|
if matchers.or_matchers.is_empty() {
|
|
return true;
|
|
}
|
|
|
|
matchers.or_matchers.iter().any(|group| {
|
|
group
|
|
.iter()
|
|
.all(|matcher| self.matcher_matches(ts, matcher))
|
|
})
|
|
}
|
|
|
|
fn matcher_matches(&self, ts: &TimeSeries, matcher: &promql_parser::label::Matcher) -> bool {
|
|
let label_value = ts.get_label(&matcher.name);
|
|
|
|
match &matcher.op {
|
|
MatchOp::Equal => label_value == Some(matcher.value.as_str()),
|
|
MatchOp::NotEqual => match label_value {
|
|
Some(value) => value != matcher.value,
|
|
None => true,
|
|
},
|
|
MatchOp::Re(regex) => label_value.is_some_and(|value| regex.is_match(value)),
|
|
MatchOp::NotRe(regex) => match label_value {
|
|
Some(value) => !regex.is_match(value),
|
|
None => true,
|
|
},
|
|
}
|
|
}
|
|
|
|
pub async fn series_metadata(
|
|
&self,
|
|
matchers: &[String],
|
|
start: Option<i64>,
|
|
end: Option<i64>,
|
|
) -> Result<Vec<HashMap<String, String>>> {
|
|
let started = self.metrics.begin_query();
|
|
let storage = self.storage.read().await;
|
|
let series = self.matching_series(&storage, matchers, start, end);
|
|
let result = Ok(series
|
|
.into_iter()
|
|
.map(|ts| {
|
|
ts.labels
|
|
.iter()
|
|
.map(|label| (label.name.clone(), label.value.clone()))
|
|
.collect()
|
|
})
|
|
.collect());
|
|
self.metrics.finish_query(started, true);
|
|
result
|
|
}
|
|
|
|
pub async fn label_values_for_matchers(
|
|
&self,
|
|
label_name: &str,
|
|
matchers: &[String],
|
|
start: Option<i64>,
|
|
end: Option<i64>,
|
|
) -> Result<Vec<String>> {
|
|
let started = self.metrics.begin_query();
|
|
let storage = self.storage.read().await;
|
|
let mut values: Vec<String> = self
|
|
.matching_series(&storage, matchers, start, end)
|
|
.into_iter()
|
|
.filter_map(|series| series.get_label(label_name).map(str::to_string))
|
|
.collect();
|
|
values.sort();
|
|
values.dedup();
|
|
self.metrics.finish_query(started, true);
|
|
Ok(values)
|
|
}
|
|
|
|
fn matching_series(
|
|
&self,
|
|
storage: &QueryableStorage,
|
|
matchers: &[String],
|
|
start: Option<i64>,
|
|
end: Option<i64>,
|
|
) -> Vec<TimeSeries> {
|
|
let parsed_matchers = parse_label_matchers(matchers);
|
|
storage
|
|
.series
|
|
.values()
|
|
.filter(|series| series_matches(series, &parsed_matchers))
|
|
.filter(|series| series_in_time_range(series, start, end))
|
|
.cloned()
|
|
.collect()
|
|
}
|
|
}
|
|
|
|
impl QueryMetrics {
|
|
fn new() -> Self {
|
|
Self {
|
|
queries_total: AtomicU64::new(0),
|
|
queries_failed: AtomicU64::new(0),
|
|
queries_active: AtomicU64::new(0),
|
|
durations_ms: Mutex::new(VecDeque::with_capacity(QUERY_DURATION_HISTORY_LIMIT)),
|
|
}
|
|
}
|
|
|
|
fn begin_query(&self) -> Instant {
|
|
self.queries_total.fetch_add(1, Ordering::Relaxed);
|
|
self.queries_active.fetch_add(1, Ordering::Relaxed);
|
|
Instant::now()
|
|
}
|
|
|
|
fn finish_query(&self, started: Instant, success: bool) {
|
|
if !success {
|
|
self.queries_failed.fetch_add(1, Ordering::Relaxed);
|
|
}
|
|
self.queries_active.fetch_sub(1, Ordering::Relaxed);
|
|
|
|
let elapsed_ms = started.elapsed().as_millis() as u64;
|
|
let mut durations = self.durations_ms.lock();
|
|
if durations.len() >= QUERY_DURATION_HISTORY_LIMIT {
|
|
durations.pop_front();
|
|
}
|
|
durations.push_back(elapsed_ms);
|
|
}
|
|
|
|
pub fn snapshot(&self) -> QueryMetricsSnapshot {
|
|
let mut sorted_durations: Vec<u64> = self.durations_ms.lock().iter().copied().collect();
|
|
sorted_durations.sort_unstable();
|
|
|
|
QueryMetricsSnapshot {
|
|
queries_total: self.queries_total.load(Ordering::Relaxed),
|
|
queries_failed: self.queries_failed.load(Ordering::Relaxed),
|
|
queries_active: self.queries_active.load(Ordering::Relaxed),
|
|
query_duration_p50: percentile(&sorted_durations, 0.50),
|
|
query_duration_p95: percentile(&sorted_durations, 0.95),
|
|
query_duration_p99: percentile(&sorted_durations, 0.99),
|
|
}
|
|
}
|
|
}
|
|
|
|
impl QueryableStorage {
|
|
/// Add or update a time series in storage
|
|
pub fn upsert_series(&mut self, series: TimeSeries) {
|
|
// Update label index
|
|
for label in &series.labels {
|
|
let series_ids = self
|
|
.label_index
|
|
.entry(label.name.clone())
|
|
.or_default()
|
|
.entry(label.value.clone())
|
|
.or_default();
|
|
if !series_ids.contains(&series.id) {
|
|
series_ids.push(series.id);
|
|
}
|
|
}
|
|
|
|
// Upsert series
|
|
self.series
|
|
.entry(series.id)
|
|
.and_modify(|existing| {
|
|
// Merge samples (append new samples)
|
|
existing.samples.extend(series.samples.clone());
|
|
// Sort by timestamp
|
|
existing.samples.sort_by_key(|s| s.timestamp);
|
|
// Deduplicate
|
|
existing.samples.dedup_by_key(|s| s.timestamp);
|
|
})
|
|
.or_insert(series);
|
|
}
|
|
|
|
/// Get label values for a specific label name
|
|
#[cfg(test)]
|
|
pub fn label_values(&self, label_name: &str) -> Vec<String> {
|
|
let mut values: Vec<String> = self
|
|
.label_index
|
|
.get(label_name)
|
|
.map(|values| values.keys().cloned().collect())
|
|
.unwrap_or_default();
|
|
values.sort();
|
|
values
|
|
}
|
|
|
|
pub fn rebuild_index(&mut self) {
|
|
self.label_index.clear();
|
|
let series: Vec<TimeSeries> = self.series.values().cloned().collect();
|
|
for series in series {
|
|
for label in &series.labels {
|
|
self.label_index
|
|
.entry(label.name.clone())
|
|
.or_default()
|
|
.entry(label.value.clone())
|
|
.or_default()
|
|
.push(series.id);
|
|
}
|
|
}
|
|
}
|
|
|
|
pub fn prune_before(&mut self, cutoff: i64) -> usize {
|
|
let mut removed_samples = 0usize;
|
|
self.series.retain(|_, series| {
|
|
let before = series.samples.len();
|
|
series.samples.retain(|sample| sample.timestamp >= cutoff);
|
|
removed_samples += before.saturating_sub(series.samples.len());
|
|
!series.samples.is_empty()
|
|
});
|
|
self.rebuild_index();
|
|
removed_samples
|
|
}
|
|
}
|
|
|
|
fn expect_vector(value: EvalValue, message: &str) -> Result<Vec<TimeSeries>> {
|
|
match value {
|
|
EvalValue::Vector(series) => Ok(series),
|
|
EvalValue::Scalar(_) => Err(Error::Query(message.to_string())),
|
|
}
|
|
}
|
|
|
|
fn expect_scalar(value: EvalValue, message: &str) -> Result<f64> {
|
|
match value {
|
|
EvalValue::Scalar(value) => Ok(value),
|
|
EvalValue::Vector(_) => Err(Error::Query(message.to_string())),
|
|
}
|
|
}
|
|
|
|
fn instant_query_from_eval_value(value: EvalValue, time: i64) -> QueryResult {
|
|
match value {
|
|
EvalValue::Scalar(value) => QueryResult {
|
|
result_type: "scalar".to_string(),
|
|
result: vec![InstantQueryResult {
|
|
metric: HashMap::new(),
|
|
value: Some((time, value)),
|
|
}],
|
|
},
|
|
EvalValue::Vector(series) => QueryResult {
|
|
result_type: "vector".to_string(),
|
|
result: series
|
|
.into_iter()
|
|
.map(|ts| InstantQueryResult {
|
|
metric: labels_to_map(&ts.labels),
|
|
value: ts
|
|
.samples
|
|
.last()
|
|
.map(|sample| (sample.timestamp, sample.value)),
|
|
})
|
|
.collect(),
|
|
},
|
|
}
|
|
}
|
|
|
|
fn append_range_step_result(
|
|
results: &mut HashMap<String, RangeResult>,
|
|
step_result: EvalValue,
|
|
time: i64,
|
|
) {
|
|
match step_result {
|
|
EvalValue::Scalar(value) => {
|
|
let entry = results
|
|
.entry("__scalar__".to_string())
|
|
.or_insert_with(|| RangeResult {
|
|
metric: HashMap::new(),
|
|
values: Vec::new(),
|
|
});
|
|
entry.values.push((time, value));
|
|
}
|
|
EvalValue::Vector(series) => {
|
|
for ts in series {
|
|
let key = labels_key(&ts.labels);
|
|
let metric = labels_to_map(&ts.labels);
|
|
let entry = results.entry(key).or_insert_with(|| RangeResult {
|
|
metric,
|
|
values: Vec::new(),
|
|
});
|
|
for sample in ts.samples {
|
|
entry.values.push((sample.timestamp, sample.value));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
fn labels_to_map(labels: &[Label]) -> HashMap<String, String> {
|
|
labels
|
|
.iter()
|
|
.map(|label| (label.name.clone(), label.value.clone()))
|
|
.collect()
|
|
}
|
|
|
|
fn normalize_labels(mut labels: Vec<Label>) -> Vec<Label> {
|
|
labels.sort_by(|lhs, rhs| lhs.name.cmp(&rhs.name).then(lhs.value.cmp(&rhs.value)));
|
|
labels
|
|
}
|
|
|
|
fn labels_key(labels: &[Label]) -> String {
|
|
let mut pairs: Vec<(String, String)> = labels
|
|
.iter()
|
|
.map(|label| (label.name.clone(), label.value.clone()))
|
|
.collect();
|
|
pairs.sort();
|
|
pairs
|
|
.into_iter()
|
|
.map(|(name, value)| format!("{name}={value}"))
|
|
.collect::<Vec<_>>()
|
|
.join(",")
|
|
}
|
|
|
|
fn aggregate_values(op: &str, values: &[f64]) -> Result<f64> {
|
|
match op {
|
|
"sum" => Ok(values.iter().sum()),
|
|
"avg" => Ok(values.iter().sum::<f64>() / values.len() as f64),
|
|
"min" => Ok(values.iter().copied().fold(f64::INFINITY, f64::min)),
|
|
"max" => Ok(values.iter().copied().fold(f64::NEG_INFINITY, f64::max)),
|
|
"count" => Ok(values.len() as f64),
|
|
other => Err(Error::Query(format!("Unsupported aggregation: {other}"))),
|
|
}
|
|
}
|
|
|
|
fn aggregation_output_labels(labels: &[Label], modifier: Option<&LabelModifier>) -> Vec<Label> {
|
|
match modifier {
|
|
None => Vec::new(),
|
|
Some(LabelModifier::Include(group)) => normalize_labels(
|
|
labels
|
|
.iter()
|
|
.filter(|label| group.labels.iter().any(|name| name == &label.name))
|
|
.cloned()
|
|
.collect(),
|
|
),
|
|
Some(LabelModifier::Exclude(group)) => normalize_labels(
|
|
labels
|
|
.iter()
|
|
.filter(|label| label.name != "__name__")
|
|
.filter(|label| !group.labels.iter().any(|name| name == &label.name))
|
|
.cloned()
|
|
.collect(),
|
|
),
|
|
}
|
|
}
|
|
|
|
fn histogram_output_labels(labels: &[Label]) -> Vec<Label> {
|
|
normalize_labels(
|
|
labels
|
|
.iter()
|
|
.filter(|label| label.name != "__name__" && label.name != "le")
|
|
.cloned()
|
|
.collect(),
|
|
)
|
|
}
|
|
|
|
fn parse_histogram_bound(value: &str) -> Result<f64> {
|
|
match value {
|
|
"+Inf" | "Inf" | "+inf" | "inf" => Ok(f64::INFINITY),
|
|
_ => value.parse::<f64>().map_err(|error| {
|
|
Error::Query(format!("invalid histogram bucket bound {value:?}: {error}"))
|
|
}),
|
|
}
|
|
}
|
|
|
|
fn histogram_quantile_value(quantile: f64, buckets: &[(f64, f64)]) -> Result<f64> {
|
|
let Some((_, total_count)) = buckets.last() else {
|
|
return Err(Error::Query(
|
|
"histogram_quantile() requires at least one bucket".into(),
|
|
));
|
|
};
|
|
if *total_count <= 0.0 {
|
|
return Ok(0.0);
|
|
}
|
|
|
|
let target = quantile * total_count;
|
|
let mut lower_bound = 0.0;
|
|
let mut previous_count = 0.0;
|
|
|
|
for (upper_bound, count) in buckets {
|
|
if *count >= target {
|
|
if upper_bound.is_infinite() {
|
|
return Ok(lower_bound);
|
|
}
|
|
|
|
let bucket_count = (*count - previous_count).max(0.0);
|
|
if bucket_count == 0.0 {
|
|
return Ok(*upper_bound);
|
|
}
|
|
|
|
let fraction = ((target - previous_count) / bucket_count).clamp(0.0, 1.0);
|
|
return Ok(lower_bound + (upper_bound - lower_bound) * fraction);
|
|
}
|
|
|
|
lower_bound = *upper_bound;
|
|
previous_count = *count;
|
|
}
|
|
|
|
Ok(lower_bound)
|
|
}
|
|
|
|
fn evaluate_scalar_binary(binary: &BinaryExpr, lhs: f64, rhs: f64) -> Result<f64> {
|
|
let op = binary.op.to_string();
|
|
if is_set_operator(&op) {
|
|
return Err(Error::Query(format!("Unsupported set operator: {op}")));
|
|
}
|
|
|
|
if is_comparison_operator(&op) {
|
|
if !binary.return_bool() {
|
|
return Err(Error::Query(
|
|
"scalar comparisons require the bool modifier".into(),
|
|
));
|
|
}
|
|
|
|
return Ok(if compare_values(&op, lhs, rhs)? {
|
|
1.0
|
|
} else {
|
|
0.0
|
|
});
|
|
}
|
|
|
|
arithmetic_value(&op, lhs, rhs)
|
|
}
|
|
|
|
fn evaluate_vector_scalar_binary(
|
|
binary: &BinaryExpr,
|
|
series_list: Vec<TimeSeries>,
|
|
scalar: f64,
|
|
vector_on_lhs: bool,
|
|
) -> Result<Vec<TimeSeries>> {
|
|
let mut results = Vec::new();
|
|
for mut series in series_list {
|
|
let labels = binary_result_labels(&series.labels, binary.modifier.as_ref());
|
|
let mut samples = Vec::new();
|
|
|
|
for sample in &mut series.samples {
|
|
let (lhs, rhs) = if vector_on_lhs {
|
|
(sample.value, scalar)
|
|
} else {
|
|
(scalar, sample.value)
|
|
};
|
|
let retained = sample.value;
|
|
if let Some(value) = apply_binary_sample(binary, lhs, rhs, retained)? {
|
|
samples.push(Sample::new(sample.timestamp, value));
|
|
}
|
|
}
|
|
|
|
if !samples.is_empty() {
|
|
results.push(TimeSeries {
|
|
id: series.id,
|
|
labels,
|
|
samples,
|
|
});
|
|
}
|
|
}
|
|
|
|
Ok(results)
|
|
}
|
|
|
|
fn evaluate_vector_vector_binary(
|
|
binary: &BinaryExpr,
|
|
lhs_series: Vec<TimeSeries>,
|
|
rhs_series: Vec<TimeSeries>,
|
|
) -> Result<Vec<TimeSeries>> {
|
|
let lhs_index = build_vector_match_index(&lhs_series, binary.modifier.as_ref())?;
|
|
let rhs_index = build_vector_match_index(&rhs_series, binary.modifier.as_ref())?;
|
|
let mut results = Vec::new();
|
|
|
|
for (key, lhs) in lhs_index {
|
|
let Some(rhs) = rhs_index.get(&key) else {
|
|
continue;
|
|
};
|
|
let Some(lhs_sample) = lhs.samples.last() else {
|
|
continue;
|
|
};
|
|
let Some(rhs_sample) = rhs.samples.last() else {
|
|
continue;
|
|
};
|
|
|
|
if let Some(value) =
|
|
apply_binary_sample(binary, lhs_sample.value, rhs_sample.value, lhs_sample.value)?
|
|
{
|
|
results.push(TimeSeries {
|
|
id: lhs.id,
|
|
labels: binary_result_labels(&lhs.labels, binary.modifier.as_ref()),
|
|
samples: vec![Sample::new(
|
|
lhs_sample.timestamp.max(rhs_sample.timestamp),
|
|
value,
|
|
)],
|
|
});
|
|
}
|
|
}
|
|
|
|
Ok(results)
|
|
}
|
|
|
|
fn build_vector_match_index<'a>(
|
|
series_list: &'a [TimeSeries],
|
|
modifier: Option<&BinModifier>,
|
|
) -> Result<HashMap<String, &'a TimeSeries>> {
|
|
let mut index = HashMap::new();
|
|
for series in series_list {
|
|
let key = vector_match_key(series, modifier);
|
|
if index.insert(key.clone(), series).is_some() {
|
|
return Err(Error::Query(format!(
|
|
"duplicate vector match key {key:?}; many-to-one matching is not supported"
|
|
)));
|
|
}
|
|
}
|
|
Ok(index)
|
|
}
|
|
|
|
fn vector_match_key(series: &TimeSeries, modifier: Option<&BinModifier>) -> String {
|
|
labels_key(&binary_result_labels(&series.labels, modifier))
|
|
}
|
|
|
|
fn binary_result_labels(labels: &[Label], modifier: Option<&BinModifier>) -> Vec<Label> {
|
|
let selected = match modifier.and_then(|modifier| modifier.matching.as_ref()) {
|
|
Some(LabelModifier::Include(group)) => labels
|
|
.iter()
|
|
.filter(|label| group.labels.iter().any(|name| name == &label.name))
|
|
.cloned()
|
|
.collect(),
|
|
Some(LabelModifier::Exclude(group)) => labels
|
|
.iter()
|
|
.filter(|label| label.name != "__name__")
|
|
.filter(|label| !group.labels.iter().any(|name| name == &label.name))
|
|
.cloned()
|
|
.collect(),
|
|
None => labels
|
|
.iter()
|
|
.filter(|label| label.name != "__name__")
|
|
.cloned()
|
|
.collect(),
|
|
};
|
|
|
|
normalize_labels(selected)
|
|
}
|
|
|
|
fn apply_binary_sample(
|
|
binary: &BinaryExpr,
|
|
lhs: f64,
|
|
rhs: f64,
|
|
retained: f64,
|
|
) -> Result<Option<f64>> {
|
|
let op = binary.op.to_string();
|
|
if is_set_operator(&op) {
|
|
return Err(Error::Query(format!("Unsupported set operator: {op}")));
|
|
}
|
|
|
|
if is_comparison_operator(&op) {
|
|
let matched = compare_values(&op, lhs, rhs)?;
|
|
if binary.return_bool() {
|
|
return Ok(Some(if matched { 1.0 } else { 0.0 }));
|
|
}
|
|
return Ok(matched.then_some(retained));
|
|
}
|
|
|
|
Ok(Some(arithmetic_value(&op, lhs, rhs)?))
|
|
}
|
|
|
|
fn compare_values(op: &str, lhs: f64, rhs: f64) -> Result<bool> {
|
|
match op {
|
|
"==" => Ok(lhs == rhs),
|
|
"!=" => Ok(lhs != rhs),
|
|
">" => Ok(lhs > rhs),
|
|
">=" => Ok(lhs >= rhs),
|
|
"<" => Ok(lhs < rhs),
|
|
"<=" => Ok(lhs <= rhs),
|
|
other => Err(Error::Query(format!(
|
|
"Unsupported comparison operator: {other}"
|
|
))),
|
|
}
|
|
}
|
|
|
|
fn arithmetic_value(op: &str, lhs: f64, rhs: f64) -> Result<f64> {
|
|
match op {
|
|
"+" => Ok(lhs + rhs),
|
|
"-" => Ok(lhs - rhs),
|
|
"*" => Ok(lhs * rhs),
|
|
"/" => Ok(lhs / rhs),
|
|
"%" => Ok(lhs % rhs),
|
|
"^" => Ok(lhs.powf(rhs)),
|
|
"atan2" => Ok(lhs.atan2(rhs)),
|
|
other => Err(Error::Query(format!(
|
|
"Unsupported arithmetic operator: {other}"
|
|
))),
|
|
}
|
|
}
|
|
|
|
fn is_comparison_operator(op: &str) -> bool {
|
|
matches!(op, "==" | "!=" | ">" | ">=" | "<" | "<=")
|
|
}
|
|
|
|
fn is_set_operator(op: &str) -> bool {
|
|
matches!(op, "and" | "or" | "unless")
|
|
}
|
|
|
|
fn percentile(values: &[u64], quantile: f64) -> f64 {
|
|
if values.is_empty() {
|
|
return 0.0;
|
|
}
|
|
|
|
let index = ((values.len() - 1) as f64 * quantile).round() as usize;
|
|
values[index.min(values.len() - 1)] as f64
|
|
}
|
|
|
|
fn parse_label_matchers(matchers: &[String]) -> Vec<(String, String)> {
|
|
matchers
|
|
.iter()
|
|
.filter_map(|matcher| matcher.split_once('='))
|
|
.map(|(key, value)| {
|
|
(
|
|
key.trim().to_string(),
|
|
value.trim().trim_matches('"').to_string(),
|
|
)
|
|
})
|
|
.collect()
|
|
}
|
|
|
|
fn series_matches(series: &TimeSeries, matchers: &[(String, String)]) -> bool {
|
|
matchers.iter().all(|(key, value)| {
|
|
series
|
|
.labels
|
|
.iter()
|
|
.any(|label| &label.name == key && &label.value == value)
|
|
})
|
|
}
|
|
|
|
fn series_in_time_range(series: &TimeSeries, start: Option<i64>, end: Option<i64>) -> bool {
|
|
let Some((series_start, series_end)) = series.time_range() else {
|
|
return true;
|
|
};
|
|
|
|
if let Some(start) = start {
|
|
if series_end < start {
|
|
return false;
|
|
}
|
|
}
|
|
if let Some(end) = end {
|
|
if series_start > end {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
true
|
|
}
|
|
|
|
/// HTTP handler for instant queries
|
|
#[axum::debug_handler]
|
|
async fn handle_instant_query(
|
|
State(service): State<QueryService>,
|
|
Query(params): Query<InstantQueryParams>,
|
|
) -> (StatusCode, Json<QueryResponse>) {
|
|
let time = params
|
|
.time
|
|
.unwrap_or_else(|| chrono::Utc::now().timestamp_millis());
|
|
|
|
let response = match service.execute_instant_query(¶ms.query, time).await {
|
|
Ok(result) => QueryResponse {
|
|
status: "success".to_string(),
|
|
data: Some(serde_json::to_value(result).unwrap()),
|
|
error: None,
|
|
error_type: None,
|
|
},
|
|
Err(e) => {
|
|
error!("Query failed: {}", e);
|
|
QueryResponse {
|
|
status: "error".to_string(),
|
|
data: None,
|
|
error: Some(e.to_string()),
|
|
error_type: Some("execution".to_string()),
|
|
}
|
|
}
|
|
};
|
|
|
|
(StatusCode::OK, Json(response))
|
|
}
|
|
|
|
/// HTTP handler for range queries
|
|
#[axum::debug_handler]
|
|
async fn handle_range_query(
|
|
State(service): State<QueryService>,
|
|
Query(params): Query<RangeQueryParams>,
|
|
) -> (StatusCode, Json<QueryResponse>) {
|
|
let response = match service
|
|
.execute_range_query(¶ms.query, params.start, params.end, params.step)
|
|
.await
|
|
{
|
|
Ok(result) => QueryResponse {
|
|
status: "success".to_string(),
|
|
data: Some(serde_json::to_value(result).unwrap()),
|
|
error: None,
|
|
error_type: None,
|
|
},
|
|
Err(e) => {
|
|
error!("Range query failed: {}", e);
|
|
QueryResponse {
|
|
status: "error".to_string(),
|
|
data: None,
|
|
error: Some(e.to_string()),
|
|
error_type: Some("execution".to_string()),
|
|
}
|
|
}
|
|
};
|
|
|
|
(StatusCode::OK, Json(response))
|
|
}
|
|
|
|
/// HTTP handler for label values
|
|
async fn handle_label_values(
|
|
State(service): State<QueryService>,
|
|
Path(label_name): Path<String>,
|
|
Query(params): Query<SeriesQueryParams>,
|
|
) -> impl IntoResponse {
|
|
match service
|
|
.label_values_for_matchers(&label_name, ¶ms.matchers, params.start, params.end)
|
|
.await
|
|
{
|
|
Ok(values) => (
|
|
StatusCode::OK,
|
|
Json(LabelValuesResponse {
|
|
status: "success".to_string(),
|
|
data: values,
|
|
}),
|
|
)
|
|
.into_response(),
|
|
Err(error) => (
|
|
StatusCode::BAD_REQUEST,
|
|
Json(serde_json::json!({
|
|
"status": "error",
|
|
"error": error.to_string(),
|
|
})),
|
|
)
|
|
.into_response(),
|
|
}
|
|
}
|
|
|
|
/// HTTP handler for series metadata
|
|
async fn handle_series(
|
|
State(service): State<QueryService>,
|
|
Query(params): Query<SeriesQueryParams>,
|
|
) -> impl IntoResponse {
|
|
match service
|
|
.series_metadata(¶ms.matchers, params.start, params.end)
|
|
.await
|
|
{
|
|
Ok(series) => (
|
|
StatusCode::OK,
|
|
Json(SeriesResponse {
|
|
status: "success".to_string(),
|
|
data: series,
|
|
}),
|
|
)
|
|
.into_response(),
|
|
Err(error) => (
|
|
StatusCode::BAD_REQUEST,
|
|
Json(serde_json::json!({
|
|
"status": "error",
|
|
"error": error.to_string(),
|
|
})),
|
|
)
|
|
.into_response(),
|
|
}
|
|
}
|
|
|
|
// Request/Response Types
|
|
|
|
#[derive(Debug, Deserialize)]
|
|
struct InstantQueryParams {
|
|
query: String,
|
|
#[serde(default)]
|
|
time: Option<i64>,
|
|
}
|
|
|
|
#[derive(Debug, Deserialize)]
|
|
struct RangeQueryParams {
|
|
query: String,
|
|
start: i64,
|
|
end: i64,
|
|
step: i64,
|
|
}
|
|
|
|
#[derive(Debug, Deserialize)]
|
|
struct SeriesQueryParams {
|
|
#[serde(default)]
|
|
#[serde(rename = "match[]")]
|
|
matchers: Vec<String>,
|
|
#[serde(default)]
|
|
start: Option<i64>,
|
|
#[serde(default)]
|
|
end: Option<i64>,
|
|
}
|
|
|
|
#[derive(Debug, Serialize)]
|
|
struct QueryResponse {
|
|
status: String,
|
|
data: Option<serde_json::Value>,
|
|
error: Option<String>,
|
|
error_type: Option<String>,
|
|
}
|
|
|
|
#[derive(Debug, Clone, Serialize)]
|
|
pub struct QueryResult {
|
|
#[serde(rename = "resultType")]
|
|
pub result_type: String,
|
|
pub result: Vec<InstantQueryResult>,
|
|
}
|
|
|
|
#[derive(Debug, Clone, Serialize)]
|
|
pub struct InstantQueryResult {
|
|
pub metric: HashMap<String, String>,
|
|
pub value: Option<(i64, f64)>,
|
|
}
|
|
|
|
#[derive(Debug, Clone, Serialize)]
|
|
pub struct RangeQueryResult {
|
|
#[serde(rename = "resultType")]
|
|
pub result_type: String,
|
|
pub result: Vec<RangeResult>,
|
|
}
|
|
|
|
#[derive(Debug, Clone, Serialize)]
|
|
pub struct RangeResult {
|
|
pub metric: HashMap<String, String>,
|
|
pub values: Vec<(i64, f64)>,
|
|
}
|
|
|
|
#[derive(Debug, Serialize)]
|
|
struct LabelValuesResponse {
|
|
status: String,
|
|
data: Vec<String>,
|
|
}
|
|
|
|
#[derive(Debug, Serialize)]
|
|
struct SeriesResponse {
|
|
status: String,
|
|
data: Vec<HashMap<String, String>>,
|
|
}
|
|
|
|
impl Default for QueryService {
|
|
fn default() -> Self {
|
|
Self::new()
|
|
}
|
|
}
|
|
|
|
impl QueryableStorage {
|
|
/// Save storage state to disk using bincode serialization
|
|
pub fn save_to_file(&self, path: &std::path::Path) -> Result<()> {
|
|
use std::fs::File;
|
|
use std::io::Write;
|
|
|
|
// Serialize to bincode
|
|
let encoded = bincode::serialize(self)
|
|
.map_err(|e| Error::Storage(format!("Serialization failed: {}", e)))?;
|
|
|
|
// Create parent directory if needed
|
|
if let Some(parent) = path.parent() {
|
|
std::fs::create_dir_all(parent)
|
|
.map_err(|e| Error::Storage(format!("Failed to create directory: {}", e)))?;
|
|
}
|
|
|
|
// Write to file atomically (write to temp, then rename)
|
|
let temp_path = path.with_extension("tmp");
|
|
let mut file = File::create(&temp_path)
|
|
.map_err(|e| Error::Storage(format!("Failed to create file: {}", e)))?;
|
|
|
|
file.write_all(&encoded)
|
|
.map_err(|e| Error::Storage(format!("Failed to write file: {}", e)))?;
|
|
|
|
file.sync_all()
|
|
.map_err(|e| Error::Storage(format!("Failed to sync file: {}", e)))?;
|
|
|
|
std::fs::rename(&temp_path, path)
|
|
.map_err(|e| Error::Storage(format!("Failed to rename file: {}", e)))?;
|
|
|
|
Ok(())
|
|
}
|
|
|
|
/// Load storage state from disk using bincode deserialization
|
|
pub fn load_from_file(path: &std::path::Path) -> Result<Self> {
|
|
use std::fs::File;
|
|
use std::io::Read;
|
|
|
|
// Check if file exists
|
|
if !path.exists() {
|
|
return Ok(Self {
|
|
series: HashMap::new(),
|
|
label_index: HashMap::new(),
|
|
});
|
|
}
|
|
|
|
// Read file
|
|
let mut file =
|
|
File::open(path).map_err(|e| Error::Storage(format!("Failed to open file: {}", e)))?;
|
|
|
|
let mut buffer = Vec::new();
|
|
file.read_to_end(&mut buffer)
|
|
.map_err(|e| Error::Storage(format!("Failed to read file: {}", e)))?;
|
|
|
|
// Deserialize from bincode
|
|
let mut storage: Self = bincode::deserialize(&buffer)
|
|
.map_err(|e| Error::Storage(format!("Deserialization failed: {}", e)))?;
|
|
storage.rebuild_index();
|
|
|
|
Ok(storage)
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
use axum::{
|
|
body::{to_bytes, Body},
|
|
http::{Method, Request, StatusCode},
|
|
};
|
|
use tower::ServiceExt;
|
|
|
|
fn test_series(id: u64, labels: &[(&str, &str)], samples: &[(i64, f64)]) -> TimeSeries {
|
|
TimeSeries {
|
|
id: SeriesId(id),
|
|
labels: labels
|
|
.iter()
|
|
.map(|(name, value)| Label::new(*name, *value))
|
|
.collect(),
|
|
samples: samples
|
|
.iter()
|
|
.map(|(timestamp, value)| Sample::new(*timestamp, *value))
|
|
.collect(),
|
|
}
|
|
}
|
|
|
|
async fn seed_series(service: &QueryService, series: Vec<TimeSeries>) {
|
|
let mut storage = service.storage.write().await;
|
|
for entry in series {
|
|
storage.upsert_series(entry);
|
|
}
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_query_service_creation() {
|
|
let service = QueryService::new();
|
|
// Verify service can be created
|
|
assert!(service.storage.read().await.series.is_empty());
|
|
}
|
|
|
|
#[test]
|
|
fn test_simple_selector_parsing() {
|
|
// Test that we can parse a simple PromQL query
|
|
let query = "http_requests_total";
|
|
let result = promql_parser::parser::parse(query);
|
|
assert!(result.is_ok());
|
|
}
|
|
|
|
#[test]
|
|
fn test_label_selector_parsing() {
|
|
let query = "http_requests_total{method=\"GET\"}";
|
|
let result = promql_parser::parser::parse(query);
|
|
assert!(result.is_ok());
|
|
}
|
|
|
|
#[test]
|
|
fn test_aggregation_parsing() {
|
|
let query = "sum(http_requests_total)";
|
|
let result = promql_parser::parser::parse(query);
|
|
assert!(result.is_ok());
|
|
}
|
|
|
|
#[test]
|
|
fn test_rate_function_parsing() {
|
|
let query = "rate(http_requests_total[5m])";
|
|
let result = promql_parser::parser::parse(query);
|
|
assert!(result.is_ok());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_instant_query_empty_storage() {
|
|
let service = QueryService::new();
|
|
let result = service.execute_instant_query("up", 1000).await;
|
|
assert!(result.is_ok());
|
|
let query_result = result.unwrap();
|
|
assert_eq!(query_result.result_type, "vector");
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_range_query_empty_storage() {
|
|
let service = QueryService::new();
|
|
let result = service.execute_range_query("up", 1000, 2000, 100).await;
|
|
assert!(result.is_ok());
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_scalar_instant_query_returns_scalar_result() {
|
|
let service = QueryService::new();
|
|
let result = service.execute_instant_query("1", 1000).await.unwrap();
|
|
assert_eq!(result.result_type, "scalar");
|
|
assert_eq!(result.result.len(), 1);
|
|
assert_eq!(result.result[0].value, Some((1000, 1.0)));
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_label_matchers_support_regex_and_negative_match() {
|
|
let service = QueryService::new();
|
|
seed_series(
|
|
&service,
|
|
vec![
|
|
test_series(
|
|
1,
|
|
&[
|
|
("__name__", "http_requests_total"),
|
|
("method", "GET"),
|
|
("status", "200"),
|
|
],
|
|
&[(1000, 10.0)],
|
|
),
|
|
test_series(
|
|
2,
|
|
&[
|
|
("__name__", "http_requests_total"),
|
|
("method", "POST"),
|
|
("status", "500"),
|
|
],
|
|
&[(1000, 20.0)],
|
|
),
|
|
],
|
|
)
|
|
.await;
|
|
|
|
let result = service
|
|
.execute_instant_query("http_requests_total{method=~\"G.*\",status!=\"500\"}", 1000)
|
|
.await
|
|
.unwrap();
|
|
|
|
assert_eq!(result.result_type, "vector");
|
|
assert_eq!(result.result.len(), 1);
|
|
assert_eq!(
|
|
result.result[0].metric.get("method"),
|
|
Some(&"GET".to_string())
|
|
);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_aggregation_groups_by_labels() {
|
|
let service = QueryService::new();
|
|
seed_series(
|
|
&service,
|
|
vec![
|
|
test_series(
|
|
1,
|
|
&[("__name__", "cpu_usage"), ("job", "api"), ("instance", "a")],
|
|
&[(1000, 2.0)],
|
|
),
|
|
test_series(
|
|
2,
|
|
&[("__name__", "cpu_usage"), ("job", "api"), ("instance", "b")],
|
|
&[(1000, 3.0)],
|
|
),
|
|
],
|
|
)
|
|
.await;
|
|
|
|
let result = service
|
|
.execute_instant_query("sum by (job)(cpu_usage)", 1000)
|
|
.await
|
|
.unwrap();
|
|
|
|
assert_eq!(result.result.len(), 1);
|
|
assert_eq!(result.result[0].metric.get("job"), Some(&"api".to_string()));
|
|
assert_eq!(result.result[0].value, Some((1000, 5.0)));
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_binary_queries_support_vector_scalar_and_vector_vector() {
|
|
let service = QueryService::new();
|
|
seed_series(
|
|
&service,
|
|
vec![
|
|
test_series(
|
|
1,
|
|
&[("__name__", "requests_total"), ("service", "api")],
|
|
&[(1000, 20.0)],
|
|
),
|
|
test_series(
|
|
2,
|
|
&[("__name__", "errors_total"), ("service", "api")],
|
|
&[(1000, 5.0)],
|
|
),
|
|
],
|
|
)
|
|
.await;
|
|
|
|
let vector_scalar = service
|
|
.execute_instant_query("requests_total / 2", 1000)
|
|
.await
|
|
.unwrap();
|
|
assert_eq!(vector_scalar.result.len(), 1);
|
|
assert_eq!(
|
|
vector_scalar.result[0].metric.get("service"),
|
|
Some(&"api".to_string())
|
|
);
|
|
assert_eq!(vector_scalar.result[0].value, Some((1000, 10.0)));
|
|
assert!(!vector_scalar.result[0].metric.contains_key("__name__"));
|
|
|
|
let ratio = service
|
|
.execute_instant_query("errors_total / requests_total", 1000)
|
|
.await
|
|
.unwrap();
|
|
assert_eq!(ratio.result.len(), 1);
|
|
assert_eq!(ratio.result[0].value, Some((1000, 0.25)));
|
|
assert_eq!(ratio.result[0].metric.len(), 1);
|
|
assert_eq!(
|
|
ratio.result[0].metric.get("service"),
|
|
Some(&"api".to_string())
|
|
);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_histogram_quantile_with_grouped_buckets() {
|
|
let service = QueryService::new();
|
|
seed_series(
|
|
&service,
|
|
vec![
|
|
test_series(
|
|
1,
|
|
&[
|
|
("__name__", "request_duration_seconds_bucket"),
|
|
("job", "api"),
|
|
("instance", "a"),
|
|
("le", "0.1"),
|
|
],
|
|
&[(1000, 5.0)],
|
|
),
|
|
test_series(
|
|
2,
|
|
&[
|
|
("__name__", "request_duration_seconds_bucket"),
|
|
("job", "api"),
|
|
("instance", "a"),
|
|
("le", "0.2"),
|
|
],
|
|
&[(1000, 10.0)],
|
|
),
|
|
test_series(
|
|
3,
|
|
&[
|
|
("__name__", "request_duration_seconds_bucket"),
|
|
("job", "api"),
|
|
("instance", "a"),
|
|
("le", "+Inf"),
|
|
],
|
|
&[(1000, 20.0)],
|
|
),
|
|
test_series(
|
|
4,
|
|
&[
|
|
("__name__", "request_duration_seconds_bucket"),
|
|
("job", "api"),
|
|
("instance", "b"),
|
|
("le", "0.1"),
|
|
],
|
|
&[(1000, 5.0)],
|
|
),
|
|
test_series(
|
|
5,
|
|
&[
|
|
("__name__", "request_duration_seconds_bucket"),
|
|
("job", "api"),
|
|
("instance", "b"),
|
|
("le", "0.2"),
|
|
],
|
|
&[(1000, 10.0)],
|
|
),
|
|
test_series(
|
|
6,
|
|
&[
|
|
("__name__", "request_duration_seconds_bucket"),
|
|
("job", "api"),
|
|
("instance", "b"),
|
|
("le", "+Inf"),
|
|
],
|
|
&[(1000, 20.0)],
|
|
),
|
|
],
|
|
)
|
|
.await;
|
|
|
|
let result = service
|
|
.execute_instant_query(
|
|
"histogram_quantile(0.5, sum by (job, le)(request_duration_seconds_bucket{job=\"api\"}))",
|
|
1000,
|
|
)
|
|
.await
|
|
.unwrap();
|
|
|
|
assert_eq!(result.result.len(), 1);
|
|
assert_eq!(result.result[0].metric.get("job"), Some(&"api".to_string()));
|
|
assert!(!result.result[0].metric.contains_key("le"));
|
|
assert_eq!(result.result[0].value, Some((1000, 0.2)));
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_query_range_route_supports_binary_expression() {
|
|
let service = QueryService::new();
|
|
seed_series(
|
|
&service,
|
|
vec![test_series(
|
|
1,
|
|
&[("__name__", "requests_total"), ("service", "api")],
|
|
&[(1000, 10.0), (2000, 20.0)],
|
|
)],
|
|
)
|
|
.await;
|
|
|
|
let app: axum::Router = service.router();
|
|
let request = Request::builder()
|
|
.method(Method::GET)
|
|
.uri("/api/v1/query_range?query=requests_total%20/%202&start=1000&end=2000&step=1000")
|
|
.body(Body::empty())
|
|
.unwrap();
|
|
let response = app.oneshot(request).await.unwrap();
|
|
assert_eq!(response.status(), StatusCode::OK);
|
|
|
|
let body = to_bytes(response.into_body(), 1024 * 1024).await.unwrap();
|
|
let payload: serde_json::Value = serde_json::from_slice(&body).unwrap();
|
|
assert_eq!(payload["status"], "success");
|
|
assert_eq!(payload["data"]["resultType"], "matrix");
|
|
assert_eq!(payload["data"]["result"][0]["metric"]["service"], "api");
|
|
assert_eq!(
|
|
payload["data"]["result"][0]["values"][0],
|
|
serde_json::json!([1000, 5.0])
|
|
);
|
|
assert_eq!(
|
|
payload["data"]["result"][0]["values"][1],
|
|
serde_json::json!([2000, 10.0])
|
|
);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_storage_upsert() {
|
|
let service = QueryService::new();
|
|
let mut storage = service.storage.write().await;
|
|
|
|
let series = TimeSeries {
|
|
id: SeriesId(1),
|
|
labels: vec![
|
|
Label::new("__name__", "test_metric"),
|
|
Label::new("job", "test"),
|
|
],
|
|
samples: vec![Sample::new(1000, 42.0)],
|
|
};
|
|
|
|
storage.upsert_series(series);
|
|
assert_eq!(storage.series.len(), 1);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_label_values() {
|
|
let service = QueryService::new();
|
|
let mut storage = service.storage.write().await;
|
|
|
|
let series = TimeSeries {
|
|
id: SeriesId(1),
|
|
labels: vec![
|
|
Label::new("__name__", "test_metric"),
|
|
Label::new("job", "test_job"),
|
|
],
|
|
samples: vec![],
|
|
};
|
|
|
|
storage.upsert_series(series);
|
|
|
|
let values = storage.label_values("job");
|
|
assert_eq!(values.len(), 1);
|
|
assert!(values.contains(&"test_job".to_string()));
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_label_values_route() {
|
|
let service = QueryService::new();
|
|
{
|
|
let mut storage = service.storage.write().await;
|
|
storage.upsert_series(TimeSeries {
|
|
id: SeriesId(1),
|
|
labels: vec![
|
|
Label::new("__name__", "test_metric"),
|
|
Label::new("job", "test_job"),
|
|
],
|
|
samples: vec![],
|
|
});
|
|
}
|
|
|
|
let app: axum::Router = service.router();
|
|
let request = Request::builder()
|
|
.method(Method::GET)
|
|
.uri("/api/v1/label/job/values")
|
|
.body(Body::empty())
|
|
.unwrap();
|
|
let response = app.oneshot(request).await.unwrap();
|
|
assert_eq!(response.status(), StatusCode::OK);
|
|
|
|
let body = to_bytes(response.into_body(), 1024 * 1024).await.unwrap();
|
|
let payload: serde_json::Value = serde_json::from_slice(&body).unwrap();
|
|
assert_eq!(payload["status"], "success");
|
|
assert!(payload["data"]
|
|
.as_array()
|
|
.unwrap()
|
|
.iter()
|
|
.any(|value| value == "test_job"));
|
|
}
|
|
|
|
#[test]
|
|
fn test_persistence_save_load_empty() {
|
|
use tempfile::tempdir;
|
|
|
|
// Create temporary directory
|
|
let dir = tempdir().unwrap();
|
|
let path = dir.path().join("test.db");
|
|
|
|
// Create empty storage and save
|
|
let storage = QueryableStorage {
|
|
series: HashMap::new(),
|
|
label_index: HashMap::new(),
|
|
};
|
|
|
|
storage.save_to_file(&path).unwrap();
|
|
assert!(path.exists());
|
|
|
|
// Load back
|
|
let loaded = QueryableStorage::load_from_file(&path).unwrap();
|
|
assert_eq!(loaded.series.len(), 0);
|
|
assert_eq!(loaded.label_index.len(), 0);
|
|
}
|
|
|
|
#[test]
|
|
fn test_persistence_save_load_with_data() {
|
|
use tempfile::tempdir;
|
|
|
|
let dir = tempdir().unwrap();
|
|
let path = dir.path().join("test.db");
|
|
|
|
// Create storage with data
|
|
let mut storage = QueryableStorage {
|
|
series: HashMap::new(),
|
|
label_index: HashMap::new(),
|
|
};
|
|
|
|
let series1 = TimeSeries {
|
|
id: SeriesId(1),
|
|
labels: vec![Label::new("__name__", "metric1"), Label::new("job", "test")],
|
|
samples: vec![Sample::new(1000, 10.0), Sample::new(2000, 20.0)],
|
|
};
|
|
|
|
let series2 = TimeSeries {
|
|
id: SeriesId(2),
|
|
labels: vec![Label::new("__name__", "metric2"), Label::new("job", "prod")],
|
|
samples: vec![Sample::new(1000, 30.0)],
|
|
};
|
|
|
|
storage.upsert_series(series1.clone());
|
|
storage.upsert_series(series2.clone());
|
|
|
|
// Save to disk
|
|
storage.save_to_file(&path).unwrap();
|
|
assert!(path.exists());
|
|
|
|
// Load back
|
|
let loaded = QueryableStorage::load_from_file(&path).unwrap();
|
|
assert_eq!(loaded.series.len(), 2);
|
|
assert_eq!(loaded.label_index.len(), 2); // __name__ and job
|
|
|
|
// Verify series data
|
|
let loaded_series1 = loaded.series.get(&SeriesId(1)).unwrap();
|
|
assert_eq!(loaded_series1.labels.len(), 2);
|
|
assert_eq!(loaded_series1.samples.len(), 2);
|
|
assert_eq!(loaded_series1.samples[0].value, 10.0);
|
|
|
|
// Verify label index
|
|
let job_values = loaded.label_values("job");
|
|
assert_eq!(job_values.len(), 2);
|
|
assert!(job_values.contains(&"test".to_string()));
|
|
assert!(job_values.contains(&"prod".to_string()));
|
|
}
|
|
|
|
#[test]
|
|
fn test_persistence_load_nonexistent_file() {
|
|
use tempfile::tempdir;
|
|
|
|
let dir = tempdir().unwrap();
|
|
let path = dir.path().join("nonexistent.db");
|
|
|
|
// Loading non-existent file should return empty storage
|
|
let loaded = QueryableStorage::load_from_file(&path).unwrap();
|
|
assert_eq!(loaded.series.len(), 0);
|
|
assert_eq!(loaded.label_index.len(), 0);
|
|
}
|
|
|
|
#[tokio::test]
|
|
async fn test_query_service_persistence() {
|
|
use tempfile::tempdir;
|
|
|
|
let dir = tempdir().unwrap();
|
|
let path = dir.path().join("service_test.db");
|
|
|
|
// Create service and add some data
|
|
let service = QueryService::new();
|
|
{
|
|
let mut storage = service.storage.write().await;
|
|
let series = TimeSeries {
|
|
id: SeriesId(42),
|
|
labels: vec![Label::new("__name__", "test_metric")],
|
|
samples: vec![Sample::new(1000, 99.5)],
|
|
};
|
|
storage.upsert_series(series);
|
|
}
|
|
|
|
// Save to disk
|
|
service.save_to_disk(&path).await.unwrap();
|
|
|
|
// Create new service loading from disk
|
|
let new_service = QueryService::new_with_persistence(&path).unwrap();
|
|
let storage = new_service.storage.read().await;
|
|
|
|
// Verify data was persisted
|
|
assert_eq!(storage.series.len(), 1);
|
|
let loaded_series = storage.series.get(&SeriesId(42)).unwrap();
|
|
assert_eq!(loaded_series.samples[0].value, 99.5);
|
|
}
|
|
}
|