Split View: PromQL 엔진 내부 구조: 파서부터 실행 엔진까지

PromQL String
    |
    v
+----------+
| Lexer    |  (tokenization)
+----------+
    |
    v
+----------+
| Parser   |  (syntax analysis)
+----------+
    |
    v
+----------+
| AST      |  (abstract syntax tree)
+----------+
    |
    v
+----------+
| Analyzer |  (optimization/validation)
+----------+
    |
    v
+----------+
| Engine   |  (evaluation/execution)
+----------+
    |
    v
Result (Matrix/Vector/Scalar)

Input: rate(http_requests_total[5m]) > 10

Token stream:
  IDENTIFIER("rate")
  LEFT_PAREN
  IDENTIFIER("http_requests_total")
  LEFT_BRACKET
  DURATION("5m")
  RIGHT_BRACKET
  RIGHT_PAREN
  GTR
  NUMBER(10)

Literals:
  NUMBER      - integer/float (e.g., 42, 3.14)
  STRING      - string literal (e.g., "hello")
  DURATION    - time range (e.g., 5m, 1h, 30s)

Identifiers:
  IDENTIFIER  - metric names, label names
  METRIC_IDENTIFIER - metric identifier

Operators:
  ADD, SUB, MUL, DIV, MOD, POW
  EQL, NEQ, LSS, GTR, LTE, GTE
  AND, OR, UNLESS

Keywords:
  BY, WITHOUT, ON, IGNORING
  GROUP_LEFT, GROUP_RIGHT
  OFFSET, BOOL
  SUM, AVG, MIN, MAX, COUNT, ...

Parser flow:
1. Read next token from stream
2. Call prefix parsing function (literals, unary ops, parentheses, etc.)
3. If higher-precedence infix operator exists, perform infix parsing
4. Return AST node

Precedence (lowest first):
1. OR
2. AND, UNLESS
3. ==, !=, <, >, <=, >= (comparison)
4. +, - (addition/subtraction)
5. *, /, % (multiplication/division)
6. ^ (power, right-associative)
7. Unary +, -

When curly braces follow a metric name, parse label matchers
Label matching operators: =, !=, =~, !~

IDENTIFIER + LEFT_PAREN -> function call
Validate name against registered function list
Verify argument count and types

Vector selector + LEFT_BRACKET + DURATION + RIGHT_BRACKET
Example: http_requests_total[5m]

Expr (expression interface)
  |
  +-- NumberLiteral        (numeric literal)
  +-- StringLiteral        (string literal)
  +-- VectorSelector       (instant vector selector)
  +-- MatrixSelector       (range vector selector)
  +-- AggregateExpr        (aggregation expression)
  +-- BinaryExpr           (binary operation)
  +-- UnaryExpr            (unary operation)
  +-- Call                 (function call)
  +-- ParenExpr            (parenthesized expression)
  +-- SubqueryExpr         (subquery expression)
  +-- StepInvariantExpr    (step-invariant expression)

AggregateExpr (sum, by=[job])
  |
  +-- Call (rate)
        |
        +-- MatrixSelector
              |
              +-- VectorSelector (http_requests_total)
              +-- Range: 5m

VectorSelector:
  Name: "http_requests_total"
  LabelMatchers:
    - __name__ = "http_requests_total"
    - job = "prometheus"       (user-specified)
    - instance =~ ".*:9090"    (regex matching)
  Offset: 0s                   (offset modifier)
  Timestamp: nil               (@ modifier)
  OriginalOffset: 0s
  StartOrEnd: 0                (start() or end())

Engine:
  +-- queryable      (TSDB storage interface)
  +-- maxSamples     (maximum sample count limit)
  +-- timeout        (query timeout)
  +-- activeQueries  (active query counter)
  +-- maxConcurrency (maximum concurrent queries)

Request: GET /api/v1/query?query=up&time=1609459200

Processing:
1. Generate AST
2. Evaluate once at time=1609459200
3. Result: Vector (single value per series)

Request: GET /api/v1/query_range?query=up&start=1609455600&end=1609459200&step=60

Processing:
1. Generate AST
2. Evaluate repeatedly from start to end at step (60s) intervals
3. Each step evaluates like an instant query
4. Result: Matrix (time-indexed value array per series)

query: rate(http_requests_total[5m])
start: T0, end: T0+300s, step: 60s

Step evaluations:
  T0:      rate(samples from T0-5m to T0)
  T0+60:   rate(samples from T0+60-5m to T0+60)
  T0+120:  rate(samples from T0+120-5m to T0+120)
  T0+180:  rate(samples from T0+180-5m to T0+180)
  T0+240:  rate(samples from T0+240-5m to T0+240)
  T0+300:  rate(samples from T0+300-5m to T0+300)

Evaluation time: T
lookback delta: 5m

Sample search range: (T - 5m, T]

Behavior:
1. Find most recent sample within 5 minutes before T
2. Include in result if not a stale marker
3. Exclude series if no sample within 5 minutes

Important: An exact sample at time T is not required
           Uses the most recent sample within lookback delta

Function registration:
  Name:       "rate"
  ArgTypes:   [ValueTypeMatrix]     (argument types)
  ReturnType: ValueTypeVector       (return type)
  Call:       funcRate              (actual implementation)
  Variadic:   0                     (variadic argument count)

rate(counter[5m]) evaluation:

1. Extract samples from 5-minute range: [s1, s2, s3, ..., sN]
2. Counter reset detection and correction:
   - If value decreases, add previous value to cumulative total
3. Calculate corrected total increase:
   resultValue = (last - first) + counterCorrection
4. Extrapolation:
   - Calculate time ratio beyond range start/end
   - durationToStart = first.time - rangeStart
   - durationToEnd = rangeEnd - last.time
   - Apply extrapolation ratio
5. Per-second rate:
   result = extrapolatedIncrease / rangeDurationSeconds

histogram_quantile(phi, buckets) evaluation:

1. Sort buckets by le label
2. Collect cumulative count for each bucket
3. Calculate target_count = phi (e.g., 0.95) * total_count
4. Find bucket containing target_count
5. Linear interpolation between bucket boundaries:
   bucketStart + (bucketEnd - bucketStart) *
   (target - countBelow) / (countInBucket)

Notes:
- le="+Inf" bucket must exist
- Returns NaN if buckets are empty
- Inaccurate if bucket boundaries do not match actual distribution

sum by (job) (metric) evaluation:

1. Collect all series of metric
2. Group by job label value
3. Sum values within each group
4. Result series: only job label retained

Implementation:
- Group management via hashmap
- Label set hash used as key
- Exact label comparison on hash collision

metric_a + metric_b

Matching algorithm:
1. Index series from both vectors by label set
2. Find pairs with identical matching label sets
3. Perform operation (addition here)
4. Result series uses left-side label set

Performance:
- Hash join approach with O(n+m) time complexity
- Smaller side built as hash table

metric_a * on(job) group_left metric_b

Processing:
1. Determine matching key with on(job)
2. Index metric_b (one side) by job in hashmap
3. For each series in metric_a (many side):
   a. Look up matching item in metric_b by job label
   b. Perform operation
   c. Retain all labels from metric_a (group_left)

max_over_time(rate(http_requests_total[5m])[30m:1m])

Evaluation:
1. Outer function (max_over_time) needs range vector
2. Subquery part: rate(http_requests_total[5m])[30m:1m]
3. Inner evaluation:
   - From 30 minutes before current time (T) to T
   - Evaluate rate(http_requests_total[5m]) at 1-minute intervals
   - Generate 30 instant vector results
4. Convert to range vector
5. max_over_time calculates maximum for each series

Key optimizations for subquery evaluation:
1. Reuse inner query range vectors (same series)
2. Step alignment: align subquery step with outer step
3. Use global evaluation_interval when resolution not specified

--query.max-samples (default 50,000,000)

Counting approach:
- Accumulate samples loaded/generated at each step
- For range vectors, count all samples within range
- Immediately abort query when limit exceeded

--query.max-concurrency (default 20)

Implementation:
- Semaphore limits concurrent queries
- New queries wait for semaphore acquisition
- Return error if acquisition fails within timeout

Point pool operation:
1. Allocate point slices from pool at query start
2. Reuse between step evaluations
3. Return to pool at query completion
4. Uses sync.Pool to reduce GC pressure

Native Histograms have different processing paths than float samples:
1. Align histogram schemas (resolution)
2. Merge at bucket level
3. Auto-convert schemas when needed during aggregation

sum(native_histogram_metric) evaluation:
1. Collect histograms from each series
2. Unify to lower resolution if schemas differ
3. Sum per-bucket counts
4. Sum sum and count fields

When same series exist across multiple blocks:
1. Search series set in each block
2. Consolidate via merge sort
3. Filter at chunk level by time range
4. Deduplicate

Detect expressions returning identical results regardless of step:
- Numeric literals
- Scalar functions without offset
- Certain aggregation results

When detected:
- Evaluate once and copy result to all steps
- Eliminate unnecessary repeated evaluation

Optimization passes:
1. Constant folding: 2 + 3 -> 5
2. Label matching optimization: generate index hints
3. Remove unnecessary label operations
4. Early return for empty results

1. "query processing would load too many samples"
   - max-samples limit exceeded
   - Fix: reduce query range or increase limit

2. "query timed out in expression evaluation"
   - query.timeout exceeded
   - Fix: optimize query or increase timeout

3. "found duplicate series for the match group"
   - Many-to-one relationship without group_left/right
   - Fix: add group_left or group_right

4. "many-to-many matching not allowed"
   - Many-on-both-sides matching not supported
   - Fix: aggregate one side to reduce to one

1. Limited join capability: no SQL-style joins
2. Limited string operations: no string comparison/manipulation
3. No sort guarantee: series order in results is non-deterministic
4. Subquery nesting limits: excessive nesting risks memory explosion