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本节重点介绍 :

range_query_log解析
range_query源码解读
querylog各个字段设置

配置开启queryLog

# global段开启log即可
global:query_log_file: /opt/logs/prometheus_query_log

range_query_log解析

{# 请求基础信息"httpRequest":{"clientIP":"192.168.43.114","method":"POST","path":"/api/v1/query_range"},# 参数段"params":{"end":"2021-05-03T02:32:45.000Z","query":"rate(node_disk_reads_completed_total{instance=~"192\\.168\\.43\\.114:9100"}[2m])","start":"2021-05-03T02:17:45.000Z","step":15},# 统计段"stats":{"timings":{"evalTotalTime":0.000331799,"resultSortTime":0.000001235,"queryPreparationTime":0.000075478,"innerEvalTime":0.00024141,"execQueueTime":0.000012595,"execTotalTime":0.000354698}},# 请求时间"ts":"2021-05-03T02:32:49.876Z"
}

range_query查询原理

D:\nyy_work\go_path\pkg\mod\github.com\prometheus\prometheus@v1.8.2-0.20210220213500-8c8de46003d1\web\api\v1\api.go

func (api *API) queryRange(r *http.Request) (result apiFuncResult) {}

step1: 参数解析

    // 解析startstart, err := parseTime(r.FormValue("start"))if err != nil {return invalidParamError(err, "start")}// 解析endend, err := parseTime(r.FormValue("end"))if err != nil {return invalidParamError(err, "end")}// 判断end是否早于startif end.Before(start) {return invalidParamError(errors.New("end timestamp must not be before start time"), "end")}// 解析 stepstep, err := parseDuration(r.FormValue("step"))if err != nil {return invalidParamError(err, "step")}if step <= 0 {return invalidParamError(errors.New("zero or negative query resolution step widths are not accepted. Try a positive integer"), "step")}

防止point过多

    // 这里怎么理解？// For safety, limit the number of returned points per timeseries.// This is sufficient for 60s resolution for a week or 1h resolution for a year.if end.Sub(start)/step > 11000 {err := errors.New("exceeded maximum resolution of 11,000 points per timeseries. Try decreasing the query resolution (?step=XX)")return apiFuncResult{nil, &apiError{errorBadData, err}, nil, nil}}

设置超时

	ctx := r.Context()if to := r.FormValue("timeout"); to != "" {var cancel context.CancelFunctimeout, err := parseDuration(to)if err != nil {return invalidParamError(err, "timeout")}ctx, cancel = context.WithTimeout(ctx, timeout)defer cancel()}

step2：根据queryEngine初始化query并解析promql

func (ng *Engine) NewRangeQuery(q storage.Queryable, qs string, start, end time.Time, interval time.Duration) (Query, error) {expr, err := parser.ParseExpr(qs)if err != nil {return nil, err}if expr.Type() != parser.ValueTypeVector && expr.Type() != parser.ValueTypeScalar {return nil, errors.Errorf("invalid expression type %q for range query, must be Scalar or instant Vector", parser.DocumentedType(expr.Type()))}qry, err := ng.newQuery(q, expr, start, end, interval)if err != nil {return nil, err}qry.q = qsreturn qry, nil
}

queryEngine从何而来？

解析promqlexpr, err := parser.ParseExpr(qs)
instance_query 和range_query调用同一newQuery

step3：执行查询

// Exec implements the Query interface.
func (q *query) Exec(ctx context.Context) *Result {if span := opentracing.SpanFromContext(ctx); span != nil {span.SetTag(queryTag, q.stmt.String())}// Exec query.res, warnings, err := q.ng.exec(ctx, q)return &Result{Err: err, Value: res, Warnings: warnings}
}

使用分布式追踪追踪查询分阶段耗时

核心函数 exec

func (ng *Engine) exec(ctx context.Context, q *query) (v parser.Value, ws storage.Warnings, err error) {// prometheus_engine_queries 计数器，表示当前query个数ng.metrics.currentQueries.Inc()defer ng.metrics.currentQueries.Dec()ctx, cancel := context.WithTimeout(ctx, ng.timeout)q.cancel = cancel// 收尾函数，记录日志或者jagerdefer func() {ng.queryLoggerLock.RLock()if l := ng.queryLogger; l != nil {params := make(map[string]interface{}, 4)params["query"] = q.qif eq, ok := q.Statement().(*parser.EvalStmt); ok {params["start"] = formatDate(eq.Start)params["end"] = formatDate(eq.End)// The step provided by the user is in seconds.params["step"] = int64(eq.Interval / (time.Second / time.Nanosecond))}f := []interface{}{"params", params}if err != nil {f = append(f, "error", err)}f = append(f, "stats", stats.NewQueryStats(q.Stats()))if span := opentracing.SpanFromContext(ctx); span != nil {if spanCtx, ok := span.Context().(jaeger.SpanContext); ok {f = append(f, "spanID", spanCtx.SpanID())}}if origin := ctx.Value(QueryOrigin{}); origin != nil {for k, v := range origin.(map[string]interface{}) {f = append(f, k, v)}}if err := l.Log(f...); err != nil {ng.metrics.queryLogFailures.Inc()level.Error(ng.logger).Log("msg", "can't log query", "err", err)}}ng.queryLoggerLock.RUnlock()}()// execTotalTime 代表exec函数执行全部耗时不算log// defer先进后出，这个GetSpanTimer最后执行execSpanTimer, ctx := q.stats.GetSpanTimer(ctx, stats.ExecTotalTime)defer execSpanTimer.Finish()// ExecQueueTime代表队列中等待时间// 命令行参数query.max-concurrency// 如果日志中这个耗时高，考虑队列被慢查询占满了。对应在data目录下的queries.active文件queueSpanTimer, _ := q.stats.GetSpanTimer(ctx, stats.ExecQueueTime, ng.metrics.queryQueueTime)// Log query in active log. The active log guarantees that we don't run over// MaxConcurrent queries.if ng.activeQueryTracker != nil {queryIndex, err := ng.activeQueryTracker.Insert(ctx, q.q)if err != nil {queueSpanTimer.Finish()return nil, nil, contextErr(err, "query queue")}defer ng.activeQueryTracker.Delete(queryIndex)}queueSpanTimer.Finish()// Cancel when execution is done or an error was raised.defer q.cancel()const env = "query execution"// EvalTotalTime代表execEvalStmt函数执行时间evalSpanTimer, ctx := q.stats.GetSpanTimer(ctx, stats.EvalTotalTime)defer evalSpanTimer.Finish()// The base context might already be canceled on the first iteration (e.g. during shutdown).if err := contextDone(ctx, env); err != nil {return nil, nil, err}switch s := q.Statement().(type) {case *parser.EvalStmt:return ng.execEvalStmt(ctx, q, s)case parser.TestStmt:return nil, nil, s(ctx)}panic(errors.Errorf("promql.Engine.exec: unhandled statement of type %T", q.Statement()))
}

queuetime实验

将查询并非限制为1个
效果查看日志，可以看到execQueueTime已经上涨到了0.77秒，对比之前很小的效果明显

012926,"execQueueTime":0.778576385,"execTotalTime":0.778688304}},"ts":"2021-09-12T07:54:01.844Z"}
{"params":{"end":"2021-09-12T07:54:01.465Z","query":"mysql_global_status_queries > 0","start":"2021-09-12T07:54:01.465Z","step":0},"ruleGroup":{"file":"/opt/app/prometheus/rule.yml","name":"alert_g_2"},"stats":{"timings":{"evalTotalTime":0.000178631,"resultSortTime":0,"queryPreparationTime":0.000030101,"innerEvalTime":0.0001427,"execQueueTime":0.378246026,"execTotalTime":0.378434315}},"ts":"2021-09-12T07:54:01.844Z"}

同时可以看到当前查询数量指标>0

核心函数 execEvalStmt

func (ng *Engine) execEvalStmt(ctx context.Context, query *query, s *parser.EvalStmt) (parser.Value, storage.Warnings, error) {// QueryPreparationTime代表准备存储上的querier+select series时间prepareSpanTimer, ctxPrepare := query.stats.GetSpanTimer(ctx, stats.QueryPreparationTime, ng.metrics.queryPrepareTime)mint, maxt := ng.findMinMaxTime(s)querier, err := query.queryable.Querier(ctxPrepare, mint, maxt)if err != nil {prepareSpanTimer.Finish()return nil, nil, err}defer querier.Close()// populateSeries调用 select返回seriesng.populateSeries(querier, s)prepareSpanTimer.Finish()// InnerEvalTime代表从存储拿到series后在本地内存中执行 evaluator.Eval(s.Expr)的时间// evaluator.Eval需要判断instance_query 还是range_queryevalSpanTimer, ctxInnerEval := query.stats.GetSpanTimer(ctx, stats.InnerEvalTime, ng.metrics.queryInnerEval)...// ResultSortTime代表排序耗时sortSpanTimer, _ := query.stats.GetSpanTimer(ctx, stats.ResultSortTime, ng.metrics.queryResultSort)sort.Sort(mat)sortSpanTimer.Finish()
}

总结查询过程

解析参数
设置超时并设置opentracing
根据queryEngine初始化query并解析promql
exec函数先设置 ExecTotalTime
exec函数进入队列排队设置并计算 ExecQueueTime
exec函数设置 EvalTotalTime 并执行execEvalStmt函数
execEvalStmt函数准备存储上的querier+select series 设置并计算QueryPreparationTime
execEvalStmt函数设置InnerEvalTime，从存储拿到series后在本地内存中执行 evaluator.Eval(s.Expr)
execEvalStmt函数设置并计算 ResultSortTime

示意图

所以这几个耗时的关系为

EvalTotalTime=QueryPreparationTime+InnerEvalTime+ResultSortTime
- 计算值 0.000075478+0.00024141+0.000001235=0.000318123
- 真实值 0.000331799 > 0.000318123
- 不一样就对了，还有中间的部分代码执行
ExecTotalTime=ExecQueueTime+EvalTotalTime
- 计算值 0.000331799+0.000012595=0.000344394
- 真实值 0.000354698 > 0.000344394

{# 请求基础信息"httpRequest":{"clientIP":"192.168.43.114","method":"POST","path":"/api/v1/query_range"},# 参数段"params":{"end":"2021-05-03T02:32:45.000Z","query":"rate(node_disk_reads_completed_total{instance=~"192\\.168\\.43\\.114:9100"}[2m])","start":"2021-05-03T02:17:45.000Z","step":15},# 统计段"stats":{"timings":{"evalTotalTime":0.000331799,"resultSortTime":0.000001235,"queryPreparationTime":0.000075478,"innerEvalTime":0.00024141,"execQueueTime":0.000012595,"execTotalTime":0.000354698}},# 请求时间"ts":"2021-05-03T02:32:49.876Z"
}

几个耗时的问题

最多的重查询在于QueryPreparationTime，也就是select series阶段
execQueueTime如果很高，不要轻易的调大query.max-concurrency，应该找出慢查并解决
- 盲目调大队列深度会导致更严重的oom问题
- 正常来说每个query在队列中等待时间很短
innerEvalTime resultSortTime一般耗时不高