Like Prometheus, but for logs.
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 
 
loki/pkg/bloomgateway/querier.go

178 lines
6.1 KiB

package bloomgateway
import (
"context"
"github.com/go-kit/log"
"github.com/go-kit/log/level"
"github.com/opentracing/opentracing-go"
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promauto"
"github.com/prometheus/common/model"
"github.com/grafana/loki/v3/pkg/logproto"
"github.com/grafana/loki/v3/pkg/querier/plan"
v1 "github.com/grafana/loki/v3/pkg/storage/bloom/v1"
"github.com/grafana/loki/v3/pkg/storage/stores/shipper/bloomshipper"
"github.com/grafana/loki/v3/pkg/util/constants"
)
type querierMetrics struct {
chunksTotal prometheus.Counter
chunksFiltered prometheus.Counter
seriesTotal prometheus.Counter
seriesFiltered prometheus.Counter
}
func newQuerierMetrics(registerer prometheus.Registerer, namespace, subsystem string) *querierMetrics {
return &querierMetrics{
chunksTotal: promauto.With(registerer).NewCounter(prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "chunks_total",
Help: "Total amount of chunks pre filtering. Does not count chunks in failed requests.",
}),
chunksFiltered: promauto.With(registerer).NewCounter(prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "chunks_filtered_total",
Help: "Total amount of chunks that have been filtered out. Does not count chunks in failed requests.",
}),
seriesTotal: promauto.With(registerer).NewCounter(prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "series_total",
Help: "Total amount of series pre filtering. Does not count series in failed requests.",
}),
seriesFiltered: promauto.With(registerer).NewCounter(prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "series_filtered_total",
Help: "Total amount of series that have been filtered out. Does not count series in failed requests.",
}),
}
}
// BloomQuerier is a store-level abstraction on top of Client
// It is used by the index gateway to filter ChunkRefs based on given line fiter expression.
type BloomQuerier struct {
c Client
logger log.Logger
metrics *querierMetrics
limits Limits
blockResolver BlockResolver
}
func NewQuerier(c Client, limits Limits, resolver BlockResolver, r prometheus.Registerer, logger log.Logger) *BloomQuerier {
return &BloomQuerier{
c: c,
logger: logger,
metrics: newQuerierMetrics(r, constants.Loki, querierMetricsSubsystem),
limits: limits,
blockResolver: resolver,
}
}
func convertToShortRef(ref *logproto.ChunkRef) *logproto.ShortRef {
return &logproto.ShortRef{From: ref.From, Through: ref.Through, Checksum: ref.Checksum}
}
func (bq *BloomQuerier) FilterChunkRefs(ctx context.Context, tenant string, from, through model.Time, chunkRefs []*logproto.ChunkRef, queryPlan plan.QueryPlan) ([]*logproto.ChunkRef, error) {
// Shortcut that does not require any filtering
if !bq.limits.BloomGatewayEnabled(tenant) || len(chunkRefs) == 0 || len(v1.ExtractTestableLineFilters(queryPlan.AST)) == 0 {
return chunkRefs, nil
}
sp, ctx := opentracing.StartSpanFromContext(ctx, "bloomquerier.FilterChunkRefs")
defer sp.Finish()
grouped := groupedChunksRefPool.Get(len(chunkRefs))
defer groupedChunksRefPool.Put(grouped)
grouped = groupChunkRefs(chunkRefs, grouped)
preFilterChunks := len(chunkRefs)
preFilterSeries := len(grouped)
result := make([]*logproto.ChunkRef, 0, len(chunkRefs))
seriesSeen := make(map[uint64]struct{}, len(grouped))
// We can perform requests sequentially, because most of the time the request
// only covers a single day, and if not, it's at most two days.
for _, s := range partitionSeriesByDay(from, through, grouped) {
day := bloomshipper.NewInterval(s.day.Time, s.day.Time.Add(Day))
blocks, skipped, err := bq.blockResolver.Resolve(ctx, tenant, day, s.series)
if err != nil {
return nil, err
}
var chunks int
for i := range s.series {
chunks += len(s.series[i].Refs)
}
sp.LogKV(
"day", s.day.Time.Time(),
"from", s.interval.Start.Time(),
"through", s.interval.End.Time(),
"series", len(s.series),
"chunks", chunks,
"blocks", len(blocks),
)
refs, err := bq.c.FilterChunks(ctx, tenant, s.interval, blocks, queryPlan)
if err != nil {
return nil, err
}
// add chunk refs from series that were not mapped to any blocks
refs = append(refs, skipped...)
for i := range refs {
seriesSeen[refs[i].Fingerprint] = struct{}{}
for _, ref := range refs[i].Refs {
result = append(result, &logproto.ChunkRef{
Fingerprint: refs[i].Fingerprint,
UserID: tenant,
From: ref.From,
Through: ref.Through,
Checksum: ref.Checksum,
})
}
}
}
level.Debug(bq.logger).Log(
"preFilterChunks", preFilterChunks,
"postFilterChunks", len(result),
"preFilterSeries", preFilterSeries,
"postFilterSeries", len(seriesSeen),
)
postFilterChunks := len(result)
postFilterSeries := len(seriesSeen)
bq.metrics.chunksTotal.Add(float64(preFilterChunks))
bq.metrics.chunksFiltered.Add(float64(preFilterChunks - postFilterChunks))
bq.metrics.seriesTotal.Add(float64(preFilterSeries))
bq.metrics.seriesFiltered.Add(float64(preFilterSeries - postFilterSeries))
return result, nil
}
// groupChunkRefs takes a slice of chunk refs sorted by their fingerprint and
// groups them by fingerprint.
// The second argument `grouped` can be used to pass a buffer to avoid allocations.
// If it's nil, the returned slice will be allocated.
func groupChunkRefs(chunkRefs []*logproto.ChunkRef, grouped []*logproto.GroupedChunkRefs) []*logproto.GroupedChunkRefs {
seen := make(map[uint64]int, len(grouped))
for _, chunkRef := range chunkRefs {
if idx, found := seen[chunkRef.Fingerprint]; found {
grouped[idx].Refs = append(grouped[idx].Refs, convertToShortRef(chunkRef))
} else {
seen[chunkRef.Fingerprint] = len(grouped)
grouped = append(grouped, &logproto.GroupedChunkRefs{
Fingerprint: chunkRef.Fingerprint,
Tenant: chunkRef.UserID,
Refs: []*logproto.ShortRef{convertToShortRef(chunkRef)},
})
}
}
return grouped
}