Пример #1
0
func ExampleAggregations() {
	// Get a client to the local Elasticsearch instance.
	client, err := elastic.NewClient()
	if err != nil {
		// Handle error
		panic(err)
	}

	// Create an aggregation for users and a sub-aggregation for a date histogram of tweets (per year).
	timeline := elastic.NewTermsAggregation().Field("user").Size(10).OrderByCountDesc()
	histogram := elastic.NewDateHistogramAggregation().Field("created").Interval("year")
	timeline = timeline.SubAggregation("history", histogram)

	// Search with a term query
	searchResult, err := client.Search().
		Index("twitter").                  // search in index "twitter"
		Query(elastic.NewMatchAllQuery()). // return all results, but ...
		SearchType("count").               // ... do not return hits, just the count
		Aggregation("timeline", timeline). // add our aggregation to the query
		Pretty(true).                      // pretty print request and response JSON
		Do()                               // execute
	if err != nil {
		// Handle error
		panic(err)
	}

	// Access "timeline" aggregate in search result.
	agg, found := searchResult.Aggregations.Terms("timeline")
	if !found {
		log.Fatalf("we sould have a terms aggregation called %q", "timeline")
	}
	for _, userBucket := range agg.Buckets {
		// Every bucket should have the user field as key.
		user := userBucket.Key

		// The sub-aggregation history should have the number of tweets per year.
		histogram, found := userBucket.DateHistogram("history")
		if found {
			for _, year := range histogram.Buckets {
				fmt.Printf("user %q has %d tweets in %q\n", user, year.DocCount, year.KeyAsString)
			}
		}
	}
}
Пример #2
0
// LSDateHistorgram builds the aggregation query using subaggregations. The result is a grouped timer series
// that Bosun can understand
func LSDateHistogram(e *State, T miniprofiler.Timer, index_root, keystring, filter, interval, sduration, eduration, stat_field, rstat string, size int) (r *Results, err error) {
	r = new(Results)
	req, err := LSBaseQuery(e.now, index_root, e.logstashHosts, keystring, filter, sduration, eduration, size)
	if err != nil {
		return nil, err
	}
	ts := elastic.NewDateHistogramAggregation().Field("@timestamp").Interval(strings.Replace(interval, "M", "n", -1)).MinDocCount(0)
	ds, err := opentsdb.ParseDuration(interval)
	if err != nil {
		return nil, err
	}
	if stat_field != "" {
		ts = ts.SubAggregation("stats", elastic.NewExtendedStatsAggregation().Field(stat_field))
		switch rstat {
		case "avg", "min", "max", "sum", "sum_of_squares", "variance", "std_deviation":
		default:
			return r, fmt.Errorf("stat function %v not a valid option", rstat)
		}
	}
	if keystring == "" {
		req.Source = req.Source.Aggregation("ts", ts)
		result, err := timeLSRequest(e, T, req)
		if err != nil {
			return nil, err
		}
		ts, found := result.Aggregations.DateHistogram("ts")
		if !found {
			return nil, fmt.Errorf("expected time series not found in elastic reply")
		}
		series := make(Series)
		for _, v := range ts.Buckets {
			val := processBucketItem(v, rstat, ds)
			if val != nil {
				series[time.Unix(v.Key/1000, 0).UTC()] = *val
			}
		}
		if len(series) == 0 {
			return r, nil
		}
		r.Results = append(r.Results, &Result{
			Value: series,
			Group: make(opentsdb.TagSet),
		})
		return r, nil
	}
	keys := req.KeyMatches
	aggregation := elastic.NewTermsAggregation().Field(keys[len(keys)-1].Key).Size(0)
	aggregation = aggregation.SubAggregation("ts", ts)
	for i := len(keys) - 2; i > -1; i-- {
		aggregation = elastic.NewTermsAggregation().Field(keys[i].Key).Size(0).SubAggregation("g_"+keys[i+1].Key, aggregation)
	}
	req.Source = req.Source.Aggregation("g_"+keys[0].Key, aggregation)
	result, err := timeLSRequest(e, T, req)
	if err != nil {
		return nil, err
	}
	top, ok := result.Aggregations.Terms("g_" + keys[0].Key)
	if !ok {
		return nil, fmt.Errorf("top key g_%v not found in result", keys[0].Key)
	}
	var desc func(*elastic.AggregationBucketKeyItem, opentsdb.TagSet, []lsKeyMatch) error
	desc = func(b *elastic.AggregationBucketKeyItem, tags opentsdb.TagSet, keys []lsKeyMatch) error {
		if ts, found := b.DateHistogram("ts"); found {
			if e.squelched(tags) {
				return nil
			}
			series := make(Series)
			for _, v := range ts.Buckets {
				val := processBucketItem(v, rstat, ds)
				if val != nil {
					series[time.Unix(v.Key/1000, 0).UTC()] = *val
				}
			}
			if len(series) == 0 {
				return nil
			}
			r.Results = append(r.Results, &Result{
				Value: series,
				Group: tags.Copy(),
			})
			return nil
		}
		if len(keys) < 1 {
			return nil
		}
		n, _ := b.Aggregations.Terms("g_" + keys[0].Key)
		for _, item := range n.Buckets {
			key := fmt.Sprint(item.Key)
			if keys[0].Pattern != nil && !keys[0].Pattern.MatchString(key) {
				continue
			}
			tags[keys[0].Key] = key
			if err := desc(item, tags.Copy(), keys[1:]); err != nil {
				return err
			}
		}
		return nil
	}
	for _, b := range top.Buckets {
		tags := make(opentsdb.TagSet)
		key := fmt.Sprint(b.Key)
		if keys[0].Pattern != nil && !keys[0].Pattern.MatchString(key) {
			continue
		}
		tags[keys[0].Key] = key
		if err := desc(b, tags, keys[1:]); err != nil {
			return nil, err
		}
	}
	return r, nil
}