// Query returns datapoints for the named time series during the supplied time
// span.  Data is returned as a series of consecutive data points.
//
// Data is queried only at the Resolution supplied: if data for the named time
// series is not stored at the given resolution, an empty result will be
// returned.
//
// All data stored on the server is downsampled to some degree; the data points
// returned represent the average value within a sample period. Each datapoint's
// timestamp falls in the middle of the sample period it represents.
//
// If data for the named time series was collected from multiple sources, each
// returned datapoint will represent the sum of datapoints from all sources at
// the same time. The returned string slices contains a list of all sources for
// the metric which were aggregated to produce the result.
func (db *DB) Query(query tspb.Query, r Resolution, startNanos, endNanos int64) ([]tspb.TimeSeriesDatapoint, []string, error) {
	// Normalize startNanos and endNanos the nearest SampleDuration boundary.
	startNanos -= startNanos % r.SampleDuration()

	var rows []client.KeyValue
	if len(query.Sources) == 0 {
		// Based on the supplied timestamps and resolution, construct start and end
		// keys for a scan that will return every key with data relevant to the
		// query.
		startKey := MakeDataKey(query.Name, "" /* source */, r, startNanos)
		endKey := MakeDataKey(query.Name, "" /* source */, r, endNanos).PrefixEnd()
		b := &client.Batch{}
		b.Scan(startKey, endKey)

		if err := db.db.Run(b); err != nil {
			return nil, nil, err
		}
		rows = b.Results[0].Rows
	} else {
		b := &client.Batch{}
		// Iterate over all key timestamps which may contain data for the given
		// sources, based on the given start/end time and the resolution.
		kd := r.KeyDuration()
		startKeyNanos := startNanos - (startNanos % kd)
		endKeyNanos := endNanos - (endNanos % kd)
		for currentTimestamp := startKeyNanos; currentTimestamp <= endKeyNanos; currentTimestamp += kd {
			for _, source := range query.Sources {
				key := MakeDataKey(query.Name, source, r, currentTimestamp)
				b.Get(key)
			}
		}
		err := db.db.Run(b)
		if err != nil {
			return nil, nil, err
		}
		for _, result := range b.Results {
			row := result.Rows[0]
			if row.Value == nil {
				continue
			}
			rows = append(rows, row)
		}
	}

	// Convert the queried source data into a set of data spans, one for each
	// source.
	sourceSpans, err := makeDataSpans(rows, startNanos)
	if err != nil {
		return nil, nil, err
	}

	// Compute a downsample function which will be used to return values from
	// each source for each sample period.
	downsampler, err := getDownsampleFunction(query.GetDownsampler())
	if err != nil {
		return nil, nil, err
	}

	// If we are returning a derivative, iteration needs to start at offset -1
	// (in order to correctly compute the rate of change at offset 0).
	var startOffset int32
	isDerivative := query.GetDerivative() != tspb.TimeSeriesQueryDerivative_NONE
	if isDerivative {
		startOffset = -1
	}

	// Create an interpolatingIterator for each dataSpan, adding each iterator
	// into a unionIterator collection. This is also where we compute a list of
	// all sources with data present in the query.
	sources := make([]string, 0, len(sourceSpans))
	iters := make(unionIterator, 0, len(sourceSpans))
	for name, span := range sourceSpans {
		sources = append(sources, name)
		iters = append(iters, span.newIterator(startOffset, downsampler))
	}

	// Choose an aggregation function to use when taking values from the
	// unionIterator.
	var valueFn func() float64
	switch query.GetSourceAggregator() {
	case tspb.TimeSeriesQueryAggregator_SUM:
		valueFn = iters.sum
	case tspb.TimeSeriesQueryAggregator_AVG:
		valueFn = iters.avg
	case tspb.TimeSeriesQueryAggregator_MAX:
//.........这里部分代码省略.........

// assertQuery generates a query result from the local test model and compares
// it against the query returned from the server.
func (tm *testModel) assertQuery(
	name string,
	sources []string,
	downsample, agg *tspb.TimeSeriesQueryAggregator,
	derivative *tspb.TimeSeriesQueryDerivative,
	r Resolution,
	start, end int64,
	expectedDatapointCount, expectedSourceCount int,
) {
	// Query the actual server.
	q := tspb.Query{
		Name:             name,
		Downsampler:      downsample,
		SourceAggregator: agg,
		Derivative:       derivative,
		Sources:          sources,
	}
	actualDatapoints, actualSources, err := tm.DB.Query(q, r, start, end)
	if err != nil {
		tm.t.Fatal(err)
	}
	if a, e := len(actualDatapoints), expectedDatapointCount; a != e {
		tm.t.Logf("actual datapoints: %v", actualDatapoints)
		tm.t.Fatal(errors.Errorf("query expected %d datapoints, got %d", e, a))
	}
	if a, e := len(actualSources), expectedSourceCount; a != e {
		tm.t.Fatal(errors.Errorf("query expected %d sources, got %d", e, a))
	}

	// Construct an expected result for comparison.
	var expectedDatapoints []tspb.TimeSeriesDatapoint
	expectedSources := make([]string, 0, 0)
	dataSpans := make(map[string]*dataSpan)

	// If no specific sources were provided, look for data from every source
	// encountered by the test model.
	var sourcesToCheck map[string]struct{}
	if len(sources) == 0 {
		sourcesToCheck = tm.seenSources
	} else {
		sourcesToCheck = make(map[string]struct{})
		for _, s := range sources {
			sourcesToCheck[s] = struct{}{}
		}
	}

	// Iterate over all possible sources which may have data for this query.
	for sourceName := range sourcesToCheck {
		// Iterate over all possible key times at which query data may be present.
		for time := start - (start % r.KeyDuration()); time < end; time += r.KeyDuration() {
			// Construct a key for this source/time and retrieve it from model.
			key := MakeDataKey(name, sourceName, r, time)
			value, ok := tm.modelData[string(key)]
			if !ok {
				continue
			}

			// Add data from the key to the correct dataSpan.
			data, err := value.GetTimeseries()
			if err != nil {
				tm.t.Fatal(err)
			}
			ds, ok := dataSpans[sourceName]
			if !ok {
				ds = &dataSpan{
					startNanos:  start - (start % r.SampleDuration()),
					sampleNanos: r.SampleDuration(),
				}
				dataSpans[sourceName] = ds
				expectedSources = append(expectedSources, sourceName)
			}
			if err := ds.addData(data); err != nil {
				tm.t.Fatal(err)
			}
		}
	}

	// Iterate over data in all dataSpans and construct expected datapoints.
	var startOffset int32
	isDerivative := q.GetDerivative() != tspb.TimeSeriesQueryDerivative_NONE
	if isDerivative {
		startOffset = -1
	}
	downsampleFn, err := getDownsampleFunction(q.GetDownsampler())
	if err != nil {
		tm.t.Fatal(err)
	}
	var iters unionIterator
	for _, ds := range dataSpans {
		iters = append(iters, ds.newIterator(startOffset, downsampleFn))
	}

	iters.init()
	currentVal := func() tspb.TimeSeriesDatapoint {
		var value float64
		switch q.GetSourceAggregator() {
		case tspb.TimeSeriesQueryAggregator_SUM:
			value = iters.sum()
//.........这里部分代码省略.........