Golang Batch.Scan方法代码示例

// 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 Query, r Resolution, startNanos, endNanos int64) ([]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()
		var b client.Batch
		b.Header.ReadConsistency = roachpb.INCONSISTENT
		b.Scan(startKey, endKey, 0)

		if err := db.db.Run(&b); err != nil {
			return nil, nil, err
		}
		rows = b.Results[0].Rows
	} else {
		b := db.db.NewBatch()
		b.Header.ReadConsistency = roachpb.INCONSISTENT
		// 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() != 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 TimeSeriesQueryAggregator_SUM:
		valueFn = iters.sum
	case TimeSeriesQueryAggregator_AVG:
//.........这里部分代码省略.........