// 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,
sampleDuration, 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(context.TODO(), q, r, sampleDuration, 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.SlabDuration()); time < end; time += r.SlabDuration() {
// 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)
}
}
}
// Verify that expected sources match actual sources.
sort.Strings(expectedSources)
sort.Strings(actualSources)
if !reflect.DeepEqual(actualSources, expectedSources) {
tm.t.Error(errors.Errorf("actual source list: %v, expected: %v", actualSources, expectedSources))
}
// Iterate over data in all dataSpans and construct expected datapoints.
var startOffset int32
isDerivative := q.GetDerivative() != tspb.TimeSeriesQueryDerivative_NONE
if isDerivative {
startOffset = -1
}
extractFn, err := getExtractionFunction(q.GetDownsampler())
if err != nil {
tm.t.Fatal(err)
}
downsampleFn, err := getDownsampleFunction(q.GetDownsampler())
if err != nil {
tm.t.Fatal(err)
}
var iters aggregatingIterator
for _, ds := range dataSpans {
iters = append(iters, newInterpolatingIterator(*ds, startOffset, sampleDuration, extractFn, downsampleFn))
}
iters.init()
if !iters.isValid() {
if a, e := 0, len(expectedDatapoints); a != e {
tm.t.Error(errors.Errorf("query had zero datapoints, expected: %v", expectedDatapoints))
}
return
}
currentVal := func() tspb.TimeSeriesDatapoint {
var value float64
switch q.GetSourceAggregator() {
case tspb.TimeSeriesQueryAggregator_SUM:
value = iters.sum()
case tspb.TimeSeriesQueryAggregator_AVG:
value = iters.avg()
case tspb.TimeSeriesQueryAggregator_MAX:
value = iters.max()
case tspb.TimeSeriesQueryAggregator_MIN:
value = iters.min()
default:
tm.t.Fatalf("unknown query aggregator %s", q.GetSourceAggregator())
}
return tspb.TimeSeriesDatapoint{
TimestampNanos: iters.timestamp(),
Value: value,
}
}
var last tspb.TimeSeriesDatapoint
if isDerivative {
last = currentVal()
if iters.offset() < 0 {
iters.advance()
}
}
for iters.isValid() && iters.timestamp() <= end {
current := currentVal()
result := current
if isDerivative {
dTime := (current.TimestampNanos - last.TimestampNanos) / int64(time.Second)
if dTime == 0 {
result.Value = 0
} else {
result.Value = (current.Value - last.Value) / float64(dTime)
}
if result.Value < 0 &&
q.GetDerivative() == tspb.TimeSeriesQueryDerivative_NON_NEGATIVE_DERIVATIVE {
result.Value = 0
}
}
expectedDatapoints = append(expectedDatapoints, result)
last = current
iters.advance()
}
if !reflect.DeepEqual(actualDatapoints, expectedDatapoints) {
tm.t.Error(errors.Errorf("actual datapoints: %v, expected: %v", actualDatapoints, expectedDatapoints))
}
}
// Query returns datapoints for the named time series during the supplied time
// span. Data is returned as a series of consecutive data points.
//
// Raw data is queried only at the queryResolution supplied: if data for the
// named time series is not stored at the given resolution, an empty result will
// be returned.
//
// Raw data is converted into query results through a number of processing
// steps, which are executed in the following order:
//
// 1. Downsampling
// 2. Rate calculation (if requested)
// 3. Interpolation and Aggregation
//
// Raw data stored on the server is already downsampled into samples with
// interval length queryResolution.SampleDuration(); however, Result data can be
// further downsampled into a longer sample intervals based on a provided
// sampleDuration. sampleDuration must have a sample duration which is a
// positive integer multiple of the queryResolution's sample duration. The
// downsampling operation can compute a sum, total, max or min. Each downsampled
// datapoint's timestamp falls in the middle of the sample period it represents.
//
// After downsampling, values can be converted into a rate if requested by the
// query. Each data point's value is replaced by the derivative of the series at
// that timestamp, computed by comparing the datapoint to its predecessor. If a
// query requests a derivative, the returned value for each datapoint is
// expressed in units per second.
//
// 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. In the case where one
// series is missing a data point that is present in other series, the missing
// data points for that series will be interpolated using linear interpolation.
func (db *DB) Query(
ctx context.Context,
query tspb.Query,
queryResolution Resolution,
sampleDuration, startNanos, endNanos int64,
) ([]tspb.TimeSeriesDatapoint, []string, error) {
// Verify that sampleDuration is a multiple of
// queryResolution.SampleDuration().
if sampleDuration < queryResolution.SampleDuration() {
return nil, nil, fmt.Errorf(
"sampleDuration %d was not less that queryResolution.SampleDuration %d",
sampleDuration,
queryResolution.SampleDuration(),
)
}
if sampleDuration%queryResolution.SampleDuration() != 0 {
return nil, nil, fmt.Errorf(
"sampleDuration %d is not a multiple of queryResolution.SampleDuration %d",
sampleDuration,
queryResolution.SampleDuration(),
)
}
// Normalize startNanos to a sampleDuration boundary.
startNanos -= startNanos % 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 */, queryResolution, startNanos)
endKey := MakeDataKey(query.Name, "" /* source */, queryResolution, endNanos).PrefixEnd()
b := &client.Batch{}
b.Scan(startKey, endKey)
if err := db.db.Run(ctx, 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 := queryResolution.SlabDuration()
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, queryResolution, currentTimestamp)
b.Get(key)
}
}
err := db.db.Run(ctx, 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
}
// Choose an extractor function which will be used to return values from
// each source for each sample period.
extractor, err := getExtractionFunction(query.GetDownsampler())
if err != nil {
return nil, nil, err
}
// Choose downsampler function.
downsampler, err := getDownsampleFunction(query.GetDownsampler())
if err != nil {
return nil, nil, err
}
// Create an interpolatingIterator for each dataSpan, adding each iterator
// into a aggregatingIterator 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(aggregatingIterator, 0, len(sourceSpans))
for name, span := range sourceSpans {
sources = append(sources, name)
iters = append(iters, newInterpolatingIterator(
*span, 0, sampleDuration, extractor, downsampler, query.GetDerivative(),
))
}
// Choose an aggregation function to use when taking values from the
// aggregatingIterator.
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:
valueFn = iters.max
case tspb.TimeSeriesQueryAggregator_MIN:
valueFn = iters.min
}
// Iterate over all requested offsets, recording a value from the
// aggregatingIterator at each offset encountered. If the query is
// requesting a derivative, a rate of change is recorded instead of the
// actual values.
iters.init()
if !iters.isValid() {
// We have no data to return.
return nil, sources, nil
}
var responseData []tspb.TimeSeriesDatapoint
for iters.isValid() && iters.timestamp() <= endNanos {
response := tspb.TimeSeriesDatapoint{
TimestampNanos: iters.timestamp(),
Value: valueFn(),
}
if query.GetDerivative() != tspb.TimeSeriesQueryDerivative_NONE {
response.Value = response.Value / float64(sampleDuration) * float64(time.Second.Nanoseconds())
}
responseData = append(responseData, response)
iters.advance()
}
return responseData, sources, nil
}