// initializeMapFunctions initialize the mapping functions for the mapper. This only applies
// to aggregate queries.
func (lm *SelectMapper) initializeMapFunctions() error {
var err error
// Set up each mapping function for this statement.
aggregates := lm.selectStmt.FunctionCalls()
lm.mapFuncs = make([]influxql.MapFunc, len(aggregates))
lm.fieldNames = make([]string, len(lm.mapFuncs))
for i, c := range aggregates {
lm.mapFuncs[i], err = influxql.InitializeMapFunc(c)
if err != nil {
return err
}
// Check for calls like `derivative(lmean(value), 1d)`
var nested *influxql.Call = c
if fn, ok := c.Args[0].(*influxql.Call); ok {
nested = fn
}
switch lit := nested.Args[0].(type) {
case *influxql.VarRef:
lm.fieldNames[i] = lit.Val
case *influxql.Distinct:
if c.Name != "count" {
return fmt.Errorf("aggregate call didn't contain a field %s", c.String())
}
lm.fieldNames[i] = lit.Val
default:
return fmt.Errorf("aggregate call didn't contain a field %s", c.String())
}
}
return nil
}
// Begin will set up the mapper to run the map function for a given aggregate call starting at the passed in time
func (l *LocalMapper) Begin(c *influxql.Call, startingTime int64) error {
// set up the buffers. These ensure that we return data in time order
mapFunc, err := influxql.InitializeMapFunc(c)
if err != nil {
return err
}
l.mapFunc = mapFunc
l.keyBuffer = make([]int64, len(l.cursors))
l.valueBuffer = make([][]byte, len(l.cursors))
l.tmin = startingTime
// determine if this is a raw data query with a single field, multiple fields, or an aggregate
var fieldName string
if c == nil { // its a raw data query
l.isRaw = true
if len(l.selectFields) == 1 {
fieldName = l.selectFields[0].Name
}
} else {
lit, ok := c.Args[0].(*influxql.VarRef)
if !ok {
return fmt.Errorf("aggregate call didn't contain a field %s", c.String())
}
fieldName = lit.Val
}
// set up the field info if a specific field was set for this mapper
if fieldName != "" {
f := l.decoder.FieldByName(fieldName)
if f == nil {
return fmt.Errorf("%s isn't a field on measurement %s", fieldName, l.job.MeasurementName)
}
l.fieldID = f.ID
l.fieldName = f.Name
}
// seek the bolt cursors and fill the buffers
for i, c := range l.cursors {
// this series may have never been written in this shard group (time range) so the cursor would be nil
if c == nil {
l.keyBuffer[i] = 0
l.valueBuffer[i] = nil
continue
}
k, v := c.Seek(u64tob(uint64(l.job.TMin)))
if k == nil {
l.keyBuffer[i] = 0
l.valueBuffer[i] = nil
continue
}
l.cursorsEmpty = false
t := int64(btou64(k))
l.keyBuffer[i] = t
l.valueBuffer[i] = v
}
return nil
}
// Begin will set up the mapper to run the map function for a given aggregate call starting at the passed in time
func (l *LocalMapper) Begin(c *influxql.Call, startingTime int64, chunkSize int) error {
// set up the buffers. These ensure that we return data in time order
mapFunc, err := influxql.InitializeMapFunc(c)
if err != nil {
return err
}
l.mapFunc = mapFunc
l.keyBuffer = make([]int64, len(l.cursors))
l.valueBuffer = make([][]byte, len(l.cursors))
l.chunkSize = chunkSize
l.tmin = startingTime
var isCountDistinct bool
// determine if this is a raw data query with a single field, multiple fields, or an aggregate
var fieldName string
if c == nil { // its a raw data query
l.isRaw = true
if len(l.selectFields) == 1 {
fieldName = l.selectFields[0]
}
// if they haven't set a limit, just set it to the max int size
if l.limit == 0 {
l.limit = math.MaxUint64
}
} else {
// Check for calls like `derivative(mean(value), 1d)`
var nested *influxql.Call = c
if fn, ok := c.Args[0].(*influxql.Call); ok {
nested = fn
}
switch lit := nested.Args[0].(type) {
case *influxql.VarRef:
fieldName = lit.Val
case *influxql.Distinct:
if c.Name != "count" {
return fmt.Errorf("aggregate call didn't contain a field %s", c.String())
}
isCountDistinct = true
fieldName = lit.Val
default:
return fmt.Errorf("aggregate call didn't contain a field %s", c.String())
}
isCountDistinct = isCountDistinct || (c.Name == "count" && nested.Name == "distinct")
}
// set up the field info if a specific field was set for this mapper
if fieldName != "" {
fid, err := l.decoder.FieldIDByName(fieldName)
if err != nil {
switch {
case c != nil && c.Name == "distinct":
return fmt.Errorf(`%s isn't a field on measurement %s; to query the unique values for a tag use SHOW TAG VALUES FROM %[2]s WITH KEY = "%[1]s`, fieldName, l.job.MeasurementName)
case isCountDistinct:
return fmt.Errorf("%s isn't a field on measurement %s; count(distinct) on tags isn't yet supported", fieldName, l.job.MeasurementName)
}
}
l.fieldID = fid
l.fieldName = fieldName
}
// seek the bolt cursors and fill the buffers
for i, c := range l.cursors {
// this series may have never been written in this shard group (time range) so the cursor would be nil
if c == nil {
l.keyBuffer[i] = 0
l.valueBuffer[i] = nil
continue
}
k, v := c.Seek(u64tob(uint64(l.job.TMin)))
if k == nil {
l.keyBuffer[i] = 0
l.valueBuffer[i] = nil
continue
}
l.cursorsEmpty = false
t := int64(btou64(k))
l.keyBuffer[i] = t
l.valueBuffer[i] = v
}
return nil
}
// Open opens the aggregate mapper.
func (am *AggMapper) Open() error {
var err error
// Get a read-only transaction.
tx, err := am.shard.DB().Begin(false)
if err != nil {
return err
}
am.tx = tx
// Set up each mapping function for this statement.
aggregates := am.stmt.FunctionCalls()
am.mapFuncs = make([]influxql.MapFunc, len(aggregates))
am.fieldNames = make([]string, len(am.mapFuncs))
for i, c := range aggregates {
am.mapFuncs[i], err = influxql.InitializeMapFunc(c)
if err != nil {
return err
}
// Check for calls like `derivative(mean(value), 1d)`
var nested *influxql.Call = c
if fn, ok := c.Args[0].(*influxql.Call); ok {
nested = fn
}
switch lit := nested.Args[0].(type) {
case *influxql.VarRef:
am.fieldNames[i] = lit.Val
case *influxql.Distinct:
if c.Name != "count" {
return fmt.Errorf("aggregate call didn't contain a field %s", c.String())
}
am.fieldNames[i] = lit.Val
default:
return fmt.Errorf("aggregate call didn't contain a field %s", c.String())
}
}
// Set all time-related parameters on the mapper.
am.queryTMin, am.queryTMax = influxql.TimeRangeAsEpochNano(am.stmt.Condition)
// For GROUP BY time queries, limit the number of data points returned by the limit and offset
d, err := am.stmt.GroupByInterval()
if err != nil {
return err
}
am.intervalSize = d.Nanoseconds()
if am.queryTMin == 0 || am.intervalSize == 0 {
am.numIntervals = 1
am.intervalSize = am.queryTMax - am.queryTMin
} else {
intervalTop := am.queryTMax/am.intervalSize*am.intervalSize + am.intervalSize
intervalBottom := am.queryTMin / am.intervalSize * am.intervalSize
am.numIntervals = int((intervalTop - intervalBottom) / am.intervalSize)
}
if am.stmt.Limit > 0 || am.stmt.Offset > 0 {
// ensure that the offset isn't higher than the number of points we'd get
if am.stmt.Offset > am.numIntervals {
return nil
}
// Take the lesser of either the pre computed number of GROUP BY buckets that
// will be in the result or the limit passed in by the user
if am.stmt.Limit < am.numIntervals {
am.numIntervals = am.stmt.Limit
}
}
// If we are exceeding our MaxGroupByPoints error out
if am.numIntervals > MaxGroupByPoints {
return errors.New("too many points in the group by interval. maybe you forgot to specify a where time clause?")
}
// Ensure that the start time for the results is on the start of the window.
am.queryTMinWindow = am.queryTMin
if am.intervalSize > 0 && am.numIntervals > 1 {
am.queryTMinWindow = am.queryTMinWindow / am.intervalSize * am.intervalSize
}
// Create the TagSet cursors for the Mapper.
for _, src := range am.stmt.Sources {
mm, ok := src.(*influxql.Measurement)
if !ok {
return fmt.Errorf("invalid source type: %#v", src)
}
m := am.shard.index.Measurement(mm.Name)
if m == nil {
// This shard have never received data for the measurement. No Mapper
// required.
return nil
}
// Create tagset cursors and determine various field types within SELECT statement.
tsf, err := createTagSetsAndFields(m, am.stmt)
if err != nil {
return err
}
tagSets := tsf.tagSets
am.selectFields = tsf.selectFields
am.selectTags = tsf.selectTags
am.whereFields = tsf.whereFields
// Validate that group by is not a field
if err := m.ValidateGroupBy(am.stmt); err != nil {
return err
}
// SLIMIT and SOFFSET the unique series
if am.stmt.SLimit > 0 || am.stmt.SOffset > 0 {
if am.stmt.SOffset > len(tagSets) {
tagSets = nil
} else {
if am.stmt.SOffset+am.stmt.SLimit > len(tagSets) {
am.stmt.SLimit = len(tagSets) - am.stmt.SOffset
}
tagSets = tagSets[am.stmt.SOffset : am.stmt.SOffset+am.stmt.SLimit]
}
}
// Create all cursors for reading the data from this shard.
for _, t := range tagSets {
cursors := []*seriesCursor{}
for i, key := range t.SeriesKeys {
c := createCursorForSeries(am.tx, am.shard, key)
if c == nil {
// No data exists for this key.
continue
}
cm := newSeriesCursor(c, t.Filters[i])
cursors = append(cursors, cm)
}
tsc := newTagSetCursor(m.Name, t.Tags, cursors, am.shard.FieldCodec(m.Name))
am.cursors = append(am.cursors, tsc)
}
sort.Sort(tagSetCursors(am.cursors))
}
return nil
}