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column_timeseries.go
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column_timeseries.go
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package queries
import (
"context"
"database/sql"
"encoding/json"
"fmt"
"io"
"math"
"reflect"
"slices"
"strconv"
"time"
runtimev1 "github.com/rilldata/rill/proto/gen/rill/runtime/v1"
"github.com/rilldata/rill/runtime"
"github.com/rilldata/rill/runtime/drivers"
"github.com/rilldata/rill/runtime/pkg/pbutil"
"google.golang.org/protobuf/types/known/structpb"
"google.golang.org/protobuf/types/known/timestamppb"
// Load IANA time zone data
_ "time/tzdata"
)
const IsoFormat string = "2006-01-02T15:04:05.000Z"
type ColumnTimeseriesResult struct {
Meta []*runtimev1.MetricsViewColumn
Results []*runtimev1.TimeSeriesValue
Spark []*runtimev1.TimeSeriesValue
TimeRange *runtimev1.TimeSeriesTimeRange
SampleSize int32
}
type ColumnTimeseries struct {
Connector string `json:"connector"`
Database string `json:"database"`
DatabaseSchema string `json:"database_schema"`
TableName string `json:"table_name"`
Measures []*runtimev1.ColumnTimeSeriesRequest_BasicMeasure `json:"measures"`
TimestampColumnName string `json:"timestamp_column_name"`
TimeRange *runtimev1.TimeSeriesTimeRange `json:"time_range"`
Pixels int32 `json:"pixels"`
SampleSize int32 `json:"sample_size"`
TimeZone string `json:"time_zone,omitempty"`
Result *ColumnTimeseriesResult `json:"-"`
FirstDayOfWeek uint32
FirstMonthOfYear uint32
// MetricsView-related fields. These can be removed when MetricsViewTimeSeries is refactored to a standalone implementation.
MetricsView *runtimev1.MetricsViewSpec `json:"-"`
MetricsViewFilter *runtimev1.MetricsViewFilter `json:"filters"`
MetricsViewPolicy *runtime.ResolvedMetricsViewSecurity `json:"security"`
}
var _ runtime.Query = &ColumnTimeseries{}
func (q *ColumnTimeseries) Key() string {
r, err := json.Marshal(q)
if err != nil {
panic(err)
}
return fmt.Sprintf("ColumnTimeseries:%s", r)
}
func (q *ColumnTimeseries) Deps() []*runtimev1.ResourceName {
return []*runtimev1.ResourceName{
{Kind: runtime.ResourceKindSource, Name: q.TableName},
{Kind: runtime.ResourceKindModel, Name: q.TableName},
}
}
func (q *ColumnTimeseries) MarshalResult() *runtime.QueryResult {
return &runtime.QueryResult{
Value: q.Result,
Bytes: approxSize(q.Result),
}
}
func (q *ColumnTimeseries) UnmarshalResult(v any) error {
res, ok := v.(*ColumnTimeseriesResult)
if !ok {
return fmt.Errorf("ColumnTimeseries: mismatched unmarshal input")
}
q.Result = res
return nil
}
func (q *ColumnTimeseries) Resolve(ctx context.Context, rt *runtime.Runtime, instanceID string, priority int) error {
olap, release, err := rt.OLAP(ctx, instanceID, q.Connector)
if err != nil {
return err
}
defer release()
if olap.Dialect() != drivers.DialectDuckDB && olap.Dialect() != drivers.DialectClickHouse {
return fmt.Errorf("not available for dialect '%s'", olap.Dialect())
}
timeRange, err := q.ResolveNormaliseTimeRange(ctx, rt, instanceID, priority)
if err != nil {
return err
}
if timeRange.Interval == runtimev1.TimeGrain_TIME_GRAIN_UNSPECIFIED {
q.Result = &ColumnTimeseriesResult{}
return nil
}
timezone := "UTC"
if q.TimeZone != "" {
timezone = q.TimeZone
}
return olap.WithConnection(ctx, priority, false, false, func(ctx context.Context, ensuredCtx context.Context, _ *sql.Conn) error {
tsAlias := tempName("_ts_")
temporaryTableName := tempName("_timeseries_")
if q.FirstDayOfWeek > 7 || q.FirstDayOfWeek <= 0 {
q.FirstDayOfWeek = 1
}
if q.FirstMonthOfYear > 12 || q.FirstMonthOfYear <= 0 {
q.FirstMonthOfYear = 1
}
var querySQL string
var args []any
switch olap.Dialect() {
case drivers.DialectDuckDB:
querySQL, args = timeSeriesDuckDBSQL(timeRange, q, temporaryTableName, tsAlias, timezone, olap.Dialect())
case drivers.DialectClickHouse:
querySQL, args = timeSeriesClickHouseSQL(timeRange, q, temporaryTableName, tsAlias, timezone, olap.Dialect())
default:
return fmt.Errorf("not available for dialect '%s'", olap.Dialect())
}
err = olap.Exec(ctx, &drivers.Statement{
Query: querySQL,
Args: args,
Priority: priority,
ExecutionTimeout: defaultExecutionTimeout,
})
if err != nil {
return err
}
defer func() {
// NOTE: Using ensuredCtx
_ = olap.Exec(ensuredCtx, &drivers.Statement{
Query: `DROP TABLE "` + temporaryTableName + `"`,
Priority: priority,
ExecutionTimeout: defaultExecutionTimeout,
})
}()
rows, err := olap.Execute(ctx, &drivers.Statement{
Query: fmt.Sprintf(`SELECT * FROM %q`, temporaryTableName),
Priority: priority,
ExecutionTimeout: defaultExecutionTimeout,
})
if err != nil {
return err
}
// Omit the time value from the result schema
schema := rows.Schema
if schema != nil {
for i, f := range schema.Fields {
if f.Name == tsAlias {
schema.Fields = slices.Delete(schema.Fields, i, i+1)
break
}
}
}
var data []*runtimev1.TimeSeriesValue
rowMap := make(map[string]any)
for rows.Next() {
err := rows.MapScan(rowMap)
if err != nil {
rows.Close()
return err
}
var t time.Time
switch v := rowMap[tsAlias].(type) {
case time.Time:
t = v
default:
rows.Close()
panic(fmt.Sprintf("unexpected type for timestamp column: %T", v))
}
delete(rowMap, tsAlias)
records, err := pbutil.ToStruct(rowMap, schema)
if err != nil {
rows.Close()
return err
}
tpb := timestamppb.New(t)
if err := tpb.CheckValid(); err != nil {
rows.Close()
return err
}
data = append(data, &runtimev1.TimeSeriesValue{
Ts: tpb,
Records: records,
})
}
if err := rows.Err(); err != nil {
return err
}
meta := structTypeToMetricsViewColumn(rows.Schema)
rows.Close()
var sparkValues []*runtimev1.TimeSeriesValue
if q.Pixels != 0 {
sparkValues, err = q.CreateTimestampRollupReduction(ctx, rt, olap, instanceID, priority, temporaryTableName, tsAlias, "count")
if err != nil {
return err
}
}
q.Result = &ColumnTimeseriesResult{
Meta: meta,
Results: data,
Spark: sparkValues,
}
return nil
})
}
func timeSeriesClickHouseSQL(timeRange *runtimev1.TimeSeriesTimeRange, q *ColumnTimeseries, temporaryTableName, tsAlias, timezone string, dialect drivers.Dialect) (string, []any) {
dateTruncSpecifier := dialect.ConvertToDateTruncSpecifier(timeRange.Interval)
measures := normaliseMeasures(q.Measures, q.Pixels != 0)
filter := ""
var args []any
var timeSQL, colSQL, unit string
var offset uint32
if timeRange.Interval == runtimev1.TimeGrain_TIME_GRAIN_WEEK && q.FirstDayOfWeek > 1 {
offset = 8 - q.FirstDayOfWeek
unit = "day"
} else if timeRange.Interval == runtimev1.TimeGrain_TIME_GRAIN_YEAR && q.FirstMonthOfYear > 1 {
offset = 13 - q.FirstMonthOfYear
unit = "month"
} else {
unit = "day" // never mind since offset is zero
}
timeSQL = `date_sub(` + unit + `, ?, date_trunc(?, date_add(` + unit + `, ?, toTimeZone(?::DATETIME64, ?))))`
// start and end are not null else we would have an empty time range but column can still have null values
colSQL = `date_sub(` + unit + `, ?, date_trunc(?, date_add(` + unit + `, ?, toTimeZone(` + safeName(q.TimestampColumnName) + `::Nullable(DATETIME64), ?))))`
// nolint
args = append(args, offset, dateTruncSpecifier, offset, timeRange.Start.AsTime(), timezone) // compute start
args = append(args, offset, dateTruncSpecifier, offset, timeRange.End.AsTime(), timezone) // compute end
args = append(args, offset, dateTruncSpecifier, offset, timeRange.Start.AsTime(), timezone) // compute start again to generate series
args = append(args, offset, dateTruncSpecifier, offset, timezone) // convert column
args = append(args, timezone)
return `CREATE TEMPORARY TABLE ` + temporaryTableName + ` AS (
WITH time_range AS
(
SELECT ` + timeSQL + ` AS start,
` + timeSQL + ` AS end,
date_diff(` + dateTruncSpecifier + `, start, end) AS interval
),
number_range AS (
SELECT
arrayJoin(range(interval::UInt64)) AS number
FROM time_range
),
-- generate a time series column that has the intended range
template AS (
SELECT ` + timeSQL + ` AS start,
date_add(` + dateTruncSpecifier + `, number, start) AS ` + tsAlias + `
FROM number_range
),
-- transform the original data, and optionally sample it.
series AS (
SELECT
` + colSQL + ` AS ` + tsAlias + `,` + getExpressionColumnsFromMeasures(measures) + `
FROM ` + dialect.EscapeTable(q.Database, q.DatabaseSchema, q.TableName) + ` ` + filter + `
GROUP BY ` + tsAlias + ` ORDER BY ` + tsAlias + `
)
-- an additional grouping is required for time zone DST (see unit tests for examples)
SELECT ` + tsAlias + `,` + getCoalesceStatementsMeasuresLast(dialect, measures) + ` FROM (
-- join the transformed data with the generated time series column,
-- coalescing the first value to get the 0-default when the rolled up data
-- does not have that value.
SELECT
` + getCoalesceStatementsMeasures(measures) + `,
toTimeZone(template.` + tsAlias + `::DATETIME64, ?) AS ` + tsAlias + ` FROM template
LEFT OUTER JOIN series ON template.` + tsAlias + ` = series.` + tsAlias + `
ORDER BY template.` + tsAlias + `
) GROUP BY 1 ORDER BY 1
)`, args
}
func timeSeriesDuckDBSQL(timeRange *runtimev1.TimeSeriesTimeRange, q *ColumnTimeseries, temporaryTableName, tsAlias, timezone string, dialect drivers.Dialect) (string, []any) {
dateTruncSpecifier := drivers.DialectDuckDB.ConvertToDateTruncSpecifier(timeRange.Interval)
measures := normaliseMeasures(q.Measures, q.Pixels != 0)
filter := ""
timeOffsetClause1 := ""
timeOffsetClause2 := ""
if timeRange.Interval == runtimev1.TimeGrain_TIME_GRAIN_WEEK && q.FirstDayOfWeek > 1 {
dayOffset := 8 - q.FirstDayOfWeek
timeOffsetClause1 = fmt.Sprintf(" + INTERVAL '%d DAY'", dayOffset)
timeOffsetClause2 = fmt.Sprintf(" - INTERVAL '%d DAY'", dayOffset)
} else if timeRange.Interval == runtimev1.TimeGrain_TIME_GRAIN_YEAR && q.FirstMonthOfYear > 1 {
monthOffset := 13 - q.FirstMonthOfYear
timeOffsetClause1 = fmt.Sprintf(" + INTERVAL '%d MONTH'", monthOffset)
timeOffsetClause2 = fmt.Sprintf(" - INTERVAL '%d MONTH'", monthOffset)
}
return `CREATE TEMPORARY TABLE ` + temporaryTableName + ` AS (
-- generate a time series column that has the intended range
WITH template as (
SELECT
unnest(list_prepend(
-- prepend the first value in case a range is empty
date_trunc('` + dateTruncSpecifier + `', timezone(?, ?::TIMESTAMPTZ) ` + timeOffsetClause1 + `) ` + timeOffsetClause2 + `,
-- take a tail of a range considering the first value is prepended
range(
date_trunc('` + dateTruncSpecifier + `', timezone(?, ?::TIMESTAMPTZ) ` + timeOffsetClause1 + `) ` + timeOffsetClause2 + `,
date_trunc('` + dateTruncSpecifier + `', timezone(?, ?::TIMESTAMPTZ) ` + timeOffsetClause1 + `) ` + timeOffsetClause2 + `,
INTERVAL '1 ` + dateTruncSpecifier + `'
)[1:]
)) as ` + tsAlias + `
),
-- transform the original data, and optionally sample it.
series AS (
SELECT
date_trunc('` + dateTruncSpecifier + `', timezone(?, ` + safeName(q.TimestampColumnName) + `::TIMESTAMPTZ) ` + timeOffsetClause1 + `) ` + timeOffsetClause2 + ` as ` + tsAlias + `,` + getExpressionColumnsFromMeasures(measures) + `
FROM ` + dialect.EscapeTable(q.Database, q.DatabaseSchema, q.TableName) + ` ` + filter + `
GROUP BY ` + tsAlias + ` ORDER BY ` + tsAlias + `
)
-- an additional grouping is required for time zone DST (see unit tests for examples)
SELECT ` + tsAlias + `,` + getCoalesceStatementsMeasuresLast(dialect, measures) + ` FROM (
-- join the transformed data with the generated time series column,
-- coalescing the first value to get the 0-default when the rolled up data
-- does not have that value.
SELECT
` + getCoalesceStatementsMeasures(measures) + `,
timezone(?, template.` + tsAlias + `) as ` + tsAlias + ` from template
LEFT OUTER JOIN series ON template.` + tsAlias + ` = series.` + tsAlias + `
ORDER BY template.` + tsAlias + `
) GROUP BY 1 ORDER BY 1
)`, []any{
timezone,
timeRange.Start.AsTime(),
timezone,
timeRange.Start.AsTime(),
timezone,
timeRange.End.AsTime(),
timezone,
timezone,
}
}
func (q *ColumnTimeseries) Export(ctx context.Context, rt *runtime.Runtime, instanceID string, w io.Writer, opts *runtime.ExportOptions) error {
return ErrExportNotSupported
}
func (q *ColumnTimeseries) ResolveNormaliseTimeRange(ctx context.Context, rt *runtime.Runtime, instanceID string, priority int) (*runtimev1.TimeSeriesTimeRange, error) {
rtr := q.TimeRange
if rtr == nil {
rtr = &runtimev1.TimeSeriesTimeRange{}
}
var result runtimev1.TimeSeriesTimeRange
if rtr.Interval == runtimev1.TimeGrain_TIME_GRAIN_UNSPECIFIED {
q := &RollupInterval{
TableName: q.TableName,
ColumnName: q.TimestampColumnName,
}
err := rt.Query(ctx, instanceID, q, priority)
if err != nil {
return nil, err
}
r := q.Result
if r == nil || r.Interval == runtimev1.TimeGrain_TIME_GRAIN_UNSPECIFIED {
return &result, nil
}
result = runtimev1.TimeSeriesTimeRange{
Interval: r.Interval,
Start: r.Start,
End: timestamppb.New(addInterval(r.End.AsTime(), r.Interval)),
}
} else if rtr.Start == nil || rtr.End == nil {
q := &ColumnTimeRange{
TableName: q.TableName,
ColumnName: q.TimestampColumnName,
}
err := rt.Query(ctx, instanceID, q, priority)
if err != nil {
return nil, err
}
tr := q.Result
result = runtimev1.TimeSeriesTimeRange{
Interval: rtr.Interval,
Start: tr.Min,
End: timestamppb.New(addInterval(tr.Max.AsTime(), rtr.Interval)),
}
}
if rtr.Start != nil {
result.Start = rtr.Start
}
if rtr.End != nil {
result.End = rtr.End
}
if rtr.Interval != runtimev1.TimeGrain_TIME_GRAIN_UNSPECIFIED {
result.Interval = rtr.Interval
}
return &result, nil
}
/**
* Contains an as-of-this-commit unpublished algorithm for an M4-like line density reduction.
* This will take in an n-length time series and produce a pixels * 4 reduction of the time series
* that preserves the shape and trends.
*
* This algorithm expects the source table to have a timestamp column and some kind of value column,
* meaning it expects the data to essentially already be aggregated.
*
* It's important to note that this implemention is NOT the original M4 aggregation method, but a method
* that has the same basic understanding but is much faster.
*
* Nonetheless, we mostly use this to reduce a many-thousands-point-long time series to about 120 * 4 pixels.
* Importantly, this function runs very fast. For more information about the original M4 method,
* see http://www.vldb.org/pvldb/vol7/p797-jugel.pdf
*/
func (q *ColumnTimeseries) CreateTimestampRollupReduction(
ctx context.Context,
rt *runtime.Runtime,
olap drivers.OLAPStore,
instanceID string,
priority int,
tableName string,
timestampColumnName string,
valueColumn string,
) ([]*runtimev1.TimeSeriesValue, error) {
safeTimestampColumnName := safeName(timestampColumnName)
rowCount, err := q.resolveRowCount(ctx, olap, priority)
if err != nil {
return nil, err
}
if rowCount < int64(q.Pixels*4) {
rows, err := olap.Execute(ctx, &drivers.Statement{
Query: `SELECT ` + safeTimestampColumnName + ` as ts, "` + valueColumn + `"::DOUBLE as count FROM "` + tableName + `"`,
Priority: priority,
ExecutionTimeout: defaultExecutionTimeout,
})
if err != nil {
return nil, err
}
defer rows.Close()
results := make([]*runtimev1.TimeSeriesValue, 0, (q.Pixels+1)*4)
for rows.Next() {
var ts time.Time
var count *float64
err = rows.Scan(&ts, &count)
if err != nil {
return nil, err
}
tsv := &runtimev1.TimeSeriesValue{
Ts: timestamppb.New(ts),
Records: &structpb.Struct{
Fields: make(map[string]*structpb.Value),
},
}
if count != nil {
tsv.Records.Fields["count"] = structpb.NewNumberValue(*count)
} else {
tsv.Records.Fields["count"] = structpb.NewNullValue()
}
results = append(results, tsv)
}
return results, nil
}
querySQL := ` -- extract unix time
WITH Q as (
SELECT ` + epochFromTimestamp(safeTimestampColumnName, olap.Dialect()) + `::BIGINT as t, "` + valueColumn + `"::DOUBLE as v FROM "` + tableName + `"
),
-- generate bounds
M as (
SELECT min(t) as t1, max(t) as t2, max(t) - min(t) as diff FROM Q
)
-- core logic
SELECT
-- left boundary point
min(t) * 1000 as min_t,
` + argMin(olap.Dialect()) + `(v, t) as argmin_tv,
-- right boundary point
max(t) * 1000 as max_t,
` + argMax(olap.Dialect()) + `(v, t) as argmax_tv,
-- smallest point within boundary
min(v) as min_v,
` + argMin(olap.Dialect()) + `(t, v) * 1000 as argmin_vt,
-- largest point within boundary
max(v) as max_v,
` + argMax(olap.Dialect()) + `(t, v) * 1000 as argmax_vt,
round(` + strconv.FormatInt(int64(q.Pixels), 10) + ` * (t - (SELECT t1 FROM M)) / (SELECT diff FROM M)::Decimal(18,3)) AS bin
FROM Q GROUP BY bin
ORDER BY bin
`
rows, err := olap.Execute(ctx, &drivers.Statement{
Query: querySQL,
Priority: priority,
ExecutionTimeout: defaultExecutionTimeout,
})
if err != nil {
return nil, err
}
defer rows.Close()
toTSV := func(ts int64, value *float64, bin *float64) *runtimev1.TimeSeriesValue {
tsv := &runtimev1.TimeSeriesValue{
Records: &structpb.Struct{
Fields: make(map[string]*structpb.Value),
},
}
tsv.Ts = timestamppb.New(time.UnixMilli(ts))
tsv.Bin = math.NaN()
if bin != nil {
tsv.Bin = *bin
}
if value != nil {
tsv.Records.Fields["count"] = structpb.NewNumberValue(*value)
} else {
tsv.Records.Fields["count"] = structpb.NewNullValue()
}
return tsv
}
results := make([]*runtimev1.TimeSeriesValue, 0, (q.Pixels+1)*4)
for rows.Next() {
var minT, maxT int64
var argminVT, argmaxVT *int64
var argminTV, argmaxTV, minV, maxV *float64
var bin *float64
err = rows.Scan(&minT, &argminTV, &maxT, &argmaxTV, &minV, &argminVT, &maxV, &argmaxVT, &bin)
if err != nil {
return nil, err
}
argminVTSafe := minT
if argminVT != nil {
argminVTSafe = *argminVT
}
argmaxVTSafe := maxT
if argmaxVT != nil {
argmaxVTSafe = *argmaxVT
}
results = append(results, toTSV(minT, argminTV, bin), toTSV(argminVTSafe, minV, bin), toTSV(argmaxVTSafe, maxV, bin), toTSV(maxT, argmaxTV, bin))
if argminVT != nil && argmaxVT != nil && *argminVT > *argmaxVT {
i := len(results)
results[i-3], results[i-2] = results[i-2], results[i-3]
}
}
return results, nil
}
func (q *ColumnTimeseries) resolveRowCount(ctx context.Context, olap drivers.OLAPStore, priority int) (int64, error) {
rows, err := olap.Execute(ctx, &drivers.Statement{
Query: fmt.Sprintf("SELECT count(*) AS count FROM %s", olap.Dialect().EscapeTable(q.Database, q.DatabaseSchema, q.TableName)),
Priority: priority,
})
if err != nil {
return 0, err
}
defer rows.Close()
var count int64
for rows.Next() {
err = rows.Scan(&count)
if err != nil {
return 0, err
}
}
err = rows.Err()
if err != nil {
return 0, err
}
return count, nil
}
// normaliseMeasures is called before this method so measure.SqlName will be non empty
func getExpressionColumnsFromMeasures(measures []*runtimev1.ColumnTimeSeriesRequest_BasicMeasure) string {
var result string
for i, measure := range measures {
result += measure.Expression + " as " + safeName(measure.SqlName)
if i < len(measures)-1 {
result += ", "
}
}
return result
}
// normaliseMeasures is called before this method so measure.SqlName will be non empty
func getCoalesceStatementsMeasures(measures []*runtimev1.ColumnTimeSeriesRequest_BasicMeasure) string {
var result string
for i, measure := range measures {
result += fmt.Sprintf(`series.%[1]s as %[1]s`, safeName(measure.SqlName))
if i < len(measures)-1 {
result += ", "
}
}
return result
}
func getCoalesceStatementsMeasuresLast(dialect drivers.Dialect, measures []*runtimev1.ColumnTimeSeriesRequest_BasicMeasure) string {
var result string
for i, measure := range measures {
result += fmt.Sprintf(` `+lastValue(dialect)+`(%[1]s) as %[1]s`, safeName(measure.SqlName))
if i < len(measures)-1 {
result += ", "
}
}
return result
}
func normaliseMeasures(measures []*runtimev1.ColumnTimeSeriesRequest_BasicMeasure, generateCount bool) []*runtimev1.ColumnTimeSeriesRequest_BasicMeasure {
if len(measures) == 0 {
return []*runtimev1.ColumnTimeSeriesRequest_BasicMeasure{
{
Expression: "count(*)",
SqlName: "count",
Id: "",
},
}
}
var countExists bool
for i, measure := range measures {
if measure.SqlName == "" {
measure.SqlName = fmt.Sprintf("measure_%d", i)
}
if measure.SqlName == "count" {
countExists = true
}
}
if !countExists && generateCount {
measures = append(measures, &runtimev1.ColumnTimeSeriesRequest_BasicMeasure{
Expression: "count(*)",
SqlName: "count",
Id: "",
})
}
return measures
}
func approxSize(c *ColumnTimeseriesResult) int64 {
var size int64
if len(c.Meta) > 0 {
size += sizeProtoMessage(c.Meta[0]) * int64(len(c.Meta))
}
if len(c.Results) > 0 {
size += sizeProtoMessage(c.Results[0]) * int64(len(c.Results))
}
if len(c.Spark) > 0 {
size += sizeProtoMessage(c.Spark[0]) * int64(len(c.Spark))
}
size += sizeProtoMessage(c.TimeRange)
size += int64(reflect.TypeOf(c.SampleSize).Size())
return size
}
func lastValue(dialect drivers.Dialect) string {
switch dialect {
case drivers.DialectClickHouse:
return "last_value"
default:
return "last"
}
}
func argMin(dialect drivers.Dialect) string {
switch dialect {
case drivers.DialectClickHouse:
return "argMin"
default:
return "arg_min"
}
}
func argMax(dialect drivers.Dialect) string {
switch dialect {
case drivers.DialectClickHouse:
return "argMax"
default:
return "arg_max"
}
}
func epochFromTimestamp(safeColName string, dialect drivers.Dialect) string {
switch dialect {
case drivers.DialectClickHouse:
return `toUnixTimestamp(` + safeColName + `)`
default:
return `extract('epoch' from ` + safeColName + `)`
}
}