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38826: exec: Add support for vectorized engine to use builtin functions. r=jordanlewis a=rohany Add support for the vectorized engine to use all builtin functions that are provided by distsql, but in a relatively slow manner. Future work can specialize some functions for improved performance. Co-authored-by: Rohan Yadav <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| // Copyright 2019 The Cockroach Authors. | ||
| // | ||
| // Use of this software is governed by the Business Source License | ||
| // included in the file licenses/BSL.txt. | ||
| // | ||
| // As of the Change Date specified in that file, in accordance with | ||
| // the Business Source License, use of this software will be governed | ||
| // by the Apache License, Version 2.0, included in the file | ||
| // licenses/APL.txt. | ||
|
|
||
| package exec | ||
|
|
||
| import ( | ||
| "context" | ||
|
|
||
| "github.com/cockroachdb/cockroach/pkg/sql/exec/coldata" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/exec/types" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/exec/types/conv" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/sem/tree" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/sqlbase" | ||
| semtypes "github.com/cockroachdb/cockroach/pkg/sql/types" | ||
| ) | ||
|
|
||
| type defaultBuiltinFuncOperator struct { | ||
| input Operator | ||
| evalCtx *tree.EvalContext | ||
| funcExpr *tree.FuncExpr | ||
| columnTypes []semtypes.T | ||
| inputCols []int | ||
| outputIdx int | ||
| outputType *semtypes.T | ||
| outputPhysType types.T | ||
| converter func(tree.Datum) (interface{}, error) | ||
|
|
||
| row tree.Datums | ||
| da sqlbase.DatumAlloc | ||
| } | ||
|
|
||
| func (b *defaultBuiltinFuncOperator) Init() { | ||
| b.input.Init() | ||
| } | ||
|
|
||
| func (b *defaultBuiltinFuncOperator) Next(ctx context.Context) coldata.Batch { | ||
| batch := b.input.Next(ctx) | ||
| n := batch.Length() | ||
| if n == 0 { | ||
| return batch | ||
| } | ||
|
|
||
| if b.outputIdx == batch.Width() { | ||
| batch.AppendCol(b.outputPhysType) | ||
| } | ||
|
|
||
| sel := batch.Selection() | ||
| for i := uint16(0); i < n; i++ { | ||
| rowIdx := i | ||
| if sel != nil { | ||
| rowIdx = sel[i] | ||
| } | ||
|
|
||
| hasNulls := false | ||
|
|
||
| for j := range b.inputCols { | ||
| col := batch.ColVec(b.inputCols[j]) | ||
| if col.MaybeHasNulls() && col.Nulls().NullAt(rowIdx) { | ||
| hasNulls = true | ||
| b.row[j] = tree.DNull | ||
| } else { | ||
| b.row[j] = PhysicalTypeColElemToDatum(col, rowIdx, b.da, b.columnTypes[b.inputCols[j]]) | ||
| } | ||
| } | ||
|
|
||
| var ( | ||
| res tree.Datum | ||
| err error | ||
| ) | ||
| // Some functions cannot handle null arguments. | ||
| if hasNulls && !b.funcExpr.CanHandleNulls() { | ||
| res = tree.DNull | ||
| } else { | ||
| res, err = b.funcExpr.ResolvedOverload().Fn(b.evalCtx, b.row) | ||
| if err != nil { | ||
| panic(err) | ||
| } | ||
| } | ||
|
|
||
| // Convert the datum into a physical type and write it out. | ||
| if res == tree.DNull { | ||
| batch.ColVec(b.outputIdx).Nulls().SetNull(uint16(rowIdx)) | ||
| } else { | ||
| converted, err := b.converter(res) | ||
| if err != nil { | ||
| panic(err) | ||
| } | ||
| coldata.SetValueAt(batch.ColVec(b.outputIdx), converted, rowIdx, b.outputPhysType) | ||
| } | ||
| } | ||
| return batch | ||
| } | ||
|
|
||
| // NewBuiltinFunctionOperator returns an operator that applies builtin functions. | ||
| func NewBuiltinFunctionOperator( | ||
| evalCtx *tree.EvalContext, | ||
| funcExpr *tree.FuncExpr, | ||
| columnTypes []semtypes.T, | ||
| inputCols []int, | ||
| outputIdx int, | ||
| input Operator, | ||
| ) Operator { | ||
|
|
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| outputType := funcExpr.ResolvedType() | ||
|
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| // For now, return the default builtin operator. Future work can specialize | ||
| // out the operators to efficient implementations of specific builtins. | ||
| return &defaultBuiltinFuncOperator{ | ||
| input: input, | ||
| evalCtx: evalCtx, | ||
| funcExpr: funcExpr, | ||
| outputIdx: outputIdx, | ||
| columnTypes: columnTypes, | ||
| outputType: outputType, | ||
| outputPhysType: conv.FromColumnType(outputType), | ||
| converter: conv.GetDatumToPhysicalFn(outputType), | ||
| row: make(tree.Datums, len(inputCols)), | ||
| inputCols: inputCols, | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,165 @@ | ||
| // Copyright 2019 The Cockroach Authors. | ||
| // | ||
| // Use of this software is governed by the Business Source License | ||
| // included in the file licenses/BSL.txt. | ||
| // | ||
| // As of the Change Date specified in that file, in accordance with | ||
| // the Business Source License, use of this software will be governed | ||
| // by the Apache License, Version 2.0, included in the file | ||
| // licenses/APL.txt. | ||
|
|
||
| package exec | ||
|
|
||
| import ( | ||
| "context" | ||
| "fmt" | ||
| "math/rand" | ||
| "testing" | ||
|
|
||
| "github.com/cockroachdb/cockroach/pkg/settings/cluster" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/exec/coldata" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/exec/types" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/parser" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/sem/builtins" | ||
| "github.com/cockroachdb/cockroach/pkg/sql/sem/tree" | ||
| semtypes "github.com/cockroachdb/cockroach/pkg/sql/types" | ||
| ) | ||
|
|
||
| // Mock typing context for the typechecker. | ||
| type mockTypeContext struct { | ||
| typs []semtypes.T | ||
| } | ||
|
|
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| func (p *mockTypeContext) IndexedVarEval(idx int, ctx *tree.EvalContext) (tree.Datum, error) { | ||
| return tree.DNull.Eval(ctx) | ||
| } | ||
|
|
||
| func (p *mockTypeContext) IndexedVarResolvedType(idx int) *semtypes.T { | ||
| return &p.typs[idx] | ||
| } | ||
|
|
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| func (p *mockTypeContext) IndexedVarNodeFormatter(idx int) tree.NodeFormatter { | ||
| n := tree.Name(fmt.Sprintf("$%d", idx)) | ||
| return &n | ||
| } | ||
|
|
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| func TestBasicBuiltinFunctions(t *testing.T) { | ||
| // Trick to get the init() for the builtins package to run. | ||
| _ = builtins.AllBuiltinNames | ||
|
|
||
| testCases := []struct { | ||
| desc string | ||
| expr string | ||
| inputCols []int | ||
| inputTuples tuples | ||
| inputTypes []semtypes.T | ||
| outputTypes []semtypes.T | ||
| outputTuples tuples | ||
| }{ | ||
| { | ||
| desc: "Absolute value", | ||
| expr: "abs(@1)", | ||
| inputCols: []int{0}, | ||
| inputTuples: tuples{{1}, {-1}}, | ||
| inputTypes: []semtypes.T{*semtypes.Int}, | ||
| outputTuples: tuples{{1}, {1}}, | ||
| outputTypes: []semtypes.T{*semtypes.Int, *semtypes.Int}, | ||
| }, | ||
| { | ||
| desc: "String length", | ||
| expr: "length(@1)", | ||
| inputCols: []int{0}, | ||
| inputTuples: tuples{{"Hello"}, {"The"}}, | ||
| inputTypes: []semtypes.T{*semtypes.String}, | ||
| outputTuples: tuples{{5}, {3}}, | ||
| outputTypes: []semtypes.T{*semtypes.String, *semtypes.Int}, | ||
| }, | ||
| } | ||
|
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| tctx := tree.NewTestingEvalContext(cluster.MakeTestingClusterSettings()) | ||
|
|
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| for _, tc := range testCases { | ||
| t.Run(tc.desc, func(t *testing.T) { | ||
| runTests(t, []tuples{tc.inputTuples}, tc.outputTuples, orderedVerifier, []int{1}, | ||
| func(input []Operator) (Operator, error) { | ||
| expr, err := parser.ParseExpr(tc.expr) | ||
| if err != nil { | ||
| t.Fatal(err) | ||
| } | ||
|
|
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| p := &mockTypeContext{typs: tc.inputTypes} | ||
| typedExpr, err := tree.TypeCheck(expr, &tree.SemaContext{IVarContainer: p}, semtypes.Any) | ||
| if err != nil { | ||
| t.Fatal(err) | ||
| } | ||
|
|
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| return NewBuiltinFunctionOperator(tctx, typedExpr.(*tree.FuncExpr), tc.outputTypes, tc.inputCols, 1, input[0]), nil | ||
| }) | ||
| }) | ||
| } | ||
| } | ||
|
|
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| func benchmarkBuiltinFunctions(b *testing.B, useSelectionVector bool, hasNulls bool) { | ||
| ctx := context.Background() | ||
| tctx := tree.NewTestingEvalContext(cluster.MakeTestingClusterSettings()) | ||
|
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| batch := coldata.NewMemBatch([]types.T{types.Int64}) | ||
| col := batch.ColVec(0).Int64() | ||
|
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| for i := int64(0); i < coldata.BatchSize; i++ { | ||
| if float64(i) < coldata.BatchSize*selectivity { | ||
| col[i] = -1 | ||
| } else { | ||
| col[i] = 1 | ||
| } | ||
| } | ||
|
|
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| if hasNulls { | ||
| for i := 0; i < coldata.BatchSize; i++ { | ||
| if rand.Float64() < nullProbability { | ||
| batch.ColVec(0).Nulls().SetNull(uint16(i)) | ||
| } | ||
| } | ||
| } | ||
|
|
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| batch.SetLength(coldata.BatchSize) | ||
|
|
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| if useSelectionVector { | ||
| batch.SetSelection(true) | ||
| sel := batch.Selection() | ||
| for i := int64(0); i < coldata.BatchSize; i++ { | ||
| sel[i] = uint16(i) | ||
| } | ||
| } | ||
|
|
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| source := NewRepeatableBatchSource(batch) | ||
| source.Init() | ||
|
|
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| expr, err := parser.ParseExpr("abs(@1)") | ||
| if err != nil { | ||
| b.Fatal(err) | ||
| } | ||
| p := &mockTypeContext{typs: []semtypes.T{*semtypes.Int}} | ||
| typedExpr, err := tree.TypeCheck(expr, &tree.SemaContext{IVarContainer: p}, semtypes.Any) | ||
| if err != nil { | ||
| b.Fatal(err) | ||
| } | ||
| op := NewBuiltinFunctionOperator(tctx, typedExpr.(*tree.FuncExpr), []semtypes.T{*semtypes.Int}, []int{0}, 1, source) | ||
|
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| b.SetBytes(int64(8 * coldata.BatchSize)) | ||
| b.ResetTimer() | ||
| for i := 0; i < b.N; i++ { | ||
| op.Next(ctx) | ||
| } | ||
| } | ||
|
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| func BenchmarkBuiltinFunctions(b *testing.B) { | ||
| _ = builtins.AllBuiltinNames | ||
| for _, useSel := range []bool{true, false} { | ||
| for _, hasNulls := range []bool{true, false} { | ||
| b.Run(fmt.Sprintf("useSel=%t,hasNulls=%t", useSel, hasNulls), func(b *testing.B) { | ||
| benchmarkBuiltinFunctions(b, useSel, hasNulls) | ||
| }) | ||
| } | ||
| } | ||
| } |
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