milvus/internal/storage/insert_data.go

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// Licensed to the LF AI & Data foundation under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package storage
import (
"encoding/binary"
"fmt"
"github.com/milvus-io/milvus-proto/go-api/v2/schemapb"
"github.com/milvus-io/milvus/pkg/common"
"github.com/milvus-io/milvus/pkg/util/merr"
)
// TODO: fill it
// info for each blob
type BlobInfo struct {
Length int
}
// InsertData example row_schema: {float_field, int_field, float_vector_field, string_field}
// Data {<0, row_id>, <1, timestamp>, <100, float_field>, <101, int_field>, <102, float_vector_field>, <103, string_field>}
//
// system filed id:
// 0: unique row id
// 1: timestamp
// 100: first user field id
// 101: second user field id
// 102: ...
type InsertData struct {
// TODO, data should be zero copy by passing data directly to event reader or change Data to map[FieldID]FieldDataArray
Data map[FieldID]FieldData // field id to field data
Infos []BlobInfo
}
func NewInsertData(schema *schemapb.CollectionSchema) (*InsertData, error) {
if schema == nil {
return nil, fmt.Errorf("Nil input schema")
}
idata := &InsertData{
Data: make(map[FieldID]FieldData),
}
for _, fSchema := range schema.Fields {
fieldData, err := NewFieldData(fSchema.DataType, fSchema)
if err != nil {
return nil, err
}
idata.Data[fSchema.FieldID] = fieldData
}
return idata, nil
}
func (iData *InsertData) IsEmpty() bool {
if iData == nil {
return true
}
timeFieldData, ok := iData.Data[common.TimeStampField]
return (!ok) || (timeFieldData.RowNum() <= 0)
}
func (i *InsertData) GetRowNum() int {
if i.Data == nil || len(i.Data) == 0 {
return 0
}
data, ok := i.Data[common.RowIDField]
if !ok {
return 0
}
return data.RowNum()
}
func (i *InsertData) GetMemorySize() int {
var size int
if i.Data == nil || len(i.Data) == 0 {
return size
}
for _, data := range i.Data {
size += data.GetMemorySize()
}
return size
}
func (i *InsertData) Append(row map[FieldID]interface{}) error {
for fID, v := range row {
field, ok := i.Data[fID]
if !ok {
return fmt.Errorf("Missing field when appending row, got %d", fID)
}
if err := field.AppendRow(v); err != nil {
return err
}
}
return nil
}
func (i *InsertData) GetRow(idx int) map[FieldID]interface{} {
res := make(map[FieldID]interface{})
for field, data := range i.Data {
res[field] = data.GetRow(idx)
}
return res
}
// FieldData defines field data interface
type FieldData interface {
GetMemorySize() int
RowNum() int
GetRow(i int) any
AppendRow(row interface{}) error
AppendRows(rows interface{}) error
feat: support inverted index (#28783) issue: https://github.com/milvus-io/milvus/issues/27704 Add inverted index for some data types in Milvus. This index type can save a lot of memory compared to loading all data into RAM and speed up the term query and range query. Supported: `INT8`, `INT16`, `INT32`, `INT64`, `FLOAT`, `DOUBLE`, `BOOL` and `VARCHAR`. Not supported: `ARRAY` and `JSON`. Note: - The inverted index for `VARCHAR` is not designed to serve full-text search now. We will treat every row as a whole keyword instead of tokenizing it into multiple terms. - The inverted index don't support retrieval well, so if you create inverted index for field, those operations which depend on the raw data will fallback to use chunk storage, which will bring some performance loss. For example, comparisons between two columns and retrieval of output fields. The inverted index is very easy to be used. Taking below collection as an example: ```python fields = [ FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100), FieldSchema(name="int8", dtype=DataType.INT8), FieldSchema(name="int16", dtype=DataType.INT16), FieldSchema(name="int32", dtype=DataType.INT32), FieldSchema(name="int64", dtype=DataType.INT64), FieldSchema(name="float", dtype=DataType.FLOAT), FieldSchema(name="double", dtype=DataType.DOUBLE), FieldSchema(name="bool", dtype=DataType.BOOL), FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=1000), FieldSchema(name="random", dtype=DataType.DOUBLE), FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim), ] schema = CollectionSchema(fields) collection = Collection("demo", schema) ``` Then we can simply create inverted index for field via: ```python index_type = "INVERTED" collection.create_index("int8", {"index_type": index_type}) collection.create_index("int16", {"index_type": index_type}) collection.create_index("int32", {"index_type": index_type}) collection.create_index("int64", {"index_type": index_type}) collection.create_index("float", {"index_type": index_type}) collection.create_index("double", {"index_type": index_type}) collection.create_index("bool", {"index_type": index_type}) collection.create_index("varchar", {"index_type": index_type}) ``` Then, term query and range query on the field can be speed up automatically by the inverted index: ```python result = collection.query(expr='int64 in [1, 2, 3]', output_fields=["pk"]) result = collection.query(expr='int64 < 5', output_fields=["pk"]) result = collection.query(expr='int64 > 2997', output_fields=["pk"]) result = collection.query(expr='1 < int64 < 5', output_fields=["pk"]) ``` --------- Signed-off-by: longjiquan <jiquan.long@zilliz.com>
2023-12-31 19:50:47 +08:00
GetDataType() schemapb.DataType
}
func NewFieldData(dataType schemapb.DataType, fieldSchema *schemapb.FieldSchema) (FieldData, error) {
typeParams := fieldSchema.GetTypeParams()
switch dataType {
case schemapb.DataType_Float16Vector:
dim, err := GetDimFromParams(typeParams)
if err != nil {
return nil, err
}
return &Float16VectorFieldData{
Data: make([]byte, 0),
Dim: dim,
}, nil
case schemapb.DataType_FloatVector:
dim, err := GetDimFromParams(typeParams)
if err != nil {
return nil, err
}
return &FloatVectorFieldData{
Data: make([]float32, 0),
Dim: dim,
}, nil
case schemapb.DataType_BinaryVector:
dim, err := GetDimFromParams(typeParams)
if err != nil {
return nil, err
}
return &BinaryVectorFieldData{
Data: make([]byte, 0),
Dim: dim,
}, nil
case schemapb.DataType_Bool:
return &BoolFieldData{
Data: make([]bool, 0),
}, nil
case schemapb.DataType_Int8:
return &Int8FieldData{
Data: make([]int8, 0),
}, nil
case schemapb.DataType_Int16:
return &Int16FieldData{
Data: make([]int16, 0),
}, nil
case schemapb.DataType_Int32:
return &Int32FieldData{
Data: make([]int32, 0),
}, nil
case schemapb.DataType_Int64:
return &Int64FieldData{
Data: make([]int64, 0),
}, nil
case schemapb.DataType_Float:
return &FloatFieldData{
Data: make([]float32, 0),
}, nil
case schemapb.DataType_Double:
return &DoubleFieldData{
Data: make([]float64, 0),
}, nil
case schemapb.DataType_JSON:
return &JSONFieldData{
Data: make([][]byte, 0),
}, nil
case schemapb.DataType_Array:
return &ArrayFieldData{
Data: make([]*schemapb.ScalarField, 0),
ElementType: fieldSchema.GetElementType(),
}, nil
case schemapb.DataType_String, schemapb.DataType_VarChar:
return &StringFieldData{
feat: support inverted index (#28783) issue: https://github.com/milvus-io/milvus/issues/27704 Add inverted index for some data types in Milvus. This index type can save a lot of memory compared to loading all data into RAM and speed up the term query and range query. Supported: `INT8`, `INT16`, `INT32`, `INT64`, `FLOAT`, `DOUBLE`, `BOOL` and `VARCHAR`. Not supported: `ARRAY` and `JSON`. Note: - The inverted index for `VARCHAR` is not designed to serve full-text search now. We will treat every row as a whole keyword instead of tokenizing it into multiple terms. - The inverted index don't support retrieval well, so if you create inverted index for field, those operations which depend on the raw data will fallback to use chunk storage, which will bring some performance loss. For example, comparisons between two columns and retrieval of output fields. The inverted index is very easy to be used. Taking below collection as an example: ```python fields = [ FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100), FieldSchema(name="int8", dtype=DataType.INT8), FieldSchema(name="int16", dtype=DataType.INT16), FieldSchema(name="int32", dtype=DataType.INT32), FieldSchema(name="int64", dtype=DataType.INT64), FieldSchema(name="float", dtype=DataType.FLOAT), FieldSchema(name="double", dtype=DataType.DOUBLE), FieldSchema(name="bool", dtype=DataType.BOOL), FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=1000), FieldSchema(name="random", dtype=DataType.DOUBLE), FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim), ] schema = CollectionSchema(fields) collection = Collection("demo", schema) ``` Then we can simply create inverted index for field via: ```python index_type = "INVERTED" collection.create_index("int8", {"index_type": index_type}) collection.create_index("int16", {"index_type": index_type}) collection.create_index("int32", {"index_type": index_type}) collection.create_index("int64", {"index_type": index_type}) collection.create_index("float", {"index_type": index_type}) collection.create_index("double", {"index_type": index_type}) collection.create_index("bool", {"index_type": index_type}) collection.create_index("varchar", {"index_type": index_type}) ``` Then, term query and range query on the field can be speed up automatically by the inverted index: ```python result = collection.query(expr='int64 in [1, 2, 3]', output_fields=["pk"]) result = collection.query(expr='int64 < 5', output_fields=["pk"]) result = collection.query(expr='int64 > 2997', output_fields=["pk"]) result = collection.query(expr='1 < int64 < 5', output_fields=["pk"]) ``` --------- Signed-off-by: longjiquan <jiquan.long@zilliz.com>
2023-12-31 19:50:47 +08:00
Data: make([]string, 0),
DataType: dataType,
}, nil
default:
return nil, fmt.Errorf("Unexpected schema data type: %d", dataType)
}
}
type BoolFieldData struct {
Data []bool
}
type Int8FieldData struct {
Data []int8
}
type Int16FieldData struct {
Data []int16
}
type Int32FieldData struct {
Data []int32
}
type Int64FieldData struct {
Data []int64
}
type FloatFieldData struct {
Data []float32
}
type DoubleFieldData struct {
Data []float64
}
type StringFieldData struct {
feat: support inverted index (#28783) issue: https://github.com/milvus-io/milvus/issues/27704 Add inverted index for some data types in Milvus. This index type can save a lot of memory compared to loading all data into RAM and speed up the term query and range query. Supported: `INT8`, `INT16`, `INT32`, `INT64`, `FLOAT`, `DOUBLE`, `BOOL` and `VARCHAR`. Not supported: `ARRAY` and `JSON`. Note: - The inverted index for `VARCHAR` is not designed to serve full-text search now. We will treat every row as a whole keyword instead of tokenizing it into multiple terms. - The inverted index don't support retrieval well, so if you create inverted index for field, those operations which depend on the raw data will fallback to use chunk storage, which will bring some performance loss. For example, comparisons between two columns and retrieval of output fields. The inverted index is very easy to be used. Taking below collection as an example: ```python fields = [ FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100), FieldSchema(name="int8", dtype=DataType.INT8), FieldSchema(name="int16", dtype=DataType.INT16), FieldSchema(name="int32", dtype=DataType.INT32), FieldSchema(name="int64", dtype=DataType.INT64), FieldSchema(name="float", dtype=DataType.FLOAT), FieldSchema(name="double", dtype=DataType.DOUBLE), FieldSchema(name="bool", dtype=DataType.BOOL), FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=1000), FieldSchema(name="random", dtype=DataType.DOUBLE), FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim), ] schema = CollectionSchema(fields) collection = Collection("demo", schema) ``` Then we can simply create inverted index for field via: ```python index_type = "INVERTED" collection.create_index("int8", {"index_type": index_type}) collection.create_index("int16", {"index_type": index_type}) collection.create_index("int32", {"index_type": index_type}) collection.create_index("int64", {"index_type": index_type}) collection.create_index("float", {"index_type": index_type}) collection.create_index("double", {"index_type": index_type}) collection.create_index("bool", {"index_type": index_type}) collection.create_index("varchar", {"index_type": index_type}) ``` Then, term query and range query on the field can be speed up automatically by the inverted index: ```python result = collection.query(expr='int64 in [1, 2, 3]', output_fields=["pk"]) result = collection.query(expr='int64 < 5', output_fields=["pk"]) result = collection.query(expr='int64 > 2997', output_fields=["pk"]) result = collection.query(expr='1 < int64 < 5', output_fields=["pk"]) ``` --------- Signed-off-by: longjiquan <jiquan.long@zilliz.com>
2023-12-31 19:50:47 +08:00
Data []string
DataType schemapb.DataType
}
type ArrayFieldData struct {
ElementType schemapb.DataType
Data []*schemapb.ScalarField
}
type JSONFieldData struct {
Data [][]byte
}
type BinaryVectorFieldData struct {
Data []byte
Dim int
}
type FloatVectorFieldData struct {
Data []float32
Dim int
}
type Float16VectorFieldData struct {
Data []byte
Dim int
}
// RowNum implements FieldData.RowNum
func (data *BoolFieldData) RowNum() int { return len(data.Data) }
func (data *Int8FieldData) RowNum() int { return len(data.Data) }
func (data *Int16FieldData) RowNum() int { return len(data.Data) }
func (data *Int32FieldData) RowNum() int { return len(data.Data) }
func (data *Int64FieldData) RowNum() int { return len(data.Data) }
func (data *FloatFieldData) RowNum() int { return len(data.Data) }
func (data *DoubleFieldData) RowNum() int { return len(data.Data) }
func (data *StringFieldData) RowNum() int { return len(data.Data) }
func (data *ArrayFieldData) RowNum() int { return len(data.Data) }
func (data *JSONFieldData) RowNum() int { return len(data.Data) }
func (data *BinaryVectorFieldData) RowNum() int { return len(data.Data) * 8 / data.Dim }
func (data *FloatVectorFieldData) RowNum() int { return len(data.Data) / data.Dim }
func (data *Float16VectorFieldData) RowNum() int { return len(data.Data) / 2 / data.Dim }
// GetRow implements FieldData.GetRow
func (data *BoolFieldData) GetRow(i int) any { return data.Data[i] }
func (data *Int8FieldData) GetRow(i int) any { return data.Data[i] }
func (data *Int16FieldData) GetRow(i int) any { return data.Data[i] }
func (data *Int32FieldData) GetRow(i int) any { return data.Data[i] }
func (data *Int64FieldData) GetRow(i int) any { return data.Data[i] }
func (data *FloatFieldData) GetRow(i int) any { return data.Data[i] }
func (data *DoubleFieldData) GetRow(i int) any { return data.Data[i] }
func (data *StringFieldData) GetRow(i int) any { return data.Data[i] }
func (data *ArrayFieldData) GetRow(i int) any { return data.Data[i] }
func (data *JSONFieldData) GetRow(i int) any { return data.Data[i] }
func (data *BinaryVectorFieldData) GetRow(i int) interface{} {
return data.Data[i*data.Dim/8 : (i+1)*data.Dim/8]
}
func (data *FloatVectorFieldData) GetRow(i int) interface{} {
return data.Data[i*data.Dim : (i+1)*data.Dim]
}
func (data *Float16VectorFieldData) GetRow(i int) interface{} {
return data.Data[i*data.Dim*2 : (i+1)*data.Dim*2]
}
// AppendRow implements FieldData.AppendRow
func (data *BoolFieldData) AppendRow(row interface{}) error {
v, ok := row.(bool)
if !ok {
return merr.WrapErrParameterInvalid("bool", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *Int8FieldData) AppendRow(row interface{}) error {
v, ok := row.(int8)
if !ok {
return merr.WrapErrParameterInvalid("int8", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *Int16FieldData) AppendRow(row interface{}) error {
v, ok := row.(int16)
if !ok {
return merr.WrapErrParameterInvalid("int16", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *Int32FieldData) AppendRow(row interface{}) error {
v, ok := row.(int32)
if !ok {
return merr.WrapErrParameterInvalid("int32", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *Int64FieldData) AppendRow(row interface{}) error {
v, ok := row.(int64)
if !ok {
return merr.WrapErrParameterInvalid("int64", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *FloatFieldData) AppendRow(row interface{}) error {
v, ok := row.(float32)
if !ok {
return merr.WrapErrParameterInvalid("float32", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *DoubleFieldData) AppendRow(row interface{}) error {
v, ok := row.(float64)
if !ok {
return merr.WrapErrParameterInvalid("float64", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *StringFieldData) AppendRow(row interface{}) error {
v, ok := row.(string)
if !ok {
return merr.WrapErrParameterInvalid("string", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *ArrayFieldData) AppendRow(row interface{}) error {
v, ok := row.(*schemapb.ScalarField)
if !ok {
return merr.WrapErrParameterInvalid("*schemapb.ScalarField", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *JSONFieldData) AppendRow(row interface{}) error {
v, ok := row.([]byte)
if !ok {
return merr.WrapErrParameterInvalid("[]byte", row, "Wrong row type")
}
data.Data = append(data.Data, v)
return nil
}
func (data *BinaryVectorFieldData) AppendRow(row interface{}) error {
v, ok := row.([]byte)
if !ok || len(v) != data.Dim/8 {
return merr.WrapErrParameterInvalid("[]byte", row, "Wrong row type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *FloatVectorFieldData) AppendRow(row interface{}) error {
v, ok := row.([]float32)
if !ok || len(v) != data.Dim {
return merr.WrapErrParameterInvalid("[]float32", row, "Wrong row type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *Float16VectorFieldData) AppendRow(row interface{}) error {
v, ok := row.([]byte)
if !ok || len(v) != data.Dim*2 {
return merr.WrapErrParameterInvalid("[]byte", row, "Wrong row type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *BoolFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]bool)
if !ok {
return merr.WrapErrParameterInvalid("[]bool", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *Int8FieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]int8)
if !ok {
return merr.WrapErrParameterInvalid("[]int8", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *Int16FieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]int16)
if !ok {
return merr.WrapErrParameterInvalid("[]int16", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *Int32FieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]int32)
if !ok {
return merr.WrapErrParameterInvalid("[]int32", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *Int64FieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]int64)
if !ok {
return merr.WrapErrParameterInvalid("[]int64", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *FloatFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]float32)
if !ok {
return merr.WrapErrParameterInvalid("[]float32", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *DoubleFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]float64)
if !ok {
return merr.WrapErrParameterInvalid("[]float64", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *StringFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]string)
if !ok {
return merr.WrapErrParameterInvalid("[]string", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *ArrayFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]*schemapb.ScalarField)
if !ok {
return merr.WrapErrParameterInvalid("[]*schemapb.ScalarField", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
func (data *JSONFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([][]byte)
if !ok {
return merr.WrapErrParameterInvalid("[][]byte", rows, "Wrong rows type")
}
data.Data = append(data.Data, v...)
return nil
}
// AppendRows appends FLATTEN vectors to field data.
func (data *BinaryVectorFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]byte)
if !ok {
return merr.WrapErrParameterInvalid("[]byte", rows, "Wrong rows type")
}
if len(v)%(data.Dim/8) != 0 {
return merr.WrapErrParameterInvalid(data.Dim/8, len(v), "Wrong vector size")
}
data.Data = append(data.Data, v...)
return nil
}
// AppendRows appends FLATTEN vectors to field data.
func (data *FloatVectorFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]float32)
if !ok || len(v)%(data.Dim) != 0 {
return merr.WrapErrParameterInvalid("[]float32", rows, "Wrong rows type")
}
if len(v)%(data.Dim) != 0 {
return merr.WrapErrParameterInvalid(data.Dim, len(v), "Wrong vector size")
}
data.Data = append(data.Data, v...)
return nil
}
// AppendRows appends FLATTEN vectors to field data.
func (data *Float16VectorFieldData) AppendRows(rows interface{}) error {
v, ok := rows.([]byte)
if !ok || len(v)%(data.Dim*2) != 0 {
return merr.WrapErrParameterInvalid("[]byte", rows, "Wrong rows type")
}
if len(v)%(data.Dim*2) != 0 {
return merr.WrapErrParameterInvalid(data.Dim*2, len(v), "Wrong vector size")
}
data.Data = append(data.Data, v...)
return nil
}
// GetMemorySize implements FieldData.GetMemorySize
func (data *BoolFieldData) GetMemorySize() int { return binary.Size(data.Data) }
func (data *Int8FieldData) GetMemorySize() int { return binary.Size(data.Data) }
func (data *Int16FieldData) GetMemorySize() int { return binary.Size(data.Data) }
func (data *Int32FieldData) GetMemorySize() int { return binary.Size(data.Data) }
func (data *Int64FieldData) GetMemorySize() int { return binary.Size(data.Data) }
func (data *FloatFieldData) GetMemorySize() int { return binary.Size(data.Data) }
func (data *DoubleFieldData) GetMemorySize() int { return binary.Size(data.Data) }
func (data *BinaryVectorFieldData) GetMemorySize() int { return binary.Size(data.Data) + 4 }
func (data *FloatVectorFieldData) GetMemorySize() int { return binary.Size(data.Data) + 4 }
func (data *Float16VectorFieldData) GetMemorySize() int { return binary.Size(data.Data) + 4 }
feat: support inverted index (#28783) issue: https://github.com/milvus-io/milvus/issues/27704 Add inverted index for some data types in Milvus. This index type can save a lot of memory compared to loading all data into RAM and speed up the term query and range query. Supported: `INT8`, `INT16`, `INT32`, `INT64`, `FLOAT`, `DOUBLE`, `BOOL` and `VARCHAR`. Not supported: `ARRAY` and `JSON`. Note: - The inverted index for `VARCHAR` is not designed to serve full-text search now. We will treat every row as a whole keyword instead of tokenizing it into multiple terms. - The inverted index don't support retrieval well, so if you create inverted index for field, those operations which depend on the raw data will fallback to use chunk storage, which will bring some performance loss. For example, comparisons between two columns and retrieval of output fields. The inverted index is very easy to be used. Taking below collection as an example: ```python fields = [ FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100), FieldSchema(name="int8", dtype=DataType.INT8), FieldSchema(name="int16", dtype=DataType.INT16), FieldSchema(name="int32", dtype=DataType.INT32), FieldSchema(name="int64", dtype=DataType.INT64), FieldSchema(name="float", dtype=DataType.FLOAT), FieldSchema(name="double", dtype=DataType.DOUBLE), FieldSchema(name="bool", dtype=DataType.BOOL), FieldSchema(name="varchar", dtype=DataType.VARCHAR, max_length=1000), FieldSchema(name="random", dtype=DataType.DOUBLE), FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=dim), ] schema = CollectionSchema(fields) collection = Collection("demo", schema) ``` Then we can simply create inverted index for field via: ```python index_type = "INVERTED" collection.create_index("int8", {"index_type": index_type}) collection.create_index("int16", {"index_type": index_type}) collection.create_index("int32", {"index_type": index_type}) collection.create_index("int64", {"index_type": index_type}) collection.create_index("float", {"index_type": index_type}) collection.create_index("double", {"index_type": index_type}) collection.create_index("bool", {"index_type": index_type}) collection.create_index("varchar", {"index_type": index_type}) ``` Then, term query and range query on the field can be speed up automatically by the inverted index: ```python result = collection.query(expr='int64 in [1, 2, 3]', output_fields=["pk"]) result = collection.query(expr='int64 < 5', output_fields=["pk"]) result = collection.query(expr='int64 > 2997', output_fields=["pk"]) result = collection.query(expr='1 < int64 < 5', output_fields=["pk"]) ``` --------- Signed-off-by: longjiquan <jiquan.long@zilliz.com>
2023-12-31 19:50:47 +08:00
// GetDataType implements FieldData.GetDataType
func (data *BoolFieldData) GetDataType() schemapb.DataType { return schemapb.DataType_Bool }
func (data *Int8FieldData) GetDataType() schemapb.DataType { return schemapb.DataType_Int8 }
func (data *Int16FieldData) GetDataType() schemapb.DataType { return schemapb.DataType_Int16 }
func (data *Int32FieldData) GetDataType() schemapb.DataType { return schemapb.DataType_Int32 }
func (data *Int64FieldData) GetDataType() schemapb.DataType { return schemapb.DataType_Int64 }
func (data *FloatFieldData) GetDataType() schemapb.DataType { return schemapb.DataType_Float }
func (data *DoubleFieldData) GetDataType() schemapb.DataType { return schemapb.DataType_Double }
func (data *StringFieldData) GetDataType() schemapb.DataType { return data.DataType }
func (data *ArrayFieldData) GetDataType() schemapb.DataType { return schemapb.DataType_Array }
func (data *JSONFieldData) GetDataType() schemapb.DataType { return schemapb.DataType_JSON }
func (data *BinaryVectorFieldData) GetDataType() schemapb.DataType {
return schemapb.DataType_BinaryVector
}
func (data *FloatVectorFieldData) GetDataType() schemapb.DataType {
return schemapb.DataType_FloatVector
}
func (data *Float16VectorFieldData) GetDataType() schemapb.DataType {
return schemapb.DataType_Float16Vector
}
// why not binary.Size(data) directly? binary.Size(data) return -1
// binary.Size returns how many bytes Write would generate to encode the value v, which
// must be a fixed-size value or a slice of fixed-size values, or a pointer to such data.
// If v is neither of these, binary.Size returns -1.
func (data *StringFieldData) GetMemorySize() int {
var size int
for _, val := range data.Data {
size += len(val) + 16
}
return size
}
func (data *ArrayFieldData) GetMemorySize() int {
var size int
for _, val := range data.Data {
switch data.ElementType {
case schemapb.DataType_Bool:
size += binary.Size(val.GetBoolData().GetData())
case schemapb.DataType_Int8:
size += binary.Size(val.GetIntData().GetData()) / 4
case schemapb.DataType_Int16:
size += binary.Size(val.GetIntData().GetData()) / 2
case schemapb.DataType_Int32:
size += binary.Size(val.GetIntData().GetData())
case schemapb.DataType_Int64:
size += binary.Size(val.GetLongData().GetData())
case schemapb.DataType_Float:
size += binary.Size(val.GetFloatData().GetData())
case schemapb.DataType_Double:
size += binary.Size(val.GetDoubleData().GetData())
case schemapb.DataType_String, schemapb.DataType_VarChar:
size += (&StringFieldData{Data: val.GetStringData().GetData()}).GetMemorySize()
}
}
return size
}
func (data *JSONFieldData) GetMemorySize() int {
var size int
for _, val := range data.Data {
size += len(val) + 16
}
return size
}