2024-03-07 12:42:37 +08:00
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// Licensed to the LF AI & Data foundation under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package storage
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import (
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"encoding/json"
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"fmt"
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enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
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"go.uber.org/zap"
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2024-03-07 12:42:37 +08:00
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"github.com/milvus-io/milvus-proto/go-api/v2/schemapb"
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enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
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"github.com/milvus-io/milvus/internal/util/bloomfilter"
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2024-03-07 12:42:37 +08:00
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"github.com/milvus-io/milvus/pkg/common"
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enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
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"github.com/milvus-io/milvus/pkg/log"
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2024-03-07 12:42:37 +08:00
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"github.com/milvus-io/milvus/pkg/util/merr"
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"github.com/milvus-io/milvus/pkg/util/paramtable"
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)
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// FieldStats contains statistics data for any column
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// todo: compatible to PrimaryKeyStats
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type FieldStats struct {
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enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
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FieldID int64 `json:"fieldID"`
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Type schemapb.DataType `json:"type"`
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Max ScalarFieldValue `json:"max"` // for scalar field
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Min ScalarFieldValue `json:"min"` // for scalar field
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BFType bloomfilter.BFType `json:"bfType"` // for scalar field
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BF bloomfilter.BloomFilterInterface `json:"bf"` // for scalar field
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Centroids []VectorFieldValue `json:"centroids"` // for vector field
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2024-03-07 12:42:37 +08:00
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}
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2024-06-10 21:34:08 +08:00
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func (stats *FieldStats) Clone() FieldStats {
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return FieldStats{
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FieldID: stats.FieldID,
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Type: stats.Type,
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Max: stats.Max,
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Min: stats.Min,
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BFType: stats.BFType,
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BF: stats.BF,
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Centroids: stats.Centroids,
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}
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}
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2024-03-07 12:42:37 +08:00
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// UnmarshalJSON unmarshal bytes to FieldStats
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func (stats *FieldStats) UnmarshalJSON(data []byte) error {
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var messageMap map[string]*json.RawMessage
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err := json.Unmarshal(data, &messageMap)
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if err != nil {
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return err
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}
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if value, ok := messageMap["fieldID"]; ok && value != nil {
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err = json.Unmarshal(*messageMap["fieldID"], &stats.FieldID)
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if err != nil {
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return err
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}
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} else {
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return fmt.Errorf("invalid fieldStats, no fieldID")
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}
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stats.Type = schemapb.DataType_Int64
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value, ok := messageMap["type"]
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if !ok {
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value, ok = messageMap["pkType"]
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}
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if ok && value != nil {
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var typeValue int32
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err = json.Unmarshal(*value, &typeValue)
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if err != nil {
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return err
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}
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if typeValue > 0 {
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stats.Type = schemapb.DataType(typeValue)
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}
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}
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isScalarField := false
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switch stats.Type {
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case schemapb.DataType_Int8:
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stats.Max = &Int8FieldValue{}
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stats.Min = &Int8FieldValue{}
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isScalarField = true
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case schemapb.DataType_Int16:
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stats.Max = &Int16FieldValue{}
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stats.Min = &Int16FieldValue{}
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isScalarField = true
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case schemapb.DataType_Int32:
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stats.Max = &Int32FieldValue{}
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stats.Min = &Int32FieldValue{}
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isScalarField = true
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case schemapb.DataType_Int64:
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stats.Max = &Int64FieldValue{}
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stats.Min = &Int64FieldValue{}
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isScalarField = true
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case schemapb.DataType_Float:
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stats.Max = &FloatFieldValue{}
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stats.Min = &FloatFieldValue{}
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isScalarField = true
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case schemapb.DataType_Double:
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stats.Max = &DoubleFieldValue{}
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stats.Min = &DoubleFieldValue{}
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isScalarField = true
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case schemapb.DataType_String:
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stats.Max = &StringFieldValue{}
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stats.Min = &StringFieldValue{}
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isScalarField = true
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case schemapb.DataType_VarChar:
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stats.Max = &VarCharFieldValue{}
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stats.Min = &VarCharFieldValue{}
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isScalarField = true
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case schemapb.DataType_FloatVector:
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stats.Centroids = []VectorFieldValue{}
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isScalarField = false
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default:
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// unsupported data type
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}
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if isScalarField {
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if value, ok := messageMap["max"]; ok && value != nil {
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err = json.Unmarshal(*messageMap["max"], &stats.Max)
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if err != nil {
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return err
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}
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}
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if value, ok := messageMap["min"]; ok && value != nil {
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err = json.Unmarshal(*messageMap["min"], &stats.Min)
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if err != nil {
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return err
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}
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}
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// compatible with primaryKeyStats
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if maxPkMessage, ok := messageMap["maxPk"]; ok && maxPkMessage != nil {
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err = json.Unmarshal(*maxPkMessage, stats.Max)
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if err != nil {
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return err
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}
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}
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if minPkMessage, ok := messageMap["minPk"]; ok && minPkMessage != nil {
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err = json.Unmarshal(*minPkMessage, stats.Min)
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if err != nil {
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return err
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}
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}
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enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
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bfType := bloomfilter.BasicBF
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if bfTypeMessage, ok := messageMap["bfType"]; ok && bfTypeMessage != nil {
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err := json.Unmarshal(*bfTypeMessage, &bfType)
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2024-03-07 12:42:37 +08:00
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if err != nil {
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return err
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}
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enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
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stats.BFType = bfType
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}
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if bfMessage, ok := messageMap["bf"]; ok && bfMessage != nil {
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bf, err := bloomfilter.UnmarshalJSON(*bfMessage, bfType)
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if err != nil {
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log.Warn("Failed to unmarshal bloom filter, use AlwaysTrueBloomFilter instead of return err", zap.Error(err))
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bf = bloomfilter.AlwaysTrueBloomFilter
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}
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stats.BF = bf
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2024-03-07 12:42:37 +08:00
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}
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} else {
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stats.initCentroids(data, stats.Type)
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err = json.Unmarshal(*messageMap["centroids"], &stats.Centroids)
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if err != nil {
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return err
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}
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}
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return nil
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}
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func (stats *FieldStats) initCentroids(data []byte, dataType schemapb.DataType) {
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type FieldStatsAux struct {
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enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
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FieldID int64 `json:"fieldID"`
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Type schemapb.DataType `json:"type"`
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Max json.RawMessage `json:"max"`
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Min json.RawMessage `json:"min"`
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BF bloomfilter.BloomFilterInterface `json:"bf"`
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Centroids []json.RawMessage `json:"centroids"`
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2024-03-07 12:42:37 +08:00
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}
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// Unmarshal JSON into the auxiliary struct
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var aux FieldStatsAux
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if err := json.Unmarshal(data, &aux); err != nil {
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return
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}
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for i := 0; i < len(aux.Centroids); i++ {
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switch dataType {
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case schemapb.DataType_FloatVector:
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stats.Centroids = append(stats.Centroids, &FloatVectorFieldValue{})
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default:
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// other vector datatype
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}
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}
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}
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func (stats *FieldStats) UpdateByMsgs(msgs FieldData) {
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switch stats.Type {
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case schemapb.DataType_Int8:
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data := msgs.(*Int8FieldData).Data
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// return error: msgs must has one element at least
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if len(data) < 1 {
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return
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}
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b := make([]byte, 8)
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for _, int8Value := range data {
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pk := NewInt8FieldValue(int8Value)
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stats.UpdateMinMax(pk)
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common.Endian.PutUint64(b, uint64(int8Value))
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stats.BF.Add(b)
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}
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case schemapb.DataType_Int16:
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data := msgs.(*Int16FieldData).Data
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// return error: msgs must has one element at least
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if len(data) < 1 {
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return
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}
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b := make([]byte, 8)
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for _, int16Value := range data {
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pk := NewInt16FieldValue(int16Value)
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|
|
stats.UpdateMinMax(pk)
|
|
|
|
common.Endian.PutUint64(b, uint64(int16Value))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
}
|
|
|
|
case schemapb.DataType_Int32:
|
|
|
|
data := msgs.(*Int32FieldData).Data
|
|
|
|
// return error: msgs must has one element at least
|
|
|
|
if len(data) < 1 {
|
|
|
|
return
|
|
|
|
}
|
|
|
|
b := make([]byte, 8)
|
|
|
|
for _, int32Value := range data {
|
|
|
|
pk := NewInt32FieldValue(int32Value)
|
|
|
|
stats.UpdateMinMax(pk)
|
|
|
|
common.Endian.PutUint64(b, uint64(int32Value))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
}
|
|
|
|
case schemapb.DataType_Int64:
|
|
|
|
data := msgs.(*Int64FieldData).Data
|
|
|
|
// return error: msgs must has one element at least
|
|
|
|
if len(data) < 1 {
|
|
|
|
return
|
|
|
|
}
|
|
|
|
b := make([]byte, 8)
|
|
|
|
for _, int64Value := range data {
|
|
|
|
pk := NewInt64FieldValue(int64Value)
|
|
|
|
stats.UpdateMinMax(pk)
|
|
|
|
common.Endian.PutUint64(b, uint64(int64Value))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
}
|
|
|
|
case schemapb.DataType_Float:
|
|
|
|
data := msgs.(*FloatFieldData).Data
|
|
|
|
// return error: msgs must has one element at least
|
|
|
|
if len(data) < 1 {
|
|
|
|
return
|
|
|
|
}
|
|
|
|
b := make([]byte, 8)
|
|
|
|
for _, floatValue := range data {
|
|
|
|
pk := NewFloatFieldValue(floatValue)
|
|
|
|
stats.UpdateMinMax(pk)
|
|
|
|
common.Endian.PutUint64(b, uint64(floatValue))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
}
|
|
|
|
case schemapb.DataType_Double:
|
|
|
|
data := msgs.(*DoubleFieldData).Data
|
|
|
|
// return error: msgs must has one element at least
|
|
|
|
if len(data) < 1 {
|
|
|
|
return
|
|
|
|
}
|
|
|
|
b := make([]byte, 8)
|
|
|
|
for _, doubleValue := range data {
|
|
|
|
pk := NewDoubleFieldValue(doubleValue)
|
|
|
|
stats.UpdateMinMax(pk)
|
|
|
|
common.Endian.PutUint64(b, uint64(doubleValue))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
}
|
|
|
|
case schemapb.DataType_String:
|
|
|
|
data := msgs.(*StringFieldData).Data
|
|
|
|
// return error: msgs must has one element at least
|
|
|
|
if len(data) < 1 {
|
|
|
|
return
|
|
|
|
}
|
|
|
|
for _, str := range data {
|
|
|
|
pk := NewStringFieldValue(str)
|
|
|
|
stats.UpdateMinMax(pk)
|
|
|
|
stats.BF.AddString(str)
|
|
|
|
}
|
|
|
|
case schemapb.DataType_VarChar:
|
|
|
|
data := msgs.(*StringFieldData).Data
|
|
|
|
// return error: msgs must has one element at least
|
|
|
|
if len(data) < 1 {
|
|
|
|
return
|
|
|
|
}
|
|
|
|
for _, str := range data {
|
|
|
|
pk := NewVarCharFieldValue(str)
|
|
|
|
stats.UpdateMinMax(pk)
|
|
|
|
stats.BF.AddString(str)
|
|
|
|
}
|
|
|
|
default:
|
|
|
|
// TODO::
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
func (stats *FieldStats) Update(pk ScalarFieldValue) {
|
|
|
|
stats.UpdateMinMax(pk)
|
|
|
|
switch stats.Type {
|
|
|
|
case schemapb.DataType_Int8:
|
|
|
|
data := pk.GetValue().(int8)
|
|
|
|
b := make([]byte, 8)
|
|
|
|
common.Endian.PutUint64(b, uint64(data))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
case schemapb.DataType_Int16:
|
|
|
|
data := pk.GetValue().(int16)
|
|
|
|
b := make([]byte, 8)
|
|
|
|
common.Endian.PutUint64(b, uint64(data))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
case schemapb.DataType_Int32:
|
|
|
|
data := pk.GetValue().(int32)
|
|
|
|
b := make([]byte, 8)
|
|
|
|
common.Endian.PutUint64(b, uint64(data))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
case schemapb.DataType_Int64:
|
|
|
|
data := pk.GetValue().(int64)
|
|
|
|
b := make([]byte, 8)
|
|
|
|
common.Endian.PutUint64(b, uint64(data))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
case schemapb.DataType_Float:
|
|
|
|
data := pk.GetValue().(float32)
|
|
|
|
b := make([]byte, 8)
|
|
|
|
common.Endian.PutUint64(b, uint64(data))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
case schemapb.DataType_Double:
|
|
|
|
data := pk.GetValue().(float64)
|
|
|
|
b := make([]byte, 8)
|
|
|
|
common.Endian.PutUint64(b, uint64(data))
|
|
|
|
stats.BF.Add(b)
|
|
|
|
case schemapb.DataType_String:
|
|
|
|
data := pk.GetValue().(string)
|
|
|
|
stats.BF.AddString(data)
|
|
|
|
case schemapb.DataType_VarChar:
|
|
|
|
data := pk.GetValue().(string)
|
|
|
|
stats.BF.AddString(data)
|
|
|
|
default:
|
|
|
|
// todo support vector field
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// UpdateMinMax update min and max value
|
|
|
|
func (stats *FieldStats) UpdateMinMax(pk ScalarFieldValue) {
|
|
|
|
if stats.Min == nil {
|
|
|
|
stats.Min = pk
|
|
|
|
} else if stats.Min.GT(pk) {
|
|
|
|
stats.Min = pk
|
|
|
|
}
|
|
|
|
|
|
|
|
if stats.Max == nil {
|
|
|
|
stats.Max = pk
|
|
|
|
} else if stats.Max.LT(pk) {
|
|
|
|
stats.Max = pk
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// SetVectorCentroids update centroids value
|
|
|
|
func (stats *FieldStats) SetVectorCentroids(centroids ...VectorFieldValue) {
|
|
|
|
stats.Centroids = centroids
|
|
|
|
}
|
|
|
|
|
|
|
|
func NewFieldStats(fieldID int64, pkType schemapb.DataType, rowNum int64) (*FieldStats, error) {
|
|
|
|
if pkType == schemapb.DataType_FloatVector {
|
|
|
|
return &FieldStats{
|
|
|
|
FieldID: fieldID,
|
|
|
|
Type: pkType,
|
|
|
|
}, nil
|
|
|
|
}
|
enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
|
|
|
bfType := paramtable.Get().CommonCfg.BloomFilterType.GetValue()
|
2024-03-07 12:42:37 +08:00
|
|
|
return &FieldStats{
|
|
|
|
FieldID: fieldID,
|
|
|
|
Type: pkType,
|
enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
|
|
|
BFType: bloomfilter.BFTypeFromString(bfType),
|
|
|
|
BF: bloomfilter.NewBloomFilterWithType(
|
|
|
|
uint(rowNum),
|
|
|
|
paramtable.Get().CommonCfg.MaxBloomFalsePositive.GetAsFloat(),
|
|
|
|
bfType),
|
2024-03-07 12:42:37 +08:00
|
|
|
}, nil
|
|
|
|
}
|
|
|
|
|
|
|
|
// FieldStatsWriter writes stats to buffer
|
|
|
|
type FieldStatsWriter struct {
|
|
|
|
buffer []byte
|
|
|
|
}
|
|
|
|
|
|
|
|
// GetBuffer returns buffer
|
|
|
|
func (sw *FieldStatsWriter) GetBuffer() []byte {
|
|
|
|
return sw.buffer
|
|
|
|
}
|
|
|
|
|
|
|
|
// GenerateList writes Stats slice to buffer
|
|
|
|
func (sw *FieldStatsWriter) GenerateList(stats []*FieldStats) error {
|
|
|
|
b, err := json.Marshal(stats)
|
|
|
|
if err != nil {
|
|
|
|
return err
|
|
|
|
}
|
|
|
|
sw.buffer = b
|
|
|
|
return nil
|
|
|
|
}
|
|
|
|
|
|
|
|
// GenerateByData writes data from @msgs with @fieldID to @buffer
|
|
|
|
func (sw *FieldStatsWriter) GenerateByData(fieldID int64, pkType schemapb.DataType, msgs ...FieldData) error {
|
|
|
|
statsList := make([]*FieldStats, 0)
|
enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
|
|
|
|
|
|
|
bfType := paramtable.Get().CommonCfg.BloomFilterType.GetValue()
|
2024-03-07 12:42:37 +08:00
|
|
|
for _, msg := range msgs {
|
|
|
|
stats := &FieldStats{
|
|
|
|
FieldID: fieldID,
|
|
|
|
Type: pkType,
|
enhance: Use Blocked Bloom Filter instead of basic bloom fitler impl. (#33405)
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
2024-05-31 17:49:45 +08:00
|
|
|
BFType: bloomfilter.BFTypeFromString(bfType),
|
|
|
|
BF: bloomfilter.NewBloomFilterWithType(
|
|
|
|
uint(msg.RowNum()),
|
|
|
|
paramtable.Get().CommonCfg.MaxBloomFalsePositive.GetAsFloat(),
|
|
|
|
bfType),
|
2024-03-07 12:42:37 +08:00
|
|
|
}
|
|
|
|
|
|
|
|
stats.UpdateByMsgs(msg)
|
|
|
|
statsList = append(statsList, stats)
|
|
|
|
}
|
|
|
|
return sw.GenerateList(statsList)
|
|
|
|
}
|
|
|
|
|
|
|
|
// FieldStatsReader reads stats
|
|
|
|
type FieldStatsReader struct {
|
|
|
|
buffer []byte
|
|
|
|
}
|
|
|
|
|
|
|
|
// SetBuffer sets buffer
|
|
|
|
func (sr *FieldStatsReader) SetBuffer(buffer []byte) {
|
|
|
|
sr.buffer = buffer
|
|
|
|
}
|
|
|
|
|
|
|
|
// GetFieldStatsList returns buffer as FieldStats
|
|
|
|
func (sr *FieldStatsReader) GetFieldStatsList() ([]*FieldStats, error) {
|
|
|
|
var statsList []*FieldStats
|
|
|
|
err := json.Unmarshal(sr.buffer, &statsList)
|
|
|
|
if err != nil {
|
|
|
|
// Compatible to PrimaryKey Stats
|
|
|
|
stats := &FieldStats{}
|
|
|
|
errNew := json.Unmarshal(sr.buffer, &stats)
|
|
|
|
if errNew != nil {
|
|
|
|
return nil, merr.WrapErrParameterInvalid("valid JSON", string(sr.buffer), err.Error())
|
|
|
|
}
|
|
|
|
return []*FieldStats{stats}, nil
|
|
|
|
}
|
|
|
|
|
|
|
|
return statsList, nil
|
|
|
|
}
|
|
|
|
|
|
|
|
func DeserializeFieldStats(blob *Blob) ([]*FieldStats, error) {
|
|
|
|
if len(blob.Value) == 0 {
|
|
|
|
return []*FieldStats{}, nil
|
|
|
|
}
|
|
|
|
sr := &FieldStatsReader{}
|
|
|
|
sr.SetBuffer(blob.Value)
|
|
|
|
stats, err := sr.GetFieldStatsList()
|
|
|
|
if err != nil {
|
|
|
|
return nil, err
|
|
|
|
}
|
|
|
|
return stats, nil
|
|
|
|
}
|