milvus/internal/core/unittest/test_inverted_index.cpp

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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>
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// Copyright (C) 2019-2020 Zilliz. All rights reserved.
//
// Licensed 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
#include <gtest/gtest.h>
#include <functional>
#include <boost/filesystem.hpp>
#include <unordered_set>
#include "common/Tracer.h"
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>
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#include "index/InvertedIndexTantivy.h"
#include "storage/Util.h"
#include "storage/InsertData.h"
#include "indexbuilder/IndexFactory.h"
#include "index/IndexFactory.h"
#include "test_utils/indexbuilder_test_utils.h"
#include "index/Meta.h"
using namespace milvus;
namespace milvus::test {
auto
gen_field_meta(int64_t collection_id = 1,
int64_t partition_id = 2,
int64_t segment_id = 3,
int64_t field_id = 101,
DataType data_type = DataType::NONE,
DataType element_type = DataType::NONE,
bool nullable = false) -> storage::FieldDataMeta {
auto meta = storage::FieldDataMeta{
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>
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.collection_id = collection_id,
.partition_id = partition_id,
.segment_id = segment_id,
.field_id = field_id,
};
meta.field_schema.set_data_type(
static_cast<proto::schema::DataType>(data_type));
meta.field_schema.set_element_type(
static_cast<proto::schema::DataType>(element_type));
meta.field_schema.set_nullable(nullable);
return meta;
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>
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}
auto
gen_index_meta(int64_t segment_id = 3,
int64_t field_id = 101,
int64_t index_build_id = 1000,
int64_t index_version = 10000) -> storage::IndexMeta {
return storage::IndexMeta{
.segment_id = segment_id,
.field_id = field_id,
.build_id = index_build_id,
.index_version = index_version,
};
}
auto
gen_local_storage_config(const std::string& root_path)
-> storage::StorageConfig {
auto ret = storage::StorageConfig{};
ret.storage_type = "local";
ret.root_path = root_path;
return ret;
}
struct ChunkManagerWrapper {
ChunkManagerWrapper(storage::ChunkManagerPtr cm) : cm_(cm) {
}
~ChunkManagerWrapper() {
for (const auto& file : written_) {
cm_->Remove(file);
}
boost::filesystem::remove_all(cm_->GetRootPath());
}
void
Write(const std::string& filepath, void* buf, uint64_t len) {
written_.insert(filepath);
cm_->Write(filepath, buf, len);
}
const storage::ChunkManagerPtr cm_;
std::unordered_set<std::string> written_;
};
} // namespace milvus::test
template <typename T,
DataType dtype,
DataType element_type = DataType::NONE,
bool nullable = false>
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>
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void
test_run() {
int64_t collection_id = 1;
int64_t partition_id = 2;
int64_t segment_id = 3;
int64_t field_id = 101;
int64_t index_build_id = 4000;
int64_t index_version = 4000;
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>
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auto field_meta = test::gen_field_meta(collection_id,
partition_id,
segment_id,
field_id,
dtype,
element_type,
nullable);
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>
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auto index_meta = test::gen_index_meta(
segment_id, field_id, index_build_id, index_version);
std::string root_path = "/tmp/test-inverted-index/";
auto storage_config = test::gen_local_storage_config(root_path);
auto cm = storage::CreateChunkManager(storage_config);
size_t nb = 10000;
std::vector<T> data_gen;
boost::container::vector<T> data;
FixedVector<bool> valid_data;
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>
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if constexpr (!std::is_same_v<T, bool>) {
data_gen = GenSortedArr<T>(nb);
} else {
for (size_t i = 0; i < nb; i++) {
data_gen.push_back(rand() % 2 == 0);
}
}
if (nullable) {
valid_data.reserve(nb);
for (size_t i = 0; i < nb; i++) {
valid_data.push_back(rand() % 2 == 0);
}
}
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 11:50:47 +00:00
for (auto x : data_gen) {
data.push_back(x);
}
auto field_data = storage::CreateFieldData(dtype, nullable);
if (nullable) {
int byteSize = (nb + 7) / 8;
uint8_t* valid_data_ = new uint8_t[byteSize];
for (int i = 0; i < nb; i++) {
bool value = valid_data[i];
int byteIndex = i / 8;
int bitIndex = i % 8;
if (value) {
valid_data_[byteIndex] |= (1 << bitIndex);
} else {
valid_data_[byteIndex] &= ~(1 << bitIndex);
}
}
field_data->FillFieldData(data.data(), valid_data_, data.size());
delete[] valid_data_;
} else {
field_data->FillFieldData(data.data(), data.size());
}
// std::cout << "length:" << field_data->get_num_rows() << std::endl;
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 11:50:47 +00:00
storage::InsertData insert_data(field_data);
insert_data.SetFieldDataMeta(field_meta);
insert_data.SetTimestamps(0, 100);
auto serialized_bytes = insert_data.Serialize(storage::Remote);
auto get_binlog_path = [=](int64_t log_id) {
return fmt::format("{}/{}/{}/{}/{}",
collection_id,
partition_id,
segment_id,
field_id,
log_id);
};
auto log_path = get_binlog_path(0);
auto cm_w = test::ChunkManagerWrapper(cm);
cm_w.Write(log_path, serialized_bytes.data(), serialized_bytes.size());
storage::FileManagerContext ctx(field_meta, index_meta, cm);
std::vector<std::string> index_files;
{
Config config;
config["index_type"] = milvus::index::INVERTED_INDEX_TYPE;
config["insert_files"] = std::vector<std::string>{log_path};
auto index = indexbuilder::IndexFactory::GetInstance().CreateIndex(
dtype, config, ctx);
index->Build();
auto create_index_result = index->Upload();
auto memSize = create_index_result->GetMemSize();
auto serializedSize = create_index_result->GetSerializedSize();
ASSERT_GT(memSize, 0);
ASSERT_GT(serializedSize, 0);
index_files = create_index_result->GetIndexFiles();
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 11:50:47 +00:00
}
{
index::CreateIndexInfo index_info{};
index_info.index_type = milvus::index::INVERTED_INDEX_TYPE;
index_info.field_type = dtype;
Config config;
config["index_files"] = index_files;
ctx.set_for_loading_index(true);
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 11:50:47 +00:00
auto index =
index::IndexFactory::GetInstance().CreateIndex(index_info, ctx);
index->Load(milvus::tracer::TraceContext{}, config);
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 11:50:47 +00:00
auto cnt = index->Count();
ASSERT_EQ(cnt, nb);
using IndexType = index::ScalarIndex<T>;
auto real_index = dynamic_cast<IndexType*>(index.get());
if constexpr (!std::is_floating_point_v<T>) {
// hard to compare floating-point value.
{
boost::container::vector<T> test_data;
std::unordered_set<T> s;
size_t nq = 10;
for (size_t i = 0; i < nq && i < nb; i++) {
test_data.push_back(data[i]);
s.insert(data[i]);
}
auto bitset =
real_index->In(test_data.size(), test_data.data());
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
if (nullable && !valid_data[i]) {
ASSERT_EQ(bitset[i], false);
} else {
ASSERT_EQ(bitset[i], s.find(data[i]) != s.end());
}
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 11:50:47 +00:00
}
}
{
boost::container::vector<T> test_data;
std::unordered_set<T> s;
size_t nq = 10;
for (size_t i = 0; i < nq && i < nb; i++) {
test_data.push_back(data[i]);
s.insert(data[i]);
}
auto bitset =
real_index->NotIn(test_data.size(), test_data.data());
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
if (nullable && !valid_data[i]) {
ASSERT_EQ(bitset[i], false);
} else {
ASSERT_NE(bitset[i], s.find(data[i]) != s.end());
}
}
}
{
auto bitset = real_index->IsNull();
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
if (nullable && !valid_data[i]) {
ASSERT_EQ(bitset[i], true);
} else {
ASSERT_EQ(bitset[i], false);
}
}
}
{
auto bitset = real_index->IsNotNull();
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
if (nullable && !valid_data[i]) {
ASSERT_EQ(bitset[i], false);
} else {
ASSERT_EQ(bitset[i], true);
}
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 11:50:47 +00:00
}
}
}
using RefFunc = std::function<bool(int64_t)>;
if constexpr (!std::is_same_v<T, bool>) {
// range query on boolean is not reasonable.
{
std::vector<std::tuple<T, OpType, RefFunc>> test_cases{
{20,
OpType::GreaterThan,
[&](int64_t i) -> bool { return data[i] > 20; }},
{20,
OpType::GreaterEqual,
[&](int64_t i) -> bool { return data[i] >= 20; }},
{20,
OpType::LessThan,
[&](int64_t i) -> bool { return data[i] < 20; }},
{20,
OpType::LessEqual,
[&](int64_t i) -> bool { return data[i] <= 20; }},
};
for (const auto& [test_value, op, ref] : test_cases) {
auto bitset = real_index->Range(test_value, op);
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < nb; i++) {
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 11:50:47 +00:00
auto ans = bitset[i];
auto should = ref(i);
if (nullable && !valid_data[i]) {
ASSERT_EQ(ans, false);
} else {
ASSERT_EQ(ans, should)
<< "op: " << op << ", @" << i
<< ", ans: " << ans << ", ref: " << should;
}
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 11:50:47 +00:00
}
}
}
{
std::vector<std::tuple<T, bool, T, bool, RefFunc>> test_cases{
{1,
false,
20,
false,
[&](int64_t i) -> bool {
return 1 < data[i] && data[i] < 20;
}},
{1,
false,
20,
true,
[&](int64_t i) -> bool {
return 1 < data[i] && data[i] <= 20;
}},
{1,
true,
20,
false,
[&](int64_t i) -> bool {
return 1 <= data[i] && data[i] < 20;
}},
{1,
true,
20,
true,
[&](int64_t i) -> bool {
return 1 <= data[i] && data[i] <= 20;
}},
};
for (const auto& [lb, lb_inclusive, ub, ub_inclusive, ref] :
test_cases) {
auto bitset =
real_index->Range(lb, lb_inclusive, ub, ub_inclusive);
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < nb; i++) {
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 11:50:47 +00:00
auto ans = bitset[i];
auto should = ref(i);
if (nullable && !valid_data[i]) {
ASSERT_EQ(ans, false);
} else {
ASSERT_EQ(ans, should)
<< "@" << i << ", ans: " << ans
<< ", ref: " << should;
}
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 11:50:47 +00:00
}
}
}
}
}
}
template <bool nullable = false>
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 11:50:47 +00:00
void
test_string() {
using T = std::string;
DataType dtype = DataType::VARCHAR;
int64_t collection_id = 1;
int64_t partition_id = 2;
int64_t segment_id = 3;
int64_t field_id = 101;
int64_t index_build_id = 4001;
int64_t index_version = 4001;
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 11:50:47 +00:00
auto field_meta = test::gen_field_meta(collection_id,
partition_id,
segment_id,
field_id,
dtype,
DataType::NONE,
nullable);
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 11:50:47 +00:00
auto index_meta = test::gen_index_meta(
segment_id, field_id, index_build_id, index_version);
std::string root_path = "/tmp/test-inverted-index/";
auto storage_config = test::gen_local_storage_config(root_path);
auto cm = storage::CreateChunkManager(storage_config);
size_t nb = 10000;
boost::container::vector<T> data;
FixedVector<bool> valid_data;
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 11:50:47 +00:00
for (size_t i = 0; i < nb; i++) {
data.push_back(std::to_string(rand()));
}
if (nullable) {
valid_data.reserve(nb);
for (size_t i = 0; i < nb; i++) {
valid_data.push_back(rand() % 2 == 0);
}
}
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 11:50:47 +00:00
auto field_data = storage::CreateFieldData(dtype, nullable);
if (nullable) {
int byteSize = (nb + 7) / 8;
uint8_t* valid_data_ = new uint8_t[byteSize];
for (int i = 0; i < nb; i++) {
bool value = valid_data[i];
int byteIndex = i / 8;
int bitIndex = i % 8;
if (value) {
valid_data_[byteIndex] |= (1 << bitIndex);
} else {
valid_data_[byteIndex] &= ~(1 << bitIndex);
}
}
field_data->FillFieldData(data.data(), valid_data_, data.size());
delete[] valid_data_;
} else {
field_data->FillFieldData(data.data(), data.size());
}
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 11:50:47 +00:00
storage::InsertData insert_data(field_data);
insert_data.SetFieldDataMeta(field_meta);
insert_data.SetTimestamps(0, 100);
auto serialized_bytes = insert_data.Serialize(storage::Remote);
auto get_binlog_path = [=](int64_t log_id) {
return fmt::format("{}/{}/{}/{}/{}",
collection_id,
partition_id,
segment_id,
field_id,
log_id);
};
auto log_path = get_binlog_path(0);
auto cm_w = test::ChunkManagerWrapper(cm);
cm_w.Write(log_path, serialized_bytes.data(), serialized_bytes.size());
storage::FileManagerContext ctx(field_meta, index_meta, cm);
std::vector<std::string> index_files;
{
Config config;
config["index_type"] = milvus::index::INVERTED_INDEX_TYPE;
config["insert_files"] = std::vector<std::string>{log_path};
auto index = indexbuilder::IndexFactory::GetInstance().CreateIndex(
dtype, config, ctx);
index->Build();
auto create_index_result = index->Upload();
auto memSize = create_index_result->GetMemSize();
auto serializedSize = create_index_result->GetSerializedSize();
ASSERT_GT(memSize, 0);
ASSERT_GT(serializedSize, 0);
index_files = create_index_result->GetIndexFiles();
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 11:50:47 +00:00
}
{
index::CreateIndexInfo index_info{};
index_info.index_type = milvus::index::INVERTED_INDEX_TYPE;
index_info.field_type = dtype;
Config config;
config["index_files"] = index_files;
ctx.set_for_loading_index(true);
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 11:50:47 +00:00
auto index =
index::IndexFactory::GetInstance().CreateIndex(index_info, ctx);
index->Load(milvus::tracer::TraceContext{}, config);
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 11:50:47 +00:00
auto cnt = index->Count();
ASSERT_EQ(cnt, nb);
using IndexType = index::ScalarIndex<T>;
auto real_index = dynamic_cast<IndexType*>(index.get());
{
boost::container::vector<T> test_data;
std::unordered_set<T> s;
size_t nq = 10;
for (size_t i = 0; i < nq && i < nb; i++) {
test_data.push_back(data[i]);
s.insert(data[i]);
}
auto bitset = real_index->In(test_data.size(), test_data.data());
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
if (nullable && !valid_data[i]) {
ASSERT_EQ(bitset[i], false);
} else {
ASSERT_EQ(bitset[i], s.find(data[i]) != s.end());
}
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 11:50:47 +00:00
}
}
{
boost::container::vector<T> test_data;
std::unordered_set<T> s;
size_t nq = 10;
for (size_t i = 0; i < nq && i < nb; i++) {
test_data.push_back(data[i]);
s.insert(data[i]);
}
auto bitset = real_index->NotIn(test_data.size(), test_data.data());
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
if (nullable && !valid_data[i]) {
ASSERT_EQ(bitset[i], false);
} else {
ASSERT_NE(bitset[i], s.find(data[i]) != s.end());
}
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 11:50:47 +00:00
}
}
using RefFunc = std::function<bool(int64_t)>;
{
std::vector<std::tuple<T, OpType, RefFunc>> test_cases{
{"20",
OpType::GreaterThan,
[&](int64_t i) -> bool { return data[i] > "20"; }},
{"20",
OpType::GreaterEqual,
[&](int64_t i) -> bool { return data[i] >= "20"; }},
{"20",
OpType::LessThan,
[&](int64_t i) -> bool { return data[i] < "20"; }},
{"20",
OpType::LessEqual,
[&](int64_t i) -> bool { return data[i] <= "20"; }},
};
for (const auto& [test_value, op, ref] : test_cases) {
auto bitset = real_index->Range(test_value, op);
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
auto ans = bitset[i];
auto should = ref(i);
if (nullable && !valid_data[i]) {
ASSERT_EQ(ans, false);
} else {
ASSERT_EQ(ans, should)
<< "op: " << op << ", @" << i << ", ans: " << ans
<< ", ref: " << should;
}
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 11:50:47 +00:00
}
}
}
{
std::vector<std::tuple<T, bool, T, bool, RefFunc>> test_cases{
{"1",
false,
"20",
false,
[&](int64_t i) -> bool {
return "1" < data[i] && data[i] < "20";
}},
{"1",
false,
"20",
true,
[&](int64_t i) -> bool {
return "1" < data[i] && data[i] <= "20";
}},
{"1",
true,
"20",
false,
[&](int64_t i) -> bool {
return "1" <= data[i] && data[i] < "20";
}},
{"1",
true,
"20",
true,
[&](int64_t i) -> bool {
return "1" <= data[i] && data[i] <= "20";
}},
};
for (const auto& [lb, lb_inclusive, ub, ub_inclusive, ref] :
test_cases) {
auto bitset =
real_index->Range(lb, lb_inclusive, ub, ub_inclusive);
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < nb; i++) {
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 11:50:47 +00:00
auto ans = bitset[i];
auto should = ref(i);
if (nullable && !valid_data[i]) {
ASSERT_EQ(ans, false);
} else {
ASSERT_EQ(ans, should) << "@" << i << ", ans: " << ans
<< ", ref: " << should;
}
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 11:50:47 +00:00
}
}
}
{
auto dataset = std::make_shared<Dataset>();
auto prefix = data[0];
dataset->Set(index::OPERATOR_TYPE, OpType::PrefixMatch);
dataset->Set(index::PREFIX_VALUE, prefix);
auto bitset = real_index->Query(dataset);
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
auto should = boost::starts_with(data[i], prefix);
if (nullable && !valid_data[i]) {
should = false;
}
ASSERT_EQ(bitset[i], should);
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>
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}
}
{
ASSERT_TRUE(real_index->SupportRegexQuery());
auto prefix = data[0];
auto bitset = real_index->RegexQuery(prefix + "(.|\n)*");
ASSERT_EQ(cnt, bitset.size());
for (size_t i = 0; i < bitset.size(); i++) {
auto should = boost::starts_with(data[i], prefix);
if (nullable && !valid_data[i]) {
should = false;
}
ASSERT_EQ(bitset[i], should);
}
}
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>
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}
}
TEST(InvertedIndex, Naive) {
test_run<int8_t, DataType::INT8>();
test_run<int16_t, DataType::INT16>();
test_run<int32_t, DataType::INT32>();
test_run<int64_t, DataType::INT64>();
test_run<bool, DataType::BOOL>();
test_run<float, DataType::FLOAT>();
test_run<double, DataType::DOUBLE>();
test_string();
test_run<int8_t, DataType::INT8, DataType::NONE, true>();
test_run<int16_t, DataType::INT16, DataType::NONE, true>();
test_run<int32_t, DataType::INT32, DataType::NONE, true>();
test_run<int64_t, DataType::INT64, DataType::NONE, true>();
test_run<bool, DataType::BOOL, DataType::NONE, true>();
test_run<float, DataType::FLOAT, DataType::NONE, true>();
test_run<double, DataType::DOUBLE, DataType::NONE, true>();
test_string<true>();
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 11:50:47 +00:00
}