Files
milvus/internal/core/src/exec/expression/JsonContainsExpr.cpp
T
ef166a673a enhance: support building core on macOS (Apple Silicon) with clang 19 (#50616)
issue: #50615

## What this PR does
Makes the C++ core build cleanly on **macOS (Apple Silicon) with
Homebrew LLVM/clang 19**.

> [!IMPORTANT]
> **Scope / impact is NOT macOS-only.** Although this PR is framed as
macOS/clang-19 enablement, two of the changes are **top-level
`requires(...)` in `internal/core/conanfile.py`, so they apply to every
platform, including Linux production builds:**
> - **`azure-sdk-for-cpp` `1.11.3@milvus/dev` → `1.16.0@milvus/dev`** (5
minor versions, `force=True`). This is the SDK Arrow uses for the
**Azure Blob remote-storage path**, so this is effectively a major Azure
SDK bump on the **production Azure object-storage path for Linux
deployments** too.
> - **`arrow` recipe `17.0.0@milvus/dev-2.6#c743ea7a…` →
`17.0.0@milvus/dev#f411aa73…`** — same Arrow version, but **rebuilt
against azure 1.16.0** with a patched `FindAzure.cmake`. The Arrow
**`package_id` (recipe revision) DID change**; it is not the same binary
as before.
> - **`libbson/1.30.6@milvus/dev`** added (also global, but new — no
pre-existing behavior to regress).
>
> **Testing blind spot:** CI green proves compile/link/UT/e2e pass, but
the UT and integration suites **do not exercise real Azure Blob
read/write**, so the Azure object-storage path is **not covered** by
this PR's CI. A real Azure Blob remote-storage regression is recommended
before merge (see Verification).

### Why the dependency bumps are required for clang 19
- **azure 1.16.0**: azure 1.11.3 bundles an older `nlohmann/json` that
uses `std::char_traits<unsigned char>`, which was **removed in libc++
19** → 1.11.3 fails to compile on clang 19. 1.16.0 fixes this.
- **arrow rebuilt against azure 1.16.0**: when Arrow is built from
source on clang 19 it must link the same azure 1.16.0; the recipe also
patches `FindAzure.cmake` to the monolithic `Azure` config so
`find_package(Azure)` resolves. Hence a new Arrow recipe revision.

### Toolchain / build enablement
- **`scripts/setenv.sh`**: probe LLVM 19/20/21 first (libc++ 17/18
`chrono operator<<` clashes with Arrow's vendored `date`; libc++ 19
fixes it).
- **`scripts/3rdparty_build.sh`**: arm64 `-march=armv8-a+crypto+crc` so
folly's F14 (SimdAndCrc on Apple Silicon) matches core/knowhere — fixes
`F14LinkCheck` undefined-symbol link errors.
- **`InvertedIndexTantivy.cpp`**: drop spurious `->template` on
non-template members (clang 19
`-Wmissing-template-arg-list-after-template-kw`).

### Drop bsoncxx / libmongoc → libbson only
The BSON layer used the **bsoncxx** C++ driver, whose CMake
unconditionally builds mongo-cxx-driver → **libmongoc** → bundled
**utf8proc**, which fails to configure under newer CMake/clang on macOS.
Milvus only needs BSON (de)serialization.

- New Conan package **`libbson/1.30.6@milvus/dev`** (libbson-only,
`ENABLE_MONGOC=OFF`; recipe published to milvus conanfiles &
production).
- New **`src/common/bson_shim.h`** — a thin `milvus::bson` C++ layer
over libbson's C API.
- Migrated `bson_view.h`, `bson_builder.{h,cpp}`, json_stats consumers
and unit tests off bsoncxx.
- CMake: `find_package(bson-1.0)` + link `mongo::bson_shared`; removed
FetchContent `thirdparty/bsoncxx`.

All three global Conan requires (arrow / libbson / azure) pin an
explicit recipe `#revision` for reproducibility.

## Verification
- Full clean rebuild on macOS arm64 / clang 19: `bin/milvus` builds;
`libmilvus_core` links **only `libbson-1.0.dylib`** (no
libmongoc/mongocxx/utf8proc).
- libbson / arrow / azure resolve from production Conan at the pinned
revisions.
- All 21 BSON unit tests (BsonView / BsonBuilder / DomNode) pass.
- Full ci-v2 suite green on Linux (build / build-ut-cov / ut-cpp / ut-go
/ integration / e2e / go-sdk).
- **TODO before merge:** real Azure Blob remote-storage read/write
regression to cover the azure-1.16.0 / arrow-rebuild blast radius (not
exercised by UT/integration).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Signed-off-by: xiaofanluan <xf@hjjaq.com>
Co-authored-by: xiaofanluan <xf@hjjaq.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-19 20:27:10 -07:00

2376 lines
95 KiB
C++

// Licensed to the LF AI & Data foundation under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "JsonContainsExpr.h"
#include <simdjson.h>
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <type_traits>
#include <unordered_set>
#include <utility>
#include <variant>
#include "boost/container/vector.hpp"
#include "boost/cstdint.hpp"
#include "common/Array.h"
#include "common/Json.h"
#include "common/Tracer.h"
#include "common/Types.h"
#include "common/type_c.h"
#include "common/ScopedTimer.h"
#include "exec/expression/EvalCtx.h"
#include "fmt/core.h"
#include "folly/FBVector.h"
#include "monitor/Monitor.h"
#include "index/ScalarIndex.h"
#include "index/json_stats/JsonKeyStats.h"
#include "index/json_stats/utils.h"
#include "opentelemetry/trace/span.h"
#include "segcore/SegmentInterface.h"
#include "segcore/SegmentSealed.h"
namespace milvus {
namespace exec {
// Replaces per-row std::set copy with a value->bit-index map built once.
// For <= 64 targets uses uint64_t bitmask (zero heap alloc per row).
// For > 64 targets uses vector<uint64_t> dynamic bitset.
template <typename T>
class ContainsAllMatcher {
public:
explicit ContainsAllMatcher(const std::set<T>& targets) {
target_count_ = targets.size();
use_small_ = (target_count_ <= 64);
uint32_t idx = 0;
for (const auto& t : targets) {
value_to_bit_[t] = idx++;
}
if (use_small_) {
full_mask_ = (target_count_ == 64)
? ~uint64_t(0)
: (uint64_t(1) << target_count_) - 1;
} else {
num_words_ = (target_count_ + 63) / 64;
}
}
// Small path: look up a value and set its bit. Returns true when all
// targets have been found.
bool
set_if_found(const T& val, uint64_t& found) const {
auto it = value_to_bit_.find(val);
if (it != value_to_bit_.end()) {
found |= (uint64_t(1) << it->second);
return found == full_mask_;
}
return false;
}
// Large path: returns true when all targets found.
bool
set_if_found(const T& val,
std::vector<uint64_t>& found,
size_t& remaining) const {
auto it = value_to_bit_.find(val);
if (it != value_to_bit_.end()) {
uint32_t idx = it->second;
uint64_t bit = uint64_t(1) << (idx % 64);
uint64_t& word = found[idx / 64];
if (!(word & bit)) {
word |= bit;
return --remaining == 0;
}
}
return false;
}
bool
use_small() const {
return use_small_;
}
size_t
target_count() const {
return target_count_;
}
uint64_t
full_mask() const {
return full_mask_;
}
size_t
num_words() const {
return num_words_;
}
private:
ankerl::unordered_dense::map<T, uint32_t> value_to_bit_;
size_t target_count_{0};
bool use_small_{true};
uint64_t full_mask_{0};
size_t num_words_{0};
};
void
PhyJsonContainsFilterExpr::Eval(EvalCtx& context, VectorPtr& result) {
tracer::AutoSpan span(
"PhyJsonContainsFilterExpr::Eval", tracer::GetRootSpan(), true);
span.GetSpan()->SetAttribute("data_type",
static_cast<int>(expr_->column_.data_type_));
span.GetSpan()->SetAttribute("json_filter_expr_type", "json_contains");
auto input = context.get_offset_input();
SetHasOffsetInput((input != nullptr));
if (expr_->vals_.empty()) {
auto real_batch_size = has_offset_input_
? context.get_offset_input()->size()
: GetNextBatchSize();
if (real_batch_size == 0) {
result = nullptr;
return;
}
if (expr_->op_ == proto::plan::JSONContainsExpr_JSONOp_ContainsAll) {
result = std::make_shared<ColumnVector>(
TargetBitmap(real_batch_size, true),
TargetBitmap(real_batch_size, true));
} else {
result = std::make_shared<ColumnVector>(
TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
}
MoveCursor();
return;
}
switch (expr_->column_.data_type_) {
case DataType::ARRAY: {
if (exec_path_ == ExprExecPath::ScalarIndex && !has_offset_input_) {
result = EvalArrayContainsForIndexSegment(
expr_->column_.element_type_);
} else {
result = EvalJsonContainsForDataSegment(context);
}
break;
}
case DataType::JSON: {
if (exec_path_ == ExprExecPath::ScalarIndex && !has_offset_input_) {
result = EvalArrayContainsForIndexSegment(
value_type_ == DataType::INT64 ? DataType::DOUBLE
: value_type_);
} else {
result = EvalJsonContainsForDataSegment(context);
}
break;
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported data type: {}",
expr_->column_.data_type_);
}
}
VectorPtr
PhyJsonContainsFilterExpr::EvalJsonContainsForDataSegment(EvalCtx& context) {
auto data_type = expr_->column_.data_type_;
switch (expr_->op_) {
case proto::plan::JSONContainsExpr_JSONOp_Contains:
case proto::plan::JSONContainsExpr_JSONOp_ContainsAny: {
if (IsArrayDataType(data_type)) {
auto val_type = expr_->column_.element_type_;
switch (val_type) {
case DataType::BOOL: {
return ExecArrayContains<bool>(context);
}
case DataType::INT8:
case DataType::INT16:
case DataType::INT32:
case DataType::INT64: {
return ExecArrayContains<int64_t>(context);
}
case DataType::FLOAT:
case DataType::DOUBLE: {
return ExecArrayContains<double>(context);
}
case DataType::STRING:
case DataType::VARCHAR: {
return ExecArrayContains<std::string>(context);
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported array sub element type {}",
val_type);
}
} else {
if (expr_->same_type_) {
auto val_type = expr_->vals_[0].val_case();
switch (val_type) {
case proto::plan::GenericValue::kBoolVal: {
return ExecJsonContains<bool>(context);
}
case proto::plan::GenericValue::kInt64Val: {
return ExecJsonContains<int64_t>(context);
}
case proto::plan::GenericValue::kFloatVal: {
return ExecJsonContains<double>(context);
}
case proto::plan::GenericValue::kStringVal: {
return ExecJsonContains<std::string>(context);
}
case proto::plan::GenericValue::kArrayVal: {
return ExecJsonContainsArray(context);
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported data type:{}",
val_type);
}
} else {
return ExecJsonContainsWithDiffType(context);
}
}
}
case proto::plan::JSONContainsExpr_JSONOp_ContainsAll: {
if (IsArrayDataType(data_type)) {
auto val_type = expr_->column_.element_type_;
switch (val_type) {
case DataType::BOOL: {
return ExecArrayContainsAll<bool>(context);
}
case DataType::INT8:
case DataType::INT16:
case DataType::INT32:
case DataType::INT64: {
return ExecArrayContainsAll<int64_t>(context);
}
case DataType::FLOAT:
case DataType::DOUBLE: {
return ExecArrayContainsAll<double>(context);
}
case DataType::STRING:
case DataType::VARCHAR: {
return ExecArrayContainsAll<std::string>(context);
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported array sub element type {}",
val_type);
}
} else {
if (expr_->same_type_) {
auto val_type = expr_->vals_[0].val_case();
switch (val_type) {
case proto::plan::GenericValue::kBoolVal: {
return ExecJsonContainsAll<bool>(context);
}
case proto::plan::GenericValue::kInt64Val: {
return ExecJsonContainsAll<int64_t>(context);
}
case proto::plan::GenericValue::kFloatVal: {
return ExecJsonContainsAll<double>(context);
}
case proto::plan::GenericValue::kStringVal: {
return ExecJsonContainsAll<std::string>(context);
}
case proto::plan::GenericValue::kArrayVal: {
return ExecJsonContainsAllArray(context);
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported data type:{}",
val_type);
}
} else {
return ExecJsonContainsAllWithDiffType(context);
}
}
}
default:
ThrowInfo(ExprInvalid,
"unsupported json contains type {}",
proto::plan::JSONContainsExpr_JSONOp_Name(expr_->op_));
}
}
template <typename ExprValueType>
VectorPtr
PhyJsonContainsFilterExpr::ExecArrayContains(EvalCtx& context) {
using GetType =
std::conditional_t<std::is_same_v<ExprValueType, std::string>,
std::string_view,
ExprValueType>;
// Typed cached set used directly inside the array scan loop, mirroring
// the pattern in ExecArrayContainsAll. Skips the MultiElement variant
// round-trip, virtual dispatch and runtime type checks in In().
// string: owning std::string set with transparent hash, so string_view
// lookups are zero-copy and the set never holds dangling views.
// bool: std::unordered_set<bool>, since std::hash<bool> is safe.
// ankerl::unordered_dense::set<bool> is avoided for the same
// reason as SetElement<bool> in Element.h (wyhash 8-byte read).
// other: ankerl::unordered_dense::set<ExprValueType>.
using TypedSet = std::conditional_t<
std::is_same_v<ExprValueType, std::string>,
ankerl::unordered_dense::set<std::string, StringHash, std::equal_to<>>,
std::conditional_t<std::is_same_v<ExprValueType, bool>,
std::unordered_set<bool>,
ankerl::unordered_dense::set<ExprValueType>>>;
auto* input = context.get_offset_input();
const auto& bitmap_input = context.get_bitmap_input();
auto real_batch_size =
has_offset_input_ ? input->size() : GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
AssertInfo(expr_->column_.nested_path_.size() == 0,
"[ExecArrayContains]nested path must be null");
auto res_vec =
std::make_shared<ColumnVector>(TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
TargetBitmapView res(res_vec->GetRawData(), real_batch_size);
TargetBitmapView valid_res(res_vec->GetValidRawData(), real_batch_size);
if (!arg_inited_) {
auto elements = std::make_shared<TypedSet>();
elements->max_load_factor(0.5f);
for (const auto& val : expr_->vals_) {
elements->insert(GetValueWithCastNumber<ExprValueType>(val));
}
arg_cached_set_ = elements;
arg_inited_ = true;
}
auto elements = std::static_pointer_cast<TypedSet>(arg_cached_set_);
int processed_cursor = 0;
auto execute_sub_batch =
[&processed_cursor, &
bitmap_input ]<FilterType filter_type = FilterType::sequential>(
const milvus::ArrayView* data,
const bool* valid_data,
const int32_t* offsets,
const int size,
TargetBitmapView res,
TargetBitmapView valid_res,
const TypedSet& elements) {
// If data is nullptr, this chunk was skipped by SkipIndex.
// We only need to update processed_cursor for bitmap_input indexing.
if (data == nullptr) {
processed_cursor += size;
return;
}
auto executor = [&](size_t i) {
const auto& array = data[i];
for (int j = 0; j < array.length(); ++j) {
if (elements.find(array.template get_data<GetType>(j)) !=
elements.end()) {
return true;
}
}
return false;
};
bool has_bitmap_input = !bitmap_input.empty();
for (int i = 0; i < size; ++i) {
auto offset = i;
if constexpr (filter_type == FilterType::random) {
offset = (offsets) ? offsets[i] : i;
}
if (valid_data != nullptr && !valid_data[offset]) {
res[i] = valid_res[i] = false;
continue;
}
if (has_bitmap_input && !bitmap_input[processed_cursor + i]) {
continue;
}
res[i] = executor(offset);
}
processed_cursor += size;
};
int64_t processed_size;
if (has_offset_input_) {
processed_size =
ProcessDataByOffsets<milvus::ArrayView>(execute_sub_batch,
std::nullptr_t{},
input,
res,
valid_res,
*elements);
} else {
processed_size = ProcessDataChunks<milvus::ArrayView>(
execute_sub_batch, std::nullptr_t{}, res, valid_res, *elements);
}
AssertInfo(processed_size == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
processed_size,
real_batch_size);
return res_vec;
}
template <typename ExprValueType>
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContains(EvalCtx& context) {
using GetType =
std::conditional_t<std::is_same_v<ExprValueType, std::string>,
std::string_view,
ExprValueType>;
auto* input = context.get_offset_input();
const auto& bitmap_input = context.get_bitmap_input();
FieldId field_id = expr_->column_.field_id_;
if (!has_offset_input_ && exec_path_ == ExprExecPath::JsonStats) {
milvus::ScopedTimer timer("json_contains_by_stats", [this](double us) {
json_filter_stats_latency_us_ += us;
});
return ExecJsonContainsByStats<ExprValueType>();
}
milvus::ScopedTimer timer("json_contains_bruteforce", [this](double us) {
json_filter_bruteforce_latency_us_ += us;
});
auto real_batch_size =
has_offset_input_ ? input->size() : GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto res_vec =
std::make_shared<ColumnVector>(TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
TargetBitmapView res(res_vec->GetRawData(), real_batch_size);
TargetBitmapView valid_res(res_vec->GetValidRawData(), real_batch_size);
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
if (!arg_inited_) {
arg_set_ = std::make_shared<SetElement<GetType>>(expr_->vals_);
arg_inited_ = true;
}
size_t processed_cursor = 0;
auto execute_sub_batch =
[&processed_cursor, &
bitmap_input ]<FilterType filter_type = FilterType::sequential>(
const milvus::Json* data,
const bool* valid_data,
const int32_t* offsets,
const int size,
TargetBitmapView res,
TargetBitmapView valid_res,
const std::string& pointer,
const std::shared_ptr<MultiElement>& elements) {
// If data is nullptr, this chunk was skipped by SkipIndex.
// We only need to update processed_cursor for bitmap_input indexing.
if (data == nullptr) {
processed_cursor += size;
return;
}
auto executor = [&](size_t i) {
auto doc = data[i].doc();
auto array = doc.at_pointer(pointer).get_array();
if (array.error()) {
return false;
}
for (auto&& it : array) {
auto val = it.template get<GetType>();
if (val.error()) {
if constexpr (std::is_same_v<GetType, int64_t>) {
auto double_val = it.template get<double>();
if (!double_val.error() &&
double_val.value() ==
std::floor(double_val.value())) {
if (elements->In(static_cast<int64_t>(
double_val.value())) > 0) {
return true;
}
}
}
continue;
}
if (elements->In(val.value()) > 0) {
return true;
}
}
return false;
};
bool has_bitmap_input = !bitmap_input.empty();
for (size_t i = 0; i < size; ++i) {
auto offset = i;
if constexpr (filter_type == FilterType::random) {
offset = (offsets) ? offsets[i] : i;
}
if (valid_data != nullptr && !valid_data[offset]) {
res[i] = valid_res[i] = false;
continue;
}
if (has_bitmap_input && !bitmap_input[processed_cursor + i]) {
continue;
}
res[i] = executor(offset);
}
processed_cursor += size;
};
int64_t processed_size;
if (has_offset_input_) {
processed_size = ProcessDataByOffsets<Json>(execute_sub_batch,
std::nullptr_t{},
input,
res,
valid_res,
pointer,
arg_set_);
} else {
processed_size = ProcessDataChunks<Json>(execute_sub_batch,
std::nullptr_t{},
res,
valid_res,
pointer,
arg_set_);
}
AssertInfo(processed_size == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
processed_size,
real_batch_size);
return res_vec;
}
template <typename ExprValueType>
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsByStats() {
using GetType =
std::conditional_t<std::is_same_v<ExprValueType, std::string>,
std::string_view,
ExprValueType>;
auto real_batch_size = GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
std::unordered_set<GetType> elements;
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
if (!arg_inited_) {
arg_set_ = std::make_shared<SetElement<GetType>>(expr_->vals_);
if constexpr (std::is_same_v<GetType, int64_t>) {
// for int64_t, we need to a double vector to store the values
auto int_arg_set =
std::dynamic_pointer_cast<SetElement<int64_t>>(arg_set_);
std::vector<double> double_vals;
double_vals.reserve(int_arg_set->GetElements().size());
for (const auto& val : int_arg_set->GetElements()) {
double_vals.emplace_back(static_cast<double>(val));
}
arg_set_double_ = std::make_shared<SetElement<double>>(double_vals);
} else if constexpr (std::is_same_v<GetType, double>) {
arg_set_double_ = arg_set_;
}
arg_inited_ = true;
}
if (arg_set_->Empty()) {
MoveCursor();
return std::make_shared<ColumnVector>(
TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
}
if (cached_index_chunk_id_ != 0 && TryCacheGet()) {
// Cache hit — skip Stats computation.
} else if (cached_index_chunk_id_ != 0 &&
segment_->type() == SegmentType::Sealed) {
auto cache_compute_start = CacheClock::now();
auto* segment = dynamic_cast<const segcore::SegmentSealed*>(segment_);
auto field_id = expr_->column_.field_id_;
auto index = segment->GetJsonStats(op_ctx_, field_id);
Assert(index.get() != nullptr);
cached_index_chunk_res_ = std::make_shared<TargetBitmap>(active_count_);
cached_index_chunk_valid_res_ =
std::make_shared<TargetBitmap>(active_count_, true);
TargetBitmapView res_view(*cached_index_chunk_res_);
TargetBitmapView valid_res_view(*cached_index_chunk_valid_res_);
// process shredding data for ARRAY type (non-shared)
{
milvus::ScopedTimer timer(
"json_contains_stats_shredding_data",
[this](double us) { json_stats_shredding_latency_us_ += us; });
auto target_field = index->GetShreddingField(
pointer, milvus::index::JSONType::ARRAY);
if (!target_field.empty()) {
ShreddingArrayBsonContainsAnyExecutor<GetType> executor(
arg_set_, arg_set_double_);
index->ExecutorForShreddingData<std::string_view>(
op_ctx_,
target_field,
executor,
nullptr,
res_view,
valid_res_view);
}
}
// process shared data
auto shared_executor = [this, &res_view](milvus::BsonView bson,
uint32_t row_offset,
uint32_t value_offset) {
auto val = bson.ParseAsArrayAtOffset(value_offset);
if (!val.has_value()) {
res_view[row_offset] = false;
return;
}
for (const auto& element : val.value()) {
if constexpr (std::is_same_v<GetType, int64_t> ||
std::is_same_v<GetType, double>) {
auto value = milvus::BsonView::GetValueFromBsonView<double>(
element.get_value());
if (value.has_value() &&
this->arg_set_double_->In(value.value())) {
res_view[row_offset] = true;
return;
}
} else {
auto value =
milvus::BsonView::GetValueFromBsonView<GetType>(
element.get_value());
if (value.has_value() &&
this->arg_set_->In(value.value())) {
res_view[row_offset] = true;
return;
}
}
}
};
{
milvus::ScopedTimer timer(
"json_contains_stats_shared_data",
[this](double us) { json_stats_shared_latency_us_ += us; });
index->ExecuteForSharedData(
op_ctx_, bson_index_, pointer, shared_executor);
}
cached_index_chunk_id_ = 0;
CachePut(CacheElapsedUs(cache_compute_start));
}
auto res = MoveOrSliceBitmap(
*cached_index_chunk_res_, current_data_global_pos_, real_batch_size);
MoveCursor();
return res;
}
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsArray(EvalCtx& context) {
auto* input = context.get_offset_input();
const auto& bitmap_input = context.get_bitmap_input();
FieldId field_id = expr_->column_.field_id_;
if (!has_offset_input_ && exec_path_ == ExprExecPath::JsonStats) {
milvus::ScopedTimer timer(
"json_contains_array_by_stats",
[this](double us) { json_filter_stats_latency_us_ += us; });
return ExecJsonContainsArrayByStats();
}
milvus::ScopedTimer timer(
"json_contains_array_bruteforce",
[this](double us) { json_filter_bruteforce_latency_us_ += us; });
auto real_batch_size =
has_offset_input_ ? input->size() : GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto res_vec =
std::make_shared<ColumnVector>(TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
TargetBitmapView res(res_vec->GetRawData(), real_batch_size);
TargetBitmapView valid_res(res_vec->GetValidRawData(), real_batch_size);
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
if (!arg_inited_) {
auto elements = std::make_shared<std::vector<proto::plan::Array>>();
for (auto const& element : expr_->vals_) {
elements->emplace_back(
GetValueFromProto<proto::plan::Array>(element));
}
arg_cached_set_ = elements;
arg_inited_ = true;
}
auto elements = std::static_pointer_cast<std::vector<proto::plan::Array>>(
arg_cached_set_);
size_t processed_cursor = 0;
auto execute_sub_batch =
[&processed_cursor, &
bitmap_input ]<FilterType filter_type = FilterType::sequential>(
const milvus::Json* data,
const bool* valid_data,
const int32_t* offsets,
const int size,
TargetBitmapView res,
TargetBitmapView valid_res,
const std::string& pointer,
const std::vector<proto::plan::Array>& elements) {
// If data is nullptr, this chunk was skipped by SkipIndex.
// We only need to update processed_cursor for bitmap_input indexing.
if (data == nullptr) {
processed_cursor += size;
return;
}
auto executor = [&](size_t i) -> bool {
auto doc = data[i].doc();
auto array = doc.at_pointer(pointer).get_array();
if (array.error()) {
return false;
}
for (auto&& it : array) {
auto val = it.get_array();
if (val.error()) {
continue;
}
std::vector<
simdjson::simdjson_result<simdjson::ondemand::value>>
json_array;
json_array.reserve(val.count_elements());
for (auto&& e : val) {
json_array.emplace_back(e);
}
for (auto const& element : elements) {
if (CompareTwoJsonArray(json_array, element)) {
return true;
}
}
}
return false;
};
bool has_bitmap_input = !bitmap_input.empty();
for (size_t i = 0; i < size; ++i) {
auto offset = i;
if constexpr (filter_type == FilterType::random) {
offset = (offsets) ? offsets[i] : i;
}
if (valid_data != nullptr && !valid_data[offset]) {
res[i] = valid_res[i] = false;
continue;
}
if (has_bitmap_input && !bitmap_input[processed_cursor + i]) {
continue;
}
res[i] = executor(offset);
}
processed_cursor += size;
};
int64_t processed_size;
if (has_offset_input_) {
processed_size = ProcessDataByOffsets<milvus::Json>(execute_sub_batch,
std::nullptr_t{},
input,
res,
valid_res,
pointer,
*elements);
} else {
processed_size = ProcessDataChunks<milvus::Json>(execute_sub_batch,
std::nullptr_t{},
res,
valid_res,
pointer,
*elements);
}
AssertInfo(processed_size == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
processed_size,
real_batch_size);
return res_vec;
}
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsArrayByStats() {
auto real_batch_size = GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
std::vector<proto::plan::Array> elements;
elements.reserve(expr_->vals_.size());
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
for (auto const& element : expr_->vals_) {
elements.emplace_back(GetValueFromProto<proto::plan::Array>(element));
}
if (elements.empty()) {
MoveCursor();
return std::make_shared<ColumnVector>(
TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
}
if (cached_index_chunk_id_ != 0 && TryCacheGet()) {
// Cache hit — skip Stats computation.
} else if (cached_index_chunk_id_ != 0 &&
segment_->type() == SegmentType::Sealed) {
auto cache_compute_start = CacheClock::now();
auto* segment = dynamic_cast<const segcore::SegmentSealed*>(segment_);
auto field_id = expr_->column_.field_id_;
auto index = segment->GetJsonStats(op_ctx_, field_id);
Assert(index.get() != nullptr);
cached_index_chunk_res_ = std::make_shared<TargetBitmap>(active_count_);
cached_index_chunk_valid_res_ =
std::make_shared<TargetBitmap>(active_count_, true);
TargetBitmapView res_view(*cached_index_chunk_res_);
TargetBitmapView valid_res_view(*cached_index_chunk_valid_res_);
// process shredding data for ARRAY type (non-shared)
{
milvus::ScopedTimer timer(
"json_contains_array_stats_shredding_data",
[this](double us) { json_stats_shredding_latency_us_ += us; });
auto target_field = index->GetShreddingField(
pointer, milvus::index::JSONType::ARRAY);
if (!target_field.empty()) {
ShreddingArrayBsonContainsArrayExecutor executor(elements);
index->ExecutorForShreddingData<std::string_view>(
op_ctx_,
target_field,
executor,
nullptr,
res_view,
valid_res_view);
}
}
auto shared_executor = [&elements, &res_view](milvus::BsonView bson,
uint32_t row_offset,
uint32_t value_offset) {
auto array = bson.ParseAsArrayAtOffset(value_offset);
if (!array.has_value()) {
res_view[row_offset] = false;
}
for (const auto& sub_value : array.value()) {
auto sub_array = milvus::BsonView::GetValueFromBsonView<
milvus::bson::array_view>(sub_value.get_value());
if (!sub_array.has_value())
continue;
for (const auto& element : elements) {
if (CompareTwoJsonArray(sub_array.value(), element)) {
return true;
}
}
}
return false;
};
{
milvus::ScopedTimer timer(
"json_contains_array_stats_shared_data",
[this](double us) { json_stats_shared_latency_us_ += us; });
index->ExecuteForSharedData(
op_ctx_, bson_index_, pointer, shared_executor);
}
cached_index_chunk_id_ = 0;
CachePut(CacheElapsedUs(cache_compute_start));
}
auto res = MoveOrSliceBitmap(
*cached_index_chunk_res_, current_data_global_pos_, real_batch_size);
MoveCursor();
return res;
}
template <typename ExprValueType>
VectorPtr
PhyJsonContainsFilterExpr::ExecArrayContainsAll(EvalCtx& context) {
using GetType =
std::conditional_t<std::is_same_v<ExprValueType, std::string>,
std::string_view,
ExprValueType>;
auto* input = context.get_offset_input();
const auto& bitmap_input = context.get_bitmap_input();
AssertInfo(expr_->column_.nested_path_.size() == 0,
"[ExecArrayContainsAll]nested path must be null");
auto real_batch_size =
has_offset_input_ ? input->size() : GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto res_vec =
std::make_shared<ColumnVector>(TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
TargetBitmapView res(res_vec->GetRawData(), real_batch_size);
TargetBitmapView valid_res(res_vec->GetValidRawData(), real_batch_size);
if (!arg_inited_) {
auto elements = std::make_shared<std::set<GetType>>();
for (auto const& element : expr_->vals_) {
elements->insert(GetValueWithCastNumber<GetType>(element));
}
arg_cached_set_ = elements;
arg_inited_ = true;
}
auto elements =
std::static_pointer_cast<std::set<GetType>>(arg_cached_set_);
int processed_cursor = 0;
ContainsAllMatcher<GetType> matcher(*elements);
std::vector<uint64_t> found_large(
matcher.use_small() ? 0 : matcher.num_words());
auto execute_sub_batch =
[&processed_cursor, &bitmap_input, &matcher, &
found_large ]<FilterType filter_type = FilterType::sequential>(
const milvus::ArrayView* data,
const bool* valid_data,
const int32_t* offsets,
const int size,
TargetBitmapView res,
TargetBitmapView valid_res,
const std::set<GetType>& elements) {
// If data is nullptr, this chunk was skipped by SkipIndex.
// We only need to update processed_cursor for bitmap_input indexing.
if (data == nullptr) {
processed_cursor += size;
return;
}
auto executor = [&](size_t i) {
if (static_cast<size_t>(data[i].length()) <
matcher.target_count()) {
return false;
}
if (matcher.use_small()) {
uint64_t found = 0;
for (int j = 0; j < data[i].length(); ++j) {
if (matcher.set_if_found(
data[i].template get_data<GetType>(j), found)) {
return true;
}
}
return found == matcher.full_mask();
} else {
std::fill(found_large.begin(), found_large.end(), 0);
size_t remaining = matcher.target_count();
for (int j = 0; j < data[i].length(); ++j) {
if (matcher.set_if_found(
data[i].template get_data<GetType>(j),
found_large,
remaining)) {
return true;
}
}
return remaining == 0;
}
};
bool has_bitmap_input = !bitmap_input.empty();
for (int i = 0; i < size; ++i) {
auto offset = i;
if constexpr (filter_type == FilterType::random) {
offset = (offsets) ? offsets[i] : i;
}
if (valid_data != nullptr && !valid_data[offset]) {
res[i] = valid_res[i] = false;
continue;
}
if (has_bitmap_input && !bitmap_input[processed_cursor + i]) {
continue;
}
res[i] = executor(offset);
}
processed_cursor += size;
};
int64_t processed_size;
if (has_offset_input_) {
processed_size =
ProcessDataByOffsets<milvus::ArrayView>(execute_sub_batch,
std::nullptr_t{},
input,
res,
valid_res,
*elements);
} else {
processed_size = ProcessDataChunks<milvus::ArrayView>(
execute_sub_batch, std::nullptr_t{}, res, valid_res, *elements);
}
AssertInfo(processed_size == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
processed_size,
real_batch_size);
return res_vec;
}
template <typename ExprValueType>
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsAll(EvalCtx& context) {
using GetType =
std::conditional_t<std::is_same_v<ExprValueType, std::string>,
std::string_view,
ExprValueType>;
auto* input = context.get_offset_input();
const auto& bitmap_input = context.get_bitmap_input();
FieldId field_id = expr_->column_.field_id_;
if (!has_offset_input_ && exec_path_ == ExprExecPath::JsonStats) {
milvus::ScopedTimer timer(
"json_contains_all_by_stats",
[this](double us) { json_filter_stats_latency_us_ += us; });
return ExecJsonContainsAllByStats<ExprValueType>();
}
milvus::ScopedTimer timer(
"json_contains_all_bruteforce",
[this](double us) { json_filter_bruteforce_latency_us_ += us; });
auto real_batch_size =
has_offset_input_ ? input->size() : GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto res_vec =
std::make_shared<ColumnVector>(TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
TargetBitmapView res(res_vec->GetRawData(), real_batch_size);
TargetBitmapView valid_res(res_vec->GetValidRawData(), real_batch_size);
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
if (!arg_inited_) {
auto elements = std::make_shared<std::set<GetType>>();
for (auto const& element : expr_->vals_) {
elements->insert(GetValueFromProto<GetType>(element));
}
arg_cached_set_ = elements;
arg_inited_ = true;
}
auto elements =
std::static_pointer_cast<std::set<GetType>>(arg_cached_set_);
int processed_cursor = 0;
ContainsAllMatcher<GetType> matcher(*elements);
std::vector<uint64_t> found_large(
matcher.use_small() ? 0 : matcher.num_words());
auto execute_sub_batch =
[&processed_cursor, &bitmap_input, &matcher, &
found_large ]<FilterType filter_type = FilterType::sequential>(
const milvus::Json* data,
const bool* valid_data,
const int32_t* offsets,
const int size,
TargetBitmapView res,
TargetBitmapView valid_res,
const std::string& pointer,
const std::set<GetType>& elements) {
// If data is nullptr, this chunk was skipped by SkipIndex.
// We only need to update processed_cursor for bitmap_input indexing.
if (data == nullptr) {
processed_cursor += size;
return;
}
auto executor = [&](const size_t i) -> bool {
auto doc = data[i].doc();
auto array = doc.at_pointer(pointer).get_array();
if (array.error()) {
return false;
}
if (matcher.use_small()) {
uint64_t found = 0;
for (auto&& it : array) {
auto val = it.template get<GetType>();
if (val.error()) {
if constexpr (std::is_same_v<GetType, int64_t>) {
auto double_val = it.template get<double>();
if (!double_val.error() &&
double_val.value() ==
std::floor(double_val.value())) {
if (matcher.set_if_found(
static_cast<int64_t>(
double_val.value()),
found)) {
return true;
}
}
}
continue;
}
if (matcher.set_if_found(val.value(), found)) {
return true;
}
}
return found == matcher.full_mask();
} else {
std::fill(found_large.begin(), found_large.end(), 0);
size_t remaining = matcher.target_count();
for (auto&& it : array) {
auto val = it.template get<GetType>();
if (val.error()) {
if constexpr (std::is_same_v<GetType, int64_t>) {
auto double_val = it.template get<double>();
if (!double_val.error() &&
double_val.value() ==
std::floor(double_val.value())) {
if (matcher.set_if_found(
static_cast<int64_t>(
double_val.value()),
found_large,
remaining)) {
return true;
}
}
}
continue;
}
if (matcher.set_if_found(
val.value(), found_large, remaining)) {
return true;
}
}
return remaining == 0;
}
};
bool has_bitmap_input = !bitmap_input.empty();
for (size_t i = 0; i < size; ++i) {
auto offset = i;
if constexpr (filter_type == FilterType::random) {
offset = (offsets) ? offsets[i] : i;
}
if (valid_data != nullptr && !valid_data[offset]) {
res[i] = valid_res[i] = false;
continue;
}
if (has_bitmap_input && !bitmap_input[processed_cursor + i]) {
continue;
}
res[i] = executor(offset);
}
processed_cursor += size;
};
int64_t processed_size;
if (has_offset_input_) {
processed_size = ProcessDataByOffsets<Json>(execute_sub_batch,
std::nullptr_t{},
input,
res,
valid_res,
pointer,
*elements);
} else {
processed_size = ProcessDataChunks<Json>(execute_sub_batch,
std::nullptr_t{},
res,
valid_res,
pointer,
*elements);
}
AssertInfo(processed_size == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
processed_size,
real_batch_size);
return res_vec;
}
template <typename ExprValueType>
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsAllByStats() {
using GetType =
std::conditional_t<std::is_same_v<ExprValueType, std::string>,
std::string_view,
ExprValueType>;
auto real_batch_size = GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
if (!arg_inited_) {
auto elements = std::make_shared<std::set<GetType>>();
for (auto const& element : expr_->vals_) {
elements->insert(GetValueFromProto<GetType>(element));
}
arg_cached_set_ = elements;
arg_inited_ = true;
}
auto elements =
std::static_pointer_cast<std::set<GetType>>(arg_cached_set_);
if (elements->empty()) {
MoveCursor();
return std::make_shared<ColumnVector>(
TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
}
if (cached_index_chunk_id_ != 0 && TryCacheGet()) {
// Cache hit — skip Stats computation.
} else if (cached_index_chunk_id_ != 0 &&
segment_->type() == SegmentType::Sealed) {
auto cache_compute_start = CacheClock::now();
auto* segment = dynamic_cast<const segcore::SegmentSealed*>(segment_);
auto field_id = expr_->column_.field_id_;
auto index = segment->GetJsonStats(op_ctx_, field_id);
Assert(index.get() != nullptr);
cached_index_chunk_res_ = std::make_shared<TargetBitmap>(active_count_);
cached_index_chunk_valid_res_ =
std::make_shared<TargetBitmap>(active_count_, true);
TargetBitmapView res_view(*cached_index_chunk_res_);
TargetBitmapView valid_res_view(*cached_index_chunk_valid_res_);
// process shredding data for ARRAY type (non-shared)
{
milvus::ScopedTimer timer(
"json_contains_all_stats_shredding_data",
[this](double us) { json_stats_shredding_latency_us_ += us; });
auto target_field = index->GetShreddingField(
pointer, milvus::index::JSONType::ARRAY);
if (!target_field.empty()) {
ShreddingArrayBsonContainsAllExecutor<GetType> executor(
*elements);
index->ExecutorForShreddingData<std::string_view>(
op_ctx_,
target_field,
executor,
nullptr,
res_view,
valid_res_view);
}
}
// process shared data
ContainsAllMatcher<GetType> shared_matcher(*elements);
std::vector<uint64_t> shared_found_large(
shared_matcher.use_small() ? 0 : shared_matcher.num_words());
auto shared_executor = [&shared_matcher,
&res_view,
&shared_found_large](milvus::BsonView bson,
uint32_t row_offset,
uint32_t value_offset) {
auto val = bson.ParseAsArrayAtOffset(value_offset);
if (!val.has_value()) {
res_view[row_offset] = false;
return;
}
if (shared_matcher.use_small()) {
uint64_t found = 0;
for (const auto& element : val.value()) {
auto value =
milvus::BsonView::GetValueFromBsonView<GetType>(
element.get_value());
if (!value.has_value()) {
if constexpr (std::is_same_v<GetType, int64_t>) {
auto double_value =
milvus::BsonView::GetValueFromBsonView<double>(
element.get_value());
if (double_value.has_value() &&
double_value.value() ==
std::floor(double_value.value())) {
if (shared_matcher.set_if_found(
static_cast<int64_t>(
double_value.value()),
found)) {
res_view[row_offset] = true;
return;
}
}
}
continue;
}
if (shared_matcher.set_if_found(value.value(), found)) {
res_view[row_offset] = true;
return;
}
}
res_view[row_offset] = (found == shared_matcher.full_mask());
} else {
std::fill(
shared_found_large.begin(), shared_found_large.end(), 0);
size_t remaining = shared_matcher.target_count();
for (const auto& element : val.value()) {
auto value =
milvus::BsonView::GetValueFromBsonView<GetType>(
element.get_value());
if (!value.has_value()) {
if constexpr (std::is_same_v<GetType, int64_t>) {
auto double_value =
milvus::BsonView::GetValueFromBsonView<double>(
element.get_value());
if (double_value.has_value() &&
double_value.value() ==
std::floor(double_value.value())) {
if (shared_matcher.set_if_found(
static_cast<int64_t>(
double_value.value()),
shared_found_large,
remaining)) {
res_view[row_offset] = true;
return;
}
}
}
continue;
}
if (shared_matcher.set_if_found(
value.value(), shared_found_large, remaining)) {
res_view[row_offset] = true;
return;
}
}
res_view[row_offset] = (remaining == 0);
}
};
{
milvus::ScopedTimer timer(
"json_contains_all_stats_shared_data",
[this](double us) { json_stats_shared_latency_us_ += us; });
index->ExecuteForSharedData(
op_ctx_, bson_index_, pointer, shared_executor);
}
cached_index_chunk_id_ = 0;
CachePut(CacheElapsedUs(cache_compute_start));
}
auto res = MoveOrSliceBitmap(
*cached_index_chunk_res_, current_data_global_pos_, real_batch_size);
MoveCursor();
return res;
}
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsAllWithDiffType(EvalCtx& context) {
auto* input = context.get_offset_input();
const auto& bitmap_input = context.get_bitmap_input();
FieldId field_id = expr_->column_.field_id_;
if (!has_offset_input_ && exec_path_ == ExprExecPath::JsonStats) {
milvus::ScopedTimer timer(
"json_contains_all_difftype_by_stats",
[this](double us) { json_filter_stats_latency_us_ += us; });
return ExecJsonContainsAllWithDiffTypeByStats();
}
milvus::ScopedTimer timer(
"json_contains_all_difftype_bruteforce",
[this](double us) { json_filter_bruteforce_latency_us_ += us; });
auto real_batch_size =
has_offset_input_ ? input->size() : GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto res_vec =
std::make_shared<ColumnVector>(TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
TargetBitmapView res(res_vec->GetRawData(), real_batch_size);
TargetBitmapView valid_res(res_vec->GetValidRawData(), real_batch_size);
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
const auto& elements = expr_->vals_;
std::unordered_set<int> elements_index;
for (int i = 0; i < static_cast<int>(elements.size()); i++) {
elements_index.insert(i);
}
int processed_cursor = 0;
auto execute_sub_batch =
[&processed_cursor, &
bitmap_input ]<FilterType filter_type = FilterType::sequential>(
const milvus::Json* data,
const bool* valid_data,
const int32_t* offsets,
const int size,
TargetBitmapView res,
TargetBitmapView valid_res,
const std::string& pointer,
const std::vector<proto::plan::GenericValue>& elements,
const std::unordered_set<int>& elements_index) {
// If data is nullptr, this chunk was skipped by SkipIndex.
// We only need to update processed_cursor for bitmap_input indexing.
if (data == nullptr) {
processed_cursor += size;
return;
}
auto executor = [&](size_t i) -> bool {
const auto& json = data[i];
auto doc = json.dom_doc();
auto array = doc.at_pointer(pointer).get_array();
if (array.error()) {
return false;
}
std::unordered_set<int> tmp_elements_index(elements_index);
for (auto&& it : array) {
int i = -1;
for (auto& element : elements) {
i++;
switch (element.val_case()) {
case proto::plan::GenericValue::kBoolVal: {
auto val = it.template get<bool>();
if (val.error()) {
continue;
}
if (val.value() == element.bool_val()) {
tmp_elements_index.erase(i);
}
break;
}
case proto::plan::GenericValue::kInt64Val: {
auto val = it.template get<int64_t>();
if (val.error()) {
auto double_val = it.template get<double>();
if (!double_val.error() &&
double_val.value() == element.int64_val()) {
tmp_elements_index.erase(i);
}
continue;
}
if (val.value() == element.int64_val()) {
tmp_elements_index.erase(i);
}
break;
}
case proto::plan::GenericValue::kFloatVal: {
auto val = it.template get<double>();
if (val.error()) {
continue;
}
if (val.value() == element.float_val()) {
tmp_elements_index.erase(i);
}
break;
}
case proto::plan::GenericValue::kStringVal: {
auto val = it.template get<std::string_view>();
if (val.error()) {
continue;
}
if (val.value() == element.string_val()) {
tmp_elements_index.erase(i);
}
break;
}
case proto::plan::GenericValue::kArrayVal: {
auto val = it.get_array();
if (val.error()) {
continue;
}
if (CompareTwoJsonArray(val, element.array_val())) {
tmp_elements_index.erase(i);
}
break;
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported data type {}",
element.val_case());
}
if (tmp_elements_index.size() == 0) {
return true;
}
}
if (tmp_elements_index.size() == 0) {
return true;
}
}
return tmp_elements_index.size() == 0;
};
bool has_bitmap_input = !bitmap_input.empty();
for (size_t i = 0; i < size; ++i) {
auto offset = i;
if constexpr (filter_type == FilterType::random) {
offset = (offsets) ? offsets[i] : i;
}
if (valid_data != nullptr && !valid_data[offset]) {
res[i] = valid_res[i] = false;
continue;
}
if (has_bitmap_input && !bitmap_input[processed_cursor + i]) {
continue;
}
res[i] = executor(offset);
}
processed_cursor += size;
};
int64_t processed_size;
if (has_offset_input_) {
processed_size = ProcessDataByOffsets<Json>(execute_sub_batch,
std::nullptr_t{},
input,
res,
valid_res,
pointer,
elements,
elements_index);
} else {
processed_size = ProcessDataChunks<Json>(execute_sub_batch,
std::nullptr_t{},
res,
valid_res,
pointer,
elements,
elements_index);
}
AssertInfo(processed_size == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
processed_size,
real_batch_size);
return res_vec;
}
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsAllWithDiffTypeByStats() {
auto real_batch_size = GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
const auto& elements = expr_->vals_;
std::set<int> elements_index;
for (int i = 0; i < static_cast<int>(elements.size()); i++) {
elements_index.insert(i);
}
if (elements.empty()) {
MoveCursor();
return std::make_shared<ColumnVector>(
TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
}
if (cached_index_chunk_id_ != 0 && TryCacheGet()) {
// Cache hit — skip Stats computation.
} else if (cached_index_chunk_id_ != 0 &&
segment_->type() == SegmentType::Sealed) {
auto cache_compute_start = CacheClock::now();
auto* segment = dynamic_cast<const segcore::SegmentSealed*>(segment_);
auto field_id = expr_->column_.field_id_;
auto index = segment->GetJsonStats(op_ctx_, field_id);
Assert(index.get() != nullptr);
cached_index_chunk_res_ = std::make_shared<TargetBitmap>(active_count_);
cached_index_chunk_valid_res_ =
std::make_shared<TargetBitmap>(active_count_, true);
TargetBitmapView res_view(*cached_index_chunk_res_);
TargetBitmapView valid_res_view(*cached_index_chunk_valid_res_);
// process shredding data for ARRAY type (non-shared)
{
milvus::ScopedTimer timer(
"json_contains_all_difftype_stats_shredding_data",
[this](double us) { json_stats_shredding_latency_us_ += us; });
auto target_field = index->GetShreddingField(
pointer, milvus::index::JSONType::ARRAY);
if (!target_field.empty()) {
ShreddingArrayBsonContainsAllWithDiffTypeExecutor executor(
elements, elements_index);
index->ExecutorForShreddingData<std::string_view>(
op_ctx_,
target_field,
executor,
nullptr,
res_view,
valid_res_view);
}
}
auto shared_executor = [&elements, &elements_index, &res_view](
milvus::BsonView bson,
uint32_t row_offset,
uint32_t value_offset) {
std::set<int> tmp_elements_index(elements_index);
auto array = bson.ParseAsArrayAtOffset(value_offset);
if (!array.has_value()) {
res_view[row_offset] = false;
return;
}
for (const auto& sub_value : array.value()) {
int i = -1;
for (auto& element : elements) {
i++;
switch (element.val_case()) {
case proto::plan::GenericValue::kBoolVal: {
auto val =
milvus::BsonView::GetValueFromBsonView<bool>(
sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (val.value() == element.bool_val()) {
tmp_elements_index.erase(i);
}
break;
}
case proto::plan::GenericValue::kInt64Val: {
// get double/int64 from bson
auto val =
milvus::BsonView::GetValueFromBsonView<double>(
sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (val.value() == element.int64_val()) {
tmp_elements_index.erase(i);
}
break;
}
case proto::plan::GenericValue::kFloatVal: {
auto val =
milvus::BsonView::GetValueFromBsonView<double>(
sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (val.value() == element.float_val()) {
tmp_elements_index.erase(i);
}
break;
}
case proto::plan::GenericValue::kStringVal: {
auto val = milvus::BsonView::GetValueFromBsonView<
std::string>(sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (val.value() == element.string_val()) {
tmp_elements_index.erase(i);
}
break;
}
case proto::plan::GenericValue::kArrayVal: {
auto val = milvus::BsonView::GetValueFromBsonView<
milvus::bson::array_view>(
sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (CompareTwoJsonArray(val.value(),
element.array_val())) {
tmp_elements_index.erase(i);
}
break;
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported data type {}",
element.val_case());
}
if (tmp_elements_index.size() == 0) {
res_view[row_offset] = true;
return;
}
}
if (tmp_elements_index.size() == 0) {
res_view[row_offset] = true;
return;
}
}
res_view[row_offset] = tmp_elements_index.size() == 0;
};
{
milvus::ScopedTimer timer(
"json_contains_all_difftype_stats_shared_data",
[this](double us) { json_stats_shared_latency_us_ += us; });
index->ExecuteForSharedData(
op_ctx_, bson_index_, pointer, shared_executor);
}
cached_index_chunk_id_ = 0;
CachePut(CacheElapsedUs(cache_compute_start));
}
auto res = MoveOrSliceBitmap(
*cached_index_chunk_res_, current_data_global_pos_, real_batch_size);
MoveCursor();
return res;
}
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsAllArray(EvalCtx& context) {
auto* input = context.get_offset_input();
const auto& bitmap_input = context.get_bitmap_input();
FieldId field_id = expr_->column_.field_id_;
if (!has_offset_input_ && exec_path_ == ExprExecPath::JsonStats) {
milvus::ScopedTimer timer(
"json_contains_all_array_by_stats",
[this](double us) { json_filter_stats_latency_us_ += us; });
return ExecJsonContainsAllArrayByStats();
}
milvus::ScopedTimer timer(
"json_contains_all_array_bruteforce",
[this](double us) { json_filter_bruteforce_latency_us_ += us; });
auto real_batch_size =
has_offset_input_ ? input->size() : GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto res_vec =
std::make_shared<ColumnVector>(TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
TargetBitmapView res(res_vec->GetRawData(), real_batch_size);
TargetBitmapView valid_res(res_vec->GetValidRawData(), real_batch_size);
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
std::vector<proto::plan::Array> elements;
elements.reserve(expr_->vals_.size());
for (auto const& element : expr_->vals_) {
elements.emplace_back(GetValueFromProto<proto::plan::Array>(element));
}
size_t processed_cursor = 0;
auto execute_sub_batch =
[&processed_cursor, &
bitmap_input ]<FilterType filter_type = FilterType::sequential>(
const milvus::Json* data,
const bool* valid_data,
const int32_t* offsets,
const int size,
TargetBitmapView res,
TargetBitmapView valid_res,
const std::string& pointer,
const std::vector<proto::plan::Array>& elements) {
// If data is nullptr, this chunk was skipped by SkipIndex.
// We only need to update processed_cursor for bitmap_input indexing.
if (data == nullptr) {
processed_cursor += size;
return;
}
auto executor = [&](const size_t i) {
auto doc = data[i].doc();
auto array = doc.at_pointer(pointer).get_array();
if (array.error()) {
return false;
}
std::unordered_set<int> exist_elements_index;
for (auto&& it : array) {
auto val = it.get_array();
if (val.error()) {
continue;
}
std::vector<
simdjson::simdjson_result<simdjson::ondemand::value>>
json_array;
json_array.reserve(val.count_elements());
for (auto&& e : val) {
json_array.emplace_back(e);
}
for (int index = 0; index < elements.size(); ++index) {
if (CompareTwoJsonArray(json_array, elements[index])) {
exist_elements_index.insert(index);
}
}
if (exist_elements_index.size() == elements.size()) {
return true;
}
}
return exist_elements_index.size() == elements.size();
};
bool has_bitmap_input = !bitmap_input.empty();
for (size_t i = 0; i < size; ++i) {
auto offset = i;
if constexpr (filter_type == FilterType::random) {
offset = (offsets) ? offsets[i] : i;
}
if (valid_data != nullptr && !valid_data[offset]) {
res[i] = valid_res[i] = false;
continue;
}
if (has_bitmap_input && !bitmap_input[processed_cursor + i]) {
continue;
}
res[i] = executor(offset);
}
processed_cursor += size;
};
int64_t processed_size;
if (has_offset_input_) {
processed_size = ProcessDataByOffsets<Json>(execute_sub_batch,
std::nullptr_t{},
input,
res,
valid_res,
pointer,
elements);
} else {
processed_size = ProcessDataChunks<Json>(execute_sub_batch,
std::nullptr_t{},
res,
valid_res,
pointer,
elements);
}
AssertInfo(processed_size == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
processed_size,
real_batch_size);
return res_vec;
}
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsAllArrayByStats() {
auto real_batch_size = GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
std::vector<proto::plan::Array> elements;
elements.reserve(expr_->vals_.size());
for (auto const& element : expr_->vals_) {
elements.emplace_back(GetValueFromProto<proto::plan::Array>(element));
}
if (elements.empty()) {
MoveCursor();
return std::make_shared<ColumnVector>(
TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
}
if (cached_index_chunk_id_ != 0 && TryCacheGet()) {
// Cache hit — skip Stats computation.
} else if (cached_index_chunk_id_ != 0 &&
segment_->type() == SegmentType::Sealed) {
auto cache_compute_start = CacheClock::now();
auto* segment = dynamic_cast<const segcore::SegmentSealed*>(segment_);
auto field_id = expr_->column_.field_id_;
auto index = segment->GetJsonStats(op_ctx_, field_id);
Assert(index.get() != nullptr);
cached_index_chunk_res_ = std::make_shared<TargetBitmap>(active_count_);
cached_index_chunk_valid_res_ =
std::make_shared<TargetBitmap>(active_count_, true);
TargetBitmapView res_view(*cached_index_chunk_res_);
TargetBitmapView valid_res_view(*cached_index_chunk_valid_res_);
// process shredding data for ARRAY type (non-shared)
{
milvus::ScopedTimer timer(
"json_contains_all_array_stats_shredding_data",
[this](double us) { json_stats_shredding_latency_us_ += us; });
auto target_field = index->GetShreddingField(
pointer, milvus::index::JSONType::ARRAY);
if (!target_field.empty()) {
ShreddingArrayBsonContainsAllArrayExecutor executor(elements);
index->ExecutorForShreddingData<std::string_view>(
op_ctx_,
target_field,
executor,
nullptr,
res_view,
valid_res_view);
}
}
auto shared_executor = [&elements, &res_view](milvus::BsonView bson,
uint32_t row_offset,
uint32_t value_offset) {
auto array = bson.ParseAsArrayAtOffset(value_offset);
if (!array.has_value()) {
res_view[row_offset] = false;
return;
}
std::set<int> exist_elements_index;
for (const auto& sub_value : array.value()) {
auto sub_array = milvus::BsonView::GetValueFromBsonView<
milvus::bson::array_view>(sub_value.get_value());
if (!sub_array.has_value())
continue;
for (int index = 0; index < elements.size(); ++index) {
if (CompareTwoJsonArray(sub_array.value(),
elements[index])) {
exist_elements_index.insert(index);
}
}
if (exist_elements_index.size() == elements.size()) {
res_view[row_offset] = true;
return;
}
}
res_view[row_offset] =
exist_elements_index.size() == elements.size();
};
{
milvus::ScopedTimer timer(
"json_contains_all_array_stats_shared_data",
[this](double us) { json_stats_shared_latency_us_ += us; });
index->ExecuteForSharedData(
op_ctx_, bson_index_, pointer, shared_executor);
}
cached_index_chunk_id_ = 0;
CachePut(CacheElapsedUs(cache_compute_start));
}
auto res = MoveOrSliceBitmap(
*cached_index_chunk_res_, current_data_global_pos_, real_batch_size);
MoveCursor();
return res;
}
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsWithDiffType(EvalCtx& context) {
auto* input = context.get_offset_input();
const auto& bitmap_input = context.get_bitmap_input();
FieldId field_id = expr_->column_.field_id_;
if (!has_offset_input_ && exec_path_ == ExprExecPath::JsonStats) {
milvus::ScopedTimer timer(
"json_contains_difftype_by_stats",
[this](double us) { json_filter_stats_latency_us_ += us; });
return ExecJsonContainsWithDiffTypeByStats();
}
milvus::ScopedTimer timer(
"json_contains_difftype_bruteforce",
[this](double us) { json_filter_bruteforce_latency_us_ += us; });
auto real_batch_size =
has_offset_input_ ? input->size() : GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto res_vec =
std::make_shared<ColumnVector>(TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
TargetBitmapView res(res_vec->GetRawData(), real_batch_size);
TargetBitmapView valid_res(res_vec->GetValidRawData(), real_batch_size);
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
const auto& elements = expr_->vals_;
size_t processed_cursor = 0;
auto execute_sub_batch =
[&processed_cursor, &
bitmap_input ]<FilterType filter_type = FilterType::sequential>(
const milvus::Json* data,
const bool* valid_data,
const int32_t* offsets,
const int size,
TargetBitmapView res,
TargetBitmapView valid_res,
const std::string& pointer,
const std::vector<proto::plan::GenericValue>& elements) {
// If data is nullptr, this chunk was skipped by SkipIndex.
// We only need to update processed_cursor for bitmap_input indexing.
if (data == nullptr) {
processed_cursor += size;
return;
}
auto executor = [&](const size_t i) {
auto& json = data[i];
auto doc = json.dom_doc();
auto array = doc.at_pointer(pointer).get_array();
if (array.error()) {
return false;
}
// Note: array can only be iterated once
for (auto&& it : array) {
for (auto const& element : elements) {
switch (element.val_case()) {
case proto::plan::GenericValue::kBoolVal: {
auto val = it.template get<bool>();
if (val.error()) {
continue;
}
if (val.value() == element.bool_val()) {
return true;
}
break;
}
case proto::plan::GenericValue::kInt64Val: {
auto val = it.template get<int64_t>();
if (val.error()) {
auto double_val = it.template get<double>();
if (!double_val.error() &&
double_val.value() == element.int64_val()) {
return true;
}
continue;
}
if (val.value() == element.int64_val()) {
return true;
}
break;
}
case proto::plan::GenericValue::kFloatVal: {
auto val = it.template get<double>();
if (val.error()) {
continue;
}
if (val.value() == element.float_val()) {
return true;
}
break;
}
case proto::plan::GenericValue::kStringVal: {
auto val = it.template get<std::string_view>();
if (val.error()) {
continue;
}
if (val.value() == element.string_val()) {
return true;
}
break;
}
case proto::plan::GenericValue::kArrayVal: {
auto val = it.get_array();
if (val.error()) {
continue;
}
if (CompareTwoJsonArray(val, element.array_val())) {
return true;
}
break;
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported data type {}",
element.val_case());
}
}
}
return false;
};
bool has_bitmap_input = !bitmap_input.empty();
for (size_t i = 0; i < size; ++i) {
auto offset = i;
if constexpr (filter_type == FilterType::random) {
offset = (offsets) ? offsets[i] : i;
}
if (valid_data != nullptr && !valid_data[offset]) {
res[i] = valid_res[i] = false;
continue;
}
if (has_bitmap_input && !bitmap_input[processed_cursor + i]) {
continue;
}
res[i] = executor(offset);
}
processed_cursor += size;
};
int64_t processed_size;
if (has_offset_input_) {
processed_size = ProcessDataByOffsets<Json>(execute_sub_batch,
std::nullptr_t{},
input,
res,
valid_res,
pointer,
elements);
} else {
processed_size = ProcessDataChunks<Json>(execute_sub_batch,
std::nullptr_t{},
res,
valid_res,
pointer,
elements);
}
AssertInfo(processed_size == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
processed_size,
real_batch_size);
return res_vec;
}
VectorPtr
PhyJsonContainsFilterExpr::ExecJsonContainsWithDiffTypeByStats() {
auto real_batch_size = GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
auto pointer = milvus::Json::pointer(expr_->column_.nested_path_);
const auto& elements = expr_->vals_;
if (elements.empty()) {
MoveCursor();
return std::make_shared<ColumnVector>(
TargetBitmap(real_batch_size, false),
TargetBitmap(real_batch_size, true));
}
if (cached_index_chunk_id_ != 0 && TryCacheGet()) {
// Cache hit — skip Stats computation.
} else if (cached_index_chunk_id_ != 0 &&
segment_->type() == SegmentType::Sealed) {
auto cache_compute_start = CacheClock::now();
auto* segment = dynamic_cast<const segcore::SegmentSealed*>(segment_);
auto field_id = expr_->column_.field_id_;
auto index = segment->GetJsonStats(op_ctx_, field_id);
Assert(index.get() != nullptr);
cached_index_chunk_res_ = std::make_shared<TargetBitmap>(active_count_);
cached_index_chunk_valid_res_ =
std::make_shared<TargetBitmap>(active_count_, true);
TargetBitmapView res_view(*cached_index_chunk_res_);
TargetBitmapView valid_res_view(*cached_index_chunk_valid_res_);
// process shredding data for ARRAY type (non-shared)
{
milvus::ScopedTimer timer(
"json_contains_difftype_stats_shredding_data",
[this](double us) { json_stats_shredding_latency_us_ += us; });
auto target_field = index->GetShreddingField(
pointer, milvus::index::JSONType::ARRAY);
if (!target_field.empty()) {
ShreddingArrayBsonContainsAnyWithDiffTypeExecutor executor(
elements);
index->ExecutorForShreddingData<std::string_view>(
op_ctx_,
target_field,
executor,
nullptr,
res_view,
valid_res_view);
}
}
auto shared_executor = [&elements, &res_view](milvus::BsonView bson,
uint32_t row_offset,
uint32_t value_offset) {
auto array = bson.ParseAsArrayAtOffset(value_offset);
if (!array.has_value()) {
res_view[row_offset] = false;
return;
}
for (const auto& sub_value : array.value()) {
for (auto const& element : elements) {
switch (element.val_case()) {
case proto::plan::GenericValue::kBoolVal: {
auto val =
milvus::BsonView::GetValueFromBsonView<bool>(
sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (val.value() == element.bool_val()) {
res_view[row_offset] = true;
return;
}
break;
}
case proto::plan::GenericValue::kInt64Val: {
auto val =
milvus::BsonView::GetValueFromBsonView<double>(
sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (val.value() == element.int64_val()) {
res_view[row_offset] = true;
return;
}
break;
}
case proto::plan::GenericValue::kFloatVal: {
auto val =
milvus::BsonView::GetValueFromBsonView<double>(
sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (val.value() == element.float_val()) {
res_view[row_offset] = true;
return;
}
break;
}
case proto::plan::GenericValue::kStringVal: {
auto val = milvus::BsonView::GetValueFromBsonView<
std::string>(sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (val.value() == element.string_val()) {
res_view[row_offset] = true;
return;
}
break;
}
case proto::plan::GenericValue::kArrayVal: {
auto val = milvus::BsonView::GetValueFromBsonView<
milvus::bson::array_view>(
sub_value.get_value());
if (!val.has_value()) {
continue;
}
if (CompareTwoJsonArray(val.value(),
element.array_val())) {
res_view[row_offset] = true;
return;
}
break;
}
default:
ThrowInfo(DataTypeInvalid,
"unsupported data type {}",
element.val_case());
}
}
}
};
{
milvus::ScopedTimer timer(
"json_contains_difftype_stats_shared_data",
[this](double us) { json_stats_shared_latency_us_ += us; });
index->ExecuteForSharedData(
op_ctx_, bson_index_, pointer, shared_executor);
}
cached_index_chunk_id_ = 0;
CachePut(CacheElapsedUs(cache_compute_start));
}
auto res = MoveOrSliceBitmap(
*cached_index_chunk_res_, current_data_global_pos_, real_batch_size);
MoveCursor();
return res;
}
VectorPtr
PhyJsonContainsFilterExpr::EvalArrayContainsForIndexSegment(
DataType data_type) {
switch (data_type) {
case DataType::BOOL: {
return ExecArrayContainsForIndexSegmentImpl<bool>();
}
case DataType::INT8: {
return ExecArrayContainsForIndexSegmentImpl<int8_t>();
}
case DataType::INT16: {
return ExecArrayContainsForIndexSegmentImpl<int16_t>();
}
case DataType::INT32: {
return ExecArrayContainsForIndexSegmentImpl<int32_t>();
}
case DataType::INT64: {
return ExecArrayContainsForIndexSegmentImpl<int64_t>();
}
case DataType::FLOAT: {
return ExecArrayContainsForIndexSegmentImpl<float>();
}
case DataType::DOUBLE: {
return ExecArrayContainsForIndexSegmentImpl<double>();
}
case DataType::VARCHAR:
case DataType::STRING: {
return ExecArrayContainsForIndexSegmentImpl<std::string>();
}
default:
ThrowInfo(DataTypeInvalid,
fmt::format("unsupported data type for "
"ExecArrayContainsForIndexSegmentImpl: {}",
expr_->column_.element_type_));
}
}
template <typename ExprValueType>
VectorPtr
PhyJsonContainsFilterExpr::ExecArrayContainsForIndexSegmentImpl() {
typedef std::conditional_t<std::is_same_v<ExprValueType, std::string_view>,
std::string,
ExprValueType>
GetType;
using Index = index::ScalarIndex<GetType>;
auto real_batch_size = GetNextBatchSize();
if (real_batch_size == 0) {
return nullptr;
}
std::unordered_set<GetType> elements;
for (auto const& element : expr_->vals_) {
elements.insert(GetValueWithCastNumber<GetType>(element));
}
boost::container::vector<GetType> elems(elements.begin(), elements.end());
// Get array offsets for nested index (needed for element-to-row conversion)
auto array_offsets = segment_->GetArrayOffsets(expr_->column_.field_id_);
auto execute_sub_batch =
[this, &array_offsets](
Index* index_ptr,
const boost::container::vector<GetType>& vals) -> TargetBitmap {
// Query helper: for nested index, convert element-level to row-level
auto query_in = [&](size_t n, const GetType* data) -> TargetBitmap {
auto element_bitset = index_ptr->In(n, data);
if (!index_ptr->IsNestedIndex()) {
return element_bitset;
}
AssertInfo(array_offsets != nullptr,
"array offsets not found for field {}",
expr_->column_.field_id_.get());
return array_offsets->ForEachRowElementRange(
[&element_bitset](int32_t elem_start, int32_t elem_end) {
for (int32_t i = elem_start; i < elem_end; ++i) {
if (element_bitset[i]) {
return true;
}
}
return false;
},
0,
active_count_);
};
switch (expr_->op_) {
case proto::plan::JSONContainsExpr_JSONOp_Contains:
case proto::plan::JSONContainsExpr_JSONOp_ContainsAny:
return query_in(vals.size(), vals.data());
case proto::plan::JSONContainsExpr_JSONOp_ContainsAll: {
TargetBitmap result(active_count_);
result.set();
for (size_t i = 0; i < vals.size(); i++) {
result &= query_in(1, &vals[i]);
}
return result;
}
default:
ThrowInfo(
ExprInvalid,
"unsupported array contains type {}",
proto::plan::JSONContainsExpr_JSONOp_Name(expr_->op_));
}
};
// Use WithRowLevel version since func handles element-to-row conversion for nested index
auto res =
ProcessIndexChunksWithRowLevel<GetType>(execute_sub_batch, elems);
AssertInfo(res->size() == real_batch_size,
"internal error: expr processed rows {} not equal "
"expect batch size {}",
res->size(),
real_batch_size);
return res;
}
} //namespace exec
} // namespace milvus