issue: #43427
This pr's main goal is merge #37417 to milvus 2.5 without conflicts.
# Main Goals
1. Create and describe collections with geospatial type
2. Insert geospatial data into the insert binlog
3. Load segments containing geospatial data into memory
4. Enable query and search can display geospatial data
5. Support using GIS funtions like ST_EQUALS in query
6. Support R-Tree index for geometry type
# Solution
1. **Add Type**: Modify the Milvus core by adding a Geospatial type in
both the C++ and Go code layers, defining the Geospatial data structure
and the corresponding interfaces.
2. **Dependency Libraries**: Introduce necessary geospatial data
processing libraries. In the C++ source code, use Conan package
management to include the GDAL library. In the Go source code, add the
go-geom library to the go.mod file.
3. **Protocol Interface**: Revise the Milvus protocol to provide
mechanisms for Geospatial message serialization and deserialization.
4. **Data Pipeline**: Facilitate interaction between the client and
proxy using the WKT format for geospatial data. The proxy will convert
all data into WKB format for downstream processing, providing column
data interfaces, segment encapsulation, segment loading, payload
writing, and cache block management.
5. **Query Operators**: Implement simple display and support for filter
queries. Initially, focus on filtering based on spatial relationships
for a single column of geospatial literal values, providing parsing and
execution for query expressions.Now only support brutal search
7. **Client Modification**: Enable the client to handle user input for
geospatial data and facilitate end-to-end testing.Check the modification
in pymilvus.
---------
Signed-off-by: Yinwei Li <yinwei.li@zilliz.com>
Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
Co-authored-by: ZhuXi <150327960+Yinwei-Yu@users.noreply.github.com>
And support set function mode and boost mode when run search with boost.
RandomScore support get random function score between [0, weight).
FunctionMode decide how to calculate boost score for multiple boost
function scores.
BoostMode decide how to calculate final score for origin score and boost
score.
relate: https://github.com/milvus-io/milvus/issues/43867
---------
Signed-off-by: aoiasd <zhicheng.yue@zilliz.com>
issue: https://github.com/milvus-io/milvus/issues/27467
>My plan is as follows.
>- [x] M1 Create collection with timestamptz field
>- [x] M2 Insert timestamptz field data
>- [x] M3 Retrieve timestamptz field data
>- [x] M4 Implement handoff
>- [x] M5 Implement compare operator
>- [x] M6 Implement extract operator
>- [x] M8 Support database/collection level default timezone
>- [x] M7 Support STL-SORT index for datatype timestamptz
---
The third PR of issue: https://github.com/milvus-io/milvus/issues/27467,
which completes M5, M6, M7, M8 described above.
## M8 Default Timezone
We will be able to use alter_collection() and alter_database() in a
future Python SDK release to modify the default timezone at the
collection or database level.
For insert requests, the timezone will be resolved using the following
order of precedence: String Literal-> Collection Default -> Database
Default.
For retrieval requests, the timezone will be resolved in this order:
Query Parameters -> Collection Default -> Database Default.
In both cases, the final fallback timezone is UTC.
## M5: Comparison Operators
We can now use the following expression format to filter on the
timestamptz field:
- `timestamptz_field [+/- INTERVAL 'interval_string'] {comparison_op}
ISO 'iso_string' `
- The interval_string follows the ISO 8601 duration format, for example:
P1Y2M3DT1H2M3S.
- The iso_string follows the ISO 8601 timestamp format, for example:
2025-01-03T00:00:00+08:00.
- Example expressions: "tsz + INTERVAL 'P0D' != ISO
'2025-01-03T00:00:00+08:00'" or "tsz != ISO
'2025-01-03T00:00:00+08:00'".
## M6: Extract
We will be able to extract sepecific time filed by kwargs in a future
Python SDK release.
The key is `time_fields`, and value should be one or more of "year,
month, day, hour, minute, second, microsecond", seperated by comma or
space. Then the result of each record would be an array of int64.
## M7: Indexing Support
Expressions without interval arithmetic can be accelerated using an
STL-SORT index. However, expressions that include interval arithmetic
cannot be indexed. This is because the result of an interval calculation
depends on the specific timestamp value. For example, adding one month
to a date in February results in a different number of added days than
adding one month to a date in March.
---
After this PR, the input / output type of timestamptz would be iso
string. Timestampz would be stored as timestamptz data, which is int64_t
finally.
> for more information, see https://en.wikipedia.org/wiki/ISO_8601
---------
Signed-off-by: xtx <xtianx@smail.nju.edu.cn>
Remove redundant `VectorPlanNode` subclasses and simplify the visitor
pattern by consolidating to a single `VectorPlanNode`.
The previous design used distinct `VectorPlanNode` subclasses and a
templated `VectorVisitorImpl` for type-directed dispatch. However, the
template parameter was not functionally used to implement different
logic for each vector type, making the subclasses redundant for their
intended purpose.
This PR is created by Cursor Agent and manually moved from
https://github.com/zhengbuqian/milvus/pull/14.
Signed-off-by: zhengbuqian <zhengbuqian@gmail.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: buqian.zheng <buqian.zheng@zilliz.com>
Ref https://github.com/milvus-io/milvus/issues/42148
This PR supports create index for vector array (now, only for
`DataType.FLOAT_VECTOR`) and search on it.
The index type supported in this PR is `EMB_LIST_HNSW` and the metric
type is `MAX_SIM` only.
The way to use it:
```python
milvus_client = MilvusClient("xxx:19530")
schema = milvus_client.create_schema(enable_dynamic_field=True, auto_id=True)
...
struct_schema = milvus_client.create_struct_array_field_schema("struct_array_field")
...
struct_schema.add_field("struct_float_vec", DataType.ARRAY_OF_VECTOR, element_type=DataType.FLOAT_VECTOR, dim=128, max_capacity=1000)
...
schema.add_struct_array_field(struct_schema)
index_params = milvus_client.prepare_index_params()
index_params.add_index(field_name="struct_float_vec", index_type="EMB_LIST_HNSW", metric_type="MAX_SIM", index_params={"nlist": 128})
...
milvus_client.create_index(COLLECTION_NAME, schema=schema, index_params=index_params)
```
Note: This PR uses `Lims` to convey offsets of the vector array to
knowhere where vectors of multiple vector arrays are concatenated and we
need offsets to specify which vectors belong to which vector array.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>
issue: #41435
this is to prevent AI from thinking of our exception throwing as a
dangerous PANIC operation that terminates the program.
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
Related to #42640
The search/query plan holded a reference to schema, which could be
destructed after schema change. This PR make plan hold a shared ptr to
it fixing dangling reference problem under concurrent read & schema
change.
This PR also remove field binlog check for loading index for old segment
with old schema may have binlog lack.
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
issue: #39541
This PR implements random sample, the syntax is:
```
filter="random_sample(factor)"
or
filter="boolean_expression && random_sample(factor)"
where
factor is a float between (0, 1) and
boolean_expression is like
"1 <= number < 10", "color in ["read, "blue"]" or others
```
---------
Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
issue:https://github.com/milvus-io/milvus/issues/27576
# Main Goals
1. Create and describe collections with geospatial fields, enabling both
client and server to recognize and process geo fields.
2. Insert geospatial data as payload values in the insert binlog, and
print the values for verification.
3. Load segments containing geospatial data into memory.
4. Ensure query outputs can display geospatial data.
5. Support filtering on GIS functions for geospatial columns.
# Solution
1. **Add Type**: Modify the Milvus core by adding a Geospatial type in
both the C++ and Go code layers, defining the Geospatial data structure
and the corresponding interfaces.
2. **Dependency Libraries**: Introduce necessary geospatial data
processing libraries. In the C++ source code, use Conan package
management to include the GDAL library. In the Go source code, add the
go-geom library to the go.mod file.
3. **Protocol Interface**: Revise the Milvus protocol to provide
mechanisms for Geospatial message serialization and deserialization.
4. **Data Pipeline**: Facilitate interaction between the client and
proxy using the WKT format for geospatial data. The proxy will convert
all data into WKB format for downstream processing, providing column
data interfaces, segment encapsulation, segment loading, payload
writing, and cache block management.
5. **Query Operators**: Implement simple display and support for filter
queries. Initially, focus on filtering based on spatial relationships
for a single column of geospatial literal values, providing parsing and
execution for query expressions.
6. **Client Modification**: Enable the client to handle user input for
geospatial data and facilitate end-to-end testing.Check the modification
in pymilvus.
---------
Signed-off-by: tasty-gumi <1021989072@qq.com>
OSPP 2024 project:
https://summer-ospp.ac.cn/org/prodetail/247410235?list=org&navpage=org
Solutions:
- parser (planparserv2)
- add CallExpr in planparserv2/Plan.g4
- update parser_visitor and show_visitor
- grpc protobuf
- add CallExpr in plan.proto
- execution (`core/src/exec`)
- add `CallExpr` `ValueExpr` and `ColumnExpr` (both logical and
physical) for function call and function parameters
- function factory (`core/src/exec/expression/function`)
- create a global hashmap when starting milvus (see server.go)
- the global hashmap stores function signatures and their function
pointers, the CallExpr in execution engine can get the function pointer
by function signature.
- custom functions
- empty(string)
- starts_with(string, string)
- add cpp/go unittests and E2E tests
closes: #36559
Signed-off-by: Yinzuo Jiang <jiangyinzuo@foxmail.com>
related: #33544
mainly changes in three aspects:
1. enable setting group_size for group by function
2. separate normal reduce and group by reduce
3. eleminate uncessary padding in search result for reducing
Signed-off-by: MrPresent-Han <chun.han@gmail.com>
Co-authored-by: MrPresent-Han <chun.han@gmail.com>
issue: #29892
This PR:
1. Move the process of gathering materialized search info to when the
search plan is created, before it goes to each segment, to avoid
repeated work and access the plan node under multi-threaded
circumstances.
2. Enforce the supported MV type to `VARCHAR`
3. Add integration test
Signed-off-by: Patrick Weizhi Xu <weizhi.xu@zilliz.com>
issue: #29892
This PR
1. Pass Materialized View (MV) search information obtained from the
expression parsing planning procedure to Knowhere. It only performs when
MV is enabled and the partition key is involved in the expression. The
search information includes:
1. Touched field_id and the count of related categories in the
expression. E.g., `color == red && color == blue` yields `field_id ->
2`.
2. Whether the expression only includes AND (&&) logical operator,
default `true`.
3. Whether the expression has NOT (!) operator, default `false`.
4. Store if turning on MV on the proxy to eliminate reading from
paramtable for every search request.
5. Renames to MV.
## Rebuttals
1. Did not write in `ExtractInfoPlanNodeVisitor` since the new scalar
framework was introduced and this part might be removed in the future.
2. Currently only interested in `==` and `in` expression, `string` data
type, anything else is a bonus.
3. Leave handling expressions like `F == A || F == A` for future works
of the optimizer.
## Detailed MV Info
Signed-off-by: Patrick Weizhi Xu <weizhi.xu@zilliz.com>
This PR adds the ability to search/get sparse float vectors in segcore,
and added unit tests by modifying lots of existing tests into
parameterized ones.
https://github.com/milvus-io/milvus/issues/29419
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
related: #25324
Search GroupBy function, used to aggregate result entities based on a
specific scalar column.
several points to mention:
1. Temporarliy, the whole groupby is implemented separated from
iterative expr framework **for the first period**
2. In the long term, the groupBy operation will be incorporated into the
iterative expr framework:https://github.com/milvus-io/milvus/pull/28166
3. This pr includes some unrelated mocked interface regarding alterIndex
due to some unworth-to-mention reasons. All these un-associated content
will be removed before the final pr is merged. This version of pr is
only for review
4. All other related details were commented in the files comparison
Signed-off-by: MrPresent-Han <chun.han@zilliz.com>
