- Refactor connection logic to prioritize uri and token parameters over
host/port/user/password for a more modern connection approach
- Add explicit limit parameter (limit=5) to search and query operations
in chaos checkers to avoid returning unlimited results
- Migrate test_all_collections_after_chaos.py from Collection wrapper to
MilvusClient API style
- Update pytest fixtures in chaos test files to support uri/token params
Signed-off-by: zhuwenxing <wenxing.zhu@zilliz.com>
Issue: #45756
1. add bulk insert scenario
2. fix small issue in e2e cases
3. add search group by test case
4. add timestampstz to gen_all_datatype_collection_schema
5. modify partial update testcase to ensure correct result from
timestamptz field
On branch feature/timestamps
Changes to be committed:
modified: common/bulk_insert_data.py
modified: common/common_func.py
modified: common/common_type.py
modified: milvus_client/test_milvus_client_partial_update.py
modified: milvus_client/test_milvus_client_timestamptz.py
modified: pytest.ini
modified: testcases/test_bulk_insert.py
Signed-off-by: Eric Hou <eric.hou@zilliz.com>
Co-authored-by: Eric Hou <eric.hou@zilliz.com>
To prevent this issue from blocking other PRs, we are temporarily
disabling this test. A proper fix will be implemented before the 2.6.6
release.
issue: https://github.com/milvus-io/milvus/issues/45511
---------
Signed-off-by: sunby <sunbingyi1992@gmail.com>
issue: #44320
This change adds deduplication logic to handle duplicate primary keys
within a single upsert batch, keeping the last occurrence of each
primary key.
Key changes:
- Add DeduplicateFieldData function to remove duplicate PKs from field
data, supporting both Int64 and VarChar primary keys
- Refactor fillFieldPropertiesBySchema into two separate functions:
validateFieldDataColumns for validation and fillFieldPropertiesOnly for
property filling, improving code clarity and reusability
- Integrate deduplication logic in upsertTask.PreExecute to
automatically deduplicate data before processing
- Add comprehensive unit tests for deduplication with various PK types
(Int64, VarChar) and field types (scalar, vector)
- Add Python integration tests to verify end-to-end behavior
---------
Signed-off-by: Wei Liu <wei.liu@zilliz.com>
issue: #45403, #45463
- fix the Nightly E2E failures.
- fix the wrong update timetick of altering collection to fix the
related load failure.
Signed-off-by: chyezh <chyezh@outlook.com>
Issue: #45129
<test>: <add new test case>
<also delete duplicate test case>
On branch feature/partial-update
Changes to be committed:
modified: milvus_client/test_milvus_client_partial_update.py
modified: milvus_client/test_milvus_client_upsert.py
---------
Signed-off-by: Eric Hou <eric.hou@zilliz.com>
Co-authored-by: Eric Hou <eric.hou@zilliz.com>
issue: #43897
- Alter collection/database is implemented by WAL-based DDL framework
now.
- Support AlterCollection/AlterDatabase in wal now.
- Alter operation can be synced by new CDC now.
- Refactor some UT for alter DDL.
---------
Signed-off-by: chyezh <chyezh@outlook.com>
Issue: #44989
On branch feature/json-shredding
Changes to be committed:
modified: milvus_client/test_milvus_client_query.py
Signed-off-by: Eric Hou <eric.hou@zilliz.com>
Co-authored-by: Eric Hou <eric.hou@zilliz.com>
issue: https://github.com/milvus-io/milvus/issues/44399
This PR implements STL_SORT for VARCHAR data type for both RAM and MMAP
mode.
The general idea is that we deduplicate field values and maintains a
posting list for each unique value.
The serialization format of the index is:
```
[unique_count][string_offsets][string_data][post_list_offsets][post_list_data][magic_code]
string_offsets: array of offsets into string_data section
string_data: str_len1, str1, str_len2, str2, ...
post_list_offsets: array of offsets into post_list_data section
post_list_data: post_list_len1, row_id1, row_id2, ..., post_list_len2, row_id1, row_id2, ...
```
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Issue: #44518
On branch feature/timestamps
Changes to be committed:
modified: common/common_func.py
new file: milvus_client/test_milvus_client_timestamptz.py
---------
Signed-off-by: Eric Hou <eric.hou@zilliz.com>
Co-authored-by: Eric Hou <eric.hou@zilliz.com>
related issue: #44425
1. split insert.py into a few files: upsert.py, insert.py,
partial_upsert.py ...
2. add test for allow insert auto id
---------
Signed-off-by: yanliang567 <yanliang.qiao@zilliz.com>
The collection not found err could contains db id in err message, which
is not meaningful to users.
This patch make error message wrapping dbname instead of db id.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
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>
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>
