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17 Commits (a2b517523dbecde8d96514fb3b7ef3b217747ecf)

Author SHA1 Message Date
congqixia de8a266d8a
enhance: Enable linux code checker (#35084) 
See also #34483

---------

Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
 2024-07-30 15:53:51 +08:00
wei liu c45f38aa61
enhance: Update protobuf-go to protobuf-go v2 (#34394) 
issue: #34252

Signed-off-by: Wei Liu <wei.liu@zilliz.com>
 2024-07-29 11:31:51 +08:00
congqixia 531092c031
enhance: Add lint rule to forbid gogo protobuf (#34594) 
github.com/gogo/protobuf is deprecated and could be error prune after
upgrade protobuf message to v2.

Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
 2024-07-12 10:19:35 +08:00
smellthemoon 2a1356985d
enhance: support null in go payload (#32296) 
#31728

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Signed-off-by: lixinguo <xinguo.li@zilliz.com>
Co-authored-by: lixinguo <xinguo.li@zilliz.com>
 2024-06-19 17:08:00 +08:00
congqixia 512ea6be5f
enhance: Avoid merging insert data when buffering insert msgs (#33562) 
See also #33561

This PR:
- Use zero copy when buffering insert messages
- Make `storage.InsertCodec` support serialize multiple insert data
chunk into same batch binlog files

Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>

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Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
 2024-06-13 11:15:56 +08:00
Cai Yudong 4004e4c545
enhance: Optimize bulk insert unittest (#33224) 
Issue: #22837

Signed-off-by: Cai Yudong <yudong.cai@zilliz.com>
 2024-05-24 10:23:41 +08:00
Cai Yudong bcdbd1966e
feat: Support sparse float vector bulk insert for binlog/json/parquet (#32649) 
Issue: #22837

Signed-off-by: Cai Yudong <yudong.cai@zilliz.com>
 2024-05-07 18:43:30 +08:00
Cai Yudong 5fc439c600
feat: Bulk insert support fp16/bf16 (#32157) 
Issue: #22837

Signed-off-by: Cai Yudong <yudong.cai@zilliz.com>
 2024-04-22 10:05:22 +08:00
Buqian Zheng 3c80083f51
feat: [Sparse Float Vector] add sparse vector support to milvus components (#30630) 
add sparse float vector support to different milvus components,
including proxy, data node to receive and write sparse float vectors to
binlog, query node to handle search requests, index node to build index
for sparse float column, etc.

https://github.com/milvus-io/milvus/issues/29419

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Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
 2024-03-13 14:32:54 -07:00
yihao.dai a434d33e75
feat: Add import scheduler and manager (#29367) 
This PR introduces novel managerial roles for importv2:
1. ImportMeta: To manage all the import tasks;
2. ImportScheduler: To process tasks and modify their states;
3. ImportChecker: To ascertain the completion of all tasks and instigate
relevant operations.

issue: https://github.com/milvus-io/milvus/issues/28521

---------

Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
 2024-03-01 18:31:02 +08:00
yihao.dai c5918290e6
feat: Add import executor and manager for datanode (#29438) 
This PR introduces novel importv2 roles for datanode:
1. Executor: To execute tasks, a import task will be divided into the
following steps: read data -> hash data -> sync data;
2. Manager: To manage all the tasks;

issue: https://github.com/milvus-io/milvus/issues/28521

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Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
 2024-01-31 20:45:04 +08:00
Xu Tong e429965f32
Add float16 approve for multi-type part (#28427) 
issue：https://github.com/milvus-io/milvus/issues/22837

Add bfloat16 vector, add the index part of float16 vector.

Signed-off-by: Writer-X <1256866856@qq.com>
 2024-01-11 15:48:51 +08:00
yihao.dai 3d07b6682c
feat: Add import reader for numpy (#29253) 
This PR implements a new numpy reader for import.

issue: https://github.com/milvus-io/milvus/issues/28521

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Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
 2024-01-08 19:42:49 +08:00
yihao.dai 23183ffb0f
feat: Add import reader for json (#29252) 
This PR implements a new json reader for import.

issue: https://github.com/milvus-io/milvus/issues/28521

---------

Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
 2024-01-05 18:12:48 +08:00
yihao.dai 3561586edf
feat: Add import reader for binlog (#28910) 
This PR defines the new import reader interfaces and implement a binlog
reader for import.

issue: https://github.com/milvus-io/milvus/issues/28521

---------

Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
 2024-01-05 11:48:47 +08:00
Jiquan Long 3f46c6d459
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 19:50:47 +08:00
XuanYang-cn 2f16339aac
Enhance InsertData and FieldData (#27436) 
1. Add NewInsertData
2. Add GetRowNum(), GetMemorySize(), and, Append() for InsertData
3. Add AppendRow() for FieldData for compaction

Signed-off-by: yangxuan <xuan.yang@zilliz.com>
 2023-10-17 17:36:11 +08:00