See also #30404
`PrimaryKey` is used to hold pk values for both int64 & varchar data
type. Since it is an interface it may occupies more memory than pure
slices when holding a group of pks.
This PR add `PrimaryKeys` interface when some other module need to hold
lots of PrimaryKeys.
By using this interface, it could reduce the memory of pk slice to half
when using Int64 Pk data type and reduce interface cost for each row of
varchar as well.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
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
---------
Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
fix: #29757
In previous code, `ColumnBasedInsertMsgToInsertData` adds empty field if
the insertMsg parameter does not have the column schema defined. This
may lead to unexpected behavior of caller functions.
This PR:
- Add column missing check
- Add column length check
- Generate BlobInfo for ColumnBasedInsertMsgToInsertData result
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
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>
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>
We have implemented the chunkcache (in cpp) to retrieve vectors, hence
rendering the vectorchunkcache (in golang) obsolete.
issue: https://github.com/milvus-io/milvus/issues/28568
---------
Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
see also: https://github.com/milvus-io/milvus/issues/28509
Currently Minio latency monitoring for get operation only collects the
duration of getting object (which just returns an io.Reader and does not
really read from minio), this pr will correct this behavior.
Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
See also #27675
When L0 segment contains only delta data, merged statslog shall be
skiped when performing sync task
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
See also #28575
Add zero-length check for `storage.NewPrimaryKeyStats`. This function
shall return error when non-positive rowNum passed.
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
Benchmark Milvus with https://github.com/qdrant/vector-db-benchmark and
specify the datasets as 'deep-image-96-angular'. Meanwhile, do perf
profiling during 'upload + index' stage of vector-db-benchmark and see
the following hot spots.
39.59%--github.com/milvus-io/milvus/internal/storage.MergeInsertData
|
|--21.43%--github.com/milvus-io/milvus/internal/storage.MergeFieldData
| |
| |--17.22%--runtime.memmove
| |
| |--1.53%--asm_exc_page_fault
| ......
|
|--18.16%--runtime.memmove
|
|--1.66%--asm_exc_page_fault
......
The hot code path is in storage.MergeInsertData() which updates
buffer.buffer by creating a new 'InsertData' instance and merging both
the old buffer.buffer and addedBuffer into it. When it calls golang
runtime.memmove to move buffer.buffer which is with big size (>1M), the
hot spots appear.
To avoid the above overhead, update storage.MergeInsertData() by
appending addedBuffer to buffer.buffer, instead of moving buffer.buffer
and addedBuffer to a new 'InsertData'. This change removes the hot spots
'runtime.memmove' from perf profiling output. Additionally, the 'upload
+ index' time, which is one performance metric of vector-db-benchmark,
is reduced around 60% with this change.
Signed-off-by: Cathy Zhang <cathy.zhang@intel.com>
Ted Xu
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