issue: #37579
If the schema includes large varchar fields, a few thousand rows can
reach hundreds of MB in size. Therefore, if the batch size of the
segment writer is large, it will produce relatively large `binlogs`,
which can cause datanode to run out of memory (OOM) during compaction.
Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
Native support for Google cloud storage using the Google Cloud Storage
libraries. Authentication is performed using GCS service account
credentials JSON.
Currently, Milvus supports Google Cloud Storage using S3-compatible APIs
via the AWS SDK. This approach has the following limitations:
1. Overhead: Translating requests between S3-compatible APIs and GCS can
introduce additional overhead.
2. Compatibility Limitations: Some features of the original S3 API may
not fully translate or work as expected with GCS.
To address these limitations, This enhancement is needed.
Related Issue: #36212
issue: #33744
This PR includes the following changes:
1. Added a new task type to the task scheduler in datacoord: stats task,
which sorts segments by primary key.
2. Implemented segment sorting in indexnode.
3. Added a new field `FieldStatsLog` to SegmentInfo to store token index
information.
---------
Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
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>
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
Adding a collection id to the index node log allows you to associate an
index building task with a specific collection.
If the host CPU usage is too high due to index build, you can use the
collection id to quickly locate a specific collection, improving fault
locating efficiency.
Signed-off-by: dengxiaohai <rolkdengxiaohai@didiglobal.com>
Co-authored-by: dengxiaohai <rolkdengxiaohai@didiglobal.com>
issue: #19095,#29655,#31718
- Change `ListWithPrefix` to `WalkWithPrefix` of OOS into a pipeline
mode.
- File garbage collection is performed in other goroutine.
- Segment Index Recycle clean index file too.
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Signed-off-by: chyezh <chyezh@outlook.com>
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>
1. add coordinator graceful stop timeout to 5s
2. change the order of datacoord component while stop
3. change querynode grace stop timeout to 900s, and we should
potentially change this to 600s when graceful stop is smooth
issue: #30310
also see pr: #30306
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Signed-off-by: chyezh <chyezh@outlook.com>
don't store logPath in meta to reduce memory, when service get
segmentinfo, generate logpath from logid.
#28885
Signed-off-by: lixinguo <xinguo.li@zilliz.com>
Co-authored-by: lixinguo <xinguo.li@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>
issue:https://github.com/milvus-io/milvus/issues/29230
this pr do two things about cagra index:
a.milvus yaml config support gpu memory settings
b.add cagra-params check
Signed-off-by: cqy123456 <qianya.cheng@zilliz.com>
Co-authored-by: yusheng.ma <yusheng.ma@zilliz.com>
support enable/disable mmap for index, the user could alter the index's
mode by `AlterIndex` method
related: https://github.com/milvus-io/milvus/issues/21866
---------
Signed-off-by: yah01 <yah2er0ne@outlook.com>
Signed-off-by: yah01 <yang.cen@zilliz.com>
