If `is_all_data_type` is true, the case will add float32, f16, bf16, and
sparse vectors, but the created indexes are all `flat` indexes by
default. The sparse type cannot create a flat index. Fix the test code
to create a `SPARSE_INVERTED_INDEX` index for the sparse vector when
is_all_data_type is true
Signed-off-by: elstic <hao.wang@zilliz.com>
See also #34483
Some lint issues are introduced due to lack of static check run. This PR
fixes these problems.
---------
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
issue: #34304
cosine is more widely used in float vectors, and cosine and hamming
distance are 'metrics' which have good geometric properties
Signed-off-by: chasingegg <chao.gao@zilliz.com>
Some lint issue is not detect due to recent static check pipeline issue.
This PR fixes these problem and Go milvusclient testcases.
Signed-off-by: Congqi Xia <congqi.xia@zilliz.com>
Refactor the function to improve performance and readability. Instead of
making API requests to Docker Hub, the function now retrieves tags from
the Harbor registry. It also filters the tags based on the provided
architecture and selects the latest tag that matches the prefix. This
change enhances the efficiency of retrieving image tags by short name.
Signed-off-by: zhuwenxing <wenxing.zhu@zilliz.com>
* add coo format sparse vector
* search data and insert data in the same sparse format or a different
format
Signed-off-by: zhuwenxing <wenxing.zhu@zilliz.com>
1. Fill log ID of stats log from import
2. Add a check to validate the log ID before writing to meta
issue: https://github.com/milvus-io/milvus/issues/33476
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Signed-off-by: bigsheeper <yihao.dai@zilliz.com>
issue: #32995
To speed up the construction and querying of Bloom filters, we chose a
blocked Bloom filter instead of a basic Bloom filter implementation.
WARN: This PR is compatible with old version bf impl, but if fall back
to old milvus version, it may causes bloom filter deserialize failed.
In single Bloom filter test cases with a capacity of 1,000,000 and a
false positive rate (FPR) of 0.001, the blocked Bloom filter is 5 times
faster than the basic Bloom filter in both querying and construction, at
the cost of a 30% increase in memory usage.
- Block BF construct time {"time": "54.128131ms"}
- Block BF size {"size": 3021578}
- Block BF Test cost {"time": "55.407352ms"}
- Basic BF construct time {"time": "210.262183ms"}
- Basic BF size {"size": 2396308}
- Basic BF Test cost {"time": "192.596229ms"}
In multi Bloom filter test cases with a capacity of 100,000, an FPR of
0.001, and 100 Bloom filters, we reuse the primary key locations for all
Bloom filters to avoid repeated hash computations. As a result, the
blocked Bloom filter is also 5 times faster than the basic Bloom filter
in querying.
- Block BF TestLocation cost {"time": "529.97183ms"}
- Basic BF TestLocation cost {"time": "3.197430181s"}
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Signed-off-by: Wei Liu <wei.liu@zilliz.com>
