issue: #43088
issue: #43038
The current loading process:
* When loading an index, we first download the index files into a list
of buffers, say A
* then constructing(copying) them into a vector of FieldDatas(each file
is a FieldData), say B
* assembles them together as a huge BinarySet, say C
* lastly, copy into the actual index data structure, say D
The problem:
* We can see that, after each step, we don't need the data in previous
step.
* But currently, we release the memory of A, B, C only after we have
finished constructing D
* This leads to a up to 4x peak memory usage comparing with the raw
index size, during the loading process
* This PR allows timely releasing of B after we assembled C. So after
this PR, the peak memory usage during loading will be up to 3x of the
raw index size.
I will create another PR to release A after we created B, that seems
more complicated and need more work.
Signed-off-by: Buqian Zheng <zhengbuqian@gmail.com>
Ref https://github.com/milvus-io/milvus/issues/42053
This PR enable ngram to support more kinds of matches such as prefix and
postfix match.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Ref #42053
This is the first PR for optimizing `LIKE` with ngram inverted index.
Now, only VARCHAR data type is supported and only InnerMatch LIKE
(%xxx%) query is supported.
How to use it:
```
milvus_client = MilvusClient("http://localhost:19530")
schema = milvus_client.create_schema()
...
schema.add_field("content_ngram", DataType.VARCHAR, max_length=10000)
...
index_params = milvus_client.prepare_index_params()
index_params.add_index(field_name="content_ngram", index_type="NGRAM", index_name="ngram_index", min_gram=2, max_gram=3)
milvus_client.create_collection(COLLECTION_NAME, ...)
```
min_gram and max_gram controls how we tokenize the documents. For
example, for min_gram=2 and max_gram=4, we will tokenize each document
with 2-gram, 3-gram and 4-gram.
---------
Signed-off-by: SpadeA <tangchenjie1210@gmail.com>
Signed-off-by: SpadeA-Tang <tangchenjie1210@gmail.com>
