New Features
* Several streaming query methods have been added to the core ClickHouse Connect client. Each of these methods returns a StreamContext object, which must be used as a Python `with` Context to stream data (this ensures the underlying
streaming response is properly closed/consumed.) For simple examples, see the basic [tests](https://github.com/ClickHouse/clickhouse-connect/blob/main/tests/integration_tests/test_streaming.py).
* `query_column_block_stream` -- returns a generator of blocks in column oriented (Native) format. Fastest method for retrieving data in native Python format
* `query_row_block_stream` -- returns a generator of blocks in row oriented format. Used for processing data in a "batch" of rows at time while limiting memory usage
* `query_rows_stream` -- returns a convenience generator to process rows one at a time (data is still loaded in ClickHouse blocks to preserve memory)
* `query_np_stream` -- returns a generator where each ClickHouse data block is transformed into a Numpy array
* `query_df_stream` -- returns a generator where each ClickHouse data block is transformed into a Pandas Dataframe
* The `client_name` is now reported in a standardized way to ClickHouse (as the `http_user_agent`). For better tracking of your
Python application, use the new `product_name` common setting or set `client_name` `get_client` parameter to identify your product
as `<your-product-name>/<product-version>`.
Performance Improvements
* C/Cython optimizations for transforming ClickHouse data to Python types have been improved, and additional datatypes have been
optimized in Cython. The performance increase over the previous 0.5.x version is approximately 10% for "normal" read queries.
* Transformation of Numpy arrays and Pandas Dataframes has been completely rewritten to avoid an intermediate conversion to
Python types. As a result, querying in Numpy format, and especially Pandas format, has been **significantly** improved -- from 2x
for small datasets to 5x or more for very large Pandas DataFrames (even without streaming). Queries including Numpy datetime64 or
Pandas Timestamp objects have particularly benefited from the new implementation.
Bug Fixes
* The default `maxsize` for concurrent HTTP connections to a single host was accidentally dropped in the 0.5.x release. It
has been restored to 8 for better performance when using multiple client objects.
* A single low level retry has been restored for HTTP connections on ConnectionReset or RemoteDisconnected exceptions. This
should reduce connection errors related to ClickHouse closing expired KeepAlive connections.
Internal Changes
* As noted above, streaming, contexts and exception handling have been tightened up to avoid leaving HTTP responses open
when querying streams.
* Previous versions used `threading.local()` variables to store context information during query processing. The architecture
has been changed to pass the relevant Query or Insert Context to transformation methods instead of relying on thread local
variables. This is significantly safer in an environment where multiple queries can conceivably be open at the same on the
same thread (for example, if using async functions).
* Per query formatting logic has moved from `ClickHouseType` to the `QueryContext`.
* `ClickHouseType` methods have been renamed to remove outdated references to `native` format (everything is native now)
* Upgraded Cython Build to 3.0.11alpha release