Python 3.8 was EOL on 2024-10-07. It is no longer tested, and versions after 2025-04-07 will not include Python 3.8 wheel distributions.
The internal serialization format for experimental JSON was updated in ClickHouse version 24.10. clickhouse-connect
will set the compatibility level on a global basis based on the last client created, so multiple clients using the
library with mixed versions 22.8/22.9 and 22.10 and later versions will break. If you need JSON support for mixed
versions you must use different Python interpreters for each version.
When creating a DBAPI Connection method using the Connection constructor or a SQLAlchemy DSN, the library currently
converts any unrecognized keyword argument/query parameter to a ClickHouse server setting. Starting in the next minor
release (0.9.0), unrecognized arguments/keywords for these methods of creating a DBAPI connection will raise an exception
instead of being passed as ClickHouse server settings. This is in conjunction with some refactoring in Client construction.
The supported method of passing ClickHouse server settings is to prefix such arguments/query parameters withch_
.
- The experimental JSON type would break in some circumstances with ClickHouse server version 24.10 and later. This has been fixed. The fix is incompatible with ClickHouse version 24.8 and 24.9 however, so see the above WARNING about mixing JSON types
- Experimental JSON types within a Tuple was broken. This has been fixed; however, the fix fails on ClickHouse server versions 24.8 and 24.9. If you need Tuple(JSON) support, you must use ClickHouse server version 24.10 or later. Closes #436.
- Roll back some timezone changes that caused incorrect usage of "local time" objects for some ClickHouse queries. Note that has deprecated "naive" timestamps; however converting everything to timezone aware objects (with the UTC timezone as appropriate) causes some numpy and possibly Pandas side effects. Eventually naive datetime object support will be deprecated/eliminated, but it will take some time to ensure no breaking changes. Fixes #433
- Handle low level HTTP errors as "Stream Complete". This provides better compatibility with the most recent ClickHouse version when the HTTP stream is abruptly closed after a server error.
- Added basic support for ClickHouse geometric types Ring, Polygon, MultiPolygon, LineString, and MultiLineString. Closes #427
- Settings/parameters from one Client will no longer leak into later client instantiations. Fixes #426
- Correctly stream unchunked HTTP responses. Fixes #417.
- Don't use
wait_end_of_query
for any streaming requests. Fixes #416
- Inserts into a Nullable integer/float column could throw an exception if the first value was
None
and the column required conversion to the numeric type (such as Python str to float). This has been fixed. Note that "mixed" Python types in an insert data set will still throw an exception (i.e., Python strings and ints should not be combined into the same column for insert. Closes #414
- Python 3.13 is now included in CI tests and 3.13 wheels are built for distribution. Note that PyArrow is not yet available for Python 3.13.
- ClickHouse errors are now detected and throw an exception even if the HTTP status code returned by ClickHouse is a 200.
This can happen when there is a long-running query (such as a large
INSERT INTO ... SELECT FROM ...
) andsend_progress_in_http_headers
is enabled to keep the HTTP connection open. - Pandas NA (which is equivalent to Float NaN for Float values) is now inserted as NULL into Nullable(Float*) columns. Closes #412
- Add an optional
executor_threads
argument to theget_async_client
method. This controls the number of concurrent threads that each AsyncClient has available for queries. Defaults to "number of CPU cores plus four". Closes #407
- Ensure lz4 compression does not exit on an empty block. May fix #403.
- Compress Arrow inserts (using pyarrow compression) if compression is set to
lz4
orzstd
. Closes #267.
- Fixed an edge case where the HTTP buffer could theoretically return empty blocks.
- JSON data can be inserted as either a Python dictionary or a JSON string containing a JSON object
{}
. Other forms of JSON data are not supported - Valid formats for the JSON type are 'native', which returns a Python dictionary, or 'string', which returns a JSON string
- Any value can be inserted into a Variant column, and ClickHouse will try to correctly determine the correct Variant Type for the value, based on its String representation.
- More complete documentation for the new types will be provided in the future.
- Each of these types must be enabled in the ClickHouse settings before using. The "new" JSON type is available started with the 24.8 release
- Returned JSON objects will only return the
max_dynamic_paths
number of elements (which defaults to 1024). This will be fixed in a future release. - Inserts into
Dynamic
columns will always be the String representation of the Python value. This will be fixed in a future release. - The implementation for the new types has not been optimized in C code, so performance may be somewhat slower than for simpler, established data types.
This is the first time that a new clickhouse_connect
features has been labeled "experimental", but these new
datatypes are complex and still experimental in ClickHouse server. Current test coverage for these types is also
quite limited. Please don't hesitate to report issues with the new types.
- When operating ClickHouse Server in
strict
TLS mode, HTTPS connections require a client certificate even if that certificate is not used for authentication. A new client parametertls_mode='strict'
can be used in this situation where username/password authentication is being used with client certificates. Other valid values for the newtls_mode
setting are'proxy'
when TLS termination occurs at a proxy, and'mutual'
to specify mutual TLS authentication is used by the ClickHouse server. Iftls_mode
is not set, and a client certificate and key are provided,mutual
is assumed. - The server timezone was not being used for parameter binding if parameters were sent as a list instead of a dictionary. This should fully fix the reopened #377.
- String port numbers (such as from environmental variables) are now correctly interpreted to determine the correct interface/protocol. Fixes #395
- Insert commands with a
SELECT FROM ... LIMIT 0
will no longer raise an exception. Closes #389.
- Some low level errors for problems with Native format inserts and queries now include the relevant column name in the error message. Thanks to Angus Holder for the PR!
- There is a new intermediate buffer for HTTP streaming/chunked queries. The buffer will store raw data from the HTTP request
until it is actually requested in a stream. This allows some lag between reading the data from ClickHouse and processing
the same data. Previously, if processing the data stream fell 30 seconds behind the ClickHouse HTTP writes to the stream,
the ClickHouse server would close the connection, aborting the query and stream processing. This will now be mitigated by
storing the data stream in the new intermediate buffer. By default, this buffer is set to 10 megabytes, but for slow
processing of large queries where memory is not an issue, the buffer size can be increasing using the new
common
settinghttp_buffer_size
. This is a fix in some cases of #399, but note that slow processing of large queries will still cause connection and processing failures if the data cannot be buffered. - It is now possible to correctly bind
DateTime64
type parameters when calling Clientquery
methods through one of two approaches:- Wrap the Python
datetime.datetime
value in the new DT64Param class, e.g.
query = 'SELECT {p1:DateTime64(3)}' # Server side binding with dictionary parameters={'p1': DT64Param(dt_value)} query = 'SELECT %s as string, toDateTime64(%s,6) as dateTime' # Client side binding with list parameters=['a string', DT64Param(datetime.now())]
- If using a dictionary of parameter values, append the string
_64
to the parameter name
This closes #396, see also the similar issue #212query = 'SELECT {p1:DateTime64(3)}, {a1:Array(DateTime(3))}' # Server side binding with dictionary parameters={'p1_64': dt_value, 'a1_64': [dt_value1, dt_value2]}
- Wrap the Python
- Insertion of large strings was triggering an exception. This has been fixed.
- In some cases retrieving the os_user as part of the
client data
in the HTTP User-Agent header could throw an exception. This has been fixed (os_user will not be sent in those cases). Closes #380.
- The client server_tz was not being correctly set if the server timezone was not UTC. This should close #377
- The os user can now be sent as part of the User-Agent HTTP header. To disable this functionality for privacy reasons,
set the new common/global setting
send_os_user
to False. Closes #371.
- Added the
AsyncClient
wrapper which is intended forasyncio
environment usage.AsyncClient
has the same methods with the same parameters as the standardClient
, but they are coroutines when applicable. Internally, these methods from theClient
that perform I/O operations are wrapped in a run_in_executor call. See also the updated run_async example.
- If the ClickHouse server was behind an https proxy that required mutual TLS authentication, the client would incorrectly
attempt to use ClickHouse mutual TLS instead and authentication would fail. It should now be possible to authenticate
correctly in this situation by settings the
verify
parameter toproxy
. This should close #370
- Fix insert of UUID strings including dashes. Closes #368
- Set required minimum version for optional tzlocal dependency. Thanks to drew-talon for reporting the issue and submitting the fix. Closes #360.
- Extended the effect of the
show_clickhouse_errors
client setting to exclude showing hostname and port for errors when that setting is False. Thanks to Andy for the PR!
- Add the ability to bind arbitrary, "heredoc" data (including binary data) into the query, as described
here. To use this functionality, use a single heredoc
tag, such as
$my_tag$
, in the query, and add that tag and the associated data into the query methodparameters
argument. For some examples, see thetest_embedded_binary
test in test_client.py. Closes #363.
- When using
query_df
with a FixedString column with a read format of 'string' (and the defaultquery_df
settinguse_extended_dtypes=True
), the resulting column in the dataframe will now be correctly set to the (extended) String dtype. Fixes #356
- Python or Pandas float value to ClickHouse Decimal now correctly rounds Float values for more accurate conversions. Thanks to Frederik Eychenié for the investigation and PR!
- Clean up pandas series concatenation issue
- query_df would raise a deprecation warning with recent Pandas version if there were empty blocks. This should be fixed. #349
- avoid a warning in timezone handling using the tzlocal library. Thanks to Tanner for the fix
- The new client keyword argument
show_clickhouse_errors
controls whether the full ClickHouse error (including possibly sensitive information) is displayed when there is an error in ClickHouse processing. It defaults to True. If False, the simple string 'The ClickHouse server returned an error.' will be displayed. Closes #344. - Updated to Cython 3.0.10
- The default behavior of applying the client timezone if the GMT offset of the client matched the GMT offset
of the server for the current time has been changed. The new default is to always apply the server timezone
unless the optional
apply_server_timezone
get_client
parameter is explicitly set toFalse
. The previous behavior could cause confusing results where datetime values would be rendered in a Daylight Savings Time/Summer Time zone when DST was not active, and vice versa.
- Fixed client side binding for complex types containing floats or integers that was broken in version 0.7.5. Closes #335.
- Added a
raw_stream
method to the Client the returns an io.Base. Use this instead of theraw_query
method with the (now removed) optionalstream
keyword boolean. Thanks to Martijn Thé for the PR that highlighted the somewhat messy public API.
- Fixed issue with SQLAlchemy Point type. Closes #332.
- Fixed client side binding for Python format strings using
%d
(int) and%f
(float) format patterns. Closes #327 - Allows empty
data
argument in the initializer ofExternalFile
/ExternalData
objects. Thanks to martijnthe for the PR!
- Added the new client method
query_arrow_stream
for streaming PyArrow queries from ClickHouse. Big thanks to NotSimone for the feature and tests! Closes #155.
- Add summary field to Cursor object to retrieve the result of 'X-Clickhouse-Summary' header. Thanks to elchyn-cheliabiyeu for the PR!
- Inserts into columns with multibyte UTF-8 names were broken. This has been fixed. #312
- If the result of applying the precedence of timezones to a column results in an explicit UTC timezone, the datetime object returned should now be timezone naive. This should make the behavior consistent with the documentation. Closes #308 (except for a documentation update)
- Extraneous semicolons are automatically removed from the end of queries. Addresses the most basic behavior in #310.
- Pandas DataFrame returned from the client
query_df
method should be constructed somewhat faster in cases where the data returned in ClickHouse is in many small blocks. Note that performance gains in this use case are somewhat limited because of the memory and copying cost of building a large DataFrame from many smaller ClickHouse Native block structures, so such performance problems should normally be addressed at the query or ClickHouse data storage level (by for example, reducing the number of partitions and/or shards referenced by the query). This may partially address #307.
- Changed type hint of the
query
parameter in Clientquery*
methods toOptional[str]
to work correctly with type analyzers. This also highlights that using a query_context instead of a query in these methods is supported (and preferred for repeated queries). Thanks to Avery Fischer for the PR! - Fixed sending a full table name to the
insert_file
tools function. Closes #305
- Python 3.7 builds are no longer part of the wheels deployed to PyPI
- Due to a change in default ClickHouse settings, inserts with "named" Tuple types no longer worked with ClickHouse version 24.1 and later. This has been fixed.
- Some types of security and other proxies require additional query parameters on any call to ClickHouse server behind
such a proxy. Because the HTTPClient makes certain initialization queries to ClickHouse before any query parameters
are set, it was difficult or impossible to create a Client successfully. You can now modify the HTTPClient class level
properties
params
andvalid_transport_settings
before callingget_client
so that such "special" query parameters will be included even on initialization queries. Thanks to Aleksey Astafiev for highlighting the problem and contributing a PR. - In some cases the user make want to disable urllib3 timeout settings
connect_timeout
andsend_receive_timeout
by setting them to none. The same PR from Aleksey Astafiev now allows setting to values toNone
- Update to Cython 3.0.8
- Add missing Nothing SQLAlchemy datatype, which fixes some edge case Superset queries. Thanks to elchyn-cheliabiyeu for the PR!
- Avoid concatenation of empty dataframes during
query_df
due to Pandas future warning. Thanks to Dylan Modesitt for the PR!
- Fix typo in log message for bad inserts. Thanks to Stas for the fix.
- Allow non ClickHouse Cloud tests to run on community Pull Requests
- Update to Cython 3.0.6
ATTACH
queries were not be correctly processed as "commands". Thanks to Aleksei Palshin for the PR!
- Added support for Point type. Closes #151. Thanks to Dhruvit Maniya for the PR!
- Upgraded to Cython 3.0.5
- Change exception handling in C API to stop spamming stderr
- Fixed an issue where client side binding of datetimes with timezones would produce the incorrect time string if timezones differed between the client and ClickHouse server. Closes #268
- In some circumstances it was possible to insert a
None
value into a non-Nullable String column. As this could mask invalid input data, any attempt to insert None into a non-Nullable String or LowCardinality(String) will now throw a DataError - Reading a named Tuple column where the Tuple element names contained spaces would fail. In particular this would cause expected failures reading the experimental JSON column type with spaces in the keys. This has been fixed. Closes #265. Note that handling spaces in column types is tricky and fragile in several respects, so the best approach remains to use simple column names without spaces.
- Reduce the estimated insert block size from 16-32MB to 1-2MB for large inserts. The large data transfers could cause "write timeout" errors in the Python code or "empty query" responses from ClickHouse over HTTPS connections. Should fix #258
- Ensure that the internal client _progress_interval is positive even if a very small
send_receive_timeout
value is specified. Closes #259. Note that a very shortsend_receive_timeout
is not recommended.
- Fix "negative" Date32 (before 1970-01-01) values for numpy and Pandas queries. Closes #254
- Remove bad private import to fix C Linkage. Closes #252
- Added Python 3.12 wheels and CI tests. Note that PyArrow is not yet available for 3.12, but should be soon. See apache/arrow#37880
- The main
clickhouse-connect.get_client
method now displays type hints and ignores non-keyword arguments. Thanks to Avery Fischer for the usability improvement! - Log messages regarding C optimization availability and JSON library selection have been change from INFO to DEBUG. Closes #249
- Fixed insert error when inserting a zero length string into a FixedString column. Closes #244
- Removed unnecessary validate_entrypoints import from top level package init that was breaking Python 3.7. Note that Python 3.7 is EOL and will no longer be supported as of January 1, 2024.
- Fixed an issue with the automatic retry of "connection reset errors". This should prevent exceptions when the ClickHouse server closes a Keep Alive connection while a new request is in flight.
- Improved support for typing tools by adding a
py.typed
file. Thanks to Avery Fischer for the contribution.
- Nested empty Maps would return an IndexError when queried. #239. Thanks to Ashton Hudson for the report and the fix
- Inserts using Pandas 2.1 would fail due to a removed method in the Pandas library. There is now a workaround/fix for this. Closes #234
- Inserts into a FixedString column that were not the expected size could cause corrupt insert blocks and mysterious errors from the ClickHouse server. Validation has been added so that more meaningful error messages are generated if a fixed string value is an invalid size. A reminder that strings which are "too short" for a FixedString column will be padded with 0 bytes, while strings that are "too long" will generate an exception during the insert.
- Add support and tests for the
Object(Nullable('json'))
type, which is sometimes detected by schema inference.
- Logging and exception handling for failed insert transformations has been reworked. If an exception is thrown when attempting to convert Python, Pandas, or Numpy data into ClickHouse Native format, the column name and type will be logged, as well as a stack trace of actual exception (note this may be in the C/Cython code, so the exception data may still be difficult to interpret). This partially addresses #229. Unfortunately determining data errors on a row level in addition to the column level is not practical in most cases without seriously impacting performance.
- Version information has been moved from a top level
VERSION
to a Python__version__
file in the package. This removes the Python 3.7 dependency on importlib_metadata. - Cython
.pyx
, and.pxd
files are now included in the PyPI source distribution to improve compatibility with 3rd party build tools.
- Fixed client
raw_insert
method when a compression method specified. #223
- Add compression parameter to the clickhouse
tools.insert_file
method. '.gz' and '.gzip' extensions are automatically recognized.
- Fixed an issue for older versions of ClickHouse where the server would send an initial block of 0 rows for larger queries. This would break some queries with LowCardinality columns. Closes #221
- Fixed the
compression
alias for thecompress
client setting in SQLAlchemy/Superset DSN urls.
- Upgraded to Cython 3.0.0 final release!
- Reversed the internal variable names of keys and indexes for low cardinality columns to be consistent with the ClickHouse server nomenclature.
- Inserting into an Enum column from a Pandas DataFrame with integer values only inserted 0 values. This is fixed. #219
- The Client min_version method now ignores unrecognized "text" elements. This could cause issues for unofficial ClickHouse releases. Thanks to Diego Nieto for the fix!
- In most cases insert query is now sent as part of the POST body instead of as a query parameter. This fixes
#213. Note that this does not happen for direct file inserts
using the
driver.tools
module, since these rely on an unmodified buffered input stream to efficiently upload files. In that case the actual insert query will still be passed as a query parameter. - All datetime objects returned from a query will now be timezone aware. This fixes #210. There remains one exception to this -- if the calculated timezone and the local timezone are both UTC, then naive timezones will be used to improve performance in such "all UTC" environments.
- Inserting Python dictionaries into a ClickHouse "named" Tuple column now works correctly. Fixes #215. Note that using dictionaries for inserts will be noticeably slower than inserting the equivalent Python tuple value (with elements in the correct order)
- Client error messages used to be cut off at 240 characters to avoid creating huge log files. This value is now
configurable using the
common.max_error_size
setting. Use0
for this setting to get the full ClickHouse error message. In addition, the default has been changed to1024
to capture more SQL errors without needing to modify the global setting value. Thanks to Ramlah Aziz for the update! - All Client insert methods now return a simple QuerySummary object, which includes properties
written_rows
,written_bytes
, andquery_id
calculated from ClickHouse HTTP response headers. A QuerySummary object is also returned from the Clientcommand
method if the command does not return other data. Closes #216 - Version determination no longer indirectly depends on the setuptools
pkg_resources
package. This also avoids some indirect dependency problems. Thanks to cwegener for the PR!
- Quote database name when retrieving tables via SQLAlchemy. Fixes the Superset issue apache/superset#24372 for recent versions of Superset using clickhouse-connect
- Don't rely on the ClickHouse currentDatabase() function to set an explicit database parameter. This should not change functionality when no database is specified in Client creation since ClickHouse will use the user's default database in that situation regardless. Fixes #207
- Inserts into decimal columns first convert the source value to a Python Decimal to work around floating point rounding issues. Fixes #203
- DateTime64 values were broken for dates before 01-01-1970. This is fixed. #204
- Cython version upgraded to 3.0.0b3
- Inserts for string columns are now C optimized (approximately 2x faster)
- Very long running queries could break because ClickHouse returned too many progress headers. Thanks to Ivan for the fix
Minor documentation clean up regarding Superset compatibility
- Use uuid4 instead of uuid1 for generating client level session_ids, as well as use a new urllib3 PoolManager
when multiprocessing mode is detected. This should fix #194.
Thanks to Guillaume Matheron for filing the issue and digging into details.
The underlying problem is that the Python uuid1() is not guaranteed to be unique in a
forked
multiprocessing environment. - Change log warning to debug message if numpy is not available for C bindings. This check is harmless if numpy is not installed and should not have produced a warning. Fixes #195
- Cython version upgraded to 3.0.0b2
- The block size (number of rows) for chunked/streaming inserts is now dynamically determined based on sample of the insert data. This allows more efficient streaming of large inserts and significantly improves insert performance in some circumstances.
- Pivoting row based data to native columns for inserts has been optimized in C. This improves insert performance for large inserts of row oriented data.
- The client will now validate that the
client_protocol_version
query parameter is actually received and used by the ClickHouse server before assuming that data returned confirms to the expected protocol version. This fixes an incompatibility with the current versions of CHProxy (and possibly other proxies that restrict the query parameters passed to the ClickHouse Server). Note that other features that require the use of query parameters (such as server side bound query parameters) may also fail because of this behavior in CHProxy. Fixes #191
- The client
command
method now accepts ClickHouse "external data." Closes #186 - Arrays of Python date and datetime objects are now correctly formatted when use as server side parameters. Fixes #188
- Fixed inserts of SimpleAggregateFunction columns with a LowCardinality type parameter. #187
- SQLAlchemy table reflection threw an exception for
SimpleAggregateFunction
columns. This has been fixed. #180 - The client no longer logs an invalid warning for query types that did not return a timezone header. #181
- Querying
SimpleAggregateFunction
columns with a LowCardinality type parameter was broken. This has been fixed. #182 - The
query_arrow
method now correctly accepts the external_data parameter. #183 - The
query_arrow
method has been fixed for read only queries/settings. #184
- A common setting
max_connection_age
has been added, which will ensure that HTTP connections are not reused forever (this can help with certain load balancing issues. It defaults to 10 minutes
- There was a critical issue when using zstd compression (the default) with urllib3 version 2.0+. This has been fixed.
- Logging "Unexpected Http Driver Exception" only as WARNING instead of ERROR. Use the raised OperationalError if you depend on this. Thanks to Alexandro Sandre for the fix.
- The
wait_end_of_query
setting is no longer automatically sent with inserts. This caused unnecessary buffering on the ClickHouse server file system, especially in the case of many small inserts. It can still be added using thesettings
dictionary of the client*insert
methods if needed for some reason. - The query setting
use_na_values
has been renamed touse_extended_dtypes
and now applies to all extended/special Pandas dtypes (except the Pandas Timestamp type). Set this toFalse
to limit the dtypes returned in Pandas dataframes to the "basic" numpy types. (Note that this will force the use of numpy object arrays for most "nullable types") This should allow creating "basic" dataframes for greater compatibility. Closes #172.
- Fix Pandas dataframe inserts where the Dataframe index does not match the data values (after, for example, creating a new DataFrame from a subset of the original.) #167 Thanks to Georgi Peev for the report and suggested fix, and his continued stress testing of Pandas functionality.
- Compression and other control settings were not properly sent with the request if the corresponding setting was not enabled on the server. Many thanks to Alexander Khmelevskiy for the extended investigation and subsequent fix. #157
- Fix quoting and escaping of array literals in server parameters. See #159. Big thanks to Joachim Jablon for the report and the fix.
- Pandas and numpy Date values were incorrect for values after 2050. This has been fixed. #164
- Fixed server side parameter binding of the NULL value for Nullable types
- Added support for
no_proxy
/NO_PROXY
environment variable. Also added support for lower casehttp_proxy
andhttps_proxy
variables. Note that lower case versions have precedence over upper case versions. Fixes #163
- The server timezone will not be applied (and Python datetime types will be timezone naive) if the client and server timezones match
and the
get_client
apply_server_timezone parameter is True (the default). This improves performance where client and server have the same (non-UTC) timezone. To override this behavior and always apply a server timezone to the result, useapply_server_timezone='always'
. This should fix #157
- The client
query_df
andquery_df_stream
methods now acceptquery_tz
andcolumn_tzs
parameters like otherquery*
methods. - A new boolean parameter
apply_server_timezone
has been added to the mainget_client
method. Setting this parameter toTrue
(the default) will apply the server timezone (if not UTC) to values returned by the clientquery*
methods. The previous behavior would always return timezone naive, UTC based Python and Pandasdatetime
objects for ClickHouse DateTime and DateTime64 columns without a defined timezone. To revert to the previous behavior, set theapply_server_timezone
parameter toFalse
. Closes #152 - The timezone logic applied to query results has been simplified and now uses the following order of precedence:
- Use the column timezone for the column if it is specified using the
column_tzs
parameter - Use the column timezone for the column if specified in the ClickHouse column definition (only works for ClickHouse versions 23.2 and later)
- Use the query timezone for the query if it is set using the
query_tz
parameter - Use the "response" timezone for the query as read from the
X-ClickHouse-Timezone
header if different from the server timezone. This closes #138. - Use the ClickHouse server timezone (if the client parameter
apply_server_timezone
isTrue
)
- Use the column timezone for the column if it is specified using the
- Note if the detected timezone according to the above precedence is UTC,
clickhouse-connect
will always return a naive datetime object with no timezone information
- ClickHouse external data is now support for all client
query
methods. To send external data, construct adriver.external.ExternalData
object and send it as theexternal_data
parameter in the appropriate query method. See the ClickHouse documentation for additional details. There are also examples in the test file . Closes #98
- Creating a client would fail if for some reason the user did not have access to the
system.settings
table. Thanks to Filipp Balakin for the fix.
- String columns now accept values of bytes-like objects (bytes/bytearray/etc.) for inserts (as with other inserts, all
values for the inserted column should be the same types, either a bytes-like object or
str
). A correspondingbytes
read format has been enabled for String columns as well. Thanks to Tim Nooran for opening the issue and providing unit tests. #148 - Cython version upgraded to 3.0.0b1
- Remove unnecessary addition of the client database to the table name for inserts. Fixes #145
- The driver should now work for older versions of ClickHouse back to 19.16. Note that older versions are not
officially tested or supported (like the main ClickHouse database, we officially support the last three monthly ClickHouse
releases and the last two LTS ClickHouse releases). For versions prior to 19.17, you may want change the new
readonly
clickhouse_connect.common
setting to '1' to allow sending ClickHouse settings with individual queries (if the user has write permissions). Thanks to Aleksey Astafiev for this contribution and for updating the tests to run with these legacy versions!
- Remove direct pandas import that caused an unrecoverable error when pandas was not installed. #139
- By default, reading Pandas Dataframes with query_df and query_df_stream now sets a new QueryContext property
of
use_pandas_na
toTrue
. Whenuse_pandas_na
is True, clickhouse_connect will attempt to use Pandas "missing" values, such as pandas.NaT and pandas.NA, for ClickHouse NULLs (in Nullable columns only), and use the associated extended Pandas dtype. Closes #132 - There are new low level optimizations for reading some Nullable columns, and writing Pandas dataframes
- Timezone information from ClickHouse DateTime columns with a timezone was lost. There was a workaround implemented for this issue in v0.5.8 that allowed assigned timezones to the query or columns on the client side. ClickHouse now support sending this timezone data with the column, but only in server versions 23.2 and later. If such a version is detected, clickhouse-connect will return timezone aware DateTime values without a workaround. Fixes #120
- For certain queries, an incorrect, non-zero "zero value" would be returned for queries where
use_none
was set toFalse
. All NULL values are now properly converted. - Timezone data was lost when a DateTime64 column with a timezone was converted to a Pandas DataFrame. This has been fixed. #136
- send_progress headers were not being correctly requested, which could result in unexpected timeouts for long-running queries. This has been fixed.
- A new keyword parameter
server_host_name
is now recognized by theclickhouse_connect.get_client
method. This identifies the "real" ClickHouse server hostname that should be used for HTTPS/TLS certificate validation, in cases where access to the server is through an ssh tunnel or other proxy with a different hostname. For examples of how to use the new parameter, see the updated file https://github.com/ClickHouse/clickhouse-connect/blob/main/examples/ssh_tunnels.py.
- The
database
element of a DSN was not recognized when present in thedsn
parameter ofclickhouse_connect.get_client
. This has been fixed.
- Referencing the QueryResult
named_results
property after other properties such asrow_count
would incorrectly raise a StreamClosedError. Thanks to Stas for the fix.
- A better error message is returned when trying to read a "non-standard" DateTime64 column function for a numpy array
or Pandas DataFrame. "non-standard" means a DateTime64 precision not conforming to seconds, milliseconds, microseconds,
or nanoseconds (0, 3, 6, or 9 respectively). These DateTime64 types are not supported for numpy or Pandas because there is
no corresponding standard numpy datetime64 type and conversion would be unacceptably slow (supported numpy types are
datetime64[s]
,datetime64[ms]
,datetime64[us]
, anddatetime64[ns]
). A workaround is to cast the DateTime64 type to a supported type, i.e.SELECT toDateTime64(col_name, 3)
for a millisecond column. - The base configuration required for a urllib PoolManager has been broken out into its own help method,
clickhouse_connect.driver.http_util.get_pool_manager_options
. This makes it simpler to configure a SOCKSProxyManager as in the new example file https://github.com/ClickHouse/clickhouse-connect/blob/main/examples/ssh_tunnels.py
- Reading Nullable(String) columns has been optimized and should be approximately 2x faster. (This does yet not include LowCardinality(Nullable(String)) columns.)
- Extraction of ClickHouse error messages included in the HTTP Response has been improved
- When reading native Python integer columns, the
use_none=False
query parameter would not be respected, and ClickHouse NULLS would be returned as None instead of 0.use_none=False
should now work correctly for Nullable(Int) columns - Starting with release 0.5.0, HTTP Connection pools were not always cleanly closed on exit. This has been fixed.
- Large query results using
zstd
compression incorrectly buffered all incoming data at the start of the query, consuming an excessive amount of memory. This has been fixed. #122 Big thanks to Denny Crane for his detailed investigation of the problem. Note that this affected large queries using the defaultcompress=True
client setting, as ClickHouse would preferzstd
compression in those cases. - Fixed an issue where a small query_limit would break client initialization due to an incomplete read of the
system.settings
table. #123
- Stream error handling has been improved so exceptions thrown while consuming a stream should be correctly propagated. This includes unexpected stream closures by the ClickHouse server. Errors inserted into the HTTP response by ClickHouse during a query should also be reported as part of a StreamFailureError
- Return empty dataframe instead of empty list when no records returned from
query_df
method Fixes #118
- The client
query_limit
now defaults to 0 (unlimited rows returned), since the previous default of 5000 was unintuitive and led to confusion when limited results were returned.
- Allow client side control of datetime.datetime timezones for query results. The client
query
methods for native Python results now accept two new parameters:query_tz
is the timezone to be assigned for any DateTime or DateTime64 objects in the results, while timezones can be set per column using thecolumn_tzs
dictionary of column names to timezones. See the test file for simple examples. This is a workaround for #120 and the underlying ClickHouse issue ClickHouse/ClickHouse#40397 Note that this issue only affects DateTime columns, not DateTime64, although the query context parameters will override the returned DateTime64 timezone as well.
- Http proxies did not work after removing the requests library. #114.
This should be fixed. Note that socks proxies are still not supported directly, but can be added by creating a correctly
configured urllib3 SOCKSProxyManager and using it as the
pool_mgr
argument to tehclickhouse_connect.create_client
method.
- Dataframe inserts would incorrectly modify null-like elements of the inserted dataframe. #112. This should be fixed
- Queries of LowCardinality columns using pandas or numpy query methods would result in an exception. #108 This has been fixed.
- 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.query_column_block_stream
-- returns a generator of blocks in column oriented (Native) format. Fastest method for retrieving data in native Python formatquery_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 usagequery_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 arrayquery_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 thehttp_user_agent
). For better tracking of your Python application, use the newproduct_name
common setting or setclient_name
get_client
parameter to identify your product as<your-product-name>/<product-version>
.
- 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.
- 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.
- 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 theQueryContext
. ClickHouseType
methods have been renamed to remove outdated references tonative
format (everything is native now)- Upgraded Cython Build to 3.0.11alpha release
- Correctly return QueryResult object when created as a context using a
with
statement. This fixes examples and the preferred context syntax for processing query results. Thanks to John McCann Cunniff Jr
- Fix issue where client database is set to None (this normally only happens when deleting the initial database)
- Fix ping check in http client. Closes #96.
The clickhouse_connect get_client
method (which proxies the driver.Client constructor) previously accepted arbitrary
keyword arguments that were interpreted as ClickHouse server settings sent with every request. To be consistent with
other client methods, get_client
now accepts an optional settings
Dict[str, Any] argument that should be used instead
to set ClickHouse server settings.
The driver.HttpClient constructor previously accepted the optional keyword argument http_adapter
, which could be used to
pass a custom requests.adapter.HttpAdapter
to the client. ClickHouse Connect no longer uses the requests
library (see
Dependency Changes below). Instead, the HttpClient constructor now accepts an optional pool_mgr
keyword argument which
can be used to set a custom urllib.poolmanager.PoolManager
for the client. In most cases the default PoolManager is
all that is needed, but multiple PoolManagers may be required for advanced server/proxy applications with many client instances.
- ClickHouse Connect no longer requires the popular
requests
library. Therequests
library is built on urllib3, but ClickHouse Connect was utilizing very little of the added functionality. Requests also has very restricted access to theurllib3
streaming API, which made adding additional compression methods difficult. Accordingly, the project now interfaces tourllib3
directly. This should not change the public API (except as noted in the warning above), but the HttpClient internals have changed to use the lower level library. - ClickHouse Connect now requires the zstandard and lz4 binding libraries to support zstd and lz4 compression. ClickHouse itself uses these compression algorithms extensively and is optimized to work with them, so ClickHouse Connect now takes advantages of them when compression is desired.
- The core client
query
method now supports streaming. The returnedQueryResult
object has new streaming methods:stream_column_blocks
- returns a generator of smaller result sets matching the ClickHouse blocks returned by the native interface.stream_row_blocks
- returns a generator of smaller result sets matching the ClickHouse blocks returned by the native interface, but "pivoted" to return data rows.stream_rows
- returns a generator that returns a row of data with each iteration.
These methods should be used within awith
context to ensure the stream is properly closed when done. In addition, two new propertiesresult_columns
andresult_rows
have been added toQueryResult
. Referencing either of these properties will consume the stream and return the full dataset. Note that these properties should be used instead of the ambiguousresult_set
, which returns the data oriented based on thecolumn_oriented
boolean property. With the addition ofresult_rows
andresult_columns
theresult_set
property and thecolumn_oriented
property are unnecessary and may be removed in a future release.
- More compression methods. As noted above, ClickHouse Connect now supports
zstd
andlz4
compression, as well as brotli (br
), if the brotli library is installed. If the clientcompress
method is set toTrue
(the default), ClickHouse Connect will request compression from the ClickHouse server in the orderlz4,zstd,br,gzip,deflate
, and will compress inserts to ClickHouse usinglz4
. Otherwise, the clientcompress
argument can be set to any oflz4
,zstd
,br
, orgzip
, and the specific compression method will be used for both queries and inserts. Whilegzip
is available, it doesn't perform as well as the other options and should normally not be used.
- More data conversions for query data have been ported to optimized C/Cython code. Rough benchmarks suggest that this improves query performance approximately 20% for standard data types.
- Using the new streaming API to process data in blocks significantly improves performance for large datasets (largely because Python has to allocate significantly less memory and do much less internal data copying otherwise required to build and hold the full dataset). For datasets of a million rows or more, streaming can improve query performance 2x or more.
- As mentioned, ClickHouse
gzip
performance is poor compared tolz4
andzstd
. Using those compression methods by default avoids the major performance degradation seen in #89. - Passing SqlAlchemy query parameters to the driver.Client constructor was broken by changes in release 0.4.8. #94. This has been fixed.
- Documentation has been expanded to cover recent updates.
- File upload support. The new
driver.tools
module adds the functioninsert_file
to simplify directly inserting data files into a table. See the test file for examples. This closes #41. - Added support for server side http query parameters
For queries that contain bindings of the form
{<name>:<datatype>}
, the client will automatically convert the query* methodparameters
dictionary to the appropriate http query parameters. Closes #49. - The main
clickhouse_connect.get_client
command will now accept a standard Pythondsn
argument and extract host, port, user, password, and settings (query parameters) from the dsn. Note that values for other keyword parameters will take precedence over values extracted from the dsn. - The QueryResult object now contains convenience properties for the
first_item
,first_row
, androw_count
in the result.
- JSON inserts with the ujson failed, this has been fixed. #84
- The JSON/Object datatype now supports writes using JSON strings as well as Python native types
- Fixed a major settings issue with connecting to a readonly database (introduced in v0.4.4)
- Fix for broken database setup dialog with recent Superset versions using SQLAlchemy 1.4
- Common settings were stored in an immutable named tuple and could not be changed. This is fixed.
- Fixed issue where the query_arrow method would not use the client database
- Ignore all "transport settings" when validating settings. This should fix #80 for older ClickHouse versions
- The get_client method now accepts a http_adapter parameter to allow sharing a requests.HTTPAdapter (and its associated connection pool) across multiple clients.
- The VERSION file is now included in every package installation. Closes #76
- Global/common configuration options are now available in the
clickhouse_connect.common
module. The available settings are:autogenerate_session_id
[bool] Whether to generate a UUID1 session id used for every client request. Defaults to True. Disabling this can facilitate client sharing and load balancing in some use cases.dict_parameter_format
[str] Options are 'json' and 'map'. This controls whether parameterized queries convert a Python dictionary to JSON or ClickHouse Map syntax. Default tojson
for insert into Object('json') columns.invalid_setting_action
[str] Options are 'send' and 'drop'. Client Connect normally validates and drops (with a warning any settings that aren't recognized by the Server or are readonly). Changing this setting to 'send' will include such settings with the request anyway -- which will normally result in an error being returned.
- The
clickhouse_connect.get_client
method now accepts asettings
dictionary argument for consistency with other client methods.
- Fixed insert of Pandas Dataframes for Timestamp columns with timezones #77
- Fixed exception when inserting a Pandas Dataframes with NaType values into ClickHouse Float column (see known issue)
When inserting Pandas DataFrame values into a ClickHouse Nullable(Float*)
column, a Float NaN value will be converted to a ClickHouse NULL.
This is a side effect of a Pandas issue where df.replace
cannot distinguish between NaT and NaN values: pandas-dev/pandas#29024
- Numpy array read and write compatibility has been refined and performance has been improved. This fixes #69
- Pandas Timestamp objects are now correctly handled for all supported ClickHouse Date* types. This fixes #68
- SQLAlchemy datatypes are now correctly mapped to the underlying ClickHouse type regardless of case. This fixes an issue with migrating Superset datasets and queries from clickhouse-sqlalchemy to clickhouse-connect. Thanks to Eugene Torap
- The settings, table information, and insert progress used for client inserts has been centralized in a new reusable InsertContext object. Client insert methods can now accept such objects to simplify code and reduce overhead
- Query results can now be returned in a column oriented format. This is useful to efficiently construct other objects (like Pandas dataframes) that use column storage internally
- The transformation of Pandas data to Python types now bypasses Numpy. As a result compatibility for ClickHouse date, integer, and NULL types has been significantly improved
- An insert using chunked transfer encode could fail in progress during serialization to ClickHouse native format. This would "hang" the request after throwing the exception, leading to ClickHouse reporting "concurrent session" errors. This has been fixed.
- Pandas DataFrame inserts into tables with a "large" integer column would throw an exception. This has been fixed.
- Pandas DataFrame inserts with NaT/NA/nan values would fail, even if inserted into Nullable column types. This has been fixed.
- Numpy inserts into large integer columns are not supported. #69
- Insert of Pandas timestamps with nanosecond precision will lose the nanosecond value. #68
- Fix read compression typo
- Insert performance and memory usage for large inserts has been significantly improved
- Insert blocks now use chunked transfer encoding (by sending a generator instead of a bytearray to the requests POST method)
- If the client is initialized with compress = True, gzip compression is now enabled for inserts
- Pandas DataFrame inserts have been optimized by keep the data in columnar format during the entire insert process
- Fix inserts for date and datetime columns from Pandas dataframes.
- Fix serialization issues for Decimal128 and Decimal256 types
- Update QueryContext.updated_copy method to preserve settings, parameters, etc. #65
- Build Python 3.11 Wheels
- Correctly handle insert into JSON/Object('json') column via SQLAlchemy
- Fix some incompatibilities with SQLAlchemy 1.4
- Fix 'SHOW CREATE' issue. #61
- "Queries" that do not return data results (like DDL and SET queries) are now automatically treated as commands. Closes #59
- A UUID session_id is now generated by default if
session_id
is not specified inclickhouse_connect.get_client
- Test infrastructure has been simplified and test configuration has moved from pytest options to environment files
- UInt64 types were incorrectly returned as signed Python ints even outside of Superset. This has been fixed
- Superset Engine Spec will now format (U)Int256 and (U)Int128 types as strings to avoid throwing a conversion exception
- The row_binary option for ClickHouse serialization has been removed. The performance is significantly lower than Native format and maintaining the option added complexity with no corresponding benefit
- The Database Connection dialog was broken in the latest Superset development builds. This has been fixed
- IPv6 Addresses fixed for default Superset configuration
- Add single retry for HTTP RemoteDisconnected errors from the ClickHouse Server. This prevents exception spam when requests (in particular inserts) are sent at approximately the same time as the ClickHouse server closes a keep alive connection.
- Fix incorrect validation errors in the Superset connection dialog
- This release updates the build process to include binary wheels for the majority of platforms, include MacOS M1 and Linux Aarch64. This should also fix installation errors on lightweight platforms without build tools.
- Builds are now included for Python 3.11
- Docker images built on MacOS directly from source do not correctly build the C extensions for Linux. However, installing the official wheels from PyPI should work correctly.
- The HTTP client now raises an OperationalError instead of a DatabaseError when the HTTP status code is 429 (too many requests), 503 (service unavailable), or 504 (gateway timeout) to make it easier to determine if it is a retryable exception
- Add
query_retries
client parameter (default 2) for "retryable" HTTP queries. Does not apply to "commands" like DDL or to inserts
- Fixed an SQLAlchemy dialect issue with SQLAlchemy 1.4 that would cause problems in the most recent Superset version
- Fixed an issue where DBAPI cursors returned an invalid description object for columns. This would cause
'property' object has no attribute 'startswith'
errors for some SqlAlchemy and SuperSet queries. - Fixed an issue where datetime parameters would not be correctly rendered as ClickHouse compatible strings
- The "parameters" object passed to client query methods can now be a sequence instead of a dictionary, for compatibility with query strings that contain simple format unnamed format directives, such as
'SELECT * FROM table WHERE value = %s'
- The wait_end_of_query parameter/setting was incorrectly being stripped. This is fixed
- Fix encoding insert of multibyte characters
- Improve identifier handling/quoting for Clickhouse column, table, and database names
- Add client arrow_insert method to directly insert a PyArrow Table insert ClickHouse using Arrow format
- Fix issue when query_limit set to 0
- Fix SQL comment problems in DBAPI cursor
- Support (experimental) JSON/Object datatype. ClickHouse Connect will take advantage of the fast orjson library if available. Note that inserts for JSON columns require ClickHouse server version 22.6.1 or later
- Standardize read format handling and allow specifying a return data format per column or per query.
- Added convenience min_version method to client to see if the server is at least the requested level
- Increase default HTTP timeout to 300 seconds to match ClickHouse server default
- Fixed multiple issues with SQL comments that would cause some queries to fail
- Fixed problem with SQLAlchemy literal binds that would cause an error in Superset filters
- Fixed issue with parameterized queries
- Named Tuples were not supported and would result in throwing an exception. This has been fixed.
- The client query_arrow function would return incomplete results if the query result exceeded the ClickHouse max_block_size. This has been fixed. As part of the fix query_arrow method returns a PyArrow Table object. While this is a breaking change in the API it should be easy to work around.
- Support Nested data types.
- Fix issue with native reads of Nullable(LowCardinality) numeric and date types.
- Empty inserts will now just log a debug message instead of throwing an IndexError.