alamb commented on code in PR #43553: URL: https://github.com/apache/arrow/pull/43553#discussion_r1896745819
########## docs/source/format/CDataInterfaceStatistics.rst: ########## @@ -0,0 +1,339 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _c-data-interface-statistics: + +===================================================== +Passing statistics through the Arrow C data interface +===================================================== + +.. warning:: This specification should be considered experimental. + +Rationale +========= + +Statistics are useful for fast query processing. Many query engines +use statistics to optimize their query plan. + +Apache Arrow format doesn't have statistics but other formats that can +be read as Apache Arrow data may have statistics. For example, the +Apache Parquet C++ implementation can read an Apache Parquet file as +Apache Arrow data and the Apache Parquet file may have statistics. + +Use case +-------- + +One of :ref:`c-stream-interface` use cases is the following: + +1. Module A reads Apache Parquet file as Apache Arrow data. +2. Module A passes the read Apache Arrow data to module B through the + Arrow C stream interface. +3. Module B processes the passed Apache Arrow data. + +If module A can pass the statistics associated with the Apache Parquet +file to module B through the Arrow C stream interface, module B can +use the statistics to optimize its query plan. + +For example, DuckDB uses this approach but DuckDB couldn't use +statistics because there wasn't the standardized way to pass +statistics. + +.. seealso:: + + `duckdb::ArrowTableFunction::ArrowScanBind() in DuckDB 1.1.3 + <https://github.com/duckdb/duckdb/blob/v1.1.3/src/function/table/arrow.cpp#L373-L403>`_ + +Goals +----- + +TODO: Remove the C data interface limitation? + +* Establish a standard way to pass statistics through the Arrow C data + interface. +* Provide this in a manner that enables compatibility and ease of + implementation for existing users of the Arrow C data interface. + +Non-goals +--------- + +TODO: Remove the C data interface limitation? + +* Provide a common way to pass statistics that can be used for + other interfaces such Arrow Flight too. + +For example, ADBC has `the statistics related APIs +<https://arrow.apache.org/adbc/current/format/specification.html#statistics>`__. +This specification doesn't replace them. + +TODO: Should we deprecate the current ADBC's statistics API and +redesign with this specification? + +This specification may fit some use cases of :ref:`format-ipc` not the +Arrow data interface. But we don't recommend this specification for +the Arrow IPC format for now. Because we may be able to define better +specification for the Arrow IPC format. The Arrow IPC format has some +different features compared with the Arrow C data interface. For +example, the Arrow IPC format can have :ref:`metadata for each message +<ipc-message-format>`. If you're interested in the specification for +passing statistics through the Arrow IPC format, please start a +discussion on the `Arrow development mailing-list +<https://arrow.apache.org/community/>`__. + +.. _c-data-interface-statistics-schema: + +Schema +====== + +This specification provides only the schema for statistics. The +producer passes statistics through the Arrow C data interface as an +Arrow map array that uses this schema. + +Here is the outline of the schema for statistics:: + + struct< + column: int32, + statistics: map< + key: dictionary< + indices: int32, + dictionary: utf8 + >, + items: dense_union<...all needed types...> + > + > + +Here is the details of top-level ``struct``: + +.. list-table:: + :header-rows: 1 + + * - Name + - Data type + - Nullable + - Notes + * - ``column`` + - ``int32`` + - ``true`` + - The zero-based column index, or null if the statistics + describe the whole table or record batch. + + The column index is computed as the same rule used by + :ref:`ipc-recordbatch-message`. + * - ``statistics`` + - ``map`` + - ``false`` + - Statistics for the target column, table or record batch. See + the separate table below for details. + +Here is the details of the ``map`` of the ``statistics``: + +.. list-table:: + :header-rows: 1 + + * - Key or items + - Data type + - Nullable + - Notes + * - key + - ``dictionary<indices: int32, dictionary: utf8>`` + - ``false`` + - Statistics key is string. Dictionary is used for + efficiency. Different keys are assigned for exact value and + approximate value. Also see the separate description below for + statistics key. + * - items + - ``dense_union`` + - ``false`` + - Statistics value is dense union. It has at least all needed + types based on statistics kinds in the keys. For example, you + need at least ``int64`` and ``float64`` types when you have a + ``int64`` distinct count statistic and a ``float64`` average + byte width statistic. Also see the separate description below + for statistics key. + + We don't standardize field names for the dense union because + consumers can access to proper field by type code not name. So + producers can use any valid name for fields. + + TODO: Should we standardize field names? + +.. _c-data-interface-statistics-key: + +Statistics key Review Comment: Nit is I would find the term "statistics type" rather than "statistics key" easier to understand ########## docs/source/format/CDataInterfaceStatistics.rst: ########## @@ -0,0 +1,339 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _c-data-interface-statistics: + +===================================================== +Passing statistics through the Arrow C data interface +===================================================== + +.. warning:: This specification should be considered experimental. + +Rationale +========= + +Statistics are useful for fast query processing. Many query engines +use statistics to optimize their query plan. + +Apache Arrow format doesn't have statistics but other formats that can +be read as Apache Arrow data may have statistics. For example, the +Apache Parquet C++ implementation can read an Apache Parquet file as +Apache Arrow data and the Apache Parquet file may have statistics. + +Use case +-------- + +One of :ref:`c-stream-interface` use cases is the following: + +1. Module A reads Apache Parquet file as Apache Arrow data. +2. Module A passes the read Apache Arrow data to module B through the + Arrow C stream interface. +3. Module B processes the passed Apache Arrow data. + +If module A can pass the statistics associated with the Apache Parquet +file to module B through the Arrow C stream interface, module B can +use the statistics to optimize its query plan. + +For example, DuckDB uses this approach but DuckDB couldn't use +statistics because there wasn't the standardized way to pass +statistics. + +.. seealso:: + + `duckdb::ArrowTableFunction::ArrowScanBind() in DuckDB 1.1.3 + <https://github.com/duckdb/duckdb/blob/v1.1.3/src/function/table/arrow.cpp#L373-L403>`_ + +Goals +----- + +TODO: Remove the C data interface limitation? + +* Establish a standard way to pass statistics through the Arrow C data + interface. +* Provide this in a manner that enables compatibility and ease of + implementation for existing users of the Arrow C data interface. + +Non-goals +--------- + +TODO: Remove the C data interface limitation? + +* Provide a common way to pass statistics that can be used for + other interfaces such Arrow Flight too. + +For example, ADBC has `the statistics related APIs +<https://arrow.apache.org/adbc/current/format/specification.html#statistics>`__. +This specification doesn't replace them. + +TODO: Should we deprecate the current ADBC's statistics API and +redesign with this specification? + +This specification may fit some use cases of :ref:`format-ipc` not the +Arrow data interface. But we don't recommend this specification for +the Arrow IPC format for now. Because we may be able to define better +specification for the Arrow IPC format. The Arrow IPC format has some +different features compared with the Arrow C data interface. For +example, the Arrow IPC format can have :ref:`metadata for each message +<ipc-message-format>`. If you're interested in the specification for +passing statistics through the Arrow IPC format, please start a +discussion on the `Arrow development mailing-list +<https://arrow.apache.org/community/>`__. + +.. _c-data-interface-statistics-schema: + +Schema +====== + +This specification provides only the schema for statistics. The +producer passes statistics through the Arrow C data interface as an +Arrow map array that uses this schema. + +Here is the outline of the schema for statistics:: + + struct< + column: int32, + statistics: map< + key: dictionary< + indices: int32, + dictionary: utf8 + >, + items: dense_union<...all needed types...> + > + > + +Here is the details of top-level ``struct``: + +.. list-table:: + :header-rows: 1 + + * - Name + - Data type + - Nullable + - Notes + * - ``column`` + - ``int32`` + - ``true`` + - The zero-based column index, or null if the statistics + describe the whole table or record batch. + + The column index is computed as the same rule used by + :ref:`ipc-recordbatch-message`. + * - ``statistics`` + - ``map`` + - ``false`` + - Statistics for the target column, table or record batch. See + the separate table below for details. + +Here is the details of the ``map`` of the ``statistics``: + +.. list-table:: + :header-rows: 1 + + * - Key or items + - Data type + - Nullable + - Notes + * - key + - ``dictionary<indices: int32, dictionary: utf8>`` + - ``false`` + - Statistics key is string. Dictionary is used for Review Comment: Maybe we could be more explicitly about what the key means (it seems like it is the "statistics type" )? ########## docs/source/format/CDataInterfaceStatistics.rst: ########## @@ -0,0 +1,339 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _c-data-interface-statistics: + +===================================================== +Passing statistics through the Arrow C data interface +===================================================== + +.. warning:: This specification should be considered experimental. + +Rationale +========= + +Statistics are useful for fast query processing. Many query engines +use statistics to optimize their query plan. + +Apache Arrow format doesn't have statistics but other formats that can +be read as Apache Arrow data may have statistics. For example, the +Apache Parquet C++ implementation can read an Apache Parquet file as +Apache Arrow data and the Apache Parquet file may have statistics. + +Use case +-------- + +One of :ref:`c-stream-interface` use cases is the following: + +1. Module A reads Apache Parquet file as Apache Arrow data. +2. Module A passes the read Apache Arrow data to module B through the + Arrow C stream interface. +3. Module B processes the passed Apache Arrow data. + +If module A can pass the statistics associated with the Apache Parquet +file to module B through the Arrow C stream interface, module B can +use the statistics to optimize its query plan. + +For example, DuckDB uses this approach but DuckDB couldn't use +statistics because there wasn't the standardized way to pass +statistics. + +.. seealso:: + + `duckdb::ArrowTableFunction::ArrowScanBind() in DuckDB 1.1.3 + <https://github.com/duckdb/duckdb/blob/v1.1.3/src/function/table/arrow.cpp#L373-L403>`_ + +Goals +----- + +TODO: Remove the C data interface limitation? + +* Establish a standard way to pass statistics through the Arrow C data + interface. +* Provide this in a manner that enables compatibility and ease of + implementation for existing users of the Arrow C data interface. + +Non-goals +--------- + +TODO: Remove the C data interface limitation? + +* Provide a common way to pass statistics that can be used for + other interfaces such Arrow Flight too. + +For example, ADBC has `the statistics related APIs +<https://arrow.apache.org/adbc/current/format/specification.html#statistics>`__. +This specification doesn't replace them. + +TODO: Should we deprecate the current ADBC's statistics API and +redesign with this specification? + +This specification may fit some use cases of :ref:`format-ipc` not the +Arrow data interface. But we don't recommend this specification for +the Arrow IPC format for now. Because we may be able to define better +specification for the Arrow IPC format. The Arrow IPC format has some +different features compared with the Arrow C data interface. For +example, the Arrow IPC format can have :ref:`metadata for each message +<ipc-message-format>`. If you're interested in the specification for +passing statistics through the Arrow IPC format, please start a +discussion on the `Arrow development mailing-list +<https://arrow.apache.org/community/>`__. + +.. _c-data-interface-statistics-schema: + +Schema +====== + +This specification provides only the schema for statistics. The +producer passes statistics through the Arrow C data interface as an +Arrow map array that uses this schema. + +Here is the outline of the schema for statistics:: + + struct< + column: int32, + statistics: map< + key: dictionary< + indices: int32, + dictionary: utf8 + >, + items: dense_union<...all needed types...> + > + > + +Here is the details of top-level ``struct``: + +.. list-table:: + :header-rows: 1 + + * - Name + - Data type + - Nullable + - Notes + * - ``column`` + - ``int32`` + - ``true`` + - The zero-based column index, or null if the statistics + describe the whole table or record batch. + + The column index is computed as the same rule used by + :ref:`ipc-recordbatch-message`. + * - ``statistics`` + - ``map`` + - ``false`` + - Statistics for the target column, table or record batch. See + the separate table below for details. + +Here is the details of the ``map`` of the ``statistics``: + +.. list-table:: + :header-rows: 1 + + * - Key or items + - Data type + - Nullable + - Notes + * - key + - ``dictionary<indices: int32, dictionary: utf8>`` + - ``false`` + - Statistics key is string. Dictionary is used for + efficiency. Different keys are assigned for exact value and + approximate value. Also see the separate description below for + statistics key. + * - items + - ``dense_union`` + - ``false`` + - Statistics value is dense union. It has at least all needed + types based on statistics kinds in the keys. For example, you + need at least ``int64`` and ``float64`` types when you have a + ``int64`` distinct count statistic and a ``float64`` average + byte width statistic. Also see the separate description below + for statistics key. + + We don't standardize field names for the dense union because + consumers can access to proper field by type code not name. So + producers can use any valid name for fields. + + TODO: Should we standardize field names? + +.. _c-data-interface-statistics-key: + +Statistics key +-------------- + +Statistics key is string. ``dictionary<int32, utf8>`` is used for +efficiency. + +We assign different statistics keys for individual statistics instead of using +flags. For example, we assign different statistics keys for exact +value and approximate value. + +The colon symbol ``:`` is to be used as a namespace separator like +:ref:`format_metadata`. It can be used multiple times in a key. + +The ``ARROW`` pattern is a reserved namespace for pre-defined +statistics keys. User-defined statistics must not use it. +For example, you can use your product name as namespace +such as ``MY_PRODUCT:my_statistics:exact``. + +Here are pre-defined statistics keys: + +.. list-table:: + :header-rows: 1 + + * - Key + - Data type + - Notes + * - ``ARROW:average_byte_width:exact`` + - ``float64`` + - The average size in bytes of a row in the target column. (exact) + * - ``ARROW:average_byte_width:approximate`` + - ``float64``: TODO: Should we use ``int64`` instead? + - The average size in bytes of a row in the target column. (approximate) + * - ``ARROW:distinct_count:exact`` + - ``int64`` + - The number of distinct values in the target column. (exact) + * - ``ARROW:distinct_count:approximate`` + - ``float64`` + - The number of distinct values in the target column. (approximate) + * - ``ARROW:max_byte_width:exact`` + - ``int64`` + - The maximum size in bytes of a row in the target column. (exact) + * - ``ARROW:max_byte_width:approximate`` + - ``float64`` + - The maximum size in bytes of a row in the target column. (approximate) + * - ``ARROW:max_value:exact`` + - Target dependent + - The maximum value in the target column. (exact) + * - ``ARROW:max_value:approximate`` + - Target dependent + - The maximum value in the target column. (approximate) + * - ``ARROW:min_value:exact`` + - Target dependent + - The minimum value in the target column. (exact) + * - ``ARROW:min_value:approximate`` + - Target dependent + - The minimum value in the target column. (approximate) + * - ``ARROW:null_count:exact`` + - ``int64`` + - The number of nulls in the target column. (exact) + * - ``ARROW:null_count:approximate`` + - ``float64`` + - The number of nulls in the target column. (approximate) + * - ``ARROW:row_count:exact`` + - ``int64`` + - The number of rows in the target table or record batch. (exact) + * - ``ARROW:row_count:approximate`` + - ``float64`` + - The number of rows in the target table or record + batch. (approximate) + +If you find a missing statistics key that is usable for multiple +systems, please propose it on the `Arrow development mailing-list +<https://arrow.apache.org/community/>`__. + +Examples +======== + +Here are some examples to help you understand. + +Simple record batch +------------------- + +Schema:: + + vendor_id: int32 + passenger_count: int64 + +Data:: + + vendor_id: [5, 1, 5, 1, 5] + passenger_count: [1, 1, 2, 0, null] + +Statistics schema:: + + struct< + column: int32, + statistics: map< + key: dictionary< + indices: int32, + dictionary: utf8 + >, + items: dense_union<int64> + > + > + +Statistics array:: Review Comment: I didn't understand this example. I thought the statistics were structs, so I would have expected the data to look something like this (perhaps we could give the "logical contents" and then the specific array encoding): ``` [ // first struct element { column: null, # record batch statistics: { "ARROW:row_count:exact": 0 } }, { column: 0, # vendor_id statistics: { "ARROW:null_count:exact": 0, "ARROW:distinct_count:exact": 2, "ARROW:max_value:exact": 5, "ARROW:min_value:exact": 1, } }, ... ] ``` I can help work out the example if people think this is a good idea ########## docs/source/format/CDataInterfaceStatistics.rst: ########## @@ -0,0 +1,339 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _c-data-interface-statistics: + +===================================================== +Passing statistics through the Arrow C data interface +===================================================== + +.. warning:: This specification should be considered experimental. + +Rationale +========= + +Statistics are useful for fast query processing. Many query engines +use statistics to optimize their query plan. + +Apache Arrow format doesn't have statistics but other formats that can +be read as Apache Arrow data may have statistics. For example, the +Apache Parquet C++ implementation can read an Apache Parquet file as +Apache Arrow data and the Apache Parquet file may have statistics. + +Use case +-------- + +One of :ref:`c-stream-interface` use cases is the following: + +1. Module A reads Apache Parquet file as Apache Arrow data. +2. Module A passes the read Apache Arrow data to module B through the + Arrow C stream interface. +3. Module B processes the passed Apache Arrow data. + +If module A can pass the statistics associated with the Apache Parquet +file to module B through the Arrow C stream interface, module B can +use the statistics to optimize its query plan. + +For example, DuckDB uses this approach but DuckDB couldn't use +statistics because there wasn't the standardized way to pass +statistics. + +.. seealso:: + + `duckdb::ArrowTableFunction::ArrowScanBind() in DuckDB 1.1.3 + <https://github.com/duckdb/duckdb/blob/v1.1.3/src/function/table/arrow.cpp#L373-L403>`_ + +Goals +----- + +TODO: Remove the C data interface limitation? + +* Establish a standard way to pass statistics through the Arrow C data + interface. +* Provide this in a manner that enables compatibility and ease of + implementation for existing users of the Arrow C data interface. + +Non-goals +--------- + +TODO: Remove the C data interface limitation? + +* Provide a common way to pass statistics that can be used for + other interfaces such Arrow Flight too. + +For example, ADBC has `the statistics related APIs +<https://arrow.apache.org/adbc/current/format/specification.html#statistics>`__. +This specification doesn't replace them. + +TODO: Should we deprecate the current ADBC's statistics API and +redesign with this specification? + +This specification may fit some use cases of :ref:`format-ipc` not the +Arrow data interface. But we don't recommend this specification for +the Arrow IPC format for now. Because we may be able to define better +specification for the Arrow IPC format. The Arrow IPC format has some +different features compared with the Arrow C data interface. For +example, the Arrow IPC format can have :ref:`metadata for each message +<ipc-message-format>`. If you're interested in the specification for +passing statistics through the Arrow IPC format, please start a +discussion on the `Arrow development mailing-list +<https://arrow.apache.org/community/>`__. + +.. _c-data-interface-statistics-schema: + +Schema +====== + +This specification provides only the schema for statistics. The +producer passes statistics through the Arrow C data interface as an +Arrow map array that uses this schema. + +Here is the outline of the schema for statistics:: + + struct< + column: int32, + statistics: map< + key: dictionary< + indices: int32, + dictionary: utf8 + >, + items: dense_union<...all needed types...> + > + > + +Here is the details of top-level ``struct``: + +.. list-table:: + :header-rows: 1 + + * - Name + - Data type + - Nullable + - Notes + * - ``column`` + - ``int32`` + - ``true`` + - The zero-based column index, or null if the statistics + describe the whole table or record batch. + + The column index is computed as the same rule used by + :ref:`ipc-recordbatch-message`. + * - ``statistics`` + - ``map`` + - ``false`` + - Statistics for the target column, table or record batch. See + the separate table below for details. + +Here is the details of the ``map`` of the ``statistics``: + +.. list-table:: + :header-rows: 1 + + * - Key or items + - Data type + - Nullable + - Notes + * - key + - ``dictionary<indices: int32, dictionary: utf8>`` + - ``false`` + - Statistics key is string. Dictionary is used for + efficiency. Different keys are assigned for exact value and + approximate value. Also see the separate description below for + statistics key. + * - items + - ``dense_union`` + - ``false`` + - Statistics value is dense union. It has at least all needed + types based on statistics kinds in the keys. For example, you + need at least ``int64`` and ``float64`` types when you have a + ``int64`` distinct count statistic and a ``float64`` average + byte width statistic. Also see the separate description below + for statistics key. + + We don't standardize field names for the dense union because + consumers can access to proper field by type code not name. So + producers can use any valid name for fields. + + TODO: Should we standardize field names? + +.. _c-data-interface-statistics-key: + +Statistics key +-------------- + +Statistics key is string. ``dictionary<int32, utf8>`` is used for +efficiency. + +We assign different statistics keys for individual statistics instead of using +flags. For example, we assign different statistics keys for exact +value and approximate value. + +The colon symbol ``:`` is to be used as a namespace separator like +:ref:`format_metadata`. It can be used multiple times in a key. + +The ``ARROW`` pattern is a reserved namespace for pre-defined +statistics keys. User-defined statistics must not use it. +For example, you can use your product name as namespace +such as ``MY_PRODUCT:my_statistics:exact``. + +Here are pre-defined statistics keys: + +.. list-table:: + :header-rows: 1 + + * - Key + - Data type + - Notes + * - ``ARROW:average_byte_width:exact`` + - ``float64`` + - The average size in bytes of a row in the target column. (exact) + * - ``ARROW:average_byte_width:approximate`` + - ``float64``: TODO: Should we use ``int64`` instead? + - The average size in bytes of a row in the target column. (approximate) + * - ``ARROW:distinct_count:exact`` + - ``int64`` + - The number of distinct values in the target column. (exact) + * - ``ARROW:distinct_count:approximate`` + - ``float64`` + - The number of distinct values in the target column. (approximate) + * - ``ARROW:max_byte_width:exact`` + - ``int64`` + - The maximum size in bytes of a row in the target column. (exact) + * - ``ARROW:max_byte_width:approximate`` + - ``float64`` + - The maximum size in bytes of a row in the target column. (approximate) + * - ``ARROW:max_value:exact`` + - Target dependent + - The maximum value in the target column. (exact) + * - ``ARROW:max_value:approximate`` + - Target dependent + - The maximum value in the target column. (approximate) + * - ``ARROW:min_value:exact`` + - Target dependent + - The minimum value in the target column. (exact) + * - ``ARROW:min_value:approximate`` + - Target dependent + - The minimum value in the target column. (approximate) + * - ``ARROW:null_count:exact`` + - ``int64`` + - The number of nulls in the target column. (exact) + * - ``ARROW:null_count:approximate`` + - ``float64`` + - The number of nulls in the target column. (approximate) + * - ``ARROW:row_count:exact`` + - ``int64`` + - The number of rows in the target table or record batch. (exact) + * - ``ARROW:row_count:approximate`` + - ``float64`` + - The number of rows in the target table or record + batch. (approximate) + +If you find a missing statistics key that is usable for multiple +systems, please propose it on the `Arrow development mailing-list +<https://arrow.apache.org/community/>`__. + +Examples +======== + +Here are some examples to help you understand. + +Simple record batch +------------------- + +Schema:: + + vendor_id: int32 + passenger_count: int64 + +Data:: + + vendor_id: [5, 1, 5, 1, 5] + passenger_count: [1, 1, 2, 0, null] + +Statistics schema:: Review Comment: It is probably too late, but I figured I would point out another format we use for statistics in DataFusion is a columnar form rather than a nested structure https://docs.rs/datafusion/latest/datafusion/physical_optimizer/pruning/trait.PruningStatistics.html For example to represent this data in DataFusion it would be | `vendor_id::null_count` | `vendor_id::min` | `vendor_id::max` | ... | `passenger_count::max` | |--------|--------|--------|--------|--------| | 0 | 1 | 5 | ... | 2 | The benefit of this encoding is that it can be used to quickly evaluate predicates on ranges (e.g. figure out if `vendor_id = 6` could ever be true) We use this format to read statistics from the parquet files (see [`ParquetStatisticsConverter`](https://docs.rs/parquet/latest/parquet/arrow/arrow_reader/statistics/struct.StatisticsConverter.html) to rule out row groups) It does result in potentially very wide schemas however ########## docs/source/format/CDataInterfaceStatistics.rst: ########## @@ -0,0 +1,271 @@ +.. Licensed to the Apache Software Foundation (ASF) under one +.. or more contributor license agreements. See the NOTICE file +.. distributed with this work for additional information +.. regarding copyright ownership. The ASF licenses this file +.. to you under the Apache License, Version 2.0 (the +.. "License"); you may not use this file except in compliance +.. with the License. You may obtain a copy of the License at + +.. http://www.apache.org/licenses/LICENSE-2.0 + +.. Unless required by applicable law or agreed to in writing, +.. software distributed under the License is distributed on an +.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +.. KIND, either express or implied. See the License for the +.. specific language governing permissions and limitations +.. under the License. + +.. _c-data-interface-statistics: + +===================================================== +Passing statistics through the Arrow C data interface +===================================================== + +.. warning:: This specification should be considered experimental. + +Rationale +========= + +Statistics are useful for fast query processing. Many query engines +use statistics to optimize their query plan. + +Apache Arrow format doesn't have statistics but other formats that can +be read as Apache Arrow data may have statistics. For example, the +Apache Parquet C++ implementation can read an Apache Parquet file as +Apache Arrow data and the Apache Parquet file may have statistics. + +One of :ref:`c-data-interface` use cases is the following: + +1. Module A reads Apache Parquet file as Apache Arrow data. +2. Module A passes the read Apache Arrow data to module B through the + Arrow C data interface. +3. Module B processes the passed Apache Arrow data. + +If module A can pass the statistics associated with the Apache Parquet +file to module B through the Arrow C data interface, module B can use +the statistics to optimize its query plan. + +Goals +----- + +* Establish a standard way to pass statistics through the Arrow C data + interface. +* Provide this in a manner that enables compatibility and ease of + implementation for existing users of the Arrow C data interface. + +Non-goals +--------- + +* Provide a common way to pass statistics that can be used for + other interfaces such Arrow Flight too. + +For example, ADBC has `the statistics related APIs +<https://arrow.apache.org/adbc/current/format/specification.html#statistics>`__. +This specification doesn't replace them. + +This specification may fit some use cases of :ref:`format-ipc` not the +Arrow data interface. But we don't recommend this specification for +the Arrow IPC format for now. Because we may be able to define better +specification for the Arrow IPC format. The Arrow IPC format has some +different features compared with the Arrow C data interface. For +example, the Arrow IPC format can have :ref:`metadata for each message +<ipc-message-format>`. If you're interested in the specification for +passing statistics through the Arrow IPC format, please start a +discussion on the `Arrow development mailing-list +<https://arrow.apache.org/community/>`__. + +.. _c-data-interface-statistics-schema: + +Schema +====== + +This specification provides only the schema for statistics. The +producer passes statistics through the Arrow C data interface as an +Arrow map array that uses this schema. + +Here is the outline of the schema for statistics:: + + struct< + column: int32, + statistics: map< + key: dictionary< + indices: int32, + dictionary: utf8 + >, + items: dense_union<...all needed types...>, + > + > + +Here is the details of top-level ``struct``: + +.. list-table:: + :header-rows: 1 + + * - Name + - Data type + - Nullable + - Notes + * - ``column`` + - ``int32`` + - ``true`` + - The zero-based column index, or null if the statistics + describe the whole table or record batch. + * - ``statistics`` + - ``map`` + - ``false`` + - Statistics for the target column, table or record batch. See + the separate table below for details. + +Here is the details of the ``map`` of the ``statistics``: + +.. list-table:: + :header-rows: 1 + + * - Key or items + - Data type + - Nullable + - Notes + * - key + - ``dictionary<indices: int32, dictionary: utf8>`` + - ``false`` + - Statistics key is string. Dictionary is used for + efficiency. Different keys are assigned for exact value and + approximate value. Also see the separate description below for + statistics key. + * - items + - ``dense_union`` + - ``false`` + - Statistics value is dense union. It has at least all needed + types based on statistics kinds in the keys. For example, you + need at least ``int64`` and ``float64`` types when you have a + ``int64`` distinct count statistic and a ``float64`` average + byte width statistic. Also see the separate description below + for statistics key. + + We don't standardize field names for the dense union because + consumers can access to proper field by index not name. So + producers can use any valid name for fields. Review Comment: We have had trouble in the arrow-rs implementation or other places where schema's contain names but they aren't standardized For example, we have an embedded field name for `List`s and some implementations use `"item"` and some `"element"` which causes spurious schema mismatch errors Therefore I also recommend removing field names unless there is some good reason for doing so (it isn't clear to me from the text why there are arbitrary field namds) -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: github-unsubscr...@arrow.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
