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Posted to github@arrow.apache.org by "edponce (via GitHub)" <gi...@apache.org> on 2023/04/26 04:05:26 UTC
[GitHub] [arrow] edponce commented on a diff in pull request #35320: GH-32335: [C++][Docs] Add design document for Acero
edponce commented on code in PR #35320:
URL: https://github.com/apache/arrow/pull/35320#discussion_r1177298441
##########
docs/source/cpp/acero/overview.rst:
##########
@@ -0,0 +1,262 @@
+.. 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.
+
+.. default-domain:: cpp
+.. highlight:: cpp
+.. cpp:namespace:: arrow::acero
+
+==============
+Acero Overview
+==============
+
+This page gives an overview of the basic Acero concepts and helps distinguish Acero
+from other modules in the Arrow code base. It's intended for users, developers,
+potential contributors, and for those that would like to extend Acero, either for
+research or for business use. This page assumes the reader is already familiar with
+core Arrow concepts. This page does not expect any existing knowledge in relational
+algebra.
+
+What is Acero?
+==============
+
+Acero is a C++ library that can be used to analyze large (potentially infinite) streams
+of data. Acero allows computation to be expressed as an "execution plan" (:class:`ExecPlan`).
+An execution plan takes in zero or more streams of input data and emits a single
+stream of output data. The plan describes how the data will be transformed as it
+passes through. For example, a plan might:
+
+ * Merge two streams of data using a common column
+ * Create additional columns by evaluating expressions against the existing columns
+ * Consume a stream of data by writing it to disk in a partitioned layout
+
+.. image:: simple_graph.svg
+ :alt: A sample execution plan that joins three streams of data and writes to disk
+
+Acero is not...
+---------------
+
+A Library for Data Scientists
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Acero is not intended to be used directly by data scientists. It is expected that
+end users will typically be using some kind of frontend. For example, Pandas, Ibis,
+or SQL. The API for Acero is focused around capabilities and available algorithms.
+However, such users may be intersted in knowing more about how Acero works so that
+they can better understand how the backend processing for their libraries operates.
+
+A Database
+^^^^^^^^^^
+
+A database (or DBMS) is typically a much more expansive application and often packaged
+as a standalone service. Acero could be a component in a database (most databases have
+some kind of execution engine) or could be a component in some other data processing
+application that hardly resembles a database. Acero does not concern itself with
+user management, external communication, isolation, durability, or consistency. In
+addition, Acero is focused primarily on the read path, and the write utilities lack
+any sort of transaction support.
+
+An Optimizer
+^^^^^^^^^^^^
+
+Acero does not have an SQL parser. It does not have a query planner. It does not have
+any sort of optimizer. Acero expects to be given very detailed and low-level instructions
+on how to manipulate data and then it will perform that manipulation exactly as described.
+
+Creating the best execution plan is very hard. Small details can have a big impact on
+performance. We do think optimizers are important and we hope that tools will emerge
+someday which can provide these capabilities using standards such as Substrait.
+
+Distributed
+^^^^^^^^^^^
+
+Acero does not provide distributed execution. However, Acero aims to be usable by a distributed
+query execution engine. In other words, Acero will not configure and coordinate workers but
+it does except to be used as a worker. Sometimes, the distinction is a bit fuzzy. For example,
Review Comment:
except -> expect
##########
docs/source/cpp/acero/overview.rst:
##########
@@ -0,0 +1,262 @@
+.. 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.
+
+.. default-domain:: cpp
+.. highlight:: cpp
+.. cpp:namespace:: arrow::acero
+
+==============
+Acero Overview
+==============
+
+This page gives an overview of the basic Acero concepts and helps distinguish Acero
+from other modules in the Arrow code base. It's intended for users, developers,
+potential contributors, and for those that would like to extend Acero, either for
+research or for business use. This page assumes the reader is already familiar with
+core Arrow concepts. This page does not expect any existing knowledge in relational
+algebra.
+
+What is Acero?
+==============
+
+Acero is a C++ library that can be used to analyze large (potentially infinite) streams
+of data. Acero allows computation to be expressed as an "execution plan" (:class:`ExecPlan`).
+An execution plan takes in zero or more streams of input data and emits a single
+stream of output data. The plan describes how the data will be transformed as it
+passes through. For example, a plan might:
+
+ * Merge two streams of data using a common column
+ * Create additional columns by evaluating expressions against the existing columns
+ * Consume a stream of data by writing it to disk in a partitioned layout
+
+.. image:: simple_graph.svg
+ :alt: A sample execution plan that joins three streams of data and writes to disk
+
+Acero is not...
+---------------
+
+A Library for Data Scientists
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Acero is not intended to be used directly by data scientists. It is expected that
+end users will typically be using some kind of frontend. For example, Pandas, Ibis,
+or SQL. The API for Acero is focused around capabilities and available algorithms.
+However, such users may be intersted in knowing more about how Acero works so that
+they can better understand how the backend processing for their libraries operates.
+
+A Database
+^^^^^^^^^^
+
+A database (or DBMS) is typically a much more expansive application and often packaged
+as a standalone service. Acero could be a component in a database (most databases have
+some kind of execution engine) or could be a component in some other data processing
+application that hardly resembles a database. Acero does not concern itself with
+user management, external communication, isolation, durability, or consistency. In
+addition, Acero is focused primarily on the read path, and the write utilities lack
+any sort of transaction support.
+
+An Optimizer
+^^^^^^^^^^^^
+
+Acero does not have an SQL parser. It does not have a query planner. It does not have
+any sort of optimizer. Acero expects to be given very detailed and low-level instructions
+on how to manipulate data and then it will perform that manipulation exactly as described.
+
+Creating the best execution plan is very hard. Small details can have a big impact on
+performance. We do think optimizers are important and we hope that tools will emerge
+someday which can provide these capabilities using standards such as Substrait.
+
+Distributed
+^^^^^^^^^^^
+
+Acero does not provide distributed execution. However, Acero aims to be usable by a distributed
+query execution engine. In other words, Acero will not configure and coordinate workers but
+it does except to be used as a worker. Sometimes, the distinction is a bit fuzzy. For example,
+an Acero source may be a smart storage device that is capable of performing filtering or other
+advanced analytics. One might consider this a distributed plan. The key distinction is Acero
+does not have the capability of transforming a logical plan into a distributed execution plan.
+That step will need to be done elsewhere.
+
+Acero vs...
+-----------
+
+Arrow Compute
+^^^^^^^^^^^^^
+
+This is described in more detail in the overview but the key difference is that Acero handles
+streams of data and Arrow Compute handles situations where all the data is in memory.
+
+Arrow Datasets
+^^^^^^^^^^^^^^
+
+The Arrow datasets library provides some basic routines for discovering, scanning, and
+writing collections of files. The datasets module depends on Acero. Both scanning and
+writing datasets uses Acero. The scan node and the write node are part of the datasets
+module. This helps to keep the complexity of file formats and filesystems out of the core
+Acero logic.
+
+Substrait
+^^^^^^^^^
+
+Substrait is a project establishing standards for query plans. Acero executes query plans
+and generates data. This makes Acero a Substrait consumer. There are more details on the
+Substrait capabilities below.
+
+Datafusion / DuckDb / Velox / Etc.
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are many columnar data engines emerging. We view this as a good thing and encourage
+projects like Substrait to help allow switching between engines as needed. We generally
+discourage comparative benchmarks as they are almost inevitably going to be workload-driven
+and rarely manage to capture an apples-vs-apples comparison. Discussions of the pros and
+cons of each is beyond the scope of this guide.
+
+Relation to Arrow C++
+=====================
+
+The Acero module is part of the Arrow C++ implementation. It is built as a separate
+module but it depends on core Arrow modules and does not stand alone. Acero uses
+and extends the capabilities from the core Arrow module and the Arrow compute kernels.
+
+.. image:: layers.svg
+ :alt: A diagram of layers with core on the left, compute in the middle, and acero on the right
+
+The core Arrow library provides containers for buffers and arrays that are laid out according
+to the Arrow columnar format. With few exceptions the core Arrow library does not examine
+or modify the contents of buffers. For example, converting a string array from lowercase
+strings to uppercase strings would not be a part of the core Arrow library because that would
+require examining the contents of the array.
+
+The compute module expands on the core library and provides functions which analyze and
+transform data. The compute module's capabilites are all exposed via a function registry.
+An Arrow "function" accepts zero or more arrays, batches, or tables, and produces an array,
+batch, or table. In addition, function calls can be combined, along with field references
+and literals, to form an expression (a tree of function calls) which the compute module can
+evaluate. For example, calculating ``x + (y * 3)`` given a table with columns ``x`` and ``y``.
+
+.. image:: expression_ast.svg
+ :alt: A sample expression tree
+
+Acero expands on these capabilities by adding compute operations for streams of data. For
+example, a project node can apply a compute expression on a stream of batches. This will
+create a new stream of batches with the result of the expression added as a new column. These
+nodes can be combined into a graph to form a more complex execution plan. This is very similar
+to the way functions are combined into a tree to form a complex expression.
+
+.. image:: simple_plan.svg
+ :alt: A simple plan that uses compute expressions
+
+.. note::
+ Acero does not use the :class:`arrow::Table` or :class:`arrow::ChunkedArray` containers
+ from the core Arrow library. This is because Acero operates on streams of batches and
+ so there is no need for a multi-batch container of data. This helps to reduce the
+ complexity of Acero and avoids tricky situations that can arise from tables whose
+ columns have different chunk sizes. Acero will often use :class:`arrow::Datum`
+ which is a variant from the core module that can hold many different types. Within
+ Acero, a datum will always hold either an :class:`arrow::Array` or a :class:`arrow::Scalar`.
+
+Core Concepts
+=============
+
+ExecNode
+--------
+
+The most basic concept in Acero is the ExecNode. An ExecNode has zero or more inputs and
+zero or one outputs. If an ExecNode has zero inputs we call it a source and if an ExecNode
+does not have an output then we call it a sink. There are many different kinds of nodes and
+each one transforms is inputs in different ways. For example:
+
+ * A scan node is a source node that reads data from files
+ * An aggregate node accumulates batches of data to compute summary statistics
+ * A filter node removes rows from the data according to a filter expression
+ * A table sink node accumulates data into a table
+
+.. note::
+ A full list of the available compute modules is included in the :ref:`user's guide<ExecNode List>`
+
+.. _exec-batch:
+
+ExecBatch
+---------
+
+Batches of data are represented by the ExecBatch class. An ExecBatch is a 2D structure that
+is very similar to a RecordBatch. It can have zero or more columns and all of the columns
+must have the same length. There are a few key differences from ExecBatch:
+
+.. figure:: rb_vs_eb.svg
+
+ Both the record batch and the exec batch have strong ownership of the arrays & buffers
+
+* An `ExecBatch` does not have a schema. This is because an `ExecBatch` is assumed to be
+ part of a stream of batches and the stream is assumed to have a consistent schema. So
+ the schema for an `ExecBatch` is typically stored in the ExecNode.
+* Columns in an `ExecBatch` are either an `Array` or a `Scalar`. When a column is a `Scalar`
+ this means that the column has a single value for every row in the batch. An `ExecBatch`
+ also has a length property which describes how many rows are in a batch. So another way to
+ view a `Scalar` is a constant array with `length` elements.
+* An `ExecBatch` contains additional information used by the exec plan. For example, an
+ `index` can be used to describe a batche's position in an ordered stream. We expect
+ that `ExecBatch` will also evolve to contain additional fields such as a selection vector.
+
+.. figure:: scalar_vs_array.svg
+
+ There are four different ways to represent the given batch of data using different combinations
+ of arrays and scalars. All four exec batches should be considered semantically equivalent.
+
+Converting from a record batch to an exec batch is is always zero copy. Both RecordBatch and ExecBatch
Review Comment:
is ~~is~~
##########
docs/source/cpp/acero/user_guide.rst:
##########
@@ -0,0 +1,735 @@
+.. 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.
+
+.. default-domain:: cpp
+.. highlight:: cpp
+.. cpp:namespace:: arrow::acero
+
+==================
+Acero User's Guide
+==================
+
+This page describes how to use Acero. It's recommended that you read the
+overview first and familiarize yourself with the basic concepts.
+
+Using Acero
+===========
+
+The basic workflow for Acero is this:
+
+#. First, create a graph of :class:`Declaration` objects describing the plan
+
+#. Call one of the DeclarationToXyz methods to execute the Declaration.
+
+ a. A new ExecPlan is created from the graph of Declarations. Each Declaration will correspond to one
+ ExecNode in the plan. In addition, a sink node will be added, depending on which DeclarationToXyz method
+ was used.
+
+ b. The ExecPlan is executed. Typically this happens as part of the DeclarationToXyz call but in
+ DeclarationToReader the reader is returned before the plan is finished executing.
+
+ c. Once the plan is finished it is destroyed
+
+Creating a Plan
+===============
+
+Using Substrait
+---------------
+
+Substrait is the preferred mechanism for creating a plan (graph of :class:`Declaration`). There are a few
+reasons for this:
+
+* Substrait producers spend a lot of time and energy in creating user-friendly APIs for producing complex
+ execution plans in a simple way. For example, the ``pivot_wider`` operation can be achieved using a complex
+ series of ``aggregate`` nodes. Rather than create all of those ``aggregate`` nodes by hand a producer will
+ give you a much simpler API.
+
+* If you are using Substrait then you can easily switch out to any other Substrait-consuming engine should you
+ at some point find that it serves your needs better than Acero.
+
+* We hope that tools will eventually emerge for Substrait-based optimizers and planners. By using Substrait
+ you will be making it much easier to use these tools in the future.
+
+You could create the Substrait plan yourself but you'll probably have a much easier time finding an existing
+Susbstrait producer. For example, you could use `ibis-substrait <https://github.com/ibis-project/ibis-substrait>`_
Review Comment:
Susbstrait -> Substrait
##########
docs/source/cpp/acero/overview.rst:
##########
@@ -0,0 +1,262 @@
+.. 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.
+
+.. default-domain:: cpp
+.. highlight:: cpp
+.. cpp:namespace:: arrow::acero
+
+==============
+Acero Overview
+==============
+
+This page gives an overview of the basic Acero concepts and helps distinguish Acero
+from other modules in the Arrow code base. It's intended for users, developers,
+potential contributors, and for those that would like to extend Acero, either for
+research or for business use. This page assumes the reader is already familiar with
+core Arrow concepts. This page does not expect any existing knowledge in relational
+algebra.
+
+What is Acero?
+==============
+
+Acero is a C++ library that can be used to analyze large (potentially infinite) streams
+of data. Acero allows computation to be expressed as an "execution plan" (:class:`ExecPlan`).
+An execution plan takes in zero or more streams of input data and emits a single
+stream of output data. The plan describes how the data will be transformed as it
+passes through. For example, a plan might:
+
+ * Merge two streams of data using a common column
+ * Create additional columns by evaluating expressions against the existing columns
+ * Consume a stream of data by writing it to disk in a partitioned layout
+
+.. image:: simple_graph.svg
+ :alt: A sample execution plan that joins three streams of data and writes to disk
+
+Acero is not...
+---------------
+
+A Library for Data Scientists
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Acero is not intended to be used directly by data scientists. It is expected that
+end users will typically be using some kind of frontend. For example, Pandas, Ibis,
+or SQL. The API for Acero is focused around capabilities and available algorithms.
+However, such users may be intersted in knowing more about how Acero works so that
+they can better understand how the backend processing for their libraries operates.
+
+A Database
+^^^^^^^^^^
+
+A database (or DBMS) is typically a much more expansive application and often packaged
+as a standalone service. Acero could be a component in a database (most databases have
+some kind of execution engine) or could be a component in some other data processing
+application that hardly resembles a database. Acero does not concern itself with
+user management, external communication, isolation, durability, or consistency. In
+addition, Acero is focused primarily on the read path, and the write utilities lack
+any sort of transaction support.
+
+An Optimizer
+^^^^^^^^^^^^
+
+Acero does not have an SQL parser. It does not have a query planner. It does not have
+any sort of optimizer. Acero expects to be given very detailed and low-level instructions
+on how to manipulate data and then it will perform that manipulation exactly as described.
+
+Creating the best execution plan is very hard. Small details can have a big impact on
+performance. We do think optimizers are important and we hope that tools will emerge
+someday which can provide these capabilities using standards such as Substrait.
+
+Distributed
+^^^^^^^^^^^
+
+Acero does not provide distributed execution. However, Acero aims to be usable by a distributed
+query execution engine. In other words, Acero will not configure and coordinate workers but
+it does except to be used as a worker. Sometimes, the distinction is a bit fuzzy. For example,
+an Acero source may be a smart storage device that is capable of performing filtering or other
+advanced analytics. One might consider this a distributed plan. The key distinction is Acero
+does not have the capability of transforming a logical plan into a distributed execution plan.
+That step will need to be done elsewhere.
+
+Acero vs...
+-----------
+
+Arrow Compute
+^^^^^^^^^^^^^
+
+This is described in more detail in the overview but the key difference is that Acero handles
+streams of data and Arrow Compute handles situations where all the data is in memory.
+
+Arrow Datasets
+^^^^^^^^^^^^^^
+
+The Arrow datasets library provides some basic routines for discovering, scanning, and
+writing collections of files. The datasets module depends on Acero. Both scanning and
+writing datasets uses Acero. The scan node and the write node are part of the datasets
+module. This helps to keep the complexity of file formats and filesystems out of the core
+Acero logic.
+
+Substrait
+^^^^^^^^^
+
+Substrait is a project establishing standards for query plans. Acero executes query plans
+and generates data. This makes Acero a Substrait consumer. There are more details on the
+Substrait capabilities below.
+
+Datafusion / DuckDb / Velox / Etc.
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are many columnar data engines emerging. We view this as a good thing and encourage
+projects like Substrait to help allow switching between engines as needed. We generally
+discourage comparative benchmarks as they are almost inevitably going to be workload-driven
+and rarely manage to capture an apples-vs-apples comparison. Discussions of the pros and
+cons of each is beyond the scope of this guide.
+
+Relation to Arrow C++
+=====================
+
+The Acero module is part of the Arrow C++ implementation. It is built as a separate
+module but it depends on core Arrow modules and does not stand alone. Acero uses
+and extends the capabilities from the core Arrow module and the Arrow compute kernels.
+
+.. image:: layers.svg
+ :alt: A diagram of layers with core on the left, compute in the middle, and acero on the right
+
+The core Arrow library provides containers for buffers and arrays that are laid out according
+to the Arrow columnar format. With few exceptions the core Arrow library does not examine
+or modify the contents of buffers. For example, converting a string array from lowercase
+strings to uppercase strings would not be a part of the core Arrow library because that would
+require examining the contents of the array.
+
+The compute module expands on the core library and provides functions which analyze and
+transform data. The compute module's capabilites are all exposed via a function registry.
+An Arrow "function" accepts zero or more arrays, batches, or tables, and produces an array,
+batch, or table. In addition, function calls can be combined, along with field references
+and literals, to form an expression (a tree of function calls) which the compute module can
+evaluate. For example, calculating ``x + (y * 3)`` given a table with columns ``x`` and ``y``.
+
+.. image:: expression_ast.svg
+ :alt: A sample expression tree
+
+Acero expands on these capabilities by adding compute operations for streams of data. For
+example, a project node can apply a compute expression on a stream of batches. This will
+create a new stream of batches with the result of the expression added as a new column. These
+nodes can be combined into a graph to form a more complex execution plan. This is very similar
+to the way functions are combined into a tree to form a complex expression.
+
+.. image:: simple_plan.svg
+ :alt: A simple plan that uses compute expressions
+
+.. note::
+ Acero does not use the :class:`arrow::Table` or :class:`arrow::ChunkedArray` containers
+ from the core Arrow library. This is because Acero operates on streams of batches and
+ so there is no need for a multi-batch container of data. This helps to reduce the
+ complexity of Acero and avoids tricky situations that can arise from tables whose
+ columns have different chunk sizes. Acero will often use :class:`arrow::Datum`
+ which is a variant from the core module that can hold many different types. Within
+ Acero, a datum will always hold either an :class:`arrow::Array` or a :class:`arrow::Scalar`.
+
+Core Concepts
+=============
+
+ExecNode
+--------
+
+The most basic concept in Acero is the ExecNode. An ExecNode has zero or more inputs and
+zero or one outputs. If an ExecNode has zero inputs we call it a source and if an ExecNode
+does not have an output then we call it a sink. There are many different kinds of nodes and
+each one transforms is inputs in different ways. For example:
+
+ * A scan node is a source node that reads data from files
+ * An aggregate node accumulates batches of data to compute summary statistics
+ * A filter node removes rows from the data according to a filter expression
+ * A table sink node accumulates data into a table
+
+.. note::
+ A full list of the available compute modules is included in the :ref:`user's guide<ExecNode List>`
+
+.. _exec-batch:
+
+ExecBatch
+---------
+
+Batches of data are represented by the ExecBatch class. An ExecBatch is a 2D structure that
+is very similar to a RecordBatch. It can have zero or more columns and all of the columns
+must have the same length. There are a few key differences from ExecBatch:
+
+.. figure:: rb_vs_eb.svg
+
+ Both the record batch and the exec batch have strong ownership of the arrays & buffers
+
+* An `ExecBatch` does not have a schema. This is because an `ExecBatch` is assumed to be
+ part of a stream of batches and the stream is assumed to have a consistent schema. So
+ the schema for an `ExecBatch` is typically stored in the ExecNode.
+* Columns in an `ExecBatch` are either an `Array` or a `Scalar`. When a column is a `Scalar`
+ this means that the column has a single value for every row in the batch. An `ExecBatch`
+ also has a length property which describes how many rows are in a batch. So another way to
+ view a `Scalar` is a constant array with `length` elements.
+* An `ExecBatch` contains additional information used by the exec plan. For example, an
+ `index` can be used to describe a batche's position in an ordered stream. We expect
+ that `ExecBatch` will also evolve to contain additional fields such as a selection vector.
+
+.. figure:: scalar_vs_array.svg
+
+ There are four different ways to represent the given batch of data using different combinations
+ of arrays and scalars. All four exec batches should be considered semantically equivalent.
+
+Converting from a record batch to an exec batch is is always zero copy. Both RecordBatch and ExecBatch
+refer to the exact same underlying arrays. Converting from an exec batch to a record batch is
+only zero copy if there are no scalars in the exec batch.
+
+.. note::
+ Both Acero and the compute module have "lightweight" versions of batches and arrays.
+ In the compute module these are called `BatchSpan`, `ArraySpan`, and `BufferSpan`. In
+ Acero the concept is called `KeyColumnArray`. These types were developed concurrently
+ and serve the same purpose. They aim to provide an array container that can be completely
+ stack allocated (provided the data type is non-nested) in order to avoid heap allocation
+ overhead. Ideally these two concepts will be merged someday.
+
+ExecPlan
+--------
+
+An ExecPlan represents a graph of ExecNode objects. A valid ExecPlan must always have at
+least one source node but it does not technically need to have a sink node. The ExecPlan contains
+resources shared by all of the nodes and has utility functions to control starting and stopping
+execution of the nodes. Both ExecPlan and ExecNode are tied to the lifecycle of a single execution.
+They have state and are not expected to be restartable.
+
+.. warning::
+ The structures within Acero, including `ExecBatch`, are still experimental. The `ExecBatch`
+ class should not be used outside of Acero. Instead, an `ExecBatch` should be converted to
+ a more standard structure such as a `RecordBatch`.
+
+ Similarly, an ExecPlan is an internal concept. Users creating plans should be using Declaration
+ objects. APIs for consuming and executing plans should abstract away the details of the underlying
+ plan and not expose the object itself.
+
+Declaration
+-----------
+
+A Declaration is a blueprint for an ExecNode. Declarations can be combined into a graph to
+form the blueprint for an ExecPlan. A Declaration describes the computation that needs to be
+done but is not actually responsible for carrying out the computation. In this way, a Declaration is
+analgous to an expression. It is expected that Declarations will need to be converted to and from
Review Comment:
analgous -> analogous
##########
docs/source/cpp/acero/overview.rst:
##########
@@ -0,0 +1,262 @@
+.. 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.
+
+.. default-domain:: cpp
+.. highlight:: cpp
+.. cpp:namespace:: arrow::acero
+
+==============
+Acero Overview
+==============
+
+This page gives an overview of the basic Acero concepts and helps distinguish Acero
+from other modules in the Arrow code base. It's intended for users, developers,
+potential contributors, and for those that would like to extend Acero, either for
+research or for business use. This page assumes the reader is already familiar with
+core Arrow concepts. This page does not expect any existing knowledge in relational
+algebra.
+
+What is Acero?
+==============
+
+Acero is a C++ library that can be used to analyze large (potentially infinite) streams
+of data. Acero allows computation to be expressed as an "execution plan" (:class:`ExecPlan`).
+An execution plan takes in zero or more streams of input data and emits a single
+stream of output data. The plan describes how the data will be transformed as it
+passes through. For example, a plan might:
+
+ * Merge two streams of data using a common column
+ * Create additional columns by evaluating expressions against the existing columns
+ * Consume a stream of data by writing it to disk in a partitioned layout
+
+.. image:: simple_graph.svg
+ :alt: A sample execution plan that joins three streams of data and writes to disk
+
+Acero is not...
+---------------
+
+A Library for Data Scientists
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Acero is not intended to be used directly by data scientists. It is expected that
+end users will typically be using some kind of frontend. For example, Pandas, Ibis,
+or SQL. The API for Acero is focused around capabilities and available algorithms.
+However, such users may be intersted in knowing more about how Acero works so that
+they can better understand how the backend processing for their libraries operates.
+
+A Database
+^^^^^^^^^^
+
+A database (or DBMS) is typically a much more expansive application and often packaged
+as a standalone service. Acero could be a component in a database (most databases have
+some kind of execution engine) or could be a component in some other data processing
+application that hardly resembles a database. Acero does not concern itself with
+user management, external communication, isolation, durability, or consistency. In
+addition, Acero is focused primarily on the read path, and the write utilities lack
+any sort of transaction support.
+
+An Optimizer
+^^^^^^^^^^^^
+
+Acero does not have an SQL parser. It does not have a query planner. It does not have
+any sort of optimizer. Acero expects to be given very detailed and low-level instructions
+on how to manipulate data and then it will perform that manipulation exactly as described.
+
+Creating the best execution plan is very hard. Small details can have a big impact on
+performance. We do think optimizers are important and we hope that tools will emerge
+someday which can provide these capabilities using standards such as Substrait.
+
+Distributed
+^^^^^^^^^^^
+
+Acero does not provide distributed execution. However, Acero aims to be usable by a distributed
+query execution engine. In other words, Acero will not configure and coordinate workers but
+it does except to be used as a worker. Sometimes, the distinction is a bit fuzzy. For example,
+an Acero source may be a smart storage device that is capable of performing filtering or other
+advanced analytics. One might consider this a distributed plan. The key distinction is Acero
+does not have the capability of transforming a logical plan into a distributed execution plan.
+That step will need to be done elsewhere.
+
+Acero vs...
+-----------
+
+Arrow Compute
+^^^^^^^^^^^^^
+
+This is described in more detail in the overview but the key difference is that Acero handles
+streams of data and Arrow Compute handles situations where all the data is in memory.
+
+Arrow Datasets
+^^^^^^^^^^^^^^
+
+The Arrow datasets library provides some basic routines for discovering, scanning, and
+writing collections of files. The datasets module depends on Acero. Both scanning and
+writing datasets uses Acero. The scan node and the write node are part of the datasets
+module. This helps to keep the complexity of file formats and filesystems out of the core
+Acero logic.
+
+Substrait
+^^^^^^^^^
+
+Substrait is a project establishing standards for query plans. Acero executes query plans
+and generates data. This makes Acero a Substrait consumer. There are more details on the
+Substrait capabilities below.
+
+Datafusion / DuckDb / Velox / Etc.
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are many columnar data engines emerging. We view this as a good thing and encourage
+projects like Substrait to help allow switching between engines as needed. We generally
+discourage comparative benchmarks as they are almost inevitably going to be workload-driven
+and rarely manage to capture an apples-vs-apples comparison. Discussions of the pros and
+cons of each is beyond the scope of this guide.
+
+Relation to Arrow C++
+=====================
+
+The Acero module is part of the Arrow C++ implementation. It is built as a separate
+module but it depends on core Arrow modules and does not stand alone. Acero uses
+and extends the capabilities from the core Arrow module and the Arrow compute kernels.
+
+.. image:: layers.svg
+ :alt: A diagram of layers with core on the left, compute in the middle, and acero on the right
+
+The core Arrow library provides containers for buffers and arrays that are laid out according
+to the Arrow columnar format. With few exceptions the core Arrow library does not examine
+or modify the contents of buffers. For example, converting a string array from lowercase
+strings to uppercase strings would not be a part of the core Arrow library because that would
+require examining the contents of the array.
+
+The compute module expands on the core library and provides functions which analyze and
+transform data. The compute module's capabilites are all exposed via a function registry.
+An Arrow "function" accepts zero or more arrays, batches, or tables, and produces an array,
+batch, or table. In addition, function calls can be combined, along with field references
+and literals, to form an expression (a tree of function calls) which the compute module can
+evaluate. For example, calculating ``x + (y * 3)`` given a table with columns ``x`` and ``y``.
+
+.. image:: expression_ast.svg
+ :alt: A sample expression tree
+
+Acero expands on these capabilities by adding compute operations for streams of data. For
+example, a project node can apply a compute expression on a stream of batches. This will
+create a new stream of batches with the result of the expression added as a new column. These
+nodes can be combined into a graph to form a more complex execution plan. This is very similar
+to the way functions are combined into a tree to form a complex expression.
+
+.. image:: simple_plan.svg
+ :alt: A simple plan that uses compute expressions
+
+.. note::
+ Acero does not use the :class:`arrow::Table` or :class:`arrow::ChunkedArray` containers
+ from the core Arrow library. This is because Acero operates on streams of batches and
+ so there is no need for a multi-batch container of data. This helps to reduce the
+ complexity of Acero and avoids tricky situations that can arise from tables whose
+ columns have different chunk sizes. Acero will often use :class:`arrow::Datum`
+ which is a variant from the core module that can hold many different types. Within
+ Acero, a datum will always hold either an :class:`arrow::Array` or a :class:`arrow::Scalar`.
+
+Core Concepts
+=============
+
+ExecNode
+--------
+
+The most basic concept in Acero is the ExecNode. An ExecNode has zero or more inputs and
+zero or one outputs. If an ExecNode has zero inputs we call it a source and if an ExecNode
+does not have an output then we call it a sink. There are many different kinds of nodes and
+each one transforms is inputs in different ways. For example:
Review Comment:
is -> its
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