[GitHub] [arrow] bkietz commented on a diff in pull request #35320: GH-32335: [C++][Docs] Add design document for Acero

["bkietz (via GitHub)" <gi...@apache.org>]

bkietz commented on code in PR #35320:
URL: https://github.com/apache/arrow/pull/35320#discussion_r1179166745


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docs/source/cpp/acero/overview.rst:
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+.. 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.

Review Comment:
   ```suggestion
   Creating the best execution plan is very hard.  Small details can have a big impact on
   performance.  We do think an optimizer is important but we believe it should be
   implemented independent of acero, hopefully in a composable way through standards such
   as Substrait so that any backend could leverage it.
   ```



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