vibhatha commented on a change in pull request #12033:
URL: https://github.com/apache/arrow/pull/12033#discussion_r788700062
##########
File path: docs/source/cpp/streaming_execution.rst
##########
@@ -305,3 +305,497 @@ Datasets may be scanned multiple times; just make
multiple scan
nodes from that dataset. (Useful for a self-join, for example.)
Note that producing two scan nodes like this will perform all
reads and decodes twice.
+
+Constructing ``ExecNode`` using Options
+=======================================
+
+Using the execution plan we can construct various queries.
+To construct such queries, we have provided a set of building blocks
+referred to as :class:`ExecNode` s. These nodes provide the ability to
+construct operations like filtering, projection, join, etc.
+
+This is the list of operations associated with the execution plan:
+
+.. list-table:: Operations and Options
+ :widths: 50 50
+ :header-rows: 1
+
+ * - Operation
+ - Options
+ * - ``source``
+ - :class:`arrow::compute::SourceNodeOptions`
+ * - ``filter``
+ - :class:`arrow::compute::FilterNodeOptions`
+ * - ``project``
+ - :class:`arrow::compute::ProjectNodeOptions`
+ * - ``aggregate``
+ - :class:`arrow::compute::ScalarAggregateOptions`
+ * - ``sink``
+ - :class:`arrow::compute::SinkNodeOptions`
+ * - ``consuming_sink``
+ - :class:`arrow::compute::ConsumingSinkNodeOptions`
+ * - ``order_by_sink``
+ - :class:`arrow::compute::OrderBySinkNodeOptions`
+ * - ``select_k_sink``
+ - :class:`arrow::compute::SelectKSinkNodeOptions`
+ * - ``scan``
+ - :class:`arrow::compute::ScanNodeOptions`
+ * - ``hash_join``
+ - :class:`arrow::compute::HashJoinNodeOptions`
+ * - ``write``
+ - :class:`arrow::dataset::WriteNodeOptions`
+ * - ``union``
+ - N/A
+
+
+.. _stream_execution_source_docs:
+
+``source``
+----------
+
+A `source` operation can be considered as an entry point to create a streaming
execution plan.
+:class:`arrow::compute::SourceNodeOptions` are used to create the ``source``
operation. The
+`source` operation is the most generic and flexible type of source currently
available but it can
+be quite tricky to configure. To process data from files the scan operation
is likely a simpler choice.
+The source node requires some kind of function that can be called to poll for
more data. This
+function should take no arguments and should return an
+``arrow::Future<std::shared_ptr<arrow::util::optional<arrow::RecordBatch>>>``.
+This function might be reading a file, iterating through an in memory
structure, or receiving data
+from a network connection. The arrow library refers to these functions as
`arrow::AsyncGenerator`
+and there are a number of utilities for working with these functions. For
this example we use
+a vector of record batches that we've already stored in memory.
+In addition, the schema of the data must be known up front. Arrow's streaming
execution
+engine must know the schema of the data at each stage of the execution graph
before any
+processing has begun. This means we must supply the schema for a source node
separately
+from the data itself.
+
+Struct to hold the data generator definition:
+
+.. literalinclude::
../../../cpp/examples/arrow/execution_plan_documentation_examples.cc
+ :language: cpp
+ :start-after: (Doc section: BatchesWithSchema Definition)
+ :end-before: (Doc section: BatchesWithSchema Definition)
+ :linenos:
+ :lineno-match:
+
+Generating sample Batches for computation:
+
+.. literalinclude::
../../../cpp/examples/arrow/execution_plan_documentation_examples.cc
+ :language: cpp
+ :start-after: (Doc section: MakeBasicBatches Definition)
+ :end-before: (Doc section: MakeBasicBatches Definition)
+ :linenos:
+ :lineno-match:
+
+Example of using ``source`` (usage of sink is explained in detail in
:ref:`sink<stream_execution_sink_docs>`):
+
+.. literalinclude::
../../../cpp/examples/arrow/execution_plan_documentation_examples.cc
+ :language: cpp
+ :start-after: (Doc section: Source Example)
+ :end-before: (Doc section: Source Example)
+ :linenos:
+ :lineno-match:
+
+.. _stream_execution_filter_docs:
+
+``filter``
+----------
+
+``filter`` operation, as the name suggests, provides an option to define data
filtering
+criteria. It selects rows matching a given expression.
+Filters can be written using :class:`arrow::compute::Expression`.
+For example, if we wish to keep rows where the value of column ``b``
+is greater than 3, then we can use the following expression::
+
+ // a > 3
+ arrow::compute::Expression filter_opt = arrow::compute::greater(
+ arrow::compute::field_ref("a"),
+ arrow::compute::literal(3));
+
+Using this option, the filter node can be constructed as follows::
+
+ // creating filter node
+ arrow::compute::ExecNode* filter;
+ ARROW_ASSIGN_OR_RAISE(filter, arrow::compute::MakeExecNode("filter",
+ // plan
+ plan.get(),
+ // previous node
+ {scan},
+ //filter node options
+ arrow::compute::FilterNodeOptions{filter_opt}));
+
+Filter example:
+
+.. literalinclude::
../../../cpp/examples/arrow/execution_plan_documentation_examples.cc
+ :language: cpp
+ :start-after: (Doc section: Filter Example)
+ :end-before: (Doc section: Filter Example)
+ :linenos:
+ :lineno-match:
+
+.. _stream_execution_project_docs:
+
+``project``
+-----------
+
+``project`` operation rearranges, deletes, transforms, and creates columns.
+Each output column is computed by evaluating an expression
+against the source record batch. This is exposed via
+:class:`arrow::compute::ProjectNodeOptions` which requires,
+an :class:`arrow::compute::Expression` and name for each of the output columns
(if names are not
+provided, the string representations of exprs will be used).
+
+Sample Expression for projection::
+
+ // a * 2 (multiply values in a column by 2)
+ arrow::compute::Expression a_times_2 = arrow::compute::call("multiply",
+ {arrow::compute::field_ref("a"), arrow::compute::literal(2)});
+
+
+Creating a project node::
+
+ arrow::compute::ExecNode* project;
+ ARROW_ASSIGN_OR_RAISE(project,
+ arrow::compute::MakeExecNode("project",
+ // plan
+ plan.get(),
+ // previous node
+ {scan},
+ // project node options
+ arrow::compute::ProjectNodeOptions{{a_times_2}}));
+
+Project example:
+
+.. literalinclude::
../../../cpp/examples/arrow/execution_plan_documentation_examples.cc
+ :language: cpp
+ :start-after: (Doc section: Project Example)
+ :end-before: (Doc section: Project Example)
+ :linenos:
+ :lineno-match:
+
+.. _stream_execution_aggregate_docs:
+
+``aggregate``
+-------------
+
+``aggregate`` operation provides various data aggregation options.
+The :class:`arrow::compute::AggregateNodeOptions` is used to
+define the aggregation criteria. An aggregate node can first group data by
+one or more key columns or the keys can be left off to compute aggregates
+across the entire dataset. Each aggregate node can compute any number of
+aggregation functions. Each aggregation function will be applied to every
+field specified as a target. The aggregation functions can be
+selected from :ref:`this list of aggregation functions
<aggregation-option-list>`.
+Note: This node is a "pipeline breaker" and will fully materialize the dataset
in memory.
+In the future, spillover mechanisms will be added which should alleviate this
constraint.
+
+An example for creating an aggregate node::
+
+ arrow::compute::CountOptions
options(arrow::compute::CountOptions::ONLY_VALID);
+
+ auto aggregate_options = arrow::compute::AggregateNodeOptions{
+ /*aggregates=*/{{"hash_count", &options}},
+ /*targets=*/{"a"},
+ /*names=*/{"count(a)"},
+ /*keys=*/{"b"}};
+
+ ARROW_ASSIGN_OR_RAISE(cp::ExecNode * aggregate,
+ cp::MakeExecNode("aggregate", plan.get(), {source},
+ aggregate_options));
+
+Aggregate example:
+
+.. literalinclude::
../../../cpp/examples/arrow/execution_plan_documentation_examples.cc
+ :language: cpp
+ :start-after: (Doc section: Aggregate Example)
+ :end-before: (Doc section: Aggregate Example)
+ :linenos:
+ :lineno-match:
+
+.. _stream_execution_sink_docs:
+
+``sink``
+--------
+
+``sink`` operation provides output and is the final node of a streaming
+execution definition. :class:`arrow::compute::SinkNodeOptions` interface is
used to pass
+the required options. Similar to the source operator the sink operator exposes
the output
+with a function that returns a record batch future each time it is called. It
is expected the
+caller will repeatedly call this function until the generator function is
exhausted (returns
+arrow::util::optional::nullopt). If this function is not called often enough
then record batches
+will accumulate in memory. An execution plan should only have one
+"terminal" node (one sink node). An execution plan may "finish" by marking
+`exec_plan->finished()` as complete before the sink generator is fully
consumed and the
+execution plan can be safely destroyed without harming the sink generator
(which will hold
+references to the unconsumed batches).
+
+Example::
+
+ arrow::AsyncGenerator<arrow::util::optional<cp::ExecBatch>> sink_gen;
+
+ arrow::compute::ExecNode* sink;
+
+ ARROW_ASSIGN_OR_RAISE(sink, arrow::compute::MakeExecNode("sink", plan.get(),
{source},
+
arrow::compute::SinkNodeOptions{&sink_gen}));
Review comment:
Yeah, I understand what you are pointing out. May be since we add the
excerpts, we can omit these.
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