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new 9acb3df5 docs: add guide to adding a new expression (#422)
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commit 9acb3df5161e590c7c262cfaf2a35a80177bc505
Author: Trent Hauck <[email protected]>
AuthorDate: Mon May 20 13:41:22 2024 -0700
docs: add guide to adding a new expression (#422)
* docs: add guide to adding a new expression
* docs: revise with presentation info
* docs: fix warning
* docs: fix header level
* docs: better info about handling datafusion udfs
* docs: grammar/typos/etc
* docs: clarify datafusion/datafusion comet path
* docs: clarify language about `isSpark32`
* docs: fix error
---
.../contributor-guide/adding_a_new_expression.md | 212 +++++++++++++++++++++
docs/source/index.rst | 1 +
2 files changed, 213 insertions(+)
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@@ -0,0 +1,212 @@
+<!---
+ 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.
+-->
+
+# Adding a New Expression
+
+There are a number of Spark expression that are not supported by DataFusion
Comet yet, and implementing them is a good way to contribute to the project.
+
+Before you start, have a look through [these
slides](https://docs.google.com/presentation/d/1H0fF2MOkkBK8fPBlnqK6LejUeLcVD917JhVWfp3mb8A/edit#slide=id.p)
as they provide a conceptual overview. And a video of a presentation on those
slides is available
[here](https://drive.google.com/file/d/1POU4lFAZfYwZR8zV1X2eoLiAmc1GDtSP/view?usp=sharing).
+
+## Finding an Expression to Add
+
+You may have a specific expression in mind that you'd like to add, but if not,
you can review the [expression coverage
document](https://github.com/apache/datafusion-comet/blob/f08fcadd5fbdb5b04293d33e654f6c16f81b70c4/doc/spark_builtin_expr_coverage.txt)
to see which expressions are not yet supported.
+
+## Implementing the Expression
+
+Once you have the expression you'd like to add, you should take inventory of
the following:
+
+1. What is the Spark expression's behavior across different Spark versions?
These make good test cases and will inform you of any compatibility issues,
such as an API change that will have to be addressed.
+2. Check if the expression is already implemented in DataFusion and if it is
compatible with the Spark expression.
+ 1. If it is, you can potentially reuse the existing implementation though
you'll need to add tests to verify compatibility.
+ 2. If it's not, consider an initial version in DataFusion for expressions
that are common across different engines. For expressions that are specific to
Spark, consider an initial version in DataFusion Comet.
+3. Test cases for the expression. As mentioned, you can refer to Spark's test
cases for a good idea of what to test.
+
+Once you know what you want to add, you'll need to update the query planner to
recognize the new expression in Scala and potentially add a new expression
implementation in the Rust package.
+
+### Adding the Expression in Scala
+
+The `QueryPlanSerde` object has a method `exprToProto`, which is responsible
for converting a Spark expression to a protobuf expression. Within that method
is an `exprToProtoInternal` method that contains a large match statement for
each expression type. You'll need to add a new case to this match statement for
your new expression.
+
+For example, the `unhex` function looks like this:
+
+```scala
+case e: Unhex if !isSpark32 =>
+ val unHex = unhexSerde(e)
+
+ val childExpr = exprToProtoInternal(unHex._1, inputs)
+ val failOnErrorExpr = exprToProtoInternal(unHex._2, inputs)
+
+ val optExpr =
+ scalarExprToProtoWithReturnType("unhex", e.dataType, childExpr,
failOnErrorExpr)
+ optExprWithInfo(optExpr, expr, unHex._1)
+```
+
+A few things to note here:
+
+* The `isSpark32` check is used to fall back to Spark's implementation of
`unhex` in Spark 3.2. This is somewhat context specific, because in this case,
due to a bug in Spark 3.2 for `unhex`, we want to use the Spark implementation
and not a Comet implementation that would behave differently if correct.
+* The function is recursively called on child expressions, so you'll need to
make sure that the child expressions are also converted to protobuf.
+* `scalarExprToProtoWithReturnType` is for scalar functions that need return
type information. Your expression may use a different method depending on the
type of expression.
+
+#### Adding Spark-side Tests for the New Expression
+
+It is important to verify that the new expression is correctly recognized by
the native execution engine and matches the expected spark behavior. To do
this, you can add a set of test cases in the `CometExpressionSuite`, and use
the `checkSparkAnswerAndOperator` method to compare the results of the new
expression with the expected Spark results and that Comet's native execution
engine is able to execute the expression.
+
+For example, this is the test case for the `unhex` expression:
+
+```scala
+test("unhex") {
+ assume(!isSpark32, "unhex function has incorrect behavior in 3.2") // used
to skip the test in Spark 3.2
+
+ val table = "unhex_table"
+ withTable(table) {
+ sql(s"create table $table(col string) using parquet")
+
+ sql(s"""INSERT INTO $table VALUES
+ |('537061726B2053514C'),
+ |('737472696E67'),
+ |('\\0'),
+ |(''),
+ |('###'),
+ |('G123'),
+ |('hello'),
+ |('A1B'),
+ |('0A1B')""".stripMargin)
+
+ checkSparkAnswerAndOperator(s"SELECT unhex(col) FROM $table")
+ }
+}
+```
+
+### Adding the Expression To the Protobuf Definition
+
+Once you have the expression implemented in Scala, you might need to update
the protobuf definition to include the new expression. You may not need to do
this if the expression is already covered by the existing protobuf definition
(e.g. you're adding a new scalar function).
+
+You can find the protobuf definition in `expr.proto`, and in particular the
`Expr` or potentially the `AggExpr`. These are similar in theory to the large
case statement in `QueryPlanSerde`, but in protobuf format. So if you were to
add a new expression called `Add2`, you would add a new case to the `Expr`
message like so:
+
+```proto
+message Expr {
+ oneof expr_struct {
+ ...
+ Add2 add2 = 100; // Choose the next available number
+ }
+}
+```
+
+Then you would define the `Add2` message like so:
+
+```proto
+message Add2 {
+ Expr left = 1;
+ Expr right = 2;
+}
+```
+
+### Adding the Expression in Rust
+
+With the serialization complete, the next step is to implement the expression
in Rust and ensure that the incoming plan can make use of it.
+
+How this works, is somewhat dependent on the type of expression you're adding,
so see the `core/src/execution/datafusion/expressions` directory for examples
of how to implement different types of expressions.
+
+#### Generally Adding a New Expression
+
+If you're adding a new expression, you'll need to review `create_plan` and
`create_expr`. `create_plan` is responsible for translating the incoming plan
into a DataFusion plan, and may delegate to `create_expr` to create the
physical expressions for the plan.
+
+If you added a new message to the protobuf definition, you'll add a new match
case to the `create_expr` method to handle the new expression. For example, if
you added an `Add2` expression, you would add a new case like so:
+
+```rust
+match spark_expr.expr_struct.as_ref().unwrap() {
+ ...
+ ExprStruct::Add2(add2) => self.create_binary_expr(...)
+}
+```
+
+`self.create_binary_expr` is for a binary expression, but if something out of
the box is needed, you can create a new `PhysicalExpr` implementation. For
example, see `if_expr.rs` for an example of an implementation that doesn't fit
the `create_binary_expr` mold.
+
+#### Adding a New Scalar Function Expression
+
+For a new scalar function, you can reuse a lot of code by updating the
`create_comet_physical_fun` method to match on the function name and make the
scalar UDF to be called. For example, the diff to add the `unhex` function is:
+
+```diff
+macro_rules! make_comet_scalar_udf {
+ ($name:expr, $func:ident, $data_type:ident) => {{
+
++ "unhex" => {
++ let func = Arc::new(spark_unhex);
++ make_comet_scalar_udf!("unhex", func, without data_type)
++ }
+
+ }}
+}
+```
+
+With that addition, you can now implement the spark function in Rust. This
function will look very similar to DataFusion code. For examples, see the
`core/src/execution/datafusion/expressions/scalar_funcs` directory.
+
+Without getting into the internals, the function signature will look like:
+
+```rust
+pub(super) fn spark_unhex(args: &[ColumnarValue]) -> Result<ColumnarValue,
DataFusionError> {
+ // Do the work here
+}
+```
+
+> **_NOTE:_** If you call the `make_comet_scalar_udf` macro with the data
type, the function signature will look include the data type as a second
argument.
+
+### API Differences Between Spark Versions
+
+If the expression you're adding has different behavior across different Spark
versions, you'll need to account for that in your implementation. There are two
tools at your disposal to help with this:
+
+1. Shims that exist in
`spark/src/main/spark-$SPARK_VERSION/org/apache/comet/shims/CometExprShim.scala`
for each Spark version. These shims are used to provide compatibility between
different Spark versions.
+2. Variables that correspond to the Spark version, such as `isSpark32`, which
can be used to conditionally execute code based on the Spark version.
+
+## Shimming to Support Different Spark Versions
+
+By adding shims for each Spark version, you can provide a consistent interface
for the expression across different Spark versions. For example, `unhex` added
a new optional parameter is Spark 3.4, for if it should `failOnError` or not.
So for version 3.2 and 3.3, the shim is:
+
+```scala
+trait CometExprShim {
+ /**
+ * Returns a tuple of expressions for the `unhex` function.
+ */
+ def unhexSerde(unhex: Unhex): (Expression, Expression) = {
+ (unhex.child, Literal(false))
+ }
+}
+```
+
+And for version 3.4, the shim is:
+
+```scala
+trait CometExprShim {
+ /**
+ * Returns a tuple of expressions for the `unhex` function.
+ */
+ def unhexSerde(unhex: Unhex): (Expression, Expression) = {
+ (unhex.child, unhex.failOnError)
+ }
+}
+```
+
+Then when `unhexSerde` is called in the `QueryPlanSerde` object, it will use
the correct shim for the Spark version.
+
+## Resources
+
+* [Variance PR](https://github.com/apache/datafusion-comet/pull/297)
+ * Aggregation function
+* [Unhex PR](https://github.com/apache/datafusion-comet/pull/342)
+ * Basic scalar function with shims for different Spark versions
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 819f7201..1e9eba21 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -59,6 +59,7 @@ as a native runtime to achieve improvement in terms of query
efficiency and quer
Development Guide <contributor-guide/development>
Debugging Guide <contributor-guide/debugging>
Benchmarking Guide <contributor-guide/benchmarking>
+ Adding a New Expression <contributor-guide/adding_a_new_expression>
Profiling Native Code <contributor-guide/profiling_native_code>
Github and Issue Tracker <https://github.com/apache/datafusion-comet>
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