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[SPARK-8117] [SQL] Push codegen implementation into each Expression

Davies Liu authored 9 years ago

This PR move codegen implementation of expressions into Expression class itself, make it easy to manage.

It introduces two APIs in Expression:
```
def gen(ctx: CodeGenContext): GeneratedExpressionCode
def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): Code
```

gen(ctx) will call genSource(ctx, ev) to generate Java source code for the current expression. A expression needs to override genSource().

Here are the types:
```
type Term String
type Code String

/**
 * Java source for evaluating an [[Expression]] given a [[Row]] of input.
 */
case class GeneratedExpressionCode(var code: Code,
                               nullTerm: Term,
                               primitiveTerm: Term,
                               objectTerm: Term)
/**
 * A context for codegen, which is used to bookkeeping the expressions those are not supported
 * by codegen, then they are evaluated directly. The unsupported expression is appended at the
 * end of `references`, the position of it is kept in the code, used to access and evaluate it.
 */
class CodeGenContext {
  /**
   * Holding all the expressions those do not support codegen, will be evaluated directly.
   */
  val references: Seq[Expression] = new mutable.ArrayBuffer[Expression]()
}
```

This is basically #6660, but fixed style violation and compilation failure.

Author: Davies Liu <davies@databricks.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #6690 from rxin/codegen and squashes the following commits:

e1368c2 [Reynold Xin] Fixed tests.
73db80e [Reynold Xin] Fixed compilation failure.
19d6435 [Reynold Xin] Fixed style violation.
9adaeaf [Davies Liu] address comments
f42c732 [Davies Liu] improve coverage and tests
bad6828 [Davies Liu] address comments
e03edaa [Davies Liu] consts fold
86fac2c [Davies Liu] fix style
02262c9 [Davies Liu] address comments
b5d3617 [Davies Liu] Merge pull request #5 from rxin/codegen
48c454f [Reynold Xin] Some code gen update.
2344bc0 [Davies Liu] fix test
12ff88a [Davies Liu] fix build
c5fb514 [Davies Liu] rename
8c6d82d [Davies Liu] update docs
b145047 [Davies Liu] fix style
e57959d [Davies Liu] add type alias
3ff25f8 [Davies Liu] refactor
593d617 [Davies Liu] pushing codegen into Expression

5e7b6b67

5e7b6b67 9 years ago

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Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1000:

>>> sc.parallelize(range(1000)).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn-cluster" or "yarn-client" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.

Configuration

Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.