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Liwei Lin authored
## What changes were proposed in this pull request?

Prior to this patch, we'll generate `compare(...)` for `GeneratedClass$SpecificOrdering` like below, leading to Janino exceptions saying the code grows beyond 64 KB.

``` scala
/* 005 */ class SpecificOrdering extends o.a.s.sql.catalyst.expressions.codegen.BaseOrdering {
/* ..... */   ...
/* 10969 */   private int compare(InternalRow a, InternalRow b) {
/* 10970 */     InternalRow i = null;  // Holds current row being evaluated.
/* 10971 */
/* 1.... */     code for comparing field0
/* 1.... */     code for comparing field1
/* 1.... */     ...
/* 1.... */     code for comparing field449
/* 15012 */
/* 15013 */     return 0;
/* 15014 */   }
/* 15015 */ }
```

This patch would break `compare(...)` into smaller `compare_xxx(...)` methods when necessary; then we'll get generated `compare(...)` like:

``` scala
/* 001 */ public SpecificOrdering generate(Object[] references) {
/* 002 */   return new SpecificOrdering(references);
/* 003 */ }
/* 004 */
/* 005 */ class SpecificOrdering extends o.a.s.sql.catalyst.expressions.codegen.BaseOrdering {
/* 006 */
/* 007 */     ...
/* 1.... */
/* 11290 */   private int compare_0(InternalRow a, InternalRow b) {
/* 11291 */     InternalRow i = null;  // Holds current row being evaluated.
/* 11292 */
/* 11293 */     i = a;
/* 11294 */     boolean isNullA;
/* 11295 */     UTF8String primitiveA;
/* 11296 */     {
/* 11297 */
/* 11298 */       Object obj = ((Expression) references[0]).eval(null);
/* 11299 */       UTF8String value = (UTF8String) obj;
/* 11300 */       isNullA = false;
/* 11301 */       primitiveA = value;
/* 11302 */     }
/* 11303 */     i = b;
/* 11304 */     boolean isNullB;
/* 11305 */     UTF8String primitiveB;
/* 11306 */     {
/* 11307 */
/* 11308 */       Object obj = ((Expression) references[0]).eval(null);
/* 11309 */       UTF8String value = (UTF8String) obj;
/* 11310 */       isNullB = false;
/* 11311 */       primitiveB = value;
/* 11312 */     }
/* 11313 */     if (isNullA && isNullB) {
/* 11314 */       // Nothing
/* 11315 */     } else if (isNullA) {
/* 11316 */       return -1;
/* 11317 */     } else if (isNullB) {
/* 11318 */       return 1;
/* 11319 */     } else {
/* 11320 */       int comp = primitiveA.compare(primitiveB);
/* 11321 */       if (comp != 0) {
/* 11322 */         return comp;
/* 11323 */       }
/* 11324 */     }
/* 11325 */
/* 11326 */
/* 11327 */     i = a;
/* 11328 */     boolean isNullA1;
/* 11329 */     UTF8String primitiveA1;
/* 11330 */     {
/* 11331 */
/* 11332 */       Object obj1 = ((Expression) references[1]).eval(null);
/* 11333 */       UTF8String value1 = (UTF8String) obj1;
/* 11334 */       isNullA1 = false;
/* 11335 */       primitiveA1 = value1;
/* 11336 */     }
/* 11337 */     i = b;
/* 11338 */     boolean isNullB1;
/* 11339 */     UTF8String primitiveB1;
/* 11340 */     {
/* 11341 */
/* 11342 */       Object obj1 = ((Expression) references[1]).eval(null);
/* 11343 */       UTF8String value1 = (UTF8String) obj1;
/* 11344 */       isNullB1 = false;
/* 11345 */       primitiveB1 = value1;
/* 11346 */     }
/* 11347 */     if (isNullA1 && isNullB1) {
/* 11348 */       // Nothing
/* 11349 */     } else if (isNullA1) {
/* 11350 */       return -1;
/* 11351 */     } else if (isNullB1) {
/* 11352 */       return 1;
/* 11353 */     } else {
/* 11354 */       int comp = primitiveA1.compare(primitiveB1);
/* 11355 */       if (comp != 0) {
/* 11356 */         return comp;
/* 11357 */       }
/* 11358 */     }
/* 1.... */
/* 1.... */   ...
/* 1.... */
/* 12652 */     return 0;
/* 12653 */   }
/* 1.... */
/* 1.... */   ...
/* 15387 */
/* 15388 */   public int compare(InternalRow a, InternalRow b) {
/* 15389 */
/* 15390 */     int comp_0 = compare_0(a, b);
/* 15391 */     if (comp_0 != 0) {
/* 15392 */       return comp_0;
/* 15393 */     }
/* 15394 */
/* 15395 */     int comp_1 = compare_1(a, b);
/* 15396 */     if (comp_1 != 0) {
/* 15397 */       return comp_1;
/* 15398 */     }
/* 1.... */
/* 1.... */     ...
/* 1.... */
/* 15450 */     return 0;
/* 15451 */   }
/* 15452 */ }
```
## How was this patch tested?
- a new added test case which
  - would fail prior to this patch
  - would pass with this patch
- ordering correctness should already be covered by existing tests like those in `OrderingSuite`

## Acknowledgement

A major part of this PR - the refactoring work of `splitExpression()` - has been done by ueshin.

Author: Liwei Lin <lwlin7@gmail.com>
Author: Takuya UESHIN <ueshin@happy-camper.st>
Author: Takuya Ueshin <ueshin@happy-camper.st>

Closes #15480 from lw-lin/spec-ordering-64k-.

(cherry picked from commit acfc5f35)
Signed-off-by: default avatarWenchen Fan <wenchen@databricks.com>
65c866ef
History

Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, 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. 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.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see http://spark.apache.org/developer-tools.html.

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" 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.

Configuration

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

## Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.