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[SPARK-19959][SQL] Fix to throw NullPointerException in df[java.lang.Long].collect

Kazuaki Ishizaki authored 8 years ago

## What changes were proposed in this pull request?

This PR fixes `NullPointerException` in the generated code by Catalyst. When we run the following code, we get the following `NullPointerException`. This is because there is no null checks for `inputadapter_value`  while `java.lang.Long inputadapter_value` at Line 30 may have `null`.

This happen when a type of DataFrame is nullable primitive type such as `java.lang.Long` and the wholestage codegen is used. While the physical plan keeps `nullable=true` in `input[0, java.lang.Long, true].longValue`, `BoundReference.doGenCode` ignores `nullable=true`. Thus, nullcheck code will not be generated and `NullPointerException` will occur.

This PR checks the nullability and correctly generates nullcheck if needed.
```java
sparkContext.parallelize(Seq[java.lang.Long](0L, null, 2L), 1).toDF.collect
```

```java
Caused by: java.lang.NullPointerException
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(generated.java:37)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:393)
...
```

Generated code without this PR
```java
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */   private Object[] references;
/* 007 */   private scala.collection.Iterator[] inputs;
/* 008 */   private scala.collection.Iterator inputadapter_input;
/* 009 */   private UnsafeRow serializefromobject_result;
/* 010 */   private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder;
/* 011 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter;
/* 012 */
/* 013 */   public GeneratedIterator(Object[] references) {
/* 014 */     this.references = references;
/* 015 */   }
/* 016 */
/* 017 */   public void init(int index, scala.collection.Iterator[] inputs) {
/* 018 */     partitionIndex = index;
/* 019 */     this.inputs = inputs;
/* 020 */     inputadapter_input = inputs[0];
/* 021 */     serializefromobject_result = new UnsafeRow(1);
/* 022 */     this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0);
/* 023 */     this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1);
/* 024 */
/* 025 */   }
/* 026 */
/* 027 */   protected void processNext() throws java.io.IOException {
/* 028 */     while (inputadapter_input.hasNext() && !stopEarly()) {
/* 029 */       InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 030 */       java.lang.Long inputadapter_value = (java.lang.Long)inputadapter_row.get(0, null);
/* 031 */
/* 032 */       boolean serializefromobject_isNull = true;
/* 033 */       long serializefromobject_value = -1L;
/* 034 */       if (!false) {
/* 035 */         serializefromobject_isNull = false;
/* 036 */         if (!serializefromobject_isNull) {
/* 037 */           serializefromobject_value = inputadapter_value.longValue();
/* 038 */         }
/* 039 */
/* 040 */       }
/* 041 */       serializefromobject_rowWriter.zeroOutNullBytes();
/* 042 */
/* 043 */       if (serializefromobject_isNull) {
/* 044 */         serializefromobject_rowWriter.setNullAt(0);
/* 045 */       } else {
/* 046 */         serializefromobject_rowWriter.write(0, serializefromobject_value);
/* 047 */       }
/* 048 */       append(serializefromobject_result);
/* 049 */       if (shouldStop()) return;
/* 050 */     }
/* 051 */   }
/* 052 */ }
```

Generated code with this PR

```java
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */   private Object[] references;
/* 007 */   private scala.collection.Iterator[] inputs;
/* 008 */   private scala.collection.Iterator inputadapter_input;
/* 009 */   private UnsafeRow serializefromobject_result;
/* 010 */   private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder;
/* 011 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter;
/* 012 */
/* 013 */   public GeneratedIterator(Object[] references) {
/* 014 */     this.references = references;
/* 015 */   }
/* 016 */
/* 017 */   public void init(int index, scala.collection.Iterator[] inputs) {
/* 018 */     partitionIndex = index;
/* 019 */     this.inputs = inputs;
/* 020 */     inputadapter_input = inputs[0];
/* 021 */     serializefromobject_result = new UnsafeRow(1);
/* 022 */     this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0);
/* 023 */     this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1);
/* 024 */
/* 025 */   }
/* 026 */
/* 027 */   protected void processNext() throws java.io.IOException {
/* 028 */     while (inputadapter_input.hasNext() && !stopEarly()) {
/* 029 */       InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 030 */       boolean inputadapter_isNull = inputadapter_row.isNullAt(0);
/* 031 */       java.lang.Long inputadapter_value = inputadapter_isNull ? null : ((java.lang.Long)inputadapter_row.get(0, null));
/* 032 */
/* 033 */       boolean serializefromobject_isNull = true;
/* 034 */       long serializefromobject_value = -1L;
/* 035 */       if (!inputadapter_isNull) {
/* 036 */         serializefromobject_isNull = false;
/* 037 */         if (!serializefromobject_isNull) {
/* 038 */           serializefromobject_value = inputadapter_value.longValue();
/* 039 */         }
/* 040 */
/* 041 */       }
/* 042 */       serializefromobject_rowWriter.zeroOutNullBytes();
/* 043 */
/* 044 */       if (serializefromobject_isNull) {
/* 045 */         serializefromobject_rowWriter.setNullAt(0);
/* 046 */       } else {
/* 047 */         serializefromobject_rowWriter.write(0, serializefromobject_value);
/* 048 */       }
/* 049 */       append(serializefromobject_result);
/* 050 */       if (shouldStop()) return;
/* 051 */     }
/* 052 */   }
/* 053 */ }
```

## How was this patch tested?

Added new test suites in `DataFrameSuites`

Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>

Closes #17302 from kiszk/SPARK-19959.

(cherry picked from commit bb823ca4)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

92f0b012

92f0b012 8 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, 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.