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[SPARK-19082][SQL] Make ignoreCorruptFiles work for Parquet

Liang-Chi Hsieh authored 8 years ago

## What changes were proposed in this pull request?

We have a config `spark.sql.files.ignoreCorruptFiles` which can be used to ignore corrupt files when reading files in SQL. Currently the `ignoreCorruptFiles` config has two issues and can't work for Parquet:

1. We only ignore corrupt files in `FileScanRDD` . Actually, we begin to read those files as early as inferring data schema from the files. For corrupt files, we can't read the schema and fail the program. A related issue reported at http://apache-spark-developers-list.1001551.n3.nabble.com/Skip-Corrupted-Parquet-blocks-footer-tc20418.html
2. In `FileScanRDD`, we assume that we only begin to read the files when starting to consume the iterator. However, it is possibly the files are read before that. In this case, `ignoreCorruptFiles` config doesn't work too.

This patch targets Parquet datasource. If this direction is ok, we can address the same issue for other datasources like Orc.

Two main changes in this patch:

1. Replace `ParquetFileReader.readAllFootersInParallel` by implementing the logic to read footers in multi-threaded manner

We can't ignore corrupt files if we use `ParquetFileReader.readAllFootersInParallel`. So this patch implements the logic to do the similar thing in `readParquetFootersInParallel`.

2. In `FileScanRDD`, we need to ignore corrupt file too when we call `readFunction` to return iterator.

One thing to notice is:

We read schema from Parquet file's footer. The method to read footer `ParquetFileReader.readFooter` throws `RuntimeException`, instead of `IOException`, if it can't successfully read the footer. Please check out https://github.com/apache/parquet-mr/blob/df9d8e415436292ae33e1ca0b8da256640de9710/parquet-hadoop/src/main/java/org/apache/parquet/hadoop/ParquetFileReader.java#L470. So this patch catches `RuntimeException`. One concern is that it might also shadow other runtime exceptions other than reading corrupt files.

## How was this patch tested?

Jenkins tests.

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #16474 from viirya/fix-ignorecorrupted-parquet-files.

61e48f52

61e48f52 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

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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 "Useful Developer Tools".

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()

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./bin/run-example SparkPi

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

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./dev/run-tests

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

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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.

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