CODEWITHSUNDEEP
apache-sparkspark-csv
Makrushin Evgenii
Makrushin Evgenii

Reputation: 1211

Is there an explanation when spark-csv won't save a DataFrame to file?

dataFrame.coalesce(1).write().save("path") sometimes writes only _SUCCESS and ._SUCCESS.crc files without an expected *.csv.gz even on non-empty input DataFrame

file save code:

private static void writeCsvToDirectory(Dataset<Row> dataFrame, Path directory) {
    dataFrame.coalesce(1)
            .write()
            .format("csv")
            .option("header", "true")
            .option("delimiter", "\t")
            .option("codec", "org.apache.hadoop.io.compress.GzipCodec")
            .mode(SaveMode.Overwrite)
            .save("file:///" + directory);
}

file get code:

static Path getTemporaryCsvFile(Path directory) throws IOException {
    String glob = "*.csv.gz";
    try (DirectoryStream<Path> stream = Files.newDirectoryStream(directory, glob)) {
        return stream.iterator().next();
    } catch (NoSuchElementException e) {
        throw new RuntimeException(getNoSuchElementExceptionMessage(directory, glob), e);
    }
}

file get error example:

java.lang.RuntimeException: directory /tmp/temp5889805853850415940 does not contain a file with glob *.csv.gz. Directory listing:
    /tmp/temp5889805853850415940/_SUCCESS, 
    /tmp/temp5889805853850415940/._SUCCESS.crc

I rely on this expectation, can someone explain me why it work this way?

Upvotes: 6

Views: 4511

Answers (3)

Hamid
Hamid

Reputation: 231

It is depend on your storage that you choose to write your csv file. if you write on hdfs everything's ok. but whenever you decide to write in your local files system you must care that nothing will be written in driver local files system and your data will be in worker's files system and you should find them in worker's storage.

two solution's:

  1. Run Spark in Local Mode

set mater local[NUMBER_OF_CORES] that you can submit your job by --master local[10] config

  1. Write In Distributed File System

write your data in distributed file system like s3,hdfs,...

Upvotes: 3

Makrushin Evgenii
Makrushin Evgenii

Reputation: 1211

My own solution solved this problem.

I replace .save("file://" with hadoopFileSystem.copyToLocalFile

The thing is .save("file:// works expectedly only with SparkSession.builder().master("local"), where hdfs:// is emulated by master's file://.

I may be wrong in theory, but it works.

static Path writeCsvToTemporaryDirectory(Dataset<Row> dataFrame) throws IOException {
    String temporaryDirectoryName = getTemporaryDirectoryName();

    writeCsvToDirectory(dataFrame, temporaryDirectoryName, sparkContext);

    return Paths.get(temporaryDirectoryName);
}

static void writeCsvToDirectory(Dataset<Row> dataFrame, String directory) throws IOException {
    dataFrame.coalesce(1)
        .write()
        .option("header", "true")
        .option("delimiter", "\t")
        .option("codec", "org.apache.hadoop.io.compress.GzipCodec")
        .mode(SaveMode.Overwrite)
        .csv(directory);

    FileSystem hadoopFileSystem = FileSystem.get(sparkContext.hadoopConfiguration());

    hadoopFileSystem.copyToLocalFile(true,
        new org.apache.hadoop.fs.Path(directory),
        new org.apache.hadoop.fs.Path(directory));
}

static Path getTemporaryCsvFile(Path directory) throws IOException {
    String glob = "*.csv.gz";

    try (DirectoryStream<Path> stream = Files.newDirectoryStream(directory, glob)) {
        return stream.iterator().next();
    } catch (NoSuchElementException e) {
        throw new RuntimeException(getNoSuchElementExceptionMessage(directory, glob), e);
    }
}

Path temporaryDirectory = writeCsvToTemporaryDirectory(dataFrame);
Path temporaryFile = DataFrameIOUtils.getTemporaryCsvFile(temporaryDirectory);

try {
    return otherStorage.upload(temporaryFile, name, fields).join();
} catch (InterruptedException | ExecutionException e) {
    throw new RuntimeException(e);
} finally {
    removeTemporaryDirectory(temporaryDirectory);
}

Upvotes: 1

Artem Vovsia
Artem Vovsia

Reputation: 1570

Output file should (must by logic) contain at least the header line and some data lines. But he does not exist at all

This comment was a bit misleading. According to the code on Github, this will happen only if the Dataframe is empty, and won't produce SUCCESS files. Considering that those files are present - Dataframe is not empty and the writeCsvToDirectory from your code is triggered.

I have a couple of questions:

  • Does your Spark job finish without errors?
  • Does the timestamp of SUCCESS file gets updated?

My two main suspects are:

  1. coalesce(1) - if you have a lot of data, this might fail
  2. SaveMode.Overwrite - I have a feeling that those SUCCESS files are in that folder from previous runs

Upvotes: 5

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