XML data source for Spark SQL and DataFrames
Scala Python Shell Java
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BioQwer and HyukjinKwon Add support for values with self-closing tags
Signed-off-by: Anton Alexandrov <afalexandrovluxoft.com>

Author: Antony Alexandrov <af.alexsandrov@gmail.com>

Closes #305 from HyukjinKwon/self-closing-1.
Latest commit f3772b2 May 15, 2018

README.md

XML Data Source for Apache Spark

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  • A library for parsing and querying XML data with Apache Spark, for Spark SQL and DataFrames. The structure and test tools are mostly copied from CSV Data Source for Spark.

  • This package supports to process format-free XML files in a distributed way, unlike JSON datasource in Spark restricts in-line JSON format.

Requirements

This library requires Spark 2.0+ for 0.4.x.

For version that works with Spark 1.x, please check for branch-0.3.

Linking

You can link against this library in your program at the following coordinates:

Scala 2.10

groupId: com.databricks
artifactId: spark-xml_2.10
version: 0.4.1

Scala 2.11

groupId: com.databricks
artifactId: spark-xml_2.11
version: 0.4.1

Using with Spark shell

This package can be added to Spark using the --packages command line option. For example, to include it when starting the spark shell:

Spark compiled with Scala 2.10

$SPARK_HOME/bin/spark-shell --packages com.databricks:spark-xml_2.10:0.4.1

Spark compiled with Scala 2.11

$SPARK_HOME/bin/spark-shell --packages com.databricks:spark-xml_2.11:0.4.1

Features

This package allows reading XML files in local or distributed filesystem as Spark DataFrames. When reading files the API accepts several options:

  • path: Location of files. Similar to Spark can accept standard Hadoop globbing expressions.
  • rowTag: The row tag of your xml files to treat as a row. For example, in this xml <books> <book><book> ...</books>, the appropriate value would be book. Default is ROW. At the moment, rows containing self closing xml tags are not supported.
  • samplingRatio: Sampling ratio for inferring schema (0.0 ~ 1). Default is 1. Possible types are StructType, ArrayType, StringType, LongType, DoubleType, BooleanType, TimestampType and NullType, unless user provides a schema for this.
  • excludeAttribute : Whether you want to exclude attributes in elements or not. Default is false.
  • treatEmptyValuesAsNulls : (DEPRECATED: use nullValue set to "") Whether you want to treat whitespaces as a null value. Default is false
  • mode: The mode for dealing with corrupt records during parsing. Default is PERMISSIVE.
    • PERMISSIVE : sets other fields to null when it meets a corrupted record, and puts the malformed string into a new field configured by columnNameOfCorruptRecord. When a schema is set by user, it sets null for extra fields.
    • DROPMALFORMED : ignores the whole corrupted records.
    • FAILFAST : throws an exception when it meets corrupted records.
  • columnNameOfCorruptRecord: The name of new field where malformed strings are stored. Default is _corrupt_record.
  • attributePrefix: The prefix for attributes so that we can differentiate attributes and elements. This will be the prefix for field names. Default is _.
  • valueTag: The tag used for the value when there are attributes in the element having no child. Default is _VALUE.
  • charset: Defaults to 'UTF-8' but can be set to other valid charset names
  • ignoreSurroundingSpaces: Defines whether or not surrounding whitespaces from values being read should be skipped. Default is false.

When writing files the API accepts several options:

  • path: Location to write files.
  • rowTag: The row tag of your xml files to treat as a row. For example, in this xml <books> <book><book> ...</books>, the appropriate value would be book. Default is ROW.
  • rootTag: The root tag of your xml files to treat as the root. For example, in this xml <books> <book><book> ...</books>, the appropriate value would be books. Default is ROWS.
  • nullValue: The value to write null value. Default is string null. When this is null, it does not write attributes and elements for fields.
  • attributePrefix: The prefix for attributes so that we can differentiating attributes and elements. This will be the prefix for field names. Default is _.
  • valueTag: The tag used for the value when there are attributes in the element having no child. Default is _VALUE.
  • compression: compression codec to use when saving to file. Should be the fully qualified name of a class implementing org.apache.hadoop.io.compress.CompressionCodec or one of case-insensitive shorten names (bzip2, gzip, lz4, and snappy). Defaults to no compression when a codec is not specified.

Currently it supports the shortened name usage. You can use just xml instead of com.databricks.spark.xml from Spark 1.5.0+

Structure Conversion

Due to the structure differences between DataFrame and XML, there are some conversion rules from XML data to DataFrame and from DataFrame to XML data. Note that handling attributes can be disabled with the option excludeAttribute.

Conversion from XML to DataFrame

  • Attributes: Attributes are converted as fields with the heading prefix, attributePrefix.

    ...
    <one myOneAttrib="AAAA">
        <two>two</two>
        <three>three</three>
    </one>
    ...

    produces a schema below:

    root
     |-- _myOneAttrib: string (nullable = true)
     |-- two: string (nullable = true)
     |-- three: string (nullable = true)
    
  • Value in an element that has no child elements but attributes: The value is put in a separate field, valueTag.

    ...
    <one>
        <two myTwoAttrib="BBBBB">two</two>
        <three>three</three>
    </one>
    ...

    produces a schema below:

    root
     |-- two: struct (nullable = true)
     |    |-- _VALUE: string (nullable = true)
     |    |-- _myTwoAttrib: string (nullable = true)
     |-- three: string (nullable = true)
    

Conversion from DataFrame to XML

  • Element as an array in an array: Writing a XML file from DataFrame having a field ArrayType with its element as ArrayType would have an additional nested field for the element. This would not happen in reading and writing XML data but writing a DataFrame read from other sources. Therefore, roundtrip in reading and writing XML files has the same structure but writing a DataFrame read from other sources is possible to have a different structure.

    DataFrame with a schema below:

     |-- a: array (nullable = true)
     |    |-- element: array (containsNull = true)
     |    |    |-- element: string (containsNull = true)
    

    with data below:

    +------------------------------------+
    |                                   a|
    +------------------------------------+
    |[WrappedArray(aa), WrappedArray(bb)]|
    +------------------------------------+
    

    produces a XML file below:

    ...
    <a>
        <item>aa</item>
    </a>
    <a>
        <item>bb</item>
    </a>
    ...

Examples

These examples use a XML file available for download here:

$ wget https://github.com/databricks/spark-xml/raw/master/src/test/resources/books.xml

SQL API

XML data source for Spark can infer data types:

CREATE TABLE books
USING com.databricks.spark.xml
OPTIONS (path "books.xml", rowTag "book")

You can also specify column names and types in DDL. In this case, we do not infer schema.

CREATE TABLE books (author string, description string, genre string, _id string, price double, publish_date string, title string)
USING com.databricks.spark.xml
OPTIONS (path "books.xml", rowTag "book")

Scala API

import org.apache.spark.sql.SQLContext
import com.databricks.spark.xml._

val sqlContext = new SQLContext(sc)
val df = sqlContext.read
  .option("rowTag", "book")
  .xml("books.xml")

val selectedData = df.select("author", "_id")
selectedData.write
  .option("rootTag", "books")
  .option("rowTag", "book")
  .xml("newbooks.xml")

Alternatively you can specify the format to use instead:

import org.apache.spark.sql.SQLContext

val sqlContext = new SQLContext(sc)
val df = sqlContext.read
  .format("com.databricks.spark.xml")
  .option("rowTag", "book")
  .load("books.xml")

val selectedData = df.select("author", "_id")
selectedData.write
  .format("com.databricks.spark.xml")
  .option("rootTag", "books")
  .option("rowTag", "book")
  .save("newbooks.xml")

You can manually specify the schema when reading data:

import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.types.{StructType, StructField, StringType, DoubleType};

val sqlContext = new SQLContext(sc)
val customSchema = StructType(Array(
  StructField("_id", StringType, nullable = true),
  StructField("author", StringType, nullable = true),
  StructField("description", StringType, nullable = true),
  StructField("genre", StringType ,nullable = true),
  StructField("price", DoubleType, nullable = true),
  StructField("publish_date", StringType, nullable = true),
  StructField("title", StringType, nullable = true)))


val df = sqlContext.read
  .format("com.databricks.spark.xml")
  .option("rowTag", "book")
  .schema(customSchema)
  .load("books.xml")

val selectedData = df.select("author", "_id")
selectedData.write
  .format("com.databricks.spark.xml")
  .option("rootTag", "books")
  .option("rowTag", "book")
  .save("newbooks.xml")

Java API

import org.apache.spark.sql.SQLContext

SQLContext sqlContext = new SQLContext(sc);
DataFrame df = sqlContext.read()
  .format("com.databricks.spark.xml")
  .option("rowTag", "book")
  .load("books.xml");

df.select("author", "_id").write()
  .format("com.databricks.spark.xml")
  .option("rootTag", "books")
  .option("rowTag", "book")
  .save("newbooks.xml");

You can manually specify schema:

import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.types.*;

SQLContext sqlContext = new SQLContext(sc);
StructType customSchema = new StructType(new StructField[] {
  new StructField("_id", DataTypes.StringType, true, Metadata.empty()),
  new StructField("author", DataTypes.StringType, true, Metadata.empty()),
  new StructField("description", DataTypes.StringType, true, Metadata.empty()),
  new StructField("genre", DataTypes.StringType, true, Metadata.empty()),
  new StructField("price", DataTypes.DoubleType, true, Metadata.empty()),
  new StructField("publish_date", DataTypes.StringType, true, Metadata.empty()),
  new StructField("title", DataTypes.StringType, true, Metadata.empty())
});

DataFrame df = sqlContext.read()
  .format("com.databricks.spark.xml")
  .option("rowTag", "book")
  .schema(customSchema)
  .load("books.xml");

df.select("author", "_id").write()
  .format("com.databricks.spark.xml")
  .option("rootTag", "books")
  .option("rowTag", "book")
  .save("newbooks.xml");

Python API

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

df = sqlContext.read.format('com.databricks.spark.xml').options(rowTag='book').load('books.xml')
df.select("author", "_id").write \
    .format('com.databricks.spark.xml') \
    .options(rowTag='book', rootTag='books') \
    .save('newbooks.xml')

You can manually specify schema:

from pyspark.sql import SQLContext
from pyspark.sql.types import *

sqlContext = SQLContext(sc)
customSchema = StructType([ \
    StructField("_id", StringType(), True), \
    StructField("author", StringType(), True), \
    StructField("description", StringType(), True), \
    StructField("genre", StringType(), True), \
    StructField("price", DoubleType(), True), \
    StructField("publish_date", StringType(), True), \
    StructField("title", StringType(), True)])

df = sqlContext.read \
    .format('com.databricks.spark.xml') \
    .options(rowTag='book') \
    .load('books.xml', schema = customSchema)

df.select("author", "_id").write \
    .format('com.databricks.spark.xml') \
    .options(rowTag='book', rootTag='books') \
    .save('newbooks.xml')

R API

Automatically infer schema (data types)

library(SparkR)

Sys.setenv('SPARKR_SUBMIT_ARGS'='"--packages" "com.databricks:spark-xml_2.10:0.4.1" "sparkr-shell"')
sqlContext <- sparkRSQL.init(sc)

df <- read.df(sqlContext, "books.xml", source = "com.databricks.spark.xml", rowTag = "book")

# In this case, `rootTag` is set to "ROWS" and `rowTag` is set to "ROW".
write.df(df, "newbooks.csv", "com.databricks.spark.xml", "overwrite")

You can manually specify schema:

library(SparkR)

Sys.setenv('SPARKR_SUBMIT_ARGS'='"--packages" "com.databricks:spark-csv_2.10:0.4.1" "sparkr-shell"')
sqlContext <- sparkRSQL.init(sc)
customSchema <- structType(
    structField("_id", "string"),
    structField("author", "string"),
    structField("description", "string"),
    structField("genre", "string"),
    structField("price", "double"),
    structField("publish_date", "string"),
    structField("title", "string"))

df <- read.df(sqlContext, "books.xml", source = "com.databricks.spark.xml", rowTag = "book")

# In this case, `rootTag` is set to "ROWS" and `rowTag` is set to "ROW".
write.df(df, "newbooks.csv", "com.databricks.spark.xml", "overwrite")

Hadoop InputFormat

The library contains a Hadoop input format for reading XML files by a start tag and an end tag. This is similar with XmlInputFormat.java in Mahout but supports to read compressed files, different encodings and read elements including attributes, which you may make direct use of as follows:

import com.databricks.spark.xml.XmlInputFormat

// This will detect the tags including attributes
sc.hadoopConfiguration.set(XmlInputFormat.START_TAG_KEY, "<book>")
sc.hadoopConfiguration.set(XmlInputFormat.END_TAG_KEY, "</book>")
sc.hadoopConfiguration.set(XmlInputFormat.ENCODING_KEY, "utf-8")

val records = sc.newAPIHadoopFile(
  path,
  classOf[XmlInputFormat],
  classOf[LongWritable],
  classOf[Text])

Building From Source

This library is built with SBT, which is automatically downloaded by the included shell script. To build a JAR file simply run sbt/sbt package from the project root. The build configuration includes support for both Scala 2.10 and 2.11.

Acknowledgements

This project was initially created by HyukjinKwon and donated to Databricks.