Spark Create DataFrame with Examples

In Spark, createDataFrame() and toDF() methods are used to create a DataFrame manually, using these methods you can create a Spark DataFrame from already existing RDD, DataFrame, Dataset, List, Seq data objects, here I will examplain these with Scala examples.

You can also create a DataFrame from different sources like Text, CSV, JSON, XML, Parquet, Avro, ORC, Binary files, RDBMS Tables, Hive, HBase, and many more.

DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R/Python, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing RDDs.

-Databricks

• Spark Create DataFrame from RDD
• Create DataFrame from List and Seq collection
• Creating Spark DataFrame from CSV file
• Creating from TXT file
• Creating from JSON file
• Creating from an XML file
• Creating from HIVE
• Creating from RDBMS Database table
• Creating from HBase table
• Other sources (Avro, Parquet e.t.c)

First, let’s import spark implicits as it needed for our examples ( for example when we want to use .toDF() function) and create the sample data.

val spark:SparkSession = SparkSession.builder()
   .master("local[1]").appName("SparkByExamples.com")
   .getOrCreate()

import spark.implicits._
val columns = Seq("language","users_count")
val data = Seq(("Java", "20000"), ("Python", "100000"), ("Scala", "3000"))

1. Spark Create DataFrame from RDD

One easy way to create Spark DataFrame manually is from an existing RDD. first, let’s create an RDD from a collection Seq by calling parallelize().

I will be using this rdd object for all our examples below.

val rdd = spark.sparkContext.parallelize(data)

1.1 Using toDF() function

Once we have an RDD, let’s use toDF() to create DataFrame in Spark. By default, it creates column names as “_1” and “_2” as we have two columns for each row.

val dfFromRDD1 = rdd.toDF()
dfFromRDD1.printSchema()

Since RDD is schema-less without column names and data type, converting from RDD to DataFrame gives you default column names as _1, _2 and so on and data type as String. Use DataFrame printSchema() to print the schema to console.

root
 |-- _1: string (nullable = true)
 |-- _2: string (nullable = true)

toDF() has another signature to assign a column name, this takes a variable number of arguments for column names as shown below.

val dfFromRDD1 = rdd.toDF("language","users_count")
dfFromRDD1.printSchema()

yields below output. Remember here we just assigned column names still it takes all data types as Strings.

root
 |-- language: string (nullable = true)
 |-- users: string (nullable = true)

By default, the datatype of these columns assigns to String. We can change this behavior by supplying schema – where we can specify a column name, data type and nullable for each field/column.

1.2 Using Spark createDataFrame() from SparkSession

Using createDataFrame() from SparkSession is another way to create and it takes rdd object as an argument. and chain with toDF() to specify names to the columns.

val dfFromRDD2 = spark.createDataFrame(rdd).toDF(columns:_*)

1.3 Using createDataFrame() with the Row type

createDataFrame() has another signature which takes the RDD[Row] type and schema for column names as arguments. To use this first we need to convert our “rdd” object from RDD[T] to RDD[Row] and define a schema using StructType & StructField.

import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.sql.Row
val schema = StructType( Array(
                 StructField("language", StringType,true),
                 StructField("users", StringType,true)
             ))
val rowRDD = rdd.map(attributes => Row(attributes._1, attributes._2))
val dfFromRDD3 = spark.createDataFrame(rowRDD,schema)

2. Create Spark DataFrame from List and Seq Collection

In this section, we will see several approaches to create Spark DataFrame from collection Seq[T] or List[T]. These examples would be similar to what we have seen in the above section with RDD, but we use “data” object instead of “rdd” object.

2.1 Using toDF() on List or Seq collection

toDF() on collection (Seq, List) object creates a DataFrame. make sure importing import spark.implicits._ to use toDF()

import spark.implicits._
val dfFromData1 = data.toDF() 

2.2 Using createDataFrame() from SparkSession

Calling createDataFrame() from SparkSession is another way to create and it takes collection object (Seq or List) as an argument. and chain with toDF() to specify names to the columns.

//From Data (USING createDataFrame)
var dfFromData2 = spark.createDataFrame(data).toDF(columns:_*)

2.3 Using createDataFrame() with the Row type

createDataFrame() has another signature in Spark which takes the util.List of Row type and schema for column names as arguments. To use this first we need to import scala.collection.JavaConversions._

import scala.collection.JavaConversions._
//From Data (USING createDataFrame and Adding schema using StructType)
val rowData= Seq(Row("Java", "20000"), 
               Row("Python", "100000"), 
               Row("Scala", "3000"))
var dfFromData3 = spark.createDataFrame(rowData,schema)

3. Create Spark DataFrame from CSV

In all the above examples, you have learned Spark to create DataFrame from RDD and data collection objects. In real-time these are less used, In this and following sections, you will learn how to create DataFrame from data sources like CSV, text, JSON, Avro e.t.c

Spark by default provides an API to read a delimiter files like comma, pipe, tab separated files and it also provides several options on handling with header, with out header, double quotes, data types e.t.c.

For detailed example, refer to create DataFrame from a CSV file.

val df2 = spark.read.csv("/src/resources/file.csv")

4. Creating from text (TXT) file

Here, will see how to create from a TXT file.

val df2 = spark.read
.text("/src/resources/file.txt")

5. Creating from JSON file

Here, will see how to create from a JSON file.

val df2 = spark.read
.json("/src/resources/file.json")

6. Creating from an XML file

To create DataFrame by parse XML, we should use DataSource "com.databricks.spark.xml" spark-xml api from Databricks.

<dependency>
     <groupId>com.databricks</groupId>
     <artifactId>spark-xml_2.11</artifactId>
     <version>0.6.0</version>
 </dependency>

val df = spark.read
      .format("com.databricks.spark.xml")
      .option("rowTag", "person")
      .xml("src/main/resources/persons.xml")

7. Creating from Hive

val hiveContext = new org.apache.spark.sql.hive.HiveContext(spark.sparkContext)
val hiveDF = hiveContext.sql(“select * from emp”)

8. Spark Create DataFrame from RDBMS Database

8.a) From Mysql table

Make sure you have MySQL library as a dependency in your pom.xml file or MySQL jars in your classpath.

val df_mysql = spark.read.format(“jdbc”)
   .option(“url”, “jdbc:mysql://localhost:port/db”)
   .option(“driver”, “com.mysql.jdbc.Driver”)
   .option(“dbtable”, “tablename”) 
   .option(“user”, “user”) 
   .option(“password”, “password”) 
   .load()

8.1 From DB2 table

Make sure you have DB2 library as a dependency in your pom.xml file or DB2 jars in your classpath.

val df_db2 = spark.read.format(“jdbc”)
   .option(“url”, “jdbc:db2://localhost:50000/dbname”)
   .option(“driver”, “com.ibm.db2.jcc.DB2Driver”)
   .option(“dbtable”, “tablename”) 
   .option(“user”, “user”) 
   .option(“password”, “password”) 
   .load()

Similarly, we can create DataFrame in Spark from most of the relational databases which I’ve not covered here and I will leave this to you to explore.

9. Create DataFrame from HBase table

To create Spark DataFrame from the HBase table, we should use DataSource defined in Spark HBase connectors. for example use DataSource “org.apache.spark.sql.execution.datasources.hbase” from Hortonworks or use “org.apache.hadoop.hbase.spark” from spark HBase connector.

    val hbaseDF = sparkSession.read
      .options(Map(HBaseTableCatalog.tableCatalog -> catalog))
      .format("org.apache.spark.sql.execution.datasources.hbase")
      .load()

Detail example explained at Generating DataFrame from HBase table

10. Other sources (Avro, Parquet, Kafka)

We can also create DataFrame from Avro, Parquet, HBase and reading data from Kafka which I’ve explained in the below articles, I would recommend reading these when you have time.

• Creating DataFrame from Parquet source
• Creating DataFrame from Avro source
• Creating DataFrame by Streaming data from Kafka

The complete code can be downloaded from GitHub

Happy Learning !!

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