Friday, May 20, 2016

Overview of Spark DataFrame API


Spark DataFrames were introduced in early 2015, in Spark 1.3. Since then, a lot of new functionality has been added in Spark 1.4, 1.5, and 1.6. More than a year later, Spark's DataFrame API provides a rich set of operations for data munging, SQL queries, and analytics. This post will give an overview of all the major features of Spark's DataFrame API, focusing on the Scala API in 1.6.1.


  • Classes and Objects
  • Reading Data
  • Traditional Dataframe Operations
  • Lazy Eval and collect()
  • SQL (Relational) Operations
  • Data Munging
  • Analytics

Classes and Objects

Let us start by reviewing the major classes and objects in the DataFrame API. The main ones are SQLContext, DataFrame, Column, and functions.

SQLContext, DataFrame, and Column Classes

  • SQLContext is the main entry point for creating DataFrames.
  • DataFrame is the main class representing the DataFrame data and operations.
  • The Column class represents an individual column of a DataFrame.

Functions Object

The functions object contains functions for aggregation, math, and date/time and string manipulation that can be applied on DataFrame columns.

Reading Data into a DataFrame

JSON, Parquet, JDBC, Hive, CSV

DataFrames can read from a large number of source data formats, such as JSON, Parquet, JDBC, and Hive. See the DataFrameReader class for some of the natively supported formats and Spark Packages for packages available for other formats, such as CSV and many others.

Reading JSON Example 

Here is an example of reading JSON data into a DataFrame. The input file must contain one JSON object on each line:

val df ="/home/data.json")
df: org.apache.spark.sql.DataFrame = [col1: int, col2: int]

In the above example, sqlContext is of type SQLContext, its read() method returns a DataFrameReader, and the reader's json() method reads the specified data file.

Traditional Dataframe Operations

Spark DataFrames support traditional dataframe operations that you might expect from working with Pandas or R dataframes. You can select columns and rows, create new columns, and apply functions on column values.

Selecting Columns

To select one or more columns:
|col1| +----+ | 1| | 2|

Selecting Rows

To select rows based on a boolean filter:
> df.filter(df("col1") > 1)  
|col1|col2| +----+----+ | 2| 6|
In the above example, df("col1") is of type Column, and ">" is a method defined in the Column class. Alternatively, a column can be specified with the $"col1" syntax.

More methods in class Column: ===, !==, isNaN, isNull, isin, like, startsWith, endsWith
See also in class DataFrame: sample

Creating New Columns

To create a new column derived from existing ones, use the withColumn() method:
> df.withColumn("col3", df("col1") + df("col2")) 
|col1|col2|col3| +----+----+----+ | 1| 5| 6| | 2| 6| 8|

More methods in class Column%, *, -, /, bitwiseAND, bitwiseOR, cast, &&, ||

Math Functions on Columns

A number of math functions, defined in the functions object, such as sqrt(), can be applied to column values:
import org.apache.spark.sql.functions._
>"col1"), sqrt(df("col1")))
|col1| SQRT(col1)| +----+------------------+ | 1| 1.0| | 2|1.4142135623730951|

You can also define your only functions on columns, via UDFs. See the "UDF" section below. Here are some more predefined math functions (not comprehensive) in functions
cos, sin, tan, exp, log, pow, cbrt, hypot, toDegrees, toRadians, ceil, floor, round, rint, pmod, shiftLeft, shiftRight

Displaying Data and Schema

To display a DataFrame:
|col1|col2| +----+----+ | 1| 5| | 2| 6|

To display a DataFrame's column names and types:
> df.printSchema()
root |-- col1: integer (nullable = false) |-- col2: integer (nullable = false)

See also: headtake, count

Lazy Eval and collect()

DataFrames are evaluated lazily, which means that no computation takes place until you perform an action. Any non-action method will thus return immediately, in most cases. An action is any method that produces output that is not a DataFrame, such as displaying data on the console, converting data into Scala Arrays, or saving data into a file or database. 

To convert a DataFrame into an array, use the collect() method:
> df.collect()
res0: Array[org.apache.spark.sql.Row] = Array([1,5], [2,6])

To convert only the first n rows, use head or take.

SQL (Relational) Operations

DataFrames also support SQL (relational) operations, such as SELECT, WHERE, GROUP BY, Aggregate, and JOIN. You can also define UDFs (user-defined functions).


To do a SELECT with a WHERE clause:
>"col1", "col2")
    .where($"col1" === 1)
|col1|col2| +----+----+ | 1| 5|

Note that "===" is a Column method that tests for equality. More methods in class Column
>, <!==isNaNisNullisinlikestartsWithendsWith

GROUP BY, Aggregate

To do a GROUP BY and aggregation:
|col1|col2| +----+----+ | 1| 5| | 2| 6| | 2| 7|
import org.apache.spark.sql.functions._
> df1.groupBy("col1")
|col1|sum_col2| +----+--------+ | 1| 5| | 2| 13|

Note that the groupBy() method returns a GroupedData object, on which we call the agg() method to perform one or more aggregations. The sum() function is one of the aggregation functions defined in the functions object.

More aggregation functions in functions: approxCountDistinct, avg, corr, count, countDistinct, first, last, max, mean, min, skewness, stddev, sumDistinct, variance (and more)


To do a JOIN between two DataFrames:
| id| name| +---+-----+ | 1|Alice| | 2| Bob|
> people.join(df, people("id") === df("col1"))
| id| name|col1|col2| +---+-----+----+----+ | 1|Alice| 1| 5| | 2| Bob| 2| 6|

The above example shows an inner join; other join types, such as outer join, are also supported.

More SQL operations

See also in class DataFrame:
alias, as, cube, distinct, drop, dropDuplicates, intersect, limit, na, orderBy, repartition, rollup, selectExpr, sort, unionAll, withColumnRenamed


To define a UDF, use the udf function:
import org.apache.spark.sql.functions.udf
val myUdf = udf {(n: Int) => (n * 2) + 1}

You can then apply the UDF on one or more Columns:
>"col1"), myUdf(df("col1")))
|col1|UDF(col1)| +----+---------+ | 1| 3| | 2| 5|

Functions for Data Munging

There are a variety of functions to simplify data munging on date, timestamp, string, and nested data in DataFrames. These functions are defined in the functions object.

Dates and Timestamps

Here is an example of working with dates and timestamps. The date_add() function adds or subtracts days from a given date. The unix_timestamp() function returns a Unix timestamp corresponding to a timestamp string in a specified format:

import org.apache.spark.sql.functions._
> df6.withColumn("day_before", date_add(df6("date"), -1))
     .withColumn("unix_time", unix_timestamp(df6("date"), "yyyy-MM-dd"))

| date|day_before| unix_time| +----------+----------+----------+ |2016-01-01|2015-12-31|1451606400| |2016-09-05|2016-09-04|1473033600|

See also (not comprehensive) in functions: current_date, current_timestamp, date_sub, datediff, dayofmonth, dayofyear, from_unixtime, hour, last_day, minute, month, next_day, quarter, second, trunc, weekofyear, year


There are also a number of functions for working with strings. Here is one example, with the substring() function, which returns a substring given an input string, position, and length:

> df6.withColumn("month_day", substring(df6("date"), 6, 5))
| date|month_day|
+----------+---------+ |2016-01-01| 01-01| |2016-09-05| 09-05|

See also (not comprehensive) in functions: ascii, concat, decode, encode, format_number, format_string, length, levenshtein, lower, lpad, ltrim, regexp_extract, regexp_replace, repeat, rtrim, split, translate, trim, upper

Nested Data Structures

With certain data formats, such as JSON, it is common to have nested arrays and structs in the schema. The functions object includes functions for working with nested columns. For example, if a column is of type Array, such as "col2" below, you can use the explode() function to flatten the data inside that column:

> df8
|col1| col2| +----+--------+ | 1|[1a, 1b]| | 2| [2a]|

>"col1"), explode(df8("col2")).as("col2_flat"))
|col1|col2_flat| +----+---------+ | 1| 1a| | 1| 1b| | 2| 2a|

The new flattened column, "col2_flat", can now be manipulated as an ordinary top-level column. For more about nested array data, please see my post on the topic.

See also: 
array_contains, size, sort_array, struct, array in functions.


The DataFrame API includes functionality for analytics, namely, summary statistics, window functions, and pivot tables.

Summary Statistics

The describe() method computes summary statistics for numerical columns and is meant for exploratory data analysis:

> df.describe()
|summary| col1| col2| +-------+------------------+------------------+ | count| 2| 2| | mean| 1.5| 5.5| | stddev|0.7071067811865476|0.7071067811865476| | min| 1| 5| | max| 2| 6|

The stat() method returns a DataFrameStatFunctions object, which provides additional statistics functions such as:
corr, cov, crosstab, freqItems, sampleBy

Window Functions

Window functions allow you to perform calculations over a moving window of rows, and are the basis for calculating a moving average or cumulative sum. You can apply window functions on DataFrames by defining a WindowSpec:

import org.apache.spark.sql.expressions.Window
val wSpec2 = Window.partitionBy("name").orderBy("date").rowsBetween(-1, 1)

The above window spec for a moving average consists of three components: (1) partition by, (2) order by, and (3) a frame. To use this WindowSpec in a DataFrame, you would apply a window function or aggregation function, such as avg() over this WindowSpec:

> customers.withColumn("movingAvg", avg(customers("amountSpent")).over(wSpec2))
| name| date|amountSpent|movingAvg| +-----+----------+-----------+---------+ |Alice|2016-05-01| 50.0| 47.5| |Alice|2016-05-03| 45.0| 50.0| |Alice|2016-05-04| 55.0| 50.0| | Bob|2016-05-01| 25.0| 27.0| | Bob|2016-05-04| 29.0| 27.0| | Bob|2016-05-06| 27.0| 28.0|

For a list of all the possible window functions and aggregation functions, please see the functions object. For more examples of using window functions, please see my blog post on the topic.

For more window functions, see in functions: cume_dist, lag, lead, ntile, percent_rank, rank, row_number (and more)

Pivot Tables

You can create pivot tables with the pivot() method:
> df7.groupBy("col1").pivot("col2").avg("col3")
|col1| A| B| +----+----+----+ | 1|10.0|21.0| | 2|12.0|20.0|

In the above example, the DataFrame is grouped by col1, pivoted along col2, which contains the values "A" and "B", and computes the average of col3 in each group. The pivot() method is defined in the GroupedData class. For more information about pivoting, please see this Databricks article: Reshaping Data with Pivot in Apache Spark.


By now, you should have a good feel for what is possible with the Spark DataFrame Scala API. From data munging, to SQL, to analytics, this API provides a broad range of functionality for working with big data. For more information about DataFrames, see the Spark programming guide.

Saturday, May 14, 2016

Reading JSON Nested Array in Spark DataFrames

In a previous post on JSON data, I showed how to read nested JSON arrays with Spark DataFrames. Now that I am more familiar with the API, I can describe an easier way to access such data, using the explode() function. All of the example code is in Scala, on Spark 1.6.

Loading JSON data

Suppose you have a file with JSON data, with one JSON object per line:

{"name":"Michael", "schools":[{"sname":"stanford", "year":2010}, {"sname":"berkeley", "year":2012}]}
{"name":"Andy", "schools":[{"sname":"ucsb", "year":2011}]}

You can read it into a DataFrame with the SqlContext read() method:

>> val people ="people.json")
people: org.apache.spark.sql.DataFrame

+-------+--------------------+ | name| schools| +-------+--------------------+ |Michael|[[stanford,2010],...| | Andy| [[ucsb,2011]]| +-------+--------------------+

Notice that the second column "schools", is an Array type, and each element of the array is a Struct:

>> people.printSchema()
root |-- name: string (nullable = true) |-- schools: array (nullable = true) | |-- element: struct (containsNull = true) | | |-- sname: string (nullable = true) | | |-- year: long (nullable = true)

Nested Array of Struct

Flatten / Explode an Array

If your JSON object contains nested arrays of structs, how will you access the elements of an array? One way is by flattening it. For instance, in the example above, each JSON object contains a "schools" array. We can simply flatten "schools" with the explode() function.

>> import org.apache.spark.sql.functions._
val flattened =$"name", explode($"schools").as("schools_flat"))
flattened: org.apache.spark.sql.DataFrame

+-------+---------------+ | name| schools_flat| +-------+---------------+ |Michael|[stanford,2010]| |Michael|[berkeley,2012]| | Andy| [ucsb,2011]| +-------+---------------+

Now each school is on a separate row. The new column "schools_flat" is of type Struct.

Select into Struct

Now you can select, for instance, all the school names within each struct, by using the DataFrame select() method. The struct has two fields: "sname" and "year". We will select only the school name, "sname":

>> val schools ="name""schools_flat.sname")
schools: org.apache.spark.sql.DataFrame = [sname: string]

| name| sname| +-------+--------+ |Michael|stanford| |Michael|berkeley| | Andy| ucsb| +-------+--------+

There you have it! We have taken data that was nested as structs inside an array column and bubbled it up to a first-level column in a DataFrame. You can now manipulate that column with the standard DataFrame methods.


  1. The DataFrame API:
  2. The explode() function:$