1. Concept & Examples of pyspark

2. Apache Spark? Roughly speaking, Spark = RDD + Interface
Support RDD-based processing interface
This ppt is based on pyspark

3. Apache Spark? Roughly speaking, Spark = RDD + Interface Lazy Execution
Immutable
Lineage
Essential concepts

4. What is RDD? RDD(Resilient Distributed Dataset)
Resilient : If data in memory is lost, it can be recreated
Distributed : Stored in memory across the cluster
Dataset : Initial data can come from a file or be created programmatically
Fundamental data unit of data in Spark

5. Apache Spark? Roughly speaking, Spark = RDD + Interface
Lazy Execution
Immutable
Lineage
Essential concepts
RDD cannot be modified but newly generated

6. Apache Spark? Roughly speaking, Spark = RDD + Interface
Lazy Execution
Immutable
Lineage
Essential concepts
RDD processing doesn’t occur until action operation comes

7. Apache Spark? Roughly speaking, Spark = RDD + Interface
Lazy Execution
Immutable
Lineage
Essential concepts
Fault tolerant policy of Spark system(logging on lineage)

8. Creating RDD RDD can be created from
- A file or set of files(.txt, .json, .csv, object file etc.)
- Data in memory
- From another RDD

9. Creating RDD with pyspark
Initializing pyspark
Configured appName
Define configuration option and create a SparkContext

10. Creating RDD with pyspark
Create RDD with the text file “purplecow.txt” from local
Print out the lines of the number of lines of the “purplecow.txt”

11. RDD Operations Two types of RDD Operations Actions : return values
Transformations : Define a new RDD based on the current ones
[Actions]
[Transformations]

12. Action Operations with pyspark
Action operations : operations that return values.
count : return the number of elements
take(n) : return an array of the first n-elements

13. Transformations Operations with pyspark
Transformation opeations : create a new RDD from an existing one
map(function) : creates a new RDD by performing a function on each record in the base RDD
filter(function) : creates a new RDD by including or excluding each record in the base RDD according to a boolean function

14. Lazy Execution
Data in RDDs is not processed until an action is performed
RDD process is not performed
RDD process is performed

15. Lazy Execution
When using transformation funtions, there are no RDD process.
It creates the lineage log.
When using action functions, RDD process are done based on the lineage log.

16. Functional Programming in spark
Key Concepts
Passing functions as input to other functions
Anonymous functions
Passing functions example with pyspark
Define a function ‘toUpper’
Passing function as input to map function

17. Functional Programming in spark
Anonymous Functions
Functions defined in-line without an identifier
Supported in python and scala(ex. lambda x: …)

18. Working with RDDs RDDs can hold any type of element
Primitive types : integers, characters, booleans, etc.
Sequence types : strings, lists, arrays, tuples, dicts, etc.
Scala/Java objects(if serializable)
Mixed types
Some types of RDDs have additional functionality
Pair RDDs : RDDs consisting of key-value pairs
Double RDDs : RDDs consisting of numeric data

19. Some other general RDD operations
Transformations
flatMap : maps one element in the base RDD to multiple elements
distinct : filter out duplicates
 union : add all elements of two RDDs into a single new RDD
Other RDD operations
– first – return the first element of the RDD
– foreach – apply a function to each element in an RDD
– top(n) – return the largest n elements using natural ordering

20. flatMap and distinct Example of flatMap and distinct
[without distinct]
[with distinct]

21. Pair RDD Pair RDDs are a special form of RDD
Each element must be a key –value pair(a two element tuple)
Keys and values can be any type.
Commonly used functions to create pair RDDs
map
flatMap/flatMapValues
keyBy

22. Pair RDD Examples Examples creating example
Split by ‘\t’ and map the data into key-value pair RDD
[Sample.txt]

23. Example. Keying web log by User ID
Split by empty space and key by 3rd element

24. Mapping Single Rows to Multiple Pairs
Textfile format
Number and IDs are delimited by ‘\t’
Multiple IDs are delimited by ‘:’
[Original textfile]

25. Mapping Single Rows to Multiple Pairs
Split the text by ‘\t’ line by line
Create pair RDD
flatMapValues by delimiter ‘:’
Multiple pairs

26. MapReduce
- Mapping the textfile data

27. MapReduce
- Reducing phase

28. MapReduce with pyspark

29. Other Pair RDD Operations

30. Other Pair RDD Operations

31. Example. Join Web log with KB articles
Join the requested file which has a format ‘KBDOC-[0-9]* of weblogs with Article ID of kblist data

32. Example. Join Web log with KB articles
Mapping kblist.txt

33. Example. Join Web log with KB articles
Mapping ‘ log’

34. Example. Join Web log with KB articles
Joining two RDD by key