Introduction to Apache Spark: Simplifying Big Data Analytics

Explore the advantages of Apache Spark over traditional systems like MapReduce for big data analytics. Learn about Resilient Distributed Datasets (RDDs), fault tolerance, and efficient data processing on commodity clusters through coarse-grained transformations. Discover how Spark simplifies batch processing and enables iterative and interactive applications.

• Apache Spark
• Big Data Analytics
• RDDs
• Fault Tolerance
• Data Processing

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1. Apache Spark Lecture by: Faria Kalim (lead TA) CS425 Fall 2019 UIUC (See Video)

2. Why Spark? Another system for big data analytics Isn t MapReduce good enough? Simplifies batch processing on large commodity clusters

3. Expensive save to disk for fault tolerance

4. Why Spark? MapReduce can be expensive for some applications e.g., Iterative Interactive Lacks efficient data sharing Specialized frameworks did evolve for different programming models Bulk Synchronous Processing (Pregel) Iterative MapReduce (Haloop) .

5. Solution: Resilient Distributed Datasets (RDDs) RDDs Immutable, partitioned collection of records Built through coarse grained transformations (map, join ) Can be cached for efficient reuse

6. RDD RDD RDD Read HDFS Cache Read Reduce Map

7. Solution: Resilient Distributed Datasets (RDDs) RDDs Immutable, partitioned collection of records Built through coarse grained, ordered transformations (map, join ) Fault Recovery? Lineage! Log the coarse grained operation applied to a partitioned dataset Simply recompute the lost partition if failure occurs! No cost if no failure

8. RDD RDD RDD Read HDFS Cache Read Map Reduce Read Reduce Map Lineage HDFS RDD RDD RDD

9. RDD RDD RDD Read HDFS Cache RDDs track the graph of transformations that built them (their lineage) to rebuild lost data Read Map Reduce Read Reduce Map Lineage HDFS RDD RDD RDD

10. What can you do with Spark? RDD operations Transformations e.g., filter, join, map, group-by Actions e.g., count, print Control Partitioning Persistence

11. Partitioning PageRank Links Ranks (url, ranks) Joins take place repeatedly (url, neighbors) Good partitioning reduces shuffles Contributions Ranks (url, ranks) Contributions

12. Generality RDDs allow unification of different programming models Stream Processing Graph Processing Machine Learning ..

13. Gather-Apply-Scatter on GraphX A B Vertices Neighbors B A A B A C A C B C D C B C D C D C Graph Represented In a Table Triplets

14. Gather-Apply-Scatter on GraphX A B B A A C B C D C D C Group-By A Gather at A

15. Gather-Apply-Scatter on GraphX A B B A A C B D C C D C Apply Map

16. Gather-Apply-Scatter on GraphX A B B C A B B A A C C B D C B C A C C D D C Scatter Triplets Join

17. Summary RDDs provide a simple and efficient programming model Generalized to a broad set of applications Leverages coarse-grained nature of parallel algorithms for failure recovery