Spark_Intro_Syed_Academy

Aug 1, 20172 likes80 views

Apache Spark is an open-source cluster computing framework originally developed at UC Berkeley in 2009. It is faster than Hadoop for interactive queries and stream processing due to its use of caching and RAM. Spark supports functional programming APIs in Java, Scala, Python and R. It provides functionality for SQL processing, streaming, machine learning and graph processing. RDDs (Resilient Distributed Datasets) are Spark's primary abstraction, acting as fault-tolerant collections of data partitioned across a cluster.

Apache Spark
Syed
Solutions Engineer - Big Data
mail.syed786@gmail.com
info.syedacademy@gmail.com
+91-9030477368

History
Developed in 2009 at UC Berkeley AMPLab.
● Open sourced in 2010.
● Spark becomes one of the largest big-data
projects with more 400 contributors in 50+ organizations such
as:
– Databricks, Yahoo!, Intel, Cloudera, IBM, …

• Fast and general cluster computing system
interoperable with Hadoop datasets.
What is Spark?

Where Does Big Data Come From?
It’s all happening online – could record every:
» Click
» Ad impression
» Billing event
» Fast Forward, pause,…
» Server request
» Transaction
» Network message
» Fault
» Facebook
» Instagram
» TripAdvisor
» Twitter
» YouTube
»…

Graph Data
Lots of interesting data has a graph structure:
• Social networks
• Telecommunication Networks
• Computer Networks
• Road networks
• Collaborations/Relationships
• …
Some of these graphs can get quite large
(e.g., Facebook user graph)
Log Files – Apache Web Server Log

Why Apache Spark?
General purpose cluster computing system
• Originally developed at UC Berkeley, now one of the
largest Apache projects
• Typically faster than Hadoop due to main-memory
processing
• High-level APIs in Java, Scala, Python and R
Functionality for:
• Map/Reduce
• SQL processing
• Real-time stream processing
• Machine learning
• Graph processing

Apache Spark EcoSystem
• Apache Spark
• RDDs
• Spark SQL
• Once known as Shark
before completely
integrated into Spark
• For SQL, structured and
semi-structured data
processing
• Spark Streaming
• Processing of live data
streams
• MLlib/ML
• Machine Learning
Algorithms
• GraphX
• Graph Processing

MapReduce vs Spark
PIG HIVE MAHOUT
(machine
learning)
MapReduce

Programming Models
• MapReduce – 50 lines of code
• Spark – 1 line of code

MapReduce Bottlenecks and Improvements
• Bottlenecks
• MapReduce is a very I/O heavy operation
• Map phase needs to read from disk then write back out
• Reduce phase needs to read from disk and then write
back out
• How can we improve it?
• RAM is becoming very cheap and abundant
• Use RAM for in-data sharing

MapReduce vs. Spark (Performance)
MapReduce Record Spark Record Spark Record 1PB
Data Size 102.5 TB 100 TB 1000 TB
# Nodes 2100 206 190
# Cores 50400 physical 6592 virtualized 6080 virtualized
Elapsed Time 72 mins 23 mins 234 mins
Sort rate 1.42 TB/min 4.27 TB/min 4.27 TB/min
Sort rate/node 0.67 GB/min 20.7 GB/min 22.5 GB/min

RDDs
• Primary abstraction object used by Apache Spark
• Resilient Distributed Dataset
• Fault-tolerant
• Collection of elements that can be operated on in parallel
• Distributed collection of data from any source
• Contained in an RDD:
• Set of dependencies on parent RDDs
• Lineage (Directed Acyclic Graph – DAG)
• Set of partitions
• Atomic pieces of a dataset
• A function for computing the RDD based on its parents
• Metadata about its partitioning scheme and data
placement
• RDDs are Immutable
• Allows for more effective fault tolerance
• Intended to support abstract datasets while also maintain
MapReduce properties like automatic fault tolerance,
locality-aware scheduling and scalability.

Spark Streaming
• Spark Streaming is an extension of the core Spark API that
enables scalable, high-throughput, fault-tolerant stream
processing of live data streams

Thank you!
www.syedacademy.com
mail.syed786@gmail.com
info.syedacademy@gmail.com
+91-9030477368

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Spark_Intro_Syed_Academy

1. Apache Spark Syed Solutions Engineer - Big Data mail.syed786@gmail.com info.syedacademy@gmail.com +91-9030477368

2. History Developed in 2009 at UC Berkeley AMPLab. ● Open sourced in 2010. ● Spark becomes one of the largest big-data projects with more 400 contributors in 50+ organizations such as: – Databricks, Yahoo!, Intel, Cloudera, IBM, …

3. • Fast and general cluster computing system interoperable with Hadoop datasets. What is Spark?

4. Where Does Big Data Come From? It’s all happening online – could record every: » Click » Ad impression » Billing event » Fast Forward, pause,… » Server request » Transaction » Network message » Fault » Facebook » Instagram » TripAdvisor » Twitter » YouTube »…

5. Graph Data Lots of interesting data has a graph structure: • Social networks • Telecommunication Networks • Computer Networks • Road networks • Collaborations/Relationships • … Some of these graphs can get quite large (e.g., Facebook user graph) Log Files – Apache Web Server Log

6. Why Apache Spark? General purpose cluster computing system • Originally developed at UC Berkeley, now one of the largest Apache projects • Typically faster than Hadoop due to main-memory processing • High-level APIs in Java, Scala, Python and R Functionality for: • Map/Reduce • SQL processing • Real-time stream processing • Machine learning • Graph processing

7. Apache Spark EcoSystem • Apache Spark • RDDs • Spark SQL • Once known as Shark before completely integrated into Spark • For SQL, structured and semi-structured data processing • Spark Streaming • Processing of live data streams • MLlib/ML • Machine Learning Algorithms • GraphX • Graph Processing

8. MapReduce vs Spark PIG HIVE MAHOUT (machine learning) MapReduce

9. Hadoop MapReduce

14. Programming Models • MapReduce – 50 lines of code • Spark – 1 line of code

18. MapReduce Bottlenecks and Improvements • Bottlenecks • MapReduce is a very I/O heavy operation • Map phase needs to read from disk then write back out • Reduce phase needs to read from disk and then write back out • How can we improve it? • RAM is becoming very cheap and abundant • Use RAM for in-data sharing

19. MapReduce vs. Spark (Performance) MapReduce Record Spark Record Spark Record 1PB Data Size 102.5 TB 100 TB 1000 TB # Nodes 2100 206 190 # Cores 50400 physical 6592 virtualized 6080 virtualized Elapsed Time 72 mins 23 mins 234 mins Sort rate 1.42 TB/min 4.27 TB/min 4.27 TB/min Sort rate/node 0.67 GB/min 20.7 GB/min 22.5 GB/min

21. Spark Architecture

22. RDDs • Primary abstraction object used by Apache Spark • Resilient Distributed Dataset • Fault-tolerant • Collection of elements that can be operated on in parallel • Distributed collection of data from any source • Contained in an RDD: • Set of dependencies on parent RDDs • Lineage (Directed Acyclic Graph – DAG) • Set of partitions • Atomic pieces of a dataset • A function for computing the RDD based on its parents • Metadata about its partitioning scheme and data placement • RDDs are Immutable • Allows for more effective fault tolerance • Intended to support abstract datasets while also maintain MapReduce properties like automatic fault tolerance, locality-aware scheduling and scalability.

23. SPARK SQL • DataFrames • DataSets

24. Spark Streaming • Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams

26. Thank you! www.syedacademy.com mail.syed786@gmail.com info.syedacademy@gmail.com +91-9030477368

Spark_Intro_Syed_Academy

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