Keynote – From MapReduce to Spark: An Ecosystem Evolves by Doug Cutting, Chief Architect, Cloudera

Mar 23, 20164 likes1,041 views

The document summarizes the evolution from MapReduce to Apache Spark for data processing. Some key points: - MapReduce provided breakthroughs like data locality, fault tolerance, and scalability but the programming model required developing generally scalable solutions. - Apache Spark provides a richer, more expressive API that allows developing applications with 2-5x less code than MapReduce. It also provides fast in-memory execution up to an order of magnitude faster than MapReduce. - A survey found 82% of developers replaced MapReduce with Spark for its speed and ability to handle large datasets faster than MapReduce. Spark is now an important part of the Hadoop ecosystem.

MapReduce to Apache Spark:
An Ecosystem Evolves
Doug Cutting (@cutting)
Chief Architect & Co-founder of Apache Hadoop

Hadoop’s Original Architecture
MapReduce
Data Processing and Resource Management
HDFS
Filesystem/Storage

The MapReduce Breakthrough
Key advances in MapReduce:
• Data locality: Automatic split computation and appropriate launch of mappers
• Fault-tolerance: Write-out of intermediate results and restartable mappers provides ability to run on commodity
hardware
• Linear scalability: Combination of locality + programming model forces developers to write generally scalable
solutions
Map Map Map Map Map Map Map Map Map Map Map Map
Reduce Reduce Reduce Reduce

Apache Spark: A Better MapReduce
Easy, Expressive API
• Rich API (Java, Scala, and Python)
• Interactive shell
• 2-5x less code needed than MR
Fast Execution
• General execution graphs
• In-memory storage
• Order-of-magnitude improvement
over MR

Big Data Developers are Rapidly Sparking Up
Source: Typesafe Apache Spark
Adoption Survey, Jan. 2015
• 82% have replaced MapReduce
with Spark
• 78% need faster processing for
large data sets
• 62% load data into Spark via HDFS
• 22% of respondents run CDH, more
than twice as many as any other
Hadoop platform

Spark is now an important part of the Hadoop Platform

A Platform That Just Won’t Stop Growing
NEWPROJECTS
EXISTINGPROJECTS
*CDHSUPPORTED
Core Hadoop
(HDFS,
MapReduce)
Solr
Pig
Core Hadoop
HBase
ZooKeeper
Solr
Pig
Core Hadoop
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
Core Hadoop
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
Core Hadoop
Flume
Bigtop
Oozie
HCatalog
Hue
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
YARN
Core Hadoop
Spark
Tez
Impala
Kafka
Drill
Flume
Bigtop
Oozie
HCatalog
Hue
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
YARN
Core Hadoop
Parquet
Sentry
Spark
Tez
Impala
Kafka
Drill
Flume
Bigtop
Oozie
HCatalog
Hue
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
YARN
Core Hadoop
Knox
Flink
Parquet
Sentry
Spark
Tez
Impala
Kafka
Drill
Flume
Bigtop
Oozie
HCatalog
Hue
Sqoop
Avro
Hive
Mahout
HBase
ZooKeeper
Solr
Pig
YARN
Core Hadoop
Kudu*
RecordService*
Ibis*
Falcon
Knox
Flink
Parquet*
Sentry*
Spark*
Tez
Impala*
Kafka*
Drill
Flume*
Bigtop*
Oozie*
Hcatalog*
Hue*
Sqoop*
Avro*
Hive*
Mahout*
Hbase*
ZooKeeper*
Solr*
Pig*
YARN*
Core Hadoop*
2006 2008 2009 2010 2011 2012 20132007 2014 2015

Hadoop’s Next 10 Years
Interest in public-cloud
deployments are driving
native support for them
into the platform.
Rapid hardware advances
are forcing the
community to re-think
Hadoop’s foundations.
Data sources are more
numerous, distributed,
and diverse (IoT), and
Hadoop will adapt.

Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there? Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.

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1. MapReduce to Apache Spark: An Ecosystem Evolves Doug Cutting (@cutting) Chief Architect & Co-founder of Apache Hadoop

2. Hadoop’s Original Architecture MapReduce Data Processing and Resource Management HDFS Filesystem/Storage

3. The MapReduce Breakthrough Key advances in MapReduce: • Data locality: Automatic split computation and appropriate launch of mappers • Fault-tolerance: Write-out of intermediate results and restartable mappers provides ability to run on commodity hardware • Linear scalability: Combination of locality + programming model forces developers to write generally scalable solutions Map Map Map Map Map Map Map Map Map Map Map Map Reduce Reduce Reduce Reduce

4. Apache Spark: A Better MapReduce Easy, Expressive API • Rich API (Java, Scala, and Python) • Interactive shell • 2-5x less code needed than MR Fast Execution • General execution graphs • In-memory storage • Order-of-magnitude improvement over MR

5. Big Data Developers are Rapidly Sparking Up Source: Typesafe Apache Spark Adoption Survey, Jan. 2015 • 82% have replaced MapReduce with Spark • 78% need faster processing for large data sets • 62% load data into Spark via HDFS • 22% of respondents run CDH, more than twice as many as any other Hadoop platform

6. Spark is now an important part of the Hadoop Platform

7. A Platform That Just Won’t Stop Growing NEWPROJECTS EXISTINGPROJECTS *CDHSUPPORTED Core Hadoop (HDFS, MapReduce) Solr Pig Core Hadoop HBase ZooKeeper Solr Pig Core Hadoop Hive Mahout HBase ZooKeeper Solr Pig Core Hadoop Sqoop Avro Hive Mahout HBase ZooKeeper Solr Pig Core Hadoop Flume Bigtop Oozie HCatalog Hue Sqoop Avro Hive Mahout HBase ZooKeeper Solr Pig YARN Core Hadoop Spark Tez Impala Kafka Drill Flume Bigtop Oozie HCatalog Hue Sqoop Avro Hive Mahout HBase ZooKeeper Solr Pig YARN Core Hadoop Parquet Sentry Spark Tez Impala Kafka Drill Flume Bigtop Oozie HCatalog Hue Sqoop Avro Hive Mahout HBase ZooKeeper Solr Pig YARN Core Hadoop Knox Flink Parquet Sentry Spark Tez Impala Kafka Drill Flume Bigtop Oozie HCatalog Hue Sqoop Avro Hive Mahout HBase ZooKeeper Solr Pig YARN Core Hadoop Kudu* RecordService* Ibis* Falcon Knox Flink Parquet* Sentry* Spark* Tez Impala* Kafka* Drill Flume* Bigtop* Oozie* Hcatalog* Hue* Sqoop* Avro* Hive* Mahout* Hbase* ZooKeeper* Solr* Pig* YARN* Core Hadoop* 2006 2008 2009 2010 2011 2012 20132007 2014 2015

8. Hadoop’s Next 10 Years Interest in public-cloud deployments are driving native support for them into the platform. Rapid hardware advances are forcing the community to re-think Hadoop’s foundations. Data sources are more numerous, distributed, and diverse (IoT), and Hadoop will adapt.

9. Learn More cloudera.com/hadoop10

Editor's Notes

#6: This data is from Typesafe’s 2015 survey of 2100+ developers, data scientists, and IT executives whose orgs are either running or researching Spark
#9: What does the future hold for Hadoop? There are many possible permutations, but these are just a couple of the obvious influences going forward.

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