Way too many enterprise decision makers have clouded and uninformed views of how Hadoop works and what it does. Donald Miner offers high-level observations about Hadoop technologies and explains how Hadoop can shift the paradigms inside of an organization, based on his report Hadoop: What You Need To Know—Hadoop Basics for the Enterprise Decision Maker, forthcoming from O’Reilly Media. After a basic introduction to Hadoop and the Hadoop ecosystem, Donald outlines 10 basic concepts you need to understand to master Hadoop: Hadoop masks being a distributed system: what it means for Hadoop to abstract away the details of distributed systems and why that’s a good thing Hadoop scales out linearly: why Hadoop’s linear scalability is a paradigm shift (but one with a few downsides) Hadoop runs on commodity hardware: an honest definition of commodity hardware and why this is a good thing for enterprises Hadoop handles unstructured data: why Hadoop is better for unstructured data than other data systems from a storage and computation perspective In Hadoop, you load data first and ask questions later: the differences between schema-on-read and schema-on-write and the drawbacks this represents Hadoop is open source: what it really means for Hadoop to be open source from a practical perspective, not just a “feel good” perspective HDFS stores the data but has some major limitations: an overview of HDFS (replication, not being able to edit files, and the NameNode) YARN controls everything going on and is mostly behind the scenes: an overview of YARN and the pitfalls of sharing resources in a distributed environment and the capacity scheduler MapReduce may be getting a bad rap, but it’s still really important: an overview of MapReduce (what it’s good at and bad at and why, while it isn’t used as much these days, it still plays an important role) The Hadoop ecosystem is constantly growing and evolving: an overview of current tools such as Spark and Kafka and a glimpse of some things on the horizon

1. 10 concepts the enterprise
decision maker needs to
understand about Hadoop
Donald Miner
Strata + Hadoop World 2016 – San Jose
March 31st, 2016

2. dminer@minerkasch.com
@donaldpminer
Donald Miner

3. Purpose of this talk
An honest and minimal introduction to Hadoop
Why is Hadoop popular?
What does Hadoop do well and why?
What is bad about Hadoop?

4. #1 - Hadoop masks being a distributed system

5. #1 - Hadoop masks being a distributed system
// This block of code defines the behavior of the map phase
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
// Split the line of text into words
StringTokenizer itr = new StringTokenizer(value.toString());
// Go through each word and send it
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
// "I've seen this word once!"
context.write(word, one);
}
}
[1]$ hadoop fs -put hamlet.txt datz/hamlet.txt
[2]$ hadoop fs -put macbeth.txt data/macbeth.txt
[3]$ hadoop fs -mv datz/hamlet.txt data/hamlet.txt
[4]$ hadoop fs -ls data/
-rw-r–r– 1 don don 139k 2012-01-31 23:49 /user/don/data/caesar.txt
-rw-r–r– 1 don don 180k 2013-09-25 20:45 /user/don/data/hamlet.txt
-rw-r–r– 1 don don 117k 2013-09-25 20:46 /user/don/data/macbeth.txt

6. #1 - Hadoop masks being a distributed system
Why is this so important?
What does it not do for me?

7. #2 - Hadoop scales out linearly
The amount of data, the amount of time something takes,
and the amount of hardware you have are linearly linked1
1. usually

8. #2 - Hadoop scales out linearly
Double the compute,
Half the time!

9. #2 - Hadoop scales out linearly
Double the data,
twice the time!

10. #2 - Hadoop scales out linearly
Double the compute,
Double the compute
The same time!

11. #2 - Hadoop scales out linearly
Data locality!

12. #2 - Hadoop scales out linearly
Why is this so important?
What does it not do for me?

13. #3 - Hadoop runs on commodity hardware

14. #3 - Hadoop runs on commodity hardware
• Non-proprietary
• Easy to acquire (all it takes is $)
• Value (not necessarily cheap)
• Let software handle the hard problems

15. #3 - Hadoop runs on commodity hardware
Why is this so important?
What does it not do for me?

16. #4 - Hadoop handles unstructured data
Query languages like SQL assume some sort of structure
Relational databases and other databases require structure
MapReduce/Spark is just Java/Scala/Python/etc
You can do anything Java can do
HDFS just stores files
You can store anything in a file

17. #4 - Hadoop handles unstructured data
Why is this so important?
What does it not do for me?

18. #5 - In Hadoop, you load data first and ask questions later
BEFORE:
ETL
Years of planning
Schemas & ER Diagrams

19. LOAD DATA FIRST, ASK QUESTIONS LATER
Data is parsed/interpreted as it is loaded out of HDFS
WITH HADOOP:
#5 - In Hadoop, you load data first and ask questions later

20. #5 - In Hadoop, you load data first and ask questions later

21. Why is this so important?
What does it not do for me?
#5 - In Hadoop, you load data first and ask questions later

22. #6 - HDFS stores the data but has some major limitations
• Stores files in folders
• Nobody cares what’s in your files
• Chunks large files into blocks (~64MB-2GB)
• 3 replicas of each block
• Blocks are scattered all over the place
• Can scale to thousands of nodes and hundreds of petabytes
FILE BLOCKS

23. #6 - HDFS stores the data but has some major limitations
Limitations:
• Low IOPs
• Higher latency
• Can’t edit files
• Can’t handle small files
• Low storage efficiency (33%)
• Low throughput on single files
• But…
• High aggregate throughput
• Massive scale
• Software only
• Few bottlenecks

24. Why is this so important?
What does it not do for me?
#6 - HDFS stores the data but has some major limitations

25. #7 - YARN controls everything going on and is
mostly behind the scenes
• Controls the compute resources on the cluster
• Was the key new feature in Hadoop 2.0
• Abstracted resource management from MapReduce to be more
general
• MapReduce became just any other application
• YARN is key in enabling multiple compute engines at once

26. Why is this so important?
What does it not do for me?
#7 - YARN controls everything going on and is
mostly behind the scenes

27. #8 - MapReduce may be getting a bad rap, but it’s
still really important (but other engines are
important, too)
• Analyzes raw data in HDFS where the data is
• Jobs are split into Mappers and Reducers
Reducers (you code this, too)
Automatically Groups by the
mapper’s output key
Aggregate, count, statistics
Outputs to HDFS
Mappers (you code this)
Loads data from HDFS
Filter, transform, parse
Outputs (key, value) pairs

28. #8 - MapReduce may be getting a bad rap, but it’s
still really important (but other engines are
important, too)
“MapReduce is slow”
“MapReduce is hard to use”

29. Real-time Large-scale analyticsAd-hoc
MapReduce!
#8 - MapReduce may be getting a bad rap, but it’s
still really important (but other engines are
important, too)

30. #8 - MapReduce may be getting a bad rap, but it’s
still really important (but other engines are
important, too)
Real-time Large-scale analyticsAd-hoc
MapReduce!Storm/streaming

31. #8 - MapReduce may be getting a bad rap, but it’s
still really important (but other engines are
important, too)
Real-time Large-scale analyticsAd-hoc
MapReduce!Storm/streaming Impala/HAWQ/Stinger

32. #8 - MapReduce may be getting a bad rap, but it’s
still really important (but other engines are
important, too)
Real-time Large-scale analyticsAd-hoc
MapReduce!Storm/streaming Impala/HAWQ/Stinger Spark

33. #8 - MapReduce may be getting a bad rap, but it’s
still really important (but other engines are
important, too)
Real-time Large-scale analyticsAd-hoc
MapReduce!Storm/streaming Spark

34. #8 - MapReduce may be getting a bad rap, but it’s
still really important (but other engines are
important, too)
Real-time Large-scale analyticsAd-hoc
MapReduce!Spark
Not everyone has this problem, but it’s a really interesting problem!

35. Why is this so important?
What does it not do for me?
#8 - MapReduce may be getting a bad rap, but
it’s still really important

36. #9 - Hadoop is open source
Free – money isn’t just a financial barrier, but also a bureaucratic one, too
Help yourself – Hadoop is a complex system underneath and sometimes
you need to figure something out for yourself
Adoption – it’s easier to adopt, so adoption is more widespread
Expansion – can be extended by anyone

37. Why is this so important?
What does it not do for me?
#9 - Hadoop is open source

38. #10 - The Hadoop ecosystem is constantly growing and evolving
Not only do individual Hadoop
components improve…
But Hadoop overall improves with new
components that do new things
differently.
And they piece together into something
that gets a lot of work done.

39. Why is this so important?
What does it not do for me?
#10 - The Hadoop ecosystem is constantly growing and evolving

40. Play by Hadoop’s rules and it’ll give you what you want

41. 10 concepts the enterprise
decision maker needs to
understand about Hadoop
Donald Miner
Strata + Hadoop World 2016 – San Jose
March 31st, 2016
dminer@minerkasch.com
@donaldpminer