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10 concepts the enterprise decision maker needs to understand about Hadoop

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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

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10 concepts the enterprise decision maker needs to understand about Hadoop

  1. 1. 10 concepts the enterprise decision maker needs to understand about Hadoop Donald Miner Strata + Hadoop World 2016 – San Jose March 31st, 2016
  2. 2. dminer@minerkasch.com @donaldpminer Donald Miner
  3. 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. 4. #1 - Hadoop masks being a distributed system
  5. 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. 6. #1 - Hadoop masks being a distributed system Why is this so important? What does it not do for me?
  7. 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. 8. #2 - Hadoop scales out linearly Double the compute, Half the time!
  9. 9. #2 - Hadoop scales out linearly Double the data, twice the time!
  10. 10. #2 - Hadoop scales out linearly Double the compute, Double the compute The same time!
  11. 11. #2 - Hadoop scales out linearly Data locality!
  12. 12. #2 - Hadoop scales out linearly Why is this so important? What does it not do for me?
  13. 13. #3 - Hadoop runs on commodity hardware
  14. 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. 15. #3 - Hadoop runs on commodity hardware Why is this so important? What does it not do for me?
  16. 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. 17. #4 - Hadoop handles unstructured data Why is this so important? What does it not do for me?
  18. 18. #5 - In Hadoop, you load data first and ask questions later BEFORE: ETL Years of planning Schemas & ER Diagrams
  19. 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. 20. #5 - In Hadoop, you load data first and ask questions later
  21. 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. 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. 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. 24. Why is this so important? What does it not do for me? #6 - HDFS stores the data but has some major limitations
  25. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 37. Why is this so important? What does it not do for me? #9 - Hadoop is open source
  38. 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. 39. Why is this so important? What does it not do for me? #10 - The Hadoop ecosystem is constantly growing and evolving
  40. 40. Play by Hadoop’s rules and it’ll give you what you want
  41. 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

Notizen

  • 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 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
  • Importance:
    Get more done faster
    Barrier of entry

    Downsides:
    Knowing what you are doing
    Abstraction bleeding through
  • Hadoop scales out linearly: why Hadoop’s linear scalability is a paradigm shift (but one with a few downsides)
  • Hadoop scales out linearly: why Hadoop’s linear scalability is a paradigm shift (but one with a few downsides)
  • Hadoop scales out linearly: why Hadoop’s linear scalability is a paradigm shift (but one with a few downsides)
  • Hadoop scales out linearly: why Hadoop’s linear scalability is a paradigm shift (but one with a few downsides)
  • Hadoop scales out linearly: why Hadoop’s linear scalability is a paradigm shift (but one with a few downsides)
  • Importance:
    Code stays the same as your cluster and problem grows
    Massively scalable

    Downsides:
    Need to do things in a linear way
    It’s not always true
  • Hadoop runs on commodity hardware: an honest definition of commodity hardware and why this is a good thing for enterprises
  • Hadoop runs on commodity hardware: an honest definition of commodity hardware and why this is a good thing for enterprises
  • importance:

    Ease of accessibility
    Cloud

    Downsides:

    Sometimes have a hard time leveraging fancier hardware
  • Hadoop handles unstructured data: why Hadoop is better for unstructured data than other data systems from a storage and computation perspective
  • importance:
    Unstructured data

    Downsides:
    Cost of flexibility
  • 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
  • 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
  • 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
  • Importance
    Solve the chicken + egg


    Cost of flexibility
  • HDFS stores the data but has some major limitations: an overview of HDFS (replication, not being able to edit files, and the NameNode)
  • HDFS stores the data but has some major limitations: an overview of HDFS (replication, not being able to edit files, and the NameNode)
  • importance:

    Scalable data storage that works for analytics

    Downsides:

    It’s bad storage
  • 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
  • Importance: YARN brings people closer to universal distributed system without getting in the way (same path)


    Downsides:
    Cost of abstraction + system complication
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • Importance:
    MapReduce – fault tolerance, long running jobs, reliability
    Other parts of the ecosystem work together to solve a problem

    Downside:
    Lack of a universal interface – Spark? Holy grail?
  • Hadoop is open source: what it really means for Hadoop to be open source from a practical perspective, not just a “feel good” perspective
  • Importance:

    Organic growth
    Competition

    Downsides:
    ????
  • 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

  • Importance:

    Innovation
    Organization

    Downside:
    Fractured
    Hard to track
    Lack of cohesiveness
  • ×