The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. Learn the serious advantages to the new tools, and get an analysis of the current state--including pros and cons as well as what's needed to bootstrap and operate the various options.

1. The New
Analytics Toolbox
Going beyond Hadoop

2. whoami
Robbie Strickland
Software Dev Manager
linkedin.com/in/robbiestrickland
@rs_atl
rostrickland@gmail.com

3. Hadoop works fine, right?

4. Hadoop works fine, right?
● It’s 11 years old (!!)

5. Hadoop works fine, right?
● It’s 11 years old (!!)
● It’s relatively slow and inefficient

6. Hadoop works fine, right?
● It’s 11 years old (!!)
● It’s relatively slow and inefficient
● … because everything gets written to disk

7. Hadoop works fine, right?
● It’s 11 years old (!!)
● It’s relatively slow and inefficient
● … because everything gets written to disk
● Writing mapreduce code sucks

8. Hadoop works fine, right?
● It’s 11 years old (!!)
● It’s relatively slow and inefficient
● … because everything gets written to disk
● Writing mapreduce code sucks
● Lots of code for even simple tasks

9. Hadoop works fine, right?
● It’s 11 years old (!!)
● It’s relatively slow and inefficient
● … because everything gets written to disk
● Writing mapreduce code sucks
● Lots of code for even simple tasks
● Boilerplate

10. Hadoop works fine, right?
● It’s 11 years old (!!)
● It’s relatively slow and inefficient
● … because everything gets written to disk
● Writing mapreduce code sucks
● Lots of code for even simple tasks
● Boilerplate
● Configuration isn’t for the faint of heart

11. Landscape has changed ...
Lots of new tools available:
● Cloudera Impala
● Apache Drill
● Proprietary (Splunk, keen.io, etc.)
● Spark / Spark SQL
● Shark

12. Landscape has changed ...
Lots of new tools available:
● Cloudera Impala
● Apache Drill
● Proprietary (Splunk, keen.io, etc.)
● Spark / Spark SQL
● Shark
MPP queries for Hadoop data

13. Landscape has changed ...
Lots of new tools available:
● Cloudera Impala
● Apache Drill
● Proprietary (Splunk, keen.io, etc.)
● Spark / Spark SQL
● Shark
Varies by vendor

14. Landscape has changed ...
Lots of new tools available:
● Cloudera Impala
● Apache Drill
● Proprietary (Splunk, keen.io, etc.)
● Spark / Spark SQL
● Shark
Generic in-memory analysis

15. Landscape has changed ...
Lots of new tools available:
● Cloudera Impala
● Apache Drill
● Proprietary (Splunk, keen.io, etc.)
● Spark / Spark SQL
● Shark Hive queries on Spark, replaced by Spark SQL

16. Spark wins?

17. Spark wins?
● Can be a Hadoop replacement

18. Spark wins?
● Can be a Hadoop replacement
● Works with any existing InputFormat

19. Spark wins?
● Can be a Hadoop replacement
● Works with any existing InputFormat
● Doesn’t require Hadoop

20. Spark wins?
● Can be a Hadoop replacement
● Works with any existing InputFormat
● Doesn’t require Hadoop
● Supports batch & streaming analysis

21. Spark wins?
● Can be a Hadoop replacement
● Works with any existing InputFormat
● Doesn’t require Hadoop
● Supports batch & streaming analysis
● Functional programming model

22. Spark wins?
● Can be a Hadoop replacement
● Works with any existing InputFormat
● Doesn’t require Hadoop
● Supports batch & streaming analysis
● Functional programming model
● Direct Cassandra integration

23. Spark vs. Hadoop

24. MapReduce:

25. MapReduce:

26. MapReduce:

27. MapReduce:

28. MapReduce:

29. MapReduce:

30. Spark (angels sing!):

31. What is Spark?

32. What is Spark?
● In-memory cluster computing

33. What is Spark?
● In-memory cluster computing
● 10-100x faster than MapReduce

34. What is Spark?
● In-memory cluster computing
● 10-100x faster than MapReduce
● Collection API over large datasets

35. What is Spark?
● In-memory cluster computing
● 10-100x faster than MapReduce
● Collection API over large datasets
● Scala, Python, Java

36. What is Spark?
● In-memory cluster computing
● 10-100x faster than MapReduce
● Collection API over large datasets
● Scala, Python, Java
● Stream processing

37. What is Spark?
● In-memory cluster computing
● 10-100x faster than MapReduce
● Collection API over large datasets
● Scala, Python, Java
● Stream processing
● Supports any existing Hadoop input / output
format

38. What is Spark?
● Native graph processing via GraphX

39. What is Spark?
● Native graph processing via GraphX
● Native machine learning via MLlib

40. What is Spark?
● Native graph processing via GraphX
● Native machine learning via MLlib
● SQL queries via SparkSQL

41. What is Spark?
● Native graph processing via GraphX
● Native machine learning via MLlib
● SQL queries via SparkSQL
● Works out of the box on EMR

42. What is Spark?
● Native graph processing via GraphX
● Native machine learning via MLlib
● SQL queries via SparkSQL
● Works out of the box on EMR
● Easily join datasets from disparate sources

43. Spark on Cassandra

44. Spark on Cassandra
● Direct integration via DataStax driver -
cassandra-driver-spark on github

45. Spark on Cassandra
● Direct integration via DataStax driver -
cassandra-driver-spark on github
● No job config cruft

46. Spark on Cassandra
● Direct integration via DataStax driver -
cassandra-driver-spark on github
● No job config cruft
● Supports server-side filters (where clauses)

47. Spark on Cassandra
● Direct integration via DataStax driver -
cassandra-driver-spark on github
● No job config cruft
● Supports server-side filters (where clauses)
● Data locality aware

48. Spark on Cassandra
● Direct integration via DataStax driver -
cassandra-driver-spark on github
● No job config cruft
● Supports server-side filters (where clauses)
● Data locality aware
● Uses HDFS, CassandraFS, or other
distributed FS for checkpointing

49. Cassandra with Hadoop
Cassandra
DataNode
TaskTracker
Cassandra
DataNode
TaskTracker
Cassandra
DataNode
TaskTracker
NameNode
2ndaryNN
JobTracker

50. Cassandra with Spark (using HDFS)
Cassandra
Spark Worker
DataNode
Cassandra
Spark Worker
DataNode
Cassandra
Spark Worker
DataNode
Spark Master
NameNode
2ndaryNN

51. A Typical Spark Application

52. A Typical Spark Application
● SparkContext + SparkConf

53. A Typical Spark Application
● SparkContext + SparkConf
● Data Source to RDD[T]

54. A Typical Spark Application
● SparkContext + SparkConf
● Data Source to RDD[T]
● Transformations/Actions

55. A Typical Spark Application
● SparkContext + SparkConf
● Data Source to RDD[T]
● Transformations/Actions
● Saving/Displaying

56. Resilient Distributed Dataset (RDD)

57. Resilient Distributed Dataset (RDD)
● A distributed collection of items

58. Resilient Distributed Dataset (RDD)
● A distributed collection of items
● Transformations
○ Similar to those found in scala collections
○ Lazily processed

59. Resilient Distributed Dataset (RDD)
● A distributed collection of items
● Transformations
○ Similar to those found in scala collections
○ Lazily processed
● Can recalculate from any point of failure

60. RDD Transformations vs Actions
Transformations:
Produce new RDDs
Actions:
Require the materialization of the records to produce a
value

61. RDD Transformations/Actions
Transformations:
filter, map, flatMap, collect(λ):RDD[T], distinct, groupBy,
subtract, union, zip, reduceByKey ...
Actions:
collect:Array[T], count, fold, reduce ...

62. Resilient Distributed Dataset (RDD)
val numOfAdults = persons.filter(_.age > 17).count()
Transformation Action

63. Example

64. Demo

65. Spark SQL Example

66. Spark Streaming

67. Spark Streaming
● Creates RDDs from stream source on a
defined interval

68. Spark Streaming
● Creates RDDs from stream source on a
defined interval
● Same ops as “normal” RDDs

69. Spark Streaming
● Creates RDDs from stream source on a
defined interval
● Same ops as “normal” RDDs
● Supports a variety of sources
○ Queues
○ Twitter
○ Flume
○ etc.

70. Spark Streaming

71. The Output

72. Real World Task Distribution

73. Real World Task Distribution
Transformations and Actions:
Similar to Scala but …
Your choice of transformations need be made with task
distribution and memory in mind

74. Real World Task Distribution
How do we do that?
● Partition the data appropriately for number of cores (at
least 2 x number of cores)
● Filter early and often (Queries, Filters, Distinct...)
● Use pipelines

75. Real World Task Distribution
How do we do that?
● Partial Aggregation
● Create an algorithm to be as simple/efficient as it can be
to appropriately answer the question

76. Real World Task Distribution

77. Real World Task Distribution
Some Common Costly Transformations:
● sorting
● groupByKey
● reduceByKey
● ...

78. Thank you!
Robbie Strickland
@rs_atl
rostrickland@gmail.com
https://github.com/rstrickland/sparkdemo