Have you ever wanted to analyze sensor data that arrives every second from across the world? Or maybe your want to analyze intra-day trading prices of millions of financial instruments? Or take all the page views from Wikipedia and compare the hourly statistics? To do this or any other similar analysis, you will need to analyze large sequences of measurements over time. And what better way to do this then with Apache Spark? In this session we will dig into how to consume data, and analyze it with Spark, and then store the results in Apache Cassandra.

1. Analyzing Time-Series Data with
Apache Spark and Cassandra
Andrew Psaltis
HDF / IoT Product Solution Architect
@itmdata
StampedeCon 2016

2. If you every wanted to….
Build models over measurements coming in
every second from sensors across the
world?
Dig into intra-day trading prices of millions
of financial instruments?
Compare hourly page view statistics across
every page on Wikipedia?

3. You need to do it over a large
sequence of measurements
over time.

4. A problem perfect for
Cassandra and Spark

5. Time-series data
consists of sequences
of measurements,
each occurring at a
point in time.

6. Example: Weather Station
Weather station collects data
Cassandra stores in sequence
Application reads in sequence

7. Query use cases
Weather Station ID
Get weather data given:
Weather Station ID and Time
Weather Station ID and
Range of Time

8. Aggregation use cases
Weather Station ID
Get temperature stats given:
Weather Station ID and Time
Weather Station ID and
Range of Time

9. Cassandra Overview

10. Cassandra architecture is
Shared nothing[1]
Materless peer-to-peer
Shard Free
Based on Amazon Dynamo and Google BigTable
[1] 1986 paper “The Case for Shared Nothing” -http://db.cs.berkeley.edu/papers/hpts85-
nothing.pdf.

11. Row

12. Partition

13. Table

14. Keyspace
Table 1 Table 2

15. Partition Key Clustering Columns
Order Override

16. Partition Key
Clustering Columns
10010:99999
2016:07:28:1
2
-5.6
2016:07:28:1
1
-5.1
2016:07:28:1
0
-4.9
2016:07:28:0
9
-5.3
Primary key relationship

17. Tokens
Consistent hash between 2-63 and 264
Each node owns a range of those values
The token is the beginning of that range to
the next node’s token value
Virtual Nodes break these down further

18. Replication
Node Primary
10.0.0.1 00-25
10.0.0.2 26-50
10.0.0.3 51-75
10.0.0.4 76-100
10.0.0.1
00-25
10.0.0.2
26-50
10.0.0.4
76-100
10.0.0.3
51-75
DC 1
DC 1 RF: 1

19. Replication
Node Primary Replica
10.0.0.1 00-25 76-100
10.0.0.2 26-50 00-25
10.0.0.3 51-75 26-50
10.0.0.4 76-100 51-75
10.0.0.1
00-25
76-100
10.0.0.2
26-50
00-25
10.0.0.4
76-100
51-75
10.0.0.3
51-75
26-50
DC 1
DC 1 RF: 2

20. Replication
Node Primary Replica Replica
10.0.0.
1
00-25 76-100 51-75
10.0.0.
2
26-50 00-25 76-100
10.0.0.
3
51-75 26-50 00-25
10.0.0.1
00-25
76-100
51-75
10.0.0.2
26-50
00-25
76-100
10.0.0.4
76-100
51-75
26-50
10.0.0.3
51-75
26-50
00-25
DC 1
DC 1 RF: 3

21. Replication
10.0.0.1
00-25
76-100
51-75
10.0.0.2
26-50
00-25
76-100
10.0.0.4
76-100
51-75
26-50
10.0.0.3
51-75
26-50
00-25
DC 1 RF: 3
Client
Write to
partition 15

22. Multi-Datacenter
10.0.0.1
00-25
76-100
51-75
10.0.0.2
26-50
00-25
76-100
10.0.0.4
76-100
51-75
26-50
10.0.0.3
51-75
26-50
00-25
DC 1 RF: 3
Client
Write to partition 15
10.0.0.1
00-25
76-100
51-75
10.0.0.2
26-50
00-25
76-100
10.0.0.4
76-100
51-75
26-50
10.0.0.3
51-75
26-50
00-25
DC 2 RF: 3

23. Query use cases
Weather Station ID
Get weather data given:
Weather Station ID and Time
Weather Station ID and
Range of Time

24. Spark Overview

27. What is Spark used for?
Fast and general purpose engine for large scale
data processing
Provides a framework that supports In-Memory
Cluster Computing
Designed for iterative computations and interactive
data mining

28. Resilient Distributed Dataset (RDD)
• Created through transformations
on data (map,filter..) or other
RDDs
• Immutable
• Partitioned
• Reusable

29. RDD Partitioning
Number of RDD partitions will
control how many parallel tasks
can be run against the data stored
in the RDD
Hint: in general make it at least as large as the # of cpu cores in your cluster

30.  Transformations - Similar to scala collections API
• Produce new RDDs
• filter, flatmap, map, distinct, groupBy, union, zip,
reduceByKey, subtract
 Actions
• Require materialization of the records to generate
a value
• collect: Array[T], count, fold, reduce..
RDD Operations

31. Data Locality
Spark asks an RDD for a list of its partitions (splits)
Each split consists of one or more token-ranges
For every partition:
•Spark gets a list of preferred nodes to process on
from RDD
•Spark creates a task and sends it to one of the
nodes for execution

32. What is Spark Streaming?
•Provides efficient, fault-tolerant stateful stream
processing
•Provides a simple API for implementing complex
algorithms
•Integrates with Spark’s batch and interactive
processing
•Integrates with other Spark extensions

33. Spark Streaming Overview

35. Discretized Streams (DStreams)
•The basic abstraction provided by Spark Streaming
•Continuous series of RDDs

36. Spark on Cassandra

39. Spark on Cassandra
• Server-Side filters (where clauses)
• Cross-table operations (JOIN, UNION, etc.)
• Data locality-aware (speed)
• Data transformation, aggregation, etc.
• Natural Time Series Integration

40. Spark Cassandra Connector
• Loads data from Cassandra to Spark
• Writes data from Spark to Cassandra
• Implicit Type Conversions and Object Mapping
• Implemented in Scala (offers a Java API)
• Open Source
• Exposes Cassandra Tables as Spark RDDs +
Spark DStreams

41. Spark Cassandra Connector

42. Spark Cassandra Example

43. Locating a Row

44. Cassandra RDD Use the Token Range to Create Node
Local Spark Partitions

45. The Spark Executor uses the Java Driver to
Pull Rows from the Local Cassandra Instance

46. Transactional
10.0.0.1
00-25
10.0.0.2
26-5010.0.0.4
76-100
10.0.0.3
51-75
10.0.0.1
00-25
10.0.0.2
26-50
10.0.0.4
76-100
10.0.0.3
51-75
Analytics

47. Batch Weather Station Analysis

48. Weather Station Analysis
Weather station collects data
Cassandra stores in sequence
Spark rolls up data into new tables

50. Setup Connection

51. Get data and aggregate

52. Store back into Cassandra

54. Aggregation use cases
Weather Station ID
Get temperature stats given:
Weather Station ID and Time
Weather Station ID and
Range of Time

55. Weather Station Stream Analysis
Weather station collects data
Data processed in stream
Cassandra stores in sequence

56. Weather Station Stream Analysis
Counter

57. https://github.com/killrweather/killrweather
To explore at home….

58. Thank You