Operational Analytics Using Spark and NoSQL Data Stores

Delivering Operational Analytics Using
Spark and NoSQL Data Stores
Mike Ferguson
Managing Director
Intelligent Business Strategies
Basho Webinar
January, 2016

2
Copyright © Intelligent Business Strategies 1992-2016!
About Mike Ferguson
Mike Ferguson is Managing Director of Intelligent
Business Strategies Limited. As an analyst and
consultant he specializes in business
intelligence, data management and enterprise
business integration. With over 34 years of IT
experience, Mike has consulted for dozens of
companies, spoken at events all over the world
and written numerous articles. Formerly he was
a principal and co-founder of Codd and Date
Europe Limited – the inventors of the Relational
Model, a Chief Architect at Teradata on the
Teradata DBMS and European Managing
Director of DataBase Associates.
www.intelligentbusiness.biz
mferguson@intelligentbusiness.biz
Twitter: @mikeferguson1
Tel/Fax (+44)1625 520700

3
Topics
 The changing landscape of operational and analytical systems
 Scalable operational applications and NoSQL data stores
 Big data analytics – The era of Hadoop and Spark
 The value of operational analytics
 Operational analytics using The Basho Data Platform and
Apache Spark
 Conclusions

4
Topics
The changing landscape of operational and analytical systems
Apache Spark
 Conclusions

5
The Application Processing Spectrum
Source: BI-Research Copyright © BI-Research, 2013-Present

6
Big Data Processing – There Is A Growing Number of Data
Stores Optimized for Operational or Analytical Workloads
OLTP RDBMS
NoSQL DBMS NoSQL
• ACID support missing in many NoSQL DBMSs
• Can you live with losing a transaction?
• OK for sensor data for example
Analytical RDBMS

7
Analytical
Systems
A Closed Loop Is Still Needed – It Just Now Also
Includes NoSQL Technologies
Operational
applications
Scalable
Analytical
Systems
data data
new data
new insights
Scalable
Operational
applications
Relational &
NoSQL systems
Relational &
NoSQL systems

8
Topics - – Where Are We?
Scalable operational applications and NoSQL data stores
Apache Spark
 Conclusions

9
Analytical
Systems
Demand For Scalable Operational Systems With High
Write Processing Is Driving Demand for NoSQL DBMS
Operational
applications
Scalable
Analytical
Systems
data data
new data
new insights
Scalable
operational
applications

10
Success of Big Data Analytics Depends On Being Able
To Scale To Capture High Velocity, High Volume Data
 Successful big data analytics requires
1. Ability to scale operational systems to capture, stream and store
the required transactional and non-transactional data
– Support peak transaction rates
– Support peak capture of non-transactional data e.g. shopping
cart data
– Support peak data arrival rates e.g. sensor data
– Support peak ingestion rates
2. Scalable Big Data analytics
3. Closed loop integration of analytical systems back into core
operational transaction processing systems
– Make prescriptive insights available to all that need them to
continuously optimise operations and maximise effectiveness

11
E-Business And Mobile Means Operational Systems Are
Having To Scale To Support Masses Of Concurrent Users
Many more users
Operational
applications
Transactional
applications
dataWeb
logs
Cluster
Mobile devices
WWW
data data data
partitioned data

12
Example Operational Applications Requiring Scalability
That Are Fuelling Demand For NoSQL DBMSs
 Web and mobile commerce
• Shopping cart data, session storage
 Internet of Things (IoT) and other time series applications
• Need to scale as the number of devices / things increase
 Mobile gaming
• Player profile data, session storage, game performance stats
 Healthcare
• Store unstructured healthcare digital imaging and video data
 Social network applications

13
Types Of NoSQL Database And Product Examples
NoSQL Database Type NoSQL Product Examples
Key Value store Aerospike, Amazon DynamoDB, Basho Riak KV,
Redis, MemcacheDB, Voldemort
Document database CouchDB, IBM DB2 (XML & JSON), MongoDB, IBM
Cloudant, Marklogic, Terrastore, JackRabbit, RaptorDB
Column Family
database
Casandra, DataStax, Google BigTable, Hadoop HBase,
Hypertable, HPCC, Amazon SimpleDB
Graph database AllegroGraph, GraphBase, Horton, InfiniteGraph, IBM
DB2, Neo4j, Oracle Spatial and Graph, Titan, Cray
Research, Teradata Aster
Multi-modal database ArangoDB, CortexDB, MarkLogic , MongoDB
FoundationDB,
 Some NoSQL databases are aimed at write processing (data collection)
 Others are aimed at specific big data analytical workloads
 Issues include lack of standard APIs, weak or no optimizer and non-
immediate consistency

14
Global NoSQL Market Size And Forecast 2013 - 2020
Source: https://www.alliedmarketresearch.com/NoSQL-market

15
Key Value Stores Can Store Any Data - Examples
Key Value
10034 John Smith
82771
93441
{ "firstName": ”Wayne",
"lastName": ”Rooney",
"age": 25,
"address": {
"streetAddress": "21 Sir Matt Busby Way",
"city": ”Manchester”,
“country”: “England”,
"postalCode": “M1 6DY”
},
"phoneNumbers": [
{ "type": "home”,
"number": ”0161-123-1234”
},
{
"type": ”mobile",
"number": ”07779-123234”
}
]
}
Key value store features:
• Very simple to understand
• Very scalable - hash partitioning
• Data access is via the key
• The application controls what’s stored in
the value
• Very fast performance
• Acceleration via in-memory processing
• Eventual consistency
• Often no support for data types
• No built-in referential integrity
• No understanding of data relationships
• The application must understand any
relationships in data
• Programmer is in complete control
• Application must navigate complex data
Use for specific operational applications

16
Key Value Stores
– The Key Is Hashed To Partition The Data
Source: Microsoft
The value can be anything
• A single data field
• A JSON document
• An XML document
• Text
• Image……
Key Value
Easy to partition (hash the key)
Very fast to retrieve and store data
The application needs to know
• What is stored in the VALUE
• How the value is structured
• How to process the value
Key needs to be unique
Can use HTTP to read and write data
e.g. CURL –XPUT, CURL -XGET

17
Key Value Stores – A Basho Riak KV Cluster Has
Virtual Nodes Running on Physical Nodes
Source: Basho
SHA1 is a hashing function that hashes a key to determine the node
Riak hash partitions and replicates data (3 copies of the data is the default)
e.g. PUT,
POST, GET….
the valuethe key
hash the key
Nodes can be
added and removed
to a Riak cluster
while it is running

18
Key Value Stores - A Basho Riak KV Ring
Riak uses partitions (64 partitions
are the default) and also replicates
the partitions for high availability
Source: Basho
Writing replicas

19
Topics – Where Are We?
Big data analytics – The era of Hadoop and Spark
Apache Spark
 Conclusions

20
Analytical
Systems
Demand For Scalable Analytical Systems Is Also
Exploding
Operational
applications
Scalable
Analytical
Systems
data data
new data
new insights
Scalable
operational
applications

21
A Hadoop System
Java, Python,
Scala
file file file file file
file file
file file
webHDFS
(An HTTP
interface to
HDFS has
REST APIs)
HDFS
file
file
file
file
PIG latin
scripts
3rd Party SQL
on Hadoop
Analytic
Application
index
indexIndex
partition
SQL
BI Tools
Storm
YARN
MapReduce Tez Spark
SQL
HBase
w
e
b
H
D
F
S
APIs to HBase, APIs to
HDFS
executes on
MR, Tez &
Spark

22
Faster Execution Engines For Analytic Applications
– Apache Spark
Java, Python,
Scala
file file
file file
webHDFS
(An HTTP
interface to
HDFS has
REST APIs)
HDFS
file
file
file
file
PIG latin
scripts
3rd Party SQL
on Hadoop
Analytic
Application
index
indexIndex
partition
SQL
BI Tools
Storm
YARN
MapReduce Tez Spark
SQL
HBase
w
e
b
H
D
F
S
APIs to HBase, APIs to
HDFS

23
Spark Is A General Purpose In-Memory Execution
Framework That Can Run With Or Without Hadoop
file file
file file
HDFS
file
file
file
file
Storm
YARN
MapReduce Tez Spark
HBase
w
e
b
H
D
F
S
HDFS, S3…..
Tachyon
Spark also
includes an
HDFS
compatible
in-memory
file system
You can use
Spark with
or without
Tachyon
The Spark stack is integrated – E.g. You can use Spark Streaming,
SparkSQL and MLBase together in the same application
Applications / BI Tools
Spark Core
Spark
Streaming
R
Spark SQL
+
DataFrames
GraphX
(Graph
Computation)
MLlib
(Machine
Learning)
SQL Python Scala Java

24
Spark Core
Spark
Streaming
R
Spark SQL +
DataFrames
GraphX
(Graph Computation)
MLlib
(Machine
Learning)
Apache Spark
Provides distributed task
dispatching, scheduling,
and basic I/O.
For analysis of real-
time streaming data
A library of pre-built analytic
algorithms that can run in
parallel across a Spark cluster
A graph analysis engine
running on Spark
Query structured data in
Spark apps using SQL
or a DataFrames API

25
Spark In-Memory Analytic Applications Can Do A
Lot More Than Map Reduce Processing
 Keep only one copy
in memory in a JVM
 Track lineage of job
operators used to
derive the data
 Use the lineage to
re-compute the
data if there is a
failure
 No MapReduce
execution needed
• Just Spark APIs
map
map
join
filter
reduce
Source: Amplab
Spark application
HDFSfile file file file file file
Spark Applications / BI Tools
Spark Core
Spark
Streaming
R
Spark SQL
+
DataFrames
GraphX
(Graph
Computation)
MLlib
(Machine
Learning)

26
Spark Applications Operate On RDDs (Data)
– You Can Do A Lot More Than Map and Reduce
 RDD = Resilient Distributed Datasets
 An RDD is a read-only, partitioned collection of records
 RDDs can be only created through operators on either
1. A dataset in stable storage or
2. Other existing RDDs.
Map Reduce Sample
Filter Count Take
Groupby Fold First
Sort Reducebykey Partitionby
Union groupByKey Mapwith
Join Cogroup Mapwith
Leftouterjoin Cross Pipe
Rightouterjoin Zip Save
Spark
Operators
Spark Applications

27
Simplifying Access To Data Using Via SparkSQL
and Spark DataFrames
 A DataFrame is a distributed
collection of data organized into
named columns
 Conceptually equivalent to a
relational DBMS table or a data
frame in R/Python
 DataFrames can be constructed
from a wide array of sources:
• Structured data files
• Hive tables
• External databases
• Existing RDDs
 Uses schema on read
Image source: Databricks.com
Note: that Spark data
sources can be
relational & NoSQL
DBMSs

28
Spark Is Going Over The Top of Multiple Data Stores For
Scalable In-Memory Analytics Across The Entire Ecosystem
Streaming
data
Hadoop
data store
Data Warehouse
RDBMS
NoSQL
DBMS
EDW
DW & martsAdvanced Analytic
(multi-structured
data)
mart
Operational NoSQL
Data Stores
Streaming
analytics
e.g. Casandra,
Basho Riak
Spark Core
Spark
Streaming
R
Spark SQL +
DataFrames
GraphX
(Graph
Computation)
MLlib
(Machine
Learning)

29
The value of operational analytics
Apache Spark
 Conclusions

30
Key Business Drivers And Objectives For
Operational Analytics
 Combine operational and analytical processing at scale to:
• Improve customer engagement
• Reduce risk
• Avoid unplanned operational cost
• Optimise operational effectiveness
 Use BI/Analytics to drive and guide business operations to help
achieve specific target business goals and KPI targets
Automated analysis of operational events as they happen
Automated alerts
On-demand recommendations
 Integrate BI/Analytics into every business process to:
• Create a ‘insight driven’ employee base
• Enable mass execution of business strategy via facilitating
mass contribution towards achieve specific business goals

31
Five Types Of Operational BI/Analytics
1. Simple operational reporting of current position/state e.g.
session state
2. Situational awareness via visualisation of live operational data
typically on dashboards
3. On-demand analytics of live operational and/or historical data
to improve operational decisions and effectiveness
4. On-demand recommendations for guidance
5. Event stream processing to monitor, automatically analyse and
act on events in real-time to prevent problems arising and to
optimise business operations

32
BI/ Analytics Apps /
Services
Operational Analytics – What’s The Difference
Between On-Demand Vs Event-Driven Analysis?
BI/ Analytics Services
Application
On-Demand
Analytical service
(query, report, model,
recommendation)
Message, file arrival, pattern, trigger
Event-Driven
Analytical service
(query, report, model,
recommendation)
streaming
data

33
Analytics Need To Be Integrated Into Business
Processes To Optimize Business Operations
Customers Partners &
suppliers
Customer
relationship
management
Operations
management
Supply
chain
management
Marketing
Sales
Service/support
Operations
Finance/accounting
Procurement
Inventorycontrol
Shipping/distribution
Humanresources
Employees
Integrated Intelligent Business Operations
Integrated On-Demand Business Intelligence

34
High Value Application Use Cases for Streaming
Analytics
Streaming
Analytics
Source: Adapted from a slide by IBM

35
Responding To Events And Event Patterns Means
Reducing Action Time
The time between an event
occurring and action being
taken being as close to zero
as possible
Action distance or action time
Event-
driven data
integration
Automated
analysis
Automated
decision and
action taking
Source: Dr Richard Hackathorne

36
With Event Stream Processing The Architecture
Has To Change
Data
cleansing &
integration
Store data
Query/Analyze
(human)
Store
data
Query/Analyze
(automated)
Classic Use
of Analytics
Event / Stream
processing
Act
(automated
or human)
Data
cleansing &
integration

37
Time Series Analysis – Query Processing Uses a Time
Window to Look at Continuously Streaming Data
Time Window
T1 T2
E.g. 5 seconds
or 30 seconds
or 5 minutes
Pattern/correlation
Continuous time series
queries (CQs) operate on
the data as it flows by
Stream
processin
g server
CQs
A set of queries (continuous
queries) reside in the data stream
server to process incoming data
Data is pushed into the queries
High frequency data

38
Key Requirements For Operational Analytics
 On-demand, event-driven and scheduled invocation of analytics
 Monitor streaming events as they happen via automatic analysis
 Automatic analysis via predictive and statistical models
 Automatic interpretation of predictive/statistical model outcomes
 Rule-driven automatic actions to automate decision making
• E.g. Alerts, recommendations, transaction and process invocation
 Integrate operational analytics into operational applications
 Operational reporting
 Scale to support large numbers of events and concurrent users
 Store relevant data together to speed up analytics execution
 Run predictive and statistical models close to the data
 Run analytics on a 24x365 basis

39
The Importance of In Memory Processing
 Massively parallel in-memory processing
is mission critical for scalable operational
systems and operational analytics
 Why?
• Performance is a critical
• Large number of concurrent user requests
for on-demand analytics
• Large number of concurrent application
requests for on-demand analytics
• Event driven operational analytics on very
high velocity data needs memory

40
Operational analytics using The Basho Data Platform and
Apache Spark
 Conclusions

41
The Basho Data Platform
SERVICE
INSTANCES
STORAGE
INSTANCES
Solr
Spark
Redis
(Caching)
Solr
Elastic
Search
Web
Services
3rd Party
Web
Services &
Integrations
Riak!KV!
!Key/Value
Riak S2 !
Object
Storage
Riak TS !!
Time!Series!
Document
Store
Columnar Graph
Replicate &
Synchronize
Message
Routing
Cluster
Management
& Monitoring
Logging &
Analytics
Internal Data
Store
CORE SERVICES
BASHO DEVELOPED BASHO INTEGRATED
THE!BASHO!DATA!PLATFORM!
Source:(Basho( hash partitioning,
cluster scalability,
triple replication,
multi-datacentre
replication
co-locates time-series data,
high availability, scalability
replicates and
synchronises data
within and across
Riak KV, Redis and
Spark Clusters Automated cluster
management simplifies
administration
Integrated in-memory
caching for faster
application performance
Search based query
processing on Riak data
using Solr indexes
Integrated in-memory
analytics for Riak KV
and Riak TS data

42
Riak TS Is A New Basho Storage Instance
Optimised for Time Series Data And Analytics
 A distributed NoSQL database optimised for time series
sequenced, unstructured data capture, aggregation and
analysis from the Internet of Things (IoT)
 Highly availability
 Scalability - add nodes to a cluster without sharding
 Automated and uniform data distributed across the cluster
• Time of geohash based data co-location to ensure time series data
is located on the same node
 Data validation on input
 APIs and client libraries for Java, Ruby, Python, Go, Erlang,
Node.js or .NET.
 Spark integration for operational analysis of time series data.

43
Operational Analytics Using The Basho Data
Platform And Apache Spark
Opera&onal*
analy&cs**
web*service*
Opera&onal*
analy&c**
applica&on*
BI*Tool*
data data data
hash*par&&oned*data*
Scalable*
opera&onal
applica&on*
Spark**Core*
Spark*
Stream
<ing*
BlinkDB*
Spark*
SQL*
GraphX* SparkR*MLlib*
write*back*
Opera&onal*Analy&cs*Using*The*Basho*Data*PlaHorm*
recent data

44
Operational Analytics Using The Basho Data
Platform And Apache Spark - 2
• Can develop Spark operational analytic applications on
low latency data stored in Basho Riak KV
• Spark-based analytical web services can be invoked on-
demand to analyse data in Riak KV
• Use on-demand Spark jobs for historical analysis and predictions
• Insights produced from analysing Riak KV data in can be
written back to Riak KV for use by other applications
• A form of closed-loop processing
• Spark Streaming can be used to calculate rollups and
detect abnormalities on streaming sensor data
• Recent data can be kept in Redis for dashboard
visualization

46
Apache Spark
Conclusions

47
Conclusions
 As operational application processing scales, so too does
the need to scale operational analytics
 Basho is using in-memory processing to accelerate
operational applications (via Redis) and to introduce
scalable operational analytics (via Spark) into these
applications
 New scalable ‘smart’ operational applications are therefore
becoming possible with careful design in a NoSQL
environment

48
www.intelligentbusiness.biz
mferguson@intelligentbusiness.biz
Twitter: @mikeferguson1
Tel/Fax (+44)1625 520700
Thank You!

Operational Analytics Using Spark and NoSQL Data Stores

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