Data Distribution Patterns with Apache NiFi

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An overview of how to distribute data across a NiFi cluster. Includes pushing to listening processors, pulling on each node, pulling on primary node with redistribution, and site-to-site.

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Page1 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Data Distribution Patterns with Apache NiFi
March 2016
Bryan Bende – Member of Technical Staff

Page2 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Overview
• Frequently Asked Question:
 “How do I distribute data across my NiFi cluster?”
• Answer:
 “It depends…”
• Typically based on whether the data is being pushed or pulled

Page3 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Pushing to Listeners
NCM
Node 1
ListenHTTP
Node 2
ListenHTTP
Load
Balancer
Data Producer
Data Producer
Data Producer
• Processors listening for incoming
data on each node in the cluster
• Load balancer sitting in front of the
cluster pointing at listeners
• Data producers push data to load
balancer which distributes data
across the cluster
• Same approach works for
ListenSyslog, ListenUDP, and
HandleHttpRequest

Page4 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Pulling on All Nodes
NCM
Node 1
GetKafka
Node 2
GetKafka
Kafka Topic
• Works when data source ensures each
request gets a unique piece of data
• Kafka sees each GetKafka processor as
the same client
• Each GetKafka processor gets different
data

Page5 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Pulling With List & Fetch Operations
NCM
Node 1 (Primary)
ListHDFS
HDFS
FetchHDFS
RPG
Input Port
Node 2
ListHDFS
FetchHDFS
RPG
Input Port
• Perform “list” operation on
primary node
• Send results to Remote
Process Group pointing back
to same cluster
• Redistributes results to all
nodes to perform “fetch” in
parallel
• Same approach for ListFile +
FetchFile and ListSFTP +
FetchSFTP

Page6 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Site-To-Site Push
NCM
Node 1
Input Port
Node 2
Input Port
Standalone NiFi
RPG
• Site-To-Site makes a direct
connection between NiFi instances
• Either side can be a cluster or
standalone instance
• In a push scenario, the source
connects a Remote Process Group
to an Input Port on the destination
• If pushing to a cluster, Site-To-Site
takes care of load balancing across
the nodes in the cluster

Page7 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Site-To-Site Pull
NCM
Node 1
RPG
Node 2
RPG
Standalone NiFi
Output Port
• In a pull scenario, the destination
connects a Remote Process
Group to an Output Port on the
source
• Each node will pull different data
from the source
• If the source was a cluster, each
node would pull from each node in
the source cluster

Page8 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Summary
Several mechanism for distributing data across a cluster…
• Push to Listeners with a load balancer in front
• Pull from a source that provides a queue
• Pull with a List + Fetch approach
• Push with Site-to-Site
• Pull with Site-to-Site
Contact Info
• bbende@hortonworks.com
• Twitter - @bbende

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Data Distribution Patterns with Apache NiFi

2. Page2 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Overview • Frequently Asked Question:  “How do I distribute data across my NiFi cluster?” • Answer:  “It depends…” • Typically based on whether the data is being pushed or pulled

3. Page3 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Pushing to Listeners NCM Node 1 ListenHTTP Node 2 ListenHTTP Load Balancer Data Producer Data Producer Data Producer • Processors listening for incoming data on each node in the cluster • Load balancer sitting in front of the cluster pointing at listeners • Data producers push data to load balancer which distributes data across the cluster • Same approach works for ListenSyslog, ListenUDP, and HandleHttpRequest

4. Page4 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Pulling on All Nodes NCM Node 1 GetKafka Node 2 GetKafka Kafka Topic • Works when data source ensures each request gets a unique piece of data • Kafka sees each GetKafka processor as the same client • Each GetKafka processor gets different data

5. Page5 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Pulling With List & Fetch Operations NCM Node 1 (Primary) ListHDFS HDFS FetchHDFS RPG Input Port Node 2 ListHDFS FetchHDFS RPG Input Port • Perform “list” operation on primary node • Send results to Remote Process Group pointing back to same cluster • Redistributes results to all nodes to perform “fetch” in parallel • Same approach for ListFile + FetchFile and ListSFTP + FetchSFTP

6. Page6 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Site-To-Site Push NCM Node 1 Input Port Node 2 Input Port Standalone NiFi RPG • Site-To-Site makes a direct connection between NiFi instances • Either side can be a cluster or standalone instance • In a push scenario, the source connects a Remote Process Group to an Input Port on the destination • If pushing to a cluster, Site-To-Site takes care of load balancing across the nodes in the cluster

7. Page7 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Site-To-Site Pull NCM Node 1 RPG Node 2 RPG Standalone NiFi Output Port • In a pull scenario, the destination connects a Remote Process Group to an Output Port on the source • Each node will pull different data from the source • If the source was a cluster, each node would pull from each node in the source cluster

8. Page8 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Summary Several mechanism for distributing data across a cluster… • Push to Listeners with a load balancer in front • Pull from a source that provides a queue • Pull with a List + Fetch approach • Push with Site-to-Site • Pull with Site-to-Site Contact Info • bbende@hortonworks.com • Twitter - @bbende

Data Distribution Patterns with Apache NiFi

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