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Page 1: Apache NiFi Crash Course - San Jose Hadoop Summit

Dataflow with Apache NiFiAldrin Piri - @aldrinpiriApache NiFi Crash CourseHadoop Summit 2016 – San Jose

29 June 2016

Page 2: Apache NiFi Crash Course - San Jose Hadoop Summit

2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Key: 'Apache NiFi’ Value: 'PMC Member'Key: 'Work’ Value: ’Sr. Member of Technical Staff @ Hortonworks'Key: 'Working with NiFi Since’ Value: '2010’

Page 3: Apache NiFi Crash Course - San Jose Hadoop Summit

3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

AgendaWhat is dataflow and what are the challenges?

Apache NiFi

Architecture

Live Demo

Community

Page 4: Apache NiFi Crash Course - San Jose Hadoop Summit

4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

AgendaWhat is dataflow and what are the challenges?

Apache NiFi

Architecture

Live Demo

Community

Page 5: Apache NiFi Crash Course - San Jose Hadoop Summit

5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Let’s Connect A to BProducers A.K.A Things

AnythingAND

Everything

Internet!

Consumers• User• Storage• System• …More Things

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6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Moving data effectively is hard

Standards: http://xkcd.com/927/

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7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Why is moving data effectively hard?

Standards Formats “Exactly Once” Delivery Protocols Veracity of Information Validity of Information Ensuring Security Overcoming Security

Compliance Schemas Consumers Change Credential Management “That [person|team|group]” Network “Exactly Once” Delivery

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8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Let’s Connect Lots of As to Bs to As to Cs to Bs to Δs to Cs to ϕsLet’s consider the needs of a courier service

Physical Store

Gateway Server

Mobile Devices

Registers

Server Cluster

Distribution Center Core Data Center at HQ

Server Cluster

On Delivery Routes

Trucks Deliverers

Delivery Truck: Creative Stall, https://thenounproject.com/creativestall/Deliverer: Rigo Peter, https://thenounproject.com/rigo/Cash Register: Sergey Patutin, https://thenounproject.com/bdesign.by/Hand Scanner: Eric Pearson, https://thenounproject.com/epearson001/

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9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Great! I am collecting all this data! Let’s use it!Finding our needles in the haystack

Physical Store

Gateway Server

Mobile Devices

Registers

Server Cluster

Distribution Center

Kafka

Core Data Center at HQ

Server Cluster

Others

Storm / Spark / Flink / Apex

Kafka

Storm / Spark / Flink / Apex

On Delivery Routes

Trucks Deliverers

Delivery Truck: Creative Stall, https://thenounproject.com/creativestall/Deliverer: Rigo Peter, https://thenounproject.com/rigo/Cash Register: Sergey Patutin, https://thenounproject.com/bdesign.by/Hand Scanner: Eric Pearson, https://thenounproject.com/epearson001/

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10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Why is moving data effectively hard when scoped internally?

Standards Formats “Exactly Once” Delivery Protocols Veracity of Information Validity of Information Ensuring Security Overcoming Security

Compliance Schemas Consumers Change Credential Management “That [person|team|group]” Network “Exactly Once” Delivery

Page 11: Apache NiFi Crash Course - San Jose Hadoop Summit

11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Let’s Connect Lots of As to Bs to As to Cs to Bs to Δs to Cs to ϕsOh, that courier service is global

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12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Why is moving data effectively hard when scoped globally?

Standards Formats “Exactly Once” Delivery Protocols Veracity of Information Validity of Information Ensuring Security Overcoming Security

Compliance Schemas Consumers Change Credential Management “That [person|team|group]” Network “Exactly Once” Delivery

Page 13: Apache NiFi Crash Course - San Jose Hadoop Summit

13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

The Unassuming Line: A Case StudyWe’ve seen a few lines show up in the wild thus far

Internet! Inter- & Intra- connections inour global courier enterprise

Spotlight: Arthur Lacôte, https://thenounproject.com/turo/

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14 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Dataflow Line Anatomy 101Let’s dissect what this line typically represents

Fig 1. Lineus Worldwidewebus. Common Name: Internet!

Script or Application

Script or Application

Data Data

Disparate TransportMechanisms

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15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Dataflow Line Anatomy 201Sometimes that transport is just more lines

Fig 1. Lineus Worldwidewebus. Common Name: Internet!

Script or Application

Script or Application

Line Inception

Data Data

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16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Dataflow Line Anatomy 301But those lines could also have components…

Fig 1. Lineus Worldwidewebus. Common Name: Internet! Fig 2. Good Recursion Joke

NoSuchJokeException

footage not found

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17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

AgendaWhat is dataflow and what are the challenges?

Apache NiFi

Architecture

Live Demo

Community

Page 18: Apache NiFi Crash Course - San Jose Hadoop Summit

18 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Apache NiFiKey Features

• Guaranteed delivery• Data buffering

- Backpressure- Pressure release

• Prioritized queuing• Flow specific QoS

- Latency vs. throughput- Loss tolerance

• Data provenance• Supports push and pull

models

• Recovery/recording a rolling log of fine-grained history

• Visual command and control

• Flow templates• Pluggable/multi-role

security• Designed for extension• Clustering

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19 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Apache NiFi Subproject: MiNiFi

Let me get the key parts of NiFi close to where data begins and provide bidrectional communication

NiFi lives in the data center. Give it an enterprise server or a cluster of them. MiNiFi lives as close to where data is born and is a guest on that device or system

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20 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Let’s revisit our courier service from the perspective of NiFi

Physical Store

Gateway Server

Mobile Devices

Registers

Server Cluster

Distribution Center

Kafka

Core Data Center at HQ

Server Cluster

Others

Storm / Spark / Flink / Apex

Kafka

Storm / Spark / Flink / Apex

On Delivery Routes

Trucks Deliverers

Delivery Truck: Creative Stall, https://thenounproject.com/creativestall/Deliverer: Rigo Peter, https://thenounproject.com/rigo/Cash Register: Sergey Patutin, https://thenounproject.com/bdesign.by/Hand Scanner: Eric Pearson, https://thenounproject.com/epearson001/

Client Libraries

Client Libraries

MiNiFi

MiNiFiNiFi NiFi NiFi NiFi NiFi NiFi

Client Libraries

Page 21: Apache NiFi Crash Course - San Jose Hadoop Summit

21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Apache NiFi Managed DataflowSOURCES REGIONAL

INFRASTRUCTURECORE

INFRASTRUCTURE

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22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

NiFi is based on Flow Based Programming (FBP)FBP Term NiFi Term DescriptionInformation Packet

FlowFile Each object moving through the system.

Black Box FlowFile Processor

Performs the work, doing some combination of data routing, transformation, or mediation between systems.

Bounded Buffer

Connection The linkage between processors, acting as queues and allowing various processes to interact at differing rates.

Scheduler Flow Controller

Maintains the knowledge of how processes are connected, and manages the threads and allocations thereof which all processes use.

Subnet Process Group

A set of processes and their connections, which can receive and send data via ports. A process group allows creation of entirely new component simply by composition of its components.

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23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

FlowFiles & Data Agnosticism

NiFi is data agnostic! But, NiFi was designed understanding that users

can care about specifics and provides tooling

to interact with specific formats, protocols, etc.

ISO 8601 - http://xkcd.com/1179/

Robustness principle

Be conservative in what you do, be liberal in what you accept from others“

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24 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

FlowFiles are like HTTP dataHTTP Data FlowFile

HTTP/1.1 200 OKDate: Sun, 10 Oct 2010 23:26:07 GMTServer: Apache/2.2.8 (CentOS) OpenSSL/0.9.8gLast-Modified: Sun, 26 Sep 2010 22:04:35 GMTETag: "45b6-834-49130cc1182c0"Accept-Ranges: bytesContent-Length: 13Connection: closeContent-Type: text/html

Hello world!

Standard FlowFile AttributesKey: 'entryDate’ Value: 'Fri Jun 17 17:15:04 EDT 2016'Key: 'lineageStartDate’ Value: 'Fri Jun 17 17:15:04 EDT 2016'Key: 'fileSize’ Value: '23609'FlowFile Attribute Map ContentKey: 'filename’Value: '15650246997242'Key: 'path’ Value: './’

Binary Content *

Header

Content

Page 25: Apache NiFi Crash Course - San Jose Hadoop Summit

25 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

AgendaWhat is dataflow and what are the challenges?

Apache NiFi

Architecture

Live Demo

Community

Page 26: Apache NiFi Crash Course - San Jose Hadoop Summit

26 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Extension / Integration PointsNiFi Term DescriptionFlow File Processor

Push/Pull behavior. Custom UI

Reporting Task

Used to push data from NiFi to some external service (metrics, provenance, etc..)

Controller Service

Used to enable reusable components / shared services throughout the flow

REST API Allows clients to connect to pull information, change behavior, etc..

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27 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

OS/Host

JVM

Flow Controller

Web Server

Processor 1 Extension N

FlowFileRepository

ContentRepository

ProvenanceRepository

Local Storage

OS/Host

JVM

Flow Controller

Web Server

Processor 1 Extension N

FlowFileRepository

ContentRepository

ProvenanceRepository

Local Storage

Architecture*

OS/Host

JVM

Flow Controller

Web Server

Processor 1 Extension N

FlowFileRepository

ContentRepository

ProvenanceRepository

Local Storage

OS/Host

JVM

NiFi Cluster Manger – Request Replicator

Web Server

MasterNiFi Cluster Manager (NCM)

OS/Host

JVM

Flow Controller

Web Server

Processor 1 Extension N

FlowFileRepository

ContentRepository

ProvenanceRepository

Local Storage

SlavesNiFi Nodes

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28 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

NiFi Architecture – Repositories - Pass by reference

FlowFile Content Provenance

F1 C1 C1 P1 F1

Excerpt of demo flow… What’s happening inside the repositories…

BEFORE

AFTER

F2 C1 C1 P3 F2 – Clone (F1)

F1 C1 P2 F1 – Route

P1 F1 – Create

Page 29: Apache NiFi Crash Course - San Jose Hadoop Summit

29 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

NiFi Architecture – Repositories – Copy on Write

FlowFile Content Provenance

F1 C1 C1 P1 F1 - CREATE

Excerpt of demo flow… What’s happening inside the repositories…

BEFORE

AFTER

F1 C1

F1.1 C2C2 (encrypted)

C1 (plaintext)

P2 F1.1 - MODIFY

P1 F1 - CREATE

Page 30: Apache NiFi Crash Course - San Jose Hadoop Summit

30 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

AgendaWhat is dataflow and what are the challenges?

Apache NiFi

Architecture

Demo

Community

Page 31: Apache NiFi Crash Course - San Jose Hadoop Summit

31 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Learn, Share at Birds of a Feather Streaming, DataFlow & Cybersecurity

Thursday June 306:30 pm, Ballroom C

Page 32: Apache NiFi Crash Course - San Jose Hadoop Summit

32 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Why NiFi?

Moving data is multifaceted in its challenges and these are present in different contexts at varying scopes– Think of our courier example and organizations like it: inter vs intra, domestically, internationally

Provide common tooling and extensions that are commonly needed but be flexible for extension– Leverage existing libraries and expansive Java ecosystem for functionality– Allow organizations to integrate with their existing infrastructure

Empower folks managing your infrastructure to make changes and reason about issues that are occurring– Data Provenance to show context and data’s journey– User Interface/Experience a key component

Page 33: Apache NiFi Crash Course - San Jose Hadoop Summit

33 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Learn more and join us!

Apache NiFi sitehttp://nifi.apache.org

Subproject MiNiFi sitehttp://nifi.apache.org/minifi/

Subscribe to and collaborate [email protected]@nifi.apache.org

Submit Ideas or Issueshttps://issues.apache.org/jira/browse/NIFI

Follow us on Twitter@apachenifi

Page 34: Apache NiFi Crash Course - San Jose Hadoop Summit

34 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Our Lab for Today

We will be exploring some examples to work through creating a dataflow with Apache NiFi

Use Case: An urban planning board is evaluating the need for a new highway, dependent on current traffic patterns, particularly as other roadwork initiatives are under way. Integrating live data poses a problem because traffic analysis has traditionally been done using historical, aggregated traffic counts. To improve traffic analysis, the city planner wants to leverage real-time data to get a deeper understanding of traffic patterns. NiFi was selected for for this real-time data integration.

Labs are available at http://tinyurl.com/nificrashcourse

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35 © Hortonworks Inc. 2011 – 2016. All Rights Reserved

Thank You


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