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[211] 인공지능이 인공지능 챗봇을 만든다
[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...
[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...
[215] Druid로 쉽고 빠르게 데이터 분석하기
[215] Druid로 쉽고 빠르게 데이터 분석하기
[245]Papago Internals: 모델분석과 응용기술 개발
[245]Papago Internals: 모델분석과 응용기술 개발
[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈
[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈
[235]Wikipedia-scale Q&A
[235]Wikipedia-scale Q&A
[244]로봇이 현실 세계에 대해 학습하도록 만들기
[244]로봇이 현실 세계에 대해 학습하도록 만들기
[243] Deep Learning to help student’s Deep Learning
[243] Deep Learning to help student’s Deep Learning
[234]Fast & Accurate Data Annotation Pipeline for AI applications
[234]Fast & Accurate Data Annotation Pipeline for AI applications
Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing
Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing
[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지
[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지
[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기
[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기
[224]네이버 검색과 개인화
[224]네이버 검색과 개인화
[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기
[213] Fashion Visual Search
[213] Fashion Visual Search
[232] TensorRT를 활용한 딥러닝 Inference 최적화
[232] TensorRT를 활용한 딥러닝 Inference 최적화
[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지
[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지
[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터
[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터
[223]기계독해 QA: 검색인가, NLP인가?
[223]기계독해 QA: 검색인가, NLP인가?
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[253] apache ni fi
1.
Beyond Messaging Enterprise Dataflow
powered by Apache NiFi © Hortonworks Inc. 2011 – 2015. All Rights Reserved Aldrin Piri DEVIEW 2015 2015.09.15
2.
Page2 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved About me Member of Technical Staff Project Management Committee and Committer @aldrinpiri
3.
Page3 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Simplistic View of Enterprise Data Flow The Data Flow Thing Process and Analyze Data Acquire Data Store Data
4.
Page4 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved 4 • Remote sensor delivery (Internet of Things - IoT) • Intra-site / Inter-site / global distribution (Enterprise) • Ingest for driving analytics (Big Data) • Data Processing (Simple Event Processing) Where do we find data flow?
5.
Page5 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Basics of Connecting Systems For every connection, these must agree: 1. Protocol 2. Format 3. Schema 4. Priority 5. Size of event 6. Frequency of event 7. Authorization access 8. Relevance P1 Producer C1 Consumer
6.
Page6 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved 6 • Messaging addresses only a small subset of the problem space • Needed to understand the big picture • Needed the ability to make immediate changes • Must maintain chain of custody for data • Rigorous security and compliance requirements Challenges of dataflow in the enterprise
7.
Page7 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved 7 Great options including: • Kafka • ActiveMQ • Tibco Let us consider the perfect messaging system for this talk: • It has zero latency • It has perfect data durability • It supports unlimited consumers and producers Messaging Systems as Dataflow
8.
Page8 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved 8 “But my system needs…” • A different format and/or schema • To use a different protocol • The highest priority information first • Large objects (event batches) / Small Objects (streams) • Authorization to the data level • Only interested in a subset of data on a topic • Data needs to be enriched/sanitized before it arrives Dataflow as a messaging problem
9.
Page9 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Using Messaging Only a subset agree using messaging 1. Protocol 2. Format 3. Schema 4. Priority 5. Size of event 6. Frequency of event 7. Authorization access 8. Relevance P1 CN C1 Messaging More issues to consider: • How do you know what the data flow looks like? • How is it managed? • How is it working – today, yesterday?
10.
Page10 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved 10 • Add new systems to handle the protocol differences • Add new systems to convert the data • Add new systems to reorder the data • Add new systems to filter the unauthorized data • Add new topics to represent ‘stages of the flow’ Which leads to latency, complexity, and limited retention Ultimately, the operations teams who handle data at flow boundaries become responsible for managing. How these issues are typically solved
11.
Page11 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Real-time Data Flow It’s not just how quickly you move data – it’s about how quickly you can change behavior and seize new opportunities
12.
Page12 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Introducing Apache NiFi • Guaranteed delivery • Data buffering - Backpressure - Pressure release • Prioritized queuing • Flow specific QoS - Latency vs. throughput - Loss tolerance • Data provenance • Recovery/recording a rolling log of fine- grained history • Visual command and control • Flow templates • Pluggable/multi-role security • Designed for extension • Clustering
13.
Page13 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved 13 November 2014 NiFi is donated to the Apache Software Foundation (ASF) through NSA’s Technology Transfer Program and enters ASF’s incubator. 2006 NiagaraFiles (NiFi) was first incepted by Joe Witt at the National Security Agency (NSA) A Brief History July 2015 NiFi reaches ASF top-level project status
14.
Page14 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Flow Based Programming (FBP) FBP Term NiFi Term Description Information 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.
15.
Page15 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved OS/Host JVM Flow Controller Web Server Processor 1 Extension N FlowFile Repository Content Repository Provenance Repository Local Storage OS/Host JVM Flow Controller Web Server Processor 1 Extension N FlowFile Repository Content Repository Provenance Repository Local Storage Architecture OS/Host JVM NiFi Cluster Manager – Request Replicator Web Server Master NiFi Cluster Manager (NCM) OS/Host JVM Flow Controller Web Server Processor 1 Extension N FlowFile Repository Content Repository Provenance Repository Local Storage Slaves NiFi Nodes
16.
Page16 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Live Demonstration
17.
Page17 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Feature Proposals – Status FUTURE Better integration with Apache Kafka FUTURE Clustering redesign IN PROGRESS Configuration management of flows STARTED Extension and template registry RELEASE COMING SOON First-class Avro support 1 STARTED Interactive queue management STARTED Multi-tenant data flow FUTURE Pluggable authentication FUTURE Reference-able process groups FUTURE Variable registry FUTURE ‘Wormhole’ connections https://cwiki.apache.org/confluence/display/NIFI/NiFi+Feature+Proposals
18.
Page18 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Learn more and join us! Apache NiFi site http://nifi.apache.org Subscribe to and collaborate at dev@nifi.apache.org users@nifi.apache.org Submit Ideas or Issues https://issues.apache.org/jira/browse/NIFI Follow us on Twitter @apachenifi
19.
Page19 © Hortonworks
Inc. 2011 – 2015. All Rights Reserved Thank you!
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