Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder, DataTorrent - "Streaming Analytics with Apache Apex"
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Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder of DataTorrent presented "Streaming Analytics with Apache Apex" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.
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Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder, DataTorrent - "Streaming Analytics with Apache Apex"
- 1. Stream Processing with Apache Apex Thomas Weise <[email protected]> @thweise Big Data Berlin v8, July 14th 2016
- 2. Stream Data Processing 2 Data Sources Events Logs Sensor Data Social Databases CDC Oper1 Oper2 Oper3 Real-time analytics on data in motion Data Visualization
- 3. Industries & Use Cases 3 Financial Services Ad-Tech Telecom Manufacturing Energy IoT Fraud and risk monitoring Real-time customer facing dashboards on key performance indicators Call detail record (CDR) & extended data record (XDR) analysis Supply chain planning & optimization Smart meter analytics Data ingestion and processing Credit risk assessment Click fraud detection Understanding customer behavior AND context Preventive maintenance Reduce outages & improve resource utilization Predictive analytics Improve turn around time of trade settlement processes Billing optimization Packaging and selling anonymous customer data Product quality & defect tracking Asset & workforce management Data governance • Large scale ingest and distribution • Real-time ELTA (Extract Load Transform Analyze) • Dimensional computation & aggregation • Enforcing data quality and data governance requirements • Real-time data enrichment with reference data • Real-time machine learning model scoring HORIZONTAL
- 4. Apache Apex 4 • In-memory, distributed stream processing • Application logic broken into components (operators) that execute distributed in a cluster • Unobtrusive Java API to express (custom) logic • Maintain state and metrics in member variables • Windowing, event-time processing • Scalable, high throughput, low latency • Operators can be scaled up or down at runtime according to the load and SLA • Dynamic scaling (elasticity), compute locality • Fault tolerance & correctness • Automatically recover from node outages without having to reprocess from beginning • State is preserved, checkpointing, incremental recovery • End-to-end exactly-once • Operability • System and application metrics, record/visualize data • Dynamic changes, elasticity
- 5. Native Hadoop Integration 5 • YARN is the resource manager • HDFS for storing persistent state
- 6. Application Development Model 6 A Stream is a sequence of data tuples A typical Operator takes one or more input streams, performs computations & emits one or more output streams • Each Operator is YOUR custom business logic in java, or built-in operator from our open source library • Operator has many instances that run in parallel and each instance is single-threaded Directed Acyclic Graph (DAG) is made up of operators and streams Directed Acyclic Graph (DAG) Operator Operator Operator Operator Operator Operator Tuple Output Stream Filtered Stream Enriched Stream Filtered Stream Enriched Stream
- 7. State Checkpointing 7 Distributed, asynchronous Periodic callbacks No artificial latency Pluggable storage
- 8. End-to-End Exactly Once 8 • Important when writing to external systems • Data should not be duplicated or lost in the external system in case of application failures • Common external systems ᵒ Databases ᵒ Files ᵒ Message queues • Exactly-once = at-least-once + idempotency + consistent state • Data duplication must be avoided when data is replayed from checkpoint ᵒ Operators implement the logic dependent on the external system ᵒ Platform provides checkpointing and repeatable windowing
- 9. Event time based computation 9 (All) : 5 t=4:00 : 2 t=5:00 : 3 k=A, t=4:00 : 2 k=A, t=5:00 : 1 k=B, t=5:00 : 2 (All) : 4 t=4:00 : 2 t=5:00 : 2 k=A, t=4:00 : 2 K=B, t=5:00 : 2 k=A t=5:00 (All) : 1 t=4:00 : 1 k=A, t=4:00 : 1 k=B t=5:59 k=B t=5:00 k=A T=4:30 k=A t=4:00
- 10. Scalability 10 NxM PartitionsUnifier 0 1 2 3 Logical DAG 0 1 2 1 1 Unifier 1 20 Logical Diagram Physical Diagram with operator 1 with 3 partitions 0 Unifier 1a 1b 1c 2a 2b Unifier 3 Physical DAG with (1a, 1b, 1c) and (2a, 2b): No bottleneck Unifier Unifier0 1a 1b 1c 2a 2b Unifier 3 Physical DAG with (1a, 1b, 1c) and (2a, 2b): Bottleneck on intermediate Unifier
- 11. Advanced Partitioning 11 0 1a 1b 2 3 4Unifier Physical DAG 0 4 3a2a1a 1b 2b 3b Unifier Physical DAG with Parallel Partition Parallel Partition Container uopr uopr1 uopr2 uopr3 uopr4 uopr1 uopr2 uopr3 uopr4 dopr dopr doprunifier unifier unifier unifier Container Container NICNIC NICNIC NIC Container NIC Logical Plan Execution Plan, for N = 4; M = 1 Execution Plan, for N = 4; M = 1, K = 2 with cascading unifiers Cascading Unifiers 0 1 2 3 4 Logical DAG
- 12. Dynamic Partitioning 12 • Partitioning change while application is running ᵒ Change number of partitions at runtime based on stats ᵒ Determine initial number of partitions dynamically • Kafka operators scale according to number of kafka partitions ᵒ Supports re-distribution of state when number of partitions change ᵒ API for custom scaler or partitioner 2b 2c 3 2a 2d 1b 1a1a 2a 1b 2b 3 1a 2b 1b 2c 3b 2a 2d 3a Unifiers not shown
- 13. Application Specification (Java) 13 Java Stream API (declarative) DAG API (compositional)
- 14. Java Streams API + Windowing 14 Next Release (3.5): Support for Windowing à la Apache Beam (incubating): @ApplicationAnnotation(name = "WordCountStreamingApiDemo") public class ApplicationWithStreamAPI implements StreamingApplication { @Override public void populateDAG(DAG dag, Configuration configuration) { String localFolder = "./src/test/resources/data"; ApexStream<String> stream = StreamFactory .fromFolder(localFolder) .flatMap(new Split()) .window(new WindowOption.GlobalWindow(), new TriggerOption().withEarlyFiringsAtEvery(Duration.millis(1000)).accumulatingFiredPanes()) .countByKey(new ConvertToKeyVal()).print(); stream.populateDag(dag); } }
- 15. Operator Library 15 RDBMS • Vertica • MySQL • Oracle • JDBC NoSQL • Cassandra, Hbase • Aerospike, Accumulo • Couchbase/ CouchDB • Redis, MongoDB • Geode Messaging • Kafka • Solace • Flume, ActiveMQ • Kinesis, NiFi File Systems • HDFS/ Hive • NFS • S3 Parsers • XML • JSON • CSV • Avro • Parquet Transformations • Filters • Rules • Expression • Dedup • Enrich Analytics • Dimensional Aggregations (with state management for historical data + query) Protocols • HTTP • FTP • WebSocket • MQTT • SMTP Other • Elastic Search • Script (JavaScript, Python, R) • Solr • Twitter
- 19. Maximize Revenue w/ real-time insights 19 PubMatic is the leading marketing automation software company for publishers. Through real-time analytics, yield management, and workflow automation, PubMatic enables publishers to make smarter inventory decisions and improve revenue performance Business Need Apex based Solution Client Outcome • Ingest and analyze high volume clicks & views in real-time to help customers improve revenue - 200K events/second data flow • Report critical metrics for campaign monetization from auction and client logs - 22 TB/day data generated • Handle ever increasing traffic with efficient resource utilization • Always-on ad network • DataTorrent Enterprise platform, powered by Apache Apex • In-memory stream processing • Comprehensive library of pre-built operators including connectors • Built-in fault tolerance • Dynamically scalable • Management UI & Data Visualization console • Helps PubMatic deliver ad performance insights to publishers and advertisers in real-time instead of 5+ hours • Helps Publishers visualize campaign performance and adjust ad inventory in real-time to maximize their revenue • Enables PubMatic reduce OPEX with efficient compute resource utilization • Built-in fault tolerance ensures customers can always access ad network
- 20. Industrial IoT applications 20 GE is dedicated to providing advanced IoT analytics solutions to thousands of customers who are using their devices and sensors across different verticals. GE has built a sophisticated analytics platform, Predix, to help its customers develop and execute Industrial IoT applications and gain real-time insights as well as actions. Business Need Apex based Solution Client Outcome • Ingest and analyze high-volume, high speed data from thousands of devices, sensors per customer in real-time without data loss • Predictive analytics to reduce costly maintenance and improve customer service • Unified monitoring of all connected sensors and devices to minimize disruptions • Fast application development cycle • High scalability to meet changing business and application workloads • Ingestion application using DataTorrent Enterprise platform • Powered by Apache Apex • In-memory stream processing • Built-in fault tolerance • Dynamic scalability • Comprehensive library of pre-built operators • Management UI console • Helps GE improve performance and lower cost by enabling real-time Big Data analytics • Helps GE detect possible failures and minimize unplanned downtimes with centralized management & monitoring of devices • Enables faster innovation with short application development cycle • No data loss and 24x7 availability of applications • Helps GE adjust to scalability needs with auto-scaling
- 21. Smart energy applications 21 Silver Spring Networks helps global utilities and cities connect, optimize, and manage smart energy and smart city infrastructure. Silver Spring Networks receives data from over 22 million connected devices, conducts 2 million remote operations per year Business Need Apex based Solution Client Outcome • Ingest high-volume, high speed data from millions of devices & sensors in real-time without data loss • Make data accessible to applications without delay to improve customer service • Capture & analyze historical data to understand & improve grid operations • Reduce the cost, time, and pain of integrating with 3rd party apps • Centralized management of software & operations • DataTorrent Enterprise platform, powered by Apache Apex • In-memory stream processing • Pre-built operator • Built-in fault tolerance • Dynamically scalable • Management UI console • Helps Silver Spring Networks ingest & analyze data in real-time for effective load management & customer service • Helps Silver Spring Networks detect possible failures and reduce outages with centralized management & monitoring of devices • Enables fast application development for faster time to market • Helps Silver Spring Networks scale with easy to partition operators • Automatic recovery from failures
- 22. More about the use cases 22 • Pubmatic • https://www.youtube.com/watch?v=JSXpgfQFcU8 • GE • https://www.youtube.com/watch?v=hmaSkXhHNu0 • http://www.slideshare.net/ApacheApex/ge-iot-predix-time-series-data-ingestion-service-using- apache-apex-hadoop • SilverSpring Networks • https://www.youtube.com/watch?v=8VORISKeSjI • http://www.slideshare.net/ApacheApex/iot-big-data-ingestion-and-processing-in-hadoop-by- silver-spring-networks
- 23. Q&A + Resources 23 • http://apex.apache.org/ • Learn more: http://apex.apache.org/docs.html • Subscribe - http://apex.apache.org/community.html • Download - http://apex.apache.org/downloads.html • Follow @ApacheApex - https://twitter.com/apacheapex • Meetups – http://www.meetup.com/pro/apacheapex/ • More examples: https://github.com/DataTorrent/examples • Slideshare: http://www.slideshare.net/ApacheApex/presentations • https://www.youtube.com/results?search_query=apache+apex • Free Enterprise License for Startups - https://www.datatorrent.com/product/startup-accelerator/