Apache Apex: Stream Processing Architecture and Applications
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The document presents Apache Apex, highlighting its stream processing architecture designed for real-time data analytics across various industries including finance, manufacturing, and IoT. It details the features of Apache Apex such as fault tolerance, dynamic scalability, and an extensive library of operators for efficient data ingestion and processing. Use cases from clients like PubMatic and GE illustrate the platform's capability to handle high-volume data and provide actionable insights with low latency.
Apache Apex: Stream Processing Architecture and Applications
- 1. Apache Apex: Stream Processing Architecture and Applications Thomas Weise <[email protected]> @thweise Spark & Hadoop User Group Munich, July 19th 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. Application Specification (Java) 7 Java Stream API (declarative) DAG API (compositional)
- 8. Java Streams API + Windowing 8 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); } }
- 10. Operator Library 10 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
- 11. Stateful Processing 11 (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
- 12. State Checkpointing 12 Distributed, asynchronous Periodic callbacks No artificial latency Pluggable storage
- 13. Fault Tolerance 13 • Operator state is checkpointed to persistent store ᵒ Automatically performed by engine, no additional coding needed ᵒ Asynchronous and distributed ᵒ In case of failure operators are restarted from checkpoint state • Automatic detection and recovery of failed containers ᵒ Heartbeat mechanism ᵒ YARN process status notification • Buffering to enable replay of data from recovered point ᵒ Fast, incremental recovery, spike handling • Application master state checkpointed ᵒ Snapshot of physical (and logical) plan ᵒ Execution layer change log
- 14. • In-memory PubSub • Stores results emitted by operator until committed • Handles backpressure / spillover to local disk • Ordering, idempotency Operator 1 Container 1 Buffer Server Node 1 Operator 2 Container 2 Node 2 Buffer Server 14
- 15. Recovery Scenario … EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1 sum 0 … EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1 sum 7 … EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1 sum 10 … EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1 sum 7 15
- 16. Processing Guarantees 16 At-least-once • On recovery data will be replayed from a previous checkpoint ᵒ No messages lost ᵒ Default, suitable for most applications • Can be used to ensure data is written once to store ᵒ Transactions with meta information, Rewinding output, Feedback from external entity, Idempotent operations At-most-once • On recovery the latest data is made available to operator ᵒ Useful in use cases where some data loss is acceptable and latest data is sufficient Exactly-once ᵒ At-least-once + idempotency + transactional mechanisms (operator logic) to achieve end-to-end exactly once behavior
- 17. End-to-End Exactly Once 17 • 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
- 18. Scalability 18 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
- 19. Advanced Partitioning 19 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
- 20. Dynamic Partitioning 20 • 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
- 21. How dynamic partitioning works 21 • Partitioning decision (yes/no) by trigger (StatsListener) ᵒ Pluggable component, can use any system or custom metric ᵒ Externally driven partitioning example: KafkaInputOperator • Stateful! ᵒ Uses checkpointed state ᵒ Ability to transfer state from old to new partitions (partitioner, customizable) ᵒ Steps: • Call partitioner • Modify physical plan, rewrite checkpoints as needed • Undeploy old partitions from execution layer • Release/request container resources • Deploy new partitions (from rewritten checkpoint) ᵒ No loss of data (buffered) ᵒ Incremental operation, partitions that don’t change continue processing • API: Partitioner interface
- 22. Compute Locality 22 • By default operators are deployed in containers (processes) on different nodes across the Hadoop cluster • Locality options for streams ᵒ RACK_LOCAL: Data does not traverse network switches ᵒ NODE_LOCAL: Data transfer via loopback interface, frees up network bandwidth ᵒ CONTAINER_LOCAL: Data transfer via in memory queues between operators, does not require serialization ᵒ THREAD_LOCAL: Data passed through call stack, operators share thread • Host Locality ᵒ Operators can be deployed on specific hosts • New in 3.4.0: (Anti-)Affinity (APEXCORE-10) ᵒ Ability to express relative deployment without specifying a host
- 26. Maximize Revenue w/ real-time insights 26 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
- 27. Industrial IoT applications 27 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
- 28. Smart energy applications 28 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
- 29. More about the use cases 29 • 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
- 30. Q&A 30
- 31. Resources 31 • 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/