Architectual Comparison of Apache Apex and Spark Streaming
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This presentation discusses architectural differences between Apache Apex features with Spark Streaming. It discusses how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion. Also, it will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. Further, it will discuss how these features affect time to market and total cost of ownership.
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Architectual Comparison of Apache Apex and Spark Streaming
- 1. Architectural Comparison of Apache Apex and Spark Streaming Webinar June 8th 2016 Thomas Weise, Apache Apex PMC @thweise thw@apache.org
- 2. Next Gen Stream Data Processing • Data from variety of sources (IoT, Kafka, files, social media etc.) • Unbounded, continuous data streams ᵒ Batch can be processed as stream (but a stream is not a batch) • (In-memory) Processing with temporal boundaries (windows) • Stateful operations: Aggregation, Rules, … -> Analytics • Results stored to variety of sinks or destinations ᵒ Streaming application can also serve data with very low latency 2 Browser Web Server Kafka Input (logs) Decompress, Parse, Filter Dimensions Aggregate Kafka Logs Kafka
- 3. Batch vs. Streaming Credit: Gyula Fóra & Márton Balassi: Large-Scale Stream Processing in the Hadoop Ecosystem 3
- 4. Architecture and Features Spark Streaming Apex Model micro-batch native streaming/data-in-motion Language Java, Scala, client bindings Java (Scala) API declarative compositional (DAG), declarative* Locality data locality advanced processing locality Latency high very low (millis) Throughput very high very high Scalability scheduler limit horizontal Partitioning standard advanced (parallel pipes, unifiers) Connector Library Limited (certification), externally maintained Rich library of connectors and processors, part of Apex (Malhar) 4
- 5. Operability Spark Streaming Apex State Management RDD, user code checkpointing Recovery RDD lineage incremental (buffer server) Processing Sem. exactly-once* end-to-end exactly-once Backpressure user configuration Automatic (buffer server memory + disk) Elasticity yes w/ limited control yes w/ full user control Dynamic topology no yes Security Kerberos Kerberos, RBAC*, LDAP* Multi-tennancy depends on cluster manager YARN, full isolation DevOps tools basic REST API, DataTorrent RTS 5
- 6. Apache Apex Features • In-memory stream processing platform ᵒ Developed since 2012, ASF TLP since 04/2016 • Unobtrusive Java API to express (custom) logic • Scale out, distributed, parallel • High throughput & low latency processing • Windowing (temporal boundary) • Reliability, fault tolerance, operability • Hadoop native • Compute locality, affinity • Dynamic updates, elasticity 6
- 8. Apache Apex Malhar Library 8
- 9. Application Development Model 9 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) Output Stream Tupl e Tupl e er Operator er Operator er Operator er Operator er Operator er Operator
- 10. Streaming Windows 10 Application window Sliding window and tumbling window Checkpoint window No artificial latency
- 11. Event time & Dimensions Computation 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. Scalability • Large amount of data to process, arrival at high velocity ᵒ Pipelining and partitioning ᵒ Backpressure • Partitioning ᵒ Run same logic in multiple processes or threads ᵒ Each partition processes a subset of the data • Apex supports partitioning out of the box ᵒ Different partitioning schemes ᵒ Unification ᵒ Static & Dynamic Partitioning ᵒ Separation of processing logic from scaling decisions 12
- 13. Partitioning 13 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
- 14. Advanced Partitioning 14 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
- 15. Dynamic Partitioning 15 • 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
- 16. How dynamic partitioning works 16 • 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
- 17. Fault Tolerance 17 • 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
- 18. • 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 18
- 19. 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 19
- 20. Processing Guarantees 20 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
- 21. End-to-End Exactly Once 21 • Becomes important when writing to external systems • Data should not be duplicated or lost in the external system even in case of application failures • Common external systems ᵒ Databases ᵒ Files ᵒ Message queues • Platform support for at least once is a must so that no data is lost • Data duplication must still 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
- 22. Data Processing Pipeline Example App Builder 22
- 25. Real-Time Dashboards Real Time Visualization 25
- 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. Demo / Q&A 29
- 30. Resources 30 • 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/