Next Gen Big Data Analytics with Apache Apex
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Next Gen Big Data Analytics with Apache Apex
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Next Gen Big Data Analytics with Apache Apex
- 1. Next Gen Big Data Analytics with Apache Apex Thomas Weise, Pramod Immaneni Hadoop Summit, San Jose, June 30th 2016
- 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. Apache Apex 3 • In-memory, distributed stream processing • Application logic broken into components called operators that run in a distributed fashion across your cluster • Natural programming model • Unobtrusive Java API to express (custom) logic • Maintain state and metrics in your member variables • 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
- 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) Output Stream Tupl e Tupl e er Operator er Operator er Operator er Operator er Operator er Operator
- 7. Checkpointing 7 Application window Sliding window and tumbling window Checkpoint window No artificial latency
- 8. Event time based computation 8 (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
- 9. Scalability 9 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
- 10. Advanced Partitioning 10 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
- 11. Dynamic Partitioning 11 • 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
- 12. Fault Tolerance 12 • 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
- 13. • 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 13
- 14. End-to-End Exactly Once 14 • 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
- 15. Data Processing Pipeline Example App Builder 15
- 16. Application Specification (Java) 16 Java Stream API (declarative) DAG API (compositional)
- 17. Java Streams API + Windowing 17 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); } }
- 19. Operator Library 19 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
- 22. Maximize Revenue w/ real-time insights 22 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
- 23. Industrial IoT applications 23 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
- 24. Smart energy applications 24 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
- 25. Resources for the use cases 25 • 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
- 26. Q&A 26
- 27. Resources 27 • 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/
Notas do Editor
- Partitioning & Scaling built-in Operators can be dynamically scaled Throughput, latency or any custom logic Streams can be split in flexible ways Tuple hashcode, tuple field or custom logic Parallel partitioning for parallel pipelines MxN partitioning for generic pipelines Unifier concept for merging results from partitions Helps in handling skew imbalance Advanced Windowing support Application window configurable per operator Sliding window and tumbling window support Checkpoint window control for fault recovery Windowing does not introduce artificial latency Stateful fault tolerance out of the box Operators recover automatically from a precise point before failure At least once At most once Exactly once at window boundaries
- In-memory stream processing platform 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
- Large amount of data to process, arrival at high velocity Pipelining and partitioning Backpressure Partitioning Run same logic in multiple processes or threads Each partition processes a subset of the data Apex supports partitioning out of the box Different partitioning schemes Unification Static & Dynamic Partitioning Separation of processing logic from scaling decisions
- Partitioning decision (yes/no) by trigger (StatsListener) Pluggable component, can use any system or custom metric Externally driven partitioning example: KafkaInputOperator Stateful! Uses checkpointed state Ability to transfer state from old to new partitions (partitioner, customizable) 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) No loss of data (buffered) Incremental operation, partitions that don’t change continue processing API: Partitioner interface
- Thank Thomas, Transition to application development Quick note Talk about apex logo presence, if you see apex logo on top it’s a functionality in Apache Apex otherwise it is a addon functionality in the DataTorrent RTS enterprise offering on top of Apache Apex. Different ways of building applications, first through our application builder UI App Builder UI – Thomas talked about operators as basic building blocks of the application containing the buisness logic, UI can be used to stitch these operators together by drag and drop and configuring properties
- Creating an application in Java provides a much greater flexibility as you can create and use the operators in the same place, two ways Compositional API is low level API where you specify the individual operator implementations and connect them up in a DAG or a graph. Talk about example above. Declarative API where you specify operations on data at a high level and the ApexStream API takes care of converting it into a DAG underneath. Talk about example above.
- For next version we are working on support for Apache Beam and the different even time windowing options in the specification This is the previous example modified with windowing support. There is one global window as we are going to continue to accumulate the counts We want the results every second and we want to keep the counts in state
- With Apex you have the flexibility to create your own custom operators and be able to use it with both low level and high level API. Here are two examples
- A snaphot of our apex malhar library listing the commonly needed operators. Talk about robust support for kafka, dynamic partitioning, 0.8 and 0.9 API support with offset management and idempotent recovery. Operators for streaming and batch
- When your application is running you would want to know what is going on with your application. That is where monitoring console comes in. Touch upon a few salient features Gives you a detailed look into your application including all operators and their partitions. Gives you performance statistics, resource usage and container information for each of the partitions Helps development and operations by providing access to the individual logs right in the console and allowing users to search the logs or change log levels on the fly. Also lets you record data in a stream live at any stage. Talk about locality here quickly By default operators are deployed in containers (processes) on different nodes across the Hadoop cluster Locality options for streams RACK_LOCAL: Data does not traverse network switches NODE_LOCAL: Data transfer via loopback interface, frees up network bandwidth CONTAINER_LOCAL: Data transfer via in memory queues between operators, does not require serialization THREAD_LOCAL: Data passed through call stack, operators share thread Host Locality Operators can be deployed on specific hosts New in 3.4.0: (Anti-)Affinity (APEXCORE-10) Ability to express relative deployment without specifying a host
- Use the built-in real-time dashboards and widgets in your application or connect to your own. This picture shows the variety of widgets we have.
- Wanted to talk about some instances where Apache Apex and DataTorrent are being used in production today. These cases mentioned here, customers have talked about using Apache Apex openly and have presented them in meetups. If you want to know more in depth we have provided links to the those meetup resources at the end. Pubmatic is in advertising space and provides real time analytics around ad impressions and clicks for publishers. They are using dimensional computation operators provided by datatorrent to slice and dice the data in different ways and provide the results of those analytics through real-time dashboards.
- GE is the leader in Industrial IoT and has built a cloud platform called Predix to store and analyze machine data from all over the world. There are using datatorrent and apache apex for high speed ingestion and processing in a fault tolerant way.
- Silver spring networks also in the IoT space provides technology to collect and analyze data from smart energy meters for utilities. They perform ingestion and analytics with datatorrent to detect failures in the systems in advance and reduce outages.