From Batch to Streaming with Apache Apex Dataworks Summit 2017
2 likes912 views
This document discusses transitioning from batch to streaming data processing using Apache Apex. It provides an overview of Apex and how it can be used to build real-time streaming applications. Examples are given of how to build an application that processes Twitter data streams and visualizes results. The document also outlines Apex's capabilities for scalable stream processing, queryable state, and its growing library of connectors and transformations.
From Batch to Streaming with Apache Apex Dataworks Summit 2017
- 1. From Batch to Streaming ET(L) with Apache Apex Thomas Weise Apache Apex PMC Chair [email protected] @thweise @atrato_io
- 2. Stream Data Processing with Apache Apex 2 Mobile Devices Logs Sensor Data Social Databases CDC Oper1 Oper2 Oper3 Real-time visualization, storage, etc Data Delivery & Storage Transform / Analytics SQL Declarative API DAG API SAMOA Beam Operator Library SAMOA Beam (roadmap) Data Sources
- 4. Batch processing with several hours till insight: ● Available data stale, does no longer apply to current situation ● Current data stuck in batch pipeline ● Complex batch processing orchestration with many different components ● Hours of delay translate to high cost due to inability to make timely campaign adjustments. Batch pipeline > 5 hours Processing with Apex, reuse batch ingestion: ● Existing ingestion mechanism (files in S3, shared with other pipelines) ● Migrate transform logic to Apex ● Enable reporting from application state (“Queryable State”) ● Reduced latency, valuable as intermediate step. Batch ingest + streaming transforms ~ 20 minutes Streaming source and processing: ● Data comes directly from Kafka clusters ● Significantly reduced latency ● Balanced load (no ingestion spikes) ● Reduced resource consumption with Apex support for multi-cluster Kafka consumers ● Reporting meets SLA requirements End-to-end stream processing seconds 4 Phased Transition
- 5. Phase 1 (batch ingest) https://www.slideshare.net/ApacheApex/real-time-insights-for-advertising-tech5
- 9. Pipeline Transformations 9 Kafka/ Files Decompress & Parse Decompress & Parse Decompress & Parse Enrich & Map Enrich & Map Enrich & Map Dimensional Compute Dimensional Compute Dimensional Compute Query Results Visualization Input Tuples Input Tuples Input Tuples Parsed Tuples Parsed Tuples Parsed Tuples Enriched Tuples Enriched Tuples Enriched Tuples Partial Aggregates Partial Aggregates Partial Aggregates Visualization Results Visualization Query Aggregate Query Aggregate Results https://www.slideshare.net/ApacheApex/actionable-insights-with-apache-apex-at-apache-big-data-2017-by-devendra-tagare Store Store
- 10. Dimension Computation 10 hour advertiser location cost revenue impr clicks 10:00 6 9 => 10 22 3 10:00 Burger King 4 6 12 2 10:00 Subway 2 3 => 4 10 2 10:00 CA 4 6 15 3 10:00 WA 2 3 => 4 7 1 10:00 Burger King CA 2 3 5 1 10:00 Burger King WA 2 3 7 1 10:00 Subway CA 2 3 10 2 10:00 Subway WA 0 => 1 Advertiser: Subway Location: WA Cost: 2 Revenue: 1 Impressions: 5 Clicks: 1 Time: 10:15:30
- 11. ● 6 geographically distributed data centers ● 10 PB of data under management ● 50 TB/day of data generated from auction & client logs ● 40+ billion ad impressions and 350+ billion bids per day ● Average data inflow of 450K events/sec ● 64 Kafka Input partitions, 32 instances of in-memory distributed store ● 1.2 TB of memory for the Apex application Scale 11
- 12. ● State Management & Fault tolerance ○ Exactly-once, Checkpointing and Windowing ○ Fine grained recovery, low-latency SLA support ○ Queryable state ● Processing based on event time ○ Accuracy, Repeatable/Replay ● Native Streaming ○ Low latency + high throughput, efficient resource utilization ○ Pipelined processing (data in motion) ● Scalability ○ Process more data by adding compute resources, no platform/architecture limits ○ Dynamic scaling and resource allocation, elasticity ● Library of connectors and transformations ○ Time to value Why Apex 12
- 13. Apex Library 13 Stateful Transformations • Windowing: sliding, tumbling, session • Accumulations: sum, merge, join, sort, top n, … • Triggering, Watermarks • Dimensional Aggregations (with state management for historical data + query) • Deduplication RDBMS • JDBC • MySQL • Oracle • MemSQL NoSQL • Cassandra, HBase • Aerospike, Accumulo • Couchbase, CouchDB • Redis, MongoDB • Geode, Kudu Messaging • Kafka • JMS (ActiveMQ etc.) • Kinesis, SQS • Flume, NiFi • MQTT File Systems • HDFS / Hive • Local File • S3 • FTP Stateless Transformations • Parsers: XML, JSON, CSV, Avro • Filter • Enrich • Configurable POJO schema • Map, FlatMap (custom Java function) • Script (JavaScript, Jython) Other • Elastic Search • Solr • Twitter • WebSocket / HTTP • SMTP
- 14. How to build it 14
- 15. Example Application (Twitter) ● Top N hashtags ● Tweet stats time series ● Queryable state ● WebSocket Pub/Sub ● Visualization with Grafana Source code: https://github.com/tweise/apex-samples/tree/master/twitter 15
- 17. Top Hashtags ● Keyed sum accumulation (5 minute window, count trigger) ● TopN accumulation of upstream windowed counts 17
- 18. Queryable State A set of operators in the library that support real-time queries of operator state. 18 Hashtag Extractor TopN Window Twitter Feed Input Operator CountByKey Window Snapshot Server Result Pub/Sub Broker HTTPWebSocket Query Input ● Pub/Sub server: https://github.com/atrato/pubsub-server ● Grafana data source: https://github.com/atrato/apex-grafana-datasource-server
- 19. Queryable State ● Snapshot server ○ Stateful operator that holds last received list of objects ○ Receives query and emits the list as JSON formatted query result ● Source schema configured, result fields via query ● Predefined schemas (Apex library): “Snapshot”, “Dimensional” 19
- 20. Demo 20
- 21. ● Apex runner in Apache Beam ● Iterative processing ● Integrated with Apache Samoa, opens up ML ● Integrated with Apache Calcite, enables SQL ● Scalable, incremental state management ● User defined control tuples (watermarks, batch control, …) ● Enhanced support for Batch Processing ● Support for Mesos and Kubernetes ● Encrypted Streams ● Support for Python Apex - Recent Additions & Roadmap 21
- 22. Resources 22 • http://apex.apache.org/ • Powered by Apex - http://apex.apache.org/powered-by-apex.html • Learn more - http://apex.apache.org/docs.html • Getting involved - http://apex.apache.org/community.html • Download - http://apex.apache.org/downloads.html • Follow @ApacheApex - https://twitter.com/apacheapex • Meetups - https://www.meetup.com/topics/apache-apex/ • Examples - https://github.com/apache/apex-malhar/tree/master/examples • Slideshare - http://www.slideshare.net/ApacheApex/presentations