HBaseCon 2015: NRT Event Processing with Guaranteed Delivery of HTTP Callbacks
1 like3,554 views
The document presents Salesforce's Marketing Cloud and its implementation of webhooks, which enable near-real-time event processing with guaranteed HTTP callback delivery. It outlines the challenges and solutions related to event processing, and introduces the Cask Data Application Platform (CDAP) for building scalable data applications on Apache Hadoop. The document also discusses transaction support and data access patterns, emphasizing the importance of high data integrity in various regulated industries.
HBaseCon 2015: NRT Event Processing with Guaranteed Delivery of HTTP Callbacks
- 1. Webhooks Near-real time event processing with guaranteed delivery of HTTP callbacks HBaseCon 2015
- 2. Alan Steckley Principal Software Engineer, Salesforce 2
- 3. Poorna Chandra Software Engineer, Cask 3
- 4. Safe harbor statement under the Private Securities Litigation Reform Act of 1995: This presentation may contain forward-looking statements that involve risks, uncertainties, and assumptions. If any such uncertainties materialize or if any of the assumptions proves incorrect, the results of salesforce.com, inc. could differ materially from the results expressed or implied by the forward-looking statements we make. All statements other than statements of historical fact could be deemed forward-looking, including any projections of product or service availability, subscriber growth, earnings, revenues, or other financial items and any statements regarding strategies or plans of management for future operations, statements of belief, any statements concerning new, planned, or upgraded services or technology developments and customer contracts or use of our services. The risks and uncertainties referred to above include – but are not limited to – risks associated with developing and delivering new functionality for our service, new products and services, our new business model, our past operating losses, possible fluctuations in our operating results and rate of growth, interruptions or delays in our Web hosting, breach of our security measures, the outcome of any litigation, risks associated with completed and any possible mergers and acquisitions, the immature market in which we operate, our relatively limited operating history, our ability to expand, retain, and motivate our employees and manage our growth, new releases of our service and successful customer deployment, our limited history reselling non-salesforce.com products, and utilization and selling to larger enterprise customers. Further information on potential factors that could affect the financial results of salesforce.com, inc. is included in our annual report on Form 10-K for the most recent fiscal year and in our quarterly report on Form 10-Q for the most recent fiscal quarter. These documents and others containing important disclosures are available on the SEC Filings section of the Investor Information section of our Web site. Any unreleased services or features referenced in this or other presentations, press releases or public statements are not currently available and may not be delivered on time or at all. Customers who purchase our services should make the purchase decisions based upon features that are currently available. Salesforce.com, inc. assumes no obligation and does not intend to update these forward- looking statements. Safe Harbor 4
- 5. ● Salesforce Marketing Cloud ● Webhooks use case ● Implementation in CDAP ● Q&A Overview 5
- 6. ● Connects businesses to their customers through email, social media, and SMS. ● 1+ billion personalized messages per day ● 100,000’s of business units ● Billions of subscribers ● Hosts petabytes of customer data in our data centers ● Handles a wide range of communications ○ Marketing campaigns ○ Purchase confirmations ○ Financial notifications ○ Password resets What is the Salesforce Marketing Cloud? 6
- 7. ● Webhooks is a near-real time event delivery platform with guaranteed delivery ○ Subscribers generate events by engaging with messages ○ Deliver events to customers over HTTP within seconds ○ Customers react to events in near real time What is Webhooks? 7
- 8. A purchase receipt email fails to be delivered A mail bounce event is pushed to a service hosted by the retailer Retailer’s customer service is immediately aware of the failure Example use case 8
- 9. 1. Process a stream of near real time events based on customer defined actions. 2. Guarantee delivery of processed events emitted to third party systems. General problem statement 9
- 10. High data integrity Commerce, health, and finance messaging subject to government regulation Horizontal scalability Short time to market Accessible developer experience Existing Hadoop/YARN/HBase expertise and infrastructure Open Source Primary concerns 10
- 11. Some events need pieces of information from other event streams Example: An email click needs the email send event for contextual information Wait until other events arrive to assemble the final event Join across streams Configurable TTL to wait to join (optional) Implementation concern - Joins 11
- 12. Configurable per customer endpoint Retry Throttle TTL to deliver (optional) Reporting metrics, SLA compliance Implementation concern - Delivery guarantees 12
- 13. High level architecture Ingest Join Route Store HTTP POST Kafka Source External System 13
- 14. public class EventRouter { private Map<EventType, Route> routesMap; public void process(Event e) { Route route = routesMap.get(e.clientId()); if (null != route) { httpPost(e, route); } } } Business logic 14
- 15. public class EventJoiner { private Map<JoinKey, SendEvent> sends; public void process(ResponseEvent e) { SendEvent send = sends.get(e.getKey()); if (null != send) { Event joined = join(send, e); routeEvent(joined); } } } Business logic 15
- 16. ● Scaling data store is easy - use HBase ● Scaling application involves ○ Transactions ○ Application stack ○ Lifecycle management ○ Data movement ○ Coordination How to scale? 16
- 17. 17
- 18. ● An open source framework to build and deploy data applications on Apache™ Hadoop® ● Provides abstractions to represent data access and processing pipelines ● Framework level guarantees for exactly-once semantics ● Transaction support on HBase ● Supports real time and batch processing ● Built on YARN and HBase Cask Data Application Platform (CDAP) 18
- 20. Business logic public class EventJoiner { private Map<JoinKey, SendEvent> sends; public void process(ResponseEvent e) { SendEvent send = sends.get(e.getKey()); if (null != send) { Event joined = join(send, e); routeEvent(joined); } } } 20
- 21. Business logic in CDAP - Flowlet public class EventJoiner extends AbstractFlowlet { @UseDataSet(“sends”) private SendEventDataset sends; private OutputEmitter<Event> outQueue; @ProcessInput public void join(ResponseEvent e) { SendEvent send = sends.get(e.getKey()); if (send != null) { Event joined = join(e, send); outQueue.emit(joined); } } } 21
- 22. public class EventJoiner extends AbstractFlowlet { @UseDataSet(“sends”) private SendEventDataset sends; private OutputEmitter<Event> outQueue; @ProcessInput public void join(ResponseEvent e) { SendEvent send = sends.get(e.getKey()); if (send != null) { Event joined = join(e, send); outQueue.emit(joined); } } } Access data with Datasets 22
- 23. Chain Flowlets with Queues public class EventJoiner extends AbstractFlowlet { @UseDataSet(“sends”) private SendEventDataset sends; private OutputEmitter<Event> outQueue; @ProcessInput public void join(ResponseEvent e) { SendEvent send = sends.get(e.getKey()); if (send != null) { Event joined = join(e, send); outQueue.emit(joined); } } } 23
- 24. Tigon Flow Event Joiner Flowlet HBase Queue HBase Queue Start Tx End Tx Start Tx End Tx Event Router Flowlet ● Real time streaming processor ● Composed of Flowlets ● Exactly-once semantics HBase Queue 24
- 25. Scaling Flowlets Event Joiner Flowlets Event Router Flowlets HBase Queue YARN Containers FIFO Round Robin Hash Partitioning 25
- 26. Summary ● CDAP makes development easier by handling the overhead of scalability ○ Transactions ○ Application stack ○ Lifecycle management ○ Data movement ○ Coordination 26
- 28. Data abstraction using Dataset ● Store and retrieve data ● Reusable data access patterns ● Abstraction of underlying data storage ○ HBase ○ LevelDB ○ In-memory ● Can be shared between Flows (real-time) and MapReduce (batch) 28
- 29. ● Transactions make exactly-once semantics possible ● Multi-row and across HBase regions transactions ● Optimistic concurrency control (Omid style) ● Open source (Apache 2.0 License) ● http://tephra.io Transaction support with Tephra 29
- 30. ● Used today in enterprise cloud applications ● CDAP is open source (Apache 2.0 License) Use and contribute http://cdap.io/ 30