# 11. Stream Processing

![](../img/ch11.png)

> *A complex system that works is invariably found to have evolved from a simple system that works. The inverse proposition also appears to be true: A complex system designed from scratch never works and cannot be made to work.*
>
> ​    — John Gall, *Systemantics* (1975)

---------------

In [Chapter 10](ch10.md) we discussed batch processing—techniques that read a set of files as input and produce a new set of output files. The output is a form of *derived data*; that is, a dataset that can be recreated by running the batch process again if necessary. We saw how this simple but powerful idea can be used to create search indexes, recom‐ mendation systems, analytics, and more.

However, one big assumption remained throughout [Chapter 10](ch10.md): namely, that the input is bounded—i.e., of a known and finite size—so the batch process knows when it has finished reading its input. For example, the sorting operation that is central to MapReduce must read its entire input before it can start producing output: it could happen that the very last input record is the one with the lowest key, and thus needs to be the very first output record, so starting the output early is not an option.

In reality, a lot of data is unbounded because it arrives gradually over time: your users produced data yesterday and today, and they will continue to produce more data tomorrow. Unless you go out of business, this process never ends, and so the dataset is never “complete” in any meaningful way [1]. Thus, batch processors must artifi‐ cially divide the data into chunks of fixed duration: for example, processing a day’s worth of data at the end of every day, or processing an hour’s worth of data at the end of every hour.

The problem with daily batch processes is that changes in the input are only reflected in the output a day later, which is too slow for many impatient users. To reduce the delay, we can run the processing more frequently—say, processing a second’s worth of data at the end of every second—or even continuously, abandoning the fixed time slices entirely and simply processing every event as it happens. That is the idea behind *stream processing*.

In general, a “stream” refers to data that is incrementally made available over time. The concept appears in many places: in the stdin and stdout of Unix, programming languages (lazy lists) [2], filesystem APIs (such as Java’s `FileInputStream`), TCP con‐ nections, delivering audio and video over the internet, and so on.

In this chapter we will look at *event streams* as a data management mechanism: the unbounded, incrementally processed counterpart to the batch data we saw in the last chapter. We will first discuss how streams are represented, stored, and transmit‐ ted over a network. In “[Databases and Streams](#databases-and-streams)” we will investigate the relationship between streams and databases. And finally, in “[Processing Streams](#processing-streams)” we will explore approaches and tools for processing those streams continually, and ways that they can be used to build applications.


## ……



## Summary

In this chapter we have discussed event streams, what purposes they serve, and how to process them. In some ways, stream processing is very much like the batch pro‐ cessing we discussed in [Chapter 10](ch10.md), but done continuously on unbounded (neverending) streams rather than on a fixed-size input. From this perspective, message brokers and event logs serve as the streaming equivalent of a filesystem.

We spent some time comparing two types of message brokers:

***AMQP/JMS-style message broker***

The broker assigns individual messages to consumers, and consumers acknowl‐ edge individual messages when they have been successfully processed. Messages are deleted from the broker once they have been acknowledged. This approach is appropriate as an asynchronous form of RPC (see also “[Message-Passing Data‐ flow]()”), for example in a task queue, where the exact order of mes‐ sage processing is not important and where there is no need to go back and read old messages again after they have been processed.

***Log-based message broker***

The broker assigns all messages in a partition to the same consumer node, and always delivers messages in the same order. Parallelism is achieved through par‐ titioning, and consumers track their progress by checkpointing the offset of the last message they have processed. The broker retains messages on disk, so it is possible to jump back and reread old messages if necessary.

The log-based approach has similarities to the replication logs found in databases (see [Chapter 5](ch5.md)) and log-structured storage engines (see [Chapter 3](ch3.md)). We saw that this approach is especially appropriate for stream processing systems that consume input streams and generate derived state or derived output streams.

In terms of where streams come from, we discussed several possibilities: user activity events, sensors providing periodic readings, and data feeds (e.g., market data in finance) are naturally represented as streams. We saw that it can also be useful to think of the writes to a database as a stream: we can capture the changelog—i.e., the history of all changes made to a database—either implicitly through change data cap‐ ture or explicitly through event sourcing. Log compaction allows the stream to retain a full copy of the contents of a database.

Representing databases as streams opens up powerful opportunities for integrating systems. You can keep derived data systems such as search indexes, caches, and analytics systems continually up to date by consuming the log of changes and applying them to the derived system. You can even build fresh views onto existing data by starting from scratch and consuming the log of changes from the beginning all the way to the present.

The facilities for maintaining state as streams and replaying messages are also the basis for the techniques that enable stream joins and fault tolerance in various stream processing frameworks. We discussed several purposes of stream processing, including searching for event patterns (complex event processing), computing windowed aggregations (stream analytics), and keeping derived data systems up to date (materialized views).

We then discussed the difficulties of reasoning about time in a stream processor, including the distinction between processing time and event timestamps, and the problem of dealing with straggler events that arrive after you thought your window was complete.

We distinguished three types of joins that may appear in stream processes:

***Stream-stream joins***

Both input streams consist of activity events, and the join operator searches for related events that occur within some window of time. For example, it may match two actions taken by the same user within 30 minutes of each other. The two join inputs may in fact be the same stream (a *self-join*) if you want to find related events within that one stream.

***Stream-table joins***

One input stream consists of activity events, while the other is a database change‐ log. The changelog keeps a local copy of the database up to date. For each activity event, the join operator queries the database and outputs an enriched activity event.

***Table-table joins***

Both input streams are database changelogs. In this case, every change on one side is joined with the latest state of the other side. The result is a stream of changes to the materialized view of the join between the two tables.

Finally, we discussed techniques for achieving fault tolerance and exactly-once semantics in a stream processor. As with batch processing, we need to discard the partial output of any failed tasks. However, since a stream process is long-running and produces output continuously, we can’t simply discard all output. Instead, a finer-grained recovery mechanism can be used, based on microbatching, checkpoint‐ ing, transactions, or idempotent writes.




## References

1. Tyler Akidau, Robert Bradshaw, Craig Chambers, et al.: “[The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing](http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf),” *Proceedings of the VLDB Endowment*, volume 8, number 12, pages 1792–1803, August 2015. [doi:10.14778/2824032.2824076](http://dx.doi.org/10.14778/2824032.2824076)
1. Harold Abelson, Gerald Jay Sussman, and Julie Sussman: [*Structure and Interpretation of Computer Programs*](https://web.archive.org/web/20220807043536/https://mitpress.mit.edu/sites/default/files/sicp/index.html), 2nd edition. MIT Press, 1996. ISBN: 978-0-262-51087-5, available online at *mitpress.mit.edu*
1. Patrick Th. Eugster, Pascal A. Felber, Rachid Guerraoui, and Anne-Marie Kermarrec: “[The Many Faces of Publish/Subscribe](http://www.cs.ru.nl/~pieter/oss/manyfaces.pdf),” *ACM Computing Surveys*, volume 35, number 2, pages 114–131, June 2003. [doi:10.1145/857076.857078](http://dx.doi.org/10.1145/857076.857078)
1. Joseph M. Hellerstein and Michael Stonebraker: [*Readings in Database Systems*](http://redbook.cs.berkeley.edu/), 4th edition. MIT Press, 2005. ISBN: 978-0-262-69314-1, available online at *redbook.cs.berkeley.edu*
1. Don Carney, Uğur Çetintemel, Mitch Cherniack, et al.: “[Monitoring Streams – A New Class of Data Management Applications](http://www.vldb.org/conf/2002/S07P02.pdf),” at *28th International Conference on Very Large Data Bases* (VLDB), August 2002.
1. Matthew Sackman: “[Pushing Back](https://wellquite.org/posts/lshift/pushing_back/),” *lshift.net*, May 5, 2016.
1. Vicent Martí: “[Brubeck, a statsd-Compatible Metrics Aggregator](http://githubengineering.com/brubeck/),” *githubengineering.com*, June 15, 2015.
1. Seth Lowenberger: “[MoldUDP64 Protocol Specification V 1.00](http://www.nasdaqtrader.com/content/technicalsupport/specifications/dataproducts/moldudp64.pdf),” *nasdaqtrader.com*, July 2009.
1. Pieter Hintjens: [*ZeroMQ – The Guide*](http://zguide.zeromq.org/page:all). O'Reilly Media, 2013. ISBN: 978-1-449-33404-8
1. Ian Malpass: “[Measure Anything, Measure Everything](https://codeascraft.com/2011/02/15/measure-anything-measure-everything/),” *codeascraft.com*, February 15, 2011.
1. Dieter Plaetinck: “[25 Graphite, Grafana and statsd Gotchas](https://grafana.com/blog/2016/03/03/25-graphite-grafana-and-statsd-gotchas/),” *grafana.com*, March 3, 2016.
1. Jeff Lindsay: “[Web Hooks to Revolutionize the Web](https://web.archive.org/web/20180928201955/http://progrium.com/blog/2007/05/03/web-hooks-to-revolutionize-the-web/),” *progrium.com*, May 3, 2007.
1. Jim N. Gray: “[Queues Are Databases](https://arxiv.org/pdf/cs/0701158.pdf),” Microsoft Research Technical Report MSR-TR-95-56, December 1995.
1. Mark Hapner, Rich Burridge, Rahul Sharma, et al.: “[JSR-343 Java Message Service (JMS) 2.0 Specification](https://jcp.org/en/jsr/detail?id=343),” *jms-spec.java.net*, March 2013.
1. Sanjay Aiyagari, Matthew Arrott, Mark Atwell, et al.: “[AMQP: Advanced Message Queuing Protocol Specification](http://www.rabbitmq.com/resources/specs/amqp0-9-1.pdf),” Version 0-9-1, November 2008.
1. “[Google Cloud Pub/Sub: A Google-Scale Messaging Service](https://cloud.google.com/pubsub/architecture),” *cloud.google.com*, 2016.
1. “[Apache Kafka 0.9 Documentation](http://kafka.apache.org/documentation.html),” *kafka.apache.org*, November 2015.
1. Jay Kreps, Neha Narkhede, and Jun Rao: “[Kafka: A Distributed Messaging System for Log Processing](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/09/Kafka.pdf),” at *6th International Workshop on Networking Meets Databases* (NetDB), June 2011.
1. “[Amazon Kinesis Streams Developer Guide](http://docs.aws.amazon.com/streams/latest/dev/introduction.html),” *docs.aws.amazon.com*, April 2016.
1. Leigh Stewart and Sijie Guo: “[Building DistributedLog: Twitter’s High-Performance Replicated Log Service](https://blog.twitter.com/2015/building-distributedlog-twitter-s-high-performance-replicated-log-service),” *blog.twitter.com*, September 16, 2015.
1. “[DistributedLog Documentation](https://web.archive.org/web/20210517201308/https://bookkeeper.apache.org/distributedlog/docs/latest/),” Apache Software Foundation, *distributedlog.io*.
1. Jay Kreps: “[Benchmarking Apache Kafka: 2 Million Writes Per Second (On Three Cheap Machines)](https://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million-writes-second-three-cheap-machines),” *engineering.linkedin.com*, April 27, 2014.
1. Kartik Paramasivam: “[How We’re Improving and Advancing Kafka at LinkedIn](https://engineering.linkedin.com/apache-kafka/how-we_re-improving-and-advancing-kafka-linkedin),” *engineering.linkedin.com*, September 2, 2015.
1. Jay Kreps: “[The Log: What Every Software Engineer Should Know About Real-Time Data's Unifying Abstraction](http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying),” *engineering.linkedin.com*, December 16, 2013.
1. Shirshanka Das, Chavdar Botev, Kapil Surlaker, et al.: “[All Aboard the Databus!](http://www.socc2012.org/s18-das.pdf),” at *3rd ACM Symposium on Cloud Computing* (SoCC), October 2012.
1. Yogeshwer Sharma, Philippe Ajoux, Petchean Ang, et al.: “[Wormhole: Reliable Pub-Sub to Support Geo-Replicated Internet Services](https://www.usenix.org/system/files/conference/nsdi15/nsdi15-paper-sharma.pdf),” at *12th USENIX Symposium on Networked Systems Design and Implementation* (NSDI), May 2015.
1. P. P. S. Narayan: “[Sherpa Update](http://web.archive.org/web/20160801221400/https://developer.yahoo.com/blogs/ydn/sherpa-7992.html),” *developer.yahoo.com*, June 8, .
1. Martin Kleppmann: “[Bottled Water: Real-Time Integration of PostgreSQL and Kafka](http://martin.kleppmann.com/2015/04/23/bottled-water-real-time-postgresql-kafka.html),” *martin.kleppmann.com*, April 23, 2015.
1. Ben Osheroff: “[Introducing Maxwell, a mysql-to-kafka Binlog Processor](https://web.archive.org/web/20170208100334/https://developer.zendesk.com/blog/introducing-maxwell-a-mysql-to-kafka-binlog-processor),” *developer.zendesk.com*, August 20, 2015.
1. Randall Hauch: “[Debezium 0.2.1 Released](https://debezium.io/blog/2016/06/10/Debezium-0.2.1-Released/),” *debezium.io*, June 10, 2016.
1. Prem Santosh Udaya Shankar: “[Streaming MySQL Tables in Real-Time to Kafka](https://engineeringblog.yelp.com/2016/08/streaming-mysql-tables-in-real-time-to-kafka.html),” *engineeringblog.yelp.com*, August 1, 2016.
1. “[Mongoriver](https://github.com/stripe/mongoriver),” Stripe, Inc., *github.com*, September 2014.
1. Dan Harvey: “[Change Data Capture with Mongo + Kafka](http://www.slideshare.net/danharvey/change-data-capture-with-mongodb-and-kafka),” at *Hadoop Users Group UK*, August 2015.
1. “[Oracle GoldenGate 12c: Real-Time Access to Real-Time Information](https://web.archive.org/web/20160923105841/http://www.oracle.com/us/products/middleware/data-integration/oracle-goldengate-realtime-access-2031152.pdf),” Oracle White Paper, March 2015.
1. “[Oracle GoldenGate Fundamentals: How Oracle GoldenGate Works](https://www.youtube.com/watch?v=6H9NibIiPQE),” Oracle Corporation, *youtube.com*, November 2012.
1. Slava Akhmechet: “[Advancing the Realtime Web](http://rethinkdb.com/blog/realtime-web/),” *rethinkdb.com*, January 27, 2015.
1. “[Firebase Realtime Database Documentation](https://firebase.google.com/docs/database/),” Google, Inc., *firebase.google.com*, May 2016.
1. “[Apache CouchDB 1.6 Documentation](http://docs.couchdb.org/en/latest/),” *docs.couchdb.org*, 2014.
1. Matt DeBergalis: “[Meteor 0.7.0: Scalable Database Queries Using MongoDB Oplog Instead of Poll-and-Diff](https://web.archive.org/web/20160324055429/http://info.meteor.com/blog/meteor-070-scalable-database-queries-using-mongodb-oplog-instead-of-poll-and-diff),” *info.meteor.com*, December 17, 2013.
1. “[Chapter 15. Importing and Exporting Live Data](https://docs.voltdb.com/UsingVoltDB/ChapExport.php),” VoltDB 6.4 User Manual, *docs.voltdb.com*, June 2016.
1. Neha Narkhede: “[Announcing Kafka Connect: Building Large-Scale Low-Latency Data Pipelines](http://www.confluent.io/blog/announcing-kafka-connect-building-large-scale-low-latency-data-pipelines),” *confluent.io*, February 18, 2016.
1. Greg Young: “[CQRS and Event Sourcing](https://www.youtube.com/watch?v=JHGkaShoyNs),” at *Code on the Beach*, August 2014.
1. Martin Fowler: “[Event Sourcing](http://martinfowler.com/eaaDev/EventSourcing.html),” *martinfowler.com*, December 12, 2005.
1. Vaughn Vernon: [*Implementing Domain-Driven Design*](https://www.informit.com/store/implementing-domain-driven-design-9780321834577). Addison-Wesley Professional, 2013. ISBN: 978-0-321-83457-7
1. H. V. Jagadish, Inderpal Singh Mumick, and Abraham Silberschatz: “[View Maintenance Issues for the Chronicle Data Model](https://dl.acm.org/doi/10.1145/212433.220201),” at *14th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems* (PODS), May 1995. [doi:10.1145/212433.220201](http://dx.doi.org/10.1145/212433.220201)
1. “[Event Store 3.5.0 Documentation](http://docs.geteventstore.com/),” Event Store LLP, *docs.geteventstore.com*, February 2016.
1. Martin Kleppmann: [*Making Sense of Stream Processing*](http://www.oreilly.com/data/free/stream-processing.csp). Report, O'Reilly Media, May 2016.
1. Sander Mak: “[Event-Sourced Architectures with Akka](http://www.slideshare.net/SanderMak/eventsourced-architectures-with-akka),” at *JavaOne*, September 2014.
1. Julian Hyde: [personal communication](https://twitter.com/julianhyde/status/743374145006641153), June 2016.
1. Ashish Gupta and Inderpal Singh Mumick: *Materialized Views: Techniques, Implementations, and Applications*. MIT Press, 1999. ISBN: 978-0-262-57122-7
1. Timothy Griffin and Leonid Libkin: “[Incremental Maintenance of Views with Duplicates](http://homepages.inf.ed.ac.uk/libkin/papers/sigmod95.pdf),” at *ACM International Conference on Management of Data* (SIGMOD), May 1995. [doi:10.1145/223784.223849](http://dx.doi.org/10.1145/223784.223849)
1. Pat Helland: “[Immutability Changes Everything](http://cidrdb.org/cidr2015/Papers/CIDR15_Paper16.pdf),” at *7th Biennial Conference on Innovative Data Systems Research* (CIDR), January 2015.
1. Martin Kleppmann: “[Accounting for Computer Scientists](http://martin.kleppmann.com/2011/03/07/accounting-for-computer-scientists.html),” *martin.kleppmann.com*, March 7, 2011.
1. Pat Helland: “[Accountants Don't Use Erasers](https://web.archive.org/web/20200220161036/https://blogs.msdn.microsoft.com/pathelland/2007/06/14/accountants-dont-use-erasers/),” *blogs.msdn.com*, June 14, 2007.
1. Fangjin Yang: “[Dogfooding with Druid, Samza, and Kafka: Metametrics at Metamarkets](https://metamarkets.com/2015/dogfooding-with-druid-samza-and-kafka-metametrics-at-metamarkets/),” *metamarkets.com*, June 3, 2015.
1. Gavin Li, Jianqiu Lv, and Hang Qi: “[Pistachio: Co-Locate the Data and Compute for Fastest Cloud Compute](https://web.archive.org/web/20181214032620/https://yahoohadoop.tumblr.com/post/116365275781/pistachio-co-locate-the-data-and-compute-for),” *yahoohadoop.tumblr.com*, April 13, 2015.
1. Kartik Paramasivam: “[Stream Processing Hard Problems – Part 1: Killing Lambda](https://engineering.linkedin.com/blog/2016/06/stream-processing-hard-problems-part-1-killing-lambda),” *engineering.linkedin.com*, June 27, 2016.
1. Martin Fowler: “[CQRS](http://martinfowler.com/bliki/CQRS.html),” *martinfowler.com*, July 14, 2011.
1. Greg Young: “[CQRS Documents](https://cqrs.files.wordpress.com/2010/11/cqrs_documents.pdf),” *cqrs.files.wordpress.com*, November 2010.
1. Baron Schwartz: “[Immutability, MVCC, and Garbage Collection](https://web.archive.org/web/20161110094746/http://www.xaprb.com/blog/2013/12/28/immutability-mvcc-and-garbage-collection/),” *xaprb.com*, December 28, 2013.
1. Daniel Eloff, Slava Akhmechet, Jay Kreps, et al.: ["Re: Turning the Database Inside-out with Apache Samza](https://news.ycombinator.com/item?id=9145197)," Hacker News discussion, *news.ycombinator.com*, March 4, 2015.
1. “[Datomic Development Resources: Excision](http://docs.datomic.com/excision.html),” Cognitect, Inc., *docs.datomic.com*.
1. “[Fossil Documentation: Deleting Content from Fossil](http://fossil-scm.org/index.html/doc/trunk/www/shunning.wiki),” *fossil-scm.org*, 2016.
1. Jay Kreps: “[The irony of distributed systems is that data loss is really easy but deleting data is surprisingly hard,](https://twitter.com/jaykreps/status/582580836425330688)” *twitter.com*, March 30, 2015.
1. David C. Luckham: “[What’s the Difference Between ESP and CEP?](http://www.complexevents.com/2006/08/01/what%E2%80%99s-the-difference-between-esp-and-cep/),” *complexevents.com*, August 1, 2006.
1. Srinath Perera: “[How Is Stream Processing and Complex Event Processing (CEP) Different?](https://www.quora.com/How-is-stream-processing-and-complex-event-processing-CEP-different),” *quora.com*, December 3, 2015.
1. Arvind Arasu, Shivnath Babu, and Jennifer Widom: “[The CQL Continuous Query Language: Semantic Foundations and Query Execution](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cql.pdf),” *The VLDB Journal*, volume 15, number 2, pages 121–142, June 2006. [doi:10.1007/s00778-004-0147-z](http://dx.doi.org/10.1007/s00778-004-0147-z)
1. Julian Hyde: “[Data in Flight: How Streaming SQL Technology Can Help Solve the Web 2.0 Data Crunch](http://queue.acm.org/detail.cfm?id=1667562),” *ACM Queue*, volume 7, number 11, December 2009. [doi:10.1145/1661785.1667562](http://dx.doi.org/10.1145/1661785.1667562)
1. “[Esper Reference, Version 5.4.0](http://esper.espertech.com/release-5.4.0/esper-reference/html_single/index.html),” EsperTech, Inc., *espertech.com*, April 2016.
1. Zubair Nabi, Eric Bouillet, Andrew Bainbridge, and Chris Thomas: “[Of Streams and Storms](https://web.archive.org/web/20170711081434/https://developer.ibm.com/streamsdev/wp-content/uploads/sites/15/2014/04/Streams-and-Storm-April-2014-Final.pdf),” IBM technical report, *developer.ibm.com*, April 2014.
1. Milinda Pathirage, Julian Hyde, Yi Pan, and Beth Plale: “[SamzaSQL: Scalable Fast Data Management with Streaming SQL](https://github.com/milinda/samzasql-hpbdc2016/blob/master/samzasql-hpbdc2016.pdf),” at *IEEE International Workshop on High-Performance Big Data Computing* (HPBDC), May 2016. [doi:10.1109/IPDPSW.2016.141](http://dx.doi.org/10.1109/IPDPSW.2016.141)
1. Philippe Flajolet, Éric Fusy, Olivier Gandouet, and Frédéric Meunier: “[HyperLogLog: The Analysis of a Near-Optimal Cardinality Estimation Algorithm](http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf),” at *Conference on Analysis of Algorithms* (AofA), June 2007.
1. Jay Kreps: “[Questioning the Lambda Architecture](https://www.oreilly.com/ideas/questioning-the-lambda-architecture),” *oreilly.com*, July 2, 2014.
1. Ian Hellström: “[An Overview of Apache Streaming Technologies](https://databaseline.bitbucket.io/an-overview-of-apache-streaming-technologies/),” *databaseline.bitbucket.io*, March 12, 2016.
1. Jay Kreps: “[Why Local State Is a Fundamental Primitive in Stream Processing](https://www.oreilly.com/ideas/why-local-state-is-a-fundamental-primitive-in-stream-processing),” *oreilly.com*, July 31, 2014.
1. Shay Banon: “[Percolator](https://www.elastic.co/blog/percolator),” *elastic.co*, February 8, 2011.
1. Alan Woodward and Martin Kleppmann: “[Real-Time Full-Text Search with Luwak and Samza](http://martin.kleppmann.com/2015/04/13/real-time-full-text-search-luwak-samza.html),” *martin.kleppmann.com*, April 13, 2015.
1. “[Apache Storm 2.1.0 Documentation](https://storm.apache.org/releases/2.1.0/index.html),” *storm.apache.org*, October 2019.
1. Tyler Akidau: “[The World Beyond Batch: Streaming 102](https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102),” *oreilly.com*, January 20, 2016.
1. Stephan Ewen: “[Streaming Analytics with Apache Flink](https://www.confluent.io/resources/kafka-summit-2016/advanced-streaming-analytics-apache-flink-apache-kafka/),” at *Kafka Summit*, April 2016.
1. Tyler Akidau, Alex Balikov, Kaya Bekiroğlu, et al.: “[MillWheel: Fault-Tolerant Stream Processing at Internet Scale](http://research.google.com/pubs/pub41378.html),” at *39th International Conference on Very Large Data Bases* (VLDB), August 2013.
1. Alex Dean: “[Improving Snowplow's Understanding of Time](https://snowplow.io/blog/improving-snowplows-understanding-of-time/),” *snowplowanalytics.com*, September 15, 2015.
1. “[Windowing (Azure Stream Analytics)](https://msdn.microsoft.com/en-us/library/azure/dn835019.aspx),” Microsoft Azure Reference, *msdn.microsoft.com*, April 2016.
1. “[State Management](http://samza.apache.org/learn/documentation/0.10/container/state-management.html),” Apache Samza 0.10 Documentation, *samza.apache.org*, December 2015.
1. Rajagopal Ananthanarayanan, Venkatesh Basker, Sumit Das, et al.: “[Photon: Fault-Tolerant and Scalable Joining of Continuous Data Streams](http://research.google.com/pubs/pub41318.html),” at *ACM International Conference on Management of Data* (SIGMOD), June 2013. [doi:10.1145/2463676.2465272](http://dx.doi.org/10.1145/2463676.2465272)
1. Martin Kleppmann: “[Samza Newsfeed Demo](https://github.com/ept/newsfeed),” *github.com*, September 2014.
1. Ben Kirwin: “[Doing the Impossible: Exactly-Once Messaging Patterns in Kafka](http://ben.kirw.in/2014/11/28/kafka-patterns/),” *ben.kirw.in*, November 28, 2014.
1. Pat Helland: “[Data on the Outside Versus Data on the Inside](http://cidrdb.org/cidr2005/papers/P12.pdf),” at *2nd Biennial Conference on Innovative Data Systems Research* (CIDR), January 2005.
1. Ralph Kimball and Margy Ross: *The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling*, 3rd edition. John Wiley & Sons, 2013. ISBN: 978-1-118-53080-1
1. Viktor Klang: “[I'm coining the phrase 'effectively-once' for message processing with at-least-once + idempotent operations](https://twitter.com/viktorklang/status/789036133434978304),” *twitter.com*, October 20, 2016.
1. Matei Zaharia, Tathagata Das, Haoyuan Li, et al.: “[Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters](https://www.usenix.org/system/files/conference/hotcloud12/hotcloud12-final28.pdf),” at *4th USENIX Conference in Hot Topics in Cloud Computing* (HotCloud), June 2012.
1. Kostas Tzoumas, Stephan Ewen, and Robert Metzger: “[High-Throughput, Low-Latency, and Exactly-Once Stream Processing with Apache Flink](https://www.ververica.com/blog/high-throughput-low-latency-and-exactly-once-stream-processing-with-apache-flink),” *ververica.com*, August 5, 2015.
1. Paris Carbone, Gyula Fóra, Stephan Ewen, et al.: “[Lightweight Asynchronous Snapshots for Distributed Dataflows](http://arxiv.org/abs/1506.08603),” arXiv:1506.08603 &#91;cs.DC&#93;, June 29, 2015.
1. Ryan Betts and John Hugg: [*Fast Data: Smart and at Scale*](http://www.oreilly.com/data/free/fast-data-smart-and-at-scale.csp). Report, O'Reilly Media, October 2015.
1. Flavio Junqueira: “[Making Sense of Exactly-Once Semantics](https://web.archive.org/web/20160812172900/http://conferences.oreilly.com/strata/hadoop-big-data-eu/public/schedule/detail/49690),” at *Strata+Hadoop World London*, June 2016.
1. Jason Gustafson, Flavio Junqueira, Apurva Mehta, Sriram Subramanian, and Guozhang Wang: “[KIP-98 – Exactly Once Delivery and Transactional Messaging](https://cwiki.apache.org/confluence/display/KAFKA/KIP-98+-+Exactly+Once+Delivery+and+Transactional+Messaging),” *cwiki.apache.org*, November 2016.
1. Pat Helland: “[Idempotence Is Not a Medical Condition](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=4b6dda7fe75b51e1c543a87ca7b3b322fbf55614),” *Communications of the ACM*, volume 55, number 5, page 56, May 2012. [doi:10.1145/2160718.2160734](http://dx.doi.org/10.1145/2160718.2160734)
1. Jay Kreps: “[Re: Trying to Achieve Deterministic Behavior on Recovery/Rewind](http://mail-archives.apache.org/mod_mbox/samza-dev/201409.mbox/%3CCAOeJiJg%2Bc7Ei%3DgzCuOz30DD3G5Hm9yFY%3DUJ6SafdNUFbvRgorg%40mail.gmail.com%3E),” email to *samza-dev* mailing list, September 9, 2014.
1. E. N. (Mootaz) Elnozahy, Lorenzo Alvisi, Yi-Min Wang, and David B. Johnson: “[A Survey of Rollback-Recovery Protocols in Message-Passing Systems](http://www.cs.utexas.edu/~lorenzo/papers/SurveyFinal.pdf),” *ACM Computing Surveys*, volume 34, number 3, pages 375–408, September 2002. [doi:10.1145/568522.568525](http://dx.doi.org/10.1145/568522.568525)
1. Adam Warski: “[Kafka Streams – How Does It Fit the Stream Processing Landscape?](https://softwaremill.com/kafka-streams-how-does-it-fit-stream-landscape/),” *softwaremill.com*, June 1, 2016.
