Cassandra at no_sql

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SFJava, SFNoSQL, SFMySQL, Marakana & Microsoft come together for a presentation evening of three NoSQL technologies - Apache Cassandra, Mongodb, Hadoop. This talk lays out a few talking points for Apache Cassandra.

Technology

Apache Cassandra: NoSQL, Yes to Scale! srisatishambati @srisatish

points Usecases Why cassandra? Usecase: Hadoop, Brisk FUD:Consistency Why facebook is not using Cassandra? Community, Code, Tools Q&A

Users. Netflix. Key by Customer, read-heavy Key by Customer:Movie, write-heavy

TimeSeries: (several customers) periodic readings: dev0, dev1…deviceID:metric:timestamp ->value Metrics typically way larger dataset than users.

Replication: Multi-datacenter Multi-region ec2 Multi-availability zones

reads local dc1 dc2 Replication: Multi-datacenter Multi-region ec2, aws Multi-availability zones

4.21.2011, Amazon Web Services outage: “Movie marathons on Netflix awaiting AWS to come back up.” #ec2disabled

4.21.2011, Amazon Web Services outage: Netflix was running on AWS.

Reads Local Key & row caches, (also, jna-based 0xffheap) indexes, materialized

Clients: cql, thrift pycassa, phpcassa hector, pelops (scala, ruby, clojure)

Usecase #3: hadoop Hdfs cassandra hive Logs stats analytics

Use column families (tables) inode sblock

near-real time hadoop Low latency: cassandra_dc nodes Batch Analytics: brisk_dc nodes

Consistency: R + W > N ORACLE, 2-node: R=1, W=2, N=2,(T=2) DNS * N is replication factor. Not to be confused with T=total #of nodes

Tune-able, flexibility. For High Consistency: read:quorum, write:quorum For High Availability: high W, low R.

Inbox Search: 600+cores.120+TB (2008) Went from 100-500m users. Average NoSQL deployment size: ~6-12 nodes.

Usecase #5: search Apache Solr + Cassandra = Solandra Other inbox/file Searches: xobni, c3 github.com/tjake/solandra

“Eventual consistency is harder to program.” mostly immutable data. complex systems at scale.

Miscellaneous, Myth: data-loss, partial rows. writes are durable.

Tools AMIs, OpsCenter, DataStax AppDynamics

B e a u t i f u l C 0 d e = new code(); //less is more ~90k.java.concurrent.@annotate. bloomfilters, merkletrees. non-blocking, staged-event-driven. bigtable, dynamo.

Current & Future Focus: Distributed Counters, CQL. Simple client. operational smoothening. compaction.

Community Robust. Rapid. # Professional support from DataStax. engineers: independent,startups, large companies, Rackspace, Twitter, Netflix.. Come join the efforts!

Usecase #4: first NoSQL, then scale! simpledb Cassandra mongodb Cassandra

Copyright: plantoys … more than one way to do it!

Summary - high scale peer-to-peer distributed database.

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We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector. In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent. About the Speakers Matthias Niehoff IT-Consultant, codecentric AG works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups. Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.

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Traditionally, machines were statically partitioned across the different services at Uber. In an effort to increase the machine utilization, Uber has recently started transitioning most of its services, including the storage services, to run on top of Mesos. This presentation will describe the initial experience building and operating a framework for running Cassandra on top of Mesos running across multiple datacenters at Uber. This framework automates several Cassandra operations such as node repairs, addition of new nodes and backup/restore. It improves efficiency by co-locating CPU-intensive services as well as multiple Cassandra nodes on the same Mesos agent. It handles failure and restart of Mesos agents by using persistent volumes and dynamic reservations. This talk includes statistics about the number of Cassandra clusters in production, time taken to start a new cluster, add a new node, detect a node failure; and the observed Cassandra query throughput and latency. About the Speaker Abhishek Verma Software Engineer, Uber Dr. Abhishek Verma is currently working on running Cassandra on top of Mesos at Uber. Prior to this, he worked on BorgMaster at Google and was the first author of the Borg paper published in Eurosys 2015. He received an MS in 2010 and a PhD in 2012 in Computer Science from the University of Illinois at Urbana-Champaign, during which he authored more than 20 publications in conferences, journals and books and presented tens of talks.

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To date, Hadoop usage has focused primarily on offline analysis--making sense of web logs, parsing through loads of unstructured data in HDFS, etc. But what if you want to run map/reduce against your live data set without affecting online performance? Combining Hadoop with Cassandra's multi-datacenter replication capabilities makes this possible. If you're interested in getting value from your data without the hassle and latency of first moving it into Hadoop, this talk is for you. I'll show you how to connect all the parts, enabling you to write map/reduce jobs or run Pig queries against your live data. As a bonus I'll cover writing map/reduce in Scala, which is particularly well-suited for the task.

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Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

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Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia

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Cassandra at no_sql

1. Apache Cassandra: NoSQL, Yes to Scale! srisatishambati @srisatish

2. NoSQL- Know your queries.

3. points Usecases Why cassandra? Usecase: Hadoop, Brisk FUD:Consistency Why facebook is not using Cassandra? Community, Code, Tools Q&A

4. Users. Netflix. Key by Customer, read-heavy Key by Customer:Movie, write-heavy

5. TimeSeries: (several customers) periodic readings: dev0, dev1…deviceID:metric:timestamp ->value Metrics typically way larger dataset than users.

6. Why Cassandra?

7. Operational simplicity peer-to-peer

8. Operational simplicity peer-to-peer

9. Replication: Multi-datacenter Multi-region ec2 Multi-availability zones

10. reads local dc1 dc2 Replication: Multi-datacenter Multi-region ec2, aws Multi-availability zones

11. 4.21.2011, Amazon Web Services outage: “Movie marathons on Netflix awaiting AWS to come back up.” #ec2disabled

12. 4.21.2011, Amazon Web Services outage: Netflix was running on AWS.

13. fast durable writes. fast reads.

14. Writes Sequential, append-only. ~1-5ms

15. Reads Local Key & row caches, (also, jna-based 0xffheap) indexes, materialized

16. Clients: cql, thrift pycassa, phpcassa hector, pelops (scala, ruby, clojure)

17. Usecase #3: hadoop Hdfs cassandra hive Logs stats analytics

18. Brisk Truly peer-to-peer hadoop.

19. Namenode decomposition, explained.

20.

21.

22. Use column families (tables) inode sblock

23. near-real time hadoop Low latency: cassandra_dc nodes Batch Analytics: brisk_dc nodes

24. FUD, acronym: fear, uncertainty, doubt.

25. Consistency: R + W > N ORACLE, 2-node: R=1, W=2, N=2,(T=2) DNS * N is replication factor. Not to be confused with T=total #of nodes

26. Tune-able, flexibility. For High Consistency: read:quorum, write:quorum For High Availability: high W, low R.

27.

28. Inbox Search: 600+cores.120+TB (2008) Went from 100-500m users. Average NoSQL deployment size: ~6-12 nodes.

29. Usecase #5: search Apache Solr + Cassandra = Solandra Other inbox/file Searches: xobni, c3 github.com/tjake/solandra

30. “Eventual consistency is harder to program.” mostly immutable data. complex systems at scale.

31. Miscellaneous, Myth: data-loss, partial rows. writes are durable.

32. Three more reasons for Cassandra...

33. Tools AMIs, OpsCenter, DataStax AppDynamics

34. B e a u t i f u l C 0 d e = new code(); //less is more ~90k.java.concurrent.@annotate. bloomfilters, merkletrees. non-blocking, staged-event-driven. bigtable, dynamo.

35. Current & Future Focus: Distributed Counters, CQL. Simple client. operational smoothening. compaction.

36. Community Robust. Rapid. # Professional support from DataStax. engineers: independent,startups, large companies, Rackspace, Twitter, Netflix.. Come join the efforts!

37.

38. Usecase #4: first NoSQL, then scale! simpledb Cassandra mongodb Cassandra

39.

40.

41. Copyright: xkcd

42. Copyright: plantoys … more than one way to do it!

43. Summary - high scale peer-to-peer distributed database.

44. Q&A @srisatish

Cassandra at no_sql

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