MapReduce Agenda for Functional Big Data Analysis
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This presentation was done by Vance Shipley (CTO at Wavenet) at the SLASSCOM Tech Talk - 'Smart Data Engineering' on 26th November 2014.
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MapReduce Agenda for Functional Big Data Analysis
- 2. Agenda MapReduce Google Scaling Out Key Value Store Chaining Fault Tolerance Functional Example Business Problem Design Processes Schema Big Data Guidelines
- 3. MapReduce
- 4. Google MapReduce + Paper published in 2004 + Implemented in 2003 + Production use at Google + Built for Google + Not open sourced
- 5. Google in 2004 + Clusters of 100s or 1000s of servers o Linux o dual-processor x86 o 2-4 GB memory o 100BaseT or GigE o inexpensive IDE hard drives + Servers fail every day + Network maintenance is constant
- 6. Scaling Out + Scaling up (faster computer) doesn’t get far + Scaling out is the only next step + Hundreds/thousands of modest computers outperform the biggest single computers + Scaling one to a few is hard + Scaling a few to many is easy + Scaling many to massive is (almost) trivial
- 7. Concurrency
- 8. Intermediate Data + Input data is split between the workers + Map workers create key/value pairs + Reduce workers read in all intermediate data and sort by key + Reduce workers then iterate over the sorted data producing a result for each key
- 10. Rinse and Repeat + Often the results of one MapReduce are used as input to another + Building on a powerful basic functional model complex data processing can be accomplished
- 11. Chaining
- 12. Fault Tolerance + Likelihood of failure rises with number of servers and processing time + Resiliency is a necessity at scale + Scheduler/Supervisor (master) reassigns failed jobs and ensures reduce workers find the (right) data
- 13. Scheduling
- 14. Supervision
- 16. Example Business Problem Scenario: A mobile operator wants to know if an instant messaging (IM) service would be useful to current subscribers. Question: What percentage of text messages (SMS) are part of a conversation?
- 17. Challenge ✓ 10 million subscribers ✓ average of 100 SMS a month per subscriber ✓ ∴ one billion SMS each month ✓ call detail records (CDR) include SMS but also voice and data events ✓ ∴ 20 billion (20,000,000,000) records/month
- 18. Requirements + Identify SMS conversations o messages sent or received with one other party o interval between messages < 10 minutes o at least three messages exchanged + Provide result as o ratio of conversational to non-conversational SMS o per subscriber o per month
- 19. Process Design
- 20. Filter + Read events from CDR files o records are in chronological order o read files in chronological order + Discard non-SMS events + Distribute SMS events to Map processes o Consistent distribution by subscriber
- 21. Hashing + To analyze interval between messages one process must handle all events for a particular subscriber + Simple Hash: o M = last four digits of subscriber’s mobile number o N = number of processes available o Pid = M rem N
- 22. Map + Read subscriber’s stored data + Find other party in set + Increment total count of messages + Is previous message < 10 minutes? o Is next previous message < 10m before previous? Increment conversational messages count + Update previous and next previous times
- 23. Schema Design
- 24. Interim Data + We are using an in memory key value store + The key is the subscriber number + The value is a set of OtherParty + OtherParty data structure contains counts + When the map is complete we transfer the data to disk for persistence
- 25. Reduce + Collect intermediate data from disk copies + Iterate through all parties for each subscriber + Total all party counts + Provide result as percentage of conversational messages to total messages
- 26. Big Data Guidelines + Find opportunities for concurrency + Choose the right containers for your data + Use memory as effectively as possible + Minimize copying data + Avoid any unnecessary overhead + Anything you are going to do hundreds of billions of times should be efficient!
- 27. Thank you.
- 28. SLASSCOM TECH TALKS https://www.facebook.com/SlasscomTechnologyForum http://www.slasscom.lk/events https://twitter.com/slasscom www.slideshare.net/slasscomtechforum
Notes de l'éditeur
- In order to successfully handle really big data requires massive concurrency and in the real world this requires fault tolerance.
- Google didn’t invent map and reduce but they were the first to apply the paradigm in a general way on a massive scale.
- … or, more probably, a number of results. By dividing the work we can assign it to many servers. This concurrency is what allows scale.
- Here is an example of something which Google do as part of their core business. Google places web sites which are linked to by many other web sites higher in search results (PageRank). To determine this a map reads web pages found by crawlers and creates key/value pairs. These are written in memory and then pushed out in blocks to disk. A reduce reads these disk blocks and sorts all the intermediate data by key. The reduce function then iterates over all the pairs for a key and outputs one result for each key.
- The results from one MapReduce can, and often are, provided as input for further MapReduce runs.
- Something like RAID, maybe Reduced Array of Inexpensive Servers (RAIS)? The can and do fail individually without the system failing.
- The user process forks all of the other processes which will be used including a master process. The master then assigns those processes work to perform, either map or reduce roles.
- The master process monitors each worker by sending a ping periodically. When it detects that a server has failed (or is no longer reachable) it will reassign that server’s work to another worker. After this reassignment each of the reduce workers will be notified to ignore the failed server and instead get the interim data from the newly assigned server.
- This is a contrived example.
- That’s billion with a ‘B’. In Canada that’s 1,000 million.
- There is an obvious hole in this pseudo code, the first two messages of the conversation are not included in the conversational totals. I could have accommodated that but I left it out to keep the example as simple possible.