Introduction to NoSQL and Hadoop/Cascading for the Atlanta IASA Chapter

1. NoSQL, Hadoop and Cascading Christopher Curtin

2. About Me 20+ years in Technology Background in Factory Automation, Warehouse Management and Food Safety system development before Silverpop CTO of Silverpop

3. Topics Why NoSQL? What is NoSQL? Hadoop Cascading Questions

4. Why NoSQL? Should be ‘not just SQL’ Not all problems are relational Not all schemas are known when developing a solution Is LAMP the reason? Difficult to assign a value to a lot of the data in an enterprise

5. Cost Obligatory slam on Oracle/IBM/Microsoft CPU/Core costs Disks for fast RDBMS performance Clustering, Redundancy and DR Obligatory slam on Accenture, IBM and Corp IT Data warehouses Report writers Lead Time

6. NIH Some of this is ‘not invented here’ But many of these solutions came from the Internet-scale businesses like Facebook, Google, Twitter, Amazon Problems most of us will never see or really understand

7. Topics Why NoSQL? What is NoSQL? Hadoop Cascading Questions

8. What is NoSQL? Lots of definitions, but a few key ideas Eventual Consistency vs. ACID (usually) Distributed on commodity hardware, easy to add/remove nodes (Typically) Open Source (Usually) non-SQL interface A few examples Graph databases Key/Value stores Document databases

9. Graph Databases For handling deeply associative data (graphs/networks) Relational isn’t good at recursive structures Data both at the leaves (nodes) and edges (relationships) Very fast to navigate “Whiteboard Friendly” Neo4j (www.neo4j.org)

10. Key/Value Stores Don’t call them databases ;-) No JOIN concept, instead duplicate data Think of it as a huge Associated Array (Map) Not all values have all attributes Designed for fast reading ‘Ridiculous’ sized SimpleDB(Amazon), BigTable (Google)

11. Document Databases Inspired by Lotus Notes. Really. Collections of Key/Value stores But with some additional visibility into the data of the stores CouchDB (couchdb.apache.org) Stores JSON documents Can search in documents quickly No schema so Domain changes are easier

12. Topics Why NoSQL? What is NoSQL? Hadoop Cascading Questions

13. Map/Reduce Made famous by Google for how the index the web Parallel processing of large datasets across multiple commodity nodes Highly fault tolerant Hadoop is Apache’s implementation of Map/Reduce

14. Map Identifies what is in the input that you want to process Can be simple: occurrence of a word Can be difficult: evaluate each row and toss those older than 90 days or from IP Range 192.168.1.* Output is a list of name/value pairs Name and value do not have to be primitives

15. Reduce Takes the name/value pairs from the Map step and does something useful with them Map/Reduce Framework determines which Reduce instance to call for which Map values so a Reduce only ‘sees’ one set of ‘Name’ values Output is the ‘answer’ to the question Example: bytes streamed by IP address from Apache logs

16. HDFS Distributed File System WITHOUT NFS etc. Hadoop knows which parts of which files are on which machine (say that 5 times fast!) “Move the processing to the data” if possible Simple API to move files in and out of HDFS 3 Copies of the data in the cluster for redundancy AND performance

17. Runtime Distribution © Concurrent 2009

18. Topics Why NoSQL? What is NoSQL? Hadoop Cascading Questions

19. Getting Started with Map/Reduce First challenge: real examples Second challenge: when to map and when to reduce? Third challenge: what if I need more than one of each? How to coordinate? Fourth challenge: non-trivial business logic

20. Cascading Hadoop coding is non-trivial Hadoop is looking for a class to do Map steps and a class to do Reduce step What if you need multiple in your application? Who coordinates what can be run in parallel? What if you need to do non-Hadoop logic between Hadoop steps?

21. Tuple A single ‘row’ of data being processed Each column is named Can access data by name or position

22. TAP Abstraction on top of Hadoop files Allows you to define own parser for files Example: Input = new Hfs(new TextLine(), a_hdfsDirectory + "/" + name);

23. Operations Define what to do on the data Each – for each “tuple” in data do this to it Group – similar to a ‘group by’ in SQL CoGroup – joins of tuple streams together Every – for every key in the Group or CoGroup do this

24. Pipes Pipes tie Operations together Pipes can be thought of as ‘tuple streams’ Pipes can be split, allowing parallel execution of Operations

25. Operations - advanced Each operations allow logic on the row, such a parsing dates, creating new attributes etc. Every operations allow you to iterate over the ‘group’ of rows to do non-trivial operations. Both allow multiple operations in same function, so no nested function calls!

26. PIPE Splitting PIPEs define processing flows A SPLIT of a PIPE will tell Cascading to pass the TUPLE to another set of logic Allows single parsing EACH to feed multiple EVERY steps Or output from one EVERY to feed different EVERY steps

27. Flows Flows are reusable combinations of Taps, Pipes and Operations Allows you to build library of functions Groups of Flow are called Cascades

28. Cascading Scheduler Once the Flows and Cascades are defined, looks for dependencies When executed, tells Hadoop what Map, Reduce or Shuffle steps to take based on what Operations were used Knows what can be executed in parallel Knows when a step completes what other steps can execute

29. Example Operation RowAggregator aggr = new RowAggregator(row); Fields groupBy = new Fields(ColumnDefinition.RECIPIENT_ID_NAME); Pipe formatPipe = new Each("reformat_“ new Fields("line"), a_sentFile); formatPipe = new GroupBy(formatPipe, groupBy); formatPipe = new Every(formatPipe, Fields.ALL, aggr);

30. Runtime Distribution © Concurrent 2009

31. Dynamic Flow Creation Flows can be created at run time based on inputs. 5 input files one week, 10 the next, Java code creates 10 Flows instead of 5 Group and Every don’t care how many input Taps

32. Dynamic Tuple Definition Each operations on input Taps can parse text lines into different Fields So one source may have 5 inputs, another 10 Each operations can used meta data to know how to parse Can write Each operations to output common Tuples Every operations can output new Tuples as well Dynamically provide GroupBy fields so end users can build own ad-hoc logic

33. Mixing non-Hadoop code Cascading allows you to mix regular java between Flows in a Cascade So you can call out to databases, write intermediates to a file etc.

34. External File Creation An aggregator can write to the local file system Sometimes you don’t need or want to push back to HDFS NFS mounts needed since you don’t know where the logic is going to execute GROUP BY is critical to doing this right

35. Real Example For the hundreds of mailings sent last year To millions of recipients Show me who opened, how often Break it down by how long they have been a subscriber And their Gender And the number of times clicked on the offer

36. RDBMS solution Lots of million + row joins Lots of million + row counts Temporary tables since we want multiple answers Lots of memory Lots of CPU and I/O $$ becomes bottleneck to adding more rows or more clients to same logic

37. Cascading Solution Let Hadoop parse input files Let Cascading group all inputs by recipient’s email Let Cascading call Every functions to look at all rows for a recipient and ‘flatten’ data Split ‘flattened’ data Pipes to process in parallel: time in list, gender, clicked on links Bandwidth to export data from RDBMS becomes bottleneck

38. Pros and Cons Pros Mix java between map/reduce steps Don’t have to worry about when to map, when to reduce Don’t have to think about dependencies or how to process Data definition can change on the fly Cons Level above Hadoop – sometimes ‘black magic’ Data must (should) be outside of database to get most concurrency

39. Other Solutions Apache Pig: http://hadoop.apache.org/pig/ More ‘sql-like’ Not as easy to mix regular Java into processes More ‘ad hoc’ than Cascading Amazon Hadoop http://aws.amazon.com/elasticmapreduce/ Runs on EC2 Provide Map and Reduce functions Can use Cascading Pay as you go

40. Resources Me: ccurtin@silverpop.com @ChrisCurtin Chris Wensel: @cwensel Web site: www.cascading.org Mailing list off website AWSome Atlanta Group: http://www.meetup.com/awsomeatlanta/ O’Reilly Hadoop Book: http://oreilly.com/catalog/9780596521974/