Real time hadoop + mapreduce intro


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augmented my real-time hadoop talk to include a programming intro to mapreduce for google developer groups

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Real time hadoop + mapreduce intro

  1. 1. 2013: year of real-time access to Big Data? Geoffrey Hendrey @geoffhendrey @vertascale
  2. 2. Agenda• Hadoop MapReduce basics• Hadoop stack & data formats• File access times and mechanics• Key-based indexing systems (HBase)• MapReduce, Hive/Pig• MPP approaches & alternatives
  3. 3. A very bad* diagram*this diagram makes it appear that data flows through the master node.
  4. 4. A better picture
  5. 5. Job Configuration
  6. 6. Map and Reduce Java Code
  7. 7. Reduce
  8. 8. Reducer Group Iterators• Reducer groups values together by key• Your code will iterate over the values, emit reduced result Bear:[1,1] Bear:2• Hadoop reducer value iterators return THE SAME OBJECT each next(). Object is “reused” to reduce garbage collection load• Beware of “reused” objects (this is a VERY common cause of long and confusing debugs)• Cause for concern: you are emitting an object with non-primitive values. STALE “reused object” state from previous value.
  9. 9. Hadoop Writables• Values in Hadoop are transmitted (shuffled, emitted) in a binary format• Hadoop includes primitive types: IntWritable, Text, LongWritable, etc• You must implement Writable interface for custom objects public void write(DataOutput d) throws IOException { d.writeUTF(this.string); d.writeByte(this.column); } public void readFields(DataInput di) throws IOException { this.string = di.readUTF(); this.column = di.readByte(); }
  10. 10. Hadoop Keys (WritableComparable)• Be very careful to implement equals and hashcode consistently with compareTo()• compareTo() will control the sort order of keys arriving in reducer• Hadoop includes ability to write custom partitioner public int getPartition(Document doc, Text v, int numReducers) { return doc.getDocId()%numReducers;}
  11. 11. Typical Hadoop File Formats
  12. 12. Hadoop Stack Review
  13. 13. Distributed File System
  14. 14. HDFS performance characteristics• HDFS was designed for high throughput, not low seek latency• best-case configurations have shown HDFS to perform 92K/s random reads []• Personal experience: HDFS very robust. Fault tolerance is “real”. I’ve unplugged machines and never lost data.
  15. 15. Motivation for Real-time Hadoop• Big Data is more opaque than small data – Spreadsheets choke – BI tools can’t scale – Small samples often fail to replicate issues• Engineers, data scientists, analysts need: – Faster “time to answer” on Big Data – Rapid “find, quantify, extract”• Solve “I don’t know what I don’t know”• MapReduce jobs are hard to debug
  16. 16. Survey or real-time capabilities• Real-time, in-situ, self-service is the “Holy Grail” for the business analyst• spectrum of real-time capabilities exists on Hadoop Available in Hadoop Proprietary HDFS HBase Drill Easy Hard
  17. 17. Real-time spectrum on HadoopUse Case Support Real-timeSeek to a particular byte in a distributed HDFS YESfileSeek to a particular value in a distributed HBase YESfile, by key (1-dimensional indexing)Answer complex questions expressible in MapReduce NOcode (e.g. matching users to music (Hive, Pig)albums). Data science.Ad-hoc query for scattered records given MPP YESsimple constraints (“field*4+==“music” && Architecturesfield*9+==“dvd”)
  18. 18. Hadoop Underpinned By HDFS• Hadoop Distributed File System (HDFS)• inspired by Google FileSystem (GFS)• underpins every piece of data in “Hadoop”• Hadoop FileSystem API is pluggable• HDFS can be replaced with other suitable distributed filesystem – S3 – kosmos – etc
  19. 19. Amazon S3
  20. 20. MapFile for real-time access? – Index file must be loaded by client (slow) – Index file must fit in RAM of client by default – scan an average of 50% of the sampling interval – Large records make scanning intolerable – not a viable “real world” solution for random access
  21. 21. Apache HBase• Clone of Google’s Big Table.• Key-based access mechanism• Designed to hold billions of rows• “Tables” stored in HDFS• Supports MapReduce over tables, into tables• Requires you to think hard, and commit to a key design.
  22. 22. HBase Architecture
  23. 23. HBase random read performance• 7 servers, each with • 8 cores • 32GB DDR3 and • 24 x 146GB SAS 2.0 10K RPM disks.• Hbase table • 3 billion records, • 6600 regions. • data size is between 128-256 bytes per row, spread in 1 to 5 columns.
  24. 24. Zoomed-in “Get” time histogram
  25. 25. MapReduce• “MapReduce is a framework for processing parallelizable problems across huge datasets using a large number of computers”-wikipedia• MapReduce is strongly tied to HDFS in Hadoop.• Systems built on HDFS (i.e. HBase) leverage this common foundation for integration with the MR paradigm
  26. 26. MapReduce and Data Science• Many complex algorithms can be expressed in the MapReduce paradigm – NLP – Graph processing – Image codecs• The more complex the algorithm, the more Map and Reduce processes become complex programs in their own right.• Often cascade multiple MR jobs in succession
  27. 27. Is MapReduce real-time?• MapReduce on Hadoop has certain latencies that are hard to improve – Copy – Shuffle, sort – Iterate• time-dependent on the both the size of the input data and the number of processors available• In a nutshell, it’s a “batch process” and isn’t “real-time”
  28. 28. Hive and Pig• Run on top of MapReduce• Provide “Table” metaphor familiar to SQL users• Provide SQL-like (or actually same) syntax• Store a “schema” in a database, mapping tables to HDFS files• Translate “queries” to MapReduce jobs• No more real-time than MapReduce
  29. 29. MPP Architectures• Massively Parallel Processing• Lots of machines, so also lots of memoryExamples:• Spark – general purpose data science framework sort of like real-time MapReduce for data science• Dremel – columnar approach, geared toward answering SQL-like aggregations and BI-style questions
  30. 30. Spark• Originally designed for iterative machine learning problems at Berkeley• MapReduce does not do a great job on iterative workloads• Spark makes more explicit use of memory caches than Hadoop• Spark can load data from any Hadoop input source
  31. 31. Effect of Memory Caching in Spark
  32. 32. Is Spark Real-time?• If data fits in memory, execution time for most algorithms still depends on – amount of data to be processed – number of processors• So, it still “depends”• …but definitely more focused on fast time-to- answer• Interactive scala and java shells
  33. 33. Dremel MPP architecture• MPP architecture for ad-hoc query on nested data• Apache Drill is an OS clone of Dremel• Dremel originally developed at Google• Features “in situ” data analysis• “Dremel is not intended as a replacement for MR and is often used in conjunction with it to analyze outputs of MR pipelines or rapidly prototype larger computations.” -Dremel: Interactive Analysis of WebScaleDatasets
  34. 34. In Situ Analysis• Moving Big Data is a nightmare• In situ: ability to access data in place – In HDFS – In Big Table
  35. 35. Uses For Dremel At Google• Analysis of crawled web documents.• Tracking install data for applications on Android Market.• Crash reporting for Google products.• OCR results from Google Books.• Spam analysis.• Debugging of map tiles on Google Maps.• Tablet migrations in managed Bigtable instances.• Results of tests run on Google’s distributed build system.• Etc, etc.
  36. 36. Why so many uses for Dremel?• On any Big Data problem or application, dev team faces these problems: – “I don’t know what I don’t know” about data – Debugging often requires finding and correlating specific needles in the haystack – Support and marketing often require segmentation analysis (identify and characterize wide swaths of data)• Every developer/analyst wants – Faster time to answer – Fewer trips around the mulberry bush
  37. 37. Column Oriented Approach
  38. 38. Dremel MPP query execution tree
  39. 39. Is Dremel real-time?
  40. 40. Alternative approaches?• Both MapReduce and MPP query architectures take “throw hardware at the problem” approach.• Alternatives? – Use MapReduce to build distributed indexes on data – Combine columnar storage and inverted indexes to create columnar inverted indexes – Aim for the sweet spot for data scientist and engineer: Ad-hoc queries with results returned in seconds on a single processing node.
  41. 41. Contact Info Email: Twitter: @geoffhendrey @vertascale www:
  42. 42. references• the-elephant/• clusters-and-the-network/••••• s3_growth_2012_q1_1.png••• view1.png• Dremel: Interactive Analysis of WebScale Datasets