Seattle Scalability Meetup - Ted Dunning - MapR


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MapR is an amazing new distributed filesystem modeled after Hadoop. It maintains API compatibility with Hadoop, but far exceeds it in performance, manageability, and more.

/* Ted's MapR meeting slides incorporated here */

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  • Constant time implies constantfactor of growth. Thus the accumulation of all of history before 10 time units ago is less than half the accumulation in the last 10 units alone. This is true at all time.
  • Startups use this fact to their advantage and completely change everything to allow time-efficient development initially with conversion to computer-efficient systems later.
  • Here the later history is shown after the initial exponential growth phase. This changes the economics of the company dramatically.
  • The startup can throw away history because it is so small. That means that the startup has almost no compatibility requirement because the data lost due to lack of compatibility is a small fraction of the total data.
  • A large enterprise cannot do that. They have to have access to the old data and have to share between old data and Hadoop accessible data.This doesn’t have to happen with the proof of concept level, but it really must happen when hadoop first goes to production.
  • But stock Hadoop does not handle this well.
  • This is because Hadoop and other data silos have different foundations. What is worse, there is a semantic wall that separates HDFS from normal resources.
  • Here is a picture that shows how MapR can replace the foundation and provide compatibility. Of course, MapR provide much more than just the base, but the foundation is what provides the fundamental limitation or lack of limit in MapR’s case.
  • Seattle Scalability Meetup - Ted Dunning - MapR

    1. 1. Seattle Monthly Hadoop / Scalability / NoSQLMeetup Ted Dunning, MapR..
    2. 2. Agenda• Lightning talks / community announcements• Main Speaker• Bier @ Feierabend - 422 Yale Ave North• Hashtags #Seattle #Hadoop
    3. 3. Fast & Frugal: Running a Lean Startup with AWS – Oct 27th 10am-2pm
    4. 4. Seattle AWS User Group November 9th, 2011 – 6:30 -9pm• November were going to hear from Amy Woodward from EngineYard about keeping your systems live through outages and other problems using EngineYard atop AWS. Come check out this great talk and learn a thing or three about EngineYard& keeping high availability for your systems!•
    5. 5.• MapR is an amazing new distributed filesystem modeled after Hadoop. It maintains API compatibility with Hadoop, but far exceeds it in performance, manageability, and more.
    6. 6. MapR, Scaling, Machine Learning
    7. 7. Outline• Philosophy• Architecture• Applications
    8. 8. Physics of startup companies
    9. 9. For startups• History is always small• The future is huge• Must adopt new technology to survive• Compatibility is not as important – In fact, incompatibility is assumed
    10. 10. Physics of large companies Absolute growth still very large Startup phase
    11. 11. For large businesses• Present state is always large• Relative growth is much smaller• Absolute growth rate can be very large• Must adopt new technology to survive – Cautiously! – But must integrate technology with legacy• Compatibility is crucial
    12. 12. The startup technology picture No compatibility requirementOld computers and software Expected hardware and software growth Current computers and software
    13. 13. The large enterprise picture Must work together ? Current hardware and software Proof of concept Hadoop cluster Long-term Hadoop cluster
    14. 14. What does this mean?• Hadoop is very, very good at streaming through things in batch jobs• Hbase is good at persisting data in very write- heavy workloads• Unfortunately, the foundation of both systems is HDFS which does not export or import well
    15. 15. Narrow Foundations Big data is Pig Hive Web Services and heavy expensive to move. Sequential File Map/OLAP OLTP Hbase Processing Reduce RDBMS NAS HDFS
    16. 16. Narrow Foundations• Because big data has inertia, it is difficult to move – It costs time to move – It costs reliability because of more moving parts• The result is many duplicate copies
    17. 17. One Possible Answer• Widen the foundation• Use standard communication protocols• Allow conventional processing to share with parallel processing
    18. 18. Broad Foundation Pig Hive Web Services Sequential File Map/OLAP OLTP Hbase Processing Reduce RDBMS NAS HDFS MapR
    19. 19. Broad Foundation• Having a broad foundation allows many kinds of computation to work together• It is no longer necessary to throw data over a wall• Performance much higher for map-reduce• Enterprise grade feature sets such as snapshots and mirrors can be integrated• Operations more familiar to admin staff
    20. 20. Map-ReduceInput Output Map function Reduce function Shuffle
    21. 21. Map-reduce key details• User supplies f1 (map) and f2 (reduce) – Both are pure functions, no side effect• Framework supplies input, shuffle, output• Framework will re-run f1 and f2 on failure• Redundant task completion is OK
    22. 22. Map-ReduceInput Output
    23. 23. Map-Reduce f1 Local f2 DiskInput Output f1 Local f2 Disk f1
    24. 24. Example – WordCount• Mapper – read line, tokenize into words – emit (word, 1)• Reducer – read (word, [k1, … , kn]) – Emit (word, Σki)
    25. 25. Example – Map Tiles• Input is set of objects – Roads (polyline) – Towns (polygon) – Lakes (polygon)• Output is set of map-tiles – Graphic image of part of map
    26. 26. Bottlenecks and Issues• Read-only files• Many copies in I/O path• Shuffle based on HTTP – Can’t use new technologies – Eats file descriptors• Spills go to local file space – Bad for skewed distribution of sizes
    27. 27. MapR Areas of Development HBase Map Reduce Ecosystem Storage Management Services
    28. 28. MapR Improvements• Faster file system – Fewer copies – Multiple NICS – No file descriptor or page-buf competition• Faster map-reduce – Uses distributed file system – Direct RPC to receiver – Very wide merges
    29. 29. MapR Innovations• Volumes – Distributed management – Data placement• Read/write random access file system – Allows distributed meta-data – Improved scaling – Enables NFS access• Application-level NIC bonding• Transactionally correct snapshots and mirrors
    30. 30. MapRsContainers Files/directories are sharded into blocks, which are placed into mini NNs (containers ) on disks  Each container contains  Directories & files  Data blocks  Replicated on serversContainers are 16-  No need to manage32 GB segments of directlydisk, placed onnodes
    31. 31. MapRsContainers  Each container has a replication chain  Updates are transactional  Failures are handled by rearranging replication
    32. 32. Container locations and replication N1, N2 N1 N3, N2 N1, N2 N1, N3 N2 N3, N2 CLDB N3 Container location database (CLDB) keeps track of nodes hosting each container and replication chain order
    33. 33. MapR ScalingContainers represent 16 - 32GB of data  Each can hold up to 1 Billion files and directories  100M containers = ~ 2 Exabytes (a very large cluster)250 bytes DRAM to cache a container  25GB to cache all containers for 2EB cluster But not necessary, can page to disk  Typical large 10PB cluster needs 2GBContainer-reports are 100x - 1000x < HDFS block-reports  Serve 100x more data-nodes  Increase container size to 64G to serve 4EB cluster  Map/reduce not affected
    34. 34. MapRs Streaming Performance 2250 2250 11 x 7200rpm SATA 11 x 15Krpm SAS 2000 2000 1750 1750 1500 1500 1250 1250 Hardware MapR 1000 1000MB Hadoop 750 750persec 500 500 250 250 0 0 Read Write Read Write Higher is better Tests: i. 16 streams x 120GB ii. 2000 streams x 1GB
    35. 35. Terasort on MapR 10+1 nodes: 8 core, 24GB DRAM, 11 x 1TB SATA 7200 rpm 60 300 50 250 40 200Elapsed 150 MapR 30time Hadoop(mins) 20 100 10 50 0 0 1.0 TB 3.5 TB Lower is better
    36. 36. HBase on MapR YCSB Random Read with 1 billion 1K records 10+1 node cluster: 8 core, 24GB DRAM, 11 x 1TB 7200 RPM 25000 20000Records 15000 per MapRsecond 10000 Apache 5000 0 Zipfian Uniform Higher is better
    37. 37. Small Files (Apache Hadoop, 10 nodes) Out of box Op: - create fileRate (files/sec) - write 100 bytes Tuned - close Notes: - NN not replicated - NN uses 20G DRAM - DN uses 2G DRAM # of files (m)
    38. 38. MUCH faster for some operationsSame 10 nodes …Create Rate # of files (millions)
    39. 39. What MapR is not• Volumes != federation – MapR supports > 10,000 volumes all with independent placement and defaults – Volumes support snapshots and mirroring• NFS != FUSE – Checksum and compress at gateway – IP fail-over – Read/write/update semantics at full speed• MapR != maprfs
    40. 40. Not Your Father’s NFS• Multiple architectures possible• Export to the world – NFS gateway runs on selected gateway hosts• Local server – NFS gateway runs on local host – Enables local compression and check summing• Export to self – NFS gateway runs on all data nodes, mounted from localhost
    41. 41. Export to the world NFS NFS Server NFS Server NFS Server NFS ServerClient
    42. 42. Local server Application NFS ServerClient Cluster Nodes
    43. 43. Universal export to self Cluster Nodes Task NFS Cluster Server Node
    44. 44. Nodes are identical Task Task NFS NFSCluster ServerNode Cluster Server Node Task NFS Cluster Server Node
    45. 45. Application architecture• High performance map-reduce is nice• But algorithmic flexibility is even nicer
    46. 46. Sharded textIndex text to local disk Indexing Assign documents to shards and then copy index to distributed file store Clustered Reducer index storage Input Mapdocuments Copy to local disk Local typically disk required before Local Search index can be loaded disk Engine
    47. 47. Shardedtext indexing• Mapper assigns document to shard – Shard is usually hash of document id• Reducer indexes all documents for a shard – Indexes created on local disk – On success, copy index to DFS – On failure, delete local files• Must avoid directory collisions – can’t use shard id!• Must manage and reclaim local disk space
    48. 48. Conventional data flow Failure of search engine requires Failure of a reducer another download causes garbage to of the index from accumulate in the clustered storage. Clustered local disk Reducer index storage Input Mapdocuments Local disk Local Search disk Engine
    49. 49. Simplified NFS data flows Search Engine Reducer Input Map Clustereddocuments index storage Failure of a reducer Search engine is cleaned up by reads mirrored map-reduce index directly. framework
    50. 50. Simplified NFS data flows Search Mirroring allows Engine exact placement of index data Reducer Input Mapdocuments Search Engine Aribitrary levels of replication also possible Mirrors
    51. 51. How about another one?
    52. 52. K-means• Classic E-M based algorithm• Given cluster centroids, – Assign each data point to nearest centroid – Accumulate new centroids – Rinse, lather, repeat
    53. 53. K-means, the movie CentroidsIn Assign Aggregatep to newu Nearest centroidst centroid
    54. 54. But …
    55. 55. Parallel Stochastic Gradient Descent Model I n Train Average p sub models u model t
    56. 56. VariationalDirichlet Assignment Model I n Gather Update p sufficient model u statistics t
    57. 57. Old tricks, new dogs Read from local disk• Mapper from distributed cache – Assign point to cluster Read from – Emit cluster id, (1, point) HDFS to local disk• Combiner and reducer by distributed cache – Sum counts, weighted sum of points – Emit cluster id, (n, sum/n) Written by• Output to HDFS map-reduce
    58. 58. Old tricks, new dogs• Mapper – Assign point to cluster Read from – Emit cluster id, (1, point) NFS• Combiner and reducer – Sum counts, weighted sum of points – Emit cluster id, (n, sum/n) Written by map-reduce• Output to HDFS MapR FS
    59. 59. Poor man’s Pregel• Mapper while not done: read and accumulate input models for each input: accumulate model write model synchronize reset input format emit summary• Lines in bold can use conventional I/O via NFS 60
    60. 60. Click modeling architecture Side-data Now via NFSI Featuren Sequential extraction Datap SGD and joinu Learning downt sampling Map-reduce
    61. 61. Click modeling architecture Side-data Map-reduce cooperates Sequential with NFS SGD Learning Sequential SGDI Learning Featuren Sequential extraction Datap SGD and joinu Learning downt sampling Sequential SGD Learning Map-reduce Map-reduce
    62. 62. And another…
    63. 63. Hybrid model flowFeature extraction and Down down sampling stream modeling Map-reduce Deployed Map-reduce Model SVD (PageRank) (spectral) ??
    64. 64. Hybrid model flowFeature extraction and Down down sampling stream modeling Deployed Model SVD (PageRank) (spectral) Sequential Map-reduce
    65. 65. And visualization…
    66. 66. Trivial visualization interface• Map-reduce output is visible via NFS $R > x <- read.csv(“/mapr/my.cluster/home/ted/data/foo.out”) > plot(error ~ t, x) > q(save=„n‟)• Legacy visualization just works
    67. 67. Conclusions• We used to know all this• Tab completion used to work• 5 years of work-arounds have clouded our memories• We just have to remember the future