An Introduction to Accumulo

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This was presented for an O'Reilly Media webcast. http://www.oreilly.com/pub/e/3152?cmp=tw-na-webcast-product-webcast_an_introduction_to_apache_accumulo …

This was presented for an O'Reilly Media webcast. http://www.oreilly.com/pub/e/3152?cmp=tw-na-webcast-product-webcast_an_introduction_to_apache_accumulo

This webcast will cover the basics of Apache Accumulo architecture and how it works, along with examples of how it is used. We'll also talk about some interesting use cases, such as text indexing, fine-grained multi-level access controls, and storing large-scale graphs. We'll also briefly touch on what sets Accumulo apart from other similar and not-so similar systems and where we think the Accumulo project is headed in a technical direction.

A description of Accumulo from the Apache Accumulo website: 

The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell-based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.

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  • Two basic operators
    AND operator represented by &
    OR operator represented by |
    In the examples A,B, C, and D are security tokens
    Security Tokens are strings of alphanumeric characters
    Tokens are user defined
    Parenthesis are required to use nested logic
  • A Minor Compaction is triggered when the Tablet’s MemTable reaches it’s maximum size
    When the MemTable reaches it’s maximum size, it is flushed
    A Minor Compaction Iterator is applied during the stage when the MemTable is flushed and a new RFile is created
    Since the iterator is applied during a Minor Compaction, the iterator does affect the persistence of the data
  • A Major Compaction periodically merges as set of RFiles into one
    If a Major Compaction iterator is enabled, the iterator runs after the merge to filter data before writing the new RFile
    Since the iterator is applied during a Minor Compaction, the iterator does affect the persistence of the data

Transcript

  • 1. AN INTRODUCTION TO APACHE ACCUMULO HOW IT WORKS, WHY IT EXISTS,AND HOW IT IS USED Donald Miner CTO, ClearEdge IT Solutions @donaldpminer August 5th, 2014
  • 2. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system.
  • 3. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Adelaide Bartkowski Alyssa Files Beatriz Palmore Cecilia Ours Craig Avalos Dianna Lapointe Erma Davis Fermina Smead Garrett Harsh Gaylene Sherry Gilberto Pardue Hui Nodal Janell Tomita Jannette Betters Jeana Delk Madlyn Radke Peggie Allis Rhona Zygmont Tran Degarmo Wilhelmina Papp
  • 4. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Janell Tomita Jannette Betters Jeana Delk Madlyn Radke Peggie Allis Rhona Zygmont Tran Degarmo Wilhelmina Papp Adelaide Bartkowski Alyssa Files Beatriz Palmore Cecilia Ours Craig Avalos Dianna Lapointe Erma Davis Fermina Smead Garrett Harsh Gaylene Sherry Gilberto Pardue Hui Nodal -inf to D E to H J to +inf
  • 5. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Accumulo Master TabletServer TabletServer TabletServer ZooKeeper
  • 6. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. KEY VALUE Adelaide Bartkowski 91294124 Alyssa Files 491294 Beatriz Palmore 4124124124 Cecilia Ours 419120 Craig Avalos 940124 Dianna Lapointe 4921 Erma Davis 050194 Fermina Smead 10024599949 Garrett Harsh 140095931 Gaylene Sherry 914815 Gilberto Pardue 412414124124 Hui Nodal 962195192 Janell Tomita 12121 Jannette Betters 9192012 Jeana Delk 9120150 Madlyn Radke 4921 Peggie Allis 944944 Rhona Zygmont 123103 Tran Degarmo 9499494 Wilhelmina Papp 11221 Lookup “Garret Harsh” FAST Lookup “4921” SLOW
  • 7. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system.
  • 8. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system.
  • 9. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system.
  • 10. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system.
  • 11. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system.
  • 12. The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. MIT Lincoln Lab study: 100 Million inserts per second using Accumulo http://arxiv.org/ftp/arxiv/papers/1406/1406.4923.pdf http://sqrrl.com/media/Accumulo-Benchmark-10312013-1.pdf Booz Allen Hamilton study: 942 tablet servers, 7.56 trillion entries, 408TB, 26 hours 94MB/Sec, 15TB/hr, 80million inserts per second 11 tablet servers went down with no interruption Showed linear scalability for write throughput 22,000 queries per second
  • 13. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell- based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
  • 14. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell- based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
  • 15. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell- based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
  • 16. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell- based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
  • 17. HBase vs. Accumulo • Slight differences in visibility labels • Coprocessors vs. Iterators • Accumulo has faster write throughput* • HBase’s reads are faster* • HBase has more ecosystem integration • BatchScanner • Accumulo can shift around locality groups after the fact • Accumulo has shown to work with no problems at 1,000 nodes (BAH paper). Facebook and others run a “cell” design for HBase. Largest clusters in the hundreds*. * We believeDisclaimer: I am biased
  • 18. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell- based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
  • 19. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell- based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011. (admin & developer) | analyst
  • 20. Column Visibility Syntax Label Description A & B Both ‘A’ and ‘B’ are required A | B Either ‘A’ or ‘B’ is required A & (C | B) ‘A’ and ‘C’ or ‘A’ and ‘B’ is required A | (B & C) ‘A’ or ‘B’ and ‘C’ is required (A | B) & (C & D) ? A & (B & (C | D)) ? Patient has schizophrenia: insurer | MD & psych Patient has stomach ulcers: insurer | doctor Patient has cavity: insurer | dentist Patient has consent for general anesthesia: surgeon
  • 21. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell- based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
  • 22. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell- based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process. Other notable improvements and feature are outlined here. Google published the design of BigTable in 2006. Several other open source projects have implemented aspects of this design including HBase, Hypertable, and Cassandra. Accumulo began its development in 2008 and joined the Apache community in 2011.
  • 23. More cool features • Constraints: user-defined Java functions that allow or prevent new writes based on a condition • Large rows: no limit on data stored in a row • Multiple masters & FATE: able to execute table operations in a fault-tolerant manner • MapReduce InputFormats • Bulk import utilities: write directly to Accumulo file formats • Batch scanner: client scans multiple ranges at once • Batch writer: client buffers and organized data before writing in parallel
  • 24. More cool features • Constraints: user-defined Java functions that allow or prevent new writes based on a condition • Large rows: no limit on data stored in a row • Multiple masters & FATE: able to execute table operations in a fault-tolerant manner • MapReduce InputFormats • Bulk import utilities: write directly to Accumulo file formats • Batch scanner: client scans multiple ranges at once • Batch writer: client buffers and organized data before writing in parallel
  • 25. More cool features • Thrift proxy: access Accumulo through Ruby, Python, … • Monitor page: shows performance, status, errors, more • Locality groups: group column families together on disk for performance tuning (changeable later) • On-HDFS at rest encryption (work in progress) • Table import and export
  • 26. More cool features • Thrift proxy: access Accumulo through Ruby, Python, … • Monitor page: shows performance, status, errors, more • Locality groups: group column families together on disk for performance tuning (changeable later) • On-HDFS at rest encryption (work in progress) • Table import and export
  • 27. Scalability & Performance • Multiple HDFS volumes: Accumulo can use multiple NameNodes to store its data • Master stores metadata in an Accumulo table • Native in-memory map: data is first written into a buffer written in C++, outside of Java • Relative encoding: consecutive keys with the same values are flagged instead of rewritten • Scan pipelines: stages of the read path are parallelized into separate threads • Caching: data recently scanned is cached
  • 28. HOW IT WORKS
  • 29. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P
  • 30. Data Model Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public | private 12423523 @donaldpminer don info height public | private 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … Name email twitter picture height SSN derek de…@ad….com 9efe23aa… 6’2” don dm…@cl….com @donaldpminer 5’ 9” erica @erica aef319eaf…
  • 31. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P Lookup key
  • 32. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P Collection of data that is kept together
  • 33. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P What the data is
  • 34. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P Who can see the data
  • 35. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P When the data was created
  • 36. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P UNIQUENESS
  • 37. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P SORTED
  • 38. Data Model KEY ROW ID COLUMN FAMILY QUALIFIER VISIBILITY VALUE Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … TIMESTAM P Some piece of information
  • 39. Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info SSN private 12314514 123-45-6789 erica … … … … … Row ID Family Qualifier Visibility Timestamp Value don info picture public 13119103 dd3ae1d3b951a33f… Writing data into Accumulo
  • 40. Row ID Family Qualifier Visibility Timestamp Value don info picture public 13119103 dd3ae1d3b951a33f… Writing data into Accumulo Text rowID = new Text(”don"); Text colFam = new Text(”info"); Text colQual = new Text(”picture"); ColumnVisibility colVis = new ColumnVisibility("public"); long timestamp = System.currentTimeMillis(); Value value = new Value(MyPictureObj.getBytes()); Mutation mutation = new Mutation(rowID); mutation.put(colFam, colQual, colVis, timestamp, value); BatchWriterConfig config = new BatchWriterConfig(); BatchWriter writer = conn.createBatchWriter(”usertable", config) writer.add(mutation); writer.close();
  • 41. Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info picture public 13119103 dd3ae1d3b951a33f… don info SSN private 12314514 123-45-6789 erica … … … … … Row ID Family Qualifier Visibility Timestamp Value don info picture public 13119103 dd3ae1d3b951a33f… Writing data into Accumulo
  • 42. Writing data into Accumulo New Record
  • 43. Writing data into Accumulo Write Ahead Log (WAL) New Record MemTable sorted append
  • 44. Writing data into Accumulo New Record
  • 45. Writing data into Accumulo Write Ahead Log (WAL) New Record MemTable
  • 46. Writing data into Accumulo Write Ahead Log (WAL) New Record MemTable RFile (minc) sorted Minor Compaction
  • 47. Writing data into Accumulo Write Ahead Log (WAL) New Record MemTable RFile (minc) RFile (minc) Minor Compaction
  • 48. Writing data into Accumulo Write Ahead Log (WAL) New Record MemTable RFile (minc) RFile (minc) RFile (minc) Minor Compaction
  • 49. Writing data into Accumulo RFile (majc) RFile (minc) RFile (minc) RFile (minc) sorted Major Compaction
  • 50. Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info picture public 13119103 dd3ae1d3b951a33f… don info SSN private 12314514 123-45-6789 erica … … … … … Range Family Visibilities don-don info public Reading data
  • 51. Range Family Visibilities don-don info public Reading data Authorizations auths = new Authorizations("public”); Scanner scan = conn.createScanner(”usertable", auths); scan.setRange(new Range(”don",”don")); scan.fetchFamily(”info"); for(Entry<Key,Value> entry : scan) { String row = entry.getKey().getRow(); Value value = entry.getValue(); }
  • 52. Reading data MemTable RFile (minc) RFile (minc) RFile (minc) RFile (majc) Range Family Visibilities don-don info public Tablet: c - f
  • 53. Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info picture public 13119103 dd3ae1d3b951a33f… don info SSN private 12314514 123-45-6789 erica … … … … … Range Family Visibilities don-don info public, user, tech Reading data
  • 54. Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info picture public 13119103 dd3ae1d3b951a33f… don info SSN private 12314514 123-45-6789 erica … … … … … Range Visibilities don-don public, user, tech Reading data Scan
  • 55. Row ID Family Qualifier Visibility Timestamp Value derek … … … … … don contact email admin | private 11905014 dminer@gopivotal.com don contact email admin | private 12412412 dminer@clearedgeit.com don contact email public 12412412 dm…@cl....com don contact twitter public 12423523 @donaldpminer don info height public 12314514 5’ 9” don info picture public 13119103 dd3ae1d3b951a33f… don info SSN private 12314514 123-45-6789 erica … … … … … Range Visibilities d-e public, user, tech Reading data Scan
  • 56. Iterators • Iterators run tablet server side at these times: 1. Scan Time 2. Minor Compaction 3. Major Compaction • Multiple iterators are included with Accumulo • Custom iterators can be created using the Iterator API
  • 57. Scan Time Iterator
  • 58. Minor Compaction Iterator
  • 59. Major Compaction Iterator
  • 60. Age-Off Iterator Ro w ID Column Family Column Qualifier Colum n Visibilit y Timestam p Valu e bob attribute score public 1005 24 bob attribute score public 1004 55 bob attribute score public 1003 71 bob attribute score public 1002 66 bob attribute score public 1001 39 bob attribute score public 1000 33 Current Time: 1102 Entries < 100s old Entries > 100s old Scan time: server side filtering Major compaction time: age off
  • 61. Combiner Iterators Apply a function to all available versions of a particular key Row ID Column Family Column Qualifier Column Visibility Time Stamp Value bob attribute score public 1005 33 bob attribute score public 1004 65 bob attribute score public 1003 71 bob attribute score public 1002 59 bob attribute score public 1001 57 bob attribute score public 1000 51 MAX 71 Scan time: server side combining Minor & Major compaction time: consolidation
  • 62. USE CASES
  • 63. Basic Structured Data Row ID Column Family Column Qualifier Column Visibility Timestam p Value bob attribute surname public Jul 2013 doe bob attribute height public Jun 2012 5’11” bob insurance dental private Sep 2009 MetLife jane attribute bloodType public Jul 2011 ab- jane attribute surname public Aug 2013 doe jane contact cellPhone public Dec 2010 (808) 345- 9876 jane insurance vision private Jan 2008 VSP john allergy major private Feb 1988 amoxicillin john attribute weight public Sep 2013 180 john contact homeAddr public Mar 2003 34 Baker LN
  • 64. Indexing Everything Row ID Column Fam Column Qual Visibility Time value index Column Fam Column Qual:Row ID Visibility Time - to Column Fam Column Qual:Row ID Visibility Time - values Column Fam Column Qual:Row ID Visibility Time - Event Table Index Table
  • 65. Index Table Row ID Column Family Column Qualifier Column Visibility Timestam p Value (808) 345- 9876 contact cellPhone:jane public Dec 2010 - 180 attribute weight:john public Sep 2013 - 34 Baker LN contact homeAddr:john public Mar 2003 - 5’11” attribute height:bob public Jun 2012 - MetLife insuranc e dental:bob private Sep 2009 - VSP insuranc e vision:jane private Jan 2008 - ab- attribute bloodType:jane public Jul 2011 - amoxicillin allergy major:john private Feb 1988 - doe attribute surname:bob public Jul 2013 - doe attribute surname:jane public Aug 2013 -
  • 66. Data Lake PATIENTS MEDICINES DOCTORS INDEX
  • 67. Data Lake PATIENTS MEDICINES DOCTORS INDEX Tell me everything you know of amoxicillin amoxicillin
  • 68. Data Lake PATIENTS DISEASES DOCTORS INDEX amoxicillin bob:allergy:amoxicillin larry:takes:amoxicillin Stomach ulcer: treatment:amoxicillin smith: prescribed:amoxicillinInfection: treatment:amoxicillin Diarrhea: side effect:amoxicillin
  • 69. Graphs a bc d e a b c d e a - 1 b 1 - c - 1 d 1 1 - 1 e - Start Nodes EndNodes Row ID Column Family Column Qualifier Value a edge b 1 a edge d 1 c edge a 1 c edge d 1 d edge c 1 e edge d 1
  • 70. Term-Partitioned Index Tablet Server 1 Row ID Column Family Value baseball document docid_3 baseball document docid_2 bat document docid_2 Tablet Server 2 Row ID Column Family Value football document docid_1 football document docid_3 glove document docid_1 Tablet Server 3 Row ID Column Family Value nba document docid_1 shoes document docid_1 soccer document docid_3 RESULTS: [docid_2, docid_3] RESULTS: [docid_1, docid_3] RESULTS: [docid_3] Tablet Server knows about the terms “baseball” Tablet Server knows about the terms “football” Tablet Server knows about the terms “soccer” Query: “baseball” AND “football” AND “soccer” Client Client-side Set Intersection [docid_2, docid_3] [docid_1, docid_3] [docid_3]
  • 71. Geospacial Indexing: Z-Order Curve 33.333W, 55.555N = 3535.353535
  • 72. WHERE TO GO FROM HERE
  • 73. Resources Apache Accumulo website accumulo.apache.org Accumulo Summit 2014 accumulosummit.com slideshare.net/AccumuloSummit Multi-day in-person training UMBC Training Centers ClearEdge IT Solutions Sqrrl
  • 74. Find a job
  • 75. AN INTRODUCTION TO APACHE ACCUMULO HOW IT WORKS, WHY IT EXISTS,AND HOW IT IS USED Donald Miner CTO, ClearEdge IT Solutions @donaldpminer August 5th, 2014