Big Data Platforms:      An Overview                          C. Scyphers                   Chief Technical ArchitectDaemo...
What Is “Big Data”?• Big Data is not simply a huge pile of information• A good starting place is the following thought: “B...
What Is A Big Data Platform? Putting it simply, it is any platform which supports             those kind of large datasets.
It doesn’t have to be cutting edge technology.
Lots of legacy technologies can address the problem.
If only at a sizeable cost.
SQLOf the new technologies, the most promising are from                the “NoSQL” family.
What Is “NoSQL”?SQLA family of non-relational data storage technologies
Horizontal scalability
Distributed processing
Faster throughput
Usually with less cost than more traditional approaches.
Some of thesetechnologiesare new andinnovative
Others have been around for decades.
NoSQL Does Not Mean “SQL Is Bad”When the trend was just starting, “NoSQL” was coined. It’sunfortunate, because it implies ...
NoSQL Means “Not Only SQL”        RELATIONAL                                  RELATIONAL                                  ...
Why Won’t SQL Do?
Scale is very hard without ridiculous expense
SQL can get very complex, very quickly
Changing a schema for a large production system is             both risky and expensive
Throughput can be a challenge
How DoesNoSQL Do It?
Scale is achieved through a shared-nothing architecture,                  removing bottlenecks
Schemaless design means change becomes much less          risky and significantly cheaper
Most solutions use simple RESTful interfaces
NoSQL is based upon a better understanding of data storage, usually referred to as the           “CAP Theorem”
The CAP Theorem        Grossly simplified (with apologies to Brewer):A database can be• Consistent (All clients see the sa...
CAP In Practice• Consistent & Available (no Partition Tolerance)  • Either single machines or single site clusters.  • Typ...
CAP In Practice• Consistent & Partition Tolerant (no Availability)  • Some data may be inaccessible, but the remainder    ...
CAP In Practice• Available & Partition Tolerant (no Consistency)  • Some data may be inaccurate; a conflict resolution    ...
CAP From A Vendor POV• C-A (no P) – this is generally how most               RDBMS vendors operate• C-P (no A) – this is h...
ACID vs BASE   Traditional Databases                        NoSQL Databases Tend    Are ACID Compliant                    ...
SQL StrengthsVery well known technology
Very mature technology
Interoperability across vendors
Large talent pool from which to choose
Ad hoc operations common, if not encouraged
NoSQL Strengths Built to address massive scale
Through horizontal scalability
While remaining highly available
And handling unstructured data
NoSQL Pros/Cons       Pros                         Cons• Schema Evolution         • Querying the data is• Horizontal Scala...
A Disclaimer Before We Continue• I am not an expert on every possible Big Data  Platform• There are hundreds of them; thes...
Flavors Of NoSQLThe major four divisions of NoSQL are:•   Key-Value•   Document Store•   Columnar•   Other
Key-Value• At a very high level, key-value works essentially  by pairing a index token (a key) with a data  element (a val...
A Key-Value Example“John Smith”, “100 Century Dr. Alexandria VA 22304”“John Doe”, “16 Kozyak Street, Lozenets District, 14...
Document Store• Document stores extend the key-value paradigm  into values with multiple attributes.• The document values ...
A Document Store Example“John Smith”, “<address><street>100 Century Dr.</street>                        <city>Alexandria</...
Columnar Family• Usually has “rows” and “columns”  • Or, at least, their logical equivalents• Not a traditional, “pure” co...
The Others• Hierarchical Databases  • LDAP, Active Directory• Graph Databases  • Neo4j, Flock DB, InfiniteGraph• XML  • Ma...
Key-Value Recap Pairing a index token (a key) with a data element (a value)
Key-Value Pro/Con         Pros                                   Cons•   Schema Evolution                •   Packing & unp...
Where Did Key-Value       Come From?The concept is quite old, but most people trace thelineage back to Amazon and the Dyna...
DynamoAmazon devised the Dynamo engine as a way toaddress their scalability issues in a reliable way.• Communication betwe...
Eventually Consistent• Rather than expending the runtime resources  to ensure that all nodes are aware of a change  before...
Can I Use Dynamo?  No. It’s an Amazon only internal product.  However, AWS S3 is largely based upon it.Amazon did announce...
Riak• Riak is a key-value database largely  modeled after the Dynamo model.• Open source (free) with paid support  from Ba...
Riak Pro/Con         Pros                                   Cons•   All nodes are equal – no       •   Not meant for small...
Riak Users
Redis• Redis is a key-value in-memory datastore.• Open source (free) with support from the  community.• Main claims to fam...
Redis Pro/Con         Pros                                   Cons•   Transactional support           •   Entirely in memor...
Redis Users
Voldemort• Voldemort is a key-value in-memory database  built by LinkedIn.• Open source (free) with support from the  comm...
Voldemort Pro/Con         Pros                                 Cons•   Highly customizable – each    •   Versioning means ...
Voldemort Users
Key/Value        “Big Vendors”• Microsoft Azure Table Storage• Oracle NoSQL• BerkleyDB (Oracle)
Document Store RecapDocument stores store an index tokenwith a grouping of attributes in a semi-         structured document
Document Store Pro/Con         Pros                                  Cons•   Tends to support a more       •   The entire ...
CouchDB• CouchDB is a document store database.• Open source (free), part of the Apache  foundation with paid support avail...
CouchDB Pro/Con         Pros                                 Cons•   Very simple API for           •   The simple API for ...
CouchDB Users
MongoDB• MongoDB is a document store database.• Open source (free) with paid support available  from 10Gen.• Main claims t...
MongoDB Pro/Con         Pros                                   Cons•   Auto-sharding                   •   Does not suppor...
MongoDB Users
Document Store        “Big Vendors”• Lotus Domino
Columnar Family Recap• A key with many values attached• Usually presenting as “rows” and “columns”  • Or, at least, their ...
Columnar Pro/Con         Pros                                  Cons•   Tend to have some level of     •   Is much less eff...
Where Did Columnar      Come From?The concept has been around for a while, but most people trace the NoSQL lineage back to...
BigTableGoogle devised the BigTable engine as a way toaddress their search related scalability issues in areliable way.• D...
Can I Use BigTable?No. It’s a Google only internal product. However, quite a few open source products are built upon      ...
Cassandra• Cassandra is a hybrid of Big Table built on  Dynamo infrastructure• Open source (free), built by Facebook with ...
Cassandra Pro/Con         Pros                                      Cons•   Designed to span multiple         •   No joins...
Cassandra Users
HBase• Hbase is a columnar database built on top of the  Hadoop environment.• Open source (free) with paid support from  n...
HBase Pro/Con         Pros                                  Cons•   Map/Reduce support             •   Secondary indexes g...
HBase Users
Hadoop• Hadoop is not a columnar store as such.• Rather, Hadoop is a massively parallel data  processing engine• Main clai...
Hadoop Pro/Con         Pros                                   Cons•   While written in Java, almost   •   Large amounts of...
Hadoop Users         Plus lots, lots more
Columnar “Big Vendor”• EMC Greenplum• Teradata Aster In so far as both of these solutions are graftingMap/Reduce into a (m...
Which One Do            I Use Where?•   Key-Value for (relatively) simple, volitile data•   Document store for more comple...
Questions?
@scyphers            Additional Information Athttp://www.daemonconsulting.net/BDC-FOSE-2012Daemon Consulting, LLC     http...
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Big Data Platforms: An Overview

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A high level overview of NoSQL Big Data platforms. This presentation is targeted more towards business people than technical people.

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Transcript of "Big Data Platforms: An Overview"

  1. 1. Big Data Platforms: An Overview C. Scyphers Chief Technical ArchitectDaemon Consulting, LLC
  2. 2. What Is “Big Data”?• Big Data is not simply a huge pile of information• A good starting place is the following thought: “Big Data describes datasets so large they become very difficult to manage with traditional database tools.”
  3. 3. What Is A Big Data Platform? Putting it simply, it is any platform which supports those kind of large datasets.
  4. 4. It doesn’t have to be cutting edge technology.
  5. 5. Lots of legacy technologies can address the problem.
  6. 6. If only at a sizeable cost.
  7. 7. SQLOf the new technologies, the most promising are from the “NoSQL” family.
  8. 8. What Is “NoSQL”?SQLA family of non-relational data storage technologies
  9. 9. Horizontal scalability
  10. 10. Distributed processing
  11. 11. Faster throughput
  12. 12. Usually with less cost than more traditional approaches.
  13. 13. Some of thesetechnologiesare new andinnovative
  14. 14. Others have been around for decades.
  15. 15. NoSQL Does Not Mean “SQL Is Bad”When the trend was just starting, “NoSQL” was coined. It’sunfortunate, because it implies antagonism towards SQL.
  16. 16. NoSQL Means “Not Only SQL” RELATIONAL RELATIONAL NON-NoSQL is a complement to a traditional RDBMS, not necessarily as a replacement of them.
  17. 17. Why Won’t SQL Do?
  18. 18. Scale is very hard without ridiculous expense
  19. 19. SQL can get very complex, very quickly
  20. 20. Changing a schema for a large production system is both risky and expensive
  21. 21. Throughput can be a challenge
  22. 22. How DoesNoSQL Do It?
  23. 23. Scale is achieved through a shared-nothing architecture, removing bottlenecks
  24. 24. Schemaless design means change becomes much less risky and significantly cheaper
  25. 25. Most solutions use simple RESTful interfaces
  26. 26. NoSQL is based upon a better understanding of data storage, usually referred to as the “CAP Theorem”
  27. 27. The CAP Theorem Grossly simplified (with apologies to Brewer):A database can be• Consistent (All clients see the same data)• Available (All clients can find some available node)• Partition-Tolerant (the database will continue to function even if split into disconnected sets – e.g. a network disruption) Pick Any Two.
  28. 28. CAP In Practice• Consistent & Available (no Partition Tolerance) • Either single machines or single site clusters. • Typically uses 2 phase commits
  29. 29. CAP In Practice• Consistent & Partition Tolerant (no Availability) • Some data may be inaccessible, but the remainder is available and consistent • Sharding is an example of this implementation Customers Customers Customers A-F G-R S-Z
  30. 30. CAP In Practice• Available & Partition Tolerant (no Consistency) • Some data may be inaccurate; a conflict resolution strategy is required. • DNS is an example of this, as well as standard master-slave replication
  31. 31. CAP From A Vendor POV• C-A (no P) – this is generally how most RDBMS vendors operate• C-P (no A) – this is how many RDBMS’ attempt to address scale without incurring large costs• A-P (no C) – this is how most NoSQL approaches solve the problem
  32. 32. ACID vs BASE Traditional Databases NoSQL Databases Tend Are ACID Compliant To Be BASE CompliantAtomicity – either the entire transaction Basically completes or none of it doesConsistent – any transaction will take the Available database from one consistent state to another, with no broken constraintsIsolation – changes do not affect other users Scalable until committedDurability – committed transactions can be Eventually consistent recovered in case of system failure Eventually consistent is the key phrase here
  33. 33. SQL StrengthsVery well known technology
  34. 34. Very mature technology
  35. 35. Interoperability across vendors
  36. 36. Large talent pool from which to choose
  37. 37. Ad hoc operations common, if not encouraged
  38. 38. NoSQL Strengths Built to address massive scale
  39. 39. Through horizontal scalability
  40. 40. While remaining highly available
  41. 41. And handling unstructured data
  42. 42. NoSQL Pros/Cons Pros Cons• Schema Evolution • Querying the data is• Horizontal Scalability much harder• Simple Protocols • Paradigm Shift • Security is a big issue • May or may not support data types (BLOBs, spatial) • Generally, uniqueness cannot be enforced
  43. 43. A Disclaimer Before We Continue• I am not an expert on every possible Big Data Platform• There are hundreds of them; these are the ones I consider the leaders in the field and recommend• If you have a favorite, please let me know and I’ll update the deck for next time• The internal details on how these systems work are rather complex; I would prefer to take those questions offline
  44. 44. Flavors Of NoSQLThe major four divisions of NoSQL are:• Key-Value• Document Store• Columnar• Other
  45. 45. Key-Value• At a very high level, key-value works essentially by pairing a index token (a key) with a data element (a value).• Both index token and the data value can be of any structure.• Such a pairing is arbitrary and up to the developer of the system to determine.
  46. 46. A Key-Value Example“John Smith”, “100 Century Dr. Alexandria VA 22304”“John Doe”, “16 Kozyak Street, Lozenets District, 1408 Sofia Bulgaria” In both examples, the key is a name and the value is an address. However, the structure of the address differs between the two.
  47. 47. Document Store• Document stores extend the key-value paradigm into values with multiple attributes.• The document values tend to be semi-structured data (XML, JSON, et al) but can also be Word or PDF documents.
  48. 48. A Document Store Example“John Smith”, “<address><street>100 Century Dr.</street> <city>Alexandria</city> <state>VA</state> <postalCode>22304</postalCode> </address>”“John Doe”, “{ “address”: { “street”: “16 Kozyak Street” “district”: “Lozenets, 1408” “city”: “Sofia” “country”: “Bulgaria” } }”
  49. 49. Columnar Family• Usually has “rows” and “columns” • Or, at least, their logical equivalents• Not a traditional, “pure” column store • More of a hybridized approach leveraging key-value pairs• A key with many values attached
  50. 50. The Others• Hierarchical Databases • LDAP, Active Directory• Graph Databases • Neo4j, Flock DB, InfiniteGraph• XML • MarkLogic• Object Oriented Databases • Versant• Lotus Notes• HPCC (LexisNexis)
  51. 51. Key-Value Recap Pairing a index token (a key) with a data element (a value)
  52. 52. Key-Value Pro/Con Pros Cons• Schema Evolution • Packing & unpacking each key• Horizontal Scalability • Keys typically are not related• Simple Protocols to each other• Works well for volatile data • The entire value must be• High throughput, typically returned, not just a part of it optimized for reads or writes • Security tends to be an issue• Keys become meaningful • Hard to support reporting, rather than arbitrary analytics, aggregation or• Application logic defines ordered values object model • Generally does not support updates in place • Application logic defines object model
  53. 53. Where Did Key-Value Come From?The concept is quite old, but most people trace thelineage back to Amazon and the Dynamo paper.
  54. 54. DynamoAmazon devised the Dynamo engine as a way toaddress their scalability issues in a reliable way.• Communication between nodes is peer to peer (P2P)• Replication occurs with the end client addressing conflict resolution• Quorum Reads/Writes• Always writable (Hinted Handoff)• Eventually Consistent
  55. 55. Eventually Consistent• Rather than expending the runtime resources to ensure that all nodes are aware of a change before continuing, Dynamo uses an eventually consistent model.• In this model, a subset of nodes are changed• Those nodes then inform their neighbors until all nodes are changed (grossly simplifying).
  56. 56. Can I Use Dynamo? No. It’s an Amazon only internal product. However, AWS S3 is largely based upon it.Amazon did announce a DynamoDB offering for their AWS customers. While it’s probably the same, I cannot guarantee that it is.
  57. 57. Riak• Riak is a key-value database largely modeled after the Dynamo model.• Open source (free) with paid support from Basho.• Main claims to fame: • Extreme reliability • Performance speed
  58. 58. Riak Pro/Con Pros Cons• All nodes are equal – no • Not meant for small, discrete single point of failure and numerous datapoints.• Horizontal Scalability • Getting data in is great;• Full Text Search getting it out, not so much• RESTful interface (and HTTP) • Security is non-existent:• Consistency level tunable on “Riak assumes the internal each operation environment is trusted”• Secondary indexes available • Conflict resolution can bubble• Map/Reduce (JavaScript & up to the client if not careful. Erlang only) • Erlang is fast, but it’s got a serious learning curve.
  59. 59. Riak Users
  60. 60. Redis• Redis is a key-value in-memory datastore.• Open source (free) with support from the community.• Main claims to fame: • Fast. So very, very fast. • Transactional support • Best for rapidly changing data
  61. 61. Redis Pro/Con Pros Cons• Transactional support • Entirely in memory• Blob storage • Master-slave replication• Support for sets, lists and (instead of master-master) sorted sets • Security is non-existent:• Support for Publish-Subscribe designed to be used in (Pub-Sub) messaging trusted environments• Robust set of operators • Does not support encryption • Support can be hard to find
  62. 62. Redis Users
  63. 63. Voldemort• Voldemort is a key-value in-memory database built by LinkedIn.• Open source (free) with support from the community• Main claims to fame: • Low latency • Highly Available • Very fast reads
  64. 64. Voldemort Pro/Con Pros Cons• Highly customizable – each • Versioning means lots of disk layer of the stack can be space being used. replaced as needed • Does not support range• Data elements are versioned queries during changes • No complex query filters• All nodes are independent – • All joins must be done in no single point of failure code• Very, very fast reads • No foreign key constraints • No triggers • Support can be hard to find
  65. 65. Voldemort Users
  66. 66. Key/Value “Big Vendors”• Microsoft Azure Table Storage• Oracle NoSQL• BerkleyDB (Oracle)
  67. 67. Document Store RecapDocument stores store an index tokenwith a grouping of attributes in a semi- structured document
  68. 68. Document Store Pro/Con Pros Cons• Tends to support a more • The entire value must be complex data model than returned, not just a part of it key/value • Security tends to be an issue• Good at content • Joins are not available within management the database• Usually supports multiple • No foreign keys indexes • Application logic defines• Schemaless (can be nested) object model• Typically low latency reads• Application logic defines object model
  69. 69. CouchDB• CouchDB is a document store database.• Open source (free), part of the Apache foundation with paid support available from several vendors.• Main claims to fame: • Simple and easy to use • Good read consistency • Master-master replication
  70. 70. CouchDB Pro/Con Pros Cons• Very simple API for • The simple API for development development is somewhat• MVCC support for read limited consistency • No foreign keys• Full Map/Reduce support • Conflict resolution devolves• Data is versioned to the application• Secondary indexes supported • Versioning requires extensive• Some security support disk space• RESTful API, JSON support • Versioning places large load• Materialized views with on I/O channels incremental update support • Replication for performance, not availability
  71. 71. CouchDB Users
  72. 72. MongoDB• MongoDB is a document store database.• Open source (free) with paid support available from 10Gen.• Main claims to fame: • Index anything • Ad hoc query support • SQL like operations (not SQL syntax)
  73. 73. MongoDB Pro/Con Pros Cons• Auto-sharding • Does not support JSON: BSON• Auto-failover instead• Update in place • Master-slave replication• Spatial index support • Has had some growing pains• Ad hoc query support (e.g. Foursquare outage)• Any field in Mongo can be • Not RESTful by default indexed • Failures require a manual• Very, very popular (lots of database repair operation production deployments) (similar to MySQL)• Very easy transition from SQL • Replication for availability, not performance
  74. 74. MongoDB Users
  75. 75. Document Store “Big Vendors”• Lotus Domino
  76. 76. Columnar Family Recap• A key with many values attached• Usually presenting as “rows” and “columns” • Or, at least, their logical equivalents
  77. 77. Columnar Pro/Con Pros Cons• Tend to have some level of • Is much less efficient when rudimentary security support processing many columns• Usually include a degree of simultaneously versioning • Joins tend to not be• Can be more efficient than supported row databases when • Referential integrity not processing a limited number available of columns over a large amount of rows
  78. 78. Where Did Columnar Come From?The concept has been around for a while, but most people trace the NoSQL lineage back to Google.
  79. 79. BigTableGoogle devised the BigTable engine as a way toaddress their search related scalability issues in areliable way.• Data is organized through a set of keys: • Row • Column • Timestamp• A hybrid row/column store with a single master• Versioning is handled through the time key• Tablets are a dynamic partition of a sequence of rows – supports very efficient range scans• Columns can be grouped into column families• Column families can have access control
  80. 80. Can I Use BigTable?No. It’s a Google only internal product. However, quite a few open source products are built upon the concepts.
  81. 81. Cassandra• Cassandra is a hybrid of Big Table built on Dynamo infrastructure• Open source (free), built by Facebook with paid support available from several vendors.• Main claims to fame: • An Apache project • Very, very fast writes • Spans multiple datacenters
  82. 82. Cassandra Pro/Con Pros Cons• Designed to span multiple • No joins datacenters • No referential integrity• Peer to peer communication • Written in Java – quite between nodes complex to administer• No single point of failure and configure• Always writeable • Last update wins• Consistency level is tunable at run time• Supports secondary indexes• Supports Map/Reduce• Supports range queries
  83. 83. Cassandra Users
  84. 84. HBase• Hbase is a columnar database built on top of the Hadoop environment.• Open source (free) with paid support from numerous vendors• Main claims to fame: • Ad hoc type abilities • Easy integration with Map/Reduce
  85. 85. HBase Pro/Con Pros Cons• Map/Reduce support • Secondary indexes generally• More of a CA approach and not supported an AP • Security is non-existent• Supports predicate push • Requires a Hadoop down for performance gains infrastructure to function• Automatic partitioning and rebalancing of regions• Data is stored in a sorted order (not indexed)• RESTful API• Strong and vibrant ecosystem
  86. 86. HBase Users
  87. 87. Hadoop• Hadoop is not a columnar store as such.• Rather, Hadoop is a massively parallel data processing engine• Main claims to fame: • Specializes in unstructured data • Very flexible and popular
  88. 88. Hadoop Pro/Con Pros Cons• While written in Java, almost • Large amounts of disk space any language can leverage and bandwidth required Hadoop • Paradigm shift for IT staff• Runs on commodity servers • Quality talent is highly in• Horizontally scalable demand and expensive• Very fast and powerful • Security is non-existent• Where Map/Reduce • Name node is a single point originated of failure• Ample support from vendors • More or less only supporting• “Helper” languages like Hive batch processing and Pig • Not user friendly to anyone• Strong and vibrant ecosystem other than developers
  89. 89. Hadoop Users Plus lots, lots more
  90. 90. Columnar “Big Vendor”• EMC Greenplum• Teradata Aster In so far as both of these solutions are graftingMap/Reduce into a (more or less) SQL environment
  91. 91. Which One Do I Use Where?• Key-Value for (relatively) simple, volitile data• Document store for more complex data• Columnar for analytical processing• RBDMS for traditional processing – particularly where a lazy consistency is not acceptable • Point Of Sale, for example
  92. 92. Questions?
  93. 93. @scyphers Additional Information Athttp://www.daemonconsulting.net/BDC-FOSE-2012Daemon Consulting, LLC http://www.daemonconsulting.net/ Specializing In The Hard Stuff

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