The document provides an overview of various database architectures and their characteristics, including structured, semi-structured, unstructured, graph, column-oriented, in-memory, and distributed databases. It discusses different examples such as Hypertable, MongoDB, and Amazon AWS services, and explains concepts like horizontal scalability, auto-sharding, and eventual consistency. Additionally, it compares Hypertable and HBase in performance metrics and highlights Hypertable's design inspired by Google's Bigtable.

1. Databases ArchitecturesDatabases Architectures
& Hypertable& Hypertable
Doug JuddDoug Judd
CEO, Hypertable, Inc.CEO, Hypertable, Inc.

2. Database TerminologyDatabase Terminology

3. www.hypertable.org
Structured, Semi-Structured,Structured, Semi-Structured,
and Unstructured Dataand Unstructured Data
 Structured is what RDBMS storeStructured is what RDBMS store

Data is broken into discrete componentsData is broken into discrete components

Types associated with each component:Types associated with each component:
integer, floating point, date, stringinteger, floating point, date, string
 Unstructured is free-form textUnstructured is free-form text
 Semi-structured is combination ofSemi-structured is combination of
sturctured and semi-structuredsturctured and semi-structured

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Document-OrientedDocument-Oriented
 Semi-structured documentsSemi-structured documents
 Accepts documents in a format such asAccepts documents in a format such as
JSON, XML, YAMLJSON, XML, YAML
 Often Schema-lessOften Schema-less
 Auto-index fieldsAuto-index fields
 Examples: CouchDB, MongoDBExamples: CouchDB, MongoDB
 Best Fit: XML or Web documentsBest Fit: XML or Web documents

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Graph DatabasesGraph Databases
 Database designed to represent graphsDatabase designed to represent graphs
 APIs for performing graph operationsAPIs for performing graph operations

Traversal (depth-first, breadth-first)Traversal (depth-first, breadth-first)

Shortest/Cheapest pathShortest/Cheapest path

PartitioningPartitioning
 Some allow HypergraphsSome allow Hypergraphs
 Examples:Examples:
Neo4j, HyperGraphDB, InfoGrid,Neo4j, HyperGraphDB, InfoGrid,
AllegroGraph, Sones, DEX, FlockDB,AllegroGraph, Sones, DEX, FlockDB,
OrientDB, VertexDB, InfiniteGraph, FilamentOrientDB, VertexDB, InfiniteGraph, Filament
 More info: sones graphdb landscapeMore info: sones graphdb landscape

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Column-OrientedColumn-Oriented
 Data physically stored by columnData physically stored by column
 RDBMS typically row-orientedRDBMS typically row-oriented
 Improved performance for columnImproved performance for column
operationsoperations
 Better data compressionBetter data compression
 Examples:Examples:
Hypertable, HBase, Cassandra, VerticaHypertable, HBase, Cassandra, Vertica

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In-MemoryIn-Memory
 Data set stored in RAMData set stored in RAM
 Extremely fast accessExtremely fast access
 Limited capacityLimited capacity
 Examples:Examples:
Memcached, Redis, MonetDB, VoltDBMemcached, Redis, MonetDB, VoltDB

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Horizontal ScalabilityHorizontal Scalability
 Scale outScale out
 Increase capacity by adding machinesIncrease capacity by adding machines
 Opposite of vertical scalability (scale up)Opposite of vertical scalability (scale up)
 Commodity HardwareCommodity Hardware

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Distributed Hash Table (DHT)Distributed Hash Table (DHT)
 Horizontally ScalableHorizontally Scalable
 DecentralizedDecentralized
 Fast accessFast access
 Restricted API:Restricted API: GET,SET,DELETEGET,SET,DELETE
 Peer-to-peer file sharing systems:Peer-to-peer file sharing systems:
BitTorrent, Napster, Gnutella, FreenetBitTorrent, Napster, Gnutella, Freenet
 Examples:Examples:
Dynamo, Cassandra, Riak, Project Voldemort,Dynamo, Cassandra, Riak, Project Voldemort,
SimpleDB, S3, Redis, Scalaris, MembaseSimpleDB, S3, Redis, Scalaris, Membase

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Amazon AWSAmazon AWS
 S3S3

Online storage web serviceOnline storage web service

Designed for larger amounts of dataDesigned for larger amounts of data

Cost $0.15/GB per monthCost $0.15/GB per month
 SimpleDBSimpleDB

Designed for smaller amounts of dataDesigned for smaller amounts of data

Provides indexing and richer query capabilityProvides indexing and richer query capability

Cost $0.27/GB per month + machine utilization feeCost $0.27/GB per month + machine utilization fee
 RDSRDS

Managed MySQL instancesManaged MySQL instances

11. Scalable DatabaseScalable Database
ArchitecturesArchitectures

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Auto-ShardingAuto-Sharding
 Splits table data into horizontal “shards”Splits table data into horizontal “shards”
 Shards managed by traditional RDBMSShards managed by traditional RDBMS
(e.g. MySQL, Postgres)(e.g. MySQL, Postgres)
 Automated “glue” code to handle shardingAutomated “glue” code to handle sharding
and request routingand request routing
 Examples:Examples:
MongoDB, AsterData, GreenplumMongoDB, AsterData, Greenplum

13. www.hypertable.org
MongoDBMongoDB

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DynamoDynamo
 Developed by Amazon.com for theirDeveloped by Amazon.com for their
Shopping CartShopping Cart
 Designed for high write availabilityDesigned for high write availability
 Eventually Consistent DHTEventually Consistent DHT
 Implementations:Implementations:

CassandraCassandra

Project VoldemortProject Voldemort

RiakRiak

DynomiteDynomite

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Eventual ConsistencyEventual Consistency
 Database update semantics in aDatabase update semantics in a
distributed system with data replicationdistributed system with data replication
 Strong Consistency - after an updateStrong Consistency - after an update
completescompletes allall processes see the updatedprocesses see the updated
valuevalue
 Eventual Consistency -Eventual Consistency - eventually alleventually all
processes will see the updated valueprocesses will see the updated value
 Most well-known eventual consistencyMost well-known eventual consistency
system is DNSsystem is DNS

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Eventual ConsistencyEventual Consistency

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Consistent HashingConsistent Hashing

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Order Preserving PartitionerOrder Preserving Partitioner
(Cassandra)(Cassandra)
www.recipezaar.comwww.recipezaar.com 1091721999…6297502721091721999…629750272
++
www.ribbonprinters.comwww.ribbonprinters.com 1091721999…9652931031091721999…965293103
/ 2 =/ 2 =
www.rgb????i?pQdpwww.rgb????i?pQdp?.??? 1091721999…297521687?.??? 1091721999…297521687

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Order Preserving PartitionerOrder Preserving Partitioner
Balance ProblemBalance Problem

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Bigtable: the infrastructure thatBigtable: the infrastructure that
Google is built onGoogle is built on
 Bigtable underpins 100+ GoogleBigtable underpins 100+ Google
services, including:services, including:
YouTube, Blogger, Google Earth, GoogleYouTube, Blogger, Google Earth, Google
Maps, Orkut, Gmail, Google Analytics,Maps, Orkut, Gmail, Google Analytics,
Google Book Search, Google Code,Google Book Search, Google Code,
Crawl Database…Crawl Database…
 ImplementationsImplementations

HypertableHypertable

HBaseHBase

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Google StackGoogle Stack
 GFSGFS - Replicates data inter-machine- Replicates data inter-machine
 MapReduceMapReduce - Efficiently process data in GFS- Efficiently process data in GFS
 BigtableBigtable - Indexed table structure- Indexed table structure

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Google File SystemGoogle File System

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Google File SystemGoogle File System

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Table: Visual RepresentationTable: Visual Representation

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Table: Actual RepresentationTable: Actual Representation

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Scaling (part I)Scaling (part I)

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Scaling (part II)Scaling (part II)

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Scaling (part III)Scaling (part III)

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Request RoutingRequest Routing

30. HypertableHypertable

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Hypertable OverviewHypertable Overview
 Massively Scalable DatabaseMassively Scalable Database
 Modeled after Google’s BigtableModeled after Google’s Bigtable
 High Performance Implementation (C++)High Performance Implementation (C++)
 Thrift Interface for all popular High LevelThrift Interface for all popular High Level
Languages: Java, Ruby, Python, PHP, etcLanguages: Java, Ruby, Python, PHP, etc
 Open Source (GPL license)Open Source (GPL license)
 Project started March 2007 @ ZventsProject started March 2007 @ Zvents

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Hypertable In Use TodayHypertable In Use Today

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Hypertable vs. HBaseHypertable vs. HBase

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Hypertable vs. HBaseHypertable vs. HBase
Test Hypertable
Advantage
Relative to
HBase (%)
Random Read Zipfian 80 GB 925
Random Read Zipfian 20 GB 777
Random Read Zipfian 2.5 GB 100
Random Write 10KB values 51
Random Write 1KB values 102
Random Write 100 byte values 427
Random Write 10 byte values 931
Sequential Read 10KB values 1060
Sequential Read 1KB values 68
Sequential Read 100 byte
values
129
Scan 10KB values 2
Scan 1KB values 58
Scan 100 byte values 75
Scan 10 byte values 220

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Annual EC2 Cost SavingsAnnual EC2 Cost Savings
 Assuming 200% improvementAssuming 200% improvement
 Extra large reserved instancesExtra large reserved instances

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ResourcesResources
Project SiteProject Site www.hypertable.org
TwitterTwitter hypertable
Commercial SupportCommercial Support www.hypertable.com
Performance EvaluationPerformance Evaluation
Write-upWrite-up
blog.hypertable.com/?p=14

37. Q&AQ&A

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System OverviewSystem Overview

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Data ModelData Model
 Sparse, two-dimensional table with cell versionsSparse, two-dimensional table with cell versions
 Cells are identified by a 4-part keyCells are identified by a 4-part key

Row (string)Row (string)

Column Family (byte)Column Family (byte)

Column Qualifier (string)Column Qualifier (string)

Timestamp (long integer)Timestamp (long integer)