polyglot persistence
TRANSCRIPT
Polyglot PersistenceScott Leberknight
Polyglot?
http://memeagora.blogspot.com/2006/12/polyglot-programming.html
Neal Ford
December 2006
Polyglot Programming
http://www.amazon.com/Paradox-Choice-Why-More-Less/dp/0060005688
First web frameworks...
http://java-source.net/open-source/web-frameworks
non-Java web frameworks too!
...then AJAX and JavaScript
InitialContext ic = new InitialContext();DataSource ds = ic.lookup("java:comp/env/jdbc/coffeeDB");Connection con = null;Statement stmt = null;ResultSet rs = null;try { con = ds.getConnection(); stmt = con.createStatement(); rs = stmt.executeQuery("select name, price from coffees"); List<Coffee> coffees = new ArrayList<Cofee>(); while (rs.next()) { String name = rs.getString("name"); float price = rs.getFloat("price"); coffees.add(new Coffee(name, price); }} catch (SQLException sqlex) { log.error("Error getting coffees", sqlex);
...and nowPERSISTENCE
Why?Scalability
(on massive scales)High availability
New types of apps, e.g. social networking
Fault tolerance Distributability
Flexibility(i.e. "schemaless")
Why?
One size does not fit all
Relational
DocumentOriented
Object
Bigtable-ish
A few types of Databases...
Key-value
EAV(Entity-Attribute-Value)
Structured
Semi-Structured
UnstructuredTypes of data
ACID vs. BASE
ACID
Atomic
Consistent
Isolated
Durable
ACID in Action
1st Bank
checking savings
customers
Transfer $1000 from
1st Bankchecking to
savings
BASE
Basically Available
Soft State
Eventually Consistent
BASE in Action
1st Bank
checking savings
customers
Transfer $1000 from 1st Bank checking to Bank of Foo savings
Bank of Foo
account account_type
customer
Schedule, Cost, Quality(choose any 2)
Brewer's Conjecture
"When designing distributed web services, there
are three properties that are commonly desired:
consistency, availability, and partition tolerance.
It is impossible to achieve all three."
- "Brewer's Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services"
Seth Gilbert and Nancy Lynch (MIT)
Consistency
Partition-tolerance
Availability
(choose any 2)
We're living in interesting times...
Explosion of alternative persistence choices
Completely new philosophies on persistence
Whirlwind tour...
Relational
Document-Oriented
Key/Value
Bigtable
Ankle-deep
Relational
Databasesblog blog_entry blog_entry_comment
category
daily_statistics
blog_owner
blog_user
Relations(tables, joins, integrity)
ACID guarantees
Query using SQL Strict schema
Difficult to scale, partition
(e.g. 2-phase commit)
By far most popular persistence choice today
Mismatch withOO languages
select *from fakenames fwhere f.surname like 'Smi%' and f.city = 'Richmond' and f.state = 'VA'order by f.surname, f.given_name;
28
Scaling...
Buy a bigger machine(vertical scaling)
What if there is no bigger machine?
Horizontal scaling:
Functional
Sharding
Users 0
Users 1
Products 0 Orders 0
Orders 1
Orders 2
FunctionalShards
Document-Oriented
Databases
"As opposed to Relational Databases, document-based
databases do not store data in tables with uniform sized
fields for each record. Instead, each record is stored as a
document that has certain characteristics. Any number of
fields of any length can be added to a document. Fields can
also contain multiple pieces of data."
- Wikipedia(http://en.wikipedia.org/wiki/Document-oriented_database)
Examples:Lotus Notes
Apache CouchDB
Amazon SimpleDB(for our purposes anyway)
MongoDB
CouchDB
Architecture
Concepts:
Documents
Views
Schemaless
Distributed
RESTful...
Views
JavaScript as description language
Map/Reduce functions
Add structure to semi-structured data
Independent of actual documents(created in special Design Documents)
function(doc) { emit(null, doc);}
42
Simplest map function...
// Map function to find Seattlitesfunction(doc) { if (doc.State == "WA" && doc.City == "Seattle") { emit(doc.Number, { "GivenName":doc.GivenName, "Surname":doc.Surname }); }}
43
// Map functionfunction(doc) { emit(doc.State, 1);}
// Reduce function; aggregates countsfunction (key, values) { return sum(values);}
44
Counting people by state...
Views are not meant to be created dynamically like SQL queries!
Caution:
To keep view querying fast, the view engine maintains indexes of its views, and incrementally updates them to reflect changes in the database. CouchDB’s core design is largely optimized around the need for efficient, incremental creation of views and their indexes.
- http://couchdb.apache.org/docs/overview.html
Amazon SimpleDB
"Amazon SimpleDB is a web service for running queries on
structured data in real time. This service works in close
conjunction with Amazon Simple Storage Service (Amazon S3)
and Amazon Elastic Compute Cloud (Amazon EC2), collectively
providing the ability to store, process and query data sets in
the cloud. These services are designed to make web-scale
computing easier and more cost-effective for developers."
- SimpleDB Developer Guide(Version 2007-11-07)
"A traditional, clustered relational database requires a sizable
upfront capital outlay, is complex to design, and often requires a
DBA to maintain and administer. Amazon SimpleDB is
dramatically simpler, requiring no schema, automatically
indexing your data and providing a simple API for storage
and access. This approach eliminates the administrative
burden of data modeling, index maintenance, and performance
tuning. Developers gain access to this functionality within
Amazon’s proven computing environment, are able to scale
instantly, and pay only for what they use."
- SimpleDB Developer Guide(Version 2007-11-07)
Organize data into domains
Domains have items
Items have attributes
Attributes have value(s)
Domain: Fakenames
"5"
"6/6/1941"
"Gwendolyn"
EmailAddress
"Michael"
"1"
"9/5/1982"
"Chris"
"David"
"11/18/1963""3"
"Swinton"
ID
"Vera"
"Johnson"
Birthday
"4"
GivenName
"9/20/1951""[email protected]"
"Lewis"
"2"
"Sutton"
"7/14/1952"
Surname
"Schuler"
Items
Attributes
Values
Domain: Amazon
"Full Screen"
"Mens"
"Entertainment"
Color Size Length
"DVDs"
"White""Yellow""Beige""Pink"
Format
"Clothes""Blue""Gray""Black"
"Books"
"Sound of Music"
"Item03"
"Blouse"
"Item02"
"Full Screen""Widescreen"
"Entertainment" "174 min"
SubcategoryID Author
"KurtVonnegut "
"Womens"
"Item04"
"Item05"
"Item01" "Pulp Fiction""DVDs"
Name
"Small""Medium""Large"
"Slaugherhouse Five"
Category
"Clothes"
"Entertainment"
"154 min""168 min (special edition)"
"30x30""32x30""34x30"...
"Jeans"
"REST" API
POST / HTTP/1.1Content-Type: application/x-www-form-urlencoded; charset=utf-8User-Agent: Amazon Simple DB Java LibraryHost: sdb.amazonaws.comContent-Length: 232
Action=CreateDomain&DomainName=Fakenames&AWSAccessKeyId=[your AWS access key id]&SignatureVersion=2&SignatureMethod=HmacSHA256&Signature=[computed signature]&Timestamp=2009-03-23T23%3A58%3A55.327Z&Version=2007-11-07
Available APIs:
Java C#
Perl PHP
VB
Ruby gems:aws-simpledb
aws-sdbsimpledb
Amazon
3rd party
Python:polarrose-twisted-amazon
AmazonSimpleDB service = new AmazonSimpleDBClient(accessKeyId, secretAccessKey);
// Create a new domainCreateDomainRequest cdReq = new CreateDomainRequest().withDomainName("Fakenames");CreateDomainResponse cdResp = service.createDomain(cdReq);
// List all our domainsListDomainsRequest ldReq = new ListDomainsRequest();ListDomainsResponse ldResp = service.listDomains(ldReq);
54
Sample response:<ListDomainsResponse xmlns="http://sdb.amazonaws.com/doc/2007-11-07/"> <ListDomainsResult> <DomainName> Fakenames </DomainName> <DomainName> Movies </DomainName> </ListDomainsResult> <ResponseMetadata> <RequestId> 8c4d0240-49ea-5d2f-9573-437324cd144c </RequestId> <BoxUsage> 0.0000071759 </BoxUsage> </ResponseMetadata></ListDomainsResponse>
// Add an attribute valueReplaceableAttribute newEmail = new ReplaceableAttribute("emailAddress", "[email protected]", false);
PutAttributesRequest request = new PutAttributesRequest() .withDomainName("Fakenames") .withItemName("1") .withAttribute(newEmail);
PutAttributesResponse response = service.putAttributes(request);
56
Query API
// Query for RichmondersString query = "['city' = 'Richmond'] intersection ['state' = 'VA']";
QueryRequest request = new QueryRequest() .withDomainName("Fakenames") .withQueryExpression(query);
QueryResponse response = service.query(request);
58
// Query for Richmonders, with attributesString query = "['city' = 'Richmond'] intersection ['state' = 'VA']";
QueryWithAttributesRequest request = new QueryWithAttributesRequest() .withDomainName("Fakenames") .withQueryExpression(query);
QueryWithAttributesResponse response = service.query(request);
59
SELECT API
// Get a countString query = "select count(*) from Fakenames";
SelectRequest request = new SelectRequest().withSelectExpression(query);
SelectResponse response = service.select(request);
61
// Select RichmondersString query = "select * from Fakenames" + " where city = 'Richmond' intersection state = 'VA'" + " intersection surname like 'Smi%'";
SelectRequest request = new SelectRequest().withSelectExpression(query);
SelectResponse response = service.select(request);
62
There are Limits!
Query execution time <= 5 sec
Max items in query response = 250
See SimpleDB Developer Guide for more...
Size limits <= 1024 bytes
Attribute limit per item <= 256
(May I have another?)
<QueryResponse xmlns="http://sdb.amazonaws.com/doc/2007-11-07/"> <QueryResult> <ItemName> 131 </ItemName> ... <NextToken> rO0ABXNyACdjb20uYW1hem9uLnNkcy5RdWVyeVByb2Nlc3Nvci5Nb3JlVG9rracXLnINNqwMACkkAFGluaXRpYWxDb25qdW5jdEluZGV4WgAOaXNQYWdlQm91bmRhc... </NextToken> </QueryResult> <ResponseMetadata> ... </ResponseMetadata></QueryResponse>
NextToken
Eventually consistent(*)
"Amazon SimpleDB keeps multiple copies of each domain. When data is written or updated...all copies of the data are updated. However, it takes time for the data to propagate to all storage locations. The data will eventually be consistent, but an immediate read might not show the change. Consistency is usually reached within seconds, but a high system load or network partition might increase this time. Performing a read after a short period of time should return the updated data."
(Version 2007-11-07)- SimpleDB Developer Guide
(*) ConsistentRead
Version 2009-04-15 added consistent read option
"If eventually consistent reads are not acceptable for your application, use ConsistentRead. Although this operation might take longer than a standard read, it always returns the last updated value."
(Version 2009-04-15)
- SimpleDB Developer Guide
Distributed Key -
Value Stores
value = store.get(key)
store.put(key, value)
store.remove(key)
68
Basically...
Data stored as key/value pairs "A big hashtable"
Replication Fault tolerance
Data consistency & versioning
Horizontalscaling
Amazon Dynamo(a real-world example)
Distributed key-value storage system
Used by Amazon core and web services(e.g. your Amazon shopping cart...)
Massively scaleable
Fault tolerant Eventually consistent
The-Project-Which-Must-
Not-Be-Named
(Project Voldemort)
What is it?
"a distributed key-value storage system"
automatic replication across multiple servers
transparent server failure handling
automatic data item versioning
"Voldemort is not a relational database, it does not attempt to satisfy arbitrary relations while satisfying ACID properties. Nor is it an object database that attempts to transparently map object reference graphs. Nor does it introduce a new abstraction such as document-orientation. It is basically just a big, distributed, persistent, fault-tolerant hash table."
http://project-voldemort.com/
designed for horizontal scaling
used at LinkedIn "for certain high-scalability storage problems where simple functional
partitioning is not sufficient"
"Consistent hashing"
No single server holds all data
Data partitioned across multiple servers
Versioning using "vector clocks"
Configuration:
cluster.xml describes cluster (servers, data partitions)
stores.xml describes data stores(persistence, routing, key/value data format, replication factor,
preferred reads/writes, required reads/writes)
<cluster> <name>mycluster</name> <server> <id>0</id> <host>localhost</host> <http-port>8081</http-port> <socket-port>6666</socket-port> <partitions>0, 1, 2, 3</partitions> </server> <server> <id>1</id> <host>localhost</host> <http-port>8082</http-port> <socket-port>6667</socket-port> <partitions>4, 5, 6, 7</partitions> </server></cluster>
78
sample cluster.xml
<stores> <store> <name>people</name> <persistence>bdb</persistence> <routing>client</routing> <replication-factor>3</replication-factor> <preferred-reads>3</preferred-reads> <required-reads>2</required-reads> <preferred-writes>2</preferred-writes> <required-writes>1</required-writes> <key-serializer> <type>json</type> <schema-info>"string"</schema-info> </key-serializer> <value-serializer> <type>json</type> <schema-info>{"GivenName":"string", "Surname":"string"}</schema-info> </value-serializer> </store></stores>
79
sample stores.xml
> locate "1"Node 0host: localhostport: 6666available: yeslast checked: 96171 ms ago
Node 1host: localhostport: 6667available: yeslast checked: 96171 ms ago
Node 2host: localhostport: 6668available: yeslast checked: 96172 ms ago
80
replication
$ ./voldemort-shell.sh people tcp://localhost:6666Established connection to people via tcp://localhost:6666> put "1" { "GivenName":"Bob", "Surname":"Smith" }> get "1"version(0:1): {"GivenName":"Bob", "Surname":"Smith", }> put "1" { "GivenName":"Robert", "Surname":"Smith", }> get "1"version(0:2): {"GivenName":"Robert", "Surname":"Smith", }
81
vector clock
(master node: version)
StoreClientFactory factory = new SocketStoreClientFactory(numThreads, numThreads, maxQueuedRequests, maxConnectionsPerNode, maxTotalConnections, bootstrapUrl);
StoreClient<Integer, Map<String, Object>> client = factory.getStoreClient("fakenames");
// Update a valueVersioned versioned = client.get(1);Map<String, Object> person = versioned.getValue();person.put("EmailAddress", newEmailAddr);versioned.setObject(person);client.put(1, versioned);
82
Java API example
Bigtable
- Bigtable: A Distributed Storage System for Structured Data
http://labs.google.com/papers/bigtable.html
"Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable including web indexing, Google Earth, and Google Finance."
"A Bigtable is a sparse, distributed, persistent
multidimensional sorted map"
- Bigtable: A Distributed Storage System for Structured Data
http://labs.google.com/papers/bigtable.html
?
distributed
sparse
column-oriented
versioned
(row key, column key, timestamp) => value
The map is indexed by a row key, column key, and a timestamp; each value in the map is an uninterpreted array of bytes.
- Bigtable: A Distributed Storage Systemfor Structured Data
http://labs.google.com/papers/bigtable.html
Key Concepts:
row key => 20090407152657
column family => "name:"
column key => "name:first", "name:last"
timestamp => 1239124584398
Row Key Timestamp Column Family "info:"Column Family "info:" Column Family "content:"
20090407145045 t7 "info:summary" "An intro to..."20090407145045
t6 "info:author" "John Doe"
20090407145045
t5 "Google's Bigtable is..."
20090407145045
t4 "Google Bigtable is..."
20090407145045
t3 "info:category" "Persistence"
20090407145045
t2 "info:author" "John"
20090407145045
t1 "info:title" "Intro to Bigtable"
20090320162535 t4 "info:category" "Persistence"20090320162535
t3 "CouchDB is..."
20090320162535
t2 "info:author" "Bob Smith"
20090320162535
t1 "info:title" "Doc-oriented..."
Row Key Timestamp Column Family "info:"Column Family "info:" Column Family "content:"
20090407145045 t7 "info:summary" "An intro to..."20090407145045
t6 "info:author" "John Doe"
20090407145045
t5 "Google's Bigtable is..."
20090407145045
t4 "Google Bigtable is..."
20090407145045
t3 "info:category" "Persistence"
20090407145045
t2 "info:author" "John"
20090407145045
t1 "info:title" "Intro to Bigtable"
20090320162535 t4 "info:category" "Persistence"20090320162535
t3 "CouchDB is..."
20090320162535
t2 "info:author" "Bob Smith"
20090320162535
t1 "info:title" "Doc-oriented..."
Ask for row 20090407145045...
Apache HBase(an open source Bigtable implementation)
HBase uses a data model very similar to that of Bigtable. Applications store data rows in labeled tables. A data row has a sortable row key and an arbitrary number of columns. The table is stored sparsely, so that rows in the same table can have widely varying numbers of columns.
- http://wiki.apache.org/hadoop/Hbase/HbaseArchitecture
hbase(main):001:0> create 'blog', 'info', 'content'0 row(s) in 4.3640 secondshbase(main):002:0> put 'blog', '20090320162535', 'info:title', 'Document-oriented storage using CouchDB'0 row(s) in 0.0330 secondshbase(main):003:0> put 'blog', '20090320162535', 'info:author', 'Bob Smith'0 row(s) in 0.0030 secondshbase(main):004:0> put 'blog', '20090320162535', 'content:', 'CouchDB is a document-oriented...'0 row(s) in 0.0030 secondshbase(main):005:0> put 'blog', '20090320162535', 'info:category', 'Persistence'0 row(s) in 0.0030 secondshbase(main):006:0> get 'blog', '20090320162535'COLUMN CELL content: timestamp=1239135042862, value=CouchDB is a doc... info:author timestamp=1239135042755, value=Bob Smith info:category timestamp=1239135042982, value=Persistence info:title timestamp=1239135042623, value=Document-oriented... 4 row(s) in 0.0140 seconds
94
HBase Shell
hbase(main):015:0> get 'blog', '20090407145045', {COLUMN=>'info:author', VERSIONS=>3 }timestamp=1239135325074, value=John Doe timestamp=1239135324741, value=John 2 row(s) in 0.0060 secondshbase(main):016:0> scan 'blog', { STARTROW => '20090300', STOPROW => '20090400' }ROW COLUMN+CELL 20090320162535 column=content:, timestamp=1239135042862, value=CouchDB is... 20090320162535 column=info:author, timestamp=1239135042755, value=Bob Smith 20090320162535 column=info:category, timestamp=1239135042982, value=Persistence 20090320162535 column=info:title, timestamp=1239135042623, value=Document... 4 row(s) in 0.0230 seconds
95
Got byte[]?
// Create a new tableHBaseAdmin admin = new HBaseAdmin(new HBaseConfiguration());
HTableDescriptor descriptor = new HTableDescriptor("mytable");descriptor.addFamily(new HColumnDescriptor("family1:"));descriptor.addFamily(new HColumnDescriptor("family2:"));descriptor.addFamily(new HColumnDescriptor("family3:"));admin.createTable(descriptor);
97
// Add some data into 'mytable'HTable table = new HTable("mytable");BatchUpdate update = new BatchUpdate("row1");update.put("family1:aaa", Bytes.toBytes("some value"));table.commit(update);
// Get data backRowResult result = table.getRow("row1");Cell cell = result.get("family1:aaa");
// Overwrite earlier value and add more dataBatchUpdate update2 = new BatchUpdate("row1");update2.put("family1:aaa", Bytes.toBytes("some value"));update2.put("family2:bbb", Bytes.toBytes("another value"));table.commit(update2);
98
Finding data:
get (by row key)
scan (by row key ranges, filtering)
Secondary indexes allow scanning by different keys
(a bit more flexibility, requires more storage)
// Scan for people born during January 1960HTable table = new HTable("fakenames");
byte[][] columns = Bytes.toByteArrays(new String[]{ "name:", "gender:" });byte[] startRow = Bytes.toBytes("19600101");byte[] endRow = Bytes.toBytes("19600201");
Scanner scanner = table.getScanner(columns, startRow, endRow);for (RowResult result: scanner) { ...}scanner.close();
100
Conclusions?
one size does not fit all
lots of alternatives
think about what you really need...
(not what's currently "hot")
What do you really need?
distributed deployment?
fault tolerance?
query richness?
schema evolution?
extreme scalability?
ability to enforce relationships?
ACID or BASE?
key/value storage?
Even more alternatives...
XML databases
Semantic Web / RDF / Triplestores
Graph databases
Tuplespaces
References!
GeneralPolyglot Persistencehttp://www.sleberknight.com/blog/sleberkn/entry/polyglot_persistence
Database Thawhttp://martinfowler.com/bliki/DatabaseThaw.html
Application Design in the context of the shifting storage spectrumhttp://qconsf.com/sf2008/presentation/Application+Design+in+the+context+of+the+shifting+storage+spectrum
BASE: An Acid Alternativehttp://queue.acm.org/detail.cfm?id=1394128
The Challenges of Latencyhttp://www.infoq.com/articles/pritchett-latency
One size fits all: A concept whose time has come and gonehttp://www.databasecolumn.com/2007/09/one-size-fits-all.htmlhttp://www.cs.brown.edu/~ugur/fits_all.pdf
The End of an Architectural Era (It's Time for a Complete Rewrite)http://db.cs.yale.edu/vldb07hstore.pdf
Brewer’s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Serviceshttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.1495
GeneralSemi-Structured Datahttp://www.dcs.bbk.ac.uk/~ptw/teaching/ssd/toc.html
Latency is Everywhere and it Costs You Sales - How to Crush ithttp://highscalability.com/latency-everywhere-and-it-costs-you-sales-how-crush-it
QCon London 2009: Database projects to watch closelyhttp://gojko.net/2009/03/11/qcon-london-2009-database-projects-to-watch-closely
Memories, Guesses, and Apologiehttp://blogs.msdn.com/pathelland/archive/2007/05/15/memories-guesses-and-apologies.aspx
Column-oriented databaseshttp://en.wikipedia.org/wiki/Column-oriented_DBMS
Entity-Attribute-Value modelhttp://en.wikipedia.org/wiki/Entity-Attribute-Value_model
Read Consistency: Dumb Databases, Smart Serviceshttp://blog.labnotes.org/2007/09/20/read-consistency-dumb-databases-smart-services/
Neo4j graph databasehttp://neo4j.org/
NoSql web site - "Your Ultimate Guide to the Non-Relational Universe"http://nosql-database.org/
Document-Oriented DatabasesDocument-Oriented Databasehttp://en.wikipedia.org/wiki/Document-oriented_database
Apache CouchDBhttp://couchdb.apache.org/
Why CouchDB?http://pmuellr.blogspot.com/2008/01/why-couchdb.html
Why CouchDB Suckshttp://www.eflorenzano.com/blog/post/why-couchdb-sucks/
Damien Katz CouchDB Interviewhttp://www.infoq.com/news/2008/11/CouchDB-Damien-Katz
CouchDB: Thinking beyond the RDBMShttp://blog.labnotes.org/2007/09/02/couchdb-thinking-beyond-the-rdbms/
CouchDB Implementationhttp://horicky.blogspot.com/2008/10/couchdb-implementation.html
Dare Takes a Look at CouchDBhttp://intertwingly.net/blog/2007/09/12/Dare-Takes-a-Look-at-CouchDB
Document-Oriented DatabasesCouchDB - A Use Casehttp://kore-nordmann.de/blog/couchdb_a_use_case.html
Amazon SimpleDBhttp://aws.amazon.com/simpledb/http://en.wikipedia.org/wiki/SimpleDB
thrudb - Document Oriented Database Serviceshttp://code.google.com/p/thrudb/
thrudb - faster, cheaper than SimpleDBhttp://www.igvita.com/2007/12/28/thrudb-faster-and-cheaper-than-simpledb/
QCon 2008 track on Document-Oriented Distributed Databaseshttp://qconsf.com/sf2008/tracks/show_track.jsp?trackOID=170
Distributed K-V StoresAmazon's Dynamohttp://www.allthingsdistributed.com/2007/10/amazons_dynamo.htmlhttp://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf
Anti-RDBMS: A list of distributed key-value storeshttp://www.metabrew.com/article/anti-rdbms-a-list-of-distributed-key-value-stores/http://www.reddit.com/r/programming/comments/7qv19/antirdbms_a_list_of_distributed_keyvalue_stores/
Is the Relational Database Doomed?http://developers.slashdot.org/comments.pl?sid=1127539&cid=26849641
Project Voldemorthttp://project-voldemort.com/
Project Voldemort design (also see excellent list of references from this page)http://project-voldemort.com/design.php
Consistent Hashinghttp://en.wikipedia.org/wiki/Consistent_hashing
Bigtable / HBaseGoogle Architecturehttp://highscalability.com/google-architecturehttp://highscalability.com/google-architecture
Bigtable: A Distributed Storage System for Structured Datahttp://en.wikipedia.org/wiki/BigTablehttp://labs.google.com/papers/bigtable.htmlhttp://labs.google.com/papers/bigtable-osdi06.pdf
Apache HBasehttp://hadoop.apache.org/hbase/http://en.wikipedia.org/wiki/HBase
Apache Hadoophttp://hadoop.apache.org/
Understanding HBase and BigTablehttp://jimbojw.com/wiki/index.php?title=Understanding_Hbase_and_BigTable
Matching Impedance: When to use HBasehttp://blog.rapleaf.com/dev/?p=26
HBase Leads Discuss Hadoop, BigTable and Distributed Databaseshttp://www.infoq.com/news/2008/04/hbase-interview
Hadoop/HBase vs RDBMShttp://www.docstoc.com/docs/2996433/Hadoop-and-HBase-vs-RDBMS
Questions?
