The presentation provides an introduction to NoSQL databases, detailing their differences from traditional relational databases (RDBMS) and discussing the various types of NoSQL solutions available such as document stores, key-value stores, and graph databases. It covers the motivation for using NoSQL, highlights key features like flexibility in structure, scalability, and handling of large datasets, and compares the implementation and performance aspects of different NoSQL technologies. Additional insights into CRUD operations, data consistency models, and examples of popular NoSQL databases are also included.

1. NoSQL Introduction
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2. NoSQL Introduction
• Understand what NoSQL is and what it is not.
• Why would you want to use NoSQL within your project
and which NoSQL database would you utilize?
• Explore the relationships between NoSQL and RDBMS.
• Understand how to select between an RDBMs (MySQL
and PostgreSQL), Document Database (MongoDB), Key-
Value Store, Graph Database, and Columnar databases or
combinations of the above.
Thursday May 8th 2014, 3:00pm-3:50pm SB 139
Slides and Feedback at: http://joind.in/11012
2

3. NoSQL
• History
• Popular NoSQL Databases
• NoSQL Database Comparisons
• Terminology
• Consistency, Replication, Performance
• NoSQL Implementation CRUD Operations
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4. NoSQL Introduction
• NoSQL is a commonly adopted misnomer
• Typically does not use ANSI SQL
– SQL = Structured Query Language
– Structure exists but is more Flexible
– Queries are performed
– Language is closer to Programming Languages
4
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5. NoSQL History
5 http://www.w3resource.com/mongodb/nosql.php

6. NoSQL History
• 1998 Carlo Strozzi Command Line Database
• June 11, 2009 Meetup
– Open Source, Distributed, Non-Relational DB
– Eric Evans (Rackspace)
– Johan Oskarsson (Last.fm)
6

7. NoSQL History
7

8. NoSQL History
• Bad name, but it stuck!
• Not a definitive term
• Generally, Newer databases solving new
and different problems
• Not Only SQL http://blog.sym-
link.com/2009/10/30/nosql_whats_in_a_name.html
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9. NoSQL Origination
• Problems not solved by RDBMs
• Limitations of RDBMs, not SQL
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10. Most Popular Databases
http://db-engines.com/en/ranking
Ranking by: Web Content, Web Searches, Technical Discussion, Jobs, Resumes
10

11. Most Popular NoSQL
• MongoDB - Document Store
• Cassandra – Wide Column Store
• Solr – Search Engine
• Redis – Key-value store
• Hbase – Wide Column Store
• Memcached – Key-value Store
• CouchDB – Document Store
• Neo4j – Graph Database
• Riak – Key-value Store
• SimpleDB – Key-value Store within Amazon Cloud
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12. 12
NoSQL vs RDBMs
Image Reference: http://blogs.the451group.com/information_management/2012/11/02/updated-database-landscape-graphic/

13. Reading Recommendations
Great Overview of NoSQL:
Seven Databases in Seven Weeks
Eric Redmond and Jim Wilson
13

14. NoSQL “Bleeding Edge”
• Several solutions are mature and stable
enough to run large scale production
environments
• Not all permutations have been considered
• Several (but not all) optimization strategies
have been published
• Crucial elements such as Security may be a
secondary add-on in favor of performance.
14

15. NoSQL “Bleeding Edge”
Sun Microsystems csh man page:
“Although robust enough for
general use, adventures into the
esoteric periphery of the C shell
may reveal unexpected quirks.”
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16. NoSQL Comparison
16
Take note of patterns:
Recent Release, Open Source, Utilized at High-Volume sites
Variety of Formats:
Key-Value, Wide-Column, Document, Graph
http://db-engines.com/en/ranking

17. NoSQL Database Types
17
• Key-Value
• Column Oriented Databases (Columnar)
• Graph
• Document
• Search Database - Solr
• Key-Value Web Optimization - Memcached

18. Key-Value Stores
18
Key Value
code:java 17.316% Lowest rank on Feb 2014
code:C 18.334% Lowest rank on August 2013
code:Objective-C Lowest rank on Dec 2007 11.341%
code:C++ {“score”:”6.892%”, “low rank”: “Feb 2008”}
Key Value
drink:java coffee
drink:punch Sprite + pineapple juice
drink:pop Carbonated Soda
http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html
Codebucketdrinkbucket

19. Column Oriented Database
19

20. Neo4j
20

21. Document Oriented Database
21
{
"_id" : 1,
"name" : { "first" : "John", "last" : "Backus" },
"contribs" : [ "Fortran", "ALGOL", "FP" ],
"awards" : [
{ "award" : "W.W. McDowell Award",
"year" : 1967,
"by" : "IEEE Computer Society" },
{ "award" : "Draper Prize",
"year" : 1993,
"by" : "National Academy of Engineering" }
]
}

22. Document Oriented Database
22
{ "facutly" :
[
{
"_id" : 1,
"name" : { "first" : "John", "last" : "Backus" },
"contribs" : [ "Fortran", "ALGOL", "FP" ],
"awards" : [
{ "award" : "W.W. McDowell Award",
"year" : 1967,
"by" : "IEEE Computer Society" },
{ "award" : "Draper Prize",
"year" : 1993,
"by" : "National Academy of Engineering" }
]
},
{
"_id" : 2,
"name" : { "first" : "David", "last" : "Williams" },
"contribs" : [ "C#", "Java", "PHP" ],
"awards" : [
{ "award" : "Sherman Peabody Award II",
"year" : 2095,
"location" : "Paris",
"by" : "Intergalactic Continuum" },
{ "award" : "Sherman Peabody Award IX",
"year" : 2090,
"location" : "Paris",
"by" : "Intergalactic Continuum" },
{ "award" : "Sherman Peabody Award IV",
"year" : 2093,
"location" : "Paris",
"by" : "Intergalactic Continuum" }
]
}
]
}

23. Document Oriented Database
23
http://chris.photobooks.com/json/
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24. 24
http://visualizer.json2html.com/

25. NoSQL Comparison
25
No ANSI SQL Standards, No Predefined Schemas, Replication,
Eventual Consistency, Rarely Foreign Keys, Data Types not required
Newer Concepts: Sharding, REST API, JSON, MapReduce

26. NoSQL Characteristics
26
No Predefined Schemas
• May insert data without creating a table
• Schema Versions (v1.5, v1.6, v1.7,…)
Rarely Foreign Keys
• No JOIN operations
• Relationships are not automatically maintained
Eventual Consistency
• Old copies being replaced by new records
• Inconsistent data until all replacements are complete

27. Download NoSQL v95141.3
27
Released 4/1/2014
http://www.nosql.org/downloads/ymbkm.zip

28. NoSQL
Terminology
and
Concepts
28 Slides and Feedback at: http://joind.in/11012

29. Sharding
29
Partitions – Data distributed across disks
Sharding – Data distributed across servers

30. Map Reduce
30
Divides work across distributed systems
Parallel processing of large data sets
Divide – Conquer – Consolidate
Often Implement by defining Map and Reduce classes or functions
1+2+3+6+7+8+9=?
Google’s MapReduce Programming Model – Revisited Ralf Lammel, Microsoft, 2008
http://www.sciencedirect.com/science/article/pii/S0167642307001281
2
6
8
1
7
3
9
16
20
36

31. JSON
31
Subset of JavaScript Object Notation
Similarities to XML method for representing data
Syntax
Name : Value pairs
“salary” : “125000”
Values are: number, string, Boolean, array, object, or NULL
Objects can store Objects, Arrays can store Arrays
Separate pairs by commas
“salary” : “125000”, “gender” : “male”
Curly braces denote objects
{ “salary” : “125000”, “gender” : “male” }
Square brackets denote arrays
“phone” : [”555-1212”, ”555-3344”]
“phone” : [ {“office” : ”555-1212”}, {“mobile” : ”555-3344”} ]

32. JSON Example
32
{
"_id" : 1,
"name" : { "first" : "John", "last" : "Backus" },
"contribs" : [ "Fortran", "ALGOL", "FP" ],
"awards" : [
{ "award" : "W.W. McDowell Award",
"year" : 1967,
"by" : "IEEE Computer Society" },
{ "award" : "Draper Prize",
"year" : 1993,
"by" : "National Academy of Engineering" }
]
}
http://www.mongodb.com/json-and-bson

33. REST API
33
CRUD (Create, Read, Update, Delete) operations through the web
HTTP Methods
GET (List/Read)
POST(Update)
PUT(Create)
DELETE(Delete)
EXAMPLE API http://www.blinksale.com/api/
List/Read Data via HTTP GET to
http://www.blinksale.com/invoices
http://www.blinksale.com/invoices/invoice_id/payments
http://www.blinksale.com/invoices/?start=2006&end=2008
Returns XML results

34. REST API
34
Update data via HTTP POST to
http://www.blinksale.com/invoices/invoice_id/payments
<?xml version="1.0" encoding="UTF-8"?>
<payment xmlns="http://www.blinksale.com/api">
<amount>1000.00</amount>
<date>2006-09-27</date>
</payment>
REST = REpresentational State Transfer
Twitter Example:
https://dev.twitter.com/docs/api/1.1 (GET and POST only)

35. Database SELECT Statements
35
Oracle
SELECT * FROM relationships
MongoDB
db.relationships.find()
Cassandra (CQL)
SELECT * FROM relationships
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36. Database SELECT Statements
36
Redis – Key-Value Store
SMEMBERS relationships
Riak – Key-Value Store with REST API (+ proprietary drivers)
http://localhost:8091/riak/relationships/likes
Neo4j (Cypher)
MATCH (n)-[r:LIKES]->(m) RETURN n,r,m
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37. JOINS without Foreign Keys
37
original_id = ObjectId()
db.employer.insert({
"_id": original_id,
"name": "Broadway Tech",
"url": "bc.example.net" })
db.people.insert({
"name": "Erin",
“employer_id": original_id,
"url": "bc.example.net/Erin" })
“Erin” works at “Broadway Tech”
One of the employees at “Broadway Tech” is “Erin”
http://docs.mongodb.org/manual/reference/database-references/#document-references

38. Replication Challenge is
Write Consistency
38

39. ACID, BASE, CAP, CPR
1979 Gray, 1983 Reuter & Härder - ACID
Atomic, Consistent, Isolated, Durable
Rollback: All or Nothing, Follows Rules, Simultaneous, No Drops
1997 Brewer - BASE
Basically Available, Soft-state, Eventually consistent
2000 Brewer – CAP (Pick Two)
Consistency, Availability, Partition Tolerance
CPR (Pick Two)
Consistency, Performance, Replication/Redundancy
Contrived - Stretch Definitions
39

40. CPR
40
Consistency
Redundancy
Performance
Pick Two

41. CPR
41
Consistency Performance
A DCB
Spread data across storage or computer
Redundancy

42. 42
Redundancy
Performance
Consistency
ABCE ABCDABCDABCE
Updates may be
inconsistent across devices

43. 43
Consistency
Redundancy
Performance
ABCD ABCDABCDABCD
One Update Locks all Nodes

44. CRUD
44
Create
Read
Update
Delete
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45. SQL CRUD
45
Create
INSERT INTO table (column1, column2) VALUES (9, 'string');
Read
SELECT column1, column2 FROM table;
Update
UPDATE table SET column2 = 'text' WHERE column1= 9
Delete
DELETE FROM table WHERE column2='text'

46. Key-Value Stores
46
Key Value
code:java 17.316% Lowest rank on Feb 2014
code:C 18.334% Lowest rank on August 2013
code:Objective-C Lowest rank on Dec 2007 11.341%
code:C++ {“score”:”6.892%”, “low rank”: “Feb 2008”}
Key Value
drink:java coffee
drink:punch Sprite + pineapple juice
drink:pop Carbonated Soda
http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html
codebucketdrinkbucket

47. Redis CRUD
47
http://redis.io/commands
http://redis.io/topics/data-types-intro
http://openmymind.net/2011/11/8/Redis-Zero-To-Master-In-30-Minutes-Part-1/
Redis is an in-memory Key-Value Store which stores:
Strings, Hashes, Lists, Sets, or Ordered sets
Strings: values of strings are concrete and can not be altered
SET user:jim {lastname: ‘Mathews’, salary: 125000}
GET user:jim
Hashes: allows modification and retrieval of individual values
HSET user:jim salary 125000
HSET user:jim lastname Mathews
HGET jim salary

48. Redis CRUD
48
Lists: One-dimensional array with insert, append, pop, and
push
Redis.lpush(‘users:employees’, ‘user:jim’)
redis.mget(redis.lrange(‘users:employess’,0,5))
Sets: lists with no duplicate values (SADD = Set Add)
SADD users:employees jim
SADD users:employees krishna
SMEMBERS employees
Sorted Sets: are sets with an added sorting value
ZADD users:employees 125000 jim
ZADD users:employees 157000 Krishna
ZRANGEBYSCORE users:employees 100000 180000

49. Riak CRUD
49
Easy to install and configure test cluster
REST Queries
Create/PUT a “course:CIS2120” row
curl –v –X PUT http://localhost:8091/riak/course/CIS2120
-H “Content-Type: application/json”
-d ‘{“name”:”Database Coding”, “days”:”MWF”}’
Read/GET the value for “course:CIS2120”
curl –X GET http://localhost:8091/riak/course/CIS2120
curl http://localhost:8091/riak/course/CIS2120
Key Value
course:CIS2120 {“name”:”Database Coding”, “days”:”MWF”}

50. Riak Links
50
Riak can link on value to key:value to another with a relationship
curl –v –X PUT http://localhost:8091/riak/student/sorensen
-H “Content-Type: application/json”
-H “Link: </riak/course/CIS2120>; riaktag=”enrolled””
-d ‘{“firstname”:”Conner”}’
This does not automatically create a link from “sorensen“ to
“CIS2120”

51. Neo4j
51

52. Neo4j – Graph Database
52
http://www.neo4j.org/learn/try
http://docs.neo4j.org/refcard/2.0/
MATCH (n)-[r:LIKES]->(m) RETURN n,r,m
Matches a person “n” that likes person “m”
https://gist.github.com/peterneubauer/6019125
http://gist.neo4j.org/?6019125

53. Neo4j CRUD
53
Must try dragging nodes at: http://www.neo4j.org/learn/try
MATCH (user {name:“Bill"})-[:KNOWS]->(colleague)
WHERE colleague.employer=“LinkedIn”
RETURN user,colleague
ORDER BY colleague.name LIMIT 10
http://docs.neo4j.org/refcard/2.0/
MATCH (n)-[r:LIKES]->(m) RETURN n,r,m
Matches a person “n” that likes person “m”
MATCH (n)-[r]->(m) RETURN n,r,m
Matches any relationship between “n” and “m”
http://www.neo4j.org/learn/cypher

54. Neo4j(LUKE {name:"Luke Skywalker"}),
(HAN {name:"Han Solo"}),
(LEIA {name:"Princess Leia Organa"}),
(OBI_WAN {name:"Obi Wan Kenobi"}),
(YODA {name : "Yoda"}),
(VADER {name:"Darth Vader"}),
(C3PO {name:"C3PO", droid:true}),
(R2D2 {name:"R2D2", droid:true}),
(CHEWBACCA {name:"Chewbacca"}),
(TATOOINE {name:"Tatooine", distance:13184}),
(DAGOBAH {name:"Dagobah", distance:15407}),
(JEDI {name:"Jedi"}),
(SITH {name:"Sith"}),
(REBELLION {name:"Rebellion"}),
(EMPIRE {name:"Empire"}),
(DARK_SIDE {name:"Dark Side"}),
(LIGHT_SIDE {name:"Light Side"}),
…
(LUKE)-[:FRIENDS_WITH]->(HAN),
(LUKE)-[:FRIENDS_WITH]->(LEIA),
(HAN)-[:FRIENDS_WITH]->(CHEWBACCA),
(YODA)-[:TEACHES]->(OBI_WAN),
(YODA)-[:TEACHES]->(LUKE),
(OBI_WAN)-[:TEACHES]->(LUKE),
(OBI_WAN)-[:KNOWS]->(VADER),
(LUKE)-[:KNOWS]->(R2D2),
(R2D2)-[:KNOWS]->(C3PO),
(LUKE)-[:LIVED_ON]->(TATOOINE),
(HAN)-[:LIVED_ON]->(CORELLIA),
(LEIA)-[:LIVED_ON]->(ALDERAAN),
(YODA)-[:LIVED_ON]->(DAGOBAH),
(LUKE)-[:DEVOTED_TO]->(JEDI),
(LUKE)-[:DEVOTED_TO]->(REBELLION),
(LUKE)-[:DEVOTED_TO]->(LIGHT_SIDE),
(VADER)-[:DEVOTED_TO]->(SITH),
(VADER)-[:DEVOTED_TO]->(EMPIRE),
(VADER)-[:DEVOTED_TO]->(DARK_SIDE),
(LEIA)-[:DEVOTED_TO]->(REBELLION),
(HAN)-[:DEVOTED_TO]->(REBELLION)
…
https://gist.github.com/peterneubauer/6019125
http://gist.neo4j.org/?6019125
MATCH y-[r]-other
WHERE y.name='Yoda'
return y.name, type(r), other.name
54

55. Google BigTable
• White Paper published in 2006
• Many databases based upon BigTable
• 13 pages, readable for many non-techies
• Insightful into the early days of NoSQL
http://static.googleusercontent.com/media/research.google.com/en/us/archive/bigtable-osdi06.pdf
55

56. Hbase
56
Large-Scale, Column-oriented database
Consistency, Performance, Fault-Tolerant, ACID via Locking
Tables are created before initial data is added
Tables have
row keys are indexed row identifier strings
column families – contain one or more columns
timestamp for version control

57. Hbase
57
Row key is a unifier for column families.
If row does insert values in a column family no disk space
is utilized within the column family.
Keys are identified by column_family:column_name
text:
revision:author
revision:comment
Write-Ahead Logging
(WAL)
similar to file system
journaling

58. Hbase CRUD
58
create ‘wiki_table’, ‘text_column_family’, ‘revision_column_family’
create ‘wiki’, ‘text’, ‘revision’
put ‘wiki’, ‘first page’, ‘text:’, ‘…’
put ‘wiki’, ‘first page’, ‘revision:author’, ‘…’
get ‘wiki’, ‘first page’, [‘revision:author’, ‘revision:comment’]
delete ‘wiki’, ‘first page’, ‘revision:author’
scan ‘wiki’ = SELECT * FROM wiki
Seven Databases in Seven Weeks, Redmond & Wilson 2012

59. MongoDB Document Store
59
db.courses.insert({
name: “CIS2120”,
description: “Database Coding”,
instructor: {
name: “David Williams”,
email: “david.williams@usu.edu”
}
instructor2: {
name: “John Kerley-Weeks”,
office: “JQL230”
}
subjects: [“Python”, “MongoDB”, “3NF”, “ETL”, “Star Schema”]
})

60. MongoDB vs SQL
60
http://docs.mongodb.org/manual/reference/sql-comparison/
MongoDB -> SQL Terminology
Collection -> Table
Document -> Row
Column -> Field
db.courses.find() = SELECT * FROM courses
db.courses.count() = SELECT COUNT(*) FROM courses
db.courses.find({name: “CIS2120”})

61. MongoDB Simple Database
61
http://media.mongodb.org/zips.json
{"city": "ACMAR", "loc": [-86.51557, 33.584132], "pop": 6055, "state": "AL", "_id": "35004"}
{"city": "ADAMSVILLE", "loc": [-86.959727, 33.588437], "pop": 10616, "state": "AL", "_id": "35005"}
{"city": "ADGER", "loc": [-87.167455, 33.434277], "pop": 3205, "state": "AL", "_id": "35006"}
{"city": "KEYSTONE", "loc": [-86.812861, 33.236868], "pop": 14218, "state": "AL", "_id": "35007"}
{"city": "NEW SITE", "loc": [-85.951086, 32.941445], "pop": 19942, "state": "AL", "_id": "35010"}
{"city": "ALPINE", "loc": [-86.208934, 33.331165], "pop": 3062, "state": "AL", "_id": "35014"}
{"city": "ARAB", "loc": [-86.489638, 34.328339], "pop": 13650, "state": "AL", "_id": "35016"}
{"city": "BAILEYTON", "loc": [-86.621299, 34.268298], "pop": 1781, "state": "AL", "_id": "35019"}
{"city": "BESSEMER", "loc": [-86.947547, 33.409002], "pop": 40549, "state": "AL", "_id": "35020"}
{"city": "HUEYTOWN", "loc": [-86.999607, 33.414625], "pop": 39677, "state": "AL", "_id": "35023"}
{"city": "BLOUNTSVILLE", "loc": [-86.568628, 34.092937], "pop": 9058, "state": "AL", "_id": "35031"}
{"city": "BREMEN", "loc": [-87.004281, 33.973664], "pop": 3448, "state": "AL", "_id": "35033"}
{"city": "BRENT", "loc": [-87.211387, 32.93567], "pop": 3791, "state": "AL", "_id": "35034"}
{"city": "BRIERFIELD", "loc": [-86.951672, 33.042747], "pop": 1282, "state": "AL", "_id": "35035"}
{“city”: “Logan, UT”, “additionally”: [“Nibley, UT”, “River Heights, UT”], “state”: “UT”, “version”: “2.1”, “_id”: “84321”}
{“city”: “Olivehurst, CA”, “additionally”: [“Arboga, CA”, “Plumas Lake, CA”, “West Linda, CA”], “state”: “CA”, “version”: “2.1”,
“_id”: “95961”}

62. Cassandra Characteristics
62
Scalable, High-availability Wide-columnar datastore
Peer-to-peer rather than master-slave clusters
Tunable consistency can read/write to a single node,
quorum of nodes or all nodes
Recommends static and dynamic column families
Static column families have contain pre-defined columns
Contact Info: phone, address, email, web
Dynamic families have variable numbers of similar columns
Students enrolled in a course

63. Cassandra CRUD
63
http://www.datastax.com/docs/0.8/references/cql
http://cassandra.apache.org/doc/cql3/CQL.html#selectStmt
CREATE TABLE course (
name text PRIMARY KEY,
instructor text,
maxstudents int
)
INSERT INTO course (name, instructor, maxstudents) VALUES
(‘CIS2120’, ‘Williams’, 28)
UPDATE course SET maxstudents=26 WHERE name=‘CIS2120’
SELECT name, instructor FROM course WHERE maxstudents > 20

64. Cassandra CRUD
64
No JOIN operations or FOREIGN KEYS
CREATE TABLE people (
name text PRIMARY KEY,
email text,
phones map<text, text>
)
INSERT INTO people (name, email, phones)
VALUES (‘John Weeks’, ‘john.weeks@usu.edu’,
{‘mobile’ : ‘555-1212’, ‘office’ : ‘797-7133’, ‘fax’ : ‘555-1212’})
UPDATE people SET phones[‘office’] = ‘555-1212’
WHERE email = ‘john.weeks@usu.edu’

65. Questions
???
65 Slides and Feedback at: http://joind.in/11012