1. CS 405G: Introduction to Database Systems
24 NoSQL
Reuse some slides of Jennifer Widom
Chen Qian
University of Kentucky

2. Chen Qian @ University of Kentucky
Summary
Tree-based indexes: O(logN) for search and update, support range queries
Hash-based indexes: best for equality searches O(1), cannot support range searches.
Static and dynamic
3/14/2018
Chen University of Kentucky 16

3. NoSQL Systems: Motivation
NoSQL: The Name
“SQL” = Traditional relational DBMS
Recognition over past decade or so:
Not every data management/analysis problem is best
solved using a traditional relational DBMS
“NoSQL” = “No SQL” =
Not using traditional relational DBMS
“No SQL”  Don’t use SQL language

4. NoSQL Systems: Motivation
NoSQL: The Name
“SQL” = Traditional relational DBMS
Recognition over past decade or so:
Not every data management/analysis problem is best
solved using a traditional relational DBMS
“NoSQL” = “No SQL” =
Not using traditional relational DBMS
“No SQL”  Don’t use SQL language
“NoSQL” = “Not Only SQL”

5. NoSQL Systems: Motivation
Not every data management/analysis
problem is best solved using a traditional DBMS
Database Management System (DBMS) provides….
… efficient, reliable, convenient, and safe
multi-user storage of and access to massive
amounts of persistent data.

6. NoSQL Systems: Motivation
Alternative to traditional relational DBMS
Flexible schema
Quicker/cheaper to set up
Massive scalability
Relaxed consistency  higher performance & availability
No declarative query language  more programming
Relaxed consistency  fewer guarantees

7. NoSQL Systems: Motivation
Example #1: Web log analysis
Each record: UserID, URL, timestamp, additional-info
Task: Load into database system
Data cleaning
Data extraction
Verification
Schema
Nothing above is needed for noSQL!

8. NoSQL Systems: Motivation
Example #1: Web log analysis
Each record: UserID, URL, timestamp, additional-info
Task: Find all records for…
Given UserID
Given URL
Given timestamp
Certain construct appearing in additional-info

9. NoSQL Systems: Motivation
Example #1: Web log analysis
Each record: UserID, URL, timestamp, additional-info
Separate records: UserID, name, age, gender, …
Task: Find average age of user accessing given URL
May not require strict consistency.

10. NoSQL Systems: Motivation
Example #2: Social-network graph
Each record: UserID1, UserID2
Separate records: UserID, name, age, gender, …
Task: Find all friends of friends of friends of … friends of given user
Large number of joins?
Not efficient at all!
Specially designed graph database may be better

11. NoSQL Systems: Motivation
Example #3: Wikipedia pages
Large collection of documents
Combination of structured and unstructured data
Task: Retrieve introductory paragraph of all pages about U.S. presidents before 1900
Mix of structured and unstructured data

12. NoSQL Systems: Motivation
Alternative to traditional relational DBMS
Flexible schema
Quicker/cheaper to set up
Massive scalability
Relaxed consistency  higher performance & availability
No declarative query language  more programming
Relaxed consistency  fewer guarantees

13. NoSQL Systems
Overview

14. NoSQL Systems: Overview
Several incarnations
MapReduce framework: OLAP
Key-value stores: OLTP
Document stores
Graph database systems

15. NoSQL Systems: Overview
MapReduce Framework
Originally from Google, open source Hadoop
No data model, data stored in files
User provides specific functions
map() reduce()
System provides data processing “glue”, fault-tolerance,
scalability

16. NoSQL Systems: Overview
Map and Reduce Functions
Map: Divide problem into subproblems
Reduce: Do work on subproblems, combine results

17. NoSQL Systems: Overview
MapReduce Architecture

18. NoSQL Systems: Overview
MapReduce Example: Web log analysis
Each record: UserID, URL, timestamp, additional-info
Task: Count number of accesses for each domain (inside URL)

19. NoSQL Systems: Overview
MapReduce Example (modified #1)
Each record: UserID, URL, timestamp, additional-info
Task: Total “value” of accesses for each domain based on additional-info

20. NoSQL Systems: Overview
MapReduce Framework
No data model, data stored in files
User provides specific functions
System provides data processing “glue”, fault-tolerance,
scalability

21. NoSQL Systems: Overview
MapReduce Framework
Schemas and declarative queries are missed
Hive – schemas, SQL-like query language
Pig – more imperative but with relational operators
Both compile to “workflow” of Hadoop (MapReduce) jobs

22. NoSQL Systems: Overview
Key-Value Stores
Extremely simple interface
Data model: (key, value) pairs
Operations: Insert(key,value), Fetch(key),
Update(key), Delete(key)
Implementation: efficiency, scalability, fault-tolerance
Records distributed to nodes based on key
Replication
Single-record transactions, “eventual consistency”

23. NoSQL Systems: Overview
Key-Value Stores
Extremely simple interface
Data model: (key, value) pairs
Operations: Insert(key,value), Fetch(key),
Update(key), Delete(key)
Some allow (non-uniform) columns within value
Some allow Fetch on range of keys
Example systems
Google BigTable, Amazon Dynamo, Cassandra,
Voldemort, HBase, …

24. NoSQL Systems: Overview
Document Stores
Like Key-Value Stores except value is document
Data model: (key, document) pairs
Document: JSON, XML, other semistructured formats
Basic operations: Insert(key,document), Fetch(key),
Update(key), Delete(key)
Also Fetch based on document contents
Example systems
CouchDB, MongoDB, SimpleDB, …

25. NoSQL Systems: Overview
Graph Database Systems
Data model: nodes and edges
Nodes may have properties (including ID)
Edges may have labels or roles

26. NoSQL Systems: Overview
Graph Database Systems
Interfaces and query languages vary
Single-step versus “path expressions” versus full recursion
Example systems
Neo4j, FlockDB, Pregel, …
RDF “triple stores” can map to graph databases

27. NoSQL Systems: Overview
“NoSQL” = “Not Only SQL”
Not every data management/analysis problem
is best solved exclusively using a traditional DBMS
Current incarnations
MapReduce framework
Key-value stores
Document stores
Graph database systems