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CS 405G: Introduction to Database Systems
24 NoSQLReuse some slides of Jennifer Widom
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
04/19/23 Chen Qian @ University of Kentucky 2
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 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”
NoSQL Systems: Motivation
Not every data management/analysisproblem 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.
Database Management System (DBMS) provides….
… efficient, reliable, convenient, and safe multi-user storage of and access to massive amounts of persistent data.
NoSQL Systems: Motivation
NoSQL Systems
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
NoSQL Systems: Motivation
Example #1: Web log analysis
Each record: UserID, URL, timestamp, additional-info
Task: Load into database systemData cleaningData extractionVerificationSchemaNothing above is needed for noSQL!
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
NoSQL Systems: Motivation
Example #1: Web log analysis
Each record: UserID, URL, timestamp, additional-infoSeparate records: UserID, name, age, gender, …
Task: Find average age of user accessing given URL
May not require strict consistency.
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
NoSQL Systems: Motivation
Example #3: Wikipedia pages
Large collection of documentsCombination of structured and unstructured data
Task: Retrieve introductory paragraph of all pages about U.S. presidents before 1900
Mix of structured and unstructured data
NoSQL Systems: Motivation
NoSQL Systems
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
NoSQL Systems: Motivation
NoSQL Systems
Overview
NoSQL Systems
Several incarnations MapReduce framework: OLAP Key-value stores: OLTP Document stores Graph database systems
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
NoSQL Systems: Overview
Map and Reduce Functions
Map: Divide problem into subproblems
Reduce: Do work on subproblems, combine results
NoSQL Systems: Overview
MapReduce Architecture
NoSQL Systems: Overview
MapReduce Example: Web log analysis
Each record: UserID, URL, timestamp, additional-infoTask: Count number of accesses for each domain (inside
URL)
NoSQL Systems: Overview
MapReduce Example (modified #1)
Each record: UserID, URL, timestamp, additional-infoTask: Total “value” of accesses for each domain based on
additional-info
NoSQL Systems: Overview
MapReduce Framework
No data model, data stored in files User provides specific functions System provides data processing “glue”, fault-tolerance, scalability
NoSQL Systems: Overview
MapReduce Framework
Schemas and declarative queries are missedHive – schemas, SQL-like query language
Pig – more imperative but with relational operators Both compile to “workflow” of Hadoop (MapReduce) jobs
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”
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, …
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, …
NoSQL Systems: Overview
Graph Database Systems Data model: nodes and edges Nodes may have properties (including ID) Edges may have labels or roles
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
NoSQL Systems: Overview
NoSQL Systems
“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
NoSQL Systems: Overview
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