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CS 405G: Introduction to Database Systems

CS 405G: Introduction to Database Systems. 24 NoSQL Reuse some slides of Jennifer Widom Chen Qian University of Kentucky. Presentation next Tuesday and Thursday. Summary. Tree-based indexes: O(logN) for search and update, support range queries

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CS 405G: Introduction to Database Systems

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

  2. Presentation next Tuesday and Thursday. Chen Qian @ University of Kentucky

  3. 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 Chen Qian @ University of Kentucky

  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

  5. 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”

  6. 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.

  7. 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

  8. 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!

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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

  14. NoSQL Systems Overview

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

  16. 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

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

  18. NoSQL Systems: Overview MapReduce Architecture

  19. 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)

  20. 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

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

  22. 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

  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) Implementation: efficiency, scalability, fault-tolerance • Records distributed to nodes based on key • Replication • Single-record transactions, “eventual consistency”

  24. 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, …

  25. 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, …

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

  27. 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

  28. 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

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