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COP4710 Database Systems

COP4710 Database Systems. NoSQL Systems. Fall 2013. Courtesy of CS145@Stanford. NoSQL: The Name. Recognition over past decade or so Not every data management/analysis problem is best solved exclusively using a traditional relational DBMS “NoSQL” = “ Not Only SQL”

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COP4710 Database Systems

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  1. COP4710Database Systems NoSQL Systems Fall 2013 Courtesy of CS145@Stanford

  2. NoSQL: The Name • Recognition over past decade or so • Not every data management/analysis problem is bestsolved exclusively using a traditional relational DBMS • “NoSQL” = “Not Only SQL” • “NoSQL”  Don’t use SQL language • Alternative to traditional relational DBMS • Flexible schema • Quicker/cheaper to set up • Massive scalability • Relaxed consistency  higher performance & availability, fewer guarantees • No declarative query language  more programming

  3. Examples • Web Log Analysis • Each record: UserID, URL, timestamp, additional-info • Task:Find all records in a highly parallelizable way for given UserID, given URL, given timestamp, or certain construct appearing in additional-info • 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

  4. Database Ranking (Dec. 2013)

  5. NoSQL Systems • Several incarnations • MapReduce framework • Hadoop, Hive, Pig/Latin • Key-value stores • Google BigTable, Amazon Dynamo, Cassandra, Voldemort, HBase • Document stores • MongoDB, CouchDB • Graph database systems • Neo4j, Pregel

  6. MapReduce • 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 • Map: Divide problem into sub-problems • Map items to 0 or more <key, value>pairs • Reduce: Do work on subproblems, combine results • Reduce <key, list-of-values> to 0 or more records

  7. MapReduce: Word Count Example

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

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

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

  11. Graph Database Systems • 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

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