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![MapReduce
map(String key, String value):
// key: document name
// value: document contents
for each word w in value:
EmitIntermediate(w, "1");
reduce(String key, Iterator values):
// key: a word
// values: a list of counts
int result = 0;
for each v in values:
result += ParseInt(v);
Emit(AsString(result)); [1]](https://image.slidesharecdn.com/nosqlinperspective-120518013807-phpapp02/75/NoSQL-in-Perspective-14-2048.jpg)
Uploaded byJeff Smith
NoSQL in Perspective
This document provides an overview of NoSQL databases and discusses some of their advantages over traditional relational databases. It introduces some common NoSQL database types and properties like CAP theorem. Functional programming concepts like MapReduce that were influential in NoSQL are described. The document also compares the transactional consistency models used by SQL databases and newer NoSQL databases.
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NoSQL in Perspective
- 1. NoSQL in Perspective Jeff Smith jeffreyksmithjr@gmail.com
- 2. NoSQL on Wikipedia 92databases 8 types 6 sub-types
- 3. Easy Questions Is thisa graph? Do I already have XML or JSON? Is this a caching problem?
- 4. Paul Graham on ProgrammingLanguages Lisp C
- 5. Math Problem Lisp isjust math. Math doesn't get stale. What in databases is just math?
- 6. Putting the Rin RDBMSes Relation Attributes Tuples
- 7. Database Analogy C isto Lisp as Relational Algebra is to Relational Calculus C: Lisp::Relational Algebra: Relational Calculus
- 8. Relational Algebra inAction Relational Algebra: R ⋉S = { t : t R, s S, Fun (t s) } SQL: SELECT * FROM audience WHERE clue > 0;
- 9. Relational Calculus inAction? Relational Calculus: { t : {name} | ∃ s : {name, wage} ( Employee(s) ∧ s.wage = 50.000 ∧ t.name = s.name ) } Relevant Implemented Language: This space under construction.
- 10. Relational Model Utility Essentially,all models are wrong, but some are useful. - George E. P. Box
- 11. When relations arewrong Sparse data Irregular data Poorly understood interrelationships No definable indexes Big data No vertically scalable hardware
- 12. Papers Read Aroundthe World Google's BigTable: http://research.google.com/archive/ bigtable.html Amazon's Dynamo: http://www.allthingsdistributed.com/ 2007/10/amazons_dynamo.html
- 13. Lessons from FunctionalProgramming MapReduce: http://research.google.com/archive/ mapreduce.html
- 14. MapReduce map(String key, Stringvalue): // key: document name // value: document contents for each word w in value: EmitIntermediate(w, "1"); reduce(String key, Iterator values): // key: a word // values: a list of counts int result = 0; for each v in values: result += ParseInt(v); Emit(AsString(result)); [1]
- 15.
- 16. CAP Theorem? Availability Consistency Partition Tolerance
- 17. Sacrifice Availability Consistency Partition Tolerance
- 18. Then, sacrifice what? Availability Availability Consistency Partition Tolerance
- 19. PACELC In the eventof a Partition, does the system prioritize Availability or Consistency Else does the system prioritize Latency or Consistency?
- 20. PACELC as aTree Partition Else Availability Consistency Latency Consistency
- 21. Traditional RDBMSes: PC/EC Partition Else Consistency Consistency
- 22. Eventually Consistent: PA/EL Partition Else Availability Latency
- 23. ELC: Replication Options 1. Updateall nodes 2. Update the master node first 3. Update an arbitrary node first
- 24. Best of bothworlds? SQL
- 25.
- 26.
- 27. Riak Demo N: persisted copies R: read copies W: write copies Strong Consistency: R+W>N
- 28. Thanks Jeff Smith jeffreyksmithjr@gmail.com