Nosql For Fun & Profit — en français !

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La versions française et retravaillée de NoSQL for Fun & Profit. Cette version a été présentée au NoSQL User Group Paris le 16 février 2010.

La versions française et retravaillée de NoSQL for Fun & Profit. Cette version a été présentée au NoSQL User Group Paris le 16 février 2010.

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  • 1. NOSQLun&Profit! forF en français
  • 2. @TIMANGLADE Je ne mords pas… trop fort.
  • 3. COUCHDB MONGODB RIAK REDIS TOKYOCABINET
  • 4. TOKYOCABINET NEO4J INFOGRID SONES HYPERGRAPHDB HYPERTABLE
  • 5. HYPERTABLE SIMPLEDB TERRASTORE HADOOP MNESIA CASSANDRA
  • 6. CASSANDRA HBASE JACKRABBIT VOLDEMORT GT.M DYNOMITE
  • 7. MEMCACHEDB BIGTABLE DYNAMO SHERPA ORACLE SPATIAL
  • 8. ORACLE SPATIAL ESRI ARCGIS SAND CITRUSLEAF NEPTUNE
  • 9. 40 ANS DANS LE DÉSERT
  • 10. Information Retrieval P. BAXENDALE, Editor A Relational Model of Data for The relational view (or model) of data described in Section 1 appears to be superior in several respects to the Large Shared Data Banks graph or network model [3,4] presently in vogue for non- inferential systems. It provides a means of describing data with its natural structure only-that is, without superim- E. F. CODD posing any additional structure for machine representation IBM Research Laboratory, San Jose, California purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence be- tween programs on the one hand and machine representa- Future users of large data banks must be protected from tion and organization of data on the other. having to know how the data is organized in the machine (the A further advantage of the relational view is that it internal representation). A prompting service which supplies forms a sound basis for treating derivability, redundancy, such information is not a satisfactory solution. Activities of users and consistency of relations-these are discussedin Section at terminals and most application programs should remain 2. The network model, on the other hand, has spawned a unaffected when the internal representation of data is changed number of confusions, not the least of which is mistaking and even when some aspects of the external representation the derivation of connections for the derivation of rela- are changed. Changes in data representation will often be tions (seeremarks in Section 2 on the “connection trap”). needed as a result of changes in query, update, and report Finally, the relational view permits a clearer evaluation traffic and natural growth in the types of stored information. of the scope and logical limitations of present formatted Existing noninferential, formatted data systems provide users data systems, and also the relative merits (from a logical with tree-structured files or slightly more general network standpoint) of competing representations of data within a models of the data. In Section 1, inadequacies of these models single system. Examples of this clearer perspective are are discussed. A model based on n-ary relations, a normal cited in various parts of this paper. Implementations of form for data base relations, and the concept of a universal systems to support the relational model are not discussed. data sublanguage are introduced. In Section 2, certain opera- 1.2. DATA DEPENDENCIES PRESENTSYSTEMS IN tions on relations (other than logical inference) are discussed The provision of data description tables in recently de- and applied to the problems of redundancy and consistency veloped information systems represents a major advance in the user’s model. toward the goal of data independence [5,6,7]. Such tables KEY WORDS AND PHRASES: data bank, data base, data structure, data facilitate changing certain characteristics of the data repre- organization, hierarchies of data, networks of data, relations, derivability, sentation stored in a data bank. However, the variety of redundancy, consistency, composition, join, retrieval language, predicate calculus, security, data integrity data representation characteristics which can be changed CR CATEGORIES: 3.70, 3.73, 3.75, 4.20, 4.22, 4.29 without logically impairing some application programs is still quite limited. Further, the model of data with which users interact is still cluttered with representational prop- erties, particularly in regard to the representation of col- lections of data (as opposed to individual items). Three of the principal kinds of data dependencies which still need 1. Relational Model and Normal Form to be removed are: ordering dependence, indexing depend- ence, and accesspath dependence. In some systems these 1.I. INTR~xJ~TI~N dependencies are not clearly separable from one another. This paper is concerned with the application of ele- 1.2.1. Ordering Dependence. Elements of data in a mentary relation theory to systems which provide shared data bank may be stored in a variety of ways, someinvolv- access large banks of formatted data. Except for a paper to ing no concern for ordering, some permitting each element by Childs [l], the principal application of relations to data to participate in one ordering only, others permitting each systems has been to deductive question-answering systems. element to participate in several orderings. Let us consider Levein and Maron [2] provide numerous referencesto work those existing systems which either require or permit data in this area. elements to be stored in at least one total ordering which is In contrast, the problems treated here are those of data closely associated with the hardware-determined ordering independence-the independence of application programs of addresses.For example, the records of a file concerning and terminal activities from growth in data types and parts might be stored in ascending order by part serial changesin data representation-and certain kinds of data number. Such systems normally permit application pro- inconsistency which are expected to become troublesome grams to assumethat the order of presentation of records even in nondeductive systems. from such a file is identical to (or is a subordering of) the Volume 13 / Number 6 / June, 1970 Communications of the ACM 377
  • 11. Information Retrieval P. BAXENDALE, Editor A Relational Model of Data for The relational view (or model) of data described in Section 1 appears to be superior in several respects to the Large Shared Data Banks graph or network model [3,4] presently in vogue for non- inferential systems. It provides a means of describing data with its natural structure only-that is, without superim- E. F. CODD posing any additional structure for machine representation IBM Research Laboratory, San Jose, California purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence be- tween programs on the one hand and machine representa- Future users of large data banks must be protected from tion and organization of data on the other. having to know how the data is organized in the machine (the A further advantage of the relational view is that it internal representation). A prompting service which supplies forms a sound basis for treating derivability, redundancy, such information is not a satisfactory solution. Activities of users and consistency of relations-these are discussedin Section at terminals and most application programs should remain 2. The network model, on the other hand, has spawned a unaffected when the internal representation of data is changed number of confusions, not the least of which is mistaking and even when some aspects of the external representation the derivation of connections for the derivation of rela- are changed. Changes in data representation will often be tions (seeremarks in Section 2 on the “connection trap”). needed as a result of changes in query, update, and report Finally, the relational view permits a clearer evaluation traffic and natural growth in the types of stored information. of the scope and logical limitations of present formatted Existing noninferential, formatted data systems provide users data systems, and also the relative merits (from a logical with tree-structured files or slightly more general network standpoint) of competing representations of data within a models of the data. In Section 1, inadequacies of these models single system. Examples of this clearer perspective are are discussed. A model based on n-ary relations, a normal cited in various parts of this paper. Implementations of form for data base relations, and the concept of a universal systems to support the relational model are not discussed. data sublanguage are introduced. In Section 2, certain opera- 1.2. DATA DEPENDENCIES PRESENTSYSTEMS IN tions on relations (other than logical inference) are discussed The provision of data description tables in recently de- and applied to the problems of redundancy and consistency veloped information systems represents a major advance in the user’s model. toward the goal of data independence [5,6,7]. Such tables KEY WORDS AND PHRASES: data bank, data base, data structure, data facilitate changing certain characteristics of the data repre- organization, hierarchies of data, networks of data, relations, derivability,
  • 12. form for data base relations, and the concept of a universal systems to support the relational model are not discussed. data sublanguage are introduced. In Section 2, certain opera- 1.2. DATA DEPENDENCIES PRESENTSYSTEMS IN tions on relations (other than logical inference) are discussed The provision of data description tables in recently de- and applied to the problems of redundancy and consistency veloped information systems represents a major advance in the user’s model. toward the goal of data independence [5,6,7]. Such tables KEY WORDS AND PHRASES: data bank, data base, data structure, data facilitate changing certain characteristics of the data repre- organization, hierarchies of data, networks of data, relations, derivability, sentation stored in a data bank. However, the variety of redundancy, consistency, composition, join, retrieval language, predicate calculus, security, data integrity data representation characteristics which can be changed CR CATEGORIES: 3.70, 3.73, 3.75, 4.20, 4.22, 4.29 without logically impairing some application programs is still quite limited. Further, the model of data with which users interact is still cluttered with representational prop- erties, particularly in regard to the representation of col- lections of data (as opposed to individual items). Three of the principal kinds of data dependencies which still need 1. Relational Model and Normal Form to be removed are: ordering dependence, indexing depend- ence, and accesspath dependence. In some systems these 1.I. INTR~xJ~TI~N dependencies are not clearly separable from one another. This paper is concerned with the application of ele- 1.2.1. Ordering Dependence. Elements of data in a mentary relation theory to systems which provide shared data bank may be stored in a variety of ways, someinvolv- access large banks of formatted data. Except for a paper to ing no concern for ordering, some permitting each element by Childs [l], the principal application of relations to data to participate in one ordering only, others permitting each systems has been to deductive question-answering systems. element to participate in several orderings. Let us consider Levein and Maron [2] provide numerous referencesto work those existing systems which either require or permit data in this area. elements to be stored in at least one total ordering which is In contrast, the problems treated here are those of data closely associated with the hardware-determined ordering independence-the independence of application programs of addresses.For example, the records of a file concerning and terminal activities from growth in data types and parts might be stored in ascending order by part serial changesin data representation-and certain kinds of data number. Such systems normally permit application pro- inconsistency which are expected to become troublesome grams to assumethat the order of presentation of records even in nondeductive systems. from such a file is identical to (or is a subordering of) the Volume 13 / Number 6 / June, 1970 Communications of the ACM 377
  • 13. DÉSERT ? QUEL “DÉSERT” ?
  • 14. LE BON CÔTÉ Un écosystème solide
  • 15. LE MAUVAIS CÔTÉ Bases de données trop ACIDes
  • 16. L’HORREUR Le Paradoxe du Paradigme
  • 17. Nom commun /pa.ʁa.diɡm/ 1. (Grammaire) Modèle de déclinaison, de conjugaison. 2. Exemple parfait. « Les mathématiques sont le paradigme des sciences. » 3. Représentation du monde, manière de voir les choses, modèle cohérent de pensée, de vision du monde qui repose sur une base dé nie, sur un système de valeurs. 4.Ensemble d'expériences, de croyances et de valeurs qui in uencent la façon dont un individu perçoit la réalité et réagit à cette perception.
  • 18. UNE IDÉE PAS SI FRAÎCHE
  • 19. Information Retrieval P. BAXENDALE, Editor A Relational Model of Data for The relational view (or model) of data described in Section 1 appears to be superior in several respects to the Large Shared Data Banks graph or network model [3,4] presently in vogue for non- inferential systems. It provides a means of describing data with its natural structure only-that is, without superim- E. F. CODD posing any additional structure for machine representation IBM Research Laboratory, San Jose, California purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence be- tween programs on the one hand and machine representa- Future users of large data banks must be protected from tion and organization of data on the other. having to know how the data is organized in the machine (the A further advantage of the relational view is that it internal representation). A prompting service which supplies forms a sound basis for treating derivability, redundancy, such information is not a satisfactory solution. Activities of users and consistency of relations-these are discussedin Section at terminals and most application programs should remain 2. The network model, on the other hand, has spawned a unaffected when the internal representation of data is changed number of confusions, not the least of which is mistaking and even when some aspects of the external representation the derivation of connections for the derivation of rela- are changed. Changes in data representation will often be tions (seeremarks in Section 2 on the “connection trap”). needed as a result of changes in query, update, and report Finally, the relational view permits a clearer evaluation traffic and natural growth in the types of stored information. of the scope and logical limitations of present formatted Existing noninferential, formatted data systems provide users data systems, and also the relative merits (from a logical with tree-structured files or slightly more general network standpoint) of competing representations of data within a models of the data. In Section 1, inadequacies of these models single system. Examples of this clearer perspective are are discussed. A model based on n-ary relations, a normal cited in various parts of this paper. Implementations of form for data base relations, and the concept of a universal systems to support the relational model are not discussed. data sublanguage are introduced. In Section 2, certain opera- 1.2. DATA DEPENDENCIES PRESENTSYSTEMS IN tions on relations (other than logical inference) are discussed The provision of data description tables in recently de- and applied to the problems of redundancy and consistency veloped information systems represents a major advance in the user’s model. toward the goal of data independence [5,6,7]. Such tables KEY WORDS AND PHRASES: data bank, data base, data structure, data facilitate changing certain characteristics of the data repre- organization, hierarchies of data, networks of data, relations, derivability, sentation stored in a data bank. However, the variety of redundancy, consistency, composition, join, retrieval language, predicate calculus, security, data integrity data representation characteristics which can be changed CR CATEGORIES: 3.70, 3.73, 3.75, 4.20, 4.22, 4.29 without logically impairing some application programs is still quite limited. Further, the model of data with which users interact is still cluttered with representational prop- erties, particularly in regard to the representation of col- lections of data (as opposed to individual items). Three of the principal kinds of data dependencies which still need 1. Relational Model and Normal Form to be removed are: ordering dependence, indexing depend- ence, and accesspath dependence. In some systems these 1.I. INTR~xJ~TI~N dependencies are not clearly separable from one another. This paper is concerned with the application of ele- 1.2.1. Ordering Dependence. Elements of data in a mentary relation theory to systems which provide shared data bank may be stored in a variety of ways, someinvolv- access large banks of formatted data. Except for a paper to ing no concern for ordering, some permitting each element by Childs [l], the principal application of relations to data to participate in one ordering only, others permitting each systems has been to deductive question-answering systems. element to participate in several orderings. Let us consider Levein and Maron [2] provide numerous referencesto work those existing systems which either require or permit data in this area. elements to be stored in at least one total ordering which is In contrast, the problems treated here are those of data closely associated with the hardware-determined ordering independence-the independence of application programs of addresses.For example, the records of a file concerning and terminal activities from growth in data types and parts might be stored in ascending order by part serial changesin data representation-and certain kinds of data number. Such systems normally permit application pro- inconsistency which are expected to become troublesome grams to assumethat the order of presentation of records even in nondeductive systems. from such a file is identical to (or is a subordering of) the Volume 13 / Number 6 / June, 1970 Communications of the ACM 377
  • 20. P. BAXENDALE, Editor ata for The relational view (or model) of data described in Section 1 appears to be superior in several respects to the graph or network model [3,4] presently in vogue for non- inferential systems. It provides a means of describing data with its natural structure only-that is, without superim- posing any additional structure for machine representation ia purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence be- tween programs on the one hand and machine representa- protected from tion and organization of data on the other. he machine (the A further advantage of the relational view is that it which supplies forms a sound basis for treating derivability, redundancy, ctivities of users and consistency of relations-these are discussedin Section should remain 2. The network model, on the other hand, has spawned a data is changed number of confusions, not the least of which is mistaking representation the derivation of connections for the derivation of rela- will often be tions (seeremarks in Section 2 on the “connection trap”). e, and report Finally, the relational view permits a clearer evaluation
  • 21. EN DEUX MOTS Data Warehouse.
  • 22. UN MARIAGE BLANC
  • 23. 1. DOCUMENT 2. KEY–VALUE 3. GRAPH 4. COLUMN/BIGTABLE 5. GEO 6. OBJECT 7. FILESYSTEM
  • 24. 1. ASSOCIATIF!KEY-VALUE 2. PLAT!DOCUMENT, FILESYSTEM 3. HIERARCHIQUE!GEO 4. RÉSEAU!GRAPH 5. DIMENSIONEL!COLUMN 6. OBJECTIONEL!OBJECT
  • 25. POUR LES RELATIONEUX J’ai fait un schéma…
  • 26. join brand 1 1 paradigm document 1 7 1 flat 1 key–value 2 2 2 associative 2 graph 3 3 3 hierarchical 3 column 4 4 4 network 4 geo 5 5 5 dimensional 5 object 6 6 6 objectional 6 filesystem 7
  • 27. ASSOCIATIF (KEY–VALUE) USER-18540 ! FR_FR
  • 28. PLAT (DOCUMENT) #E763C9 ! GOOG, 2010-02-16, 13H46, 450, 400
  • 29. HIERARCHIQUE (GEO) France Paca IdF
  • 30. RÉSEAU (GRAPH) Olivier Tim Martin Bob
  • 31. DIMENSIONEL (COLUMN) Sales Fact Table +------------------------+ | sale_amount | time_id | +------------------------+ Time Dimension | 2008.08| 1234 |---+ +-----------------------------+ +------------------------+ | | time_id | timestamp | | +-----------------------------+ +---->| 1234 | 20080902 12:35:43 | +-----------------------------+
  • 32. OBJECTIONEL (OBJECT)
  • 33. LE POIDS DU NOM
  • 34. ANTI-SQL ?
  • 35. ANTI-BDD ?
  • 36. UN NOUVEAU STANDARD ? J’ai un paquet de ba es à livrer à un certain Nicolas Martignole. Il est dans le coin ?
  • 37. UN NOUVEAU LANGAGE ?
  • 38. « NOT ONLY SQL » ?
  • 39. ALORS C’EST QUOI ?
  • 40. SQL VS. NOSQL VS. NOSQL
  • 41. 1. NOSQL, ÇA PUE Si si, vraiment.
  • 42. 2. ÇA N’EST PAS UNE QUESTION DE TAILLE
  • 43. 3. C’EST VRAIMENT PAS COMPLIQUÉ
  • 44. 4. MAIS…
  • 45. ALLER PLUS LOIN
  • 46. My NoSQL http://nosql.mypopescu.com/
  • 47. NoSQL-fr http://groups.google.com/group/nosql-fr
  • 48. NØSQL BOSTON — 11 MARS L iv e! http://nosqlboston.eventbrite.com SUIVRE @NOSQLLIVE POUR PLUS DE DÉTAILS.
  • 49. NØSQL E LONDRES — 20 & 21 u rop AVRIL e! ATELIERS ET FORMATIONS LE 22. SUIVRE @NOSQLEU POUR PLUS DE DÉTAILS.
  • 50. SpeakerRate.com/timanglade
  • 51. SlideShare.net/timanglade
  • 52. MERCI ! NOSQLfit! rFu fo & n P ro
  • 53. ?