MongoDB Aug2010 SF Meetup

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This document provides an overview of MongoDB and the Morphia object-document mapper for Java. It discusses MongoDB concepts like document storage, indexing, and concurrency model. It then covers Morphia's annotations for mapping Java objects to MongoDB documents, including entity configuration, relationships, and common data access patterns using queries and updates. Code examples are provided for mapping object graphs, querying, and updating documents.

MongoDB: ObjStorSol + Des+Map’nYeah, I shortened that… but not to OSSDnM

…AppServer - J2EE (JBoss, Resin)Hosted AppServices - Google AppEngineDatastore - Objectify (GAEJ ORM/ODM)MongoDB – Morphia (ODM)Community Developer (and more)How I got here…

Basic ExpectationsNo transactions (but atomic updates)Memory mapped data filesDocument storeFast-N-Loose by default (no safe/w/GLE)Indexing order matters; query optimizer helpsOne writer (serialized writes)One JS thread (M/R, $where, group)Many query threads (non-js)BSON everywhere

What is it good for?DebuggingTestingAdministrationScripting GlueNot building apps, probably…

Single Collection, InheritanceOne – ManyEmbeddedReferenceOn the one -> Array of refsOn the Many -> Add a ref fieldTreesMany – ManyProb. best to store on only one side.Designs

${ _id : "scotthernandez", name : "Scott Hernandez",desc : "that guy", groups : [ "users", "admins", "fishermen" ]}People - Groups$

${ "_id" : "users", "desc" : "normal users“ },{ "_id" : "fishermen", "desc" : "people who fish“ },{ "_id" : "admins", "privledges" : 2, "desc" : "administrative users“ }Group - People$

blog =: { text: “I like to swim”, author: “scott”, comments: [ {author:”ralph”, text:”me2”, replies:[ {author:”liz”, text:”doesn’t every1?”} ]}, {author:”jeff”, text:”good to know”} ],ts:Date(…)}Tree

Raw MongoDB DriverMap<String, Object> view of objectsRough but dynamicMorphia (type-safe mapper)POJOsAnnotation based (similar to JPA)Syntactic sugar and helpersOthersCode generators, other jvm languagesJava Library Choices

BSON PackageTypesEncode/DecodeDBObject (Map<String, Object>)Nested MapsDirectly encoded to binary format (BSON)MongoDB PackageMongoDBObject (BasicDBObject/Builder)DB/DBColletionDBQuery/DBCursorMongoDB Java Driver

Data Typesint and longArray/ArrayListStringbyte[] – binDataDouble (IEEE 754 FP)Date (ms since epoch UTC)NullBooleanJavaScript StringRegexObjectId (ts(4)+ host(3) + pid(2)+ incr(3) ) 12-byteBSON Package

Morphia: MongoDB MapperMaps POJOType-safeAccess Patterns: DAO/Datastore/RollYourOwnData TypesLow performance overheadJPA likeMany concepts came from Objectify (GAE)

Annotations@Entity(“collectionName”)@Id@Transient (not transient)@Indexed(…)@Property(“fieldAlias”)@AlsoLoad({aliases})@Reference@Serialized[@Embedded]

Lifecycle Events@PrePersist@PreSave@PostPersist@PreLoad@PostLoadEntityListenersEntityInterceptor

Datastore Basicsget(class, id)find(class, […])save(entity, […])delete(query)getCount(query)update/First(query, upOps)findAndModify/Delete(query, upOps)

Basic POJO@Entityclass Person { @Id ObjectId name;SexEnum sex;@IndexedInteger height;}

QueriesDatastoreds = …Query q = ds.createQuery(Person.class);q.field(“height”).greaterThan(155).limit(5);for(Person p : q.fetch()) print(p);Person me = q.field(“name”).startsWith(“sc”).get();

Save whole object graphs (get/save)Update parts (embedded objects)Un/set fieldsPush/pop arrays (lists)Increment numeric fieldsAny combination of the aboveGet/Save or Update

Update Operationsset(field, val)unset(field)inc(field, [val])dec(field)add(field, val)addAdd(field, vals)removeFirst/Last(field)removeAll(field, vals)

Update existing propsUpdate w/new propsExamples

UpdateDatastoreds = …Query q = ds.find(Person.class, “name”, “scott”);UpdateOperationuo = ds.createUpdateOperations(cls)uo.set(“city”, “seattle”).set(“lastUpdated”, new Date());UpdateResults res = ds.update(q, uo);if(res.getUpdatedCount() > 0) //do something?

Morphia Relationships Annotations[@Embedded]Load/Save with EntityUpdate@ReferenceStored as DBRef(s)Loaded with EntityNot automatically savedKey<T>Stored as DBRef(s)Just a link, but resolvable by Datastore/Query

MongoDB is a document-oriented NoSQL database that uses a document-data model. It provides horizontal scaling with auto-sharding and replication. MongoDB can store documents in collections without a defined schema and support dynamic queries and indexing. RealNetworks uses MongoDB with Scala and other technologies for an Android app to send notifications to devices with installed RealNetworks applications at scale.

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This document summarizes a presentation about Apache CouchDB. Some key points: - CouchDB is a scalable, distributed key-value database that uses peer-to-peer replication. It has an append-only file structure and is designed to handle crashes well. - Data is stored in JSON documents with dynamic schemas. Views are built using JavaScript map-reduce functions. - The API is RESTful HTTP and works natively with the web. Data can be queried and rendered directly in the browser using JavaScript. - CouchDB embraces web technologies and can scale from smartphones to server clusters. It is open source and embraces an open philosophy.

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This summary provides an overview of Buddy Media's experience using MongoDB for three stages of a project: Stage 1 was a non-critical logging application where they learned MongoDB is not like MySQL and to use subdocuments instead of rows/tables. Stage 2 involved critical user data with spikes where they learned to use modifier operators carefully and implement indexing and replica sets. Stage 3 was for real-time analytics of user events, requiring flexibility and high write volumes. They store aggregated metrics instead of individual events and use upserts and $inc to update in bulk, providing faster performance than SQL for their needs.

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The document provides information about an introductory webinar on NoSQL databases and MongoDB. It includes the webinar agenda which covers why NoSQL databases exist, the different types of NoSQL databases including key-value, column, graph and document stores, and details on MongoDB including how it uses JSON-like documents, ensures data durability through replica sets, and scales through sharding. It also advertises a follow up webinar on building a first MongoDB application and provides a registration link.

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The document introduces MongoDB as a scalable, high-performance, open source, schema-free, document-oriented database. It discusses MongoDB's philosophy of flexibility and scalability over relational semantics. The main features covered are document storage, querying, indexing, replication, MapReduce and auto-sharding. Concepts like collections, documents and cursors are mapped to relational database terms. Examples uses include data warehousing and debugging.

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This document summarizes a presentation about schema design in MongoDB. It discusses embedding documents, linking documents through references, and using geospatial data for check-ins. Examples are given for modeling blog posts and comments, places with metadata, and user profiles with check-in histories. The document emphasizes designing schemas based on application needs rather than relational normalization.

Mastering the MongoDB ShellMongoDB

This document discusses the MongoDB shell, which is a JavaScript interpreter with built-in support for MongoDB. It can be used for interactive development, testing, administration, and learning MongoDB. The shell allows running queries, saving documents, and has features like command completion, history, and helpers for common tasks. While powerful, it has some limitations like performance and lack of modern JavaScript features. Overall, the document recommends using the shell to better understand and work with MongoDB.

Mongodb introchristkv

Christian Kvalheim gave an introduction to NoSQL and MongoDB. Some key points: 1) MongoDB is a scalable, high-performance, open source NoSQL database that uses a document-oriented model. 2) It supports indexing, replication, auto-sharding for horizontal scaling, and querying. 3) Documents are stored in JSON-like records which can contain various data types including nested objects and arrays.

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MongoDB Aug2010 SF Meetup

1. MongoDB: ObjStorSol + Des+Map’nYeah, I shortened that… but not to OSSDnM

2. …AppServer - J2EE (JBoss, Resin)Hosted AppServices - Google AppEngineDatastore - Objectify (GAEJ ORM/ODM)MongoDB – Morphia (ODM)Community Developer (and more)How I got here…

3. Basic ExpectationsNo transactions (but atomic updates)Memory mapped data filesDocument storeFast-N-Loose by default (no safe/w/GLE)Indexing order matters; query optimizer helpsOne writer (serialized writes)One JS thread (M/R, $where, group)Many query threads (non-js)BSON everywhere

4. What is the shell?

5. What is it good for?DebuggingTestingAdministrationScripting GlueNot building apps, probably…

6. Shell Demo

7. Single Collection, InheritanceOne – ManyEmbeddedReferenceOn the one -> Array of refsOn the Many -> Add a ref fieldTreesMany – ManyProb. best to store on only one side.Designs

8. { _id : "scotthernandez", name : "Scott Hernandez",desc : "that guy", groups : [ "users", "admins", "fishermen" ]}People - Groups

9. { "_id" : "users", "desc" : "normal users“ },{ "_id" : "fishermen", "desc" : "people who fish“ },{ "_id" : "admins", "privledges" : 2, "desc" : "administrative users“ }Group - People

10. blog =: { text: “I like to swim”, author: “scott”, comments: [ {author:”ralph”, text:”me2”, replies:[ {author:”liz”, text:”doesn’t every1?”} ]}, {author:”jeff”, text:”good to know”} ],ts:Date(…)}Tree

11. Raw MongoDB DriverMap<String, Object> view of objectsRough but dynamicMorphia (type-safe mapper)POJOsAnnotation based (similar to JPA)Syntactic sugar and helpersOthersCode generators, other jvm languagesJava Library Choices

12. BSON PackageTypesEncode/DecodeDBObject (Map<String, Object>)Nested MapsDirectly encoded to binary format (BSON)MongoDB PackageMongoDBObject (BasicDBObject/Builder)DB/DBColletionDBQuery/DBCursorMongoDB Java Driver

13. Data Typesint and longArray/ArrayListStringbyte[] – binDataDouble (IEEE 754 FP)Date (ms since epoch UTC)NullBooleanJavaScript StringRegexObjectId (ts(4)+ host(3) + pid(2)+ incr(3) ) 12-byteBSON Package

14. Morphia: MongoDB MapperMaps POJOType-safeAccess Patterns: DAO/Datastore/RollYourOwnData TypesLow performance overheadJPA likeMany concepts came from Objectify (GAE)

15. Annotations@Entity(“collectionName”)@Id@Transient (not transient)@Indexed(…)@Property(“fieldAlias”)@AlsoLoad({aliases})@Reference@Serialized[@Embedded]

16. Lifecycle Events@PrePersist@PreSave@PostPersist@PreLoad@PostLoadEntityListenersEntityInterceptor

17. Datastore Basicsget(class, id)find(class, […])save(entity, […])delete(query)getCount(query)update/First(query, upOps)findAndModify/Delete(query, upOps)

18. Basic POJO@Entityclass Person { @Id ObjectId name;SexEnum sex;@IndexedInteger height;}

19. QueriesDatastoreds = …Query q = ds.createQuery(Person.class);q.field(“height”).greaterThan(155).limit(5);for(Person p : q.fetch()) print(p);Person me = q.field(“name”).startsWith(“sc”).get();

20. Save whole object graphs (get/save)Update parts (embedded objects)Un/set fieldsPush/pop arrays (lists)Increment numeric fieldsAny combination of the aboveGet/Save or Update

21. Update Operationsset(field, val)unset(field)inc(field, [val])dec(field)add(field, val)addAdd(field, vals)removeFirst/Last(field)removeAll(field, vals)

22. Update existing propsUpdate w/new propsExamples

23. UpdateDatastoreds = …Query q = ds.find(Person.class, “name”, “scott”);UpdateOperationuo = ds.createUpdateOperations(cls)uo.set(“city”, “seattle”).set(“lastUpdated”, new Date());UpdateResults res = ds.update(q, uo);if(res.getUpdatedCount() > 0) //do something?

24. Morphia Relationships Annotations[@Embedded]Load/Save with EntityUpdate@ReferenceStored as DBRef(s)Loaded with EntityNot automatically savedKey<T>Stored as DBRef(s)Just a link, but resolvable by Datastore/Query

25. Or… [email protected]?

MongoDB Aug2010 SF Meetup

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