HealthcareMetadata

There are several common strategies for document versioning. The strategy you select will depend on the trade-offs you want to make. Please note that MongoDB does not have any support for triggers. So a few of these methods will require that you be able to do multiple writes.

=== Strategy 1: embed history === In theory, you can embed the history of a document inside of the document itself. This can even be done atomically.

=== Strategy 2: write history to separate collection ===

zopyx.versioning - A MongoDB based versioning solution for Zope and Plone

Versioning is an extra-feature that is not fundamental to document databases. MongoDB and CouchDB do not support it by default, but there are different solutions available

Versioning is not supported by either MongoDB and CouchDB. MVCC should not be confused for document versioning

Built-in Versioning: Most document databases support versioning of documents with the flip of a switch.

Placing a document under version control is very unintrusive. Vermongo only adds a version number property (an int32 called _version) to the document. It does not touch any other fields. In particular, it also does not place any requirements on the contents of the _id field. Older revisions are stored in a separate collection that shadows the original collection. They are MongoDB documents themselves, and can be queried.

When creating, updating, or deleting documents, you just have to follow the Vermongo protocol outlined here. There will be (at least) a Java library to facilitate this. The only major downside is that documents have to be updated one-by-one (no bulk updates possible), and that there is some overhead in retrieving and copying the old version. Query operations can just proceed as normal.

After inserting into collection 'foo' a document { a=>"x"}, updating it to { a: "y"}, then to { a: "z" }, and finally deleting it, the shadow collection will contain the following (the original collection will not contain the document anymore, as it has been deleted): > db.foo.vermongo.find() { "_id" : { "_id" : ObjectId("4c78da..."), "_version" : 1 }, "a" : "x", "_version" : 1 } { "_id" : { "_id" : ObjectId("4c78da..."), "_version" : 2 }, "a" : "y", "_version" : 2 } { "_id" : { "_id" : ObjectId("4c78da..."), "_version" : 3 }, "a" : "z", "_version" : 3 } { "_id" : { "_id" : ObjectId("4c78da..."), "_version" : 4 }, "_version" : "deleted:4" }

Installation There has not been a release build yet. You will have to compile v7files from its source code, using Maven.

It's more typical to keep older versions of your document inside your current revision (either as JSON or, often, as an attachment). For the JSON case; { "_id":"foo", "_rev":"3-fsfsfsdf", "foo":"current value of foo", "history": { "2": { "foo":"previous version of foo" }, "1": { "foo":"initial version of foo" } } }

t's often simpler to push the full old version of the document into an attachment instead. This pattern is common enough that CouchDB ships with a library, jquery.couch.js, that implements it (in the saveDoc(doc) function).

Ways to implement data versioning in MongoDB

will be used infrequently will be used all at once to present it in a "time machine" fashion there won't be more versions than few hundred to a single record. history won't expire.

My personal approach would be to store diffs. Because the display of these diffs is really a special action, I would put the diffs in a different "history" collection.

Modeling friend of friend relationships in MongoDB

"When a document's stored value for a index key field is an array, MongoDB indexes each element of the array. See the Multikeys page for more information." so to find all of "alex"'s friends I can just do: db.user.find( { 'friends' : '4e77bb3b8a3e000000004f7a'});

Otherwise, if the number of books per publisher is unbounded, this data model would lead to mutable, growing arrays, as in the following example:

Embedding¶ To de-normalize data, store two related pieces of data in a single document. Operations within a document are less expensive for the server than operations that involve multiple documents. In general, use embedded data models when: you have “contains” relationships between entities. See Model Embedded One-to-One Relationships Between Documents. you have one-to-many relationships where the “many” objects always appear with or are viewed in the context of their parent documents. See Model Embedded One-to-Many Relationships Between Documents. Embedding provides the following benefits: generally better performance for read operations. the ability to request and retrieve related data in a single database operation.

Referencing¶ To normalize data, store references between two documents to indicate a relationship between the data represented in each document. In general, use normalized data models: when embedding would result in duplication of data but would not provide sufficient read performance advantages to outweigh the implications of the duplication. to represent more complex many-to-many relationships. to model large hierarchical data sets. See Model Tree Structures in MongoDB. Referencing provides more flexibility than embedding; however, to resolve the references, client-side applications must issue follow-up queries. In other words, using references requires more roundtrips to the server.

we promise that your client application which uses the database is a separate work. To facilitate this, the mongodb.org supported drivers (the part you link with your application) are released under Apache license, which is copyleft free.

MongoDB Database Server and Tools¶ Free Software Foundation’s GNU AGPL v3.0. Commercial licenses are also available from 10gen, including free evaluation licenses. Drivers¶ mongodb.org supported drivers: Apache License v2.0. Third parties have created drivers too; licenses will vary there.

How to Buy Contact sales to learn more about MongoDB Commercial Subscriptions, and how to buy. MongoDB Subscriptions Basic Standard Enterprise Edition MongoDB MongoDB MongoDB Enterprise SLA 4 Hours 1 Hour 30 Minutes Support 9am – 9pm local, M – F 24 x 7 x 365 24 x 7 x 365 License AGPL Commercial Commercial Emergency Patches Not Included Included Included Price $2,500 / Server / Year $5,000 / Server / Year $7,500 / Server / Year

Commercial License. Enterprises with restrictions around the AGPL license or those interested in OEM applications may prefer a standard commercial license, which is included in the MongoDB Standard and Enterprise Subscriptions.

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To say this another way: if you modify the core database source code, the goal is that you have to contribute those modifications back to the community.

How to do it... Create your entities first. This example consists of a user, an address, and a SuperSecretData entity: @Entitypublic class User extends Model { @SerializedName("userLogin") public String login; @NoJsonExport public String password; @ManyToOne public Address address; @OneToOne public SuperSecretData secrets; public String toString() { return id + "/" + login; }}@Entitypublic class Address extends Model { public String street; public String city; public String zip;}@Entitypublic class SuperSecretData extends Model { public String secret = "foo";}

Supports Google account and OpenID authentication and access control on each JSON document. Provides admin console for setting the access control.

MongoDB includes a basic authentication scheme that works with single server deployments and replica sets. However, the built-in authentication does not work with sharding, so for this and other cases, we recommend running the database in a secure environment.

The current version of Mongo supports only very basic security.  One authenticates a username and password in the context of a particular database.  Once authenticated, a normal user has full read and write access to the database in question while a read only user only has read access.