MapReduce Views Using the Node.js SDK with Couchbase Server

how-to

You can use MapReduce views to create queryable indexes in Couchbase Data Platform.

Although still maintained and supported for legacy use, Views date from the earliest days of Couchbase Server development, and as such are rarely the best choice over, say, our Query service if you are starting a fresh application. See our discussion document on the best service for you to use.

The normal CRUD methods allow you to look up a document by its ID. A MapReduce (view query) allows you to lookup one or more documents based on various criteria. MapReduce views are comprised of a map function that is executed once per document (this is done incrementally, so this is not run each time you query the view) and an optional reduce function that performs aggregation on the results of the map function. The map and reduce functions are stored on the server and written in JavaScript.

MapReduce queries can be further customized during query time to allow only a subset (or range) of the data to be returned.

See the Incremental MapReduce Views and Querying Data with Views sections of the general documentation to learn more about views and their architecture.

By Name Views

The following example is the definition of a by_name view in a "beer" design document. This view checks whether a document is a beer and has a name. If it does, it emits the beer’s name into the index. This view allows beers to be queried for by name. For example, it’s now possible to ask the question "What beers start with A?"

var result = bucket.viewQuery('beers', 'by_name', {
  range: { start: 'A' },
  limit: 10,
})

The following example is the definition of a by_name view in a "landmarks" design document in the "travel-sample" sample dataset. This view checks whether a document is a landmark and has a name. If it does, it emits the landmark’s name into the index. This view allows landmarks to be queried for by its "name" field.

var result = await bucket.viewQuery('landmarks', 'by_name', {
  key: 'landmark_10019',
})

A Spatial View can instead be queried with a range or bounding box. For example, let’s imagine we have stored landmarks with coordinates for their home city (eg. Paris, Vienna, Berlin and New York) under geo, and each city’s coordinates is represented as two attributes, lon and lat. The following spatial view map function could be used to find landmarks within Europe, as a "by_location" view in a "spatial" design document:

function (doc, meta) {
    if (doc.type && doc.type == 'landmark' && doc.geo) {
        emit([doc.geo.lon, doc.geo.lat], null);
    }
}