Views

In part 3 of this series we looked at compound keys in more detail and also into persisting view data. Today we are going to look at what to do when Couchbase’s query model can’t fulfill what you need! (Or you need an excuse to play with more cool tech). ###Enter ElasticSearch! Elasticsearch is a search server based on Lucene, it’s great for real time analytics, flexible queries and features such as full text search, fuzzy matching and geo location queries. ...

In part 2 of this series we looked at using compound keys for more advanced querying, today we are going to modify our data set slightly so we can explore other querying methods. Our new documents are going to look like this: All that has changed is we’ve added in an ‘offers’ field which is an array that contains 0 or more offer codes that have been ‘claimed’ in our fake system. ...

Basic analytics on our data set In part one of the series here we covered setting up our Couchbase node and populating it with a sample of documents. As a quick refresher here is what one of our sample documents looks like. The query we wrote in the last article allowed us to group new users by year,month,day and so on but also allowed us to retreive a count of how many users had joined between specific dates. ...

Let’s see those new users! Views are the Couchbase way of generating indices so that you can query your data on attributes other than the document key. In this article we are going to explore the Map Reduce system of Couchbase to allow you to access your data in a variety of methods. Views are used for 3 main reasons: Additonal indices on which to query your data Calculating and producing statistics and information Fine filtering of larger data sets The following is incredibly important to remember! ...