I have a bucket of patient documents. Each patient has a single doctor. The patient document refers to a doctor by a field called doctorId. There may be other data in the patient document, but we’re mainly focused on the patient document’s key and the doctorId value. Some examples:

Next, we can assume that each doctor can have multiple patients. We can also assume that a doctor document exists, but we don’t actually need that for this tutorial, so let’s just focus on the patients for now.

Finally, what we want for our application (or report or whatever), is an aggregate grouping of the patients with their doctor. Each record would identify a doctor and a list/array/collection of patients. Something like:

This might be useful for a dashboard showing all the patients assigned to doctors, for instance. How can we get the data in this form, with N1QL or with MapReduce?

N1QL gives us the ARRAY_AGG function to make this possible.

Start by selecting the doctorId from each patient document, and the key to the patient document. Then, apply ARRAY_AGG to the patient document ID. Finally, group the results by the doctorId.

Note: don’t forget to run CREATE PRIMARY INDEX ON patients for this tutorial to enable a primary index scan.

Imagine this query without the ARRAY_AGG. It would return one record for each patient. By adding the ARRAY_AGG and the GROUP BY, it now returns one record for each doctor.

Here’s a snippet of the results on the sample data set I created:

If you don’t want to go through the trouble of creating a bucket and importing sample data, you can also try this in the N1QL tutorial sandbox. There aren’t patient documents in there, so the query will be a little different.

I’m going to group up emails by age. Start by selecting the age from each document, and the email from each document. Then, apply ARRAY_AGG to the email. Finally, group the results by the age.

Here’s a screenshot of some of the results from the sandbox:

Similar aggregate grouping can also be achieved with a MapReduce View.

Start by creating a new View. From Couchbase Console, go to Indexes, then Views. Select the "patients" bucket. Click "Create Development View". Name a design document (I called mine "_design/dev_patient". Create a view, I called mine "doctorPatientGroup".

We’ll need both a Map and a custom Reduce function.

First, for the map, we just want the doctorId (in an array, since we’ll be using grouping) and the patient’s document ID.

Next, for the reduce function, we’ll take the values and concatenate them into an array. Below is one way that you can do it. I do not claim to be a JavaScript expert or a MapReduce expert, so there might be a more efficient way to tackle this:

After you’ve created both map and reduce functions, save the index.

Finally, when actually calling this Index, set group_level to 1. You can do this in the UI:

Or you can do it from the Index URL. Here’s an example from a cluster running on my local machine:

The result of that view should look like this (truncated to look nicer in a blog post):

I think the N1QL method is easier, but there may be performance benefits to using MapReduce in some cases. In either case, you can accomplish aggregate grouping just as easily (if not more easily) as in a relational database.

Interested in learning more about N1QL? Be sure to check out the complete N1QL tutorial/sandbox. Interested in MapReduce Views? Check out the MapReduce Views documentation to get started.

Did you find this post useful? Have suggestions for improvement? Please leave a comment below, or contact me on Twitter @mgroves.

Cloud computing basically means that instead of running applications in your own data center, you run it in someone else’s data center. Why would I do that?

Running a data center is difficult and expensive. You have to purchase hardware, manage upgrades, security, networking, and even stuff like electricity, ventilation, and cooling. For some enterprises, this is either not a big deal or it’s worth the hassle. But for many enterprises, the value that you’re delivering is not in the hardware or the operating system and so on, but in the domain expertise that goes into the software you’re building. Then, cloud appeals to enterprises who would rather someone else handle all that other stuff.

A metaphor that I really like was written up by Albert Barron in a blog post called Pizza as a Service (I especially like the diagram). It makes sense for a pizza company to control the whole stack because pizza making is their core competency. But if pizza making isn’t your job, it makes sense to take another option, like dining out, so you can instead spend your time focusing on what you do best.

This isn’t to say that cloud is always the best solution, but it explains why many companies are choosing to move at least some of their infrastructure and platform to a cloud provider like Microsoft’s Azure.

If you’re on the fence, I recommend at least giving it a try, so that you’re prepared for the day that your CTO comes to you and asks "so what are we doing about the cloud?"

Signing up for Azure is easy.

When I first started with Azure, I was very worried that I’d run up a big tab if I wasn’t careful. With the $200 trial, you won’t get charged until you explicitly tell Microsoft to do so. But years later, after my initial trial, I’ve still never had a problem of an unexpectedly high bill.

If you’ve never used Azure or any cloud computing, now is your chance to get started. I’d love to hear about your experiences with Azure, with Couchbase, and your overall impressions of cloud computing. Please leave a comment below, or talk to me on Twitter @mgroves.

For this blog post, I decided to try Visual Studio Preview.

I did this because there is a handy tool for creating Azure Function projects in Visual Studio.

But it only works for the preview version at this time. You don’t have to use these tools to develop Azure Functions, but it made the process simpler for me.

Once I had this tooling in place, all I had to was to File→New→Project. Then under "Cloud", select "Azure Functions".

Once you do this, you’ll have an empty looking project with a couple of JSON files. Right click on the project, add item, and select "Azure Function".

Next, you’ll need to select what kind of Azure Function you want to create. I chose "HttpTrigger". I also chose "Anonymous" to keep this post simple, but depending on your use case, you may want to require an authentication token. After you do this, a very simple shell of a function will be generated (as a C# class). You can execute this function locally (indeed, that is what the local.settings.json file is for) so you can test it out without deploying to Azure yet.

First, I decided that I wanted two Azure Functions: one to "get" a piece of data by ID, and one to "set" a new piece of given data. I started by defining the shape of my data with a simple C# POCO:

Here is the Azure function that I wrote to "get" that document from Couchbase Server:

Some things to note:

Next, run this function locally, and you’ll get a console screen that looks similar to this:

At the bottom, you’ll notice that it tells you the Azure Function URL(s). Assuming I had a document in Couchbase (I don’t yet), I could create an HTTP request with a tool like Postman to: http://localhost:7071/api/HttpTriggerCsharpGet?id=123456

Currently, if I do that, I’ll get "null" as a response (since I don’t have any validation or error checking code). So let’s move on and create a "Set" function.

The "Set" function will be slightly different. I want document information POSTed to it, and I want it to return a message like "New document inserted with ID 123456".

This function has a similar shape to the Get, but some important things to note:

To create a request in Postman, I’ll use a URL of http://localhost:7071/api/HttpTriggerCsharpSet. In the headers, I’ll set Content-Type to "application/json". Finally, the body will be JSON:

Now, when I POST that to the endpoint, I’ll get a response message of "New document inserted with ID f05ea97e-7c2f-4f88-b72d-19756f6a6f35".

I have glossed over how these functions connect to Couchbase Server.

Previously, I mentioned two methods, GetCluster and GetBucket that will connect to the cluster and bucket, respectively.

At this point, most of this code should be familiar if you’ve used Couchbase Server and the Couchbase .NET SDK before. I’m connecting to a single node cluster, and then connecting a bucket that has a password set (I’m using Couchbase Server 4.6).

But, the important thing to point out here is the use of Configuration.AppSettings. In the local.settings.json file, I’ve added these Couchbase settings to the Value section:

When running Azure Functions locally, this file is used for configuration. I have Couchbase Server running locally with a bucket called "azurefunctions". Anything in "Values" can be accessed via Configuration.AppSettings.

Before deploying the Azure Functions, I’ll need to create a Couchbase Cluster on Azure. This is very easy to do, thanks to Ben Lackey’s great work on the Azure Marketplace. Once that’s deployed, deploying the Azure Functions are also easy, thanks to Visual Studio.

This is my first time trying out Azure Functions. This blog post shows a simple demo, but there are other factors to consider before you start using this in production:

Anything I missed? Any more tips or suggestions to share to make this process easier or better? Please leave a comment below or ping me on Twitter @mgroves.