Living in the Tech Avalanche Generation

It came to my attention recently that a site called SOA Guru have been scraping my blog and knocking off every single post and publishing under the name of their Admin user. What really annoys me is not that they are earning money off my work but more so that they are taking credit for it. If you have a story about this site then please blog it and make sure you report them to Google. I wonder if they will re-blog this post, I hope so.

UPDATE: Their re-post of this post is showing a link back here but it’s been generated by me to combat the scraping.

UPDATE: After making contact via email I received a response explaining that my material would be removed from their site and that appears to have taken place.

I’m not about to cut all ties with my relational past and abandon the RDBMS. No, indeed that’s not the appropriate response – well at least not at this stage. So an exploration is what is going to be required to help in clarifying whether MongoDB can find a place in my toolbox.

Watching Michael Dirolf recently sparked my imagination, I began to see a potential abundance of goodness that could be applied in many way – possibly alongside a relational database. The potential for its implementation on the query side in a CQRS architecture for instance, serving as a store for directly bound view model data. MongoDB at first glance offers some nice features that would blend well into an architecture that included serving data from inside the web tier; BSON (binary JSON) may resonate well, it’s unceremonious and lite weight. The future promise of sharding as a product feature also has it’s appeal for obvious reasons.

Figure 1.0 – Potential Architecture

My initial enquiry was grounded in the efficacy of putting MongoDB to work in a distributed publish / subscribe architecture where (N) subscribing instances of autonomous components in a logical service boundary could effectively update N instances of a dedicated view model database. Each view model perhaps sharing an affinity to a web server allowing scaling out both in tandem. In order to sustain eventual consistency, this architecture employs transactional queues. MongoDB achieves it’s scale efficiencies by ignoring some the properties of an RDBMS that are commonly associated with the difficulties in scaling them. What’s clear is that an architecture that heavily leverages publish / subscribe, one way messaging and transactional queues (aimed at providing high availability and scale), will also generally require it’s storage to support transactional behavioural properties. MongoDB advertises it’s replication feature set as part of it’s toolbox, this helps achieve eventual consistency across a shared grid of servers but these two worlds are not about to find a way to play well together. In effect, both seem to be coming to a similar problem with a different solution, with MongoDB addressing scale through it’s built-in auto sharding, focusing more intensely on the database alone. For MongoDB to work in this environment would require the ability to enlist database calls in distributed transactions, something that is not supported by design. It would be possible to add a transactional messaging gateway to solve this problem, however there are other database alternatives that would not require me to take that extra step and additional latency.

This of course in no way invalidates assigning value to looking more deeply at MongoDB (and the like), therefore as I plant sign posts along the road about to be travelled and in light of the friction described above, I will now change track somewhat. My next move forward is to get a better grip of what the developer experience is about (not every problem requires the same solution).

Me as Developer - Starting Out

I started out with a somewhat familiar approach, running the JavaScript based shell you can run ad-hoc queries somewhat akin to query analyzer in the SQL Server world. Once installed and having put the binaries in my path environment variable, running the following commands will start up the server and the shell.

   1: c:\users\simon.segal\mongod

   2: c:\users\simon.segal\mongo

Figure 2.0 – Mongo Shell & DB

Going from download, to install, to running the shell, inserting and querying for data, is really a relatively quick exercise. With just a dash of reading from the excellent documentation (which also has a friendly readable PDF download utility), you can be writing code in the shell very fast. Code like this doesn’t need a lot of explaining to get going.

Listing 1.0 – Mongo Shell

>customer = {firstName: "Jon", lastName: "Doe", DateJoined: new Date("2010/01/30")};
>db.Customers.save(customer);
>db.Customers.find();
{ "_id" : ObjectId("4b5016e65d3c000000006393"), "firstName" : "Jon", "lastName"
: "Doe", "DateJoined" : "Sat Jan 30 2010 00:00:00 GMT+1100 (AUS Eastern Daylight
 Time)" }

The Officially Supported Drivers

• C
• C++
• Java
• Javascript
• Perl
• PHP
• Python
• Ruby

The Unofficial Community Supported Drivers

• C#
• Cold Fusion
• Erlang
• Factor
• Fantom
• F#
• Go
• Groovy
• PHP
• Powershell
• Ruby

The C# Driver

You can see that Microsoft technology is fairly well represented in these lists but I want to focus on the C# driver and talk about using MongoDB from C# specifically. Here’s some code using the C# driver to help start getting a feel for the API.

public void CreateCustomer()
{
    var mongo = new Mongo("127.0.0.1", "27017");
    mongo.Connect();
    var db = mongo.getDB("HR");
    var employees = db.GetCollection("Employees");
    var employee = new Document();
    employee["FirstName"] = "Simon";
    employee["LastName"] = "Segal";
    employee["JoinDate"] = DateTime.Now;
    employees.Insert(employee);
    var cursor = employees.FindAll();
    foreach (var emp in cursor.Documents)
    {
        Console.WriteLine(string.Format(
            "Customer ID is {0}, first name is {1} and " +
            "last name is {2}", emp["_id"],
            emp["FirstName"], emp["LastName"]));
        Console.WriteLine(emp.ToString());
    }
}

After running this code in Visual Studio we get the following output which prints a formatted string and the JSON representation as is.

Figure 1.0

Issues

The first problem with writing code like this is that it’s about as far away from being Domain Driven as I can imagine, there is no concept of an Entity, just a Document. Well it’s a Document Database you say – so why don’t I just stop whining and get on with writing code like that above? Sure transaction script might be great for some scenario’s but when I want to work with a richer domain model I don’t think I should need to change to accommodate the drivers API. Let me be clear, I don’t think there is anything wrong with the API, it’s perfectly fine and in fact I think it’s more than pliable enough for me to use it in other ways that will suit me from time to time.

I alluded earlier in the discussion, to the fact that I originally wanted to proof of concept MongoDB for playing a role as an eventually consistent storage for the Query side of an implementation of CQRS, where views are being bound directly to view model data (as described here) via a RESTFul facade. In part 2.0 I am going to look at how we can build a Context object (think DataContext or ObjectContext) to wrap up the C# driver’s functionality and enable working directly with POCO’s, removing any direct requirement to work from the MongoDB API’s Document class. That’s it for now, let’s see where we end up in Part 2.0.

On the 19th of January 2010 Udi Dahan presented on Command Query Responsibility Segregation. The Presentation took place in front of the Victorian .NET User Group. The video jumps around a bit early on but it does settle after a few minutes or so. Also the audio is a bit low in volume and may need a bit of a boost. Many thanks to Udi and Mahesh Krishnan (the User Group Organiser) for making this happen. Watch or download the video here.