Even more checking out MongoDB: The coolness continues

One thing I started to think about after having looked at MongoDB was how to model things that are somehow connected – without the use of foreign keys and the ability to join stuff.

When working with documents, you generally just embed the data where it belongs. But what if I have the following documents:

– and I want to constrain access to the songs, allowing me to see both songs, and The Dude to see only Factory?

My first take was to simply add username in an array inside each song, like so:

– and this will work well with indexing, which can be done like this:

But here comes the problem: What if each song should be displayed along with the name of who can access that particular song? I need to embed more stuff in the array, e.g. like so:

There’s a challenge now in keeping this extra “denormalized” piece of information up-to-date in case a user changes his name, etc. – but let’s just assume we’ve handled that.

Now here comes the cool thing: It’s cool that MongoDB can index “into” an array, but it can actually index “into” anything! Just tell it where to go, using the Dot Notation.

That means I can perform the same search as I did above like so:

How cool is that?? (pretty cool, actually!)

More checking out MongoDB: Updating

In MongoDB, there’s no way to lock a database, collection, or document. The ability to work without locking is a requirement for any db that wishes to be horizontally scalable, and obviously this imposes some limitations and/or possibilities (depending on your point of view :)).

If you want all the goodness that document-orientation brings, it seems we need to cope with this non-locking database.

So how DO you update stuff in MongoDB? And, more importantly: how do you update stuff without race conditions?

In one of my previous posts on MongoDB, I mentioned that the unit of atomicity is a document – i.e., either a document gets saved/updated/deleted or it doesn’t. That must mean that we can count on updating one document only (or not), so we should build our applications so they can work without requiring multiple documents to be updated to be consistent ([1. Which is good practice anyway! In my experience, long and wide db transactions are often used, not to enforce a strict consistency as much as to allow scenarios like: “when this happens, this should also happen”. But that kind of logic can often be handled by something else, e.g. by a publishing events reliably to other processes (logically and/or physically), that handles the side-effects.]).

First, let’s take a look at how to actually update a document.

Naïve attempt to update a document

Well, we could do this:

That is, if you go and save a document that already has an ID, any existing document with that ID will be updated.

This would work if we were the only client on the db. But what if someone was editing the post in that same moment, adding another tag as well? Well, if he was unfortunate enough to save his edits when we were out for coffee, his changes would be lost.

One way to actually do it

By using the update function!

update accepts the following four arguments:

  1. criteria – document selector that specifies which document to be updated
  2. objNew – document to save
  3. upsert – bool to specify auto-insert if document does not exist (“update if present, insert if missing”)
  4. multi – bool to allow updating multiple documents that match the criteria (default is only first document)

Actually, as you can now probably see, save(doc) is just a shorthand for update({}, doc, true, false) – an upsert with the document we’re saving.

This way, we could easily add an incrementing version field to our documents to make sure that the version we’re saving is the version we retrieved.

Let’s try it out:

– good thing he didn’t get through with that one! 🙂

As you can see, we can easily implement optimistic concurrency on each document by constraining updates to the version we checked out. But, as I will show next, you can actually do a lot of things on the server.

Server-side document updates

Instead of retrieving an entire document, modifying it, and saving it back again with the risk of overwriting someone else’s edits, we can ask the server to make edits as smaller operations. E.g. our attempt to add a missing ‘test’ tag to the document could have been done like this:

See how the code $push modifier was used to push a value into the array… this stuff is great. But here, we have a race condition again – what if someone added the ‘test’ tag almost the same time as we did? Then two ‘test’ tags would be present in the array.

One way is to constrain the update by id and the absence of ‘test’ in the tags array:

– another is to use the $addToSet function, which makes MongoDB treat the array as a set:


My conclusion (so far) is that an application can get a huge benefit from using the various modifier operations – performance-wise (obviously), but probably also UX-wise as well… It’s a step in another direction from the usual CRUD scenarios that I usually compulsively associate with the word “update”, and I imagine it could be made to reflect the user’s interactions with the system.

I am thinking that the majority of the user’s interactions with the system could (and probably should) be put on the form

– and then one could implement a more-or-less generic mechanism on the client side to handle unsuccessful updates (e.g. by asking the user what to do then, reloading some data to allow [assumed state before] to be something else, or by handling certain situations with some kind of merging function, etc.).

How to perform updates across multiple documents

My first thought is that this situation should be avoided when working with a document-oriented db. I think most people will agree with this one.

I am pretty unsure of this, actually… The rest of this post is just a few thoughts on my first take on this, should I need to do this. Comments are greatly appreciated!

The problem in updating multiple documents is that we can perform an update on one document at a time, each time checking if the update went well or not. But there’s no way to (consistently) roll back update #1 if update #2 fails. So this means that there’s only one way: Forward! But how to proceed then, when an update failed?

How do we usually do stuff reliably across boundaries of multiple things that may or may not succeed, allowing us to handle errors as gracefully as possible and proceed thereafter?

I’m thinking that asynchronous reliable one-way messaging is the answer to this.

So if an application ever needs to update multiple documents in the most reliable way possible, it should probably perform one document update per “transaction” – in NServiceBus terminology that would be updating one document per message handler. And then the handler should throw an exception if an update unexpectedly fails.

But again: I’m thinking that this situation should be avoided at all costs with a document-oriented db. If ACID is required, the application should probably have a RDBMS on the side, or implement some kind of transactional mechanism in the document store.


That concludes my little learning series of MongoDB posts.

I must say that I am intrigued by all the NoSQL discussions currently going on in the communities, and I think it is always a sign of health that we question the technologies we use.

I am entirely convinced that document dbs could and should have been used for some parts of systems that I have experienced, and I am blown away by the lack of friction when starting up a project on top of a schemaless db.

As a .NET dude, I am convinced that the future will see more .NET systems built with more than one db underneath – e.g. with MongoDB for all the “soft parts” of the system, NHibernate on SQL Server for the few things that by nature require ACID, and then some NHibernate Search/Lucene/Solr.NET for full-text indexing and searching capabilities etc.

More checking out MongoDB: Querying

In my first post about MongoDB, I touched querying very lightly. Querying is of course pretty important to most systems, so it’s fair to dedicate a separate post to the subject.

Querying in MongoDB works by sending a document to the server, e.g. in the following snippet I create a document with a post ID

– which can actually be even shorter, as the find and findOne functions accept an ObjectId directly as their argument, like so:

But how can I find a post with a specific slug? Easy, like so:

But how does this perform? It’s easy to examine how queries are executed with the explain() function, like so:

– yielding some info about the execution of the query. I don’t know exactly how to interpret all this, but I think I get that "nscanned": 10000 means 10000 documents were scanned – and in a collection with 10000 documents, that’s not entirely optimal as it implies a full table scan. Now, let’s make sure that our query will execute as fast as possible by creating an index on the field ( _id is always automatically indexed):

Now lets explain() again:

Wow! That’s what I call an improvement!

What about posts with a specific tag? First I tried the following snippet, because I learned that the special $where field could be put in a query document to evaluate a predicate server-side:

– and this actually works. This syntax is sort of clunky though. Luckily, MongoDB provides a nifty mechanism for arrays that automagically checks if something is contained in it. So my query can be rewritten to this:


Now, to take advantage of indexes, the special query document fields should be used. I showed $where above, but there are more – to name a few: $gt (greater than), $gte (greater than or equal), $lt (less than), $lte (less than or equal), $ne (not equal), and many more. For example, to count the number of non-nifty posts in February 2010:

Check out the manual for some great documentation on available operators.


Querying with MongoDB actually seems pretty cool and flexible. I like the idea that it’s possible to execute ad-hoc queries, and for most usage I think the supplied operators are adequate. The ability to supply a predicate function via $where seems really cool, but it should probably only be used in conjunction with one or more of the other operators to avoid a full table scan.

More checking out MongoDB: References

This post will touch a little bit on the mechanism used for references, and then a few thoughts on how document-orientation relates to OO.

Now – if you, like me, are into OO and normalized object models – the weirdness begins….. or maybe not?! (actually, I am not sure yet :))

In an OO world (and in a normalized RDB world as well), you reference stuff, thus reducing the amount of redundant information as much as possible. E.g. the names of countries should not be put in a column in your address table, each country should have a row in the countries table, and then be referenced by a countryId in the address table.

In a document-oriented world, you generally embed objects instead of referencing them. This is done for performance reasons, and because there’s no way to join stuff – which means that a stored ID/foreign key merely remains free for the client to manually use in additional queries.

When you do need to actually reference another document, use DBRef to create a reference, supplying the collection and the ID as arguments. E.g. like so:

This way, the reference is represented in a consistent manner which may – or may not – be picked up by the driver you are using. The C# driver can create DBRefs and follow them, but you don’t get to join stuff – you still need an extra query.

Embedding objects may seem a little clunky at first, but actually this plays nicely with some common OO concepts – take aggregation, for example: a blog post has an array of comments, each of which makes no sense without an aggregating post – i.e. comments live and die with their post. That’s an obvious sign that comments should be embedded in the post. So, instead of:

– you should do this:

Actually this makes me think about the concept of an aggregate root in DDD: an aggregate root “owns” the data beneath it, and is responsible for maintaining its own integrity. If you were to delete an aggregate root, all the data beneath it would dissappear.

This also fits kind of nicely with the fact that there’s no database transactions in MongoDB – i.e. there’s no way to issue multiple statements and have them rolled back in case of an error – there’s only documents, and either a document gets inserted/updated/deleted, or it doesn’t. So obviously, the document is the unit of atomicity, which fits (sort of nicely) with the aggregate root and its responsibility of keeping itself internally consistent.


The observations stated here pretty much make a document an aggregate root in the DDD sense – especially since only documents get an _id. There’s no obvious way to reference a particular comment inside the second post shown above.

If MongoDB’s performance is up to the task, data should probably be aggregated as much as possible into large documents. MongoDB’s limit is 4 MB per document, but I am unsure of how large documents should be before you should consider splitting them.

Maybe I am thinking too much about these things? Maybe I should just try and build something and see where my document modeling goes? Suggestions and comments are welcome 🙂

Checking out MongoDB

Having experienced a lot of pain using RDBMSs ([1. Usually because of abusing RDBMSs, actually. Storing an object model in a RDBMS is not painful as long as the tooling is right – e.g. by leveraging the amazing NHibernate. The pain comes when developers suddenly start implementing overly complex queries and doing reporting on top of a pretty entity model, modeling stuff OO style… ouch!]) as a default choice of persistence, having read a couple of blog posts about MongoDB, and being generally interested in widening my horizon, I decided to check out MongoDB.

This post is a write-as-I-go summary of the information I have gathered from the following places:

More posts may follow…. 🙂

Getting MongoDB

Piece of cake! Download MongoDB from the download center and shove the binaries away somewhere on your machine. Default is for MongoDB to store its data in /data/db which translates to c:\data\db if you are using Windows – go ahead and create this directory. The MongoDB daemon can be started by running mongod.exe, which will accept connections on localhost:27017.

It will probably look something like the screenshot shown below.

An alternative data path can be specified on the command line, e.g. like so: mongod --dbpath c:\somewhere\else.

Accessing it with JavaScript

Run mongo.exe to start the Mongo Shell. It will probably look something like this:

In the MongoDB prompt, you can use JavaScript to access the db. Here’s a sample session of some commands I have found useful:

Now, I have successfully added two documents representing blog posts in a collection named posts. As you can see, MongoDB assigns some funky IDs to the documents.

That was a brief demonstration of the JavaScript API in the Mongo Shell. Now, let’s do this from C#.

Getting started with mongodb-csharp

Now, go to mongodb-csharp dowload section at GitHub and get a debug build of the driver. Create a C# project and reference the MongoDB.Driver assembly.

On my machine, punching in the following actually works:

Now, I can verify that the document is actually in there by going back to the console and doing this:

Nifty! Now lets show the posts from C#. On my machine the following snippet displays the headlines of all posts:

– which is documented in the following screenshot:

Random nuggets of information

Document IDs

All MongoDB documents must have an ID in the _id field, either assigned by you (any object can be used), or automatically by MongoDB. IDs generated by MongoDB are virtually globally unique, as they consist of the following: 4 bytes of timestamp, 3 bytes of machine identification, 2 bytes of process identification, 3 bytes of something that gets incremented.

As a nifty consequence, the time of creation can be extracted from auto-generated IDs.

The ID type used by MongoDB can be created with ObjectId('00112233445566778899aabb') (where the input must be a string representing 12 bytes in HEX).

How are documents stored?

I you have not yet figured it out, documents are serialized to JSON – with the minor modification that it’s a BINARY version of JSON, hence it’s called BSON.

String encoding

UTF-8. No worries.

What about references?

I will research this and do a separate post on the subject. As MongoDB is non-relational, a “join” is – in principle – an unknown concept. There’s a mechanism, however, that allows for consistent representation of foreign keys that may/may not give you some extra functionality (depending on the driver you are using).

What about querying?

I will research this as well, posting as I go.

OR/M? (or OD/M?)

It is not yet clear to me how to handle Object-Document Mapping. Will require some research as well. As an OO dude, I am especially interested in finding out what a schema-less persistance mechanism will do to my design.

What else?

More topics include applying indices, deleting/updating, atomicity, and more. Implies additional blog posts.


My first impression of MongoDB is really good. It’s extremely easy to get going, and the few error messages I have received were easy to understand.

I am especially in awe with how little friction I encountered – mostly because of the schema-less nature, but also because everything just worked right away.