Design problems are fun. It’s a chance to build something that lasts and do it right. Plus, those bad decisions are going to hang around forever. This is our chance to make the right decision.
We’re building a system to store events that have occurred in an our application. This is going to be the back end for an event sourcing system. The event sourcing system (in case you won’t want to read that article) will just be storing things that have happened in the system. Sometimes you don’t just want to know where an order is right now, you also want to how it got to that location. Event sourcing lets us store that information in a meaningful way.
So, back to the app – we’re creating a back end for event sourcing in our application. Events are identified by an always increasing numeric column. Events are also associated with some kind of event owner. Does
n’t really matter what that is, just know that events are owned by something. The combination of event owner + event ID uniquely identifies each event.
In effect, the event source storage is a time ordered log of activity in the system for a single application entity. In other words – if you’re tracking activity for an order, you’d have an
Implementing the Event Source Storage
What’s the best way to implement this in a database? For our purposes, we’re going to use SQL Server and talk about what we can and can’t do in there. Specifically, we’ll be working with SQL Server 2016 Developer Edition.
Unless I have to, I won’t be using any SQL Server specific features and functionality for this. Instead, we’ll be looking at this from a general design perspective. I may look at using specific features in the future. We’ll see.
What do we know about the data that we’re going to be storing?
- We have an
Owner ID. Owner ID can be virtually anything, but it should match the primary key of the table we’re tracking.
- We have an
Event IDthat should always increase.
- Data is never updated once inserted into the event table.
- Data is never deleted once inserted into the event table.
- Data is only queried by the
How should we go about implementing this physicall in the database?
Physical Design – Clustered Index
Event ID is constantly increasing, many database developers would suggest creating a table in SQL Server with a clustered index on the
Event ID column. As a best first guess, this isn’t the worst option.
Using a clustered index on
Event ID is a decent approach because the
Event ID should be always increasing. Depending on how the IDs are generated, there are scenarios where they may not be generated in purely sequential order (see rustflakes).
In this case, we have to assume that
Event IDs may be coming from anywhere and, as such, may not arrive in order. Even though we’re largely appending to the table, we may not be appending in a strict order. Using a clustered index to support the table isn’t the best option in this case – data will be inserted somewhat randomly. We’ll spend maintenance cycles defragmenting this data.
Another downside to this approach is that data is largely queried by
Owner ID. These aren’t unique, and one
Owner ID could have many events or only a few events. To support our querying pattern we need to create a multi-column clustering key or create an index to support querying patterns.
- Clustered index – We would need to cluster on
Event IDto support our table. This results in inserts throughout the table and we’re back at having fragmentation.
- Secondary index – In this case, we can create a non-clustered index on top of the clustered index with just the
Owner IDcolumn. Now we can pull back only the records we need. But, in this case, we’ll need to traverse two b-trees – one for the non-clustered index and one for the clustered index. On high throughput systems, this could become problematic.
Physical Design – Heap
What if we use a heap for base table in this case? Data in a heap is appended to the table, so this solves the problem of random inserts – it’s just not happening at the table level.
We can make use of just one index on the
Owner ID column. Sure, there will be fragmentation in this index as we add data to the database, but this is going to be considerably smaller than our clustered index in the previous example. To find our rows, we’ll have to traverse the b-tree to locate the
Owner ID, but then it’s a straight RID lookup into the heap.
Since there won’t be updates or deletes from the table, many of the benefits of the clustered index approach fall by the wayside. Instead, we need to focus on the most effective way to work with data to this table.
Using a heap for base table storage and a non-clustered index on
Owner ID solves the problem of fragmenting on insert – there won’t be any in the table. Fragmentation of the non-clustered index will be minimal. In addition, we can get directly to the rows we want through the non-clustered index.
Before implementing anything in your database, stop and think about both the physical and logical design. Both of those topics include more than just the table structure. Make sure you think about the indexes and query patterns – data retrieval and modification are important to designing the most effective database.