In my previous post about Event Store read complexity I described how the growth of reads from the event database might be quadratic in respect to amount of events per aggregate.

On the higher level, the conclusion was that the event sourced database should be optimized for reads rather that writes, which is not always obvious from the definition of the "append-only store".


In this post I want to look at NEventStore on top of Azure SQL Database which is the combination we currently use for event sourcing in Azure-based web application.

NEventStore library provides a C# abstraction over event store with multiple providers for several database backends. We use the Persistence.SQL provider. When you initialize it with a connection string to an empty database, the provider will go on and create two tables with schema, indexes etc. The most important table is Commits and it gets the following schema:

CREATE TABLE dbo.Commits
  BucketId          varchar(40),
  StreamId          char(40),
  StreamRevision    int,
  Items             tinyint,
  CommitId          uniqueidentifier,
  CommitSequence    int,
  CheckpointNumber  bigint IDENTITY(1, 1),
  Payload           varbinary(max),
  CommitStamp       datetime2
ALTER TABLE dbo.Commits 

I removed several columns, most indexes and constraints to make the script more readable.

The primary key is based upon CheckpointNumber - an IDENTITY column, which means the new events (commits) are appended to the end of the clustered index. Clearly, this is good for INSERT performance.

There is a number of secondary non-clustered indexes that are optimized for rich API of NEventStore library, e.g. dispatching events to observers, searching for streams, time-based queries etc.

Our Use Case

It turns out that we don't need those extended API provided by NEventStore. Effectively, we only need two operations to be supported:

  • Add a new event to a stream
  • Read all events of a stream

Our experience of running production-like workloads showed that the read operation performance suffers a lot when the size of a stream grows. Here is a sample query plan for the read query with the default schema:

Query Plan with default primary key

SQL Server uses non-clustered index to find all events of the given steam, and then does key lookups, which might get very expensive for large streams with hundreds or thousands of events.

Tuning for Reads

After seeing this, I decided to re-think the primary index of the Commits table. Here is what I came down to:

ALTER TABLE dbo.Commits 
PRIMARY KEY CLUSTERED (BucketId, StreamId, CommitSequence)

Now, all the commits of one stream are physically located together in the clustered index.

The change makes INSERT's less efficient. It's not a simple append to the end of the clustered index anymore.

But at this price, the reads just got much faster. Here is the plan for the same query over the new schema:

Query Plan with the new primary key

Simple, beautiful and fast!

Our Results

The results look great for us. We are able to run our 50 GB Commits table on a 100-DTU SQL Database instance, with typical load of 10 to 25 percent. The reads are still taking the biggest chunk of the load, with writes being far behind.

The mileage may vary, so be sure to test your NEventStore schema versus your workload.

Further Improvements

Here are some further steps that we might want to take to make Commits table even faster:

  • The table comes with 5 non-clustered indexes. One of them became our clustered index. Two indexes are unique, so they might be useful for duplicate prevention (e.g. in concurrency scenarios). The remaining two are non-unique, so they can probably be safely deleted unless we start using other queries that they are intended for.

  • There are several columns which are not used in our implementation: StreamIdOriginal, Dispatched and Headers to name a few. We could replace the table with a view of the same name, and always return defaults for those columns in any SELECT, ignoring the values in any INSERT.

But I expect these changes to have moderate impact on performance in contrast to the primary key change discussed above.