Database design for handling 1 billion rows and counting We receive real-time GPS data at a rate of around 5000 pr. minute (from 4 TCP servers). Each server uses a single connection to insert the data, and buffers data in between inserts. Every 15 minutes or so, a service fetches this data, and processes it into trips. Once the trips have been generated, the actual GPS data is usually not so important, only if the user wants to see the route on a map.

The problem is that it seems the database is struggling to keep up with the rate of data being inserted. Sometimes when the load increases, the insert time suddenly increases drastically (> 30 seconds), which in turn allows more data to be buffered, which in turn results in larger inserts and longer insert duration.

I hope to get some comments on the current design, and some of the ideas we have to improve performance, and answers to some of our questions - and any other tips people might have!

Current design

The data is currently separated into tables representing one week, and data older than a year is archived into a secondary database. The whole thing is joined together in an editable view, which is used for both inserts and reads. 

Table design


Id (PK, uniqueidentifier)
DeviceId (FK, int)
PersonId (FK, int)
VehicleId(FK, int)
TokenId  (FK, int)
UtcTime  (PK, datetime2(3))
Latitude (float)
Longitude(float)
Speed    (smallint)
Heading (smallint)
Satellites (tinyint)
IOData (varbinary(100))
IgnitionState (tinyint)
UserInput (tinyint)
CreateTimeUtc (datetime2(3))


Indices


DeviceId_CreateTimeUtc_Desc
DeviceId_UtcTime_Desc (Clustered)
PersonId_UtcTime_Desc 
TokenId_UtcTime_Desc 
VehicleId_UtcTime_Desc 


Every week currently takes up around 10 GB including indices, and currently there is around 300 GB data in the main database.

The data tables in the main database have their own filegroup with 1 file, but it is on the same disk as all other tables in the main database. The secondary database is on a different disk, but on the same machine.

I think we are also running an index rebuild job weekly, when a new table partition (week) is taken into use. No shrink is performed.

The machine is an 8-core HP with 12 GB memory, and the disk holding the main database is running RAID 10.

Ideas


Limit the amount of data stored in primary database to e.g. max 1 month. At the very least it would make the database more managable for backup/restoration, but could we expect to see a performance improvement by doing this?
Create 2 files in filegroup for current data, and distribute them onto 2 different physical partitions
Create master-slave databases holding current data, so inserts and reads are performed on different databases
Put files for current data on SSD disks (would mirroring make any performance difference with SSD disks?)


Please let me know if more info is needed. There are horribly many factors influencing performance, and probably equally many ways to tweak it.
5000 inserts per minute are about 83 inserts per second. With 5 indexes that's 400 physical rows inserted per second. If the workload was in-memory this would not pose a problem even to the smallest of servers. Even if this was a row-by-row insert using the most inefficient way I can think of. 83 trivial queries per second are just not interesting from a CPU standpoint.

Probably, you are disk-bound. You can verify this by looking at wait stats or STATISTICS IO.

Your queries probably touch a lot of different pages so that the buffer pool does not have space for all of them. This causes frequent page reads and probably random disk writes as well.

Imagine a table where you only physically insert at the end because of an ever-increasing key. The working set would be one page: the last one. This would generate sequential IO as well wen the lazy writer or checkpoint process writes the "end" of the table to disk.

Imagine a table with randomly-placed inserts (classic example: a guid key). Here, all pages are the working set because a random page will be touched for each insert. IOs are random. This is the worst case when it comes to working set.

You're in the middle. Your indexes are of the structure (SomeValue, SequentialDateTime). The first component partially randomizes the sequentiality provided by the second. I guess there are quite a few possible values for "SomeValue" so that you have many randomly-placed insert-points in your indexes.

You say that data is split into 10GB tables per week. That's a good starting point because the working set is now bounded by 10GB (disregarding any reads you might do). With 12GB of server memory it is unlikely, though, that all relevant pages can stay in memory.

If you could reduce the size of the weekly "partitions" or increase server memory by a bit you are probably fine.

I'd expect that inserts at the beginning of the week are faster then at the end. You can test this theory on a dev server by running a benchmark with a certain data size and gradually reducing server memory until you see performance tank.

Now even if all reads and writes fit into memory you might still have random dirty page flushing IO. The only way to get rid of that is to write into co-located positions in your indexes. If you can at all convert your indexes to use (more) sequential keys that would help a lot.

As a quick solution I'd add a buffering layer between the clients and the main table. Maybe accumulate 15min of writes into a staging table and periodically flush it. That takes away the load spikes and uses a more efficient plan to write to the big table.