Tag: Table Storage

Welcome (and a Table Storage date sorting tip)

Welcome to Azure From The Trenches, a blog where I hope to share information learned from using Azure to develop and run real production applications. The  applications themselves shall largely remain nameless but to illustrate some points I have a sample application to go along with this blog which I’ll be publishing on GitHub shortly.

I really want to cover Azure in a broad sense so expect to see topics on subjects such as:

  • Architecture
  • Business
  • Code
  • Cost modelling
  • Deployment
  • Operating an Azure system
  • Scaling
  • SLAs
  • Testing

As well as sharing information I’d love for this blog to be visited and commented on by others working on Azure so that I can learn from you and improve my own understanding and future work.

As my companion application isn’t quite ready I thought I’d kick things off with a short tip for sorting table storage within a partition by date in a descending order. This had me scratching my head for a while when I first wanted to do this during my early use of Table Storage.

The solution relies on the rows in a partition being stored sorted by the row key in an ascending fashion. The row key is a string so you can’t just drop a date in their but as long as you’re dealing with recent dates you can quickly and easily place a string in the row key that will let you sort in an ascending date count: the Tick property of the DateTime class.

This is a long value that counts the number of ticks (100 nanoseconds) that have elapsed since midnight on the 1st January 0001. If we zero pad that as a string then the partition will be sorted by date:

RowKey = String.Format("{0:D19}", DateTime.UtcNow.Ticks);

To sort in a descending date order we simply need to subtract the current number of ticks from the number of ticks for the maximum date:

RowKey = String.Format("{0:D19}", DateTime.MaxValue.Ticks - DateTime.UtcNow.Ticks);

When I first began to use Azure and Table Storage my instinct was always to run back to SQL but with some lateral thinking you can often bend Table Storage to your will and get better scalability, reliability and cheaper running costs – a topic I plan on coming back to.


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