Wrapping up Cross Join Today Mark shares with us a perfect example of the real value of the CROSS join, or using the generic term Cartesian results, in an example defined by a dimension common in international finance reporting; Currency. The value of any currency can be translated into the value of any other kind of currency through exchange rates. […]

The Power of the CROSS JOIN I have been intrigued with the power of a Cartesian query ever since the days when we had to pull a power cord to kill a query someone had written doing a Cartesian query on two very large tables. A Cartesian query, implemented in TSQL by using a CROSS JOIN, results in joining all […]

TSQL Filtering Examples Today I wanted to demonstrate a couple simple scenarios where you have options for adding filtering criteria or where you must place the criteria in the JOIN clause. For my first example I want to return all the purchase orders having at least one Purchase Order Detail with an item of 154 (shoes). The following two queries […]

Combining Join and Filter Criteria I’m kinda shy when it comes to topics like this one. It borders on trolling for comments. The reason I have chosen this topic is because it has come up in recent conversation with developers moving to writing their own SQL, and wanting to understand the difference more fully. From that perspective I thought it […]

Filtering Data in SQL In early versions of SQL Server, ported into Windows by Microsoft from the Sybase code, both filtering and join criteria were coded in the WHERE clause. Here is a short example… SELECT … FROM PurchaseOrder po PurshaseOrderDetail pod WHERE po.PurchaseOrderID = pod.PurchaseOrderID AND po.CustomerID = @CustomerID As you can see the filter for the PurchaseOrder table […]

The Art of Programming I recently read a quote by Donald Knuth in the book “Coders at Work” by Peter Seibel. Knuth stated I’ve got thousands of pages and exercises, and I write it down and put it in the book so that I don’t have it all in my head. I have to come back to it and learn […]

Getting the Big Picture – Conclusion We’ve been considering how to determine system performance degradation in a distributed, service oriented world. We considered multi layered services, service oriented monitoring and sharded data monitoring. Today I want to talk about inconsistent performance. One of the hardest bugs for any developer to track down is the inconsistent bug; the bug that is […]

Getting the Big Picture – Part 3 Today I would like to talk a little bit about monitoring for distributed processes, and how that changes the big picture when you get complaints about performance. When I am talking about distributed processes I am talking about sharded data or logic; sometimes both. Sharding your data in an SQL or NoSQL environment […]

Getting the Big Picture – Part 2 Because applications are becoming more distributed it requires a different kind of monitoring in order to understand trends, faults, or reduced performance. One of the easiest things to monitor is duration. How long does it take to call a service and receive a response? Perhaps your measurements need to be balanced by the […]

Getting the Big Picture – Part One A while back we talked about how the face of monitoring has changed. Historically we monitored individual servers and their performance because we knew so much about what servers we had, and what software was running on them. Today with the cloud or virtual servers your load can move without your knowledge or […]