What are the limitation of Windows Azure Tables

I’m pretty sure that a lot of you had heard about Windows Azure Tables. I already described how we can work with this Windows Azure Tables. You can find a series of posts about them on my blog in this link.
Today I want to talk about some limitation that we can have on Windows Azure Tables if we don’t use properly. A row in a table can store any kind of data that is serializable and one table can have more than one entity type saved. For example in the same table we can save Student entities and in the same time Dog entity and also Car entity. Each entity that is saved has 3 properties that need to exist all the time:
Partition key – based on this property we can group items from a specific table based on this partition. It is used for load balancing across storage nodes.
Row key – this a unique identifiers used to identify a unique row in a partition (the combination between partition key and row key form a unique key in the table).
Timestamp – the last modified time for a row.
The partition key exists with a scope, but a lot of people don’t use it properly. Because of this when they do intensive work on a table from Windows Azure some performance issues could appear.
Why? In this moment (July 2012), it seems that the maxim number of rows that can be processes per partition in a table is 500.
What does it means? If we have a table with only one partition key we will be able to process only 500 rows per second. If we have a table with 10 partition keys and we will be able to access maxim 5000 rows per second.
Some people could say: “OH, only 500 rows per second!”. What we don’t need to forget is that all this information’s are usually access from internet, not only from the datacenter. Because of this sending for example 500 rows per second is a lot of information and even on our side (on the client that consume the table) we could have problems with our bandwidth.
Also, we can use partition key in such a way that we could distribute the rows in a table in a manner that this limitation will not be reached.
For example if we have a table where we store audit data, we could group them based on the type. For example each partition key would represent a different audit type. A better solution is to have each audit time in a different table and the partition key could be used to group the audit data based on the type (per hour, per day, depends on how many items are logged per hour/day). Also, don’t forget that we will pay the same amount of money if we have one Azure table with 1.000.000 or 100 Azure tables with 10.000 rows each.
Don’t be afraid to split information in more than one tables. Having data in more than one table will help us to work with it more easily. Also use partition key, whenever is possible. We have them with a scope.
In conclusion, we don’t need to forget that we have a limited number of rows that we can process per second. Now are 500 rows per partition key, in the future maybe will be 10.000. What is important for us is to know that some limit exist and when we use table intensive, we need to remember that we need to distribute data across table or partitions.