azure,logging,partitioning,azure-table-storage , How to partition Azure tables used for storing logs

How to partition Azure tables used for storing logs


Tag: azure,logging,partitioning,azure-table-storage

We have recently updated our logging to use Azure table storage, which owing to its low cost and high performance when querying by row and partition is highly suited to this purpose.

We are trying to follow the guidelines given in the document Designing a Scalable Partitioning Strategy for Azure Table Storage. As we are making a great number of inserts to this table (and hopefully an increasing number, as we scale) we need to ensure that we don't hit our limits resulting in logs being lost. We structured our design as follows:

However, in practice we have found this is less than ideal, because when we look at our logs at a glance we would like them to appear in order of time. We instead end up with blocks of results grouped by context, and we would have to get all partitions if we want to order them by time.

Some ideas we had were

As this is such a common application of Azure table storage, there must be some sort of standard procedure. What is the best practice for partitioning Azure tables that are used for storing logs?

Solution constraints


I have come across similar situation you encountered, based on my experience I could say:

Whenever a query is fired on an azure storage table, it does a full table scan if a proper partition key is not provided. In other words, storage table is indexed on Partition key and partitioning the data properly is the key to get fast results.

That said, now you will have to think on what kind of queries you would fire on the table. Such as Logs occurred during a time period, for a product etc.

One way is to use reverse ticks up to hour precision instead of using the exact ticks as part of Partition Key. That way an hour worth of data can be queried based on this partition key. Depending on the number of rows which fall in to each partition, you could change the precision to a day. Also, it will be wise to store related data together, that means data for each product would go to a different table. That way you can reduce the number of partitions and number of rows in each partition.

Basically, ensure that you know the partition keys in advance (exact or range) and fire queries against such specific partition keys to get results faster.

To speed up writing to table, you can use Batch Operation. Be cautious though as if one entity on the batch fails whole batch operation fails. Proper retry and error checking can save you here.

At the same time, you could use blob storage to store lot of related data. The idea is to store a chunk of related serialized data as one blob. You can hit one such blob to get all the data in it and do further projections on the client side. For example, an hour worth of data for a product would go to a blob, you can devise a specific blob prefix naming pattern and hit the exact blob when needed. This will help you get your data pretty fast rather than doing a table scan for each query.

I used the blob approach and have been using it for couple of years with no troubles. I convert my collection to IList<IDictionary<string,string>> and use binary serialization and Gzip for storing each blob. I use Reflection.Emmit based helper methods to access entity properties pretty fast so serialization and deserialization doesn't take a toll on the CPU and memory.

Storing data in blobs help me store more for less and get my data faster.


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