Category: Compression

Melissa Connors discusses compression options and gives examples of data which will compress and that which will not:

Page Compression is what I like to refer to as “compression for real this time” as it goes well beyond the smart storage method of row and uses patterns/repeating values to condense the stored data.

First, to gain a better understanding of this method, check out a simple representation of a page of data. This is illustrated below in Figure 1. You’ll notice that there are some repeating values (e.g. SQLR) and some repeated strings of characters (e.g. SSSLL).

I really appreciate getting an idea of what kind of data does not compress well.  You’d think auto-incrementing numbers would be another scenario, but Melissa explains how that’s not necessarily the case.

Rolf Tesmer digs into the case of compression of building an index whose leading column has a low cardinality:

That first one is a cracker – it hit me once when compressing a SQL Server table (600M+ rows) on a 64 core Enterprise SQL Server.  After benchmarking several other data compression activities I thought I had a basic “rule of thumb” (based on GB data size and number of rows)… of which just happened to be coincidence!

This also begs the question of why would you use low selectivity indexes?  Well I can think of a few cases – but the one which stands out the most is the identification of a small number of rows within a greater collection – such as an Index on TYPE columns (ie; [ProcessingStatusFlag] CHAR(1) = [P]rocessed, [U]nprocessed, [W]orking, [F]ailed, etc)

… AND SO – lets do some testing to validate this puppy!

There’s a significant difference here, so check out Rolf’s post for the details.

Andy Mallon explains row-level compression:

You can think of row compression as working by treating certain fixed-length data types as variable-length data types. By removing certain metadata, NULL and 0 values, and the padding of fixed-length values, SQL Server can reduce the total size of a row.

The easiest way to think of it is that char(n) no longer takes n bytes for every row, but instead gets treated more like varchar(n) where the storage used varies for each value. The behavior for each data type varies, with some data types getting more or less (or no) savings compared to others.

Row-level compression is the “safer” of the two primary compression options, but I almost never use it.  That might just be a function of the my particular workloads, of course.

Andy Mallon looks at the costs and benefits of data compression:

The obvious benefit is that compressed data takes up less space on disk. Since you probably keep multiple copies of your database (multiple environments, DR, backups, etc), this space savings can really add up. High-performance enterprise-class storage is expensive. Compressing your data to reduce footprint can have a very real benefit to your budget. I once worked on an SAP ERP database that was 12TB uncompressed, and was reduced to just under 4TB after we implemented compression.

My experience with compression is that the benefit vastly outweighs the cost.  Do your own testing, of course.

Corey Beck on data compression:

Before we jump right into enabling either row or page compression, we can actually estimate the savings of each to determine which will provide us with the most savings on storage.  Since page compression includes row compression, we will start with row compression and the estimated savings.

EXEC sp_estimate_data_compression_savings
‘Person’,’Person’,null,null,’row’

In practice, data compression is extremely valuable and in most circumstances, the benefits outweigh the costs.  In certain workloads, you might even see CPU usage go down.