In Administration Services and in MaxL, you can view detailed compression and query statistics. You can view the number of stored level 0 members, which affects retrieval performance; the average bundle fill and average value length, which affect compression; and the level 0 size.
The following sections describe each of the compression and query related statistics.
Dimensions with a large number of stored level 0 members do not perform well if tagged Compression. As with any dynamically calculated dimension, upper-level retrievals from compression dimensions generally are slow. See Maintaining Retrieval Performance.
Compression is more effective if values are grouped together in consecutive members on dimensions or hierarchies rather than spread throughout the outline with lots of #MISSING data between values. Essbase saves memory by storing information about the location and contents of the groups rather than storing it separately for each of the members. The average bundle fill is the average number of values stored in the groups. It can vary between 1 and 16, with 16 being the best. Choosing a compression dimension that has a higher average bundle fill means that the database compresses better.
In some outlines, you can improve compression by ordering the numbers in the compression dimension so that members that are frequently populated are grouped together. When populated members are grouped together, more values fit into each bundle, increasing the average bundle fill and improving compression.
The average value length is the average storage size, in bytes, required for the stored values in the cells. It can vary between 2 bytes and 8 bytes with 2 bytes being the best. Without compression, it takes 8 bytes to store a value in a cell. With compression, it can take fewer bytes, depending on the value length. For example, 10.050001 might take 8 bytes to store even when compressed, but 10.05 may only take 2 bytes (4 bytes to store when compressed). Dimensions with a smaller average value length compress the database better.
Rounding the data values to no more that two digits after the decimal point can reduce the average value length, improving compression.