Yes, you read it correct. That shocked me too.
I was trying to understand global cache waits for a client. Naturally, I queried statspack tables and analyzed the data for just one day using a script. Surprisingly, tab$ came as top consumer of global cache waits. I was shocked by the revelations and couldn’t believe it! If something doesn’t make sense, look more closely, right?
Global cache waits
Database version is 9i. Statspack table stats$seg_stat is the source for this script and that table is populated from v$segment_stats. So, these column values (global_cache_cu_blocks_served and global_cache_cr_blocks_served) are cumulative. To find statistics for a specific period, we need to subtract column value of prior row from current row. Analytic function lag can be useful for this.
In this analytic function printed below, partitioning clause uses instance_number, startup_time and dataobj#. All rows with same value for these three columns will be considered in one data partition and then rows ordered by snap_id within that data partition. Lag will pull the row from prior snap_id in that partition. Then, we subtract from current value to get the difference. Please refer to this paper: Performance tuning with SQL new features – paper for more information about analytic functions.
... global_cache_cu_blocks_served - lag(global_cache_cu_blocks_served,1,0) over (partition by instance_number,startup_time, dataobj#, obj# order by snap_id ) global_cache_cu_blocks_served, ...
Script and output
Complete script printed below and running that against a database.