Visualize the IO source thanks to Tableau and AWR

As you know, the wait event “db file sequential read” records “single block” IO performed outside the database buffer cache. But does the IO come from:

  • Filesystem cache (If any and used)
  • Disk Array cache
  • SSD
  • Spindle Disks
  • …..

It could be interesting to visualize the distribution of the IO source:

  • Should you migrate from a cached filesystem to ASM (You may need to increase the database cache to put the previous Filesystem cached IOs into the database cache).
  • Should you use Dynamic Tiering and want to figure out where the IOs come from (SSD, Spindle Disks..).

To do so, I’ll use the AWR data coming from the dba_hist_event_histogram view and Tableau. I’ll also extract the data coming from dba_hist_snapshot (to get the begin_interval_date time).

alter session set nls_date_format='YYYY/MM/DD HH24:MI:SS';
alter session set nls_timestamp_format='YYYY/MM/DD HH24:MI:SS';

select * from dba_hist_event_histogram where
snap_id >= (select min(snap_id) from dba_hist_snapshot
where begin_interval_time >= to_date ('2014/06/01 00:00','YYYY/MM/DD HH24:MI'))
and event_name='db file sequential read';

select * from dba_hist_snapshot where begin_interval_time >= to_date ('2014/06/01 00:00','YYYY/MM/DD HH24:MI');

As you can see, there is no computation. This is just a simple extraction of the data.

Then I put those data into 2 csv files (awr_snap_for_june.csv and awr_event_histogram.csv).

1) Now, launch Tableau and select the csv files and add an inner join between those files:

Screen Shot 2014-06-28 at 13.45.19

2) Go to the worksheet and put the “begin interval time” dimension into the “column” and change it to an “exact date” (Instead of Year):

Screen Shot 2014-06-28 at 13.48.13

3) Put the “Wait count” measure into the “Rows” and create a table calculation on it:

Screen Shot 2014-06-28 at 13.55.33

Choose “difference” as the “WAIT_COUNT” field is cumulative and we want to see the delta between the AWR’s snapshots.

4) My graph now looks like:

Screen Shot 2014-06-28 at 14.02.38

The Jun 14 and Jun 20 the database has been re-started and then the difference is < 0.

5) Let’s modify the formula to take care of database restart into the delta computation:

Screen Shot 2014-06-28 at 14.04.31


Screen Shot 2014-06-28 at 14.05.19

Name: “Delta Wait Count” and change ZN(SUM([Wait Count])) – LOOKUP(ZN(SUM([Wait Count])), -1) to max(ZN(SUM([Wait Count])) – LOOKUP(ZN(SUM([Wait Count])), -1),0):

Screen Shot 2014-06-28 at 14.07.17

So that now the graph looks like:

Screen Shot 2014-06-28 at 14.09.11

6) Now we have to “split” those wait count into 2 categories based on the wait_time_milli measure coming from dba_hist_event_histogram. Let’s say that:

  • “db file sequential read” <= 4 ms are not coming from spindle disks (So from caching, SSD..).
  • “db file sequential read” > 4 ms are coming from spindle disks.

Let’s implement this in tableau with a calculated field:

Screen Shot 2014-06-28 at 14.18.36

Name: “IO Source” and use this formula:

Screen Shot 2014-06-28 at 14.21.23

Feel free to modify this formula according to your environment.

Now take the “IO Source” Dimension and put it into the Color marks:

Screen Shot 2014-06-28 at 14.23.37

So that we can now visualize the IO source repartition:

Screen Shot 2014-06-28 at 14.25.08



  • Karl Arao presented another example usage of Tableau into this blog post.
  • Should you need to retrieve “db file sequential read” buckets < 1 ms, then you can use oracle_trace_parsing from Kyle Hailey.

Update 1: Example of oracle_trace_parsing usage into “Oracle “Physical I/O” ? not always physical” blog post.

Update 2: Another way to retrieve “db file sequential read” buckets < 1 ms (With external tables this time) into Nikolay Savvinov blog post.


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