Calculating SQL Runtime Using Raw ASH Data.pdf

Calculating SQL Runtime Using Raw ASH Data
Yuan Yao

Who Am I
Yuan Yao
• Performance maven in Oracle & MySQL
• Oracle ACE
• Huawei Cloud’s Most Valuable Professional
• Former database manager@IBM
• Holder of more than 20 Oracle certificates
• Inventor of two computer patents
• Author of MySQL 8.0 Operations and Optimization (ISBN: 9787302602682)

Agenda
• How to address random outlier SQLs
• Calculating SQL runtime using raw ASH data
• Use cases
• find outlier SQLs in a time window
• find outlier executions of a specific SQL
www.byte-way.com https://x.com/YuanOracle

How to address random outlier SQLs

Random slow response incident
User: The application suddenly took a long
time to process a request!
DBA: Can you provide details?
User: It was random.
DBA: Has this happened before?
User: No, it seemed like a one-time issue.
DBA: How long does this request usually take?
User: A few seconds.
DBA: When did this slow request occur?
User: About two hours ago.
DBA: Any more specific time?
User: No, I'm not sure.

Limitations of AWR in identifying random outlier SQLs
• Averages can mask performance spikes and outliers.
• Snapshot intervals (e.g., hourly) may miss transient anomalies.
• Lack of granularity.
• Overhead of frequent snapshots.
• Storage and performance impact of detailed logging.

What we need
A method that can calculate the runtime of every single execution of
SQL with little overhead.

Calculating SQL runtime using raw ASH data
A new approach to identifying culprit SQL statements

Sources of ASH data
V$ACTIVE_SESSION_HISTORY
• Use: Recent/active analysis
• Frequency: Every second
• Storage: In-memory
• Advantage: High precision, captures short-lived
SQL
• Limitation: Limited data retention.
DBA_HIST_ACTIVE_SESS_HISTORY
• Use: Long-term analysis
• Frequency: Every 10 seconds
• Storage: Disk-based
• Advantage: Longer retention
• Limitation: May miss short SQL, less precise
The Active Session History (ASH) is a diagnostic tool in Oracle databases that takes
samples of all the active sessions and stores the sampled data in two tables.

Time related columns in ASH
SQL> desc v$active_session_history
Name Null? Type
----------------------------------------- -------- ----------------------------
SAMPLE_ID NUMBER
SAMPLE_TIME TIMESTAMP(3)
SAMPLE_TIME_UTC TIMESTAMP(3)
USECS_PER_ROW NUMBER
IS_AWR_SAMPLE VARCHAR2(1)
SESSION_ID NUMBER
SESSION_SERIAL# NUMBER
SESSION_TYPE VARCHAR2(10)
FLAGS NUMBER
USER_ID NUMBER
SQL_ID VARCHAR2(13)
…
SQL_EXEC_ID NUMBER
SQL_EXEC_START DATE

Formula
SQL Runtime = Maximum Sample Time - SQL Start Time
= max(sample_time)-sql_exec_start

Timeline of a session over the past 30 minutes
(V$ACTIVE_SESSION_HISTORY)
SELECT sample_id,
TO_CHAR(sample_time, 'HH24:MI:SS') AS sample_time,
TO_CHAR(sql_exec_start, 'HH24:MI:SS') AS sql_start_time,
sql_id,
sql_exec_id
FROM v$active_session_history
WHERE sample_time > SYSDATE - 30 / 60 / 24
AND session_id = 124
AND session_serial# = 39232
ORDER BY sample_id;

Output and analysis
SAMPLE_ID SAMPLE_T SQL_STAR SQL_ID SQL_EXEC_ID
---------- -------- -------- ------------- -----------
8937592 16:53:48 16:53:47 3wpnrxd1xpncv 16777216
8937593 16:53:49 16:53:47 3wpnrxd1xpncv 16777216
…
8937626 16:54:23 16:53:47 3wpnrxd1xpncv 16777216
8937627 16:54:24 16:53:47 3wpnrxd1xpncv 16777216
8937628 16:54:25 16:54:24 azrtjtkq79qa6 16777216
8937629 16:54:26 16:54:24 azrtjtkq79qa6 16777216
8937630 16:54:27 16:54:24 azrtjtkq79qa6 16777216
8937631 16:54:28 16:54:24 azrtjtkq79qa6 16777216
8937632 16:54:29 16:54:24 azrtjtkq79qa6 16777216
8937633 16:54:30 16:54:29 dkhax46cjukju 16777216
8937634 16:54:31 16:54:29 dkhax46cjukju 16777216
…
• For the SQL ID `3wpnrxd1xpncv`, the start
time is `16:53:47` , the last sample time
is `16:54:24`, the deduced runtime is 37
seconds.
• For the SQL ID `azrtjtkq79qa6`, the start time
is `16:54:24,` the last sample time
is `16:54:29`, the runtime is 5 seconds.

Output of query DBA_HIST_ACTIVE_SESS_HISTORY
SAMPLE_ID SAMPLE_T SQL_STAR SQL_ID SQL_EXEC_ID
---------- -------- -------- ------------- -----------
8937596 16:53:52 16:53:47 3wpnrxd1xpncv 16777216
8937606 16:54:02 16:53:47 3wpnrxd1xpncv 16777216
8937616 16:54:12 16:53:47 3wpnrxd1xpncv 16777216
8937626 16:54:23 16:53:47 3wpnrxd1xpncv 16777216
8937636 16:54:33 16:54:29 dkhax46cjukju 16777216
8937646 16:54:43 16:54:29 dkhax46cjukju 16777216
...
• For the SQL ID `3wpnrxd1xpncv`, the start
time is `16:53:47` , the maximum sample time
is `16:54:23`, the deduced runtime is 36
seconds.
• For the SQL ID `azrtjtkq79qa6`, this ID is not
present in the new output, indicating this
SQL execution has not been captured due to
the wider sampling interval.

Observations
• Execution Time:
• Ends between max sample and next sample.
• Not very accurate due to sampling intervals.
• Short-lived SQL:
• May slip through if between samples.
• Frequent executions eventually captured.

Use cases
find outlier SQLs in a time window
find outlier executions of a specific SQL

Oracle stopped responding about two hours ago
-- This query captures 9 longest SQL executions within a one-hour window before and after two hours ago
select *
from (
select sql_id,
round((EXTRACT(DAY FROM (max(sample_time) - max(sql_exec_start))) * 86400) +
(EXTRACT(HOUR FROM (max(sample_time) - max(sql_exec_start))) * 3600) +
(EXTRACT(MINUTE FROM (max(sample_time) - max(sql_exec_start))) * 60) +
(EXTRACT(SECOND FROM (max(sample_time) - max(sql_exec_start)))), 2) as run_seconds,
to_char(max(sql_exec_start), 'YYYY-Mon-DD HH24:MI:SS') as exec_start,
to_char(max(sample_time), 'YYYY-Mon-DD HH24:MI:SS') as end_time,
sql_exec_id
from dba_hist_active_sess_history
where sql_exec_start between sysdate - interval '3' hour - interval '30' minute
and sysdate - interval '1' hour + interval '30' minute
and sql_exec_id > 0
group by sql_id, sql_exec_id
order by run_seconds desc
)
where rownum < 10;

An example of output
SQL_ID RUN_SECONDS EXEC_START END_TIME SQL_EXEC_ID
7nz11m353mj22 93.25 2024-Jul-10 13:03:53 2024-Jul-10 13:05:26 16777217
c1nxkb8m44cpz 33.61 2024-Jul-10 13:03:00 2024-Jul-10 13:03:33 16777217
as2dr3ag24gay 11.37 2024-Jul-10 13:03:12 2024-Jul-10 13:03:23 16777224
cx67k8h01fp7y 8.73 2024-Jul-10 13:05:38 2024-Jul-10 13:05:46 16777217
cguxmj11h9gjx 8.09 2024-Jul-10 13:03:46 2024-Jul-10 13:03:54 16777217
b6usrg82hwsa3 8.05 2024-Jul-10 13:12:59 2024-Jul-10 13:13:07 16777228
ajfdc0t0jh5z5 4.49 2024-Jul-10 13:05:32 2024-Jul-10 13:05:36 16777216
as2dr3ag24gay 3.45 2024-Jul-10 13:06:14 2024-Jul-10 13:06:17 16777225
6t9uu481ygn89 2.85 2024-Jul-10 13:03:41 2024-Jul-10 13:03:43 16777217
The SQL statement with SQL_ID 7nz11m353mj22 and an execution time of 93.25 seconds is the most
likely culprit for the slow response reported by the user.

Measuring a specific SQL statement
-- This query retrieves and calculates the execution time of a specific SQL statement on the current date
SELECT sql_id,
sql_exec_id,
ROUND((EXTRACT(DAY FROM (MAX(sample_time) - MIN(sql_exec_start))) * 86400) +
(EXTRACT(HOUR FROM (MAX(sample_time) - MIN(sql_exec_start))) * 3600) +
(EXTRACT(MINUTE FROM (MAX(sample_time) - MIN(sql_exec_start))) * 60) +
(EXTRACT(SECOND FROM (MAX(sample_time) - MIN(sql_exec_start)))), 2) AS run_seconds,
TO_CHAR(MIN(sql_exec_start), 'YYYY-Mon-DD HH24:MI:SS') AS exec_start,
TO_CHAR(MAX(sample_time), 'YYYY-Mon-DD HH24:MI:SS') AS end_time
FROM dba_hist_active_sess_history
WHERE sql_id = 'as2dr3ag24gay'
AND TRUNC(sample_time) = TRUNC(SYSDATE)
AND sql_exec_id > 0
GROUP BY sql_id, sql_exec_id
ORDER BY run_seconds DESC;

An example of output
SQL_ID SQL_EXEC_ID RUN_SECONDS EXEC_START END_TIME
as2dr3ag24gay 16777224 45.12 2024-Jul-10 13:03:12 2024-Jul-10 13:03:57
The execution of the SQL statement at “2024-Jul-10 13:03:12” is the outlier.

Calculating SQL Runtime Using Raw ASH Data.pdf

More Related Content

Similar to Calculating SQL Runtime Using Raw ASH Data.pdf

Recently uploaded

Calculating SQL Runtime Using Raw ASH Data.pdf