Who is generating redo logs now?
One of the first question, which cover firefighter is: "Who is generating redo logs in this moment". Idea is to determine action on
ad hoc basis. For that I use following script, which I call top_redo.sql:
Purpose : Shows current redo logs generation info (RAC-non RAC environment) |
Author : Damir Vadas, damir.vadas@gmail.com |
Remarks : run as privileged user |
Changes (DD.MM.YYYY, Name, CR/TR#): |
col redo_MB for 999G990 heading "Redo |Size MB" |
column sid_serial for a13; |
lpad((b.SID || ',' || lpad(b.serial#,5)),11) sid_serial, |
from (select n.inst_id, sid, |
round(value/1024/1024) redo_mb |
from gv$statname n, gv$sesstat s |
where n.inst_id=s.inst_id |
and s.statistic# = n.statistic# |
where b.inst_id=a.inst_id |
PROMPT Top 30 from gv$sesstat view according generated redo logs |
Result is something like:
INST_ID SID_SERIAL USERNAME MACHINE OSUSER STATUS Size MB |
1 788, 1 iis1 oracle ACTIVE 2,073 |
4 788, 1 iis4 oracle ACTIVE 1,928 |
1 792, 1 iis1 oracle ACTIVE 1,168 |
1 791, 1 iis1 oracle ACTIVE 1,149 |
3 788, 1 iis3 oracle ACTIVE 1,111 |
4 792, 1 iis4 oracle ACTIVE 1,092 |
1 785, 1 iis1 oracle ACTIVE 1,064 |
4 791, 1 iis4 oracle ACTIVE 1,064 |
3 792, 1 iis3 oracle ACTIVE 757 |
3 791, 1 iis3 oracle ACTIVE 738 |
3 785, 1 iis3 oracle ACTIVE 436 |
4 785, 1 iis4 oracle ACTIVE 411 |
1 764, 4 SYS iis1 oracle ACTIVE 340 |
1 737,61477 DBSNMP iis1 oracle ACTIVE 117 |
3 703,33361 DBSNMP iis3 oracle ACTIVE 113 |
4 677,30159 DBSNMP iis4 oracle ACTIVE 86 |
4 795, 1 iis4 oracle ACTIVE 81 |
1 795, 1 iis1 oracle ACTIVE 77 |
4 794, 1 iis4 oracle ACTIVE 76 |
3 795, 1 iis3 oracle ACTIVE 75 |
1 794, 1 iis1 oracle ACTIVE 74 |
3 794, 1 iis3 oracle ACTIVE 70 |
1 645, 5393 ANPI USR\APINTARIC apintaric INACTIVE 50 |
3 758, 5 iis3 oracle ACTIVE 39 |
3 755, 2 iis3 oracle ACTIVE 24 |
3 754, 2 iis3 oracle ACTIVE 22 |
3 756, 2 iis3 oracle ACTIVE 21 |
3 757, 3 iis3 oracle ACTIVE 21 |
4 774, 5835 JAGO CLT\JGOLUZA jgoluza INACTIVE 10 |
1 619,61303 LIMI NIO\LMIHALIC lmihalic INACTIVE 9 |
Top 30 from gv$sesstat view according generated redo logs |
If you want to concentrate on real oracle users (avoid core Oracle processes in result) place next condition in outer where clause:
and b.username is not null |
When and how many redo logs generation occurred?
Beside current analyze in many times wider analyze/compare is even more interesting. So questions like:
- When do we have most of redo log generation?
- Where was the peak of log generation?
- Did we have any "strange" redo log generation?
need a little different approach-query
v$log_history view. It holds historic data which retention period is initially controlled with
MAXLOGHISTORY, defined while creating database (fixed not changeable without recreation of control file) and
CONTROL_FILE_RECORD_KEEP_TIME which is changeable. In mine case it was set to 31 days (exact number of days for longest month):
SQL> show parameter CONTROL_FILE_RECORD_KEEP_TIME |
NAME_COL_PLUS_SHOW_PARAM TYPE VALUE_COL_PLUS_SHOW_PARAM |
control_file_record_keep_time integer 31 |
Script to gather data through mentioned period looks like.
Purpose : redo logs distribution per hours on each day ... |
Author : Damir Vadas, damir.vadas@hypo-alpe-adria.com |
Remarks : run as privileged user |
Changes (DD.MM.YYYY, Name, CR/TR#): |
PROMPT Archive log distribution per hours on each day ... |
to_char(first_time,'YY-MM-DD') day, |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'00',1,0)),'999') "00", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'01',1,0)),'999') "01", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'02',1,0)),'999') "02", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'03',1,0)),'999') "03", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'04',1,0)),'999') "04", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'05',1,0)),'999') "05", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'06',1,0)),'999') "06", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'07',1,0)),'999') "07", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'08',1,0)),'999') "08", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'09',1,0)),'999') "09", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'10',1,0)),'999') "10", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'11',1,0)),'999') "11", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'12',1,0)),'999') "12", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'13',1,0)),'999') "13", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'14',1,0)),'999') "14", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'15',1,0)),'999') "15", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'16',1,0)),'999') "16", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'17',1,0)),'999') "17", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'18',1,0)),'999') "18", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'19',1,0)),'999') "19", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'20',1,0)),'999') "20", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'21',1,0)),'999') "21", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'22',1,0)),'999') "22", |
to_char(sum(decode(substr(to_char(first_time,'HH24'),1,2),'23',1,0)),'999') "23", |
group by to_char(first_time,'YY-MM-DD') |
Result looks like:
SQL>@rl
Archive log distribution per hours on each day ...
DAY 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 TOT
-------- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- ---- -----
11-01-14 0 0 23 16 17 16 16 16 22 39 23 18 22 18 18 18 22 18 19 16 19 16 16 17 425
11-01-15 24 23 17 18 21 16 16 16 22 18 20 19 19 18 18 21 20 18 20 16 18 16 16 17 447
11-01-16 40 39 43 24 17 16 16 16 22 18 18 21 21 18 19 19 22 18 19 17 18 16 16 16 509
11-01-17 25 23 18 16 17 16 16 16 22 18 21 18 20 18 18 18 24 18 18 17 20 16 16 17 446
11-01-18 25 23 21 16 18 17 16 17 21 43 18 19 26 18 20 40 21 18 20 16 18 16 16 17 500
11-01-19 24 24 22 16 17 16 16 16 23 18 19 18 23 19 18 19 19 18 22 16 21 16 16 17 453
11-01-20 24 24 19 16 17 16 16 16 22 18 19 18 24 18 18 19 70 18 19 18 17 16 16 17 495
11-01-21 24 23 20 16 17 16 16 16 22 18 18 20 48 25 46 57 22 18 18 17 20 16 16 17 546
11-01-22 24 27 22 16 17 16 16 16 22 18 18 19 21 18 18 21 20 18 18 17 20 16 16 17 451
11-01-23 24 19 19 16 17 16 16 16 22 18 18 18 21 18 20 18 21 18 20 17 18 16 16 17 439
11-01-24 24 21 19 16 17 16 16 16 22 18 20 18 21 18 21 18 21 18 19 17 17 16 16 17 442
11-01-25 24 23 18 16 17 16 16 16 43 18 18 18 25 18 115 41 23 18 19 17 20 16 16 18 589
11-01-26 23 22 19 16 17 16 16 16 22 18 21 18 30 18 43 18 20 18 22 16 21 16 16 16 478
11-01-27 21 23 23 16 17 16 16 16 22 18 18 20 22 20 40 97 155 145 155 93 109 116 164 214 1556
11-01-28 93 24 23 16 17 16 16 16 29 18 19 19 30 101 19 18 75 30 19 16 20 16 22 16 688
11-01-29 21 16 16 16 17 16 27 20 22 18 18 18 31 18 18 19 23 18 19 18 18 16 16 17 456
11-01-30 24 22 17 16 17 16 16 16 22 18 18 19 23 18 19 18 23 18 18 17 20 16 16 17 444
11-01-31 24 19 21 16 17 16 16 16 23 18 19 18 22 19 18 20 20 18 18 16 21 16 16 17 444
11-02-01 24 25 22 16 17 16 16 16 23 18 18 20 24 18 18 19 21 18 18 17 19 16 16 17 452
11-02-02 24 22 20 16 17 16 16 16 23 18 18 40 23 18 39 18 21 18 22 16 21 16 16 17 491
11-02-03 24 23 22 16 18 16 16 16 22 18 19 18 24 18 20 18 20 18 19 18 19 16 16 17 451
11-02-04 24 23 23 16 17 16 16 16 22 18 19 19 48 18 20 20 23 18 18 17 19 16 16 17 479
11-02-05 24 23 17 17 17 16 16 16 22 18 20 19 23 18 19 20 22 18 18 17 20 16 16 17 449
11-02-06 24 23 17 16 18 16 16 16 22 18 19 19 21 18 19 18 24 18 20 16 21 16 16 17 448
11-02-07 24 22 18 16 18 16 16 16 22 18 52 18 44 25 29 24 21 18 18 16 19 16 16 17 519
11-02-08 24 23 19 16 17 16 16 16 22 18 20 19 28 18 19 18 22 18 19 17 34 32 32 31 514
11-02-09 36 39 35 31 37 31 32 32 32 34 34 34 39 34 34 34 36 34 38 32 37 31 32 33 821
11-02-10 37 36 38 31 33 32 31 32 37 34 34 34 41 34 35 35 37 34 34 31 34 32 31 33 820
11-02-11 39 35 38 31 33 32 31 32 37 34 34 34 38 34 34 34 35 34 34 32 34 32 31 32 814
11-02-12 40 34 34 33 35 32 31 32 37 34 34 34 36 34 35 34 36 34 34 31 36 32 31 32 815
11-02-13 40 34 35 31 34 32 31 32 37 34 34 34 38 34 34 34 37 34 34 32 36 32 31 32 816
11-02-14 40 34 33 31 35 32 32 32 37 66 34 35 38 34 34 34 37 34 34 31 36 32 31 33 849
11-02-15 48 50 50 44 48 32 32 32 35 34 34 34 38 34 37 34 39 34 34 32 36 31 32 33 887
11-02-16 39 34 33 32 33 31 32 32 37 53 37 34 38 38 34 2 0 0 0 0 0 0 0 0 539
34 rows selected.
SQL>
Redo logs generation is grouped by hours where last column (TOT) is sum of all redo logs in one day. According this it is more then obvious where redo log generation was highest, so our interest may be focused on presented point in time.
How much is that in Mb?
Total redo logs size (and according that, archived log size) cannot be computed from previous query because not all redo log switches occur when redo log was full. For that you might want to use this very easy query:
SQL> select sum(value)/1048576 redo_MB from sys.gv_$sysstat where name = 'redo size'; |
If you want to calculate on instance grouping, then use this:
SQL> select inst_id, sum(value)/1048576 redo_MB from sys.gv_$sysstat where name = 'redo size' |
Both queries works on single instances as well.
Which segments are generating redo logs?
After we found out our point of interest, in mine case where were most of the redo logs generation, it is very useful to find out which segments (
not tables only) are causing redo log generation. For that we need to use "dba_hist" based tables, part of "Oracle AWR (Automated Workload Repository)", which usage I have described in topic
Automated AWR reports in Oracle 10g/11g. For this example I'll focus on data based on time period: 11-01-28 13:00-11-01-28 14:00. Query for such a task should be:
SELECT to_char(begin_interval_time,'YY-MM-DD HH24') snap_time, |
sum(db_block_changes_delta) BLOCK_CHANGED |
FROM dba_hist_seg_stat dhss, |
dba_hist_seg_stat_obj dhso, |
WHERE dhs.snap_id = dhss.snap_id |
AND dhs.instance_number = dhss.instance_number |
AND dhss.obj# = dhso.obj# |
AND dhss.dataobj# = dhso.dataobj# |
AND begin_interval_time BETWEEN to_date('11-01-28 13:00','YY-MM-DD HH24:MI') |
AND to_date('11-01-28 14:00','YY-MM-DD HH24:MI') |
GROUP BY to_char(begin_interval_time,'YY-MM-DD HH24'), |
HAVING sum(db_block_changes_delta) > 0 |
ORDER BY sum(db_block_changes_delta) desc ; |
Reduced result from previously shown query would be:
SNAP_TIME OBJECT_NAME BLOCK_CHANGED |
----------- ------------------------------ ------------- |
11-01-28 13 USR_RACUNI_MV 1410112 |
11-01-28 13 TROK_TAB_RESEAU_I 734592 |
11-01-28 13 TROK_VOIE_I 638496 |
11-01-28 13 TROK_DATUM_ULAZA_I 434688 |
11-01-28 13 TROK_PAIEMENT_I 428544 |
11-01-28 13 D_DPX_VP_RAD 351760 |
11-01-28 13 TROK_SVE_OK_I 161472 |
11-01-28 13 I_DATPBZ_S002 135296 |
11-01-28 13 IDS2_DATUM_I 129904 |
11-01-28 13 IDS2_PZNBR 129632 |
11-01-28 13 IDS2_IDS1_FK_I 128848 |
11-01-28 13 IDS2_DATTRAN_I 127440 |
11-01-28 13 IDS2_DATSOC_I 127152 |
11-01-28 13 IDS2_VRSTA_PROD_I 122816 |
Let us focus on first segment "USR_RACUNI_MV", segment with highest number of changed blocks (what mean directly highest redo log generation). Just for information, this is
MATERIALIZED VIEW.
What SQL was causing redo log generation
Now when we know when, how much and what, time is to find out how redo logs are generated. In next query "USR_RACUNI_MV" and mentioned period are hard codded, because we are focused on them. Just to point that SQL that start with "SELECT" are not point of our interest because they do not make any changes.
SELECT to_char(begin_interval_time,'YYYY_MM_DD HH24') WHEN, |
dbms_lob.substr(sql_text,4000,1) SQL, |
dhss.instance_number INST_ID, |
executions_delta exec_delta, |
rows_processed_delta rows_proc_delta |
FROM dba_hist_sqlstat dhss, |
WHERE upper(dhst.sql_text) LIKE '%USR_RACUNI_MV%' |
AND ltrim(upper(dhst.sql_text)) NOT LIKE 'SELECT%' |
AND dhss.snap_id=dhs.snap_id |
AND dhss.instance_number=dhs.instance_number |
AND dhss.sql_id=dhst.sql_id |
AND begin_interval_time BETWEEN to_date('11-01-28 13:00','YY-MM-DD HH24:MI') |
AND to_date('11-01-28 14:00','YY-MM-DD HH24:MI') |
Result is like:
WHEN SQL inst_id sql_id exec_delta rows_proc_delta |
------------- ------------------------------------------------- ------- ------------- ---------- --------------- |
2011_01_28 13 DECLARE 1 duwxbg5d1dw0q 0 0 |
job BINARY_INTEGER := :job; |
next_date DATE := :mydate; |
dbms_refresh.refresh('"TAB"."USR_RACUNI_MV"'); |
2011_01_28 13 delete from "TAB"."USR_RACUNI_MV" 1 5n375fxu0uv89 0 0 |
For both of examples it was impossible to find out number of rows changed according operation that was performed. Let us see output of another example (NC_TRANSACTION_OK_T table) where we can meet with DDL that generate redo logs!
WHEN SQL inst_id sql_id exec_delta rows_proc_delta |
------------- ------------------------------------------------- ------- ------------- ---------- --------------- |
2011_01_28 13 alter table TAB.NC_TRANSACTION_OK_T 4 g5gvacc8ngnb8 0 0 |
If you are focused on pure number of changes, then you might to perform query where inst_id and sql_id are irrelevant (excluded from query). Here is a little modified previous example, for "Z_PLACENO" segment (pure oracle table):
SELECT when, sql, SUM(sx) executions, sum (sd) rows_processed |
SELECT to_char(begin_interval_time,'YYYY_MM_DD HH24') when, |
dbms_lob.substr(sql_text,4000,1) sql, |
dhss.instance_number inst_id, |
sum(executions_delta) exec_delta, |
sum(rows_processed_delta) rows_proc_delta |
FROM dba_hist_sqlstat dhss, |
WHERE upper(dhst.sql_text) LIKE '%Z_PLACENO%' |
AND ltrim(upper(dhst.sql_text)) NOT LIKE 'SELECT%' |
AND dhss.snap_id=dhs.snap_id |
AND dhss.instance_Number=dhs.instance_number |
AND dhss.sql_id = dhst.sql_id |
AND begin_interval_time BETWEEN to_date('11-01-25 14:00','YY-MM-DD HH24:MI') |
AND to_date('11-01-25 15:00','YY-MM-DD HH24:MI') |
GROUP BY to_char(begin_interval_time,'YYYY_MM_DD HH24'), |
dbms_lob.substr(sql_text,4000,1), |
Result is like:
WHEN SQL exec_delta rows_proc_delta |
------------- ---------------------------------------------------------------------- ---------- --------------- |
2011_01_25 14 DELETE FROM Z_PLACENO 4 7250031 |
2011_01_25 14 INSERT INTO Z_PLACENO(OBP_ID,MT_SIFRA,A_TOT) 4 7250830 |
SELECT P.OBP_ID,P.MT_SIFRA,SUM(P.OSNOVICA) |
WHERE OPI.OBP_ID = OPO.OPI_OBP_ID |
AND OPI.RBR = OPO.OPI_RBR |
AND NVL(OPI.S_PRETPOREZA,'O') IN ( 'O','N','A','Z','S') |
GROUP BY OPI.OBP_ID,OPO.MT_SIFRA |
Here you can see directly number executions and number of involved rows.
Query based on segment directly
Sometimes you do not want to focus on period, so your investigation may start with segment as starting point. For such a tasks I use next query. This is small variation of previous example where "USR_RACUNI_MV" segment is hard codded.
SELECT to_char(begin_interval_time,'YY-MM-DD HH24') snap_time, |
sum(db_block_changes_delta) |
FROM dba_hist_seg_stat dhss, |
dba_hist_seg_stat_obj dhso, |
WHERE dhs.snap_id = dhss.snap_id |
AND dhs.instance_number = dhss.instance_number |
AND dhss.obj# = dhso.obj# |
AND dhss.dataobj# = dhso.dataobj# |
AND dhso.object_name = 'USR_RACUNI_MV' |
GROUP BY to_char(begin_interval_time,'YY-MM-DD HH24') |
ORDER BY to_char(begin_interval_time,'YY-MM-DD HH24'); |
Reduced result is:
SNAP_TIME SUM(DB_BLOCK_CHANGES_DELTA) |
----------- --------------------------- |
As you can see in accented row, the numbers are the same as at the begging of topic.
