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:/* ----------------------------------------------------------------------------- Filename: top_redo.sql CR/TR# : Purpose : Shows current redo logs generation info (RAC-non RAC environment) Date : 12.08.2008. Author : Damir Vadas, damir.vadas@gmail.com Remarks : run as privileged user Changes (DD.MM.YYYY, Name, CR/TR#): --------------------------------------------------------------------------- */ col machine for a15 col username for a10 col redo_MB for 999G990 heading "Redo |Size MB" column sid_serial for a13; select b.inst_id, lpad((b.SID || ',' || lpad(b.serial#,5)),11) sid_serial, b.username, machine, b.osuser, b.status, a.redo_mb 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 n.name = 'redo size' and s.statistic# = n.statistic# order by value desc ) a, gv$session b where b.inst_id=a.inst_id and a.sid = b.sid and rownum <= 30 ; PROMPT Top 30 from gv$sesstat view according generated redo logsResult is something like:
SQL> @top_redo Redo 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 30 rows selected. Top 30 from gv$sesstat view according generated redo logs SQL>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?
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 31Script to gather data through mentioned period looks like.
/* ----------------------------------------------------------------------------- Filename: rl.sql CR/TR# : Purpose : redo logs distribution per hours on each day ... Date : 07.08.2007. Author : Damir Vadas, damir.vadas@hypo-alpe-adria.com Remarks : run as privileged user Changes (DD.MM.YYYY, Name, CR/TR#): --------------------------------------------------------------------------- */ set pagesize 120; set linesize 200; col day for a8; spool rl.txt PROMPT Archive log distribution per hours on each day ... select 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", COUNT(*) TOT from v$log_history group by to_char(first_time,'YY-MM-DD') order by day ;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_$sysstat where name = 'redo size'; REDO_MB ---------- 1074623.75 SQL>If you want to calculate on instance grouping, then use this:
SQL> select inst_id, sum(value)/1048576 redo_MB from sys.gv_$sysstat where name = 'redo size' 2 group by inst_id; INST_ID REDO_MB ---------- ---------- 1 370325.298 2 4712.567 4 405129.283 3 294457.100 SQL>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, dhso.object_name, sum(db_block_changes_delta) BLOCK_CHANGED FROM dba_hist_seg_stat dhss, dba_hist_seg_stat_obj dhso, dba_hist_snapshot dhs 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'), dhso.object_name 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, dhss.sql_id, executions_delta exec_delta, rows_processed_delta rows_proc_delta FROM dba_hist_sqlstat dhss, dba_hist_snapshot dhs, dba_hist_sqltext dhst 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; broken BOOLEAN := FALSE; BEGIN dbms_refresh.refresh('"TAB"."USR_RACUNI_MV"'); :mydate := next_date; IF broken THEN :b := 1; ELSE :b := 0; END IF; END; 2011_01_28 13 delete from "TAB"."USR_RACUNI_MV" 1 5n375fxu0uv89 0 0For 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 shrink space cascadeIf 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 FROM ( SELECT to_char(begin_interval_time,'YYYY_MM_DD HH24') when, dbms_lob.substr(sql_text,4000,1) sql, dhss.instance_number inst_id, dhss.sql_id, sum(executions_delta) exec_delta, sum(rows_processed_delta) rows_proc_delta FROM dba_hist_sqlstat dhss, dba_hist_snapshot dhs, dba_hist_sqltext dhst 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), dhss.instance_number, dhss.sql_id ) group by when, sql;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) FROM (SELECT OPI.OBP_ID, OPO.MT_SIFRA, SUM(OPO.IZNKN) 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, dba_hist_snapshot dhs 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) ----------- --------------------------- ... 11-01-28 11 1224240 11-01-28 12 702880 11-01-28 13 1410112 11-01-28 14 806416 11-01-28 15 2008912 11-01-28 16 1103648 ...As you can see in accented row, the numbers are the same as at the begging of topic.
The End
Regardless which approach you use, redo logs generation has important effects in proper management of Oracle RMAN backups as well as stand by databases.Cheers!