Oracle: Optimieren zweimal Self-Join-Abfrage

Question

Ich habe versucht, diese Abfrage effizient in den letzten zwei Tagen zu erhalten. Ich habe mehr über das Oracle-Index-Verhalten erfahren, von dem ich denke, dass ich an dieser Stelle verwirrt bin, was funktionieren soll und was nicht.

Grundsätzlich besteht die Abfrage darin, Werte zu summieren und mit Werten von gestern und letzter Woche zu vergleichen.

Ich habe herum gespielt mit Breakdown, ich habe in meinem Kopf analytische Abfragen und wechselnde Reihenfolge der Indizes gespielt, aber nichts scheint zu funktionieren. All mein Test war auf einem Tisch mit 500K Reihen, sobald ich ihn auf einem Tisch mit 20 Millionen Reihen laufen lasse dauert es einfach ewig.

Jede Hilfe wird sehr geschätzt.

Ich habe den ursprünglichen Beitrag geändert, damit Sie mir helfen können. :)

CREATE TABLE TABLE_1 
(ORDER_LINE_ID NUMBER, OFFSET NUMBER, BREAK_ID NUMBER, ZONE NUMBER, NETWORK NUMBER, HOUR_OF_DAY NUMBER, START_TIME DATE, END_TIME DATE, SUCCESS NUMBER 
    CONSTRAINT "TABLE_1_PK" PRIMARY KEY (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, HOUR_OF_DAY)) 

-- SUCCESS is already aggregated during the insert 
-- These are last week's records 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (1,0,1, 1, 1, 2016042001,'04/20/2016 00:00:00', '04/20/2016 02:00:00', 1); 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (1,30,1, 1, 1, 2016042001,'04/20/2016 00:00:00', '04/20/2016 02:00:00', 2); 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (2,0,1, 1, 1, 2016042001,'04/20/2016 00:00:00', '04/20/2016 02:00:00', 1); 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (2,30,1, 1, 1, 2016042001,'04/20/2016 00:00:00', '04/20/2016 02:00:00', 1); 

-- These are yesterday's records 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (3,0,1, 1, 1, 2016042601,'04/26/2016 00:00:00', '04/26/2016 02:00:00', 1); 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (3,30,1, 1, 1, 2016042601,'04/26/2016 00:00:00', '04/26/2016 02:00:00', 2); 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (4,0,1, 1, 1, 2016042601,'04/26/2016 00:00:00', '04/26/2016 02:00:00', 1); 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (4,30,1, 1, 1, 2016042601,'04/26/2016 00:00:00', '04/26/2016 02:00:00', 1); 

-- This is today's records 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (5,0,1, 1, 1, 2016042701,'04/27/2016 00:00:00', '04/27/2016 02:00:00', 1); 
INSERT INTO TABLE_1 (ORDER_LINE_ID, OFFSET, BREAK_ID, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME, SUCCESS) 
VALUES (5,30,1, 1, 1, 2016042701,'04/27/2016 00:00:00', '04/27/2016 02:00:00', 1); 

-- Original twice join query 
SELECT BREAK_ID, ORDER_LINE_ID, HOUR_OF_DAY, OFFSET, ZONE, NETWORK, START_TIME, END_TIME, SUM(SUCCESS), SUM(YESTERDAY_SUCCESS), SUM(LAST_WEEK_SUCCESS) 
FROM TABLE_1 CURRENT_DAY 
LEFT OUTER JOIN (
    SELECT SUM(SUCCESS) YESTERDAY_SUCCESS, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME FROM TABLE_1 
    GROUP BY ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME 
) YESTERDAY 
    ON YESTERDAY.START_TIME + 1 = CURRENT_DAY.START_TIME 
    AND YESTERDAY.END_TIME + 1 = CURRENT_DAY.END_TIME 
    AND YESTERDAY.HOUR_OF_DAY = CURRENT_DAY.HOUR_OF_DAY 
    AND YESTERDAY.NETWORK = CURRENT_DAY.NETWORK 
    AND YESTERDAY.ZONE = CURRENT_DAY.ZONE 
LEFT OUTER JOIN (
    SELECT SUM(SUCCESS) LAST_WEEK_SUCCESS, ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME FROM TABLE_1 
    GROUP BY ZONE, NETWORK, HOUR_OF_DAY, START_TIME, END_TIME 
) LAST_WEEK 
    ON YESTERDAY.START_TIME + 7 = CURRENT_DAY.START_TIME 
    AND YESTERDAY.END_TIME + 7 = CURRENT_DAY.END_TIME 
    AND YESTERDAY.HOUR_OF_DAY = CURRENT_DAY.HOUR_OF_DAY 
    AND YESTERDAY.NETWORK = CURRENT_DAY.NETWORK 
    AND YESTERDAY.ZONE = CURRENT_DAY.ZONE 
GROUP BY BREAK_ID, ORDER_LINE_ID, HOUR_OF_DAY, OFFSET, ZONE, NETWORK, START_TIME, END_TIME; 

-- Using Analytic Query (thank you to MT0) 
SELECT BREAK_ID, ORDER_LINE_ID, HOUR_OF_DAY, OFFSET, ZONE, NETWORK, START_TIME, END_TIME, SUM(SUCCESS), SUM(YESTERDAY_SUCCESS), SUM(LAST_WEEK_SUCCESS) 
FROM (
    SUM(SUCCESS) 
    OVER (PARTITION BY ZONE, NETWORK, HOUR_OF_DAY, TO_CHAR(START_TIME, 'HH24:MI:SS'), TO_CHAR(END_TIME, 'HH24:MI:SS') 
      ORDER BY START_TIME 
      RANGE BETWEEN INTERVAL '1' DAY PRECDEDING AND INTERVAL '1' DAY PRECEDING 
     ) AS YESTERDAY_SUCCESS, 
SUM (SUCCESS) 
    OVER (PARTITION BY ZONE, NETWORK, HOUR_OF_DAY, TO_CHAR(START_TIME, 'HH24:MI:SS'), TO_CHAR(END_TIME, 'HH24:MI:SS') 
      ORDER BY START_TIME 
      RANGE BETWEEN INTERVAL '7' DAY PRECDEDING AND INTERVAL '7' DAY PRECEDING 
     ) AS LAST_WEEK_SUCCESS 
FROM TABLE_1 
) T1 
WHERE SYSDATE - INTERVAL '12' HOUR <= START_TIME 
AND START_TIME < SYSDATE - INTERVAL '1' HOUR 
GROUP BY BREAK_ID, ORDER_LINE_ID, HOUR_OF_DAY, OFFSET, ZONE, NETWORK, START_TIME, END_TIME; 

Ich muss sagen, danke für die Hilfe, um diese Frage auf etwas zu bringen, das ich hoffe, ist verständlicher. Alles funktioniert wie erwartet, aber die Performance könnte etwas tunen.

1,8 Sekunden auf einem Tisch mit 500K Reihen

400 Sekunden auf einem Tisch mit 20 Millionen Zeilen

ich mag auch einige Ausführungspläne von Oracle zur Verfügung gestellt hinzuzufügen. Ich habe Probleme beim Tuning der Leistung.

-- using twice self join 
-------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
| Id | Operation      | Name      | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | Writes | OMem | 1Mem | O/1/M | 
-------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
| 0 | SELECT STATEMENT    |       |  1 |  |  50 |00:00:00.84 | 99875 | 217 | 1705 |  |  |   | 
| 1 | HASH GROUP BY     |       |  1 | 6711 |  50 |00:00:00.84 | 99875 | 217 | 1705 | 1616K| 995K|   | 
|* 2 | FILTER      |       |  1 |  | 119K|00:00:00.65 | 99875 |  0 |  0 |  |  |   | 
| 3 | NESTED LOOPS OUTER   |       |  1 |  54M| 119K|00:00:00.64 | 99875 |  0 |  0 |  |  |   | 
|* 4 |  HASH JOIN OUTER    |       |  1 | 109 | 119K|00:00:00.52 | 99875 |  0 |  0 | 13M| 2093K|  1/0/0| 
| 5 |  TABLE ACCESS BY INDEX ROWID| TABLE_1_IDX    |  1 | 109 | 119K|00:00:00.14 | 85908 |  0 |  0 |  |  |   | 
|* 6 |  INDEX RANGE SCAN   | START_TIME_IDX   |  1 | 109 | 119K|00:00:00.02 |  320 |  0 |  0 |  |  |   | 
| 7 |  VIEW      |       |  1 | 1250 | 29311 |00:00:00.23 | 13967 |  0 |  0 |  |  |   | 
| 8 |  HASH GROUP BY    |       |  1 | 1250 | 29311 |00:00:00.22 | 13967 |  0 |  0 | 3008K| 1094K|  1/0/0| 
|* 9 |  FILTER     |       |  1 |  | 88627 |00:00:00.20 | 13967 |  0 |  0 |  |  |   | 
|* 10 |   TABLE ACCESS FULL  | TABLE_1     |  1 | 1250 | 88627 |00:00:00.19 | 13967 |  0 |  0 |  |  |   | 
| 11 |  VIEW      |       | 119K| 499K|  0 |00:00:00.10 |  0 |  0 |  0 |  |  |   | 
| 12 |  SORT GROUP BY    |       | 119K| 499K|  0 |00:00:00.08 |  0 |  0 |  0 | 1024 | 1024 |  1/0/0| 
|* 13 |  FILTER     |       | 119K|  |  0 |00:00:00.02 |  0 |  0 |  0 |  |  |   | 
| 14 |  TABLE ACCESS FULL  | TABLE_1     |  0 | 499K|  0 |00:00:00.01 |  0 |  0 |  0 |  |  |   | 
-------------------------------------------------------------------------------------------------------------------------------------------------------------------- 

Predicate Information (identified by operation id): 
--------------------------------------------------- 

    2 - filter(SYSDATE@!-17<SYSDATE@!-16) 
    4 - access("YESTERDAY"."ZONE"="T1"."ZONE" AND "YESTERDAY"."NETWORK"="T1"."NETWORK" AND "YESTERDAY"."HOUR_OF_DAY"="T1"."HOUR_OF_DAY" 
       AND "T1"."END_TIME"=INTERNAL_FUNCTION("YESTERDAY"."END_TIME")+1 AND 
       "T1"."START_TIME"=INTERNAL_FUNCTION("YESTERDAY"."START_TIME")+1) 
    6 - access("T1"."START_TIME">=SYSDATE@!-17 AND "T1"."START_TIME"<SYSDATE@!-16) 
    9 - filter(SYSDATE@!-17<SYSDATE@!-16) 
    10 - filter((INTERNAL_FUNCTION("START_TIME")+1>=SYSDATE@!-17 AND INTERNAL_FUNCTION("START_TIME")+1<SYSDATE@!-16)) 
    13 - filter(("YESTERDAY"."ZONE"="T1"."ZONE" AND "YESTERDAY"."NETWORK"="T1"."NETWORK" AND "YESTERDAY"."HOUR_OF_DAY"="T1"."HOUR_OF_DAY" 
       AND "T1"."END_TIME"=INTERNAL_FUNCTION("YESTERDAY"."END_TIME")+7 AND 
       "T1"."START_TIME"=INTERNAL_FUNCTION("YESTERDAY"."START_TIME")+7)) 

Eine weitere Ausführungsplan

-- using analytic query 
------------------------------------------------------------------------------------------------------------------------------- 
| Id | Operation    | Name    | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | O/1/M | 
------------------------------------------------------------------------------------------------------------------------------- 
| 0 | SELECT STATEMENT  |     |  1 |  |  50 |00:00:01.51 | 13967 |  |  |   | 
| 1 | HASH GROUP BY  |     |  1 | 499K|  50 |00:00:01.51 | 13967 | 98M| 7788K|   | 
|* 2 | VIEW    |     |  1 | 499K| 119K|00:00:01.15 | 13967 |  |  |   | 
| 3 | WINDOW SORT  |     |  1 | 499K| 499K|00:00:01.43 | 13967 | 66M| 2823K|  1/0/0| 
|* 4 |  FILTER   |     |  1 |  | 499K|00:00:00.16 | 13967 |  |  |   | 
| 5 |  TABLE ACCESS FULL| TABLE_1   |  1 | 499K| 499K|00:00:00.12 | 13967 |  |  |   | 
------------------------------------------------------------------------------------------------------------------------------- 

Predicate Information (identified by operation id): 
--------------------------------------------------- 

    2 - filter(("T1"."START_TIME">=SYSDATE@!-INTERVAL'+17 00:00:00' DAY(2) TO SECOND(0) AND 
       "T1"."START_TIME"<SYSDATE@!-INTERVAL'+16 00:00:00' DAY(2) TO SECOND(0))) 
    4 - filter(SYSDATE@!-INTERVAL'+17 00:00:00' DAY(2) TO SECOND(0)<SYSDATE@!-INTERVAL'+16 00:00:00' DAY(2) TO 
       SECOND(0)) 

Wie Sie sehen können mit Analytic Query (danken wieder MT0), habe ich einen Index für START_TIME hinzugefügt, die von der Abfrage Vorteile Selbstmitmach aber schätzt vs Wirklichkeiten sind ausgeschaltet . Wo Analytic Query gerade entscheidet, will es nichts mit dem Index zu tun haben. Irgendwelche Ideen, Bezugspunkte oder Hilfe werden sehr geschätzt. Vielen Dank im Voraus, alle.

Answer 1

Es ist unklar, warum Sie beitreten, nur wenn Zeilen mit genau der gleichen Zeit heute und gestern (oder letzte Woche), aber wenn Sie nur die Zeilen zwischen bestimmten Zeiten wollen, dann können Sie alle Self-Joins beseitigen und zu tun:

SELECT order_line, 
     zone, 
     network, 
     sum(
     CASE WHEN SYSDATE - INTERVAL '12' HOUR <= start_time 
      AND start_time < SYSDATE - INTERVAL '1' HOUR 
      THEN success 
      END 
     ) AS total_successes_today, 
     sum(
     CASE WHEN SYSDATE - INTERVAL '12' HOUR <= start_time 
      AND start_time < SYSDATE - INTERVAL '1' HOUR 
      THEN error 
      END 
     ) AS total_errors_today, 
     sum(
     CASE WHEN SYSDATE - INTERVAL '36' HOUR <= start_time 
      AND start_time < SYSDATE - INTERVAL '25' HOUR 
      THEN success 
      END 
     ) AS total_successes_yesterday, 
     sum(
     CASE WHEN SYSDATE - INTERVAL '180' HOUR <= start_time 
      AND start_time < SYSDATE - INTERVAL '169' HOUR 
      THEN success 
      END 
     ) AS total_successes_last_week 
FROM table_1 
WHERE ( SYSDATE - INTERVAL '12' HOUR <= start_time 
     AND start_time < SYSDATE - INTERVAL '1' HOUR) -- today 
OR  ( SYSDATE - INTERVAL '36' HOUR <= start_time 
     AND start_time < SYSDATE - INTERVAL '25' HOUR) -- yesterday = today + 24 hours 
OR  ( SYSDATE - INTERVAL '180' HOUR <= start_time 
     AND start_time < SYSDATE - INTERVAL '169' HOUR) -- last week = today + 7*24 hours 

wenn Sie jedoch wollen, um die Verbindung halten auf die Start- und Endzeiten dann könnte man eine analytische Abfrage verwenden:

SELECT order_line, 
     zone, 
     network, 
     SUM(success), 
     SUM(error), 
     SUM(yesterday_success), 
     SUM(last_week_success) 
FROM (
    SELECT t.*, 
     SUM(success) 
      OVER (PARTITION BY id, 
           TO_CHAR(start_time, 'HH24:MI:SS'), 
           TO_CHAR(end_time, 'HH24:MI:SS') 
        ORDER BY start_time 
        RANGE BETWEEN INTERVAL '1' DAY PRECEDING AND INTERVAL '1' DAY PRECEDING 
       ) AS yesterday_success, 
     SUM(success) 
      OVER (PARTITION BY id, 
           TO_CHAR(start_time, 'HH24:MI:SS'), 
           TO_CHAR(end_time, 'HH24:MI:SS') 
        ORDER BY start_time 
        RANGE BETWEEN INTERVAL '7' DAY PRECEDING AND INTERVAL '7' DAY PRECEDING 
       ) AS last_week_success 
    FROM TABLE_1 t 
) 
WHERE SYSDATE - INTERVAL '12' HOUR <= start_time 
AND start_time < SYSDATE - INTERVAL '1' HOUR 
GROUP BY 
     order_line, 
     zone, 
     network 
ORDER BY 
     order_line, 
     zone, 
     network 

Sie an, ob aussehen würden Sie Geschwindigkeitsverbesserungen durch Verwendung funktionsbasierter Indizes unter TO_CHAR(start_time, 'HH24:MI:SS') und TO_CHAR(end_time, 'HH24:MI:SS').