Posts Tagged Materialized Views

Materialized Views & Partition Change Tracking

During the 11g Data Warehouse Administration course that I delivered this week in Munich, I have shown this demonstration about Partition Change Tracking (PCT) that I’d like to share with the Oracle Community. You may have a look here for an Introduction into Materialized Views if that topic is new for you. Often, the Base Tables of Materialized Views are partitioned, which gives use additional options for the Refresh as well as for the Rewrite:

SQL> select * from v$version;

BANNER
--------------------------------------------------------------------------------
Oracle Database 11g Enterprise Edition Release 11.2.0.1.0 - Production
PL/SQL Release 11.2.0.1.0 - Production
CORE    11.2.0.1.0      Production
TNS for Linux: Version 11.2.0.1.0 - Production
NLSRTL Version 11.2.0.1.0 - Production

SQL> grant dba to adam identified by adam;

Grant succeeded.

SQL> connect adam/adam
Connected.
SQL> create table sales
 (product     varchar2(50),
  channel_id  number,
  cust_id     number,
  amount_sold number(10,2),
  time_id     date)
partition by list (channel_id)
(partition c0 values (0),
 partition c1 values (1),
 partition c2 values (2),
 partition c3 values (3),
 partition c4 values (4)
);
Table created.
SQL> alter table sales nologging;

Table altered.
SQL> insert /*+ append */ into sales
select
'Oracle Enterprise Edition' as product,
mod(rownum,5) as channel_id,
mod(rownum,1000) as cust_id ,
5000 as amount_sold,
to_date
('01.' || lpad(to_char(mod(rownum,12)+1),2,'0') || '.2010' ,'dd.mm.yyyy')
as time_id
from dual connect by level<=1e6; 
1000000 rows created.

SQL> commit;

Commit complete.

The above was my demo setup, done with 11.2.0.1 Most of the shown should work with lower versions also, though. We will now see a Materialized View that contains the Partition Key of the Base Table, which makes it easily possible for the system to associate the rows of the Materialized View with the Partitions. Otherwise, we would need to explain that with a Partition Marker.

SQL> create materialized view mv_pct
enable query rewrite as
select channel_id,sum(amount_sold) from sales group by channel_id;  

Materialized view created.

The MV is now fresh and may get used for Query Rewrite:

SQL> select mview_name,last_refresh_type,staleness from user_mviews;

MVIEW_NAME                     LAST_REF STALENESS
------------------------------ -------- -------------------
MV_PCT                         COMPLETE FRESH

SQL> set autotrace on explain
SQL> select channel_id,sum(amount_sold) from sales group by channel_id;

CHANNEL_ID SUM(AMOUNT_SOLD)
---------- ----------------
         0       1000000000
         1       1000000000
         2       1000000000
         3       1000000000
         4       1000000000

Execution Plan
----------------------------------------------------------
Plan hash value: 470332451

---------------------------------------------------------------------------------------
| Id  | Operation                    | Name   | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |        |     5 |   130 |     3   (0)| 00:00:01 |
|   1 |  MAT_VIEW REWRITE ACCESS FULL| MV_PCT |     5 |   130 |     3   (0)| 00:00:01 |
---------------------------------------------------------------------------------------

Note
-----
   - dynamic sampling used for this statement (level=2)

Now we modify one row:

SQL> set autotrace off
SQL> update sales set amount_sold=1 where rownum<2;

1 row updated.

SQL> commit;

Commit complete.:

This makes the MV stale right? Well actually not all of it:

SQL> select mview_name,last_refresh_type,staleness from user_mviews;

MVIEW_NAME                     LAST_REF STALENESS
------------------------------ -------- -------------------
MV_PCT                         COMPLETE NEEDS_COMPILE

SQL> select detail_partition_name, freshness from user_mview_detail_partition;

DETAIL_PARTITION_NAME          FRESH
------------------------------ -----
C4                             FRESH
C3                             FRESH
C2                             FRESH
C1                             FRESH
C0                             STALE

The second Dictionary View is new in 11g, not the behavior of the next query. Only Partition C0 is stale, which is why queries that do not need channel_id 0 can still use the MV:

SQL> set autotrace on explain
SQL> select sum(amount_sold) from sales where channel_id=1;

SUM(AMOUNT_SOLD)
----------------
      1000000000


Execution Plan
----------------------------------------------------------
Plan hash value: 1805549181

----------------------------------------------------------------------------------------
| Id  | Operation                     | Name   | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |        |     1 |    26 |     3   (0)| 00:00:01 |
|   1 |  SORT AGGREGATE               |        |     1 |    26 |            |          |
|*  2 |   MAT_VIEW REWRITE ACCESS FULL| MV_PCT |     1 |    26 |     3   (0)| 00:00:01 |
----------------------------------------------------------------------------------------

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

   2 - filter("MV_PCT"."CHANNEL_ID"=1)

Note
-----
   - dynamic sampling used for this statement (level=2)

The Rewrite is still possible although part of the MV is stale. We call that PCT Rewrite. Furthermore, we can now do a PCT Fast Refresh, although there is no MV Log on the Base Table sales. The PCT Refresh does not need to scan the whole Base Table but only the stale partitions, which is in this case about 5 times faster than a Complete Refresh, because we have 5 equally sized partitions. The value ‘P‘ is new in 11g, but it also works with ‘F’ or ‘?’ in earlier versions.

SQL> set autotrace off
SQL> exec dbms_mview.refresh('MV_PCT','P')

PL/SQL procedure successfully completed.

SQL> select mview_name,last_refresh_type,staleness from user_mviews;

MVIEW_NAME                     LAST_REF STALENESS
------------------------------ -------- -------------------
MV_PCT                         FAST_PCT FRESH

Conclusion: Partitioning delivers some extra benefits when used together with Materialized Views. Especially, Rewrite & Refresh can be done on the Partition layer then. As always: “Don’t believe it, test it!” :-)

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Brief introduction into Materialized Views

This week, I am teaching an Oracle Database 11g Data Warehouse Administration course in Munich. One of the focus areas of that course are Materialized Views, and I have developed some examples for that course that I like to share with the Oracle community. Other themes of that four days course are Partitioning, ETL and Parallelization.

Materialized Views have the ability to speed up queries (even dramatically) while being transparent for the queries in a similar way as indexes resp. partitioned tables are. That means that we do not have to modify our queries in order to benefit from these structures. Unlike an ordinary view which is only a stored select statement that runs if we use the view, a materialized view stores the result set of the select statement as a container table. Let me demonstrate the benefit of that. I have a (for my little database) relatively large table and do an aggregation query on it:

SQL> desc sales
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 PROD_ID                                   NOT NULL NUMBER
 CUST_ID                                   NOT NULL NUMBER
 TIME_ID                                   NOT NULL DATE
 CHANNEL_ID                                NOT NULL NUMBER
 PROMO_ID                                  NOT NULL NUMBER
 QUANTITY_SOLD                             NOT NULL NUMBER(10,2)
 AMOUNT_SOLD                               NOT NULL NUMBER(10,2)

SQL> select count(*) from sales;

 COUNT(*)
----------
 7350744

SQL> select bytes/1024/1024 as mb from user_segments where segment_name='SALES';

 MB
----------
 286

SQL> show sga

Total System Global Area  373293056 bytes
Fixed Size                  1219496 bytes
Variable Size             134218840 bytes
Database Buffers          234881024 bytes
Redo Buffers                2973696 bytes

SQL> set timing on
SQL> select channel_id,sum(amount_sold) from sales group by channel_id;

CHANNEL_ID SUM(AMOUNT_SOLD)
---------- ----------------
 2        210770739
 4        109654416
 3        463002085
 9       2219410.08

Elapsed: 00:00:04.51

As you can see, the aggregation takes round 5 seconds. Now I will speed that up using a Materialized View:

SQL> create materialized view mv1 enable query rewrite
 2  as select channel_id,sum(amount_sold) from sales group by channel_id;

Materialized view created.

Elapsed: 00:00:05.69
SQL> select channel_id,sum(amount_sold) from sales group by channel_id;

CHANNEL_ID SUM(AMOUNT_SOLD)
---------- ----------------
 2        210770739
 4        109654416
 3        463002085
 9       2219410.08

Elapsed: 00:00:00.01

The very same statement now takes way less time! Why is that so?

SQL> set autotrace on explain
SQL> select channel_id,sum(amount_sold) from sales group by channel_id;

CHANNEL_ID SUM(AMOUNT_SOLD)
---------- ----------------
 2        210770739
 4        109654416
 3        463002085
 9       2219410.08

Execution Plan
----------------------------------------------------------
Plan hash value: 2958490228
--------------------------------------------------------------------------------
| Id  | Operation                    | Name | Rows  | Bytes | Cost (%CPU)| Time
--------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |      |     4 |   104 | 3   (0)| 00:00:01
|   1 |  MAT_VIEW REWRITE ACCESS FULL| MV1  |     4 |   104 | 3   (0)| 00:00:01
--------------------------------------------------------------------------------

Because the optimizer transparently rewrote the query on the Materialized View! But what if we now change the content of the base table sales?

SQL> update sales set amount_sold=amount_sold+1 where rownum<2;

1 row updated.

SQL> commit;

Commit complete.

The materialized view is now stale and will no longer be used for query rewrite (as we can already determine by query runtime):

SQL> select mview_name,staleness from user_mviews;

MVIEW_NAME                     STALENESS
------------------------------ -------------------
MV1                            NEEDS_COMPILE

SQL> set timing on
SQL> select channel_id,sum(amount_sold) from sales group by channel_id;

CHANNEL_ID SUM(AMOUNT_SOLD)
---------- ----------------
 2        210770739
 4        109654416
 3        463002086
 9       2219410.08

Elapsed: 00:00:04.52

In order to get statements rewritten against the materialized view again, we must refresh it by some method.

One method is on demand with a procedure call like in this example:

SQL> exec dbms_mview.refresh(list=>'MV1',method=>'C')

PL/SQL procedure successfully completed.

Elapsed: 00:00:04.62

SQL> select channel_id,sum(amount_sold) from sales group by channel_id;

CHANNEL_ID SUM(AMOUNT_SOLD)
---------- ----------------
 2        210770739
 4        109654416
 3        463002086
 9       2219410.08

Elapsed: 00:00:00.01

Now the MV1 can get used again, as we see by runtime. That was a complete refresh – which can take a long time. We would like to get this done faster.

One comfortable method to get a fast refreshable materialized view is the usage of the package DBMS_ADVISOR:

SQL> vari t varchar2(50)
SQL> begin
 2   dbms_advisor.tune_mview(task_name=>:t,
 3                           mv_create_stmt=>'create materialized view mv1'
 4                               || ' refresh fast as'
 5                               || ' select channel_id,sum(amount_sold)'
 6                               || ' from sales group by channel_id');
 7  end;
 8/  
PL/SQL procedure successfully completed.

SQL> set long 5000
SQL> select statement from user_tune_mview where task_name=:t order by action_id;

STATEMENT
--------------------------------------------------------------------------------
CREATE MATERIALIZED VIEW LOG ON "ADAM"."SALES" WITH ROWID, SEQUENCE ("CHANNEL_ID
","AMOUNT_SOLD")  INCLUDING NEW VALUES

ALTER MATERIALIZED VIEW LOG FORCE ON "ADAM"."SALES" ADD ROWID, SEQUENCE ("CHANNE
L_ID","AMOUNT_SOLD")  INCLUDING NEW VALUES

CREATE MATERIALIZED VIEW ADAM.MV1   REFRESH FAST WITH ROWID DISABLE QUERY REWRIT
E AS SELECT ADAM.SALES.CHANNEL_ID C1, SUM("ADAM"."SALES"."AMOUNT_SOLD") M1, COUN
T("ADAM"."SALES"."AMOUNT_SOLD") M2, COUNT(*) M3 FROM ADAM.SALES GROUP BY ADAM.SA
LES.CHANNEL_ID

STATEMENT
--------------------------------------------------------------------------------
DROP MATERIALIZED VIEW ADAM.MV1

SQL> exec dbms_advisor.delete_task(:t)
PL/SQL procedure successfully completed.

Usually, we need Materialized View Logs on the base tables and special expressions in the materialized view query to be able to do a fast refresh.

I simply take the coding from above now:

SQL> CREATE MATERIALIZED VIEW LOG ON "ADAM"."SALES"
WITH ROWID, SEQUENCE ("CHANNEL_ID","AMOUNT_SOLD")
INCLUDING NEW VALUES;  2    3

Materialized view log created.

SQL> CREATE MATERIALIZED VIEW ADAM.MV1
 REFRESH FAST WITH ROWID
 enable QUERY REWRITE AS
 SELECT ADAM.SALES.CHANNEL_ID C1,
 SUM("ADAM"."SALES"."AMOUNT_SOLD") M1,
 COUNT("ADAM"."SALES"."AMOUNT_SOLD") M2,
 COUNT(*) M3
 FROM ADAM.SALES
 GROUP BY ADAM.SALES.CHANNEL_ID;

Materialized view created.

After again modifying the base table sales, I will then try a fast refresh of the MV1:

SQL> set timing off
SQL> update sales set amount_sold=amount_sold*1 where rownum<2;

1 row updated.

SQL> commit;

Commit complete.

SQL> select mview_name,staleness from user_mviews;

MVIEW_NAME                     STALENESS
------------------------------ -------------------
MV1                            NEEDS_COMPILE

SQL> set timing on
SQL> exec dbms_mview.refresh('MV1','F')

PL/SQL procedure successfully completed.

Elapsed: 00:00:00.62

Materialized View, Query Rewritw, MV Log, Refresh FAST

It worked! But do I always have to refresh manually? No, we can also schedule a regular refresh like this:

SQL>  alter materialized view mv1 refresh start with sysdate next sysdate + interval '1' minute;

Materialized view altered.

SQL> alter session set nls_date_format='hh24:mi:ss';

Session altered.

SQL> select MVIEW_NAME,LAST_REFRESH_TYPE,LAST_REFRESH_DATE from user_mviews;

MVIEW_NAME                     LAST_REF LAST_REF
------------------------------ -------- --------
MV1                            FAST     10:40:05

SQL> select MVIEW_NAME,LAST_REFRESH_TYPE,LAST_REFRESH_DATE from user_mviews;

MVIEW_NAME                     LAST_REF LAST_REF
------------------------------ -------- --------
MV1                            FAST     10:41:04

Of course, a one minute refresh interval is extraordinary short. An other possibility to refresh the MVs very comfortably is on commit:

CREATE MATERIALIZED VIEW ADAM.MV1
 REFRESH FAST on commit 
 WITH ROWID
 enable QUERY REWRITE AS
 SELECT ADAM.SALES.CHANNEL_ID C1,
 SUM("ADAM"."SALES"."AMOUNT_SOLD") M1,
 COUNT("ADAM"."SALES"."AMOUNT_SOLD") M2,
 COUNT(*) M3
 FROM ADAM.SALES
 GROUP BY ADAM.SALES.CHANNEL_ID;

Materialized view created.

SQL> select MVIEW_NAME,LAST_REFRESH_TYPE,LAST_REFRESH_DATE from user_mviews;

MVIEW_NAME                     LAST_REF LAST_REF
------------------------------ -------- --------
MV1                            COMPLETE 11:16:28

SQL> update sales set amount_sold=amount_sold*1 where rownum<2;

1 row updated.

SQL> commit;

Commit complete.

SQL> select MVIEW_NAME,LAST_REFRESH_TYPE,LAST_REFRESH_DATE from user_mviews;

MVIEW_NAME                     LAST_REF LAST_REF
------------------------------ -------- --------
MV1                            FAST     11:19:56

But this may (although done in asynchronous fashion) slow down the DML on the base table, so it requires testing whether the slow down is noticeable or neglectable. So far  for now about Materialized Views. There is of course much more to say, but I think that this is enough for a brief introduction of that theme.

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