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The Secrets of Materialized Views(zt)

原创 Linux操作系统 作者:vongates 时间:2019-06-16 14:42:05 0 删除 编辑

Overview

Materialized views are a data warehousing/decision support system tool that can increase by many orders of magnitude the speed of queries that access a large number of records. In basic terms, they allow a user to query potentially terabytes of detail data in seconds. They accomplish this by transparently using pre-computed summarizations and joins of data. These pre-computed summaries would typically be very small compared to the original source data.

In this article you'll find out what materialized views are, what they can do and, most importantly, how they work - a lot of the ' magic ' goes on behind the scenes. Having gone to the trouble of creating it, you'll find out how to make sure that your materialized view is used by all queries to which the view is capable of providing the answer. Sometimes, you know Oracle could use the materialized view, but it is not able to do so simply because it lacks important information.


Setup of Materialized Views

There is one mandatory INIT.ORA parameter necessary for materialized views to function, this is the COMPATIBLE parameter. The value of COMPATIBLE should be set to 8.1.0, or above, in order for query rewrites to be functional. If this value is not set appropriately, query rewrite will not be invoked.

There are two other relevant parameters that may be set at either the system-level via the INIT.ORA file, or the session-level via the ALTER SESSION command.

  • QUERY_REWRITE_ENABLED

Unless the value of this parameter is set to TRUE, query rewrites will not take place. The default value is FALSE.

  • QUERY REWRITE INTEGRITY

This parameter controls how Oracle rewrites queries and may
be set to one of three values:

ENFORCED - Queries will be rewritten using only constraints and rules that are enforced and guaranteed by Oracle. There are mechanisms by which we can tell Oracle about other inferred relationships, and this would allow for more queries to be rewritten, but since Oracle does not enforce those relationships, it would not make use of these facts at this level.

TRUSTED - Queries will be rewritten using the constraints that are enforced by Oracle, as well as any relationships existing in the data that we have told Oracle about, but are not enforced by the database.

STALE TOLERATED - Queries will be rewritten to use materialized views even if Oracle knows the data contained in the materialized view is ' stale ' (out-of-sync with the details). This might be useful in an environment where the summary tables are refreshed on a recurring basis, not on commit, and a slightly out-of-sync answer is acceptable.

The needed privileges are as follows:

  • CREATE SESSION
  • CREATE TABLE
  • CREATE MATERIALIZED VIEW
  • QUERY REWRITE

Finally, you must be using the Cost Based Optimizer CBO in order to make use of query rewrite. If you do not use the CBO, query rewrite will not take place.

Example

The example will demonstrate what a materialized view entails. The concept is that of reducing the execution time of a long running query transparently, by summarizing data in the database. A query against a large table will be transparently rewritten into a query against a very small table, without any loss of accuracy in the answer. For the example we create our own big table based on the system view ALL_OBJECTS.

Prepare the large table BIGTAB:

sqlplus scott/tiger
set echo on
set termout off

drop table bigtab;

create table bigtab
nologging
as
select * from all_objects
union all
select * from all_objects
union all
select * from all_objects
/

insert /*+ APPEND */ into bigtab
select * from bigtab;
commit;
insert /*+ APPEND */ into bigtab
select * from bigtab;
commit;
insert /*+ APPEND */ into bigtab
select * from bigtab;
commit;

analyze table bigtab compute statistics;
select count(*) from bigtab;

COUNT(*)
----------
708456

Run query against this BIGTABLE

Initially this quewry will require a full scan of the large table.

set autotrace on
set timing on
select owner, count(*) from bigtab group by owner;

OWNER COUNT(*)
------------------------------ ----------
CTXSYS 6264
ELAN 1272
HR 816
MDSYS 5640
ODM 9768
ODM_MTR 288
OE 2064
OLAPSYS 10632
ORDPLUGINS 696
ORDSYS 23232
OUTLN 168
PM 216
PUBLIC 278184
QS 984
QS_ADM 168
QS_CBADM 576
QS_CS 552
QS_ES 936
QS_OS 936
QS_WS 936
SCOTT 264
SH 4176
SYS 324048
SYSTEM 15096
TEST 4536
WKSYS 6696
WMSYS 3072
XDB 6240

28 rows selected.

Elapsed: 00:00:07.06

Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE
(Cost=2719 Card=28 Bytes=140)
1 0 SORT (GROUP BY) (Cost=2719 Card=28 Bytes=140)
2 1 TABLE ACCESS (FULL) OF 'BIGTAB'
(Cost=1226 Card=708456 Bytes=3542280)

Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
19815 consistent gets
18443 physical reads
0 redo size
973 bytes sent via SQL*Net to client
510 bytes received via SQL*Net from client
3 SQL*Net roundtrips to/from client
1 sorts (memory)
0 sorts (disk)
28 rows processed

In order to get the aggregate count, we must count 700'000+ records on over 19800 blocks. If you need this summary often per day, you can avoid counting the details each and every time by creating a materialized view of this summary data.

Create the Materialized View

sqlplus scott/tiger

grant query rewrite to scott;
alter session set query_rewrite_enabled=true;
alter session set query_rewrite_integrity=enforced;

create materialized view mv_bigtab
build immediate
refresh on commit
enable query rewrite
as
select owner, count(*)
from bigtab
group by owner
/

analyze table mv_bigtab compute statistics;

Basically, what we've done is pre-calculate the object count, and define this summary information as a materialized view. We have asked that the view be immediately built and populated with data. You'll notice that we have also specified REFRESH ON COMMIT and ENABLE QUERY REWRITE. Also notice that we may have created a materialized view, but when we ANALYZE, we are analyzing a table. A materialized view creates a real table, and this table may be indexed, analyzed, and so on.

Now let's see the materialized view in action by issuing the same query again

set timing on
set autotrace traceonly
select owner, count(*)
from bigtab
group by owner;
set autotrace off
set timing off

28 rows selected.

Elapsed: 00:00:00.03

Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE
(Cost=2 Card=28 Bytes=252)
1 0 TABLE ACCESS (FULL) OF 'MV_BIGTAB'
(Cost=2 Card=28 Bytes=252)

Statistics
----------------------------------------------------------
11 recursive calls
0 db block gets
17 consistent gets
0 physical reads
0 redo size
973 bytes sent via SQL*Net to client
510 bytes received via SQL*Net from client
3 SQL*Net roundtrips to/from client
4 sorts (memory)
0 sorts (disk)
28 rows processed

No physical I/O this time around as the data was found in the cache. Our buffer cache will be much more efficient now as it has less to cache. W could not even begin to cache the previous query's working set, but now I can. Notice how our query plan shows we are now doing a full scan of the MV_BIGTAB table, even though we queried the detail table BIGTAB. When the SELECT OWNER, ... query is issued, the database automatically directs it to the materialized view.

Now, add a new row to the BIGTAB table and commit te change

insert into bigtab
(owner, object_name, object_type, object_id)
values ('Martin', 'Zahn', 'Akadia', 1111111);

commit;

set timing on
set autotrace traceonly
select owner, count(*)
from bigtab
where owner = 'Martin'
group by owner;
set autotrace off
set timing off

Execution Plan
----------------------------------------------------------
0 SELECT STATEMENT Optimizer=CHOOSE
(Cost=2 Card=1 Bytes=9)
1 0 TABLE ACCESS (FULL) OF 'MV_BIGTAB'
(Cost=2 Card=1 Bytes=9)

Statistics
----------------------------------------------------------
0 recursive calls
0 db block gets
4 consistent gets
0 physical reads
0 redo size
439 bytes sent via SQL*Net to client
499 bytes received via SQL*Net from client
2 SQL*Net roundtrips to/from client
0 sorts (memory)
0 sorts (disk)
1 rows processed

The analysis shows that we scanned the materialized view MV_BIGTAB and found the new row. By specifying REFRESH ON COMMIT in our original definition of the view, we requested that Oracle maintain synchronization between the view and the details, the summary will be maintained as well.

Uses of Materialized Views

This is relatively straightforward and is answered in a single word - performance. By calculating the answers to the really hard questions up front (and once only), we will greatly reduce the load on the machine, We will experience:

  • Less physical reads - There is less data to scan through.

  • Less writes - We will not be sorting/aggregating as frequently.

  • Decreased CPU consumption - We will not be calculating aggregates and functions on the data, as we will have already done that.

  • Markedly faster response times - Our queries will return incredibly quickly when a summary is used, as opposed to the details. This will be a function of the amount of work we can avoid by using the materialized view, but many orders of magnitude is not out of the question.

Materialized views will increase your need for one resource - more permanently allocated disk. We need extra storage space to accommodate the materialized views, of course, but for the price of a little extra disk space, we can reap a lot of benefit.

Materialized views work best in a read-only, or read-intensive environment. They are not designed for use in a high-end OLTP environment. They will add overhead to modifications performed on the base tables in order to capture the changes.

There are concurrency issues with regards to rising the REFRESH ON COMMIT option. Consider the summary example from before. Any rows that are inserted or deleted from this table will have to update one of 28 rows in the summary table in order to maintain the count in real time. This does not preclude the use of materialized views in an OLTP environment. For example if you use full refreshes on a recurring basis (during off-peak time) there will be no overhead added to the modifications, and there would be no concurrency issues. This would allow you to report on yesterday's activities, for example, and not query the live OLTP data for reports.

How Materialized Views Work

Materialized views may appear to be hard to work with at first. There will be cases where you create a materialized view, and you know that the view holds the answer to a certain question but, for some reason, Oracle does not. The more meta data provided, the more pieces of information about the underlying data you can give to Oracle, the better.

So, now that we can create a materialized view and show that it works, what are the steps Oracle will undertake to rewrite our queries? Normally, when QUERY REWRITE ENABLED is set to FALSE, Oracle will take your SQL as is, parse it, and optimize it. With query rewrites enabled, Oracle will insert an extra step into this process. After parsing, Oracle will attempt to rewrite the query to access some materialized view, instead of the actual table that it references. If it can perform a query rewrite, the rewritten query (or queries) is parsed and then optimized along with the original query. The query plan with the lowest cost from this set is chosen for execution. If it cannot rewrite the query, the original parsed query is optimized and executed as normal.

Conclusion

Summary table management, another term for the materialized view, has actually been around for some time in tools such as Oracle Discoverer. If you ran a query in SQL*PLUS, or from your Java JDBC client, then the query rewrite would not (could not) take place. Furthermore, the synchronization between the details (original source data) and the summaries could not be performed or validated for you automatically, since the tool ran outside the database.

Furthermore, since version 7.0, the Oracle database itself has actually implemented a feature with many of the characteristics of summary tables - the Snapshot. This feature was initially designed to support replication, but many would use it to ' pre-answer ' large queries. So, we would have snapshots that did not use a database link to replicate data from database to database, but rather just summarized or pre-joined frequently accessed data. This was good, but without any query rewrite capability, it was still problematic. The application had to know to use the summary tables in the first place, and this made the application more complex to code and maintain. If we added a new summary then we would have to find the code that could make use of it, and rewrite that code.

In Oracle 8.1.5 Oracle took the query rewriting capabilities from tools like Discoverer, the automated refresh and scheduling mechanisms from snapshots (that makes the summary tables ' self maintaining ' ), and combined these with the optimizer's ability to find the best plan out of many alternatives. This produced the materialized view.

With all of this functionality centralized in the database, now every application can take advantage of the automated query rewrite facility, regardless of whether access to the database is via SQL*PLUS, JDBC, ODBC, Pro*C, OCI, or some third party tool. Every Oracle 8i enterprise database can have summary table management. Also, since everything takes place inside the database, the details can be easily synchronized with the summaries, or at least the database knows when they aren't synchronized, and might bypass stale summaries.

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