One of the most useful information that the Oracle kernel attaches to plans in the library cache are measures of various resource consumption figures, such as elapsed time, consistent and current gets, disk reads, etcetera. These can be made available for each plan line (aka "row source operation").
These figures are always cumulative, that is, include both the resource consumed by the line itself and all of its progeny. It is very often extremely useful to exclude the progeny from the measure, to get what we could name the "self" figure (following, of course, the terminology introduced by Cary Millsap and Jeff Holt in their famous book Optimizing Oracle Performance).
My sqlplus script xplan now implements the automatic calculation of the "self" for the most important measures, including elapsed time and buffer gets, the most used ones when tuning a statement.
Let's see an example, and then elaborate on their most important application: as a resource profile when tuning.
A simple example
Here's an illustrative example for the measure "elapsed time":
--------------------------------------------------- |Ela |Ela+ |Id|Operation | -last----last-------------------------------------- |801,097| =| 0|SELECT STATEMENT | |801,097| +79,017| 1| HASH JOIN | |673,010|+262,274| 2| TABLE ACCESS BY INDEX ROWID| |410,736| 410,736| 3| INDEX FULL SCAN | | 49,070| 49,070| 4| TABLE ACCESS FULL | -usec----usec--------------------------------------
The first column ("Ela"), whose values are read straight from v$sql_plan_statistics, is the cumulative elapsed time of each row source operation and all its progeny (children, grandchildren, etc). Hence for example, you can see that line#1 (HASH JOIN) run for 801msec, including the time spent by line #2,3,4 (its progeny).
The second column ("Ela+") is the corresponding "self" column, derived from "Ela" by subtracting the time spent by the children - line#1 has two children (#2 and #4), and hence we get 801-673-49=79msec.
Self measures as a resource profile for the plan
Having the "self" measures available makes extremely easy to identify the most expensive row source operations, which are (usually) the first worth considering when tuning (or studying) a SQL statement. Actually, the "self" set is the resource profile of the plan: it blames each consumer (here, the plan lines) for its share of the resource consumed.
For example, line#3 is the most expensive with its 410 msec worth of time - if we are lucky and can reduce its time consumption almost to zero, we would cut the consumption of the whole statement by (about) 50%. It is definitely a line on which to invest some of our tuning time - by e.g. investigating whether a predicate failed to being pushed down; try building a more optimal (e.g. smaller) index; try hinting it into a "FAST FULL SCAN", etc etc.
The second best option for tuning is line#2, a "TABLE ACCESS BY INDEX ROWID"... maybe we could eliminate it completely by adding the fetched columns at the end of the index read by line#3, thus possibly saving 262msec (about 25%) of time.
And so on.
I have found these "self" figures extremely useful in all my recent tuning projects - I hope that the same could turn true for some of you, and maybe that you could suggest me some way to improve xplan :)
For anyone into using their SQL skills creatively, and getting out of the boring SQL-coding daily routine ... here is a puzzle that is both entertaining and challenging, and with a real prize for the winner!
"In this challenge (see page 25), the Wicked Witch of the West needs help in creating a magic spell to ensure that the Third Annual Witching & Wizarding Ball is a grand success. The winner will receive the August Order of the Wooden Pretzel in keeping with the Steven Feuerstein’s observation that "some people can perform seeming miracles with straight SQL, but the statements end up looking like pretzels created by somebody who is experimenting with hallucinogens." There are currently four knights of the August Order of the Wooden Pretzel: Alberto Dell’Era (Italy) who won the first challenge in 2009 and Andre Araujo (Australia), Rob van Wijk (Netherlands), and Ilya Chuhnakov (Russia) who won the second challenge in 2011."
I remember having a lot of fun when I joined the first edition of this Challenge - I hope the same for you :)
Tracing a session is extremely useful when you need to investigate how a client interacts with the database - the client could be an application of yours, a third-party application, or an Oracle module such as dbms_stats or dbms_mview. To get the perfect picture of the client-server dialogue, you "simply" need to consider all EXEC lines in the trace file, and associate to each line the executed statement and the bind variable values; a very tedious and error-prone task when done manually, that Xtrace can make for you (and for free).
Let's see the tool in action. Consider tracing a call to this stored procedure, that executes a recursive SQL statement :
create or replace procedure test_proc( p_x int ) is begin for i in 1..p_x loop for k in (select count(*) from t where x > i) loop null; end loop; end loop; end;
Here is the output of Xtrace:
Reading it bottom-up, you can see that the client called the SP, which in turn executed recursively (note the indentation) the SQL statement twice.
You can also ask Xtrace to display the bind variable values used for each execution:
So - the client passed the value "2" for :p_x to the SP, which in turn executed the SQL statement first passing "1" for :B1, and then passing "2".
Interested ? Try it live (requires Java Web Start):
When Xtrace opens up, press the "options" button and then the "EXEC FLOW analysis" button. Enable/disable the bind variable values using the "display BINDS under EXEC" checkbox; color the statements as you like.
I really like (besides the light and modern look) the aggregator of the OakTable members' blogs - a window on high-quality news and investigations about Oracle ...
Xtrace is a graphical tool that can navigate Oracle trace files, manipulate them, and optionally get them back as a text file. It actually makes (much) more, but in this first post we are going to focus on its basic browsing capabilities.
Let’s see the tool in action on the trace file produced by this simple PL/SQL block:
begin for r in (select * from t) loop null; end loop; end;
The resulting trace file is
WAIT #2: nam='SQL*Net message from client' ela= 61126 driver id=1413697536 #bytes=1 p3=0 obj#=76357 tim=5789636384898 ===================== PARSING IN CURSOR #26 len=66 dep=0 uid=73 oct=47 lid=73 tim=5789636385129 hv=3421439103 ad='aeb809c8' begin for r in (select * from t) loop null; end loop; end; END OF STMT PARSE #26:c=0,e=153,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,tim=5789636385122 BINDS #26: ===================== PARSING IN CURSOR #28 len=15 dep=1 uid=73 oct=3 lid=73 tim=5789636386184 hv=1406298530 ad='a0503300' SELECT * FROM T END OF STMT PARSE #28:c=0,e=804,p=0,cr=0,cu=0,mis=1,r=0,dep=1,og=1,tim=5789636386181 BINDS #28: EXEC #28:c=0,e=64,p=0,cr=0,cu=0,mis=0,r=0,dep=1,og=1,tim=5789636386284 WAIT #28: nam='db file sequential read' ela= 19 file#=4 block#=59 blocks=1 obj#=76357 tim=5789636386383 WAIT #28: nam='db file sequential read' ela= 11 file#=4 block#=60 blocks=1 obj#=76357 tim=5789636386457 FETCH #28:c=0,e=243,p=2,cr=3,cu=0,mis=0,r=100,dep=1,og=1,tim=5789636386566 FETCH #28:c=0,e=54,p=0,cr=1,cu=0,mis=0,r=100,dep=1,og=1,tim=5789636386663 FETCH #28:c=0,e=3,p=0,cr=0,cu=0,mis=0,r=0,dep=1,og=1,tim=5789636386693 EXEC #26:c=0,e=1543,p=2,cr=4,cu=0,mis=0,r=1,dep=0,og=1,tim=5789636386746 WAIT #26: nam='SQL*Net message to client' ela= 2 driver id=1413697536 #bytes=1 p3=0 obj#=76357 tim=5789636387057 WAIT #26: nam='SQL*Net message from client' ela= 42743 driver id=1413697536 #bytes=1 p3=0 obj#=76357 tim=5789636429824 STAT #28 id=1 cnt=200 pid=0 pos=1 obj=76357 op='TABLE ACCESS FULL T (cr=4 pr=2 pw=0 time=363 us)'
Even for this artificially simple trace file, it takes a lot of effort to read and understand it; for example, it takes a while to associate the recursive SQL lines to the execution of the PL/SQL blocks (the “EXEC #26” line).
With Xtrace, the trace reading experience is remarkably much better:
Note the indentation by recursive level (which is provided out-of-the -box) and the color of the lines by statement (that takes perhaps a minute in order to be set up).
You can try this example live by pressing the “Launch” button above if you are interested; in particular, try the “Options” button of the middle pane, and the “set color” popup menus of the top pane.
Suggestion: you might even check the hyperlinks that links together the lines; for example, the xct pointer that links the SQL recursive calls to the parent “EXEC #26” (check the interactive manual for more information).
You can also get the trace back as a text file, if so desired:
000 line zero 001 xtrace: log file 'E:\localCVS30\TrilogyLectures\MioSitoWeb\xtrace\dist\xtrace.log' 002 VIRTUAL CALL #-4: 'null call - ignore this' 003 VIRTUAL CALL #-4: 'null call - ignore this' 004 +WAIT #2: nam='SQL*Net message from client' xe=ela=61126 p1='driver id'=1413697536 p2='#bytes'=1 p3=''=0 xphy=0 obj#=76357 tim=5789636384898 005 VIRTUAL CALL #-8: 'wait-for-client' 006 VIRTUAL CALL #-5: 'client-message-received' 007 ---------------------PARSING IN CURSOR #26: len=66 dep=0 uid=73 oct=47 lid=73 tim=5789636385129 hv=3421439103 ad='0eb809c8' begin for r in (select * from t) loop null; end loop; end; END OF STMT 008 PARSE #26: mis=0 r=0 dep=0 og=1 tim=5789636385122 e=153 c=0 p=0 cr=0 cu=0 009 BINDS #26: 010 ---------------------PARSING IN CURSOR #28: len=15 dep=1 uid=73 oct=3 lid=73 tim=5789636386184 hv=1406298530 ad='00503300' SELECT * FROM T END OF STMT 011 PARSE #28: mis=1 r=0 dep=1 og=1 tim=5789636386181 e=804 c=0 p=0 cr=0 cu=0 012 BINDS #28: 013 EXEC #28: mis=0 r=0 dep=1 og=1 tim=5789636386284 e=64 c=0 p=0 cr=0 cu=0 014 +WAIT #28: nam='db file sequential read' xe=ela=19 p1='file#'=4 p2='block#'=59 p3='blocks'=1 xphy=1 obj#=76357 tim=5789636386383 015 +WAIT #28: nam='db file sequential read' xe=ela=11 p1='file#'=4 p2='block#'=60 p3='blocks'=1 xphy=1 obj#=76357 tim=5789636386457 016 FETCH #28: mis=0 r=100 dep=1 og=1 tim=5789636386566 e=243 c=0 p=2 cr=3 cu=0 017 FETCH #28: mis=0 r=100 dep=1 og=1 tim=5789636386663 e=54 c=0 p=0 cr=1 cu=0 018 FETCH #28: mis=0 r=0 dep=1 og=1 tim=5789636386693 e=3 c=0 p=0 cr=0 cu=0 019 EXEC #26: mis=0 r=1 dep=0 og=1 tim=5789636386746 e=1543 c=0 p=2 cr=4 cu=0 020 -WAIT #26: nam='SQL*Net message to client' xe=ela=2 p1='driver id'=1413697536 p2='#bytes'=1 p3=''=0 xphy=0 obj#=76357 tim=5789636387057 021 +WAIT #26: nam='SQL*Net message from client' xe=ela=42743 p1='driver id'=1413697536 p2='#bytes'=1 p3=''=0 xphy=0 obj#=76357 tim=5789636429824 022 VIRTUAL CALL #-8: 'wait-for-client' 023 VIRTUAL CALL #-5: 'client-message-received' 024 STAT #28: id=1 pid=0 pos=1 obj=76357 op='TABLE ACCESS FULL T' cnt=200 avg(cnt)=200.0 card=n/a cr=4 avg(cr)=4.0 cost=n/a pr=2 pw=0 time=363 size=n/a xnexecs=1 xstatn=0 xplannum=0 025 026 VIRTUAL CALL #-4: 'null call - ignore this'
This can be obtained using the “save as text“ popup menu of the middle pane.
We are going to keep exploring Xtrace in the upcoming posts.
A devastating performance degradation of materialized view fast refreshes can happen in versions after 9i - and can be healed rather easily by simply setting the hidden parameter _mv_refresh_use_stats or, a bit surprisingly, by locking statistics on the logs. The problem can manifest at least in the currently-latest patchsets of 10g, 11gR1 and 11gR2 (10.2.0.4, 188.8.131.52 and 184.108.40.206), seems to hit a lot of people, and its root cause are the utilization of wrong hints by the Oracle refresh engine.
We will investigate the join-only MV case only, since this is the case I have investigated after a question by Christo Kutrovsky, factoring in some observations by Taral Desai and some Support notes; I have some clues that something similar may happen for other types of MVs.
The test case sets up this very common scenario for fast refreshes:
1 - two big base tables joined together by the MV;
2 - only a small fraction of rows modified (actually one deleted, two updated, one inserted);
3 - all tables and indexes with fresh statistics collected;
4 - MV logs with no statistic collected AND with not-locked statistics;
5 - indexes present on the joined columns;
6 - indexes present on the rowid columns of the MV.
Points 1 and 2 make for the ideal scenario for incremental ("fast") refreshes to be effective; 3 is very common as well, since you normally have many other statements issued on the tables; the relevance of 4 will be clear later, but it happens very often in real life, since people might perhaps consider collecting stats on the log, but locking their statistics is usually not made, at least in my experience.
To understand the importance of points 5 and 6, please check this post of mine; note how those indexes are a necessary prerequisite for the sanity of the DEL and INS steps of the MV process. Without them, the refresh cannot be incremental since it has no physical way to read and propagate only the modified rows and those related to them, but it must scan (uselessly) most of the base tables and MV. But in other for the refresh to be incremental ("fast"), those indexes have to be actually used...
Let's illustrate the issue focusing on the DEL step (the easier to discuss about). In the above mentioned post, we have seen that the DEL step uses a single SQL statement whose text, leaving out minor technical details and hints, is:
/* MV_REFRESH (DEL) */ delete from test_mv where test_t1_rowid in ( select * from ( select chartorowid (m_row$$) from mlog$_test_t1 where snaptime$$ > :1 ) as of snapshot (:2) )
In 220.127.116.11, we get this very healthy plan:
------------------------------------------------- |Id|Operation |Name | ------------------------------------------------- | 0|DELETE STATEMENT | | | 1| DELETE | | | 2| NESTED LOOPS | | | 3| VIEW | | | 4| SORT UNIQUE | | | 5| TABLE ACCESS FULL|MLOG$_TEST_T1 | | 6| INDEX RANGE SCAN |TEST_MV_TEST_T1_ROWID| -------------------------------------------------
That is: get the rowid of all modified rows from the log, and use the rowid-based index to delete the "old image" of them from the MV (inserting their "new image" is the job of the INS step). This is truly incremental, since the resource usage and elapsed time are proportional to the number of rows logged in the MV log, not to the dimension of the tables.
In 10.2.0.4, 18.104.22.168 and 22.214.171.124 the plan becomes:
------------------------------------------ |Id|Operation |Name | ------------------------------------------ | 0|DELETE STATEMENT | | | 1| DELETE |TEST_MV | | 2| HASH JOIN RIGHT SEMI | | | 3| TABLE ACCESS FULL |MLOG$_TEST_T1| | 4| MAT_VIEW ACCESS FULL|TEST_MV | ------------------------------------------
Oops, the indexes are not used ... hence the DEL step overhead is proportional to the size of the MV, and that can be definitely unacceptable.
That is due to the engine injecting an HASH_SJ hint in the outermost nested subquery:
... WHERE "TEST_T1_ROWID" IN (SELECT /*+ NO_MERGE HASH_SJ */ ...
This is recognized as a bug in many scenarios (start from Oracle Support note 578720.1 and follow the references to explore some of them) even if I have not found a clear and exhaustive note that documents the behaviour.
remedy one: set "_mv_refresh_use_stats"
To get back to the healthy plan, simply set "_mv_refresh_use_stats" to "true" (ask Oracle Support first of course for permission); this makes for a set of hint much more adequate for a fast refresh:
... WHERE "TEST_T1_ROWID" IN (SELECT /*+ NO_MERGE NO_SEMIJOIN */ ...
Note: The root cause for this bug is probably due to a change hinted in note 875532.1 - in 10.2.0.3 the meaning of _mv_refresh_use_stats was reversed, but not the default, hence (by mistake?) activating a different piece of the engine code.
The very same problem happens for the INS step; I won't go into much details here (please check the test case spools provided above if interested), but in 126.96.36.199 the base table modified rows are directly fetched using the rowid contained in the log:
----------------------------------------------------- |Id|Operation |Name | ----------------------------------------------------- | 0|INSERT STATEMENT | | | 1| TABLE ACCESS BY INDEX ROWID |TEST_T2 | | 2| NESTED LOOPS | | | 3| VIEW | | | 4| NESTED LOOPS | | | 5| VIEW | | | 6| SORT UNIQUE | | | 7| TABLE ACCESS FULL |MLOG$_TEST_T1 | | 8| TABLE ACCESS BY USER ROWID|TEST_T1 | | 9| INDEX RANGE SCAN |TEST_T2_J2_1_IDX| -----------------------------------------------------
Instead, in 10.2.0.4, 188.8.131.52 and 184.108.40.206 we get the following plan:
-------------------------------------------------- |Id|Operation |Name | -------------------------------------------------- | 0|INSERT STATEMENT | | | 1| TABLE ACCESS BY INDEX ROWID|TEST_T2 | | 2| NESTED LOOPS | | | 3| VIEW | | | 4| HASH JOIN RIGHT SEMI | | | 5| TABLE ACCESS FULL |MLOG$_TEST_T1 | | 6| TABLE ACCESS FULL |TEST_T1 | | 7| INDEX RANGE SCAN |TEST_T2_J2_1_IDX| --------------------------------------------------
Whose resource consumption is, of course, proportional to the size of the base table.
Even in this case, this is due to the nasty HASH_SJ hint:
... FROM "TEST_T1" "MAS$" WHERE ROWID IN (SELECT /*+ HASH_SJ */ ...
If you set _mv_refresh_use_stats, you get back the 220.127.116.11 plan - and thus you are back to incremental for both the DEL and INS steps. As a side note, a cardinality hint is used, where the cardinality is set to the correct value (6 in my test case):
... FROM "TEST_T1" "MAS$" WHERE ROWID IN (SELECT /*+ CARDINALITY(MAS$ 6) NO_SEMIJOIN ...
remedy two: collect and lock statistics on the logs
Very interestingly, instead of setting the hidden parameter, you have another way to get back to the healthy plan: gather statistics on the MV logs when they are empty AND lock them (as suggested in note 578720.1, albeit not in this scenario and even if setting the parameter is not necessary; thanks to Taral Desai for pointing me to the note). In this case, no hint at all is injected beside a NO_MERGE for the DEL step:
... WHERE "TEST_T1_ROWID" IN (SELECT /*+ NO_MERGE */ ... ... FROM "TEST_T1" "MAS$" WHERE ROWID IN (SELECT ...
So, the engine is confident that the CBO will come out with a good plan, and it does not inject any "intelligent" hint. Possibly, and intriguing, this is because by locking the statistics, I am assuring the engine that these statistics are representative of the data anytime. So, locking the statistics is not meant only as a way to prevent dbms_stats from changing them ... it is deeper than that. At least in this case, you are taking responsibility for them, and Oracle will take that in consideration.
As a minor but useful new feature, xplan is now able to integrate into its report the DDL of tables (and indexes) referenced by the plan, calling dbms_metadata.get_ddl transparently.
This is mostly useful to get more details about referenced tables' constraints and partitions definition - to complement their CBO-related statistics that xplan reports about.
This feature can be activated by specifing dbms_metadata=y or dbms_metadata=all (check xplan.sql header of xplan.sql for more informations).
We spoke about xplan in general here.
For people that have asked - I'm not blogging simply because I'm feverishly working on a tool of mine that I'm very fond of :)
Guess what it does from the following next two screenshots ...
This post investigates the improvements that have been made in 11gR2 to the fast refresh engine of materialized views (MVs) that are set to be automatically refreshed at commit time. We speak about join-only materialized views only in this post, as always with the help of a test case.
As noted in the post of mine "11gR2: materialized view logs changes", in 11gR2 a new column, xid$$, is now part of materialized view logs; this column records the id of the transaction that logged the changes of the base table which the log is defined on. It is important to stress that this column is added regardless of the type of the MV log, that is, to both the brand-new "commit SCN-based" logs and the old fashioned "timestamp-based" ones. That means that both types of MV logs can take advantage of the new improvements - albeit I haven't tested whether MVs (logs) migrated from a previous version are automatically upgraded by the migration scripts and get the new xid$$ column added.
algorithm before 11gR2
In versions before 11gR2, the refresh algorithm for on-commit MVs was the same as the one for on-demand ones, with only minor variants. That is, the algorithm was almost completely the same, just triggered by the commit event instead of by the user.
For an in-depth analysis of the algorithm, I will refer the reader to the discussion about the on-demand algorithm in the post "fast refresh of join-only materialized views - algorithm summary"; in passing, the test case for this post is in fact the very same three-table join MV, just redefined as "on commit" instead of "on demand". To recap, the "old" algorithm (until 18.104.22.168) was:
1) new log rows are inserted with snaptime$$=4000 A.D;
2) at refresh time (commit time), a snapshot of the new rows is taken, that is, all new rows are marked with snaptime$$= "commit time", using the statement
update MLOG$_TEST_T1 set snaptime$$ = :1 where snaptime$$ > to_date('2100-01-01:00:00:00','YYYY-MM-DD:HH24:MI:SS')
3) all modifications whose snaptime$$ is between the date of the last refresh (excluded) and the commit date(included) are propagated to the MV. The propagation consists of two steps.
First a DEL step:
/* MV_REFRESH (DEL) */ delete from test_mv where test_t1_rowid in ( select * from ( select chartorowid (m_row$$) from mlog$_test_t1 where snaptime$$ > :1 ) -- no "as of snapshot (:2)" clause )
Then an INS one:
/* MV_REFRESH (INS) */ insert into test_mv select jv.j1_2, jv.x1, jv.pk1, jv.rid$, mas2.j2_1, mas2.j2_3, mas2.x2, mas2.pk2, mas2.rowid, mas3.j3_2, mas3.x3, mas3.pk3, mas3.rowid from ( select log.rowid rid$, log.* from test_t1 log where rowid in ( select chartorowid(log.m_row$$) from mlog$_test_t1 where snaptime$$ > :1 ) ) jv, -- no "as of snapshot (:2) jv" clause test_t2 as of snapshot (:2) mas2, test_t3 as of snapshot (:2) mas3 where jv.j1_2 = mas2.j2_1 and mas2.j2_3 = mas3.j3_2
Note that the only small difference from the on-demand case is the absence of the "as of snapshot" clause, but the statements are otherwise identical. Note also that the rows in the MV log are identified in both statements by snaptime, using the subquery
select chartorowid(log.m_row$$) from mlog$_test_t1 where snaptime$$ > :1
4) all obsolete log rows are deleted, that is, all rows whose snaptime$$ is less than or equal the lowest of all refresh times are removed from the log, using the the statement
delete from mlog$_test_t1 where snaptime$$ <= :1
algorithm starting from 11gR2
In 11gR2, the on-commit algorithm is still almost the same as the on-demand one; the "only" change is how modified rows to be propagated are identified, and in general, how logs are managed. Not surprisingly, log rows are now directly identified by the transaction id, which is logged in xid$$. In detail:
1) new log rows are inserted with xid$$ = transaction id;
2) at refresh time (commit time), no snapshot is taken, that is, the MV log is not updated at all;
3) all modifications made by the committing transaction are propagated to the MV, still using the same two steps.
The DEL step is now:
/* MV_REFRESH (DEL) */ delete from test_mv where test_t1_rowid in ( select * from ( select chartorowid (m_row$$) from mlog$_test_t1 where xid$$ = :1 ) )
The INS one is:
/* MV_REFRESH (INS) */ insert into test_mv select jv.j1_2, jv.x1, jv.pk1, jv.rid$, mas2.j2_1, mas2.j2_3, mas2.x2, mas2.pk2, mas2.rowid, mas3.j3_2, mas3.x3, mas3.pk3, mas3.rowid from ( select log.rowid rid$, log.* from test_t1 log where rowid in ( select chartorowid(log.m_row$$) from mlog$_test_t1 where xid$$ = :1 ) ) jv, -- no "as of snapshot (:2) jv" clause test_t2 as of snapshot (:2) mas2, test_t3 as of snapshot (:2) mas3 where jv.j1_2 = mas2.j2_1 and mas2.j2_3 = mas3.j3_2
Hence, the big difference from the previous versions case is that rows in the MV log are identified very simply by the transaction that logged them (the committing transaction, of course), by the subquery
select chartorowid(log.m_row$$) from mlog$_test_t1 where xid$$ = :1
4) all obsolete log rows are deleted, that is, the rows logged by the committing transaction are removed, using the the statement
delete from mlog$_test_t1 where where xid$$ = :1
The new algorithm is for sure much simpler and more elegant. Performance is improved since the snapshot step has been removed, and the other steps are more or less as expensive as before.
practical implications: an example
I strongly believe that studying the internals is the best way to learn how to make the best use of any feature. Let's see an example of how the few bits of "internal knowledge" I shared here can be used in practice - that is, how a little investment in investigation makes for huge savings in effort afterwards, and huge gains in effectiveness of your work as well.
It is well-known that it can be sometimes beneficial, in pre-11gR2, to place an index on the log (indexing the log is even suggested by support note 258252 "MATERIALIZED VIEW REFRESH: Locking, Performance, Monitoring"). The scenario that benefits the most from such an index is when the log is composed of mostly-empty blocks, and hence an index access is preferable over a full table(log) scan; you get mostly-empty blocks, for example, when there are peeks in activity on the master tables that keep the log High Water Mark very high.
From the above discussion, it is obvious that in pre-11gR2, the best index for join-only MVs was on (snaptime$$, m_row$$) - not on snaptime$$ alone as it is sometimes suggested - to make the refresh operation an index-only one.
Starting from 11gR2, the best index is now on (xid$$, m_row$$). Not only that, but having no snapshot step, and hence no update on the index, makes the indexing option even more attractive.
Could you see these implications so easily, without knowing the internals? I don't.