Category Archives: oracle

Funny friday Oracle SQL quiz: query running N seconds

Posted on by Posted in curious, Funny, oracle, quiz, SQL 1,777 Page views Leave a comment

Write a pure SQL query with PL/SQL that stop after :N seconds, where :N is a bind variable.

My solution

[sourcecode language=”sql”]
with v(start_hsecs, delta, flag) as (
select
hsecs as start_hsecs, 0 as delta, 1 as flag
from v$timer
union all
select
v.start_hsecs,
(t.hsecs-v.start_hsecs)/100 as delta,
case when (t.hsecs-v.start_hsecs)/100 > :N /* seconds */ then v.flag*-1 else v.flag+1 end as flag
from v, v$timer t
where v.flag>0 and t.hsecs>=v.start_hsecs
)
select delta
from v
where 0>flag
/
–end
[/sourcecode]

[collapse]
SQL> var N number
SQL> exec :N := 3 /* seconds */;

PL/SQL procedure successfully completed.

SQL> select...

     DELTA
----------
      3.01

1 row selected.

Elapsed: 00:00:03.01

Another my solution using sys.standard.current_timestamp, so some internal pl/sql…:

select count(*) from dual 
connect by sys.standard.current_timestamp - current_timestamp <= interval'3'second;

Serial Scans failing to offload

Posted on by Posted in adaptive serial direct path reads, cell_offload, oracle, SmartScan 1,858 Page views 1 Comment

Very Large Buffer Cache

We’ve observed databases with very large buffer caches where Serial Scans don’t make use of Smart Scan when that would have executed faster: improvements to the decision making for Serial Scans have been made under bug  31626438. This fix is back-portable.

A key difference between PQ and Serial is that as part of granule generation PQ sums the sizes of all the partitions that have not been pruned and passes that total size to the buffer cache decision making logic. Because the entire size to be scanned is considered, we make an accurate determination of smart scan benefits and the risk of cache thrashing.

Serial Scans on partitioned tables do not involved the coordinator and have no opportunity to get the larger picture, instead they start work immediately so each partition is considered one at a time and only that one partition’s size is considered by the decision for using Buffer Cache or Direct Read (and hence offload). In the presence of very large buffer caches any given partition can fail the “Is Medium” test (or even the “Is Small” test) and so not get offloaded.

In order to avoid this situation an upper bound of 100MB for using a buffer cache scan has been implemented for any serially scanned segment that:

  • isn’t using Automatic Big Table Caching (ABTC).
  • hasn’t had the Small Table parameter changed to a non-default value.

Any partitions larger than 100 MB will now automatically use Direct Read and hence offload on Exadata.

See also: Part 1

See also: Part 2

NSMTIO: kxfxghwm:[HWM_NOT_FOUND]

Another case to watch out for is when NSMTIO tracing shows HWM_NOT_FOUND and then choosing a Buffer Cache scan when a Direct Read offloaded scan would have been faster. This can happen when a PQ query gets executed serially (NB: this is NOT the downgrade to serial case, this is still PQ but on a single thread). In this case the coordinator again does not have the opportunity to process all the partitions and as part of that gather the High Water Mark (HWM) for each segment and checkpoint them so we fall back on buffer cache scans. A fix for this is currently being investigated.

Mixed Block Sizes

I have consistently advised against mixing block sizes in a database without a compelling reason backed up by empirical evidence, but for those who must the “Is Medium Table” logic for whether to use buffer cache or direct read has been improved when the database has more than one block size in use. This is tracked by bug 24655250 and fixed in 20.1.

See also  Random thoughts on block sizes

Simple function returning Parallel slave info

Posted on by Posted in oracle, parallel, query optimizing, SQL, statistics, troubleshooting 1,588 Page views Leave a comment

You can add also any information from v$rtsm_sql_plan_monitor if needed

create or replace function px_session_info return varchar2 parallel_enable as
   vSID int;
   res varchar2(30);
begin
   vSID:=userenv('sid');
   select 
           to_char(s.server_group,'fm000')
    ||'-'||to_char(s.server_set,'fm0000')
    ||'-'||to_char(s.server#,'fm0000')
    ||'('||s.sid||','||s.degree||'/'||s.req_degree||')'
    into res
   from v$px_session s 
   where s.sid=vSID;
   return res;
exception when no_data_found then
   return 'no_parallel';
end;
/

Simple example:

select--+ parallel
  px_session_info, count(*)
from sys.obj$
group by px_session_info
/
PX_SESSION_INFO           COUNT(*)
------------------------  --------
001-0002-0001(630,2/2)     38298
001-0002-0002(743,2/2)     34706

Android Oracle Client 2.0

Posted on by Posted in Android, java, oracle, SQL 1,794 Page views Leave a comment

I’ve just released new version of my Simple Android Oracle Client.

New features:

  • Supported Oracle versions: 11.2, 12.1, 12.2, 18, 19, 20.
  • SQL Templates: now you can save and load own script templates
  • Server output (dbms_output)
  • Export results as JSON, CSV and HTML files (long tap on results)
  • Copy results to the Clipboard as JSON or CSV

I use it just for basic troubleshooting and small fixes, but, please, let me know if you need anything else.
Screenshots:

Smart Scan and Recursive queries

Posted on by Posted in adaptive serial direct path reads, CBO, hints, oracle, parallel, SmartScan, trace, troubleshooting 1,680 Page views Leave a comment

Since Christmas I have been asked to investigate two different “failures to use Smart Scan”. It turns out they both fell into the same little known restriction on the use of Direct Read. Smart Scan critically depends on Direct Read in order to read the synthetic output blocks into private buffers in PGA so with Direct Read disabled Smart Scan is also disabled. In these two cases the restriction is on using Direct Read on Serial Recursive queries.

Case 1: Materialized View Refresh

A customer asked me to investigate why his MView refresh was running slowly and was failing to use Smart Scan. He had used 'trace[NSMTIO] disk=highest' which showed the cause as:

Direct Read for serial qry: disabled(::recursive_call::kctfsage:::)
Continue reading→

PL/SQL functions and statement level consistency

Posted on by Posted in deterministic functions, oracle, PL/SQL, PL/SQL optimization, query optimizing, SQL 1,905 Page views Leave a comment

You may know that whenever you call PL/SQL functions from within SQL query, each query in the function is consistent to the SCN of its start and not to the SCN of parent query.

Simple example:

create table test as 
  select level a, level b from dual connect by level<=10;

create or replace function f1(a int) return int as
  res int;
begin
  select b into res 
  from test t 
  where t.a=f1.a;
  dbms_lock.sleep(1);
  return res;
end;
/

As you can see we created a simple PL/SQL function that returns the result of the query select b from test where a=:input_var

But lets check what does it return if another session changes data in the table:

-- session 2:
begin
    for i in 1..30 loop
      update test set b=b+1;
      commit;
      dbms_lock.sleep(1);
    end loop;
end;
/
-- session 1:
SQL> select t.*, f1(a) func from test t;

         A          B       FUNC
---------- ---------- ----------
         1          1          1
         2          2          3
         3          3          5
         4          4          7
         5          5          9
         6          6         11
         7          7         13
         8          8         15
         9          9         17
        10         10         19

10 rows selected.

As you can see we got inconsistent results in the column FUNC, but we can easily fix it using OPERATORs:

CREATE OPERATOR f1_op
   BINDING (INT) 
   RETURN INT 
   USING F1;

Lets revert changes back and check our query with new operator now:

--session 1:
SQL> update test set b=a;

10 rows updated.

SQL> commit;

Commit complete.

-- session 2:
begin
    for i in 1..30 loop
      update test set b=b+1;
      commit;
      dbms_lock.sleep(1);
    end loop;
end;
/

-- session 1:
SQL> select t.*, f1(a) func, f1_op(a) op from test t;

         A          B       FUNC         OP
---------- ---------- ---------- ----------
         1          2          2          2
         2          3          5          3
         3          4          8          4
         4          5         11          5
         5          6         14          6
         6          7         17          7
         7          8         20          8
         8          9         23          9
         9         10         26         10
        10         11         29         11

10 rows selected.

As you can see, all values in the column OP are equal to the values of B, while, in turn, function F1 returns inconsistent values.

My presentations from RuOUG meetups

Posted on by Posted in oracle 1,554 Page views Leave a comment

I forgot to share my files from presentations, so I’m going to keep them here:

8-sql-2596013-Использование-некоторых-новых-возможностей-SQLCBO-в-12сDownload
Intra-block-row-chaining(RuOUG)Download
Intra-block-row-chaining(RuOUG).pptxDownload
Когда-выбирается-план-с-большим-COSTDownload
Sayan Malakshinov. CBO – Query Transformations(RuOUG)Download

DMLs and the Columnar Cache on ADW

Posted on by Posted in 12.2, inmemory, oracle 1,964 Page views Leave a comment

One of the main performance technologies underlying ADW is the In-Memory format column cache and a question that has come up several times is: how does the columnar cache handle DMLs and cache invalidations. This blog post attempts to answer that question.

The Two Columnar Cache Formats

The original columnar cache was an idempotent rearrangement of Hybrid Columnar Compressed blocks into pure columnar form still in the original HCC encoding. This is known internally as “CC1” and was not applicable to row format blocks. Because this is an idempotent transformation we are able to reconstitute the original raw blocks from a CC1 cache if needed.

We then introduced the In-Memory format columnar cache where, if you had an In-Memory licence, we would run the 1 MB chunks processed by Smart Scan through the In-Memory loader and write new clean columns encoded in In-Memory formats which meant that we could then use the new SIMD based predicate evaluation and other performance improvements developed for Database In-Memory. If you do not have an In-Memory licence, the original CC1 column cache is used. Another advantage of the CC2 format is that we can load row format blocks as well as HCC format blocks into pure columnar In-Memory format.  

Continue reading→

SQL: Fast ways to count unique characters in the string

Posted on by Posted in oracle, PL/SQL optimization, query optimizing, SQL 2,185 Page views Leave a comment

Test data:

create table t_str as
select round(dbms_random.value(1e10,9e10)) str from dual connect by level&lt;=1e5
/

PL/SQL variant:

with
  function ff(s varchar2) return varchar2 
  as
      type avarchars is table of varchar2(100) index by varchar2(1);
      st  avarchars;
      idx varchar2(1);
      res varchar2(10);
      
      function iterate( idx in out nocopy varchar2, arr in out nocopy avarchars) 
         return boolean
      as --pragma inline;
      begin
         if idx is null
            then idx:=arr.first; 
            else idx:=arr.next(idx);
         end if;
         return idx is not null;
      end;  
   begin
     for i in 1..length(s) loop
        st(substr(s,i,1)):=1;
     end loop;
     while iterate(idx,st) loop
        res:=res||idx;
     end loop;
     return res;
   end;

select min(ff(str)) res
from t_str
/

SQL-only variant:

select min(fstr)
from t_str t
     cross apply (
     select listagg(c) within group (order by 1) fstr
     from (select
            distinct substr(t.str, level, 1) c
           from dual
           connect by level <= length(t.str)
          )
     )
/

Timings:

SQL> create table t_str as
  2  select round(dbms_random.value(1e10,9e10)) str from dual connect by level<=1e5
  3  /

Table created.

Elapsed: 00:00:00.55
SQL> with
  2    function ff(s varchar2) return varchar2
  3    as
  4        type avarchars is table of varchar2(100) index by varchar2(1);
  5        st  avarchars;
  6        idx varchar2(1);
  7        res varchar2(10);
  8
  9        function iterate( idx in out nocopy varchar2, arr in out nocopy avarchars)
 10           return boolean
 11        as --pragma inline;
 12        begin
 13           if idx is null
 14              then idx:=arr.first;
 15              else idx:=arr.next(idx);
 16           end if;
 17           return idx is not null;
 18        end;
 19     begin
 20       for i in 1..length(s) loop
 21          st(substr(s,i,1)):=1;
 22       end loop;
 23       while iterate(idx,st) loop
 24          res:=res||idx;
 25       end loop;
 26       return res;
 27     end;
 28
 29  select min(ff(str)) res
 30  from t_str
 31  /

RES
--------------------------------------------------------------
0123

Elapsed: 00:00:00.48
SQL> select min(fstr) res2
  2  from t_str t
  3       cross apply (
  4       select listagg(c) within group (order by 1) fstr
  5       from (select
  6              distinct substr(t.str, level, 1) c
  7             from dual
  8             connect by level <= length(t.str)
  9            )
 10       )
 11  /

RES2
--------------------------------------------------------------
0123

Elapsed: 00:00:01.01

And much easier variant if you need your strings contain digits only:

select min(translate('0123456789', translate('z0123456789','z'||str,'z'), chr(0)))
from t_str