Development

With Gitora 6, developers can work on the same code base (i.e. a package, procedure etc…) in the same database without blocking each other.

Many development teams use a single database for development. Many of them use the same database for testing, as well. They achieve this by using different schemas for different purposes. Gitora 6 enables these teams to create Git repos for these schemas and pull updates between them.

With Gitora 6, you can even create a different schema for every developer and have them merge their code using Git.

Gitora 6 enables you to implement any modern development workflow in a single database.

Gitora 6 introduces a new repo type called Single Schema Repo (SSR). As its name suggests an SSR manages database objects from a single schema. The DDL scripts in SSR’s don’t contain the schema prefixes so that Gitora can execute them in other schemas in the same database.

This enables developers to work on the same package, procedure, function, view etc… (i.e. anything that can be created with the CREATE OR REPLACE command) at the same time, in the same database in different schemas.

Let’s go through an example: Let’s assume that the team is working on a logistics software and have a schema named LOGISTICS that stores all their database objects. The team can create a schema (or already have one) called LOGISTICS_TEST in the same database. Here are the steps the team needs to follow so that they can pull their changes to LOGISTICS_TEST.

1. Create a single schema Gitora repo that manages the LOGISTICS schema. Let’s call it the REPO_LOGISTICS.
   
2. Add all relevant database objects in the LOGISTICS schema to the LOGISTICS_REPO.
   
3. Create another single schema Gitora repo that manages the LOGISTICS_TEST schema. Let’s call it the REPO_LOGISTICS_TEST
   
4. Pull from the REPO_LOGISTICS to REPO_LOGISTICS_TEST

That’s it. That’s all there is to it. From this point on, any change you make to the code in the LOGISTICS schema can be pulled to the LOGISTICS_TEST schema using Gitora (and vice versa).

Single Schema Repos can also be used to create separate development environments in the same database for every developer in the team.

Assuming we already have the LOGISTICS schema and the REPO_LOGISTICS repo from the previous example, here is how that would work:

1. Create a schema for each developer: LOGISTICS_JOE, LOGISTICS_MARY, LOGISTICS_PAUL.
   
2. Create a single schema Gitora repo for each schema. Let’s call them REPO_LOGISTICS_JOE, REPO_LOGISTICS_MARY, REPO_LOGISTICS_PAUL respectively.
   
3. Pull from the REPO_LOGISTICS to REPO_LOGISTICS_JOE, REPO_LOGISTICS_MARY and REPO_LOGISTICS_PAUL.

From this point on, all three developers can work in their own schema and edit any package, procedure, view etc… freely, without overwriting each other’s changes or being affected by them. Using Gitora, they can create new branches in their own repo, for features or bugs they work on without affecting anyone else.

When the time comes to put it all together, they can use Gitora to merge their changes.

We wrote a few articles to get you started:

Creating Single Schema Repos.

How to implement agile development in a single database

How to manage changes to tables with Single Schema Repos

Before Gitora 6, agile database development with Gitora required multiple databases to implement. With Gitora 6 you can achieve it in a single database.

We are incredibly excited to release Gitora 6. We believe it will bring a massive productivity boost to Oracle database development teams worldwide. We can’t wait for you to try it.

Gitora 6 is available now. You can download it from this link.

You use a version control system to solve certain problems, to accomplish certain tasks. These are:

• Keep track of who changes what, when and why.
  
• If necessary, use previous versions of the code base for testing, development etc…
  
• Move changes between environments faster, with fewer errors during deployment.
  
• Improve productivity of the team (i.e. less time coordinating manually and more time developing. For example different developers working on the same parts of the code base simultaneously and merging their changes at a later time, hopefully mostly automatically.).

If you are using a version control system but not accomplishing any of the things above, are you really using a version control system?

Here is a perfectly reasonable scenario that I’ve seen in the real world. I’ve seen this scenario with teams using Git with SQL Developer, SVN/Git with another Oracle PL/SQL IDE. Invariably, they all end up here:

1. In the DEV database, you make changes to the package PKG_HIRING in the HR schema and then commit the package under /HR/PKG_HIRING.sql file in Git.
   
2. Next, another developer makes changes to the same package. He commits his changes under /HR/next_version/PKG_HIRING.sql, because… why not? It’s not like there is any enforcement.
   
3. A third developer commits his changes to /HR/PKG_HIRING.pkb because for some reason, the tool he uses for editing uses the .pkb suffix for packages.
   
4. The fourth developer named John, decides to copy PKG_HIRING to a file and work on it. He thinks he can move his changes to the database later. So he creates /HR/JOHN_DO_NOT_TOUCH/PKG_HIRING.sql
   
5. A fifth developer, makes changes to the HR.PKG_HIRING package but forgets to commit the package to Git. Hey it happens…
   
6. A sixth developer makes even more changes. He commits the package to Git under HR/PKG_HIRING.sql thereby committing not only his changes but also the fifth developers changes.

Three months later, you are tasked with deploying the latest version of the HR application to the TEST database. Which PKG_HIRING package are you going to deploy to TEST?

I actually know the answer to this question. You will deploy the one in the HR schema. It’s the safest option. I cannot help but wonder tough.. in these past three months, did John save his changes to the database? If so, how did he do it? Did he simply overwrite the package? What if some changes were lost?

At this point, I have to ask… Why do you even have a version control system? Why does that Git/SVN repo even exist? Is it useful at all or are you just doing all this just to tick a box and to be able to say that you are using a version control system?

Gitora solves all these problems above. It creates a version control system around Git and enforces it at the database level.

Let’s repeat the same steps for the six developers but this time let them use Gitora:

1. In DEV, you make changes to the PKG_HIRING package in the HR schema. When you are done, you open the Gitora web app, you see the list of database objects you changed, you select the ones you want to commit (in this case PKG_HIRING) you add a comment to the commit (such as the ticket number) and click the commit button.
   
2. The second developer follows the same process as you. He cannot make any changes to the package until you are done with it. Once you are done, he can make his changes. He opens the Gitora App, commit his changes to Git and is done. Note that, he doesn’t get to choose which folder he saves the PKG_HIRING.sql file to. Gitora creates a well structured working directory with reasonable folder and file names for database objects. The locations of these files are also standard. When there is human decision involved with naming and putting files in the file system, a lot of errors can occur. Gitora prevents these errors.
   
3. The third developer’s IDE used the .pkb extension for his file. Since developers don’t get to name the files or choose the folders they save database object scripts to, the third developer also has no choice but to follow the same process as the first two developers. By the way, Gitora works with any IDE. You can use SQL Developer, SQL Navigator, TOAD or PL/SQL Developer. It doesn’t matter. Everyone in your team can use a different editor if they choose to do so. Gitora still works.
   
4. The fourth developer was impatient. He created a new file under JOHN_DO_NOT_TOUCH and saved his changes to that file. With Gitora, he has no direct access to Git and the Git working folder. (Gitora is a server side solution. You don’t install Git to developer’s computers.) So he cannot do that. However, if he wants to do mischief, he still can copy the PKG_HIRING package to a new file in his computer and make changes there. Then, he can simply overwrite everything in the PKG_HIRING package with his version at a later time. This will cause some changes to be lost in the package depending on when he does it. He can even go ahead and commit his changes to Gitora. But hold on a second. At some point, either during development or testing, the mistake he made will be detected. In the first scenario, there is no way to know for certain who erased all the functioning code and exactly what code should be put back to the package. It will be trivial to use Git to figure out who deleted the perfectly functioning code from PKG_HIRING because Gitora always keeps track of who changed what, when and why. Since we can know when the mistake happened, we will know the correct version of the package to put back to. John will take the heat for his egregious mistake and merge his changes to the correct version of the package. No one wants that kind of heat. It is easy to see that Gitora incentivizes the right kind of behavior among teammates by keeping everyone accountable for their actions as well as making it possible to recover from critical mistakes.
   
5. The fifth developer simply forgot to commit his changes to Git. With Gitora there are two ways this can be caught. First, the sixth developer who wants to edit the package will contact the fifth developer to ask when the package will be available. Second, Gitora web app always shows which objects are locked by whom. So before preparing a deployment, you can always check if there are any database objects that are locked.
   
6. In the first scenario, the sixth developer committed fifth developers changes along with his. With Gitora, the sixth developer will always commit only his changes because there is no way he can gain access to the PKG_HIRING without the fifth developer releasing the package first. Gitora enforces that if an object is being edited by someone, no one else can edit it. With Gitora, version control is not something you do on the side but it is tightly integrated into your workflow and enforced.

And finally, three months later, you are tasked with preparing the deployment to TEST. How are you going to move the changes to the TEST database? With Gitora, all you need to do is to open the Gitora app, connect to the TEST database and pull the HR repo from DEV to TEST and you are done.

Going back to the beginning of the article; Gitora makes Git a useful version control system for the Oracle Database.

As shown in the second scenario with Gitora you can:

• Keep track of who changes what, when and why.
  We caught John misbehaving.
  
• If necessary, use previous versions of the code base for testing, development etc…
  We recovered an old version of PKG_HIRING and have John merge his changes to it.
  
• Move changes between environments faster, with fewer errors during deployment.
  Done via point and click Gitora Web Application.
  
• Improve productivity of the team (i.e. less time coordinating manually and more time developing. For example different developers working on the same parts of the code base simultaneously and merging their changes at a later time, hopefully mostly automatically.).
  The second scenario reduced manual coordination significantly. Gitora can do a lot more. You can have different teams work on the same code base at the same time and merge their changes just like Java, JavaScript developers. However that’s beyond the scope of this article. Please read this article and this article to learn more about how you can implement parallel development with Gitora.

 Full live performance. I've been enjoying these funk-folk-jazz songs for some time. Allegedly the music is in "old Ukrainian" - I don't speak Ukrainian or the closely related Carpatho-Russian dialect of Transcarpathia, so I can't really discern what that means - but consider the vocals as instrumentation in any case. In any case, this music is great fun at times and always stirs the human emotion. 
--------

A barely related side note: as a case study in creative folk-culture appropriation to modern forms, I recommend the Ukrainian musical film Hutsulka Ksenya. The plot revolves a young American whose late father leaves him his fortune on the condition he marry a Ukrainian woman. Distinctly unenthusiastic he visits the Carpathian region (presumably Zakarpattia Oblast, from which the Pavlik family emigrated) and falls for a young Hutsul woman.... I won't say more, its a terribly creative film and thoroughly enjoyable.

Hutsuls, as an aside, are not strictly speaking Ukrainians in the ethno-cultural sense, but in the broader sense of Ukraine as a multi-cultural nation. 

Gitora 6, the latest version of the source control tool for the Oracle Database, enables PL/SQL developers implement agile development practices in a single database.

With Gitora 6, developers can work on the same code base (packages, procedures etc...) at the same time, in the same database.

Learn more at http://blog.gitora.com/introducing-gitora-6/

Did you know you can set most parameters for the execution of a single statement without using an Alter Session by using an OPT_PARAM hint? For example, regular parameters (here forcing the storage clause in the query plan):

SQL> select /*+ OPT_PARAM('cell_offload_plan_display' 'always') */ col1 From table1;

and underscore parameters:

SQL>  select /*+ OPT_PARAM('_arch_comp_dbg_scan',2048) */ n_name from nation;

However if you try conflicting settings that set a different value in an inner query block, the value you set in the outermost query block will prevail. In this trvial example _arch_comp_dbg_scan=2048 will prevail:

SQL> select /*+ OPT_PARAM('_arch_comp_dbg_scan',2048) */ n_name from nation
where n_nationkey = (
    select /*+ OPT_PARAM('_arch_comp_dbg_scan',0) */ min(r_regionkey)
    from region where n_name < r_name); 

Another point to remember is that not all parameters get their value from the cursor environment. For example the buffer cache gets the raw value of _serial_direct_read so it cannot be overruled in this way: 

SQL> select /*+ OPT_PARAM('_serial_direct_read', always) */ n_name from nation; 

 will not force a DR scan of the table but an alter session will.

Just one more tool to keep in your toolkit for when you need it.

—
Roger

Since the beginning Oracle has provided four compression levels to offer a trade-off between the compression ratio and various other factors including table scans and the performance of single-row retrieval. I can not emphasize enough that the various trade offs mean that YMMV with the different levels and you should always test what works best with your application and hardware when choosing the level. Historically people have rarely used Query Low since the fast compression with reduced compression ratio means that the extra disk I/O is slower than the cost of decompression with Query High. The one time that Query Low makes sense on spinning disks is if you still have a significant number of row retrieval operations (including from index access joins).

X5 introduced NVMe technology which means that the extra I/O from Query Low is faster than ZLIB decompression which makes Query Low beneficial. So we needed to reassess the sizing of Compression Units. from 11.2.0.1 to 12.1.2.4 the sizing guidelines are as follows:

So, for example, Compress for Query High aims to pivot around at least 1000 rows and create a minimum compression unit size of 32 kb and a maximum of 64 kb. Using 12.1.2.3 I ran these against a TPC-H Lineitem table than contained between 1 and 6 copies of each column.

For Query Low fixed 32 kb CUs this gave us the following:

and for Query High 32 to 64 kb CUs this gave us:

so we see that the CU size remains as 32 kb as long as we are getting a 1000 rows or more then increases in size to 64 kb to try and fit in at least 1000 rows.

It became clear that this size range was inadequate for wide tables so to get more efficient compression and longer column runs for faster predicate performance (and also better CELLMEMORY rewrites) we removed the fixed size for Query Low and increased the max:

Query Low: 32 kb to 64 kb

Query High:  32 kb to 80 kb

This will not affect narrow tables at all but wider tables should see better table compression and faster scans at the cost of slightly slower single row retrieval for data loaded by 12.2 RDBMS. If you have HCC wide tables and typically cache them on flash cache you should consider re-evaluating Query Low for data loaded in 12.2 (or use Alter Table Move Compress to recompress existing data).

Roger 

When a background activity is happening on the cell you typically can’t use RDBMS v$ views to monitor it in the same way. One such question is how to tell if a segment is fully loaded in the Exadata column cache since this does not appear in the equivalent In-Memory v$ views.

When a segment is scanned by Smart Scan sufficiently often to be eligible the AUTOKEEP pool (typically that means at least twice an hour), the eligible 1MB chunks are written to flash in 12.1.0.2 style format, and put on a background queue. Lower priority tasks pick up the queued 1MB 12.1.0.2 format chunks from the flash cache, run them though the In-Memory loader, and rewrite the pure columnar representation in place of the old 12.1.0.2 style column cache chunks.

The easiest way that I know of to tell when this completes is to monitor that background activity is to use the following query until it shows zero:

select name, sum(value) value from (
      select extractvalue(value(t),'/stat/@name') name,
            extractvalue(value(t),'/stat') value
      from v$cell_state cs,
           table(xmlsequence(extract(xmltype(cs.statistics_value),
                                     '//stats[@type="columnarcache"]/stat'))) t
     where statistics_type='CELL')
     where name in ('outstanding_imcpop_requests')
     group by name;

I normally blog about table scans on Oracle native data but Oracle also supports a wide variety of features for scanning external tables and I need to cover these too. One of the ways I learn new things is being sent a bug report and saying to myself “Oh! I didn’t know you could do that”. So today I’m going to start with the grammar:

Alter Table <xt> Project Columns [ All | Referenced ]  

This DDL changes the value in the Property column displayed in user_external_tables:

SQL> select property
2    from user_external_tables
3    where table_name='C_ET';

PROPERTY
----------
ALL       

Here we have an external table defined using the legacy driver ORACLE_LOADER. This driver defaults to projecting all the columns in the base table rather than just those needed to satisfy the query (i.e. the referenced columns) and discarding rows that have data conversion issues up to the reject limit. 

So for example we have a DATE column in our external table that contains dirty data that won’t convert cleanly to Oracle internal dates using the supplied mask we can either import it as a VARCHAR2 to not lose values or import it as a date but lose rows even on queries that don’t need that date column. We can change the behaviour to only project the referenced columns by 

SQL> alter table c_et project column referenced;
 
Table altered.
 
SQL> 
SQL> select property 
2    from user_external_tables
3    where table_name='C_ET';
 
PROPERTY   
----------
REFERENCED 

The driver will now ignore unreferenced columns and if the date column is not used we will get all the rows in the external data and the query will run faster since datatype conversion is expensive but we may get an inconsistent number of rows depending on which columns are used in any given query. This is OK if the DBA knows a priori that there are no conversion errors or if they are willing to live with inconsistency in the number of rows returned.

The big data drivers such as ORACLE_HIVE have a different default behaviour which is to only project referenced columns and to replace data with conversion errors with NULL values and i.e. they default to returning a consistent number of rows with best performance.

Take away: In order to get consistency and fast table scan performance with ORACLE_LOADER,  the trick is to define the external table with the ‘convert_error store_null’ option and switch to ‘Project Column Referenced’. For example:

CREATE TABLE "T_XT"
(
  c0 varchar(10),
  c1 varchar(10)
)
ORGANIZATION external
(
  TYPE oracle_loader
  DEFAULT DIRECTORY DMPDIR
  ACCESS PARAMETERS
  (
    RECORDS DELIMITED BY NEWLINE
    CHARACTERSET  al32utf8
    ESCAPE
    FIELDS CSV WITH EMBEDDED terminated by ',' enclosed by '|'
    REJECT ROWS WITH ALL NULL FIELDS
    nullif equal blanks
    truncate_columns
    convert_error store_null
    (
      "C0" CHAR(10),
      "C1" CHAR(10)
    )
  )
  location
  (
    'simple.dat'
  )
) REJECT LIMIT UNLIMITED
parallel 4;

If you want more information on handling conversion errors when scanning an external table please check the Utilities Guide section on each driver type

12.1.0.2 introduced the new Columnar Flash Cache where 1MB of  blocks that are all in HCC format are rewritten so as to make each column from each CU contiguous. This works by first writing all the block headers to an array, then writing all the CU headers to an array, finally writing all the Column 1 column-level-CUs, then writing all the Column2 column-level-CUs etc.

The flash cache hash table maintains a simple mapping of column numbers to 64KB flash pages so, for any given query, we can simply do asynchronous disk I/O of the minimum set of 64KB pages required to cover the referenced columns.

Within the “flashcache” cell stats there is a new nested cell stat called “columnarcache” that enables you to track how it is working.

> set long 50000000
> set pagesize 10000
> select xmltype(STATISTICS_VALUE).getclobval(2,2) from v$cell_state;

XMLTYPE(STATISTICS_VALUE).GETCLOBVAL(2,2)
--------------------------------------------------------------------------------
....

  <stats type="flashcache">
    <stats type="columnarcache">
      <stat name="columnar_cache_size">0</stat>
      <stat name="columnar_cache_keep_size">0</stat>
      <stat name="columnar_cache_keep_read">0</stat>
      <stat name="columnar_cache_keep_read_bytes">0</stat>
      <stat name="columnar_attempted_read">0</stat>
      <stat name="columnar_attempted_read_bytes">0</stat>
      <stat name="columnar_cache_hits">0</stat>
      <stat name="columnar_cache_hits_read_bytes">0</stat>
      <stat name="columnar_cache_hits_saved_bytes">0</stat>
      <stat name="columnar_cache_pop">0</stat>
      <stat name="columnar_cache_pop_bytes">0</stat>
      <stat name="columnar_cache_pop_throttled">0</stat>
      <stat name="columnar_cache_pop_invalid_buf">0</stat>
      <stat name="columnar_cache_pop_size_align">0</stat>
      <stat name="columnar_cache_pop_findchdrfailure_mainchdr">0</stat>
      <stat name="columnar_cache_pop_findchdrfailure_childchdr">0</stat>
    </stats>
  </stats>

I typically spool the output of this to wade through it an editor but if we want to monitor how it is working with some workload, we need to extract individual stats, for example I bounced the cells and verified the cache was empty:

> select xmlcast(xmlquery('/cell_stats/stats/stats/stat[@name="columnar_cache_size"]' passing xmltype(STATISTICS_VALUE) returning content) as varchar2(200) ) "COLUMNAR CACHE SIZE"
from v$cell_state
where xmlexists('/cell_stats/stats/stats[@type="columnarcache"]' passing xmltype(STATISTICS_VALUE));

COLUMNAR CACHE SIZE
--------------------------------------------------------------------------------
0

I am using the 1GB TPC-H schema which takes a little more 400MB on disk when compressed with Query Low:

SQL> select sum(bytes) from user_segments where SEGMENT_NAME in ('SUPPLIER','PARTSUPP','LINEITEM','ORDERS','PART','CUSTOMER');

SUM(BYTES)
----------
 420675584

and checking the columnar cache again shows about half of the data has been rewritten into columnar cache format instead of caching raw blocks:

SQL> select xmlcast(xmlquery('/cell_stats/stats/stats/stat[@name="columnar_cache_size"]' passing xmltype(STATISTICS_VALUE) returning content) as varchar2(200) ) "COLUMNAR CACHE SIZE"
from v$cell_state
where xmlexists('/cell_stats/stats/stats[@type="columnarcache"]' passing xmltype(STATISTICS_VALUE));

COLUMNAR CACHE SIZE
--------------------------------------------------------------------------------
179306496

So let’s look at how the cache helped:

select
   xmlcast(
      xmlquery(
         '/cell_stats/stats/stats/stat[@name="columnar_cache_hits_read_bytes"]' 
         passing xmltype(STATISTICS_VALUE) 
         returning content
      ) as varchar2(200) 
   ) "HIT READ BYTES"
from v$cell_state
where xmlexists('/cell_stats/stats/stats[@type="columnarcache"]' passing xmltype(STATISTICS_VALUE));

 
HIT READ BYTES
--------------------------------------------------------------------------------
1909456896

select
   xmlcast(
      xmlquery(
         '/cell_stats/stats/stats/stat[@name="columnar_cache_hits_saved_bytes"]'
         passing xmltype(STATISTICS_VALUE)
         returning content
      ) as varchar2(200) 
   ) "HIT SAVED BYTES"
from v$cell_state
where xmlexists('/cell_stats/stats/stats[@type="columnarcache"]' passing xmltype(STATISTICS_VALUE));

 
HIT SAVED BYTES
--------------------------------------------------------------------------------
1128267776

which shows we were able to eliminate about 35% of the disk I/O for this query run!

We could, of course, have gotten that information more simply with the regular stat “cell physical IO bytes saved by columnar cache” but I wanted to show how to pull values from v$cell_state for use in scripts.

Many people only use Query High compression as they find the increased disk I/O from Query Low more than offsets the savings from cheaper decompression costs. However, with the columnar cache in place, those trade-offs have changed. It may be worth re-evaluating the decision as to when user Query Low vs. Query High particularly on CPU-bound cells.

Addendum: performance bug 20525311 affecting the columnar cache with selective predicates is fixed in the next rpm.

I was looking through a test script and saw something I didn’t know you could do in Oracle. I mentioned it to an Oracle ACE and he didn’t know it either. I then said to the External Table engineers “Oh I see you’ve added this cool new feature” and he replied dryly – “Yes, we added it in Oracle 10.1”. Ouch! So just in case you also didn’t know, you can create an External Table using a CTAS and the ORACLE_DATAPUMP driver.

This feature only work with the ORACLE_DATAPUMP access driver (it does NOT work with with the LOADER, HIVE, or HDFS drivers) and we can use it like this:

SQL> create table cet_test organization external
  2  (
  3    type ORACLE_DATAPUMP
  4    default directory T_WORK
  5    location ('xt_test01.dmp','xt_test02.dmp')
  6  ) parallel 2
  7  as select * from lineitem
 
Table created.

Checking the results shows us

-rw-rw---- ... 786554880 Mar 9 10:48 xt_test01.dmp 
-rw-rw---- ... 760041472 Mar 9 10:48 xt_test02.dmp

This can be a great way of creating a (redacted) sample of data to give to a developer to test or for a bug repro to give to support or to move between systems. 

A friend contacted me to ask why they were having problems using the table_stats hint to influence optimizer decision making and also to influence the decision to use direct read or buffer cache scan so this is just a quick blog post to clarify the syntax as it is not well documented.

table_stats(<table_name> <method> {<keyword>=<value>} )

Method is one of: DEFAULT, SET, SCALE, SAMPLE

Keyword is one of: BLOCKS, ROWS, ROW_LENGTH

The most useful methods are SET which does for statement duration what dbms_stats.set_table_stats does globally; and SCALE which acts to scale up the current size of the segment and can therefore be used to try what if scenarios on the segment growing on performance

For example:

select /*+  table_stats(scott.emp set rows=14 blocks=1  row_length=10)  */ * from scott.emp;

Since this is a table scan blog, let’s look at the impact on table scans. Using the Scale 1 customers table with 150,000 rows

SQL> exec dbms_stats.gather_table_stats(USER,'RDM');

SQL> select sum(BLOCKS) from user_segments where segment_name='RDM';
SUM(BLOCKS)
-----------
       1792

and use trace events

event="trace[NSMTIO] disk medium"                  # Direct I/O decision making
event="10358 trace name context forever, level 2"  # Buffer cache decision making

We see this segment is smaller than the small table threshold for this buffer cache (kcbstt=9458) and so decision making is short-circuited and will use the buffer cache :

kcbism: islarge 0 next 0 nblks 1689 type 2, bpid 3, kcbisdbfc 0 kcbnhl 8192 kcbstt 9458 keep_nb 0 kcbnbh 461198 kcbnwp 1 kcbpstt 0, BCRM_ON 0
NSMTIO: qertbFetch:NoDirectRead:[- STT < OBJECT_SIZE < MTT]:Obect's size: 1689 (blocks), Threshold: MTT(46119 blocks),

Now let’s try the same query with the hint shown in the example above:

kcbism: islarge 1 next 0 nblks 66666666 type 2, bpid 3, kcbisdbfc 0 kcbnhl 8192 kcbstt 9458 keep_nb 0 kcbnbh 461198 kcbnwp 1 kcbpstt 0, BCRM_ON 0
kcbimd: nblks 66666666 kcbstt 9458 kcbnbh 46119 bpid 3 kcbisdbfc 0 is_medium 0
kcbivlo: nblks 66666666 vlot 500 pnb 461198 kcbisdbfc 0 is_large 1
NSMTIO: qertbFetch:DirectRead:[OBJECT_SIZE>VLOT]
NSMTIO: Additional Info: VLOT=2305990
Object# = 75638, Object_Size = 66666666 blocks

Now the size of the table in blocks is far larger than our small table threshold so we go on to evaluate whether it is a medium table and it is too large to be considered medium (cutoff is 10% cache i.e. kcbnbh=46119 blocks) so then it is evaluated as a very large table and that is true so direct read will be used.

If for some reason we wanted to make some value permanent (caveat emptor) after doing experiments with the hint, we can set the table stats like this:

BEGIN
  DBMS_STATS.SET_TABLE_STATS( 
    ownname => 'TPCH'
  , tabname => 'RDM'
  , numrows => 2000000
  , numblks => 10000 );
END;

SQL>  select NUM_ROWS,BLOCKS,EMPTY_BLOCKS from DBA_TAB_STATISTICS where TABLE_NAME='RDM';

NUM_ROWS     BLOCKS EMPTY_BLOCKS
---------- ---------- ------------
   2000000     10000             0

and now we see the size we decided upon after the needed experiments being used without a hint:

kcbism: islarge 1 next 0 nblks 10000 type 2, bpid 3, kcbisdbfc 0 kcbnhl 8192 kcbstt 9458 keep_nb 0 kcbnbh 461198 kcbnwp 1 kcbpstt 0, BCRM_ON 0
kcbimd: nblks 10000 kcbstt 9458 kcbnbh 46119 bpid 3 kcbisdbfc 0 is_medium 1
kcbcmt1: hit age_diff adjts last_ts nbuf nblk has_val kcbisdbfc cache_it 191 23693 23502 461198 10000 1 0 1
NSMTIO: qertbFetch:NoDirectRead:[- STT < OBJECT_SIZE < MTT]:Obect's size: 10000 (blocks), Threshold: MTT(46119 blocks),

Our table is no longer small as 10,000 blocks is larger than STT=9458 blocks so it is a medium table but as it is smaller than the medium table threshold it will still use the buffer cache.

I hope you found this useful.

Roger

One of the joys of regexp is that you can write a pattern that is painfully expensive to match and offloading these to the cell can cause significant impact on other users and overall throughput (including heartbeat issues). If you have a user who is prone to writing bad regexp expressions you as DBA can prevent regexp (or any other operator) from being offloaded to the cells.

Let’s take a very simple example using a cut down version of TPC-H Query 16 and a NOT LIKE predicate: 

SQL> explain plan for select p_brand, p_type, p_size
from part
where p_brand <> 'Brand#45'
and p_type not like 'MEDIUM POLISHED%'
and p_size in (49, 14, 23, 45, 19, 3, 36, 9)
group by p_brand, p_type, p_size;

SQL> select * FROM TABLE(DBMS_XPLAN.DISPLAY);

  |*  3 |    TABLE ACCESS STORAGE FULL| PART | 29833 |  1048K|    |   217   (2)| 00:00:01 |  1 |  8 
------------------------------------------------------------------------------------------------------------
      3 - storage(("P_SIZE"=3 OR "P_SIZE"=9 OR "P_SIZE"=14 OR "P_SIZE"=19 
      OR "P_SIZE"=23 OR "P_SIZE"=36 OR "P_SIZE"=45 OR "P_SIZE"=49) 
      AND "P_BRAND"<>'Brand#45' AND "P_TYPE" NOT LIKE 'MEDIUM POLISHED%')

Here we see all the predicates get offloaded as expected. So, for example, to stop NOT LIKE being offloaded we would need to find the operator in v$sqlfn_metadata

SQL> column descr format a18
SQL> select func_id, descr, offloadable from v$sqlfn_metadata where descr like '%LIKE%';

   FUNC_ID DESCR              OFF
---------- ------------------ ---
        26  LIKE              YES
        27  NOT LIKE          YES
        99  LIKE              NO
       120  LIKE              YES
       121  NOT LIKE          YES
       ...
       524  REGEXP_LIKE       YES
       525  NOT REGEXP_LIKE   YES
       537  REGEXP_LIKE       YES
       538  NOT REGEXP_LIKE   YES

we can ignore all but the two basic LIKE operators in this case, so to disable the offload of our LIKE predicates we use:

   FUNC_ID DESCR              OFF
---------- ------------------ ---
        26  LIKE              YES
        27  NOT LIKE          YES
        99  LIKE              NO
       120  LIKE              YES
       121  NOT LIKE          YES
       ...
       524  REGEXP_LIKE       YES
       525  NOT REGEXP_LIKE   YES
       537  REGEXP_LIKE       YES
       538  NOT REGEXP_LIKE   YES

we can ignore all but the two basic LIKE operators in this case, so to disable the offload of our LIKE predicates we use:

SQL> alter session set cell_offload_parameters="OPT_DISABLED={26,27};";

and we see this reflected in the offloadable column in v$sqlfn_metadata.

SQL> select func_id, descr, offloadable from v$sqlfn_metadata where descr like '%LIKE%';

   FUNC_ID DESCR              OFF
---------- ------------------ ---
        26  LIKE              NO
        27  NOT LIKE          NO
        99  LIKE              NO
       120  LIKE              YES
       121  NOT LIKE          YES

To re-enable them you would use:

SQL> alter session set cell_offload_parameters="OPT_DISABLED={};";

One thing to note about this param is that it doesn’t work like events (whose settings are additive), here it replaces the previous value and so every operator you want disabled has to be included in the same alter session (and the param is limited to 255 maximum characters limiting the number of operators that can be disabled). With the offload of LIKE and NOT LIKE disabled we can see the impact on the plan:

SQL> explain plan for select p_brand, p_type, p_size
from part
where p_brand <> 'Brand#45'
and p_type not like 'MEDIUM POLISHED%'
and p_size in (49, 14, 23, 45, 19, 3, 36, 9)
group by p_brand, p_type, p_size;

SQL> select * FROM TABLE(DBMS_XPLAN.DISPLAY); 

 |*  3 |    TABLE ACCESS STORAGE FULL| PART | 29833 |  1048K|    |   217   (2)| 00:00:01 |  1 |  8 
------------------------------------------------------------------------------------------------------------

     3 - storage(("P_SIZE"=3 OR "P_SIZE"=9 OR "P_SIZE"=14 OR "P_SIZE"=19 OR "P_SIZE"=23
     OR "P_SIZE"=36 OR "P_SIZE"=45 OR "P_SIZE"=49) AND "P_BRAND"<>'Brand#45')

and the NOT LIKE is no longer in the storage filter. Now lets say that you as DBA are faced with a more complex problem and want to halt all complex processing on the cells temporarily. There is a parameter that will disable everything except the simple comparison operators and NULL checks:

SQL> alter session set "_cell_offload_complex_processing"=FALSE;

Now lets see what happens:

SQL> explain plan for select p_brand, p_type, p_size
from part
where p_brand <> 'Brand#45'
and p_type not like 'MEDIUM POLISHED%'
and p_size in (49, 14, 23, 45, 19, 3, 36, 9)
group by p_brand, p_type, p_size;

SQL> select * FROM TABLE(DBMS_XPLAN.DISPLAY); 

 |*  3 |    TABLE ACCESS STORAGE FULL| PART | 29833 |  1048K|    |   217   (2)| 00:00:01 |  1 |  8 
------------------------------------------------------------------------------------------------------------

    3 - filter(("P_SIZE"=3 OR "P_SIZE"=9 OR "P_SIZE"=14 OR "P_SIZE"=19 OR "P_SIZE"=23
    OR "P_SIZE"=36 OR "P_SIZE"=45 OR "P_SIZE"=49) AND "P_BRAND"<>'Brand#45'
    AND "P_TYPE" NOT LIKE 'MEDIUM POLISHED%')

Well we got no storage predicates at all and we didn’t expect that because we had one simple predicate namely p_brand != 'Brand#45' and the IN predicate had been rewritten to a series of OR’ed comparisons so what happened? This parameter only permits simple predicates that are linked by AND’s and can be attached directly to one column. Disjuncts are not pushable so they are normally evaluated by an eva tree or by pcode neither of which are sent to the cell with this parameter set to FALSE. So why wasn’t our one simple predicate offloaded. Well, note where it is in the explain plan. It comes after the rewritten the IN and since the predicates are sorted by the optimizer on effectiveness we stop looking as soon as we see one that can’t be offloaded. Let’s remove the IN and see what happens:

SQL> explain plan for select  p_brand, p_type, p_size
from part
where p_brand <> 'Brand#45'
and p_type not like 'MEDIUM POLISHED%';

|*  2 |   TABLE ACCESS STORAGE FULL| PART |   190K|  6686K|   217   (2)| 00:00:01 | 1 | 8 |
---------------------------------------------------------------------------------------------------

    2 - storage("P_BRAND"<>'Brand#45')
       filter("P_BRAND"<>'Brand#45' AND "P_TYPE" NOT LIKE 'MEDIUM POLISHED%')

as expected the simple predicate is now offloaded. If you look at v$sqlfn_metadata you’ll see this param is reflected in the offloadable column:

SQL> select func_id, descr, offloadable from v$sqlfn_metadata where descr like '%LIKE%';

   FUNC_ID DESCR              OFF
---------- ------------------ ---
        26  LIKE              NO
        27  NOT LIKE          NO
        99  LIKE              NO
       120  LIKE              NO
       ...
       121  NOT LIKE          NO
       524  REGEXP_LIKE       NO
       525  NOT REGEXP_LIKE   NO
       537  REGEXP_LIKE       NO
       538  NOT REGEXP_LIKE   NO

I hope you never need any of this in real life but it’s good to have it in the toolbag.

There are three main tools available for cleaning up a segment (Alter Table Shrink, Alter Table Move, and export/import), but one of them isn’t as helpful as you might have thought.  

Consider the following sequence of events where we update the 256th column to cause widespread fragmentation:

SQL> update t set c256 = 'abcdefghijklmnopqrstuvwxyz'; 
2000000 rows updated.
SQL> commit;
Commit complete.

SQL> analyze table t compute statistics;
Table analyzed.

SQL> select chain_cnt from dba_tables where table_name = 'T' and owner = 'FUSION';
CHAIN_CNT
----------
   2000000

SQL> select sum(blocks) from user_segments where segment_name = 'T';

SUM(BLOCKS)
-----------
     139264
SQL> alter table t enable row movement;
Table altered.
SQL> alter table t shrink space cascade;
Table altered.
SQL> analyze table t compute statistics;
Table analyzed.

SQL> select chain_cnt from dba_tables where table_name = 'T' and owner = 'FUSION';

CHAIN_CNT
----------
   1970068

1 row selected.

Note: ‘chain_cnt” does not count chained rows, rather it counts rows whose row pieces are chained across more than one block.  A Row that is in three pieces but all three pieces are in the same block has a zero chain_cnt.

In this particular artificial scenario Shrink has not gained us much reduction in space used, and more importantly it hasn’t reduced the kind of fragmentation that affects SmartScan performance.

This is because Shrink works in two phases. In Phase 1, the segment is scanned down from the end of the segment to the beginning. Rows with their head piece in the currently scanned block are moved together with all their row pieces. The segment is scanned from beginning upwards looking for space for the entire row. When it is unable to move any more entire rows, Phase 2 starts scanning down again from the end of the segment trying to move individual row pieces to blocks with space. This meant that while Phase 1 could potentially reduce chaining for relocated rows, Phase 2 was very unlikely to reduce the chain count and could in fact increase the chain_cnt. The moral of this is that Shrink really is for freeing up blocks close to the High Water Mark and not for cleaning up fragmented rows.

Now let’s try Alter Table move with the same segment:

SQL> alter table t move; 

Table altered.

SQL> analyze table t compute statistics;
Table analyzed.

SQL> select chain_cnt from dba_tables where table_name = 'T' and owner = 'FUSION';
CHAIN_CNT
----------
     45976
1 row selected.

SQL> select sum(blocks) from user_segments where segment_name = 'T';

SUM(BLOCKS)
-----------
      92160
1 row selected.

OK, that did what we hoped: more space has been reclaimed but more importantly for SmartScan, the number of fragmented rows has been reduced considerably.

With the fix for 19433200, the mechanics of Shrink have been reworked and it is now better at reducing the chain_cnt. However, even with the improvements made, when faced with heavily fragmented rows, Alter Table Move or export/import are likely to provide significantly better table scan performance with SmartScan.

Roger MacNicol

Oracle Data Storage Technology 

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One of the easiest ways is to use diagnostic events:

alter session set events 'sql_trace {callstack: fname xctend} errorstack(1)';

Got an interesting question today in RuOUG:

Some very simple PL/SQL procedures usually are completed within ~50ms, but sometimes sporadically longer than a second. For example, the easiest one from these procedures:

create or replace PROCEDURE XXXX (
  P_ORG_NUM IN number,
  p_result OUT varchar2, 
  p_seq OUT number 
) AS 
BEGIN
  p_seq := P_ORG_NUM; p_result:='';
END;

sql_trace shows that it was executed for 1.001sec and all the time was “ON CPU”:

Trace file /opt/oracle/diag/rdbms/rdb4/rdb/trace/rdb_ora_7100.trc
Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 - Production
Version 19.10.0.0.0
Build label:    RDBMS_19.10.0.0.0DBRU_SOLARIS.SPARC64_210107
ORACLE_HOME:    /opt/oracle/product/19
System name:	SunOS
Node name:	node2.zzzzz.ru
Release:	5.11
Version:	11.3
Machine:	sun4v
Oracle process number: 137
Unix process pid: 7100, image: oracle@node2.zzzzz.ru

*** 2021-05-26T15:38:31.321550+03:00
*** SESSION ID:(68.47940) 2021-05-26T15:38:31.321609+03:00
*** CLIENT ID:() 2021-05-26T15:38:31.321633+03:00
*** SERVICE NAME:(SYS$USERS) 2021-05-26T15:38:31.321656+03:00
*** MODULE NAME:(JDBC Thin Client) 2021-05-26T15:38:31.321679+03:00
*** ACTION NAME:() 2021-05-26T15:38:31.321703+03:00
*** CLIENT DRIVER:(jdbcthin : 19.3.0.0.0) 2021-05-26T15:38:31.321728+03:00

=====================
PARSING IN CURSOR #18446744071368904384 len=53 dep=0 uid=51 oct=47 lid=51 tim=190436799743741 hv=804128640 ad='12345678' sqlid='aabbaabbaa123'
BEGIN AAAA.XXXX(:1 ,:2 ,:3 ); END;
END OF STMT
PARSE #18446744071368904384:c=28,e=28,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,plh=0,tim=190436799743741
BINDS #18446744071368904384:

 Bind#0
  oacdty=02 mxl=22(22) mxlc=00 mal=00 scl=00 pre=00
  oacflg=03 fl2=1000000 frm=01 csi=873 siz=24 off=0
  kxsbbbfp=ffffffff747c6c98  bln=22  avl=02  flg=05
  value=6
 Bind#1
  oacdty=01 mxl=32767(32767) mxlc=00 mal=00 scl=00 pre=00
  oacflg=03 fl2=1400010 frm=01 csi=873 siz=32767 off=0
  kxsbbbfp=ffffffff747b8020  bln=32767  avl=00  flg=05
 Bind#2
  oacdty=02 mxl=22(22) mxlc=00 mal=00 scl=00 pre=00
  oacflg=03 fl2=1400000 frm=01 csi=873 siz=24 off=0
  kxsbbbfp=ffffffff747c6c68  bln=22  avl=00  flg=05

*** 2021-05-26T15:38:33.172899+03:00
WAIT #18446744071368904384: nam='SQL*Net message to client' ela= 3 driver id=1413697536 #bytes=1 p3=0 obj#=-1 tim=190436800744970
EXEC #18446744071368904384:c=1104,e=1001110,p=0,cr=0,cu=0,mis=0,r=1,dep=0,og=1,plh=0,tim=190436800745125
XCTEND rlbk=0, rd_only=1, tim=190436800745192

Originally I asked for dtrace/flamegraph and got just this:

              unix`_resume_from_idle+0x280
              genunix`cv_timedwait_sig_hires+0x1c4
              genunix`cv_waituntil_sig+0x90
              genunix`nanosleep+0xd4
              unix`syscall_trap+0x228

              0xffffffff7c04dbcc
              0xffffffff7c0393ec
              0xffffffff7c02e084
              0xffffffff7c02f860
              0x1176ef724
              0x118376518
              0x118366b84
              0x118365d14
              0x118366078
              0x118314b60
              0x1183973cc
              0x1182c707c
              0x1102edef8
              0x10aa60064
              0x10c224038
              0x10aa50ca8
              0x115bbc370
              0x10aa196dc
              0x10aa2c59c
              0x10aa50ca8
  ns                                                
           value  ------------- Distribution ------------- count    
       268435456 |                                         0        
       536870912 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 1        
      1073741824 |                                         0      

But truss showed much more interesting details:

 0.000037    write(11, "   B i n d # 2\n", 8)           = 8
 0.000045    write(12, " 2 0 m 7 } M X 8\n", 9)         = 9
 0.000022    lseek(11, 0, SEEK_CUR)                             = 4673
 0.000038    write(11, "     o a c d t y = 0 2  ".., 104)       = 104
 0.000040    write(12, " 2 0 m N } 5 Y e 1\n", 10)              = 10
 0.000020    lseek(11, 0, SEEK_CUR)                             = 4777
 0.000037    write(11, "     k x s b b b f p = f".., 52)        = 52
 0.000038    write(12, " 2 0 m 7 } j Y q\n", 9)         = 9
 0.000028    pathconf("/tmp/", _PC_NAME_MAX)                    = 255
 0.000029    open("/tmp/.SHMLPESLD_rdb4_26_000000047334dc80", O_RDWR|O_CREAT|O_EXCL|O_NOFOLLOW|O_CLOEXEC, 0666) Err#17 EEXIST
 1.000029    nanosleep(0xFFFFFFFF7FFF5FF0, 0xFFFFFFFF7FFF5FE0) = 0
 0.000038    open("/tmp/.SHMLPESLD_rdb4_26_000000047334dc80", O_RDWR|O_CREAT|O_EXCL|O_NOFOLLOW|O_CLOEXEC, 0666) = 13
 0.000023    close(13)                                  = 0
 0.000026    open("/tmp/.SHMDPESLD_rdb4_26_000000047334dc80", O_RDONLY|O_NOFOLLOW|O_CLOEXEC) = 13
 0.000028    unlink("/tmp/.SHMLPESLD_rdb4_26_000000047334dc80") = 0
 0.000052    mmap(0x00000000, 528, PROT_READ|PROT_EXEC, MAP_PRIVATE, 13, 0) = 0xFFFFFFFF74700000
 0.000020    close(13)                                  = 0
 0.000023    lseek(11, 0, SEEK_CUR)                             = 4829
 0.000044    write(11, "\n * * *   2 0 2 1 - 0 5".., 38)        = 38
 0.000040    write(12, " - T + 7 } d b k t . c *".., 16)        = 16
 0.000020    lseek(11, 0, SEEK_CUR)                             = 4867
 0.000037    write(11, " W A I T   # 1 8 4 4 6 7".., 130)       = 130
 0.000037    write(12, " 8 4 j U 1 y 5 $ o 7 2 2".., 18)        = 18
 0.000020    lseek(11, 0, SEEK_CUR)                             = 4997
 0.000037    write(11, " E X E C   # 1 8 4 4 6 7".., 105)       = 105
 0.000037    write(12, " 8 0 j d 0 y 5 $ k x s t".., 20)        = 20
 0.000022    lseek(11, 0, SEEK_CUR)                             = 5102

As you can see it calls nanosleep in the line #11 and sleeps 1 sec, but more interesting a line before and a few after nanosleep: oracle tries to open (and fails “err17”) a file "/tmp/.SHMLPESLD_rdb4_XXX” , which means that it tries to open a pl/sql procedure compiled in native mode (pesld – Plsql code Execution Shared object manager native code Loader). I’ve asked to check compilation parameters of the procedure, and it was really compiled in native mode:

PLSQL_OPTIMIZE_LEVEL 2
PLSQL_CODE_TYPE NATIVE
PLSQL_DEBUG FALSE

So obvious workaround to recompile those procedures in the INTERPRETED mode fixed the issue.

UPDATE: Forgot to show what exactly is going on here: Finally I got normal stack:

              unix`_resume_from_idle+0x280
              genunix`cv_timedwait_sig_hires+0x1c4
              genunix`cv_waituntil_sig+0x90
              genunix`nanosleep+0xd4
              unix`syscall_trap+0x228

              libc.so.1`__nanosleep+0x4
              libc.so.1`sleep+0x14
              libc.so.1`__pos4obj_lock+0x68
              libc.so.1`shm_open+0x50
              oracle`pesld02_Map_Object+0xbc4
              oracle`penglur+0x1d18
              oracle`pfrins+0x8e4
              oracle`pfri7_inst_body_common+0x214
              oracle`pfri3_inst_body+0x18
              oracle`pfrrun+0x9e0
              oracle`plsql_run+0x22c
              oracle`peicnt+0xfc
              oracle`kkxexe+0x2f8
              oracle`opiexe+0x5784
              oracle`kpoal8+0x918
              oracle`opiodr+0x448
              oracle`ttcpip+0x430
              oracle`opitsk+0x83c
              oracle`opiino+0x39c
              oracle`opiodr+0x448
  ns                                                
           value  ------------- Distribution ------------- count    
       268435456 |                                         0        
       536870912 |@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 3        
      1073741824 |                                         0    

As you can see pesld02_Map_Object is trying to load (map) a user-defined function (native compilation) and calls shm_open which calls __pos4obj_lock and if we look at it, we can find why and when it calls sleep(which calls nanosleep internally):

int
__pos4obj_lock(const char *name, const char *ltype)
{
 char *dfile;
 int fd;
 int limit = 64;

 if ((dfile = __pos4obj_name(name, ltype)) == NULL) {
  return (-1);
 }

 while (limit-- > 0) {
  if ((fd = __open_nc(dfile, O_RDWR | O_CREAT | O_EXCL, 0666))
      < 0) {
   if (errno != EEXIST)
    break;
   (void) sleep(1);
   continue;
  }

  (void) __close_nc(fd);
  free(dfile);
  return (1);
 }

 free(dfile);
 return (-1);
}

@tracefile_read_last_by_mask filemask [regexp] [ignore_regexp]
– finds last trace by filemask and filters rows by regexp and filters out rows by ignore_regexp:

@ tracefile_by_mask.sql [mask] – finds and shows last 10 trace files by mask

@tracefile_current – shows a file name of the your session’s trace file
@tracefile_read_current – reads a trace file of the current session

@tracefile_last [n] – shows last N trace files (by default 10)

@tracefile_read filename – reads a specified trace file

@tracefile_read_full.sql <filename> – reads a specified trace file and shows it with additional columns: component_name, operation_name, file_name, function_name

@tracefile_spool <tracefilename> <dest_file> – saves tracefilename to dest_file on local machine:

@diag/problems_last.sql [N] – shows last N problems, N is optional, by default shows last 10 problems

@diag/incidents_last.sql [N] – shows last N incidents (by default 10)

@diag/incidents_by_problem.sql <problem_id> [N] – shows last N incidents by problem_id

@diag/adr_home_create.sql – creates a directory object for reading incident files:

@diag/incident_files_by_mask.sql filemask – shows incident file names by file mask

SQL> @diag/incident_files_by_mask.sql %incident%387.%

INCIDENT_ID   OWNER_ID      FLAGS CON_ID BFILE
----------- ---------- ---------- ------ ------------------------------------------------------------
      26387          1          1      3 <ADR_HOME>/incident/incdir_26387/ORA19_ora_2942_i26387.trc#0

1 row selected.

@diag/incident_files_by_id.sql incident_id – shows incident files by incident_id

@diag/incident_files_spool_by_id.sql incident_id – saves an incident file on local machine: