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Using trace events with an offload server

Posted on May 4, 2017 by Roger MacNicol Posted in cell_offload, oracle, SmartScan, trace 1,803 Page views 2 Comments

I’ve noticed several people who were familiar with using trace events with cellsrv, were uncertain about how to use tracing the new offload server architecture. So whereas in the past you could have added tracing to SmartScan processing with:

> cellcli -e 'alter cell events = "trace[fplib.sage_data] disk=lowest, memory=lowest"'

the new syntax to turn it on is:

> cellcli -e 'alter cell offloadgroupEvents = "trace[fplib.sage_data] disk=lowest, memory=lowest"'

and to turn it off:

> cellcli -e 'alter cell offloadgroupEvents = "trace[fplib.sage_data] off"'

Do not use anything higher than “disk=medium, memory=medium” since the higher levels of tracing will cause the heartbeat monitor to fail with timeout issues.

The SmartScan trace events available under the fplib (Filter Processing Library) facility are:

        fplib.fplib_main,  fplib.sage_cache,  fplib.sage_txn,  fplib.sage_data,  fplib.sage_index

In the cell trace directory hierarchy each offload server will have its own trace directories

diag/asm/cell/SYS_122010_150220/trace
diag/asm/cell/SYS_112331_141117/trace

In a future post I will cover how to use named offload groups to isolate the traces for a single session.

Roger MacNicol,

Data Storage Technology Group

What’s new in 12.2 CELLMEMORY Part 3

Posted on May 4, 2017 by Roger MacNicol Posted in 12.2, inmemory, oracle, SmartScan 1,596 Page views 1 Comment

The Cellmemory Stats in RDBMS

The RDBMS stats for Cellmemory are designed to closely follow the pattern used by the Inmemory stats

Query Stats

Each column in each one MB of disk blocks will be rewritten into one IMC format Column CU in flash and a set of Column CUs comprise an overall Compression Unit so these stats reflect the number of 1 MB rewrites that were processed (not the number of column CUs).

“cellmemory IM scan CUs processed for query”
– #1 MB chuncks scanned in MEMCOMPRESS FOR QUERY format
“cellmemory IM scan CUs processed for capacity”
– #1 MB chuncks scanned in MEMCOMPRESS FOR CAPACITY format
“cellmemory IM scan CUs processed no memcompress”
– #1 MB chuncks scanned in NO CELLMEMORY format (12.1.0.2 format)

Load Stats

“cellmemory IM load CUs for query”
– #1 MB chunks successfully rewritten from 12.1.0.2 to MEMCOMPRESS FOR QUERY format
“cellmemory IM load CUs for capacity”
– #1 MB chunks successfully rewritten from 12.1.0.2 to MEMCOMPRESS FOR CAPACITY format
“cellmemory IM load CUs no memcompress”
– #1 MB chunks successfully rewritten into 12.1.0.2 format

Before a rewrite happens a routine is called that looks through the blocks in the 1 MB chunk and determines if it is eligible for write. Reasons it may not be include transactional metadata from the commit cache, the presence of blocks formats that can’t be rewitten (although this list is getting smaller with each rpm), and the amount of space the rewrite will take up.

A rewrite into 12.1.0.2 format must fit in the original 1 MB of flash cache. An IMC format rewrite is not permitted to exceed 8 MB. This limit is highly unlikely to be reached by MEMCOMPRESS FOR CAPACITY but could be reached when trying to rewrite HCC blocks with much greater than 8X original compression capacity into MEMCOMPRESS FOR QUERY format. This is one reason that the default is FOR CAPACITY.

“cellmemory IM scan CUs rejected for query”
– #1 MB chunks that could not be rewritten into MEMCOMPRESS FOR QUERY for whatever reason
“cellmemory IM scan CUs rejected for capacity
– #1 MB chunks that could not be rewritten into MEMCOMPRESS FOR CAPACITY for whatever reason
“cellmemory IM scan CUs rejected no memcompress”
– #1 MB chunks that could not even be rewritten into 12.1.0.2 format for whatever reason

What’s new in 12.2 CELLMEMORY Part 2

Posted on May 4, 2017 by Roger MacNicol Posted in inmemory, oracle, SmartScan 1,628 Page views 1 Comment

Question: do I need to know anything in this blog post?

Answer: No, it is a true cache and CELLMEMORY works automatically

Question: so why should I read this blog post?

Answer: because you like to keep a toolkit of useful ways to control the system when needed

The DDL

The Exadata engineering team has done a lot of work to make the flash cache automatically handle a variety of very different workloads happening simultaneously which is why we now typically discourage users from specifying:

SQL> Alter Table T storage (cell_flash_cache keep);

and trust the AUTOKEEP pool to recognize table scans that would most benefit from caching and to cache them in a thrash resistant way. Our Best Practice for CELLMEMORY is typically not to use the DDL. That said, there will be cases when the DBA wishes to override the system’s default behavior and we will look at a couple of those reasons. But first here’s the DDL which is a very cut down portion of the INMEMORY syntax:

Alter Table T  [ [ NO ] CELLMEMORY [ MEMCOMPRESS FOR [ QUERY | CAPACITY ] [ LOW | HIGH ] ] ]

So let’s break this down piece by piece.

Examples

SQL> Alter Table T NO CELLMEMORY

The NO CELLMEMORY clause prevents a table being eligible for the rewrite from 12.1.0.2 format to 12.2 In-Memory format. There are a variety of reasons you may wish to do this and we’ll look at those at the end of this post.

SQL> Alter Table T CELLMEMORY
SQL> Alter Table T CELLMEMORY MEMCOMPRESS FOR CAPACITY

These allows a table to be cached in the default 12.2 In-Memory format (you’d only ever need this to undo a NO CELLMEMORYyou done earlier or to revert a change in the compression level used).

SQL> Alter Table T CELLMEMORY MEMCOMPRESS FOR QUERY

I mentioned above that CELLMEMORY, by default, undergoes two rounds of compression: first a round of semantic compression and secondly a round of bitwise compression using LZO is applied. This is the default to try and keep CELLMEMORY’s flash cache footprint as close as we can to HCC flash space usage but run queries much faster than HCC. But, even though LZO has a relatively low decompression cost it is not zero. There are some workloads which run faster with MEMCOMPRESS FOR QUERY but they also use typically twice as much flash space. It would not be appropriate for the system to automatically start using twice as much flash but if you wish to experiment with this, the option is there. Also compressing with LZO takes CPU time which is not needed with MEMCOMPRESS FOR QUERY.

What about [ LOW | HIGH ] ?

Currently, unlike INMEMORY, these are throw away words but we retained them in the grammar for future expansion and because people are used to specifying them in a MEMCOMPRESS clause. this means in effect:

CELLMEMORY’S MEMCOMPRESS FOR QUERY
is roughly equivalent of INMEMORY’S MEMCOMPRESS FOR QUERY HIGH
CELLMEMORY’S MEMCOMPRESS FOR CAPACITY
is roughly equivalent of INMEMORY’S MEMCOMPRESS FOR CAPACITY LOW

Why might I want to overrule the default?

There are a few reasons:

Turning CELLMEMORY off may be useful if you know an HCC table will only be queried a few times and then dropped or truncated soon and it is not worth the CPU time to create the In-Memory formats
Turning CELLMEMORY off may be useful if your cells are already under a lot of CPU pressure and you wish to avoid the CPU of creating the In-Memory formats
Switching to MEMCOMPRESS FOR QUERY may be useful to get better query performance on some workloads (YMMV) and reduce the CPU cost of creating In-Memory formats

What about INMEMORY with CELLMEMORY?

We allow both INMEMORY and CELLMEMORY on the same table

SQL> Create Table T (c1 number) INMEMORY NO CELLMEMORY;
SQL> Create Table T (c1 number) INMEMORY CELLMEMORY MEMCOMPRESS FOR QUERY;

Why would you want both INMEMORY and CELLMEMORY?

DBIM implements a priority system where the In-Memory area is first loaded with the critical tables and then finally down to the PRIORITY LOW tables. If you have a PRIORITY LOW table that is unlikely to get loaded in memory it is fine to also specify CELLMEMORY so as to still get columnar performance. Note: an HCC encoded INMEMORY table will still get automatic CELLMEMORY if you don’t use any DDL at all.

—
Roger

What’s new in 12.2 CELLMEMORY Part 1

Posted on May 4, 2017 by Roger MacNicol Posted in 12.2, inmemory, oracle, SmartScan 1,646 Page views 1 Comment

Many people know that in 12.1.0.2 we introduced a ground-breaking columnar cache that rewrote 1 MB chunks of HCC format blocks in the flash cache into pure columnar form in a way that allowed us to only do the I/O for the columns needed but also to recreate the original block when that
was required.

This showed up in stats as “cell physical IO bytes saved by columnar cache”.

But in 12.1.0.2 we had also introduced Database In-Memory (or DBIM) that rewrote heap blocks into pure columnar form in memory. That project introduced:

new columnar formats optimized for query performance
a new way of compiling predicates that supported better columnar execution
the ability to run predicates against columns using SIMD instructions which could execute the predicate against multiple rows simultaneously

so it made perfect sense to rework the columnar cache in 12.2 to take advantage of the new In-Memory optimizations.

Quick reminder of DBIM essentials

In 12.2, tables have to be marked manually for DBIM using the INMEMORY keyword:

SQL> Alter Table INMEMORY

When a scan on a table tagged as INMEMORY is queried the background process is notified to start loading it into the INMEMORY area. This design was adopted so that the user’s scans are not impeded by loading. Subsequent scans that come along will check what percentage is currently loaded in memory and make a rule based decision:

For Exadata

Greater than 80% populated, use In-Memory and the Buffer Cache for the rest
Less than 80% populated, use Smart Scan
The 80% cutoff between BC and DR is configurable with an undocumented underscore parameter
Note: if an In-Memory scan is selected even for partially populated, Smart Scan is not used

For non-Exadata

Greater than 80% populated, use In-Memory and the Buffer Cache for the rest
Less than 80% populated, use In-Memory and Direct Read for the rest
Note: this requires that segment to be check pointed first
The 80% cutoff between BC and DR is configurable with an undocumented underscore parameter

While DBIM can provides dramatic performance improvements it is limited by the amount of usable SGA on the system that can be set aside for the In-Memory area. After that performance becomes that of disk access to heap blocks from the flash cache or disk groups. What was needed was a way to increase the In-Memory area so that cooler segments could still benefit from the In-Memory formats without using valuable RAM which is often a highly constrained resource.

Cellmemory

Cellmemory works in a similar way to the 12.1.0.2 columnar cache in that 1 MB of HCC formatted blocks are cached in columnar form automatically without the DBA needing to be involved or do anything. This means columnar cache scans are cached after Smart Scan has processed the blocks rather than before as happens with ineligible scans. The 12.1.0.2 columnar cache format simply takes all the column-1 compression units (CUs) and stores them contiguously, then all the column-2 CUs stored contiguously etc. so that each column can be read directly from the cache without reading unneeded columns. This happens during Smart Scan so that the reformated blocks are returned to the cell server along with the query results for that 1 MB chunk.

In 12.2, eligible scans continue to be cached in the 12.1.0.2 format columnar cache format after Smart Scan has processed the blocks so that columnar disk I/O is available immediately. The difference is that if and only if the In-Memory area is in use on the RDBMS node (i.e. the In-Memory feature is already in use in that database), the address of the beginning of the columnar cache entry is added to a queue so that a background process running at a lower priority can read those cache entries and invoke the DBIM loader to rewrite the cache entry into In-Memory formatted data.

Unlike the columnar cache which has multiple column-1 CUs, the IMC format creates a single column using the new formats that use semantic compression and support SIMD etc on the compressed intermediate compressed data. By default a second round of compression using LZO is then applied. When 1 MB of HCC ZLIB compressed blocks are rewritten in this way they typically take around 1.2 MB (YMMV obviously).

Coming up

Up-coming blog entries will cover:

Overriding the default behaviour with DDL
New RDBMS stats for Cellmemory
New cellsrv stats for Cellmemory
Flash cache pool changes
Tracing Cellmemory

When bloggers get it wrong – part 2

Posted on May 4, 2017 by Roger MacNicol Posted in adaptive serial direct path reads, oracle, SmartScan, trace, troubleshooting 2,033 Page views 1 Comment

In Part 2 we are going to look at making use of the trace events that show what was discussed in Part 1.
NB: Oracle no longer adds new numeric trace events, going forward new trace events use the Unified Tracing Service whose grammer is much simpler. The elements we need are:

trace[[x.]y] disk = [ lowest | low | medium | high | highest ]

For example Table Scan tracing is in the DATA hierachy:

[1] DATA
[2] KDS    “Kernel Data Scan”
[3] KDSFTS  “Full Table Scan”
[3] KDSRID  “RowID”

‘trace[KDSFTS] disk low’ – only trace full table scans
‘trace[KDSRID] disk low’ – only trace fetch by rowid
‘trace[KDS.*] disk low’ – trace both table scans and fetch by rowid
NB: don’t use ‘lowest’ with KDS – it is used for memory tracing only

Tracing Full Table Scans: KDSFTS

At the beginning of a granule we see if it is possible to use Turbo Scan (which is a prerequisite for using Exadata Smart Scan) and the data object number being scanned:

Continue reading→

When bloggers get it wrong – part 1

Posted on May 4, 2017 by Roger MacNicol Posted in adaptive serial direct path reads, oracle, SmartScan 2,345 Page views 3 Comments

I’ve read a number of blog entries from various people who’ve clearly put great diligence into trying to understand how the decision to use the buffer cache for a table scan or whether it is better to use direct read for the scan is made. Since this is critical decision from a performance perspective, I’ve decided to write a more definitive account. Part 1 deals with a few principles and part 2 (which will follow very shortly) will show how this works with tracing and clear up a few misunderstandings. Part 3 will deal with PQ in more detail as soon as I have time.

Serial Scans

Small Table Threshold (STT)

Serial scans use direct path reads when the object is not a small table and, since 11.2.0.2, the size used to determine whether it is “small” has come stats rather than the size from segment header.

_small_table_threshold defaults to the 2% buffer cache or to 20 blocks whichever is bigger (NB: this parameter ignores the effects of compression). An object is small enough for buffer cache reads to be more effective than direct path read if the number of blocks it contains is less than or equal to the value of _small_table_threshold. Such scans will bump the stat “table scans (short tables)”

Medium Table Threshold (MTT)

So what happens when an object is bigger than STT? We fall into the MTT case: MTT is not directly configurable and defaults to 10% of the buffer cache blocks. But it is also depends on a cost based decision that includes how busy the cache is.
NB: PQ and Exadata have different rules.
NB: MTT also depends on _object_statistics being enabled.

For STT < #blocks < MTT, if the cost based decision means a buffer cache scan is chosen, the stats “table scans (short tables)” will still get bumped.

So when is a medium table not worth caching?

First, when MTT < #blocks (ignore VLOT in blogs, it hasn’t been used for many years but persists in the trace and blogs for historic reasons).

Second, if the scan is not a select for update: different rules apply because cache scans are more useful if the segment being updated is first the subject of a query. Table larger than MTT also do factor in storage reduction factor. The ratios used are 10X for HCC and 3.3.X for OLTP.

Third, a cost based analysis is done comprising the % currently in cache, the % buffers are dirty (check-pointing cost), the current I/O bottleneck, and any RAC access costs. If the buffer cache is still used the stat “table scans (long tables)” will get bumped. But, if Direct Read is chosen the stat “table scans (direct read)” will get bumped.

Automatic Big Table Caching (ABTC)

So does this mean the buffer cache never helps with large tables? Not at all, large tables can still use the buffer cache for a portion of their scan based on the public parameter DB_BIG_TABLE_CACHE_PERCENT_TARGET which sets aside a percentage of the buffer cache for larger tables

On a single instance, ABTC works with both Serial and PQ scans BUT mixing Serial and PQ may lead to fewer cache hits. On RAC, it only works with PQ and requires that PARALLEL_DEGREE_POLICY set to AUTO or ADAPTIVE.

ABTC works by tracking the heat of medium and large tables and is designed to take advantage of any runs of buffers currently cached. Then if the decision to use ABTC is made, it periodically checks the cache for how many buffers in the next section are already in memory to see whether Direct Read or Buffer Cache scan would be better for the next chunk. Let’s see how the heat tracking works which looking at the v$bt_scan_obj_temps view.

SQL> select DATAOBJ#,SIZE_IN_BLKS,TEMPERATURE from V$BT_SCAN_OBJ_TEMPS; 

DATAOBJ#   SIZE_IN_BLKS TEMPERATURE
---------- ------------ -----------    
     79042         1689        3000

Each time we scan a medium or larger table it will bump that object’s temperature by 1000. Then, the object with the highest temperature will get absolute priority for caching and, if needed, will evict any previously cached ABTC blocks of other objects.
If the hottest object only partially fits in the buffer cache (which is quite likely), the first section that fits will be read through the buffer cache and cached then the rest of the scan will switch to Direct Read when the ABTC allocation in the cache is fully utilized.
NB: The object will still be check-pointed at the beginning so that it is safe to switch to Direct Read at any time.

Part 2 will include tracing to how how to make use of this feature.

Offloading row level security to Smart Scan

Posted on May 4, 2017 by Roger MacNicol Posted in cell_offload, oracle, SmartScan, SQL 1,566 Page views Leave a comment

The question came up this week about whether the predicates for row level security are offloaded to Smart Scan. The simple answer is yes as long as the policy_function contains off-loadable predicates.

Let’s see this in action. Using the public TPC-H schema we can set up the customer table so that customers can only see their own entries in that table. We need a row level security policy to see if each row is one that the current user is permitted to see and a function for the policy to call to make that determination:

Continue reading→

More tricks with OPT_PARAM

Posted on May 4, 2017 by Roger MacNicol Posted in cell_offload, hints, oracle, SmartScan 2,275 Page views Leave a comment

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;

Continue reading→

Examining the new Columnar Cache with v$cell_state

Posted on May 4, 2017 by Roger MacNicol Posted in oracle, SmartScan 1,855 Page views Leave a comment

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.

Roger MacNicol
Oracle Data Storage Technology

Controlling the offload of specific operators

Posted on May 4, 2017 by Roger MacNicol Posted in cell_offload, oracle, SmartScan 1,955 Page views Leave a comment

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.

The Cellmemory Stats in RDBMS

Query Stats

Load Stats

The DDL

Examples

What about [ LOW | HIGH ] ?

Why might I want to overrule the default?

What about INMEMORY with CELLMEMORY?

Quick reminder of DBIM essentials

For Exadata

For non-Exadata

Cellmemory

Coming up

Tracing Full Table Scans: KDSFTS

Serial Scans

Small Table Threshold (STT)

Medium Table Threshold (MTT)

Roger MacNicolOracle Data Storage Technology

Roger MacNicol
Oracle Data Storage Technology