Oracle Scratchpad

September 28, 2019

Negative Offload

Filed under: Exadata,Execution plans,HCC,Oracle,Troubleshooting — Jonathan Lewis @ 5:38 pm BST Sep 28,2019

At the Trivadis Performance Days 2019 I did a presentation on using execution plans to understand what a query was doing. One of the examples I showed was a plan from an Exadata system (using that needed to go faster. The plan was from the SQL Monitor report and all I want to show you is one line that’s reporting a tablescan. To fit the screen comfortably I’ve removed a number of columns from the output.

The report had been generated while the statement was still running (hence the “->” at the left hand edge) and the query had scanned 166 segments (with no partition elimination) of a table with 4,500 data segments (450 range partitions and 10 hash sub-partitions – note the design error, by the way, hash partitioning in Oracle should always hash for a powert of 2).

SQL Plan Monitoring Details (Plan Hash Value=3764612084)  
| Id   |           Operation            | Name  | Read  | Read  | Write | Write |   Cell   | Mem  | Activity |       Activity Detail       |  
|      |                                |       | Reqs  | Bytes | Reqs  | Bytes | Offload  |      |   (%)    |         (# samples)         |   
| -> 5 |      TABLE ACCESS STORAGE FULL | TXN   |  972K | 235GB |       |       | -203.03% |   7M |    63.43 | Cpu (1303)                  | 
|      |                                |       |       |       |       |       |          |      |          | cell smart table scan (175) | 

In the presentation I pointed out that for a “cell smart table scan” (note the Activity Detail colum) this line was using a surprisingly large amount of CPU.

We had been told that the table was using hybrid columnar compression (HCC) and had been given some figures that showed the compression factor was slightly better than 4. I had also pointed out that the typical size of a read request was 256KB. (Compare Read Reqs with Read Bytes)

To explain the excessive CPU I claimed that we were seeing “double decompression” – the cell was decompressing (uncompressing) compression units (CUs), finding that the resulting decompressed data was larger than the 1MB unit that Exadata allows and sending the original compressed CU to the database server where it was decompressed again – and the server side decompression was burning up the CPU.

This claim is (almost certainly) true – but the justification I gave for the claim was at best incomplete (though, to be brutally honest, I have to admit that I’d made a mistake): I pointed out that the Cell Offload was negative 200% and that this was what told us about the double decompression. While double decompression was probably happening the implication I had made was that a negative offload automatically indicated double decompression – and that’s was an incorrect assumption on my part. Fortunately Maurice Müller caught up with me after the session was over and pointed out the error then emailed me a link to a relevant article by Ahmed Aangour.

The Cell Offload is a measure of the difference between the volume of data read and the volume of data returned to the server. If the cell reads 256KB from disc, but the column and row selection means the cell returns 128KB the Cell Offload would be 50%; if the cell returns 64KB the Cell Offload would be 75% (100 * (1 – 64KB/256KB)). But what if you select all the rows and columns from a compressed table – the volume of data after decompression would be larger than the compressed volume the cell had read from disc – and in this case we knew that we were reading 256KB at a time and the compression factor was slightly greater than 4, so the uncompressed data would probably be around 1MB, giving us a Cell Offload of 100 * (1 – 1024KB / 256KB) = negative 300%

Key Point: Any time that decompression, combined with the row and column selection, produces more data than the volume of data read from disc the Cell Offload will go negative. A negative Cell Offload is not inherently a problem (though it might hint at a suboptimal use of compression).

Follow-up Analysis

Despite the error in my initial understanding the claim that we were seeing double decompression was still (almost certainly) true – but we need to be a little more sophisticated in the analysis. The clue is in the arithmetic a few lines further up the page. We can see that we are basically reading 256KB chunks of the table, and we know that 256KB will expand to roughly 1MB so we ought to see a Cell Offload of about -300%; but the Cell Offload is -200%. This suggests fairly strongly that on some of the reads the decompressed data is slightly less than 1MB, which allows the cell to return the decompressed data to the database server, while some of the time the decompressed data is greater than 1MB, forcing the cell to send the original (compressed) CU to the databsae server.

We may even be able work the arithmetic backwards to estimate the number of times double decompression appeared.  Assume that two-thirds of the time the cell decompressed the data and successfully sent (just less than) 1MB back to the database server and one-third of the time the cell decompressed the data and found that the result was too large and sent 256KB of compressed data back to the server, and let’s work with the 972,000 read requests reported to see what drops out of the arithmetic:

  • Total data read: 972,000 * 256KB = 243,000 MB
  • Data sent to db server:  648,000 * 1MB + 324,000 * 256KB = 729,000 MB
  • Cell Offload = 100 * (1 – 729/243) = -200%   Q.E.D.

Of course it would be nice to avoid guessing – and if we were able to check the session activity stats (v$sessstat) while the query was running (or after it had completed) we could pick up several numbers that confirmed our suspicion. For, for example, we would keep an eye on:

	cell CUs sent uncompressed
	cell CUs processed for uncompressed
	EHCC {class} CUs Decompressed

Differences between these stats allows you to work out the number of compression units that failed the 1MB test on the cell server and were sent to the database server to be decompressed. There is actually another statistic named “cell CUs sent compressed” which would make life easy for us, but I’ve not seen it populated in my tests – so maybe it doesn’t mean what it seems to say.

Here’s an example from an system that I presented a few years ago showing some sample numbers.

cell CUs sent uncompressed              5,601
cell CUs processed for uncompressed     5,601

EHCC CUs Decompressed                  17,903
EHCC Query High CUs Decompressed       12,302 

This reveals an annoying feature of 11g (continued in 12.1) that results in double counting of the statistics, confusing the issue when you’re trying to analyze what’s going on. In this case the table consisted of 12,302 compression units, and the query was engineered to cause the performance problem to appear. The first two statistics show us how many CUs were decompressed successfully (we’ll see a change appearing there in 12.1). We then see that all 12,302 of the table’s “query high” compression units were decompressed – but the “total” of all CUs decompressed was 17.903.

It’s not a coincidence that 12,302 + 5,601 = 17,903; there’s some double counting going on. I don’t know how many of the statistics are affected in this way, but Oracle has counted the CUs that passsed decompression once as they were processed at the cell server and again as they arrived at the database server. In this example we can infer that 12,302 – 5,601 = 6,701 compression units failed decompression at the cell server and were sent to the database server in compressed form to be decompressed again.

Here’s a couple of sets of figures from some similar tests run on – one with a table compressed to query high another compressed to query low. There is one critical difference from the 11g figures but the same double-counting seems to have happened. In both cases the “EHCC Query [Low|High] CUs Decompressed” show the correct number of CUs in each table. Note, though that the “cell CUs processed for uncompress” in 12.1 appear to report the number of attempted decompressions rather than 11g’s number of successful decompressions.


cell CUs sent uncompressed                     19,561	-- successful decompressions at cell server
cell CUs processed for uncompressed            19,564	=> 3 failures

EHCC CUs Decompressed                          39,125	=  2 * 19,561 successes + 3 db server decompression
EHCC Query High CUs Decompressed               19,564


cell CUs sent uncompressed                     80,037	-- successful decompressions at cell server
cell CUs processed for uncompressed            82,178	=> 2,141 failures

EHCC CUs Decompressed                         162,215	=  2 * 80,037 successes + 2,141 db server decompressions
EHCC Query Low CUs Decompressed                82,178


I’ve annotated the figures to explain the arithmetic.

There has been some significant renaming and separation of statistics in 12.2, as described in this post by Roger MacNicol, and the problems of double-counting should have disappeared. I haven’t yet tested my old models in the latest versions of Oracle, though, so can’t show you anyy figures to demonstrate the change.


There are 4 key points to note in this posting.

  • Hash (sub)partitioning should be based on powers of 2, otherwise some partitions will be twice size of others.
  • There is a 1MB limit on the “data packet” sent between the cell server and database server in Exadata.
  • If you select a large fraction of the rows and columns from an HCC compressed table you may end up decompressing a lot of your data twice if the decompressed data for a read request is larger than the 1MB unit (and the cost will be highly visible at the database server as CPU usage).
  • The Cell Offload figure for a tablescan (in particular) will go negative if the volume of data sent from the cell server to the database server is larger than the volume of data read from the disk- even if double decompression hasn’t been happening.

A little corollary to the third point: if you are writing to a staging table with the expectation of doing an unfiltered tablescan (or a select *), then you probably don’t want to use hybrid columnar compression on the table as you will probably end up using a lot of CPU at the database server to compress it, then do double-decompression using even more CPU on the database server.  It’s only if you really need to minimise disk usage and have lots of CPU capacity to spare that you have a case for using hybrid columnar compression for the table (and Oracle In-Memory features may also change the degree of desirability).


I haven’t said anything about accessing table data by index when the table is subject to HCC compression. I haven’t tested the mechanism in recent versions of Oracle but it used to be the case that the cell server would supply the whole compression unit (CU) to the database server which would decompress it to construct the relevant row. One side effect of this was that the same CU could be decompressed (with a high CPU load) many times in the course of a single query.


March 29, 2016

Index Usage

Filed under: 12c,Exadata,HCC,in-memory,Indexing,Oracle,Performance — Jonathan Lewis @ 10:53 am BST Mar 29,2016

There are some questions about Oracle that are like the mythical Hydra – you think you’ve killed it, but for every head you cut off another two grow. The claim that “the optimizer will switch between using an index and doing a tablescan when you access more than X% of the data” re-appeared on the OTN database forum a little while ago – it doesn’t really matter what the specific value of X was – and it’s a statement that needs to be refuted very firmly because it’s more likely to cause problems than it is to help anyone understand what’s going on.

At a very informal level we may have an intuitive feeling that for a “precise” query accessing a “small” amount of data an indexed access path should make sense while for a “big” query accessing a “large” amount of data we might expect to see a tablescan, but any attempt to give a meaning to “small” and “large” that is both general purpose and strictly quantified will be wrong: there are too many variables involved.

Just as a quick demonstration of how badly we can be misled by a simple numeric claim here’s a quick test I created on a newly installed instance of, which I happened to set up with a locally defined tablespace using uniform extents of 1MB using the default 8KB blocksize but with manual (freelist) space management:

rem     Script:   index_usage_pct.sql
rem     Dated:    March 2016
rem     Author:   J P Lewis

drop table t1;

create table t1
with generator as (
        select  --+ materialize
                rownum id 
        from dual 
        connect by 
                level <= 1e4
        cast(rownum as number(8,0))                              id,
        cast(trunc(dbms_random.value(0,1e6)) as number(8,0))     n1,
        lpad(rownum,6,'0')              v1,
        rpad('x',10,'x')                small_vc,
        rpad('x',151,'x')               padding
        generator       v1,
        generator       v2
        rownum <= 1e6
begin dbms_stats.gather_table_stats( ownname => user,
                tabname          =>'T1',
                method_opt       => 'for all columns size 1'

create index t1_i1 on t1(id);

spool index_usage_pct.lst

select  num_rows, blocks, avg_row_len, round(num_rows/blocks) rows_per_block
from    user_tables
where   table_name = 'T1'

set autotrace on explain
select count(v1) from t1 where id between 1 and 245000;
set autotrace off

spool off

I’ve created a table with 1 million rows; the rows are about 180 bytes long (you’ll see the sizes a few lines further down the page), so it’s not an unreasonable model for lots of tables in typical systems – if you want to experiment further you can adjust the rpad() in the padding column; and I’ve created an index on a sequentially  (rownum) generated column. My call to autotrace will produce a truthful execution plan for the query supplied – there’s no risk of unexpected type conversion and no problems from bind variable peeking. As you can easily infer, my query will access 245,000 rows in the table of 1,000,000 – nearly a quarter of the table. Would you expect to see Oracle use the index ?

Here’s the output from the script on MY brand new database, instance, and tablespace:

---------- ---------- ----------- --------------
   1000000      25642         180             39

1 row selected.


1 row selected.

Execution Plan
Plan hash value: 269862921

| Id  | Operation                    | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT             |       |     1 |    10 |  6843   (1)| 00:01:23 |
|   1 |  SORT AGGREGATE              |       |     1 |    10 |            |          |
|   2 |   TABLE ACCESS BY INDEX ROWID| T1    |   245K|  2392K|  6843   (1)| 00:01:23 |
|*  3 |    INDEX RANGE SCAN          | T1_I1 |   245K|       |   553   (1)| 00:00:07 |

Predicate Information (identified by operation id):

   3 - access("ID">=1 AND "ID"<=245000)

There are no tricks involved here, no cunning fiddling with data structures or parameters – this is just a simple, straightforward, test.

Of course the result is probably a little counter-intuitive; 24.5% of the data seems a lot for the optimizer to pick an index. There are many reasons for this, the first being that the data is very well clustered relative to the index – the index’s clustering_factor is the smallest it could be for a B-tree indexing every row in this table.

Another important feature, though, is that I haven’t done anything with the system statistics so the optimizer was using various default values which tell it that a multiblock read will be quite small (eight blocks) and a lot slower than a single block read (26 ms vs. 12 ms). One simple change that many people might have made during or shortly after installation (though it shouldn’t really be done in any modern version of Oracle) is to set the db_file_multiblock_read_count parameter to 128 – with just this change the optimizer would assume that a multiblock read really would be 128 blocks, but that it would now take 266 ms. That means the optimizer will assume that the read will be ten times slower than it was, but will read 32 times as much data – a fairly significant relative improvement thanks to which the access path for my initial query will switch to a full tablescan and won’t switch back to an index range scan until I reduce the range from 245,000 to something like 160,000.

I can go further, of course. With a few background queries running to exercise the database I executed the dbms_stats.gather_system_stats() procedure with the ‘start’ and ‘stop’ options to collect some figures about the hardware and expected workload. This gave me the following results,  interpreted from the sys.aux_stats$ table:

MBRC       :126
MREADTIM   :0.902
SREADTIM   :0.386

With the optmizer using these figures to compare the relative speed and size of single and multiblock reads I had to reduce my selected range to roughly 51,000 before the optimizer would choose the index range scan.

I could go on to demonstrate the effects of the dbms_resource_manager.calibrate_io procedure and the effects of allowing different degrees of parallelism with different system stats, but I think I’ve probably made the point that there’s a lot of variation in the break point between index range scans and tablescans EVEN when you don’t change the data. With this very well-ordered (perfect clustering_factor) data I’ve seen the break point vary between 51,000 rows and 245,000 rows (5% and 25%).

And finally …

Let’s just finish with a last (and probably the most important) variation:  changing the pattern in the data we want from perfectly clustered to extremely scattered. If you check the query that generated the data you’ll see that we can do this by creating an index on column n1 instead of column id, and changing the where clause in the test query to n1 between 1 and 4500 (which, in my case, returned slightly more that 4,500 rows thanks to a small amount of duplication generated by the call to dbms_random.value()). With my most recent settings for the system statistics the optimizer chose to use a tablescan at slightly under 0.5% of the data.

Remember, there are many factors involved in the optimizer choosing between a tablescan and an index range scan and one of the most significant factors in the choice is the (apparent) clustering of the data so, if you haven’t come across it before, you should examine the “table_cached_blocks” option that appeared in for the procedure dbms_stats.set_table_prefs() as this allows you to give the optimizer a better idea of how well your data really is clustered.

Addendum (April 2016)

Following on from the demonstration of how changes in parameters, patterns and statistics can make a difference in what we (or the optimizer) might consider a “small” amount of data and whether an indexed access path would be appropriate, it’s worth mentioning that the Exadata technologies of smart scans and hybrid columnar compression and Oracle’s latest technology of In-Memory Colum Store do not change the way you think about indexes – they only change the (unspecifiable) volume at which an index ceases to be the better option to use.


May 12, 2014

Compression Units – 6

Filed under: Exadata,HCC,Oracle — Jonathan Lewis @ 1:34 pm BST May 12,2014

I received an email recently asking me if I knew how Oracle found specific rows and columns in a compression unit. This is a topic that I’ve spoken about a couple of times, and I’ve published several notes on the blog about it, including an image of a critical slide from one of my presentations, and I was expecting to find some notes somewhere about Oracle catalogues all the bits and pieces. Part 4 of this series lists some of the detail but I was slightly surprised to discover that it made the comment: “and somewhere in the CU there has to be a set of pointers to the first byte for each compressed column” – and this was the bit that my interrogator wanted to know. This posting is the answer – and I’ll start with a dump of the first block of a CU:

tab 0, row 0, @0x30
tl: 8016 fb: --H-F--N lb: 0x0  cc: 1
nrid:  0x01c06484.0
col  0: [8004]
Compression level: 03 (Archive Low)
 Length of CU row: 8004
kdzhrh: ------PC CBLK: 11 Start Slot: 00
 NUMP: 11
 PNUM: 00 POFF: 7884 PRID: 0x01c06484.0
 PNUM: 01 POFF: 15900 PRID: 0x01c06485.0
 PNUM: 02 POFF: 23916 PRID: 0x01c06486.0
 PNUM: 03 POFF: 31932 PRID: 0x01c06487.0
 PNUM: 04 POFF: 39948 PRID: 0x01c06488.0
 PNUM: 05 POFF: 47964 PRID: 0x01c06489.0
 PNUM: 06 POFF: 55980 PRID: 0x01c0648a.0
 PNUM: 07 POFF: 63996 PRID: 0x01c0648b.0
 PNUM: 08 POFF: 72012 PRID: 0x01c0648c.0
 PNUM: 09 POFF: 80028 PRID: 0x01c0648d.0
 PNUM: 10 POFF: 88044 PRID: 0x01c0648e.0
CU header:
CU version: 0   CU magic number: 0x4b445a30
CU checksum: 0x5c173142
CU total length: 93755
CU flags: NC-U-CRD-OP
ncols: 7
nrows: 4361
algo: 0
CU decomp length: 93159   len/value length: 309310
row pieces per row: 1
num deleted rows: 0
 00 00 1f 44 1f 0b 00 00 00 0b 00 00 1e cc 01 c0 64 84 00 00 00 00 3e 1c 01
 c0 64 85 00 00 00 00 5d 6c 01 c0 64 86 00 00 00 00 7c bc 01 c0 64 87 00 00
 00 00 9c 0c 01 c0 64 88 00 00 00 00 bb 5c 01 c0 64 89 00 00 00 00 da ac 01
 c0 64 8a 00 00 00 00 f9 fc 01 c0 64 8b 00 00 00 01 19 4c 01 c0 64 8c 00 00
 00 01 38 9c 01 c0 64 8d 00 00 00 01 57 ec 01 c0 64 8e 00 00 00 4b 44 5a 30
 42 31 17 5c 00 01 6e 3b eb 06 00 07 11 09 00 04 b8 3e 01 00 00 00 00 00 00
 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
...  20 repetitions of the previous line of zeros deleted
...  This is mostly the bitmap identifying deleted rows.
...  nrows=4361, so this will be about 4261, so this will be
...  about 4361/8 = 545 bytes of zeros.
 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 02 00 00 02 54 00 00 30
 bc 00 00 4a 82 00 00 64 37 00 00 a6 b8 00 00 e9 39 00 01 2b ba e6 c2 eb 8b
 00 00 2e 68 01 40 01 02 00 00 53 f0 78 9c a5 dc 07 57 23 5b 96 25 e0 ee 24

If you look at the symbolic dump there are lots of numbers that you could try to locate in the binary dump labelled START_CU:, for example the CU magic number 4b3445a30 is very obvious at the end of line 37 above (with the bytes in the right order), and the CU checksum of 5c173142 is visible at the start of line 38 (with the bytes in reverse order – no, don’t ask why me there’s a difference).

Bearing in mind how Oracle frequently uses “self-describing” strategies for data we might search for the value that gives the length of the CU as this could well be the start of the compressed data. But there are two CU lengths – the CU total length (93755) and the CU decomp length (93159). To my mind the latter is the “amount of CU requiring decompression” but let’s check for both: 93,755 (dec) = 0x16e3b, and 93,159 (dec) = 0x16be7 .

Take another look at line 38 – the second 4 bytes are: 00 01 6e 3b, the CU total length. Unfortunately we can’t see the CU decomp length anywhere, so let’s change strategy slightly – the different between the two lengths is 93755 – 93159 =  596 = 0x254, and by a convenient coincidence if you look at line 46, five bytes from the end, you find 02 54. Is this really a coincidence ? Let’s count 596 bytes from the CU total length and see where we get to – the answer is : the last 4 bytes of line 46 (e6 c2 eb 8b) This doesn’t seem to help at first sight, but take a look at where we are and what precedes that 4 bytes. There are 7 groups of 4 bytes that look like increasing values – and since I know more about the data than you do (it’s a table with 7 columns, and the last 4 columns are identical across all rows), I’ve extracted those 28 bytes and reformatted them:

                    	Offset		Delta
                    	------		-----
        00 00 02 54 	  596
        00 00 30 bc 	12476		11880
        00 00 4a 82 	19074		 6598
        00 00 64 37 	25655		 6581
        00 00 a6 b8 	42680		17025
        00 00 e9 39 	59705		17025
        00 01 2b ba 	76730		17025
                    	93755	<----

The “Offset” column is what you get by converting from Hex to decimal, and the “Delta” column is the difference between one value and the next. Notice how the last three deltas are the same, and how, if you add the same delta to the last Offset you get a number which is the CU total length. The “02 54” I found as the difference between the CU total length and the CU decomp length is actually the starting point for the list of pointers to each of the compressed columns, and the repeated 17,025 is the length of my duplicated final four columns. The e6 c2 eb 8b is the first four bytes of the first (compressed) column.

Having worked out that it’s possible to find the list of pointers quite easily from the dump, the next step (left as an exercise to the interested reader) is to work out the algorithm that Oracle uses to locate the list – since the CU decomp length is clearly derived and not actually stored. As a little clue, you might start by taking another look at line 38: bytes 11/12 are 00 07 – the number of columns in the table, and bytes 13/14 are 11 09 – the number of rows in the CU (and that’s the most significant number at this point because it tells you how long the bitmap for deleted rows will be).

August 19, 2012

Compression Units – 5

Filed under: CBO,Exadata,HCC,Indexing,Oracle — Jonathan Lewis @ 6:02 pm BST Aug 19,2012

The Enkitec Extreme Exadata Expo (E4) event is over, but I still have plenty to say about the technology. The event was a great success, with plenty of interesting speakers and presentations. I was particularly keen to hear  Frits Hoogland’s comments  on Exadata and OLTP, Richard Foote on Indexes, and Maria Colgan’s comments on how Oracle is making changes to the optimizer to understand Exadata a little better.

All three presentations were interesting – but Maria’s was possiby the most important (and entertaining). In particular she told us about two patches for, one current and one that is yet to be released (unfortunately I forgot to take  note of the patch numbers – ed: but they’ve been supplied by readers’ comment below).

August 7, 2012

Compression Units – 4

Filed under: Exadata,HCC,Oracle — Jonathan Lewis @ 5:24 pm BST Aug 7,2012

Following up a suggestion from Kerry Osborne that I show how I arrived at the observation I made in an earlier posting about the size of a compression unit, here’s a short note to show you what I did. It really isn’t rocket science (that’s just a quick nod to NASA and Curiosity – the latest Mars rover).

Step 1: you can access rows by rowid in Oracle, so what happens when you try to analyze rowids on Exadata for a table using HCC ? I created a table with the option “compress for archive high” and then ran the following query:

July 27, 2012

Compression Units – 3

Filed under: Exadata,HCC,Oracle — Jonathan Lewis @ 5:08 pm BST Jul 27,2012

For those who have read the previous posting of how I engineered an Exadata disaster and want to reproduce it, here’s the script I used to generate the data.

July 23, 2012

Compression Units – 2

Filed under: CBO,Exadata,HCC,Indexing,Oracle — Jonathan Lewis @ 4:41 pm BST Jul 23,2012

When I arrived in Edinburgh for the UKOUG Scotland conference a little while ago Thomas Presslie, one of the organisers and chairman of the committee, asked me if I’d sign up on the “unconference” timetable to give a ten-minute talk on something. So I decided to use Hybrid Columnar Compression to make a general point about choosing and testing features. For those of you who missed this excellent conference, here’s a brief note of what I said.

July 20, 2012

Compression Units

Filed under: Exadata,HCC,Oracle — Jonathan Lewis @ 6:04 am BST Jul 20,2012

If you’re starting to work with Exadata you need to work out what how much impact you can have by doing the right (or wrong, or unlucky) sorts of things with the technology. Fortunately there are only a few special features that really need investigation: Hybrid Columnar Compression (HCC), Offloading (and related Smart Scan stuff) and Storage Indexes. These are the features that will have the biggest impact on the space taken up by your data, the volume of data that will move through your system as you load and query it, and the amount of CPU it takes to do something with your data.

There are other features that are important, of course, such as the features for affecting parallel execution, and the options for resource management, but the three I’ve listed above are the core of what’s (currently) unique to Exadata. In this note I’m just going to make a few comments about how Oracle implements HCC, and what side-effects this may have.

October 5, 2011

HCC – 2

Filed under: Exadata,HCC,Infrastructure,Oracle — Jonathan Lewis @ 12:07 pm BST Oct 5,2011

Just a little follow-up to my previous note on hybrid columnar compression. The following is the critical selection of code I extracted from the trace file after tracing a run of the advisor code against a table with 1,000,000 rows in it:

October 4, 2011


Filed under: Exadata,HCC,Infrastructure,Oracle — Jonathan Lewis @ 11:56 am BST Oct 4,2011

Hybrid Columnar Compression is one of the big features of Exadata that can make fairly dramatic differences to the amount of space it takes to store your data. But how do you find out what’s going on under the covers if you haven’t got an Exadata machine in your garage ?

Here’s a simple starting point that occurred to me a couple of days ago after the product manager (or some such) pointed out that there was no need to make an Exadata emulator available to anyone because all you needed was the compression advisor which you could trust because it actually compressed a sample of your data to see how well it could compress.

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