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Blocksize and Oracle Performance

Article by author Bert Scalzo

 

As it does for many people today, the Linux movement enthralls me. I'm interested not only because I'm more of a UNIX-based DBA but also because of the amazing speed with which major vendors, such as HP, Compaq, Dell, IBM and Oracle, have embraced this open-source operating system. Last year Linux server sales accounted for approximately 30% of Compaq's, 13.7% of Dell's and 13.5% of IBM's total server sales, according to eWeek. Moreover, IBM spent a billion dollars on Linux development in 2001, after having ported Linux to all their hardware platforms in 2000. Furthermore, Intel's new 64-bit Itanium CPU lists only four supported operating systems: Windows, Linux, AIX and HP-UX. And let's not forget that Oracle released 9i on Linux months ahead of the Windows port. Then again, maybe I just like the underdog--I mean I'm writing this article on my AMD Athlon-based PC.

No matter how fashionable Linux may be, that popularity does not automatically translate into nor does it guarantee performance. Even though Linux runs on everything from IBM 3/90s to Sun SPARC-based boxes, most people at this point are still probably running Linux on Intel-based server platforms. Now without sounding condescending, let me state that the PC architecture was never really intended to scale to the heights Linux makes possible. Thus we need to make sure that we squeeze every last drop of blood out of the turnip when we deploy an Intel based Linux server--especially for enterprise databases like DB2 and Oracle. Believe it or not, it's quite easy to get upwards of 1000% database improvement through proper Linux tuning and database configuration for Linux.

As with any scientific endeavor, in this article we will attempt to evaluate different tuning techniques by establishing a controlled environment where we can ascertain a baseline, identify all the changeable relevant variables, modify one variable at a time and obtain a reliable measurement of the effects for that one change. Wow, I haven't written techno-babble like this since I was a Physics student at Ohio State. In plain English, we must test one tuning concept at a time in order to accurately measure the observable effects of only that change.

First you need a test environment. I used a Compaq quad CPU server with 512 megabytes memory and eight 7200 RPM ultra-wide SCSI disks. Then I did the exact same tests with a single CPU Athlon system with the same amount of memory, but with a single 7200 RPM ultra100 IDE disk drive. Although the raw numbers and percentages were not identical, the observed improvement pattern was. That is, every test made each system better in the same general direction and similar magnitude.

Linux servers are truly universal in function, utilized easily as web servers, application servers, database servers, routers, firewalls, e-mail servers, file servers, print servers and combinations of the above. But we need to pick one such usage; remember our single variable requirement.

For simplicity, I chose the TPC benchmark as my testing methodology. It's widely recognized as a reliable OLTP workload benchmark, it has both on-line and deferred transactions, it's non-uniform in nature and it applies to numerous databases, including Oracle and DB2. Plus the TPC can be configured to stress all aspects of the hardware: CPU, memory, bus and disk. And to be totally honest, I'm a DBA, and Quest has a wonderful tool called Benchmark Factory that makes defining, running and measuring TPC tests as simple as sending e-mail.

Remember, I said that we'd start by looking at some very high ROI approaches. That means we're looking for items so easy and apparent in terms of applicability and impact that we only need observe the runtime differences in the TPC to see if we're on the right track. So we'll be only looking at what I call the OS and DB low-hanging fruits.

NOTE: If you're positive your Oracle database has been created perfectly for Linux, you might choose to skip the database low-hanging fruits section. For many DBAs it might serve as a refresher of some obvious database configuration and tuning issues.

DB Low-Hanging Fruits

Let's begin by looking at a typical initial database. Often, people start with either the default database created by the Oracle Installer or a database they created using the Database Configuration Assistant. Either way, the default settings are generally quite useless. Plus, a novice DBA or a consultant passing as a DBA might select values that actually make things worse. The point is that databases set up with poor initialization parameters and using dictionary tablespaces as shown in table DB1 are not uncommon.

DB1: Initial Database

Database Block Size

2K

SGA Buffer Cache

64M

SGA Shared Pool

64M

SGA Redo Cache

4M

Redo Log Files

4M

Tablespaces

Dictionary

TPC Results (our baseline)

Load Time (Seconds)

49.41

Transactions / Second

8.152

The obvious first choice is to increase the SGA size. So we increase the buffer cache and shared pool as shown in table DB2.

DB2: Cache & Pool

Database Block Size

2K

SGA Buffer Cache

128M

SGA Shared Pool

128M

SGA Redo Cache

4M

Redo Log Files

4M

Tablespaces

Dictionary

TPC Results

Load Time (Seconds)

48.57

Transactions / Second

9.147

Not quite what we had hoped for; only a 1.73% improvement in load time and a 10.88% increase in TPS. Okay, so maybe we should have increased the SGA redo log as well. Of course, we don't want the redo log file to be smaller than the SGA memory allocation, so we'll need to bump up the redo log file size to match. This is shown in table DB3.

DB3: Log Buffer

Database Block Size

2K

SGA Buffer Cache

128M

SGA Shared Pool

128M

SGA Redo Cache

16M

Redo Log Files

16M

Tablespaces

Dictionary

TPC Results

Load Time (Seconds)

41.39

Transactions / Second

10.088

Now we're getting somewhere. Notice the load time improved by 10, or by 17.35%. And once again the TPS time improved about the same amount, 9.33%. This makes sense because the load and simultaneous inserts, updates and deletes needed much more room than 8M. But it seems like the memory increases are yielding very small improvements. The I/O aspect seems to be where the current problem is. So even though it's an OLTP system, let's try increasing the block size as shown in table DB4.

DB4: 4K Block

Database Block Size

4K

SGA Buffer Cache

128M

SGA Shared Pool

128M

SGA Redo Cache

16M

Redo Log Files

16M

Tablespaces

Dictionary

TPC Results

Load Time (Seconds)

17.35

Transactions / Second

10.179

Now we're cooking. Even a PC with its limited bus and I/O capabilities can reap huge benefits from a larger block size. The load time improved over 138%, with no detriment to the TPS. For the moment, let's assume we don't want to try the next block size increase for whatever reason. The next simple idea that comes to mind is to switch from dictionary to locally managed tablespaces, something Oracle has been touting pretty hard. Thus we end up with that shown in table DB5.

DB5: Local Tablespaces

Database Block Size

4K

SGA Buffer Cache

128M

SGA Shared Pool

128M

SGA Redo Cache

16M

Redo Log Files

16M

Tablespaces

Local

TPC Results

Load Time (Seconds)

15.07

Transactions / Second

10.425

So Oracle is right, locally managed tablespaces are definitely the way to go. We got over a 15% improvement on the load and about 2% on the TPS. That's okay, but we would really like to see more results like those we saw with the 4K block size. So let's try 8K, as shown in table DB6. It worked before, so maybe it will work again.

DB6: 8K Block

Database Block Size

8K

SGA Buffer Cache

128M

SGA Shared Pool

128M

SGA Redo Cache

16M

Redo Log Files

16M

Tablespaces

Local

TPC Results

Load Time (Seconds)

11.42

Transactions / Second

10.683

Not too bad. As before, the larger block size yielded improvements to the load (almost 32%) with no detriment to the TPS. In fact, the TPS improved over 2%. But notice that we have reached a critical juncture in block size increases. The load time improvement decreased quite significantly--138% to 32%--and the TPS gain was nearly three times as much as that of the 4K block size. Further, block size increases will not likely be a good source of no-brainer gains (i.e., so obvious that we don't need to use other performance measuring tools).

So we're rapidly approaching the end of the DB low-hanging fruits. The only other thought that comes to mind is that we have multiple CPUs, maybe we can set up I/O slaves to leverage them. It's worth a try and is shown in table DB7.

DB7: I/O Slaves

Database Block Size

8K

SGA Buffer Cache

128M

SGA Shared Pool

128M

SGA Redo Cache

16M

Redo Log Files

16M

Tablespaces

Local

dbwr_io_slaves

4

lgwr_io_slaves (derived)

4

TPC Results

Load Time (Seconds)

10.48

Transactions / Second

10.717

That's another 9% improvement on the load but almost nothing for the TPS. It appears we've gotten all there is from the low-hanging fruits. We got improvements of 342% for the load time and 24% for the TPS; not bad for requiring absolutely no extensive or detailed performance monitoring. The results are summarized below.

 

 

   

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