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With Modern Storage the Oracle Buffer Cache is Not So Important.

With Oracle’s move towards engineered systems we all know that “more” is being done down at the storage layer and modern storage arrays have hundreds of spindles and massive caches. Does it really matter if data is kept in the Database Buffer Cache anymore?

Yes. Yes it does.

Time for a cool beer

Time for a cool beer

With much larger data sets and the still-real issue of less disk spindles per GB of data, the Oracle database buffer cache is not so important as it was. It is even more important.

I could give you some figures but let’s put this in a context most of us can easily understand.

You are sitting in the living room and you want a beer. You are the oracle database, the beer is the block you want. Going to the fridge in the kitchen to get your beer is like you going to the Buffer Cache to get your block.

It takes 5 seconds to get to the fridge, 2 seconds to pop it open with the always-to-hand bottle opener and 5 seconds to get back to your chair. 12 seconds in total. Ahhhhh, beer!!!!

But – what if there is no beer in the fridge? The block is not in the cache. So now you have to get your car keys, open the garage, get the car out and drive to the shop to get your beer. And then come back, pop the beer in the fridge for half an hour and now you can drink it. That is like going to storage to get your block. It is that much slower.

It is only that much slower if you live 6 hours drive from your beer shop. Think taking the scenic route from New York to Washington DC.

The difference in speed really is that large. If your data happens to be in the memory cache in the storage array, that’s like the beer already being in a fridge – in that shop 6 hours away. Your storage is SSD-based? OK, you’ve moved house to Philadelphia, 2 hours closer.

Let's go get beer from the shop

Let’s go get beer from the shop

To back this up, some rough (and I mean really rough) figures. Access time to memory is measured in Microseconds (“us” – millionths of a second) to hundreds of Nanoseconds (“ns” – billionths of a second). Somewhere around 500ns seems to be an acceptable figure. Access to disc storage is more like Milliseconds (“ms” – thousandths of a second). Go check an AWR report or statspack or OEM or whatever you use, you will see that db file scattered reads are anywhere from low teens to say 2 or 3 ms, depending on what your storage and network is. For most sites, that speed has hardly altered in years as, though hard discs get bigger, they have not got much faster – and often you end up with fewer spindles holding your data as you get allocated space not spindles from storage (and the total sustainable speed of hard disc storage is limited to the total speed of all the spindles involved). Oh, the storage guys tell you that your data is spread over all those spindles? So is the data for every system then, you have maximum contention.

However, memory speed has increased over that time, and so has CPU speed (though CPU speed has really stopped improving now, it is more down to More CPUs).

Even allowing for latching and pinning and messing around, accessing a block in memory is going to be at the very least 1,000 times faster than going to disc, maybe 10,000 times. Sticking to a conservative 2,000 times faster for memory than disc , that 12 seconds trip to the fridge equates to 24,000 seconds driving. That’s 6.66 hours.

This is why you want to avoid physical IO in your database if you possibly can. You want to maximise the use of the database buffer cache as much as you can, even with all the new Exadata-like tricks. If you can’t keep all your working data in memory, in the database buffer cache (or in-memory or use the results cache) then you will have to do that achingly slow physical IO and then the intelligence-at-the-hardware comes into it’s own, true Data Warehouse territory.

So the take-home message is – avoid physical IO, design your database and apps to keep as much as you can in the database buffer cache. That way your beer is always to hand.

Cheers.

Update. Kevin Fries commented to mention this wonderful little latency table. Thanks Kevin.

“Here’s something I’ve used before in a presentation. It’s from Brendan Gregg’s book – Systems Performance: Enterprise and the Cloud”