Better call Thomas – Bayes in the Database

This is my third and last post covering my presentations from last week’s Tech Event, and it’s no doubt the craziest one :-).
Again I had the honour to be invited by my colleague Ludovico Caldara to participate in the cool “Oracle Database Lightning Talks” session. Now, there is a connection to Oracle here, but mainly, as you’ll see, it’s about a Reverend in a Casino.

So, imagine one of your customers calls you. They are going to use Oracle Transparent Data Encryption (TDE) in their main production OLTP database. They are in a bit of panic. Will performance get worse? Much worse?

Well, you can help. You perform tests using Swingbench Order Entry benchmark, and soon you can assure them: No problem. The below chart shows average response times for one of the transaction types of Order Entry, using either no encryption or TDE with one of AES-128 or AES-256.
Wait a moment – doesn’t it even look as though response times are even lower with TDE!?

Swingbench Order Entry response times (Customer Registration)

Well, they might be, or they might not be … the Swingbench results include how often a transaction was performed, and the standard deviation … so how about we ask somebody who knows something about statistics?

Cool, but … as you can see, for example, in this xkcd comic, there is no such thing as an average statistician … In short, there’s frequentist statistics and there’s Bayesian, and while there is a whole lot you can say about the respective philosophical backgrounds it mainly boils down to one thing: Bayesians take into account not just the actual data – the sample – but also the prior probability.

This is where the Reverend enters the game . The fundament of Bayesian statistics is Bayes theorem, named after Reverend Thomas Bayes.

Bayes theorem

In short, Bayes Theorem says that the probability of a hypothesis, given my measured data (POSTERIOR), is equal to the the probability of the data, given the hypothesis is correct (LIKELIHOOD), times the prior probability of the hypothesis (PRIOR), divided by the overall probability of the data (EVIDENCE). (The evidence may be neglected if we’re interested in proportionality only, not equality.)

So … why should we care? Well, from what we know how TDE works, it cannot possibly make things faster! In the best case, we’re servicing all requests from the buffer cache and so, do not have to decrypt the data. Then we shouldn’t incur any performance loss. But I cannot imagine how TDE could cause a performance boost.

So we go to our colleague the Bayesian statistian, give him our data and tell him about our prior beliefs.(Prior belief sounds subjective? It is, but prior assumptions are out in the open, to be questioned, discussed and justified.).

Now harmless as Bayes theorem looks, in practice it may be difficult to compute the posterior probability. Fortunately, there is a solution:Markov Chain Monte Carlo (MCMC). MCMC is a method to obtain the parameters of the posterior distribution not by calculating them directly, but by sampling, performing a random walk along the posterior distribution.

We assume our data is gamma distributed, the gamma distribution generally being adequate for response time data (for motivation and background, see chapter 3.5 of Analyzing Computer System Performance with Perl::PDQ by Neil Gunther).
Using R, JAGS (Just Another Gibbs Sampler), and rjags, we (oh, our colleague the statistian I meant, of course) go to work and create a model for the likelihood and the prior.
We have two groups, TDE and “no encryption”. For both, we define a gamma likelihood function, each having its own shape and rate parameters. (Shape and rate parameters can easily be calculated from mean and standard deviation.)

model {
    for ( i in 1:Ntotal ) {
      y[i] ~ dgamma( sh[x[i]] , ra[x[i]] )
    sh[1] <- pow(m[1],2) / pow(sd[1],2)
    sh[2] <- pow(m[2],2) / pow(sd[2],2)
    ra[1]  <- m[1] / pow(sd[1],2)
    ra[2]  <- m[2] / pow(sd[2],2

Second, we define prior distributions on the means and standard deviations of the likelihood functions:

    m[1] ~ dgamma(priorSha1, priorRa1)
    m[2] ~ dgamma(priorSha2, priorRa2)
    sd[1] ~ dunif(0,100)
    sd[2] ~ dunif(0,100)

As we are convinced that TDE cannot possibly make it run faster, we assume that the means of both likelihood functions are distributed according to prior distributions with the same mean. This mean is calculated from the data, averaging over all response times from both groups. As a consequence, in the above code, priorSha1 = priorSha2 and priorRa1 = priorRa2. On the other hand, we have no prior assumptions about the likelihood functions’ deviations, so we model them as uniformly distributed, choosing noninformative priors.

Now we start our sampler. Faites vos jeux … rien ne va plus … and what do we get?

Posterior distribution of means

Here we see the outcome of our random walks, the posterior distributions of means for the two conditions. Clearly, the modes are different. But what to conclude? The means of the two data sets were different too, right?

What we need to look at is the distribution of the difference of both parameters. What we see here is that the 95% highest density interval (HDI) [3] of the posterior distribution of the difference ranges from -0.69 to +1.89, and thus, includes 0. (HDI is a concept similar, but superior to the classical frequentist confidence interval, as it is composed of the 95% values with the highest probability.)

Posterior distribution of difference between means

From this we conclude that statistically, our observed difference of means is not significant – what a shame. Wouldn’t it have been nice if we could speed up our app using TDE 😉

Overhead of TDE tablespace encryption, is there?

Recently, for a customer, I conducted performance tests comparing performance under TDE tablespace encryption to a baseline. While I won’t present the results of those tests here I will describe two test series I ran in my lab environment.
So you’re an application owner – or a cautious DBA who wants to avoid trouble 😉 – and you want to know, is there an overhead if you use TDE tablespace encryption? (I think there’s some material available on this and I think it rather goes in the “no difference” direction. But it’s always nice to test for yourself, and you really need an answer – yes or no? ;-)).

For both test series, the design was the same: 2*3-factorial with factors

  • encryption: none, AES 128, AES 256, and
  • absence/presence of AES-NI kernel module (on suitable hardware)

So let’s see…

No there’s not!

The first test series were runs of the widely used Swingbench Order Entry Benchmark.
Measurement duration (excluding warm-up) per condition was 20 minutes, which was enough – in this isolated environment – to get reproducible results (as confirmed by sample).
The setup and environmental characteristics were the following:

  • Oracle on OEL 7
  • Order Entry schema size 6G
  • SGA 3G
  • 2 (v)CPUs
  • Data files on ASM
  • 4 concurrent users

Let’s look at the results:

Swingbench OE TPS

At first, it even looks like test runs using encryption had higher throughput. However, with standard deviations ranging between 25 and 30, for the single Order Entry transaction types, this must be attributed to chance. (Also, it would be quite difficult to argue why throughput would actually increase with TDE… let alone be highest when using an algorithm that requires more rounds of encryption…)
So from this test series, we would conclude that their is no performance impact of TDE tablespace encryption. But then, most of the time, at this ratio of schema size to buffer cache, blocks are mostly found in the cache:

  • Logical read (blocks) per transaction: 105.9
  • Physical read (blocks) per transaction: 2.6
  • Buffer Hit %: 97.53

Now with TDE tablespace encryption, blocks are stored in the SGA unencrypted, which means only a process that has to get the block from disk has to perform decryption.
Encryption, on the other hand, is taken care of by database writer, and so happens in the background anyway. So in this setup, it’s not astonishing at all not to find throughput differences due to absence/presence of encryption.

Yes there is…

A second type of test was run against the same database, on the same host. These were SQL Loader direct path loads of a 14G file, done serially. In this case, the server process has to read every line from disk, encrypt it, and write it to disk. This is what we see in this case:

SQL Loader Direct Path Load times

Now suddenly we DO have a substantial impact of TDE. Loading the file and storing data to disk now takes ~1.5 times as long as without encryption. And we see – which was to be expected, too – that this increase in elapsed time is mostly due to increased cpu consumption.

By the way, now that we have such a clear effect, do we see usage of AES-NI speeding up the load? No:

SQL Loader Direct Path Load, with and without AES-NI

This is surprising and probably something deserving further investigation, but not the topic of this post…


So what’s the point? Whether or not you will experience negative impact on performance will depend on your workload. No simple yes-or-no here… (but of course, no mystique either. It just means we have to phrase our questions a little bit more precisely.)