Calculating statement execution likelihood

Derek Jones from The Shape of Code

In the following code, how often will the variable b be incremented, compared to a?

If we assume that the variables x and y have values drawn from the same distribution, then the condition (x < y) will be true 50% of the time (ignoring the situation where both values are equal), i.e., b will be incremented half as often as a.

a++;
if (x < y)
   {
   b++;
   if (x < z)
      {
      c++;
      }
   }

If the value of z is drawn from the same distribution as x and y, how often will c be incremented compared to a?

The test (x < y) reduces the possible values that x can take, which means that in the comparison (x < z), the value of x is no longer drawn from the same distribution as z.

Since we are assuming that z and y are drawn from the same distribution, there is a 50% chance that (z < y).

If we assume that (z < y), then the values of x and z are drawn from the same distribution, and in this case there is a 50% change that (x < z) is true.

Combining these two cases, we deduce that, given the statement a++; is executed, there is a 25% probability that the statement c++; is executed.

If the condition (x < z) is replaced by (x > z), the expected probability remains unchanged.

If the values of x, y, and z are not drawn from the same distribution, things get complicated.

Let's assume that the probability of particular values of x and y occurring are alpha e^{-sx} and beta e^{-ty}, respectively. The constants alpha and beta are needed to ensure that both probabilities sum to one; the exponents s and t control the distribution of values. What is the probability that (x < y) is true?

Probability theory tells us that P(A < B) = int{-infty}{+infty} f_B(x) F_A(x) dx, where: f_B is the probability distribution function for B (in this case: beta e^{-tx}), and F_A the the cumulative probability distribution for A (in this case: alpha(1-e^{-sx})).

Doing the maths gives the probability of (x < y) being true as: {alpha beta s}/{s+t}.

The (x < z) case can be similarly derived, and combining everything is just a matter of getting the algebra right; it is left as an exercise to the reader :-)

Automating Windows VM Creation on Ubuntu

Chris Oldwood from The OldWood Thing

TL;DR you can find my resulting Oz and Packer configuration files in this Oz gist and this Packer gist on my GitHub account.

As someone who has worked almost exclusively on Windows for the last 25 years I was somewhat surprised to find myself needing to create Windows VMs on Linux. Ultimately these were to be build server agents and therefore I needed to automate everything from creating the VM image, to installing Windows, and eventually the build toolchain. This post looks at the first two aspects of this process.

I did have a little prior experience with Packer, but that was on AWS where the base AMIs you’re provided have already got you over the initial OS install hurdle and you can focus on baking in your chosen toolchain and application. This time I was working on-premise and so needed to unpick the Linux virtualization world too.

In the end I managed to get two approaches working – Oz and Packer – on the Ubuntu 18.04 machine I was using. (You may find these instructions useful for other distributions but I have no idea how portable this information is.)

QEMU/KVM/libvirt

On the Windows-as-host side (until fairly recently) virtualization boiled down to a few classic options, such as Hyper-V and Virtual Box. The addition of Docker-style Windows containers, along with Hyper-V containers has padded things out a bit more but to me it’s still fairly manageable.

In contrast on the Linux front, where this technology has been maturing for much longer, we have far more choice, and ultimately, for a Linux n00b like me [1], this means far more noise to wade through on top of the usual “which distribution are you running” type questions. In particular the fact that any documentation on “virtualization” could be referring to containers or hypervisors (or something in-between), when you’re only concerned with hypervisors for running Windows VMs, doesn’t exactly aid comprehension.

Luckily I was pointed towards KVM as a good starting point on the Linux hypervisor front. QEMU is one of those minor distractions as it can provide full emulation, but it also provides the other bit KVM needs to be useful in practice – device emulation. (If you’re feeling nostalgic you can fire up an MS-DOS recovery boot-disk from “All Boot Disks” under QMEU/KVM with minimal effort which gives you a quick sense of achievement.)

What I also found mentioned in the same breath as these two was a virtualization “add-on layer” called libvirt which provides a layer on top of the underlying technology so that you can use more technology agnostic tools. Confusingly you might notice that Packer doesn’t mention libvirt, presumably because it already has providers that work directly with the lower layer.

In summary, using apt, we can install this lot with:

$ sudo apt install qemu qemu-kvm libvirt-bin  bridge-utils  virt-manager -y

Windows ISO & Product Key

We’re going to need a Windows ISO along with a related product key to make this work. While in the end you’ll need a proper license key I found the Windows 10 Evaluation Edition was perfect for experimentation as the VM only lasts for a few minutes before you bin it and start all over again.

You can download the latest Windows image from the MS downloads page which, if you’ve configured your browser’s User-Agent string to appear to be from a non-Windows OS, will avoid all the sign-up nonsense. Alternatively google for “care.dlservice.microsoft.com” and you’ll find plenty of public build scripts that have direct download URLs which are beneficial for automation.

Although the Windows 10 evaluation edition doesn’t need a specific license key you will need a product key to stick in the autounattend.xml file when we get to that point. Luckily you can easily get that from the MS KMS client keys page.

Windows Answer File

By default Windows presents a GUI to configure the OS installation, but if you give it a special XML file known as autounattend.xml (in a special location, which we’ll get to later) all the configuration settings can go in there and the OS installation will be hands-free.

There is a specific Windows tool you can use to generate this file, but an online version in the guise of the Windows Answer File Generator produced a working file with fairly minimal questions. You can also generate one for different versions of the Windows OS which is important as there are many examples that appear on the Internet but it feels like pot-luck as to whether it would work or not as the format changes slightly between releases and it’s not easy to discover where the impedance mismatch lies.

So, at this point we have our Linux hypervisor installed, and downloaded a Windows installation .iso along with a generated autounattend.xml file to drive the Windows install. Now we can get onto building the VM, which I managed to do with two different tools – Oz and Packer.

Oz

I was flicking through a copy of Mastering KVM Virtualization and it mentioned a tool called Oz which was designed to make it easy to build a VM along with installing an OS. More importantly it listed having support for most Windows editions too! Plus it’s been around for a fairly long time so is relatively mature. You can install it with apt:

$ sudo apt install oz -y

To use it you create a simple configuration file (.tdl) with the basic VM details such as CPU count, memory, disk size, etc. along with the OS details, .iso filename, and product key (for Windows), and then run the tool:

$ oz-install -d2 -p windows.tdl -x windows.libvirt.xml

If everything goes according to plan you end up with a QEMU disk image and an .xml file for the VM (called a “domain”) that you can then register with libvirt:

$ virsh define windows.libvirt.xml

Finally you can start the VM via libvirt with:

$ virsh start windows-vm

I initially tried this with the Windows 8 RTM evaluation .iso and it worked right out of the box with the Oz built-in template! However, when it came to Windows 10 the Windows installer complained about there being no product key, despite the Windows 10 template having a placeholder for it and the key was defined in the .tdl configuration file.

It turns out, as you can see from Issue #268 (which I raised in the Oz GitHub repo) that the Windows 10 template is broken. The autounattend.xml file also wants the key in the <UserData> section too it seems. Luckily for me oz-install can accept a custom autounattend.xml file via the -a option as long as we fill in any details manually, like the <AutoLogin> account username / password, product key, and machine name.

$ oz-install -d2 -p windows.tdl -x windows.libvirt.xml –a autounattend.xml

That Oz GitHub issue only contains my suggestions as to what I think needs fixing in the autounattend.xml file, I also have a personal gist on GitHub that contains both the .tdl and .xml files that I successfully used. (Hopefully I’ll get a chance to submit a formal PR at some point so we can get it properly fixed; it also needs a tweak to the Python code as well I believe.)

Note: while I managed to build the basic VM I didn’t try to do any post-processing, e.g. using WinRM to drive the installation of applications and tools from the outside.

Packer

I had originally put Packer to one side because of difficulties getting anything working under Hyper-V on Windows but with my new found knowledge I decided to try again on Linux. What I hadn’t appreciated was quite how much Oz was actually doing for me under the covers.

If you use the Packer documentation [2] [3] and online examples you should happily get the disk image allocated and the VM to fire up in VNC and sit there waiting for you to configure the Windows install. However, after selecting your locale and keyboard you’ll probably find the disk partitioning step stumps you. Even if you follow some examples and put an autounattend.xml on a floppy drive you’ll still likely hit a <DiskConfiguration> error during set-up. The reason is probably because you don’t have the right Windows driver available for it to talk to the underlying virtual disk device (unless you’re lucky enough to pick an IDE based example).

One of the really cool things Oz appears to do is handle this nonsense along with the autounattend.xml file which it also slips into the .iso that it builds on-the-fly. With Packer you have to be more aware and fetch the drivers yourself (which come as part of another .iso) and then mount that explicitly as another CD-ROM drive by using the qemuargs section of the Packer builder config. (In my example it’s mapped as drive E: inside Windows.)

[ "-drive", "file=./virtio-win.iso,media=cdrom,index=3" ]

Luckily you can download the VirtIO drivers .iso from a Fedora page and stick it alongside the Windows .iso. That’s still not quite enough though, we also need to tell the Windows installer where our drivers are located; we do that with a special section in the autounattend.xml file.

<DriverPaths>
  <PathAndCredentials wcm:action="add" wcm:keyValue="1">
    <Path>E:\NetKVM\w10\amd64\</Path>

Finally, in case you’ve not already discovered it, the autounattend.xml file is presented by Packer to the Windows installer as a file in the root of a floppy drive. (The floppy drive and extra CD-ROM drives both fall away once Windows has bootstrapped itself.)

"floppy_files":
[
  "autounattend.xml",

Once again, as mentioned right at the top, I have a personal gist on GitHub that contains the files I eventually got working.

With the QEMU/KVM image built we can then register it with libvirt by using virt-install. I thought the --import switch would be enough here as we now have a runnable image, but that option appears to be for a different scenario [4], instead we have to take two steps – generate the libvirt XML config file using the --print-xml option, and then apply it:

$ virt-install --vcpus ... --disk ...  --print-xml > windows.libvert.xml
$ virsh define windows.libvert.xml

Once again you can start the finalised VM via libvirt with:

$ virsh start windows-vm

Epilogue

While having lots of documentation is generally A Good Thing™, when it’s spread out over a considerable time period it’s sometimes difficult to know if the information you’re reading still applies today. This is particularly true when looking at other people’s example configuration files alongside reading the docs. The long-winded route might still work but the tool might also do it automatically now if you just let it, which keeps your source files much simpler.

Since getting this working I’ve seen other examples which suggest I may have fallen foul of this myself and what I’ve written up may also still be overly complicated! Please feel free to use the comments section on this blog or my gists to inform any other travellers of your own wisdom in any of this.

 

[1] That’s not entirely true. I ran Linux on an Atari TT and a circa v0.85 Linux kernel on a 386 PC in the early-to-mid ‘90s.

[2] The Packer docs can be misleading. For example it says the disk_size is in bytes and you can use suffixes like M or G to simplify matters. Except they don’t work and the value is actually in megabytes. No wonder a value of 15,000,000,000 didn’t work either :o).

[3] Also be aware that the version of Packer available via apt is only 1.0.x and you need to manually download the latest 1.4.x version and unpack the .zip. (I initially thought the bug in [2] was down to a stale version but it’s not.)

[4] The --import switch still fires up the VM as it appears to assume you’re going to add to the current image, not that it is the final image.


Spryer Francis – a.k.

a.k. from thus spake a.k.

Last time we saw how we could use a sequence of Householder transformations to reduce a symmetric real matrix M to a symmetric tridiagonal matrix, having zeros everywhere other than upon the leading, upper and lower diagonals, which we could then further reduce to a diagonal matrix Λ using a sequence of Givens rotations to iteratively transform the elements upon the upper and lower diagonals to zero so that the columns of the accumulated transformations V were the unit eigenvectors of M and the elements on the leading diagonal of the result were their associated eigenvalues, satisfying

    M × V = V × Λ

and, since the transpose of V is its own inverse

    M = V × Λ × VT

which is known as the spectral decomposition of M.
Unfortunately, the way that we used Givens rotations to diagonalise tridiagonal symmetric matrices wasn't particularly efficient and I concluded by stating that it could be significantly improved with a relative minor change. In this post we shall see what it is and why it works.

Visual Lint 7.0.4.313 has been released

Products, the Universe and Everything from Products, the Universe and Everything

This is a recommended maintenance update for Visual Lint 7.0. The following changes are included:

  • Added support for wildcards to the text filters in the Analysis Status, Analysis Statistics, Analysis Results and Stack Usage Displays and Display Filter Dialog.

    This allows (for example) files to be easily excluded from analysis by using a wildcard text filter in the Analysis Status Display.
  • Added a /writevlconfigfiles switch to VisualLintConsole to allow the user to use VisualLintConsole to incrementally update analysis configuration (.vlconfig) file(s) for the current solution/workspace/project.
  • Added the environment variable _RB_CONFIGURATION to the generated PC-lint/PC-lint Plus command line. This includes the name of the configuration, and like _RB_PLATFORM can be used to dynamically select options within an indirect (.lnt) file.
  • The "Delete Analysis Results" command now correctly deletes per-project analysis results and report baggage files.
  • Improved a prompt which was shown by the Configuration Wizard if it was unable to write any affected files on completion.
  • Locked out the program information (+program_info) option on the Command Line Options page if the active analysis tool is PC-lint Plus, as this directive is currently PC-lint 9.0 specific.
  • Generated PC-lint Plus command lines now escape the pathname of the stack usage report file if it contains quotes (generated PC-lint 9.0 command lines are unaffected as spaces in the pathname do not cause issues for it).
  • Fixed a bug in the parsing of ExcludedFromBuild attributes in Visual C++ 2010-2019 (.vcxproj) project files.
  • Fixed a bug which affected projects containing more than one file with the same name.
  • Fixed a bug in the parsing of cpplint analysis results of the format: "<filename>(<lineno>): error cpplint: [<ID>] <description> [<category>]".
  • Corrected a help topic.

Download Visual Lint 7.0.4.313

Christmas books for 2019

Derek Jones from The Shape of Code

The following are the really, and somewhat, interesting books I read this year. I am including the somewhat interesting books to bulk up the numbers; there are probably more books out there that I would find interesting. I just did not read many books this year, what with Amazon recommends being so user unfriendly, and having my nose to the grindstone finishing a book.

First the really interesting.

I have already written about Good Enough: The Tolerance for Mediocrity in Nature and Society by Daniel Milo.

I have also written about The European Guilds: An economic analysis by Sheilagh Ogilvie. Around half-way through I grew weary, and worried readers of my own book might feel the same. Ogilvie nails false beliefs to the floor and machine-guns them. An admirable trait in someone seeking to dispel the false beliefs in current circulation. Some variety in the nailing and machine-gunning would have improved readability.

Moving on to first half really interesting, second half only somewhat.

“In search of stupidity: Over 20 years of high-tech marketing disasters” by Merrill R. Chapman, second edition. This edition is from 2006, and a third edition is promised, like now. The first half is full of great stories about the successes and failures of computer companies in the 1980s and 1990s, by somebody who was intimately involved with them in a sales and marketing capacity. The author does not appear to be so intimately involved, starting around 2000, and the material flags. Worth buying for the first half.

Now the somewhat interesting.

“Can medicine be cured? The corruption of a profession” by Seamus O’Mahony. All those nonsense theories and practices you see going on in software engineering, it’s also happening in medicine. Medicine had a golden age, when progress was made on finding cures for the major diseases, and now it’s mostly smoke and mirrors as people try to maintain the illusion of progress.

“Who we are and how we got here” by David Reich (a genetics professor who is a big name in the field), is the story of the various migrations and interbreeding of ‘human-like’ and human peoples over the last 50,000 years (with some references going as far back as 300,000 years). The author tries to tell two stories, the story of human migrations and the story of the discoveries made by his and other people’s labs. The mixture of stories did not work for me; the story of human migrations/interbreeding was very interesting, but I was not at all interested in when and who discovered what. The last few chapters went off at a tangent, trying to have a politically correct discussion about identity and race issues. The politically correct class are going to hate this book’s findings.

“The Digital Party: Political organization and online democracy” by Paolo Gerbaudo. The internet has enabled some populist political parties to attract hundreds of thousands of members. Are these parties living up to their promises to be truly democratic and representative of members wishes? No, and Gerbaudo does a good job of explaining why (people can easily join up online, and then find more interesting things to do than read about political issues; only a few hard code members get out from behind the screen and become activists).

Suggestions for books that you think I might find interesting welcome.

Looks like I get to redo my WireGuard VPN server

Timo Geusch from The Lone C++ Coder&#039;s Blog

I’ve blogged about setting up a WireGuard VPN server earlier this year. It’s been running well since, but I needed to take care of some overdue maintenance tasks. Trying to log into the server this morning and I am greeted with “no route to host”. Eh? A quick check on my Vultr UI showed that […]

The post Looks like I get to redo my WireGuard VPN server appeared first on The Lone C++ Coder's Blog.

A study, a replication, and a rebuttal; SE research is starting to become serious

Derek Jones from The Shape of Code

tldr; A paper makes various claims based on suspect data. A replication finds serious problems with the data extraction and analysis. A rebuttal paper spins the replication issues as being nothing serious, and actually validating the original results, i.e., the rebuttal is all smoke and mirrors.

When I first saw the paper: A Large-Scale Study of Programming Languages and Code Quality in Github, the pdf almost got deleted as soon as I started scanning the paper; it uses number of reported defects as a proxy for code quality. The number of reported defects in a program depends on the number of people using the program, more users will generate more defect reports. Unfortunately data on the number of people using a program is extremely hard to come by (I only know of one study that tried to estimate number of users); studies of Java have also found that around 40% of reported faults are requests for enhancement. Most fault report data is useless for the model building purposes to which it is put.

Two things caught my eye, and I did not delete the pdf. The authors have done good work in the past, and they were using a zero-truncated negative binomial distribution; I thought I was the only person using zero-truncated negative binomial distributions to analyze software engineering data. My data analysis alter-ego was intrigued.

Spending a bit more time on the paper confirmed my original view, it’s conclusions were not believable. The authors had done a lot of work, this was no paper written over a long weekend, but lots of silly mistakes had been made.

Lots of nonsense software engineering papers get published, nothing to write home about. Everybody gets writes a nonsense paper at some point in their career, hopefully they get caught by reviewers and are not published (the statistical analysis in this paper was probably above the level familiar to most software engineering reviewers). So, move along.

At the start of this year, the paper: On the Impact of Programming Languages on Code Quality: A Reproduction Study appeared, published in TOPLAS (the first was in CACM, both journals of the ACM).

This replication paper gave a detailed analysis of the mistakes in data extraction, and the sloppy data analyse performed in the original work. Large chunks of the first study were cut to pieces (finding many more issues than I did, but not pointing out the missing usage data). Reading this paper now, in more detail, I found it a careful, well argued, solid piece of work.

This publication is an interesting event. Replications are rare in software engineering, and this is the first time I have seen a take-down (of an empirical paper) like this published in a major journal. Ok, there have been previous published disagreements, but this is machine learning nonsense.

The Papers We Love meetup group ran a mini-workshop over the summer, and Jan Vitek gave a talk on the replication work (unfortunately a problem with the AV system means the videos are not available on the Papers We Love YouTube channel). I asked Jan why they had gone to so much trouble writing up a replication, when they had plenty of other nonsense papers to choose from. His reasoning was that the conclusions from the original work were starting to be widely cited, i.e., new, incorrect, community-wide beliefs were being created. The finding from the original paper, that has been catching on, is that programs written in some languages are more/less likely to contain defects than programs written in other languages. What I think is actually being measured is number of users of the programs written in particular languages (a factor not present in the data).

Yesterday, the paper Rebuttal to Berger et al., TOPLAS 2019 appeared, along with a Medium post by two of the original authors.

The sequence: publication, replication, rebuttal is how science is supposed to work. Scientists disagree about published work and it all gets thrashed out in a series of published papers. I’m pleased to see this is starting to happen in software engineering, it shows that researchers care and are willing to spend time analyzing each others work (rather than publishing another paper on the latest trendy topic).

From time to time I had considered writing a post about the first two articles, but an independent analysis of the data meant some serious thinking, and I was not that keen (since I did not think the data went anywhere interesting).

In the academic world, reputation and citations are the currency. When one set of academics publishes a list of mistakes, errors, oversights, blunders, etc in the published work of another set of academics, both reputation and citations are on the line.

I have not read many academic rebuttals, but one recurring pattern has been a pointed literary style. The style of this Rebuttal paper is somewhat breezy and cheerful (the odd pointed phrase pops out every now and again), attempting to wave off what the authors call general agreement with some minor differences. I have had some trouble understanding how the rebuttal points discussed are related to the problems highlighted in the replication paper. The tone of the medium post is that there is nothing to see here, let’s all move on and be friends.

An academic’s work is judged by the number of citations it has received. Citations are used to help decide whether someone should be promoted, or awarded a grant. As I write this post, Google Scholar listed 234 citations to the original paper (which is a lot, most papers have one or none). The abstract of the Rebuttal paper ends with “…and our paper is eminently citable.”

The claimed “Point-by-Point Rebuttal” takes the form of nine alleged claims made by the replication authors. In four cases the Claim paragraph ends with: “Hence the results may be wrong!”, in two cases with: “Hence, FSE14 and CACM17 can’t be right.” (these are references to the original conference and journal papers, respectively), and once with: “Thus, other problems may exist!”

The rebuttal points have a tenuous connection to the major issues raised by the replication paper, and many of them are trivial issues (compared to the real issues raised).

Summary bullet points (six of them) at the start of the Rebuttal discuss issues not covered by the rebuttal points. My favourite is the objection bullet point claiming a preference, in the replication, for the use of the Bonferroni correction rather than FDR (False Discovery Rate). The original analysis failed to use either technique, when it should have used one or the other, a serious oversight; the replication is careful and does the analysis using both.

I would be very surprised if the Rebuttal paper, in its current form, gets published in any serious journal; it’s currently on a preprint server. It is not a serious piece of work.

Somebody who has only read the Rebuttal paper would take away a strong impression that the criticisms in the replication paper were trivial, and that the paper was not a serious piece of work.

What happens next? Will the ACM appoint a committee of the great and the good to decide whether the CACM article should be retracted? We are not talking about fraud or deception, but a bunch of silly mistakes that invalidate the claimed findings. Researchers are supposed to care about the integrity of published work, but will anybody be willing to invest the effort needed to get this paper retracted? The authors will not want to give up those 234, and counting, citations.

Arbitrary Cache Timeouts

Chris Oldwood from The OldWood Thing

Like many other programmers I’ve probably added my fair share of caches to systems over the years, and as we know from the old joke, one of the two hardest problems in computer science is knowing when to invalidate them. It’s a hard question, to be sure, but a really annoying behaviour you can run into as a maintainer is when the invalidation appears to be done arbitrarily, usually by specifying some timeout seemingly plucked out of thin air and maybe even changed equally arbitrarily. (It may not be, but documenting such decisions is usually way down the list of important things to do.)

Invalidation

If there is a need for a cache in production, and let’s face it that’s the usual driver, then any automatic invalidation is likely to be based on doing it as infrequently as possible to ensure the highest hit ratio. The problem is that that value can often be hard-coded and mask cache invalidation bugs because it rarely kicks in. The knee-jerk reaction to “things behaving weirdly” in production is to switch everything off-and-on again thereby implicitly invalidating any caches, but this doesn’t help us find those bugs.

The most recent impetus for this post was just such a bug which surfaced because the cache invalidation logic never ran in practice. The cache timeout was set arbitrarily large, which seemed odd, but I eventually discovered it was supposed to be irrelevant because the service hosting it should have been rebooted at midnight every day! Due to the password for the account used to run the reboot task expiring it never happened and the invalidated items then got upset when they were requested again. Instead of simply fetching the item from the upstream source and caching it again, the cache had some remnants of the stale items and failed the request instead. Being an infrequent code path it didn’t obviously ring any bells so took longer to diagnose.

Design for Testability

While it’s useful to avoid throwing away data unnecessarily in production we know that the live environment rarely needs the most flexibility when it comes to configuration (see “Testing Drives the Need for Flexible Configuration”). On the contrary, I’d expect to have any cache being cycled reasonably quickly in a test environment to try and flush out any issues as I’d expect more side-effects from cache misses than hits.

If you are writing any automated tests around the caching behaviour that is often a good time to consider the other non-functional requirements, such as monitoring and support. For example, does the service or tool hosting the cache expose some means to flush it manually? While rebooting a service may do the trick it does nothing to help you track down issues around residual state and often ends up wreaking havoc with any connected clients if they’re not written with a proper distributed system mindset.

Another scenario to consider is if the cache gets poisoned; if there is no easy way to eject the bad data you’re looking at the sledgehammer approach again. If your cache is HA (highly available) and backed by some persistent storage getting bad data out could be a real challenge when you’re under the cosh. One system I worked on had random caches poisoned with bad data due to a threading serialization bug in an external library.

Monitoring

The monitoring side is probably equally important. If you generate no instrumentation data how do you know if your cache is even having the desired effect? One team I was on added a new cache to a service and we were bewildered to discover that it was never used. It turned out the WCF service settings were configured to create a new service instance for every request and therefore a new cache was created every time! This was despite the fact that we had unit tests for the cache and they were happily passing [1].

It’s also important to realise that a cache without an eviction policy is just another name for a memory leak. You cannot keep caching data forever unless you know there is a hard upper bound. Hence you’re going to need to use the instrumentation data to help find the sweet spot that gives you the right balance between time and space.

We also shouldn’t blindly assume that caches will continue to provide the same performance in future as they do now; our metrics will allow us to see any change in trends over time which might highlight a change in data that’s causing it to be less efficient. For example one cache I saw would see its efficiency plummet for a while because a large bunch of single use items got requested, cached, and then discarded as the common data got requested again. Once identified we disabled caching for those kinds of items, not so much for the performance benefit but to avoid blurring the monitoring data with unnecessary “glitches” [2].

 

[1] See “Man Cannot Live by Unit Testing Alone” for other tales of the perils of that mindset.

[2] This is a topic I covered more extensively in my Overload article “Monitoring: Turning Noise Into Signal”.