On brute force stupidity

Everyone who’s ever managed any internet-facing server is aware of the ridiculous amount of brute force SSH login attempts by all kinds of botnets. Some folks decide to move their SSHD to a non-standard port, some rely on complicated shenanigans like port knocking, and some use tools like fail2ban. I’m unfortunate enough to manage a little over a dozen servers, so I decided to have some fun with fail2ban.

enabled = true
banaction = %(banaction_allports)s
enabled = true

My configuration is pretty straightforward. You fuck up, you get banned. You fuck up repeatedly, you get banned for a longer time. Nothing special there. Given that I’m running a similar config on many boxes, I decided to compile some data relating to the origins of login attempts. This data was collected over a period of ~2 months on ~12 servers.

Here’s a quick plot of the number of times a certain IP address was banned. Only the top 100 abusers are included, because the chart has a very long tail indeed. I removed the IP addresses from the X-axis because there’s no way to include them without turning into a black blob.

It should be immediately obvious that a relatively small number of IP addresses is responsible for a metric fuckton of unwelcome activity. Remember that this represents the number of times an IP was banned. Left unchecked, the number of attempts increases by orders of magnitude.

The top offender (and the only one whose full IP address I’ll publish) is It’s part of a Chinese subnet. It managed to get banned a staggering 4466 number of times. More than the next 5 abusers combined.

As the following chart illustrates, a whopping 76% of these IP addresses belong to Chinese subnets.

I daresay the internet would be a slightly better place if those 100 machines were permanently disconnected. It’s likely they’re just unsuspecting folks with compromised machines. But I for one am permanently firewalling all of them on any box I have access to.

Yet Another Battery Widget (Awesome 3.5.1)

Yet another battery widget for Awesome. This one actually works (shock! horror!) on Awesome 3.5.1 on my Thinkpad x230. Your mileage may vary. Colours used are from the excellent Solarized colour scheme. Behold the mighty widget, in all its unobtrusive glory!


The implementation is in two parts: a simple shell script to output the battery status, and a bit of rc.lua tweaks to display the widget. This is mostly the result of a bit of copy/pasting from different sources I forgot to bookmark. Oh well.


capacity=`cat /sys/class/power_supply/BAT0/capacity`
if (($capacity <= 25));
status=`cat /sys/class/power_supply/BAT0/status`
if [[ "$status" = "Discharging" ]]
echo "<span color=\"$capacityColour\">$capacity%</span> <span color=\"$statusColour\">$status</span>"

Add the following snippets to /path/to/awesome/rc.lua. I’ll attempt to indicate the approximate location at the top of each snippet.

Create the widget..and don’t forget to adjust the path to the battery.sh script.

-- This goes below the line containing mytextclock = awful.widget.textclock()
-- Create a battery widget
battery = wibox.widget.textbox()
function getBatteryStatus()
   local fd= io.popen("/path/to/battery.sh")
   local status = fd:read()
   return status

Add the widget..

-- This goes above the line containing right_layout:add(mytextclock)

Get the widget to refresh every 30 seconds. Put this somewhere near the end of the config file.

-- Battery status timer
batteryTimer = timer({timeout = 30})
batteryTimer:connect_signal("timeout", function()

That’s all! Restart awesome and you’ll see a relatively purdy yet unobstrusive battery status display.

Java Date Performance Subtleties

A recent profling session pointed out that some of our processing threads were blocking on java.util.Date construction. This is troubling, because it’s something we do many thousands of times per second, and blocked threads are pretty bad!

A bit of digging led me to TimeZone.getDefault(). This, for some insanely fucked up reason, makes a synchronized call to TimeZone.getDefaultInAppContext(). The call is synchronized because it attempts to load the default time zone from the sun.awt.AppContext. What. The. Fuck. I don’t know what people were smoking when they wrote this, but I hope they enjoyed it …

Unfortunately, Date doesn’t have a constructor which takes a TimeZone argument, so it always calls getDefault() instead.

I decided to run some microbenchmarks. I benchmarked four different ways of creating Dates:

// date-short:
    new Date();
    new Date(year, month, date, hrs, min, sec);
// calendar:
    Calendar cal = Calendar.getInstance(TimeZone);
    cal.set(year, month, date, hourOfDay, minute, second)
// cached-cleared-calendar:
//    Same as calendar, but with Calendar.getInstance() outside of the loop, 
//    and a cal.clear() call in the loop.

I tested single threaded performance, where 1M Dates were created using each method in a single thread. Then multi-threaded with 4 threads, each thread creating 250k Dates. In other words: both methods ended up creating the same number of Dates.

Lower is beter.
Click to enlarge. Lower is beter.

With exception of date-long, all methods speed up by a factor of 2 when multi-threaded. (The machine only has 2 physical cores). The date-long method actually slows down when multi-threaded. This is because of lock contention in the synchronized TimeZone acquisition.

The JavaDoc for Date suggests replacing the date-long call by a calendar call. Performance-wise, this is not a very good suggestion: its single-threaded performance is twice as bad as that of Date unless you reuse the same Calendar instance. Even multi-threaded it’s outperformed by date-long. This is simply not acceptable.

Fortunately, the cached-cleared-calendar option performs very well. You could easily store a ThreadLocal reference to an instance of a Calendar and clear it whenever you need to use it.

More important than the raw duration of the Date creation, is the synchronization overhead. Every time a thread has to wait to enter a synchronized block, it could end up being rescheduled or swapped out. This reduces the predictability of performance. Keeping synchronization down to a minimum (or zero, in this case) increases predictability and liveness of the application in general.

Before anyone mentions it: yes, I’m aware that the long Date constructors are deprecated. Unfortunately, they are what Joda uses when converting to Java Dates. I’ve proposed a patch, but while doing a bit more research for this blog post, I’ve come to the conclusion that my patch needs a bit of refining as it is still too slow (though it no longer blocks). In the mean while, I hope that the -kind?- folks at Oracle will reconsider their shoddy implementation.

I’ve also heard rumours that Joda will somehow, magically, replace java.util.Date in JDK 8. Not sure how that’s going to work with backwards compatibility. I’d be much happier if java.util.Date would stop sucking quite as much. And if SimpleDateFormat were made thread-safe. And … the list goes on.

Character sets, time zones and hashes

Character sets, time zones and password hashes are pretty much the bane of my life. Whenever something breaks in a particularly spectacular fashion, you can be sure that one of those three is, in some way, responsible. Apparently the average software developer Just Doesn’t Get It™. Granted, they are pretty complex topics. I’m not expecting anyone to care about the difference between ISO-8859-15 and ISO-8859-1, know about UTC‘s subtleties or be able to implement SHA-1 using a ball of twine.

What I do expect, is for sensible folk to follow these very simple guidelines. They will make your (and everyone else’s) life substantially easier.

Use UTF-8..

Always. No exceptions. Configure your text editors to default to UTF-8. Make sure everyone on your team does the same. And while you’re at it, configure the editor to use UNIX-style line-endings (newline, without useless carriage returns).

..or not

Make sure you document the cases where you can’t use UTF-8. Write down and remember which encoding you are using, and why. Remember that iconv is your friend.

Store dates with time zone information

Always. No exceptions. A date/time is entirely meaningless unless you know which time zone it’s in. Store the time zone. If you’re using some kind of retarded age-old RDBMS which doesn’t support date/time fields with TZ data, then you can either store your dates as a string, or store the TZ in an extra column. I repeat: a date is meaningless without a time zone.

While I’m on the subject: store dates in a format described by ISO 8601, ending with a Z to designate UTC (Zulu). No fancy pansy nonsense with the first 3 letters of the English name of the month. All you need is ISO 8601.

Bonus tip: always store dates in UTC. Make the conversion to the user time zone only when presenting times to a user.

Don’t rely on platform defaults

You want your code to be cross-platform, right? So don’t rely on platform defaults. Be explicit about which time zone/encoding/language/.. you’re using or expecting.

Use bcrypt

Don’t try to roll your own password hashing mechanism. It’ll suck and it’ll be broken beyond repair. Instead, use bcrypt or PBKDF2. They’re designed to be slow, which will make brute-force attacks less likely to be successful. Implementations are available for most sensible programming environments.

If you have some kind of roll-your-own fetish, then at least use an HMAC.

Problem be gone

Keeping these simple guidelines in mind will prevent entire ranges of bugs from being introduced into your code base. Total cost of implementation: zilch. Benefit: fewer headdesk incidents.

Repeat after me: MySQL is not a filesystem

I came across this gem on DZone this morning. It’s a tutorial on storing images in a MySQL database (using PHP). There are several things in the tutorial that I don’t agree with, but I’ll let those slide. What really bugs me, is how it fails to mention that this is a very bad idea.

A relational database is not a filesystem. Files go on a filesystem. Relational data goes in an RDBMS. Repeat that a couple of times.

The most compelling argument for this, is performance. I did a quick test. I did a google image search on stupidity and downloaded the first 10 images. I then wrote PHP scripts to serve them up in two ways:

1. From a MySQL (MyISAM) table with 2 columns: ID (int, auto_increment) and DATA (mediumblob)
2. Using readfile.

The third test method, “FS”, simply loads the image over HTTP directly, without any intermediary scripts.

The results are the average of running Apache Benchmark 10 times: 10 concurrent requests, 1000 requests per run.


As you can see, the MySQL approach is a hell of a lot slower than the more sensible FS approach.

The best way to store your images (or other binary files) is on the filesystem. Every modern web server does a good (or excellent) job of serving up static content. Storing them in a database is by far the worst possible solution. Not only because it’s slow, but also because it complicates database backups: MySQL dumps with binary data don’t compress very well, causing the whole database backup to be slower and larger than needs be.

So please, be sensible. Store your files on a filesystem.

Java 7 Performance

I decided to compare Java 6 & 7 performance for $employer’s $application. Java 7 performs better — as expected. What I did not expect, was that the difference would be so big. Around 10% on average. That’s not bad for something as simple as a version bump.

Jave 6 vs Java 7

Ideally I’d like to investigate where this difference comes from. I suspect improved ergonomics have a lot to do with it.

$application uses Apache Solr rather extensively. In fact, most of the time querying is spent in Solr. With indexing it’s probably about 50% of the time. With querying it’s probably closer to 90%. All tests are run in a controlled environment, so I have a fair amount of confidence in these results.

The indexing test inserts 3 million documents in Solr. Creating these documents takes up the bulk of the time. It involves a lot of filesystem access — something which Java versions have very little influence over and heavily multi-threaded CPU-intensive processing.

If you’re not using Java 7, you really should consider upgrading. If you’re stuck with people who live in the past, maybe you can convince them with a bunch of pretty performance graphs of your own.

Gnuplot data analysis, real world example

Creating graphs in LibreOffice is a nightmare. They’re ugly, nearly impossible to customize and creating pivot tables with data is bloody tedious work. In this post, I’ll show you how I took the output of a couple of performance test scripts and turned it into reasonably pretty graphs with a few standard command line tools (gnuplot, awk, a bit of (ba)sh and a Makefile).

The Data

I ran a series of query performance tests against data sets of different sizes. The sets contain 10k, 100k, 1M, 10M, 100M and 500M documents. One of the basic constraints is that it has to be easy to add/remove sets. I don’t want to faff about with deleting columns or updating pivot tables. If I add a set to my test data, I want it automagically show up in my graphs.

The output of the test script is a simple tab separated file, and looks like this:

#Set	Iteration	QueryID	Duration
500M	1	101	10.497499465942383
500M	1	102	3.9973576068878174
500M	1	103	9.4201889038085938
500M	1	104	2.8091645240783691
500M	1	105	2.944718599319458
500M	1	106	5.1576917171478271
500M	1	107	5.7224125862121582
500M	1	108	5.7259769439697266
500M	1	109	4.7974696159362793

Each row contains the query duration (in seconds) for a single execution of a single query.

Processing the data

I don’t just want to graph random numbers. Instead, for each query in each set, I want the shortest execution time (MIN), the longest (MAX) and the average across iterations (AVG). So we’ll create a little awk script to output data in this format. In order to make life easier for gnuplot later on, we’ll create a file per dataset.

% head -n 3 output/500M.dat

500M	200	0.071	2.699	0.952	3
500M	110	0.082	5.279	1.819	3

Here’s the source of the awk script, transform.awk. The code is quite verbose, to make it a bit easier to understand.

        if($0 ~ /^[^#]/) {
                key = $1"_"$3
                first = iterations[key] > 0 ? 0 : 1
                sets[$1] = 1
                queries[$3] = 1
                totals[key] += $4
                iterations[key] += 1
                if(1 == iterations[key]) {
                        minima[key] = $4
                        maxima[key] = $4
                } else {
                        minima[key] = $4 < minima[key] ? $4 : minima[key]
                        maxima[key] = $4 > maxima[key] ? $4 : maxima[key]
        for(set in sets) {
                outfile = "output/"set".dat"
                print "#SET\tQUERY\tMIN\tMAX\tAVG\tITERATIONS" > outfile
                for(query in queries) {
                        key = set"_"query
                        iterationCount = iterations[key]
                        average = totals[key] / iterationCount
                        printf("%s\t%d\t%.3f\t%.3f\t%.3f\t%d\n", set, query, minima[key], maxima[key], average, iterationCount) >> outfile

This code will read our input data, calculate MIN, MAX, AVG, number of iterations for each query and dump the contents in a tab-separated dat file with the same name as the set. Again, this is done to make life easier for gnuplot later on.

I want to see the effect of dataset size on query performance, so I want to plot averages for each set. Gnuplot makes this nice and easy, all I have to do is name my sets and tell it where to find the data. But ah … I don’t want to tell gnuplot what my sets are, because they should be determined dynamically from the available data. Enter, a wee shellscript that outputs gnuplot commands.

# Output plot commands for all data sets in the output dir
# Usage: ./plotgenerator.sh column-number
# Example for the AVG column: ./plotgenerator.sh 5
echo -n "plot "
for s in `ls output | sed 's/\.dat//'` ;
        echo -n "$prefix \"output/$s.dat\" using 2:$1 title \"$s\""
        if [[ "$prefix" == "" ]] ; then
                prefix=", "

This script will generate a gnuplot “plot” command. Each datafile gets its own title (this is why we named our data files after their dataset name) and its own colour in the graph. We want to plot two columns: the QueryID, and the AVG duration. In order to make it easier to plot the MIN or MAX columns, I’m parameterizing the second column: the $1 value is the number of the AVG, MIN or MAX column.


Gnuplot will call the plotgenerator.sh script at runtime. All that’s left to do is write a few lines of gnuplot!

Here’s the source of average.gnp

set terminal png enhanced size 1280,768
set xlabel "Query"
set ylabel "Duration (seconds)"
set xrange [100:]
set title "Average query duration"
set key outside
set grid
set style data points
eval(system("./plotgenerator.sh 5"))

The result

% ./average.gnp > average.png

Click for full size.


Wrapping it up with a Makefile

I don’t like having to remember which steps to execute in which order, and instead of faffing about with yet another shell script, I’ll throw in another *nix favourite: a Makefile.

It looks like this:

        rm -rf output
        mkdir output
        awk -f transform.awk queries.dat
        ./average.gnp > average.png

Now all you have to do, is run


whenever you’ve updated your data file, and you’ll end up with a nice’n purdy new graph. Yay!

Having a bit of command line proficiency goes a long way. It’s so much easier and faster to analyse, transform and plot data this way than it is using graphical “tools”. Not to mention that you can easily integrate this with your build system…that way, each new build can ship with up-to-date performance graphs. Just sayin’!

Note: I’m aware that a lot of this scripting could be eliminated in gnuplot 4.6, but it doesn’t ship with Fedora yet, and I couldn’t be arsed building it.

What bugs me on the web

2013 is nearly upon us, and the web has come a very long way in the ~15 years I’ve been a netizen. And yet, even though we’ve made so many advances, it sometimes feels like we’ve been stagnant, or worse, regressed in some cases.

Each and every web developer out there should have a long, hard think about how the web has (d)evolved in their lifetime and which way we want to head next. There’s an awful lot happening at the moment: web 2.0, HTML 5, Flash’s death-throes, super-mega-ultra tracking cookies, EU cookie regulation nonsense, microdata, cloud fun, … I could go on all day. Needless to say: it’s a mixed bunch.

In any event, here’s a brief list of 3 things that bug me on the web.

Links are broken

Usability has long been the web’s sore thumb, and in spite of any number of government-sponsored usability certification programmes over the year, people still don’t seem to give a rat’s arse. Websites are still riddled with nasty drop down menus that only work with a mouse. Sometimes they’re extra nasty by virtue of being ajaxified. At least Flash menus are finally going the way of the dinosaur.

Pro tip: every single bloody link on your web site should have a working HREF, so people can use it without relying on click handlers, mice, javascript and so people can open the bloody thing in a new tab without going through hell and back.

Bonus points: make your links point to human-readable URLs.

Languages, you’re doing it wrong

The web is no longer an English-only or US-only playing field, and companies all over are starting to cotton on to this fact. What they have yet to realise, however, is that people don’t necessarily speak the language you think they do. If you rely on geolocation data to serve up translated content: stop. You’re doing it wrong. The user determines the language. Believe it or not, people do know which language(s) they speak.

Geolocation, for starters, isn’t an exact science. Depending on the kind of device this can indeed be very accurate. Or very much not. Proxies, VPNs, Onion Routers etc can obviously mislead your tracking. Geolocation tells you nothing. It doesn’t tell you why that person is there (maybe they’re on holiday?). It also doesn’t tell you what language is spoken there. This might be a shock to some people, but some countries have more than one official language. Hell, some villages do. Maybe you can find this data somewhere, and correlate it with the location, but you’d be wrong to. Language is a very sensitive issue in some places. Get it right, or pick a sensible default and make clear that it was a guess. Don’t be afraid to ask for user input.

Pro tip: My favourite HTTP header: Accept-Language. Every sensible browser sends this header with every request. In most cases, the default is the browser’s or OS’s language. Which is nearly always the user’s first language, and when it’s not, at least you know the user understands it well enough to be able to use a browser..

Bonus points: Seriously, use Accept-Language. If you don’t, you’re a dick.

Clutter is back

Remember how, back in 1999, we all thought Google looked awesome because it was so clean & crisp and didn’t get in your face and everyone copied the trend? Well, that seems to have come to an end.
Here’s Yahoo in 1997. (I love how it has an ad for 256mb of memory.)
Here’s Yahoo now.

The 1997 version was annoying to use (remember screen resolutions in the 90s? No? You’re too young to read this, go away) because it was so cluttered.
The 2012 version is worse and makes me want to gouge my eyes out.

Even Google is getting all in your face these days, with search-as-you-type and whatnot. Bah. DuckDuckGo seems to be the exception (at least as far as search engines go). It offers power without wagging it in your face.

Pro tip: don’t put a bazillion things on your pages. Duh.

2013 Wishlist

My web-wishlist for 2013 is really quite simple: I want a usable web. Not just people with the latest and greatest javascript-enabled feast-your-eyes-on-this devices. For everyone. Including those who use text-to-speech, or the blind, or people on older devices. Graceful degradation is key to this. So please, when you come up with a grand feature, think about what we might be giving up on as well. Don’t break links. Don’t break the back button. Don’t break the web.

Bad Press for Agile

So .. Agile’s been getting some bad press of late. Now, these guys are just quacks, and I probably shouldn’t feed the trolls here, but I never could resist.

Saying “agile doesn’t work” or “agile is only out to sell services(training,certification etc)” is obviously a bogus claim. The same could be said of any software methodology. Many waterfall projects have failed, and many have had the help of process improvement engineers and whatnot. Some projects will always fail. A sound development methodology & culture can help you realise imminent failure earlier, or it can help reduce chances of failure. But no methodology is a guarantee for success. A team of idiots run by idiots will always produce crap. No matter how many buzzwords they fit in their job titles or marketing blurbs.

Agile is many things, but no one has ever claimed it to be a silver bullet. As for for it being “for lazy devs”: all developers are lazy, it’s part of the job description. It’s why we automate shit. It’s why we focus on code and not on hot air.

My recommendation to you: use whatever works for you. And in doing so, you’re already on your way to being Agile :-).