Code Life

Dynamo Session Manager

We (EnergyHub) have just released a session replication plugin for Tomcat 6 using Amazon DynamoDB.


We’ve never used the bundled Tomcat clustering solutions, mainly because we run on EC2 and the multicast-based solution doesn’t work there. For about 18 months we’ve been using Memcached Session Manager (m-s-m), which stores the sessions in memcached. It works pretty well, but avoiding a single point of failure in a simple protocol like memcached is hard. m-s-m solves this problem by saving the session to a given memcached node and also a backup server. This has led to high complexity: in our case we counted 6 memcached calls for each web request. In defense of m-s-m, the author is very active on the mailing list and helped us debug a lot of corner case problems, often pushing out a release within a day or two of us reporting a problem. Also we were using the non-sticky configuration which is less well tested.

In the end, the sheer amount of code and the number of steps for each request in m-s-m made it hard to diagnose subtle intermittent bugs. We found David Dawson’s Mongo Tomcat Sessions plugin which very cleanly loads a session from Mongo at the beginning of the request and saves it back at the end. This is smartly putting the replication logic into the database layer. Mongo is great for easy-to-use replication, and we rely on it in production already, but for decent replication we’d be talking about two or three servers, which adds cost and maintenance burden. We figured we could take the general approach but use Amazon’s DynamoDB on the backend: no worries about deploying or monitoring the storage layer.


When a request comes in, we look it up by ID in a Dynamo table. If it’s not found we’ll start a new session. After the request a helper Tomcat valve calls back to save the session back to Dynamo. This approach works well, the only thing to consider before rushing to deploy is that Dynamo must be configured for a certain throughput. In our case, the throughput t that must be provisioned is

t = s*r,

where s is the session size rounded up, in kB; and r is the request rate (requests per second). For example, if the vast majority of sessions are 1 < s < 2 kB and we have a maximum request rate of 100 req/s, then we must provision the table for 2*100 = 200 read units and 200 write units.

Session Expiration by table Rotation

Moving to Dynamo loses us one key feature vs MongoDB: secondary indices. In this case, it means we can't have an index on the last modified time of the session, which could be used to delete the expired sessions. We have to workarounds in Dynamo:

  1. Scan the whole table for expired 'last modified time' sessions. This is expensive and hard to provision for: if you have a high session turnover you could have millions of sessions to scan through, but only hundred of provisioned reads per second in which to do so.
  2. Move active sessions to a new table and drop the old one. This is the approach we have taken. For example, if the expiration time is one hour, we will start by saving our sessions into table 'A'. After one hour, we will create a new table, 'B', and start saving new sessions into B. When loading an existing session, we will look in 'B', and if not found, in 'A'. In this manner active sessions will be moved to table B over the next hour. After another hour, we will create table 'C' and start saving there. At this point all sessions that only exist in table 'A' are older than one hour and can be safely dropped, so we delete the whole table.

Extra Features / Advanced Settings

  • We've added optional monitoring via statsd, which we use heavily in production.
  • We auto-provision the read-write capacity when we create tables, based on a given session size and request rate.
  • We've added the ability to ignore requests by URI or based on the presence of certain HTTP headers. This is useful for us because we have a lot of machine-generated traffic that doesn't use sessions.

See the code at or use the jars from

Code Life

Command-line face detection

This post explains how to:

  1. Take a facial portrait and detect the position of the face
  2. Cut a facial portrait down the center and remove half of the picture so that kids can fill it in themselves.
  3. Print a massive amount of JPEGs at once by putting them in a PDF.

I’m in the Eastern Cape, South Africa now, working with orphans and vulnerable children. Alex and I are spending some of our time on art projects in remote rural areas, and one of the projects is an idea we stole from an orphanage in Cape Town: take a digital portrait of a child’s face, crop it down the center, print it and let them draw the other side of the face. Like this one Alex did:

Alex's half-face portrait
Half-face portrait

The first time we did this, we went out to the rural area, took pictures of about 16 kids and then spent an hour or two processing the pictures and printing. The processing involved:

  1. Importing the pictures to Picassa, straightening some of them and cropping others. (Yes, I know Picassa is not Free/Libre, but F-spot (in Ubuntu Hardy) is dog slow to display pictures and doesn’t have the straighten function).
  2. Exporting to a directory, then opening each file in GIMP and cropping the right-or-left hand side of the face away.
  3. Combine all the JPEG images into a PDF so they’re easy to print.

The second time, we did the project at a school, for 60+ pupils. The straightening/cropping in Picassa took about ten minutes (since most of the pictures didn’t need much work). The open-crop-save-close process in GIMP took about thirty seconds per picture and was both repetitive and highly mouse intensive so that we both got hand cramps after a while.

So, after watching Alex do the process for a second class at the school, I decided there must be a better way: automatic face detection. Lo-and-behold, five minutes of Googling got me to Torch3Vision, an image recognition toolkit with built-in face detection. It definitely works, but it takes quite a little setting up, so here’s a guide.

  1. Download Torch3Vision and un-tar it: tar -zxf Torch3vision2.1.tgz
  2. Build Torch3vision: cp Linux_i686.cfg.vision2.1 Linux_i686.cfg ./torch3make
  3. Build the vison examples for face detection:cd vision2.1/examples/facedetect/
    ../../../torch3make *.cc

So now we have a working set of face-detection programmes. The command line interface isn’t too friendly, so they take a little playing around. For starters, the binaries on my Ubuntu system don’t read JPEG images (although the code seems to be there, the build system is non-standard and didn’t automatically pick up my jpeg libraries. So, I needed to convert my images to PPM format, which is one of those image formats that no-one uses but somehow is the lowest common denominator for image processing command line apps. I use the program ‘jpegtopnm’ from package ‘netpbm’.

jpegtopnm andy.jpg > andy.ppm

Of the three facial detection programs available, I found ‘mlpcascadescan’ to be the most effective and quickest, although they all have similar interfaces so this will basically be the same for all of them. We need to pass the source image and the model file, and we tell it to write the face position and to save a drawing with the face detected:

mlpcascadescan andy.ppm -savepos -draw \
-model ~/temp/models/mlp-cascade19x19-20-2-110

This command takes about 20s to run on my creaky old laptop, and creates two files. One is a greyscale visualization of the face detected (the original image was colour):

Face detected, more or less
Face detected, more or less

The other file ‘andy.pos’ contains the results of face detection. Line one is the number of detections, then each line has format x y w h, very easy to parse.

   FACE_POS=`head -n 2 “andy.pos | tail -n 1`
FACE_X=`echo $FACE_POS | awk ‘{print $1}’`
FACE_W=`echo $FACE_POS | awk ‘{print $3}’`
FACE_CENTER=`echo $FACE_X + $FACE_W/2 | bc`

I played around with the step-factors in the x and y directions to shave a second or so off the face detection routine, the values I chose were 0.1 and 0.2 respectively (I don’t need any accuracy in the y direction really, since my use is to cut the face down the middle).

Then, since these are portrait photographs, I can speed up face detection by setting a minimum size for the face. I experimented and one sixth of the total image width gave good results – any larger and the face detection would fail with a crash. Adding this constraint provides better than 10X speed up, since the algorithm doesn’t waste time searching for small faces.

WIDTH=`identify -format “%w” “andy.jpg”`
MIN_FACE_WIDTH=`echo $WIDTH / 6 | bc`

So now here’s the final face detection command

mlpcascadescan “$ppm” -dir /tmp/ -savepos -model $MODEL \
-minWsize $MIN_FACE_WIDTH -stepxfactor $STEPX -stepyfactor $STEPY

And finally, as promised, I’ll tell you how to blank-out one side of the face: of course, using Image Magick. Using the ‘chop’ or ‘crop’ commands didn’t work for this purpose, where I wanted the image to keep it’s dimensions but have one half just be white. So I decided to draw a white rectangle over half of the picture.  I apply the image manipulation to the original JPEG file, not the temporary PPM file that I used to detect the face position.

convert -fill white \
-draw “rectangle $FACE_CENTER,0 $WIDTH,$HEIGHT” \
“andy.jpg” “andy_half.jpg”

And here’s the final result:

The script I am using to tie this all together.

After processing all the portraits, I run a quick script to convert the jpegs to PDF and then join them into one master PDF file that I can easily print. The JPEG-PDF conversion uses Image Magick again (convert -rotate 90 file.jpg file.pdf). Joining together many PDFs into one document is easy with ‘pdfjoin’ from package ‘pdfjam’ (pdfjoin $tempfiles –outfile jpg2pdf.pdf). See the final jpg2pdf script.

But perhaps more enjoyable is to see the result after letting my limited creative talents loose:

A work of staggering complexity.
A work of staggering complexity.