# Building a terrible 'IoT' temperature logger

I had approximately the following exchange with a co-worker a few days ago:

Them: “Hey, do you have a spare Raspberry Pi lying around?”
Me: [thinks] “..yes, actually.”
T: “Do you want to build a temperature logger with Prometheus and a DS18B20+?
M: “Uh, okay?”

It later turned out that that co-worker had been enlisted by yet another individual to provide a temperature logger for their project of brewing cider, to monitor the temperature during fermentation. Since I had all the hardware at hand (to wit, a Raspberry Pi 2 that I wasn’t using for anything and temperature sensors provided by the above co-worker), I threw something together. It also turned out that the deadline was quite short (brewing began just two days after this initial exchange), but I made it work in time.

## Interfacing the thermometer

As noted above, the core of this temperature logger is a DS18B20 temperature sensor. Per the manufacturer:

The DS18B20 digital thermometer provides 9-bit to 12-bit Celsius temperature measurements … communicates over a 1-Wire bus that by definition requires only one data line (and ground) for communication with a central microprocessor. … Each DS18B20 has a unique 64-bit serial code, which allows multiple DS18B20s to function on the same 1-Wire bus. Thus, it is simple to use one microprocessor to control many DS18B20s distributed over a large area.

Indeed, this is a very easy device to interface with. But even given the svelte hardware needs (power, data and ground signals), writing some code that speaks 1-Wire is not necessarily something I’m interested in. Fortunately, these sensors are very commonly used with the Raspberry Pi, as illustrated by an Adafruit tutorial published in 2013.

The Linux kernel provided for the Pi in its default Raspbian (Debian-derived) distribution supports bit-banging 1-Wire over its GPIOs by default, requiring only a device tree overlay to activate it. This is as simple as adding a line to /boot/config.txt to make the machine’s boot loader instruct the kernel to apply a change to the hardware configuration at boot time:

dtoverlay=w1-gpio

With that configuration, one simply needs to wire the sensor up. The w1-gpio device tree configuration by default uses GPIO 4 on the Pi as the data line, then power and grounds need to be connected and a pull-up resistor added to the data line (since 1-Wire is an open-drain bus).

The w1-therm kernel module already understands how to interface with these sensors- meaning I don’t need to write any code to talk to the temperature sensor: Linux can do it all for me! For instance, reading the temperature out in an interactive shell to test, after booting with the 1-Wire overlay enabled:

$modprobe w1-gpio w1-therm$ cd /sys/bus/w1/devices
$ls 28-000004b926f1 w1_bus_master1$ cat 28-000004b926f1/w1_slave
9b 01 4b 46 7f ff 05 10 6e : crc=6e YES
9b 01 4b 46 7f ff 05 10 6e t=25687

The kernel periodically scans the 1-Wire bus for slaves and creates a directory for each device it detects. In this instance, there is one slave on the bus (my temperature sensor) and it has serial number 000004b926f1. Reading its w1_slave file (provided by the w1-therm driver) returns the bytes that were read on both lines, a summary of transmission integrity derived from the message checksum on the first line, and t=x on the second line, where x is the measured temperature in milli-degrees Celsius. Thus, the measured temperature above was 25.687 degrees.

While it’s fairly easy to locate and read these files in sysfs from a program, I found a Python library that further simplifies the process: w1thermsensor provides a simple API for detecting and reading 1-wire temperature sensors, which I used when implementing the bridge for capturing temperature readings (detailed more later).

### 1-Wire details

I wanted to verify for myself how the 1-wire interfacing worked so here are the details of what I’ve discovered, presented because they may be interesting or helpful to some readers. Most documentation of how to perform a given task with a Raspberry Pi is limited to comments like “just add this line to some file and do the other thing!” with no discussion of the mechanics involved, which I find very unsatisfying.

The line added to /boot/config.txt tells the Rapberry Pi’s boot loader (a version of Das U-Boot) to pass the w1-gpio.dtbo device tree overlay description to the kernel. The details of what’s in that overlay can be found in the kernel source tree at arch/arm/boot/dts/overlays/w1-gpio-overlay.dts.

This in turn pulls in the w1-gpio kernel module, which is part of the upstream kernel distribution- it’s very simple, setting or reading the value of a GPIO port as requested by the Linux 1-wire subsystem.

Confusingly, if we examine the dts file describing the device tree overlay, it can take a pullup option that controls a rpi,parasitic-power parameter. The documentation says this “enable(s) the parasitic power (2-wire, power-on-data) feature”, which is confusing. 1-Wire is inherently capable of supplying parasitic power to slaves with modest power requirements, with the slaves charging capacitors off the data line when it’s idle (and being held high, since it’s an open-collector bus). So, saying an option will enable parasitic power is confusing at best and probably flat wrong.

Further muddying the waters, there also exists a w1-gpio-pullup overlay that includes a second GPIO to drive an external pullup to provide more power, which I believe allows implementation of the strong pull-up described in Figure 6 of the DS18B20 datasheet (required because the device’s power draw while reading the temperature exceeds the capacity of a typical parasitic power setup):

By also connecting the pullup GPIO to the data line (or putting a FET in there like the datasheet suggests), the w1-gpio driver will set the pullup line to logic high for a requested time, then return it to Hi-Z where it will idle. But for my needs (cobbling something together quickly), it’s much easier to not even bother with parasite power.

In conclusion for this section: I don’t know what the pullup option for the 1-Wire GPIO overlay actually does, because enabling it and removing the external pull-up resistor from my setup causes the bus to stop working. The documentation is confusingly imprecise, so I gave up on further investigation since I already had a configuration that worked.

## Prometheus scraping

To capture store time-series data representing the temperature, per the co-worker’s original suggestion I opted to use Prometheus. While it’s designed for monitoring the state of computer systems, it’s plenty capable of storing temperature data as well. Given I’ve used Prometheus before, it seemed like a fine option for this application though on later consideration I think a more robust (and effortful) system could be build with different technology choices (explored later in this post).

The Raspberry Pi with temperature sensor in my application is expected to stay within range of a WiFi network with internet connectivity, but this network does not permit any incoming connections, nor does it permit connections between wireless clients. Given I wanted to make the temperature data available to anybody interested in the progress of brewing, there needs to be some bridge to the outside world- thus Prometheus should run on a different machine from the Pi.

The easy solution I chose was to bring up a minimum-size virtual machine on Google Cloud running Debian, then install Prometheus and InfluxDB from the Debian repositories:

$influx Visit https://enterprise.influxdata.com to register for updates, InfluxDB server management, and monitoring. Connected to http://localhost:8086 version 1.0.2 InfluxDB shell version: 1.0.2 > CREATE DATABASE prometheus; Examining the Prometheus logs again, now it was failing and complaining that the specified retention policy didn’t exist. Noting that the Influx documentation for the CREATE DATABASE command mentioned that the autogen retention policy will be used if no other is specified, setting the retention-policy flag to autogen and restarting Prometheus made data start appearing, which I verified by waiting a little while and making a query (guessing a little bit about how I would query a particular metric): > USE prometheus; > SELECT * FROM w1therm_temperature LIMIT 10; name: w1therm_temperature ------------------------- time id instance job value 1532423583303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423598303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423613303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423628303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423643303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423658303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423673303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423688303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423703303000000 000004b926f1 localhost:9000 w1therm 297.9 1532423718303000000 000004b926f1 localhost:9000 w1therm 297.9 ## Results A sample graph of the temperature over two days: The fermentation temperature is quite stable, with daily variation of less than one degree in either direction from the baseline. ## Refinements I later improved the temperature server to handle SIGHUP as a trigger to scan for sensors again, which is a slight improvement over restarting it, but not very important because the server is already so simple (and fast to restart). On reflection, using Prometheus and scraping temperatures is a very strange way to go about solving the problem of logging the temperature (though it has the advantage of using only tools I was already familiar with so it was easy to do quickly). Pushing temperature measurements from the Pi via MQTT would be a much more sensible solution, since that’s a protocol designed specifically for small sensors to report their states. Indeed, there is no shortage of published projects that do exactly that more efficiently than my Raspberry Pi, most of them using ESP8266 microcontrollers which are much lower-power and can still connect to Wi-Fi networks. ### Rambling about IoT security Getting sensor readings through an MQTT broker and storing them to be able to graph them is not quite as trivial as scraping them with Prometheus, but I suspect there does exist a software package that does most of the work already. If not, I expect a quick and dirty one could be implemented with relative ease. On the other hand, running a device like that which is internet-connected but is unlikely to ever receive anything remotely looking like a security update seems ill-advised if it’s meant to run for anything but a short amount of time. In that case having the sensor be part of a Zigbee network instead, which does not permit direct internet connectivity and thus avoids the fraught terrain of needing to protect both the device itself from attack and the data transmitted by the device from unauthorized use (eavesdropping) by taking ownership of that problem away from the sensor. It remains possible to forward messages out to an MQTT broker on the greater internet using some kind of bridge (indeed, this is the system used by many consumer “smart device” platforms, like Philips’ Hue though I don’t think they use MQTT), where individual devices connect only to the Zigbee network, and a more capable bridge is responsible for internet connectivity. The problem of keeping the bridge secure remains, but is appreciably simpler than needing to maintain the security of each individual device in what may be a heterogeneous network. It’s even possible to get inexpensive off-the-shelf temperature and humidity sensors that connect to Zigbee networks like some sold by Xiaomi, offering much better finish than a prototype-quality one I might be able to build myself, very good battery life, and still capable of operating in a heterogenous Zigbee network with arbitrary other devices (though you wouldn’t know it from the manufacturer’s documentation, since they want consumers to commit to their “platform” exclusively)! So while my solution is okay in that it works fine with hardware I already had on hand, a much more robust solution is readily available with off-the-shelf hardware and only a little bit of software to glue it together. If I needed to do this again and wanted a solution that doesn’t require my expertise to maintain it, I’d reach for those instead. 1. Hostname changed to an obviously fake one for anonymization purposes. [return] # Considering my backup systems With the recent news that Crashplan were doing away with their “Home” offering, I had reason to reconsider my choice of online backup backup provider. Since I haven’t written anything here lately and the results of my exploration (plus description of everything else I do to ensure data longevity) might be of interest to others looking to set up backup systems for their own data, a version of my notes from that process follows. ## The status quo I run a Linux-based home server for all of my long-term storage, currently 15 terabytes of raw storage with btrfs RAID on top. The choice of btrfs and RAID allows me some degree of robustness against local disk failures and accidental damage to data. If a disk fails I can replace it without losing data, and using btrfs’ RAID support it’s possible to use heterogenous disks, meaning when I need more capacity it’s possible to remove one disk (putting the volume into a degraded state) and add a new (larger) one and rebalance onto the new disk. btrfs’ ability to take copy-on-write snapshots of subvolumes at any time makes it reasonable to take regular snapshots of everything, providing a first line of defense against accidental damage to data. I use Snapper to automatically create rolling snapshots of each of the major subvolumes: • Synchronized files (mounted to other machines over the network) have 8 hourly, 7 daily, 4 weekly and 3 monthly snapshots available at any time. • Staging items (for sorting into other locations) have a snapshot for each of the last two hours only, because those items change frequently and are of low value until considered further. • Everything else keeps one snapshot from the last hour and each of the last 3 days. This configuration strikes a balance according to my needs for accident recovery and storage demands plus performance. The frequently-changed items (synchronized with other machines and containing active projects) have a lot of snapshots because most individual files are small but may change frequently, so a large number of snapshots will tend to have modest storage needs. In addition, the chances of accidental data destruction are highest there. The other subvolumes are either more static or lower-value, so I feel little need to keep many snapshots of them. I use Crashplan to back up the entire system to their “cloud”1 service for$5 per month. The rate at which I add data to the system is usually lower than the rate at which it can be uploaded back to Crashplan as a backup, so in most cases new data is backed up remotely within hours of being created.

Finally, I have a large USB-connected external hard drive as a local offline backup. Also formatted with btrfs like the server (but with the entire disk encrypted), I can use btrfs send to send incremental backups to this external disk, even without the ability to send information from the external disk back. In practice, this means I can store the external disk somewhere else completely (possibly without an Internet connection) and occasionally shuttle diffs to it to update to a more recent version. I always unplug this disk from power and its host computer when not being updated, so it should only be vulnerable to physical damage and not accidental modification of its contents.

### Synchronization and remotes

For synchronizing current projects between my home server (which I treat as the canonical repository for everything), the tools vary according to the constraints of the remote system. I mount volumes over NFS or SMB from systems that rarely or never leave my network. For portable devices (laptop computers), Syncthing (running on the server and portable device) makes bidirectional synchronization easy without requiring that both machines always be on the same network.

I keep very little data on portable devices that is not synchronized back to the server, but because it is (or, was) easy to set up, I used Crashplan’s peer-to-peer backup feature to back up my portable computers to the server. Because the Crashplan application is rather heavyweight (it’s implemented in Java!) and it refuses to include peer-to-peer backups in uploads to their storage service (reasonably so; I can’t really complain about that policy), my remote servers back up to my home server with Borg.

I also have several Android devices that aren’t always on my home network- these aren’t covered very well by backups, unfortunately. I use FolderSync to automatically upload things like photos to my server which covers the extent of most data I create on those devices, but it seems difficult to make a backup of an Android device that includes things like preferences and per-app data without rooting the device (which I don’t wish to do for various reasons).

### Summarizing the status quo

• btrfs RAID provides resilience against single-disk failures and easy growth of total storage in my server.
• Remote systems synchronize or back up most of their state to the server.
• Everything on the server is continuously backed up to Crashplan’s remote servers.
• A local offline backup can be easily moved and is rarely even connected to a computer so it should be robust against even catastrophic failures.

## Evaluating alternatives

Now that we know how things were, we can consider alternative approaches to solve the problem of Crashplan’s $5-per-month service no longer being available. The primary factors for me are cost and storage capacity. Because most of my data changes rarely but none of it is strictly immutable, I want a system that makes it possible to do incremental backups. This will of course also depend on software support, but it means that I will tend to prefer services with straightforward pricing because it is difficult to estimate how many operations (read or write) are necessary to complete an incremental backup. Some services like Dropbox or Google Drive as commonly-known examples might be appropriate for some users, but I won’t consider them. As consumer-oriented services positioned for the use case of “make these files available whenever I have Internet access,” they’re optimized for applications very different from the needs of my backups and tend to be significantly more expensive at the volumes I need. So, the contenders: • Crashplan for Small Business: just like Crashplan Home (which was going away), but costs$10/mo for unlimited storage and doesn’t support peer-to-peer backup. Can migrate existing Crashplan Home backup archives to Small Business as long as they are smaller than 5 terabytes.
• Backblaze: $50 per year for unlimited storage, but their client only runs on Mac and Windows. • Google Cloud Storage: four flavors available, where the interesting ones for backups are Nearline and Coldline. Low cost per gigabyte stored, but costs are incurred for each operation and transfer of data out. • Backblaze B2: very low cost per gigabyte, but incurs costs for download. • Online.net C14: very low cost per gigabyte, no cost for operations or data transfer in the “intensive” flavor. • AWS Glacier: lowest cost for storage, but very high latency and cost for data retrieval. The pricing is difficult to consume in this form, so I’ll make some estimates with an 8 terabyte backup archive. This somewhat exceeds my current needs, so should be a useful if not strictly accurate guide. The following table summarizes expected monthly costs for storage, addition of new data and the hypothetical cost of recovering everything from a backup stored with that service. Service Storage cost Recovery cost Notes Crashplan$10 0 "Unlimited" storage, flat fee.
Backblaze $4.17 0 "Unlimited" storage, flat fee. GCS Nearline$80 ~$80 Negligible but nonzero cost per operation. Download$0.08 to $0.23 per gigabyte depending on total monthly volume and destination. GCS Coldline$56 ~$80 Higher but still negligible cost per operation. All items must be stored for at least 90 days (kind of). B2$40 $80 Flat fee for storage and transfer per-gigabyte. C14 €40 0 "Intensive" flavor. Other flavors incur per-operation costs. Glacier$32 $740 Per-gigabyte retrieval fees plus Internet egress. Reads may take up to 12 hours for data to become available. Negligible cost per operation. Minimum storage 90 days (like Coldline). Note that for Google Cloud and AWS I’ve used the pricing quoted for the cheapest regions; Iowa on GCP and US East on AWS. ### Analysis Backblaze is easily the most attractive option, but the availability restriction for their client (which is required to use the service) to Windows and Mac makes it difficult to use. It may be possible to run a Windows virtual machine on my Linux server to make it work, but that sounds like a lot of work for something that may not be reliable. Backblaze is out. AWS Glacier is inexpensive for storage, but extremely expensive and slow when retrieving data. The pricing structure is complex enough that I’m not comfortable depending on this rough estimate for the costs, since actual costs for incremental backups would depend strongly on the details of how they were implemented (since the service incurs charges for reads and writes). The extremely high latency on bulk retrievals (up to 12 hours) and higher cost for lower-latency reads makes it questionable that it’s even reasonable to do incremental backups on Glacier. Not Glacier. C14 is attractively priced, but because they are not widely known I expect backup packages will not (yet?) support it as a destination for data. Unfortunately, that means C14 won’t do. Google Cloud is fairly reasonably-priced, but Coldline’s storage pricing is confusing in the same ways that Glacier is. Either flavor is better pricing-wise than Glacier simply because the recovery cost is so much lower, but there are still better choices than GCS. B2’s pricing for storage is competitive and download rates are reasonable (unlike Glacier!). It’s worth considering, but Crashplan still wins in cost. Plus I’m already familiar with software for doing incremental backups on their service (their client!) and wouldn’t need to re-upload everything to a new service. ## Fallout I conclude that the removal of Crashplan’s “Home” service effectively means a doubling of the monthly cost to me, but little else. There are a few extra things to consider, however. First, my backup archive at Crashplan was larger than 5 terabytes so could not be migrated to their “Business” version. I worked around that by removing some data from my backup set and waiting a while for those changes to translate to “data is actually gone from the server including old versions,” then migrating to the new service and adding the removed data back to the backup set. This means I probably lost a few old versions of the items I removed and re-added, but I don’t expect to ever need any of them. Second and more concerning in general is the newfound inability to do peer-to-peer backups from portable (and otherwise) computers to my own server. For Linux machines that are always Internet-connected Borg continues to do the job, but I needed a new package that works on Windows. I’ve eventually chosen Duplicati, which can connect to my server the same way Borg does (over SSH/SFTP) and will in general work over arbitrarily-restricted internet connections in the same way that Crashplan did. ## Concluding I’m still using Crashplan, but converting to their more-expensive service was not quite trivial. It’s still much more inexpensive to back up to their service compared to others, which means they still have some significant freedom to raise the cost until I consider some other way to back up my data remotely. As something of a corollary, it’s pretty clear that my high storage use on Crashplan is subsidized by other customers who store much less on the service; this is just something they must recognize when deciding how to price the service! # An illustrated guide to LLVM At the most recent Rust Sydney meetup (yesterday, “celebrating” Rust’s second birthday) I gave a talk intended to provide an introduction to using LLVM to build compilers, using Rust as the implementing language. The presentations were not recorded which might have been neat, but I’m publishing the slides and notes here for anybody who might find it interesting or useful. It is however not as illustrated as the title may seem to suggest. It’s embedded below, or you can view standalone in your browser or as a PDF, available with or without presenter notes. Navigate with the arrow keys on your keyboard or by swiping. Press ? to show additional keys for controls; in particular, s will open a presenter view that includes the plentiful notes I’ve included. In case of curiousity, I built the presentation with reveal.js and its preparation consumed a lot more time than I initially expected (though that’s not the fault of reveal). # Quick and dirty web image optimization Given a large pile of images that nominally live on a web server, I want to make them smaller and more friendly to serve to clients. This is hardly novel: for example, Google offer detailed advice on reducing the size of images for the web. I have mostly JPEG and PNG images, so, jpegtran and optipng are the tools of choice for bulk lossless compression. To locate and compress images, I’ll use GNU find and parallel to invoke those tools. For JPEGs I take a simple approach, preserving comment tags and creating a progressive JPEG (which can be displayed at a reduced resolution before the entire image has been downloaded). find -iname '*.jpg' -printf '%P,%s\n' \ -exec jpegtran -o -progressive -copy comments -outfile {} {} \; \ > delta.csv  Where things get a little more interesting is when I output the name and size of each located file (-printf ...) and store those in a file (> delta.csv).1 This is so I can collect more information about the changes that were made. ## Check the JPEGs Loading up a Jupyter notebook for quick computations driven with Python, I load the file names and original sizes, adding the current size of each listed file to my dataset. import os deltas = [] with open('delta.csv', 'r') as f: for line in f: name, _, size = line.partition(',') size = int(size) newsize = os.stat(name).st_size deltas.append((name, size, newsize)) From there, it’s quick work to do some list comprehensions and generate some overall statistics: original_sizes = [orig for (_, orig, _) in deltas] final_sizes = [new for (_, _, new) in deltas] shrinkage = [orig - new for (_, orig, new) in deltas] pct_total_change = 100 * (sum(original_sizes) - sum(final_sizes)) / sum(original_sizes) pct_change = [shrinkage / orig for (shrinkage, orig) in zip(shrinkage, original_sizes)] avg_pct_change = 100 * sum(pct_change) / len(pct_change) print('Total size reduction:', sum(shrinkage), 'bytes ({}%)'.format(round(pct_total_change, 2))) avg = sum(shrinkage) / len(shrinkage) print('Average reduction per file:', avg, 'bytes ({}%)'.format(round(avg_pct_change, 2))) This is by no means good code, but that’s why this short write-up is “quick and dirty”. Over the JPEGs alone, I rewrote 2162 files, saving 8820474 bytes overall; about 8.4 MiB, 8.02% of the original size of all of the files- a respectable savings for exactly zero loss in quality. ## PNGs I processed the PNGs in a similar fashion, having optipng emit interlaced PNGs which can be displayed at reduced resolution without complete data and try more combinations of compression parameters than it usually would, trading CPU time for possible gains. There was no similar tradeoff with the JPEGs, since apparently jpegtran’s optimization of encoding is entirely deterministic whereas optipng relies on experimental compression of image data with varying parameters to find a minimum size. Since I observed that many of the PNGs were already optimally-sized and interlacing made them significantly larger (by more than 10% in many cases), I also considered only files that were more than 256 KiB in size. To speed up processing overall, I used parallel to run multiple instances of optipng at once (since it’s a single-threaded program) to better utilize the multicore processors at my disposal. find -iname '*.png' -size +256k -printf '%P,%s\n' \ -exec parallel optipng -i 1 -o 9 ::: {} + \ > delta.csv  Running the same analysis over this output, I found that interlacing had a significant negative effect on image size. There were 68 inputs larger than 256 KiB, and the size of all of the files increased by 2316732 bytes; 2.2 MiB, nearly 10% of the original size. A few files had significant size reductions (about 300 KiB in the best case), but the largest increase in size had similar magnitude. Given the overall size increase, the distribution of changes must be skewed towards net increases. ### Try again Assuming most of the original images were not interlaced (most programs that write PNG don’t interlace unless specifically told to) and recognizing that interlaced PNGs tend to compress worse, I ran this again but without interlacing (-i 0) and selecting all files regardless of size. The results of this second run were much better: over 3102 files, save 12719421 bytes (12.1 MiB), 15.9% of the original combined size. One file saw a whopping 98% reduction in size, from 234 KB to only 2914 bytes- inspecting that one myself, the original was inefficiently coded 32 bits per pixel (8-bit RGBA), and it was reduced to two bits per pixel. I expect a number of other files had similar but less dramatic transformations. Some were not shrunk at all, but optipng is smart enough to skip rewriting those so it will never make a file larger- the first run was an exception because I asked it to make interlaced images. ## That’s all I saved about 20 MiB for around 5000 files- not bad. A copy of the notebook I used to do measurements is available (check out nbviewer for an online viewer), useful if you want to do something similar with your own images. I would not recommend doing it to ones that are not meant purely for web viewing, since the optimization process may strip metadata that is useful to preserve. 1. Assumption: none of the file names contain commas, since I’m calling this a CSV (comma-separated values) file. It’s true in this instance, but may not be in others. [return] # sax-ng Over on Cemetech, we’ve long had an embedded chat widget called “SAX” (“Simultaneous Asynchronous eXchange”). It behaves kind of like a traditional shoutbox, in that registered users can use the SAX widget to chat in near-real-time. There is also a bot that relays messages between the on-site widget and an IRC channel, which we call “saxjax”. The implementation of this, however, was somewhat lacking in efficiency. It was first implemented around mid-2006, and saw essentially no updates until just recently. The following is a good example of how dated the implementation was: // code for Mozilla, etc if (window.XMLHttpRequest) { xmlhttp=new XMLHttpRequest() xmlhttp.onreadystatechange=state_Change xmlhttp.open("GET",url,true) xmlhttp.send(null) } else if (window.ActiveXObject) { // code for IE xmlhttp=new ActiveXObject("Microsoft.XMLHTTP") if (xmlhttp) { xmlhttp.onreadystatechange=state_Change xmlhttp.open("GET",url,true) xmlhttp.send() } }  The presence of ActiveXObject here implies it was written at a time when a large fraction of users would have been using Internet Explorer 5 or 6 (the first version of Internet Explorer released which supported the standard form of XMLHttpRequest was version 7). Around a year ago (that’s how long this post has been a draft for!), I took it upon myself to design and implement a more modern replacement for SAX. This post discusses that process and describes the design of the replacement, which I have called “sax-ng.” ## Legacy SAX The original SAX implementation, as alluded to above, is based on AJAX polling. On the server, a set of approximately the 30 most recent messages were stored in a MySQL database and a few PHP scripts managed retrieving and modifying messages in the database. This design was a logical choice when initially built, since the web site was running on a shared web host (supporting little more than PHP and MySQL) at the time. Eventually this design became a problem, as essentially every page containing SAX that is open at any given time regularly polls for new messages. Each poll calls into PHP on the server, which opens a database connection to perform one query. Practically, this means a very large number of database connections being opened at a fairly regular pace. In mid-2012 the connection count reached levels where the shared hosting provider were displeased with it, and requested that we either pay for a more expensive hosting plan or reduce resource usage. In response, we temporarily disabled SAX, then migrated the web site to a dedicated server provided by OVH, who had opened a new North American datacenter in July. We moved to the dedicated server in August of 2012. This infrastructure change kept the system running, and opened the door to a more sophisticated solution since we gained the ability to run proper server applications. Meanwhile, the limitations of saxjax (the IRC relay bot) slowly became more evident over time. The implementation was rather ad-hoc, in Python. It used two threads to implement relay, with a dismaying amount of shared state used to relay messages between the two threads. It tended to stop working correctly in case of an error in either thread, be it due to a transient error response from polling the web server for new messages, or an encoding-related exception thrown from the IRC client (since Python 2.x uses bytestrings for most tasks unless specifically told not to, and many string operations (particularly outputting the string to somewhere) can break without warning when used with data that is not 8-bit clean (that is, basically anything that isn’t ASCII). Practically, this meant that the bot would frequently end up in a state where it would only relay messages one way, or relay none at all. I put some time into making it more robust to these kinds of failures early in 2015, such that some of the time it would manage to catch these errors and outright restart (rather than try to recover from an inconsistent state). Doing so involved some pretty ugly hacks though, which prompted a return to some longtime thoughts on how SAX could be redesigned for greater efficiently and robustness. ## sax-ng For a long time prior to beginning this work, I frequently (semi-jokingly) suggested XMPP (Jabber) as a solution to the problems with SAX. At a high level this seems reasonable: XMPP is a chat protocol with a number of different implementations available, and is relatively easy to set up as a private chat service. On the other hand, the feature set of SAX imposes a few requirements which are not inherently available for any given chat service: 1. An HTTP gateway, so clients can run inside a web browser. 2. Group chat, not just one-to-one conversation capability. 3. External authentication (logging in to the web site should permit connection to chat as well). 4. Retrieval of chat history (so a freshly-loaded page can have some amount of chat history shown). As it turns out, ejabberd enables all of these, with relatively little customization. mod_http_bind provides an HTTP gateway as specified in XEP-0206, mod_muc implements multi-user chat as specified in XEP-0045 which also includes capabilities to send chat history to clients when they connect, and authentication can be handled by an external program which speaks a simple binary protocol and is invoked by ejabberd. Main implementation of the new XMPP-based system was done in about a week, perhaps 50 hours of concerted work total (though I may be underestimating). I had about a month of “downtime” at the beginning of this past summer, the last week of which was devoted to building sax-ng. ### ejabberd The first phase involved setting up an instance of ejabberd to support the rest of the system. I opted to run it inside Docker, ideally to make the XMPP server more self-contained and avoid much custom configuration on the server. Conveniently, somebody had already built a Docker configuration for ejabberd with a wealth of configuration switches, so it was relatively easy to set up. Implementing authentication against the web site was also easy, referring to the protocol description in the ejabberd developers guide. Since this hooks into the website’s authentication system (a highly modified version of phpBB), this script simply connects to the mysql server and runs queries against the database. Actual authentication is performed with phpBB SIDs (Session ID), rather than a user’s password. It was built this way because the SID and username are stored in a cookie, which is available to a client running in a web browser. This is probably also somewhat more secure than storing a password in the web browser, since the SID is changed regularly so data exposure via some vector cannot compromise a user’s web site password. Error handling in the authentication script is mostly nonexistent. The Erlang approach to such problems is mostly “restart the component if it fails”, so in case of a problem (of which the only real possibility is a database connection error) ejabberd will restart the authentication script and attempt to carry on. In practice this has proven to be perfectly reliable. In XMPP MUC (Multi-User Chat), users are free to choose any nickname they wish. For our application, there is really only one room and we wish to enforce that the nickname used in XMPP is the same as a user’s username on the web site. There ends up being no good way in ejabberd to require that a user take a given nickname, but we can ensure that it is impossible to impersonate other users by registering all site usernames as nicknames in XMPP. Registered nicknames may only be used by the user to which they are registered, so the only implementation question is in how to automatically register nicknames. I ended up writing a small patch to mod_muc_admin, providing an ejabberdctl subcommand to register a nickname. This patch is included in its entirety below. diff --git a/src/mod_muc_admin.erl b/src/mod_muc_admin.erl index 9c69628..3666ba0 100644 --- a/src/mod_muc_admin.erl +++ b/src/mod_muc_admin.erl @@ -15,6 +15,7 @@ start/2, stop/1, % gen_mod API muc_online_rooms/1, muc_unregister_nick/1, + muc_register_nick/3, create_room/3, destroy_room/3, create_rooms_file/1, destroy_rooms_file/1, rooms_unused_list/2, rooms_unused_destroy/2, @@ -38,6 +39,9 @@ %% Copied from mod_muc/mod_muc.erl -record(muc_online_room, {name_host, pid}). +-record(muc_registered, + {us_host = {\{<<"">>, <<"">>}, <<"">>} :: {\{binary(), binary()}, binary()} | '$1',
+         nick = <<"">> :: binary()}).

%%----------------------------
%% gen_mod
@@ -73,6 +77,11 @@ commands() ->
module = ?MODULE, function = muc_unregister_nick,
args = [{nick, binary}],
result = {res, rescode}},
+     #ejabberd_commands{name = muc_register_nick, tags = [muc],
+              desc = "Register the nick in the MUC service to the JID",
+              module = ?MODULE, function = muc_register_nick,
+              args = [{nick, binary}, {jid, binary}, {domain, binary}],
+              result = {res, rescode}},

#ejabberd_commands{name = create_room, tags = [muc_room],
desc = "Create a MUC room name@service in host",
@@ -193,6 +202,16 @@ muc_unregister_nick(Nick) ->
error
end.

+muc_register_nick(Nick, JID, Domain) ->
+    {jid, UID, Host, _,_,_,_} = jlib:string_to_jid(JID),
+    F = fun (MHost, MNick) ->
+                mnesia:write(#muc_registered{us_host=MHost,
+                                             nick=MNick})
+        end,
+    case mnesia:transaction(F, [{\{UID, Host}, Domain}, Nick]) of
+        {atomic, ok} -> ok;
+        {aborted, _Error} -> error
+    end.

%%----------------------------


It took me a while to work out how exactly to best implement this feature, but considering I had never worked in Erlang before it was reasonably easy. I do suspect some familiarity with Haskell and Rust provided background to more easily understand certain aspects of the language, though. The requirement that I duplicate the muc_registered record (since apparently Erlang provides no way to import records from another file) rubs me the wrong way, though.

In practice, then, a custom script traverses the web site database, invoking ejabberdctl to register the nickname for every existing user at server startup and then periodically or on demand when the server is running.

### Web interface

The web interface into XMPP was implemented with Strophe.js, communicating with ejabberd via HTTP-bind with the standard support in both the client library and server.

The old SAX design served a small amount of chat history with every page load so it was immediately visible without performing any additional requests after page load, but since the web server never receives chat data (it all goes into XMPP directly), this is no longer possible. The MUC specification allows a server to send chat history to clients when they join a room, but that still requires several HTTP round-trips (taking up to several seconds) to even begin receiving old lines.

I ended up storing a cache of messages in the browser, which is used to populate the set of displayed messages on initial page load. Whenever a message is received and displayed, its text, sender and a timestamp are added to the local cache. On page load, messages from this cache which are less than one hour old are displayed. The tricky part with this approach is avoiding duplication of lines when messages sent as part of room history already exist, but checking the triple of sender, text and timestamp seems to handle these cases quite reliably.

### webridge

The second major feature of SAX is to announce activity on the web site’s bulletin board, such as when people create or reply to threads. Since the entire system was previously managed by code tightly integrated with the bulletin board, a complete replacement of the relevant code was required.

In the backend, SAX functionality was implemented entirely in one PHP function, so replacing the implementation was relatively easy. The function’s signature was something like saxSay($type,$who, $what,$where), where type is a magic number indicating what kind of message it is, such as the creation of a new thread, a post in a thread or a message from a user. The interpretation of the other parameters depends on the message type, and tends to be somewhat inconsistent.

The majority of that function was a maze of comparisons against the message type, emitting a string which was eventually pushed into the chat system. Rather than attempt to make sense of that code, I decided to replace it with a switch statement over symbolic values (whereas the old code just used numbers with no indication of purpose), feeding simple invocations of sprintf. Finding the purpose of each of the message types was most challenging among that, but it wasn’t terribly difficult as I ended up searching the entire web site source code for references to saxSay and determined the meaning of the types from the caller’s context.

To actually feed messages from PHP into XMPP, I wrote a simple relay bot which reads messages from a UNIX datagram socket and repeats them into a MUC room. A UNIX datagram socket was selected because there need not be any framing information in messages coming in (just read a datagram and copy its payload), and this relay should not be accessible to anything running outside the same machine (hence a UNIX socket).

The bot is implemented in Python with Twisted, utilizing Twisted’s provided protocol support for XMPP. It is run as a service under twistd, with configuration provided via environment variables because I didn’t want to write anything to handle reading a more “proper” configuration file. When the PHP code calls saxSay, that function connects to a socket with path determined from web site configuration and writes the message into that socket. The relay bot (“webridge”) receives these messages and writes them into MUC.

### saxjax-ng

Since keeping a web page open for chatting is not particularly convenient, we also operate a bridge between the SAX chat and an IRC channel called saxjax. The original version of this relay bot was of questionable quality at best: the Python implementation ran two threads, each providing one-way communication though a list. No concurrency primitives, little sanity.

Prior to creation of sax-ng I had put some amount of effort in improving the reliability of that system, since an error in either thread would halt all processing of messages in the direction corresponding to the thread in which the error occurred. Given there was essentially no error handling anywhere in the program, this sort of thing happened with dismaying frequency.

saxjax-ng is very similar in design to webridge, in that it’s Twisted-based and uses the Twisted XMPP library. On the IRC side, it uses Twisted’s IRC library (shocking!). Both ends of this end up being very robust when combined with the components that provide automatic reconnection and a little bit of custom logic for rotating through a list of IRC servers. Twisted guarantees singlethreaded operation (that’s the whole point; it’s an async event loop), so relaying a message between the two connections is simply a matter of repeating it on the other connection.

## Contact with users

This system has been perfectly reliable since deployment, after a few changes. Most notably, the http-bind interface for ejabberd was initially exposed on port 5280 (the default for http-bind). Users behind certain restrictive firewalls can’t connect to that port, so we quickly reconfigured our web server to reverse-proxy to http-bind and solve that problem. Doing so also means the XMPP server doesn’t need its own copy of the server’s SSL certificate.

There are still some pieces of the web site that emit messages containing HTML entities in accordance with the old system. The new system.. doesn’t emit HTML entities because that should be the responsibility of something doing HTML presentation (Strong Opinion) and I haven’t bothered trying to find the things that are still emitting HTML-like strings.

The reconnect logic on the web client tends to act like it’s received multiples of every message that arrives after it’s tried to reconnect to XMPP, such as when a user puts their computer to sleep and later resumes; the web client tries to detect the lost connection and reopen it, and I think some event handlers are getting duplicated at that point. Haven’t bothered working on a fix for that either.

# Conclusion

ejabberd is a solid piece of software and not hard to customize. Twisted is a good library for building reliable network programs in Python, but has enough depth that some of its features lack useful documentation so finding what you need and figuring out how to use it can be difficult. This writeup has been languishing for too long so I’m done writing now.

# Web history archival and WARC management

I’ve been a sort of ‘rogue archivist’ along the lines of the Archive Team for some time, but generally lack the combination of motivation and free time to directly take part in their activities.

That said, I do sometimes go on bursts of archival since these things do concern me; it’s just a question of when I’ll get manic enough to be useful and latch onto an archival task as the one to do. An earlier public example is when I mirrored ticalc.org.

The historical record contains plenty of instances where people maintained copies of their communications or other documentation which has proven useful to study, and in the digital world the same is likely to be true. With the ability to cheaply store large amounts of data, it is also relatively easy to generate collections in the hope of their future utility.

Something I first played with back in 2014 was extracting lists of web pages to archive from web browser history. From a public perspective this may not be particularly interesting, but if maintained over a period of time this data could be interesting as a snapshot of a typical-in-some-fashion individual’s daily life, or for purposes I can’t foresee.

Today I’m going to write a little about how I collect this data and reduce the space requirements. The products of this work that are source code can be found on Bitbucket.

## Collecting History

I use Firefox as my everyday web browser, which combined with Firefox Sync provides ready access to a reasonably complete record of my web browsing activity. The first step is extracting the actual browser history, which is a relatively straightforward process since Firefox maintains all of this data in SQLite databases. I use cookies.sqlite and places.sqlite from my Firefox profile.

Extracting history from places.sqlite is as simple as running a query that emits timestamps and corresponding URLs. For example:

sqlite3 places.sqlite \
"SELECT visit_date, url FROM moz_places, moz_historyvisits \
WHERE moz_places.id = moz_historyvisits.place_id \
AND visit_date > $LASTRUN \ ORDER BY visit_date" This will print the timestamp and URL for every page in history newer than LASTRUN (which can easily be omitted to get everything), with the fields separated by pipes (|). The timestamp (visit_date) is a UNIX timestamp expressed in microseconds. While there’s some utility in just grabbing web pages, the real advantage I’ve found in using data directly from a web browser is that it can gain a personal touch, with access to private data granted in many cases by cookies. This does imply that the data should not be shared, but as with personal letters in history this formerly-private information may become useful in the future at a point when the privacy of that data is no longer a concern for those involved. Again using sqlite and the cookies.sqlite file we got from Firefox, it’s relatively easy to extract a cookies.txt file that can be read by many tools: sqlite3 -separator ' ' cookies.sqlite << EOF .mode tabs .header off SELECT host, CASE substr(host,1,1)='.' WHEN 0 THEN 'FALSE' ELSE 'TRUE' END, path, CASE isSecure WHEN 0 THEN 'FALSE' ELSE 'TRUE' END, expiry, name, value FROM moz_cookies; EOF The output of that sqlite invocation can be redirected directly into a cookies.txt file without any further work. With the list of URLs and cookies, it’s again not difficult to capture a WARC containing every web page listed. I’ve used wget, largely out of convenience. Taking advantage of a UNIX shell, I usually do the following, piping the URL list into wget: cut -d '|' -f 2- urls.txt | \ wget --warc-file=date --warc-cdx --warc-max-size=1G \ -e robots=off -U "Inconspicuous Browser" \ --timeout 30 --tries 2 --page-requisites \ --load-cookies cookies.txt \ --delete-after -i - This will download every URL given to it with the cookies extracted earlier, and will also download external resources (like images) when they are referenced in downloaded pages. The process will be logged to a WARC file named with the time the process was started, limiting to approximately 1-gigabyte chunks. This takes a while, and the best benefits are to be had from running this at fairly short intervals which will tend to provide more unexpired cookies and catch changes over short periods of time, thus presenting a more accurate view of what the browser’s user is actually doing. ## Deduplication On completion, I’m presented with a directory containing some number of compressed WARC files. That’s a reasonable place to leave it, but this weekend after doing an archival run that yielded about 90 gigabytes of data I decided to look into making it smaller, especially considering I know my archive runs end up grabbing many copies of the same resources on web sites which I visit frequently (for example, icons on DuckDuckGo). The easy approach would be to use a compression scheme which tends to work better than gzip (the typical compression scheme for WARCs). However, doing so would destroy a useful property in that the files do not need to be completely decompressed for viewing. These are built such that with an index showing where a particular record exists in the archive, a user does not need to decompress the entire file up to that point (as would be the case with most compression schemes)- it is possible to seek to that point in the compressed file and decompress just the desired record. I had hope that the professionals in this field had already considered ways to make their archives smaller, and that ended up being true but the documentation is very sparse: the only truly useful material was a recent presentation by Youssef Eldakar from the Bibliotheca Alexandrina cursorily describing tools to deduplicate entries in WARC files using revisit records which point to a previous date-URL combination that has the same contents1. I don’t see any strong reason to keep my archives split into 1-gigabyte pieces and it’s slightly easier to perform deduplication on a single large archive, so I used megawarc to join the a number of smaller archives into one big one. It was easy enough to find the published code for the tools described in the presentation, so all I had to do was figure out how to run them.. right? ## The Process The logical procedure for deduplication is as follows: 1. Run warcsum to compute hashes of every record of interest in the specified archive(s), writing them to a file. 2. Run warccollres to examine the records and their hashes, determining which ones are actually the same and which are just hash collisions. 3. Run wardrefs to rewrite the archives with references when duplicates are found. I had a hard time actually getting that to happen, though. ### warcsum Running warcsum was relatively easy; it happily chewed on my test archive for a while and eventually spat out a long list of files. I later discovered that it wasn’t processing the whole archive, though- it stopped after about two gigabytes of data. I eventually found that the program (written in C) was using int as a type to represent file offsets, so the apparent offset in a file becomes negative after reading two gigabytes of data which causes the program to end, thinking it’s done everything. I patched the relevant bits to use 64-bit types (like off_t) where working with file offsets, and eventually got it to emit 1.7 million records rather than the few tens of thousands I was getting before. While investigating the premature termination, I found (using warcat) that wget sometimes writes record length fields that are one byte longer than the actual record. I spent a while trying to investigate that and repair the length fields in hopes of fixing warcsum’s premature termination, but it ended up being unnecessary. In practice this off-by-one doesn’t seem to be harmful, but I do find it somewhat concerning. I also discovered that warcsum assumes wrapping arithmetic for determining how large some buffers should be, which is undefined behavior in C and could cause Bad Things to happen. I fixed the instance where I saw it, but that didn’t seem to be causing any issues on my dataset. ### warccollres Moving on to warccollres, I found that it assumes a lot of infrastructure which I lack. Given the name of a WARC file, it expects to have access to a MySQL server which can indicate a URL where records from the WARC can be downloaded- a reasonable assumption if you’re a professional working within an organization like the Bibliotheca Alexandrina or Internet Archive, but excessive for my purposes and difficult to set up. I ended up rewriting all of warccollres in Python, using a self-contained database and assuming direct access to the files. There’s nothing particularly novel in there (see warccollres.py in the repository). WARC records are read from the archive and compared where they have the same hash to determine actual equality, and duplicates are marked as such. I originally imported everything into a sqlite database and did all the work in there (not importing file contents though– that would be very inefficient), but this was rather slow because sqlite tends to be slow on workloads that involve more than a little bit of writing to the database. With some changes I made it use a “real” database (MariaDB) which helped. After tuning some parameters on the database server to allow it to use much larger amounts of memory (innodb_buffer_pool_size..) and creating some indexes on the imported data, everything moved along at a nice clip. As the process went on, it seemed to slow down- early on everything was I/O-bound and status messages were scrolling by too fast for me to see, but after a few hundred thousand records had been processed I could see a significant slowdown. Looking at resource usage, the database was the limiting factor. It turned out that though I had created indexes in the database on the rows that get queried frequently, it was still performing a full table scan to satisfy the requirement that records be processed in the order which they appear in the WARC file. (I determined this by manually running some queries and having mariadb ANALYZE them for information on how it processed the query.) After creating a composite index of the copy_number and warc_offset columns (which I wasn’t even aware was possible until I read the grammar for CREATE INDEX carefully, and had to experiment to discover that the order in which they are specified matters), the process again became I/O-bound. Where the first 1.2 million records or so were processed in about 16 hours, the last 500 thousand were completed in only about an hour after I created that index. ### warcrefs Compared to the earlier parts, warcrefs is a quite docile tool, perhaps in part because it’s implemented in Java. I made a few changes to the file describing how Maven should build it so I could get a jar file containing the program and all its required libraries which would be easy to run. With the file-offset issues in warcsum fresh in my mind, I proactively checked for similar issues in warcrefs and found it used int for file offsets throughout (which in Java is always a 32-bit value). I changed the relevant parts to use long instead, avoiding further problems with large files. As I write this warccollres is still running on a large amount of data, so I can’t truly evaluate the capabilities of warcrefs. I did test it on a small archive which had some duplication and it was successful (verified by manual inspection2). ### warcrefs revisited I’m writing this section after the above-mentioned run of warccollres finished and I got to run warcrefs over about 30 gigabytes of data. It turned out a few additional changes were required. 1. I forgot to recompile the jar after changing its use of file offsets to use longs, at which point I found the error reporting was awful in that the program only printed the error message and nothing else. It bailed out on reaching a file offset not representable as an int, but I couldn’t tell that until I made it print a proper stack trace. 2. Portions of revisit records were processed as strings but have lengths in bytes. Where multibyte characters are used this yields a wrong size. Fortunately, the WARC library used to write output checks these so I just had to fix it to use byte lengths everywhere. 3. Reading records to deduplicate reopened the input file for every record and never closed them, causing the program to eventually reach the system open file limit and fail. I had to make it close those. ## Results I got surprisingly good savings out of deduplication on my initial large dataset. Turns out web browser history has a lot more duplication than a typical archive: about 50% on my data, where Eldakar cited a number closer to 15% for general archives. $ ls -lh
total 47G
-rw-r--r-- 1 tari users  14G Jan 18 15:07 mega_dedup.warc.gz
-rw-r--r-- 1 tari users  33G Jan 17 10:45 mega.warc.gz
-rw-r--r-- 1 tari users 275M Jan 17 11:39 mega.warcsum
-rw-r--r-- 1 tari users 415M Jan 18 13:50 warccollres.txt


The input file was 33 gigabytes, reduced to only 14 after deduplication. I’ve manually checked that all the records appear to be there, so that appears to be true deduplication only. There are 1709118 response records in the archive (that’s the number of lines in the warcsum file), with only 210467 unique responses3, making an average of about 8 copies per response. Perhaps predictably, this implies that the duplicated records tend to be small since the overall savings was much less than 8 times.

## Improvements

At this point deduplication is not a very automated process, since there are three different programs involved and a database must be set up. This would be relatively easy to script, but it hasn’t yet seen enough use for me to be confident in its ability to run unattended.

There are some inefficiencies, especially in warccollres.py which decompresses records in their entirety into memory (where it could stream them or back them with real files to reduce memory requirements for large records). It also requires that there be only one WARC file under consideration, which was a concession to simplicity of implementation.

In the downloading process, I found that it will sometimes get hung up on streams, particularly streaming audio like Hutton Orbital Radio where the actual stream URL appears in browser history. The result of that kind of thing is downloading a “file” of unbounded size at a rather low speed (since it’s delivered only as fast as the audio will be played back).

wpull is a useful tool to replace wget with (that is also mostly compatible, for convenience) which can help address these issues. It supports custom scripts to control its operation in a more fine-grained way, which would probably permit detection of streams so they don’t get downloaded. Also attractive is wpull’s support for running Javascript in downloaded pages, which allows it to capture data that is not served “baked in” to a web page as is often the case on modern web sites, especially “social” ones.

## Concluding

I ended up spending the majority of a weekend hammering out most of this code, from about 11:00 on Saturday through about 18:00 on Sunday with only about an hour total for food-breaks and a too-much-yet-not-enough 6-hour pause to sleep. I might not call it pleasant, but it’s a good feeling to build something like this successfully and before losing interest in it for an indeterminate amount of time.

I have long-term plans regarding software to automate archiving tasks like this one, and that was where my work here started early on Saturday. I’d hope that future manic chunks of time like this one will lead to further progress on that concept, but personal history says this kind of incredibly-productive block of time occurs at most a few times a year, and the target of my concentration is unpredictable4. Call it a goal to work toward, maybe: the ability to work on archiving as an occupation, rather than a sadly neglected hobby.

In any case, if you missed it, the collection of code I put together for deduplication is available on Bitbucket. The history-gathering portions I use are basically exactly as described in the relevant sections, leaving a lot of room for future improvement. Thanks for reading if you’ve come this far, and I hope you find my work useful!

1. I’m not entirely comfortable with that approach, since there is no particular guarantee that any record exists with the specified “coordinates” (time of retrieval and network location) in web-space. However, this approach does maintain sanity even if a WARC is split into its individual records which is another important consideration.

[return]
2. WARC files are mostly plain text with possibly-binary network traffic in between, so it’s relatively easy to browse them with tools like zless and verify everything looks correct. It’s quite convenient, really.

[return]
3. SELECT count(id) FROM warcsums WHERE copy_num = 1 [return]
4. In fact, the last time I did something like this I (re)wrote a large amount of chat infrastructure which I still have yet to finish writing up for this blog.

[return]

# HodorCSE

Localization of software, while not trivial, is not a particularly novel problem. Where it gets more interesting is in resource-constrained systems, where your ability to display strings is limited by display resolution and memory limitations may make it difficult to include multiple localized copies of any given string in a single binary. All of this is then on top of the usual (admittedly slight in well-designed systems) difficulty in selecting a language at runtime and maintaining reasonably readable code.

This all comes to mind following discussion of providing translations of Doors CSE, a piece of software for the TI-84+ Color Silver Edition1 that falls squarely into the “embedded software” category. The simple approach (and the one taken in previous versions of Doors CS) to localizing it is just replacing the hard-coded strings and rebuilding.

As something of a joke, it was proposed to make additional “joke” translations, for languages such as Klingon or pirate. I proposed a Hodor translation, along the lines of the Hodor UI patch2 for Android. After making that suggestion, I decided to exercise my skills a bit and actually make one.

## Hodor (Implementation)3

Since I don’t have access to the source code of Doors CSE, I had to modify the binary to rewrite the strings. Referring the to file format guide, we are aware that TI-8x applications are mostly Intel hex, with a short header. Additionally, I know that these applications are cryptographically signed which implies I will need to resign the application when I have made my changes.

### Dumping contents

I installed the IntelHex module in a Python virtualenv to process the file into a format easier to modify, though I ended up not needing much capability from there. I simply used a hex editor to remove the header from the 8ck file (the first 0x4D bytes).

Simply trying to convert the 8ck payload to binary without further processing doesn’t work in this case, because Doors CSE is a multipage application. On these calculators Flash applications are split into 16-kilobyte pages which get swapped into the memory bank at 0x4000. Thus the logical address of the beginning of each page is 0x4000, and programs that are not aware of the special delimiters used in the TI format (to delimit pages) handle this poorly. The raw hex file (after removing the 8ck header) looks like this:

:020000020000FC
:20400000800F00007B578012010F8021088031018048446F6F727343534580908081020382
:2040200022090002008070C39D40C39A6DC3236FC30E70C3106EC3CA7DC3FD7DC3677EC370
:20404000A97EC3FF7EC35D40C35D40C33D78C34E78C36A78C37778C35D40C3A851C9C940F3
:2040600001634001067001C36D00CA7D00BC6E00024900097A00E17200487500985800BDF8
[snip]
:020000020001FB
:20402000EF7D4721B98411AE84010900EDB0EFAA4AC302723A9B87B7CA4940FE01CA4340B9
:20404000C30272CD4F40C30272CDB540C30272EF67452100002275FE3EA03273FECD63405B


Lines 1 and 7 here are the TI-specific page markers, indicating the beginning of pages 0 and 1, respectively. The lines following each of those contain 32 (20 hex) bytes of data starting at address 0x40000 (4000). I extracted the data from each page out to its own file with a text editor, minus the page delimiter. From there, I was able to use the hex2bin.py script provided with the IntelHex module to create two binary files, one for each page.

### Modifying strings

With two binary files, I was ready to modify some strings. The calculator’s character set mostly coincides with ASCII, so I used the strings program packaged with GNU binutils to examine the strings in the image.

$strings page00.bin HDoorsCSE ##6M#60> oJ:T Uo& dQ:T [snip] xImprove BASIC editor Display clock Enable lowercase Always launch Doors CSE Launch Doors CSE with PRGM]  With some knowledge of the strings in there, it was reasonably short work to find them with a hex editor (in this case I used HxD) and replace them with variants on the string “Hodor”. I also found that page 1 of the application contains no meaningful strings, so I ended up only needing to examine page 0. Some of the reported strings require care in modification, because they refer to system-invariant strings. For example, “OFFSCRPT” appears in there, which I know from experience is the magic name which may be given to an AppVar to make the calculator execute its contents when turned off. Thus I did not modify that string, in addition to a few others (names of authors, URLs, etc). ### Repacking I ran bin2hex.py to convert the modified page 0 binary back into hex, and pasted the contents of that file back into the whole-app hex file (replacing the original contents of page 0). From there, I had to re-sign the binary.4 WikiTI points out how easy that process is, so I installed rabbitsign and went on my merry way: $ rabbitsign -g -r -o HodorCSE.8ck HodorCSE.hex


### Testing

I loaded the app up in an emulator to give it a quick test, and was met by complete nonsense, as intended.

I’m providing the final modified 8ck here, for the amusement of my readers. I don’t suggest that anybody use it seriously, not for the least reason that I didn’t test it at all thoroughly to be sure I didn’t inadvertently break something.

## Extending the concept

It’s relatively easy to extend this concept to the calculator’s OS as well (and in fact similar string replacements have been done before) with the OS signing keys in hand. I lack the inclination to do so, but surely somebody else would be able to do something fun with it using the process I outlined here.

1. That name sounds stupider every time I write it out. Henceforth, it’s just “the CSE.”

[return]
2. The programmer of that one took is surprisingly far, such that all of the code that feasibly can be is also Hodor-filled.

[return]
3. Hodor hodor hodor hodor. Hodor hodor hodor. [return]
4. This signature doesn’t identify the author, as you might assume. Once upon a time TI provided the ability for application authors to pay some amount of money to get a signing key associated with them personally, but that system never saw wide use. Nowadays everybody signs their applications with the public “freeware” keys, just because the calculator requires that all apps be signed and the public keys must be stored on the calculator (of which the freeware keys are preinstalled on all of them).

[return]

# "A Sufficiently Smart Compiler"

On a bit of a lark today, I decided to see if I could get Spasm running in a web browser via Emscripten. I was successful, but found that something seemed to be optimizing out most of main() such that I had to hack in my own main function that performed the same critical functions and (for the sake of simplicity) hard-coded the relevant command-line options.

Looking into the problem a bit further, I observed that not all of main() was being removed; there was one critical line left in. The beginning of the function in source and the generated code were as follows.

C++ source:

int main (int argc, char **argv)
{
int curr_arg = 1;
bool case_sensitive = false;
bool is_storage_initialized = false;

use_colors = true;
extern WORD user_attributes;
user_attributes = save_console_attributes ();
atexit (restore_console_attributes_at_exit);

//if there aren't enough args, show info
if (argc < 2) {


Generated Javascript (asm.js):

function _main($argc,$argv) {
$argc =$argc | 0;
$argv =$argv | 0;
HEAP8[4296] = 1;
__Z23save_console_attributesv() | 0;
return 0;
}


Spasm is known to work in general, but I found it unlikely that LLVM’s optimizer would be optimizing this code wrong as well. Building with optimizations turned off generated correct code, so it was definitely the optimizer breaking this and not some silly bug in Emscripten. Looking a little deeper into the save_console_attributes function, we see the following code:

WORD save_console_attributes () {
#ifdef WIN32
CONSOLE_SCREEN_BUFFER_INFO csbiScreenBufferInfo;
GetConsoleScreenBufferInfo (GetStdHandle (STD_OUTPUT_HANDLE), &csbiScreenBufferInfo);
return csbiScreenBufferInfo.wAttributes;
#endif
}

<!-- more -->

Since I'm not building for a Windows target (Emscripten's runtime environment
resembles a Unix-like system), this was preprocessed down to an empty function
(returning void), but it's declared with a non-void return. Smells like
[undefined behavior](http://blog.regehr.org/archives/213)! Let's make this
function return 0:

c++
WORD save_console_attributes () {
#ifdef WIN32
CONSOLE_SCREEN_BUFFER_INFO csbiScreenBufferInfo;
GetConsoleScreenBufferInfo (GetStdHandle (STD_OUTPUT_HANDLE), &csbiScreenBufferInfo);
return csbiScreenBufferInfo.wAttributes;
#else
return 0;
#endif
}


With that single change, I now get useful code in main. Evidently LLVM’s optimizer was smart enough to recognize the call to that function invoked UB and optimized out the rest of main.

## Concluding

This issue illustrates nicely the dangers of a sufficiently smart compiler, where updates to your compiler might break otherwise-working code because it’s subtly broken. This is particularly of concern in C, where the compilers tend to go to extreme measures to optimize the generated code and there are a lot of ways to inadvertently invoke undefined behavior.

Static analyzers are a big help in finding these issues. Looking more closely at the compiler output from building Spasm, it emitted a warning regarding this function, as well as several potential buffer overflows of the following form:

    char s[64];
strncat(s, "/", sizeof(s));


This looks correct, but is subtly broken because the length parameter taken by strncat should be the maximum allowed length of the string, excluding the null terminator. The third parameter should be sizeof(s) - 1 in this case, otherwise the string’s null terminator might be written out of bounds.

## Appendix

The code for my work on this is up on Bitbucket and might be of interest to some readers. I fear that by working on this project I’ve inadvertently committed to becoming the future maintainer of Spasm, which I find to contain a significant amount of poor-quality code. Perhaps I’ll have to write a replacement for Spasm in Rust, which I’ve been quite pleased with as a potential replacement for C, without the numerous pitfalls and rather more modern in its capabilities.

# Reverse-engineering Ren'py packages

Some time ago (September 3, 2013, apparently), I had just finished reading Analogue: A Hate Story (which I highly recommend, by the way) and was particularly taken with the art. At that point it seems my engineer’s instincts kicked in and it seemed reasonable to reverse-engineer the resource archives to extract the art for my own nefarious purposes.

A little examination of the game files revealed a convenient truth: it was built with Ren’Py, a (open-source) visual novel engine written in Python. Python is a language I’m quite familiar with, so the actual task promised to be well within my expertise.

## Code

Long story short, I’ve build some rudimentary tools for working with compiled Ren’py data. You can get it from my repository on BitBucket. Technically-inclined readers might also want to follow along in the code while reading.

## Background

There are a large number of games designed with Ren’py. It’s an easy tool to get started with and hack on, since the script language is fairly simple and because it’s open-source, more sophisticated users are free to bend it to their will. A few examples of (in my opinion) high-quality things built with the engine:

Since visual novels tend to live or die on the combination of art and writing, the ability to examine the assets outside the game environment offers interesting possibilities.

Since it was handy, I started my experimentation with Analogue.

## RPA resource archives

The largest files distributed with the game were .rpa files, so I investigated those first for finding art. As it turned out, this was exactly the place I needed to look. Start by examining the raw data:

$cd "Analogue A Hate Story/game"$ ls
bytecode.rpyb
data.rpa
dlc1.rpa
nd.rpa
$less data.rpa RPA-3.0 00000000035f5c75 414154bb <F0><AA><D6>^MީZ<A0><90><FB>^M6<B9><B7>^X<A3><82><F3>F<B0><DF>k8(<BF>ߦx<9C><D5>T [snip] There’s an obvious file identifier (RPA-3.0), followed by a couple numbers and a lot of compressed-looking data. The first number turns out to be very close to the total file size, so it’s probably some size or offset field, while the other one looks like some kind of signature. $ python -c 'print(0x35f5c75)'
56581237
\$ stat -c %s data.rpa
56592058

At this point I simply referred to the Ren’Py source code, rather than waste time experimenting on the data itself. Turns out the first number is the file offset of the index, and the second one is a key used for simple obfuscation of elements of the index (numbers are bitwise exclusive-or’d with the key). The archive index itself is a DEFLATE-compressed block of pickled Python objects. The index maps file names to tuples of offset and block length specifying where within the archive file the data can be found.

With that knowledge in hand, it’s short work to build a decoder for the index data and dump it all to files. This is rpa.py in my tools. Extracting the archives pulls out plenty of images and other media, as well as a number of interesting-looking .rpyb files, which we’ll discuss shortly.

### Cosplay

For a bit of amusement, I exercised my web-programming chops a little and built a standalone web page for playing with the extracted costumes and expressions of *Hyun-ae and *Mute, which I’ve included below. Here’s a link to the bare page for standalone amusement as well.

## Script guts

The basic format of compiled scripts (.rpyb files) is similar to that of resource packages. The entire thing is a tuple of (data, statements), where data is a dictionary of basic metadata and statements is a list of objects representing the script’s code.

The statements in this are just the Ren’py abstract syntax tree, so all the objects come from the renpy.ast module. Unfortunately and as I’ll discuss later, the pickle format makes this representation hard to work with.

The structure of AST members is designed such that each object can have attached bytecode. In practice this appears to never happen in archives. In my investigations of the source, it appears that Ren’py only writes Python bytecode as a performance enhancement, and most of it ends up in bytecode.rpyb. That file appears to provide some sort of bytecode cache that overrides script files in certain situations. For the purposes of reverse-engineering this is fortunate– Python bytecode is documented, but rather more difficult to translate into something human-readable than the source code that is actually present in RPYB archives.

Here’s some of the Act 1 script from Analogue run through the current version of my script decompiler:

## BEGIN Label 'dont_understand', params=None, hide=False
dont_understand:
## <class 'renpy.ast.Show'> -- don't know how to dump this!
## <class 'renpy.ast.Say'> -- don't know how to dump this!
## <class 'renpy.ast.Jump'> -- don't know how to dump this!
## BEGIN Label 'nothing', params=None, hide=False
nothing:
python:
shown_message = None

for block in store.blocks:
for message in block.contents:
if message == _message:
shown_message = str(store.blocks.index(block)+1) + "-" + str(block.contents.index(message)+1)

if shown_message:
gray_out(shown_message)
## <class 'renpy.ast.With'> -- don't know how to dump this!
## <class 'renpy.ast.Show'> -- don't know how to dump this!
## <class 'renpy.ast.With'> -- don't know how to dump this!
## <class 'renpy.ast.Say'> -- don't know how to dump this!


Clearly there are a few things my decompiler needs to learn about. It does, however, handle the more common block elements such as If statements. In any case, the Python code embedded in these scripts tends to be more interesting than the rest (which are mostly just dialogue and display manipulation) for the purposes of reverse-engineering. If you’re more interested in spoiling the game for yourself, it’s not as useful.

A few telling bits of logic from options.rpy:

init -3:
## BEGIN Python
python hide:
## BEGIN PyCode, exec mode, from game/options.rpy
renpy.demo_mode = True
init -1:
## BEGIN Python
python hide:
## BEGIN PyCode, exec mode, from game/options.rpy
config.developer = True

config.screen_width = 1024
if persistent.resolution == None:
persistent.resolution = 2
if persistent.old_resolution == None:
persistent.old_resolution = persistent.resolution

if not persistent.resolution:
config.screen_height = 600
else:
config.screen_height = 640

if renpy.demo_mode:
config.window_title = u"Analogue trial"
else:
config.window_title = u"Analogue: A Hate Story"

config.name = "Analogue"
config.version = "0.0"


The spacing here is interesting; I suspect (but haven’t attempted to verify) that the Ren’py script compiler strips comments since there haven’t been any in all of the scripts I’ve examined, so it’s likely that the unusual empty blocks in the code were comment blocks in a former life.

I’ve yet to dig much into what determines when demo_mode is set, but I doubt it would be difficult to forcibly set (or clear) if one were so inclined. Not that I condone such an action..

A little bit of interesting game-critical logic, also from Analogue (caution: minor spoilers)

store.radiation = 0
store.reactor_enabled = True
def interact_cb():

if (any_read("7-*") or (store.current_character == "mute" and get_m("6-9").enabled)) and store.radiation_levels < 0.65:

config.interact_callbacks.append(interact_cb)

You can get some idea of how specialized Ren’py’s execution environment is from this code. Particularly, store is a magic value injected into the locals of Python script blocks which maps to the RPY persistent variable store, which stores most of the game state. config is a similar magic value providing a handle to the engine configuration.

In this instance, radiation` refers to a sort of hidden timer which forces the player to solve a puzzle on expiration (assuming the preconditions have been met), then make a decision which causes the plot to fork depending on that decision. Elsewhere in the code, I found a few developer switches which allow one to display the value of this countdown and reset or force it.

## Conclusions

As the official documentation notes, the process of resource compilation is not very secure but is enough to deter casual copying. I’ve shown here that such a claim is entirely correct, though script decompilation may be somewhat harder than the developers envisioned due to the choice of pickle as a serialization format.

It’s nothing particularly new to me, but a reminder to designers of software: if it runs on your attacker’s system, it can be hacked. It’s not a question of “if”, but instead “how fast”. I was mostly interested in extracting resources with this project, which was quite easy. In that matter, I think the designers of Ren’Py made a good design decision. The compiled archives and scripts are much more robust against accidental modification in the face of curious users than not compiling anything, but the developers do not expend undue effort building something harder to break which would eventually be broken anyway by a sufficiently determined attacker.

### Portability

As I alluded to earlier, the pickle representation makes the Ren’Py AST hard to work with. This is because many of the objects contain references to the engine state, which in turn implies most of the engine needs to be initialized when unpickling the AST. To say the least, this is not easy- engine initialization is not easily separated from game startup.

To illustrate the problem, observe that the Ren’Py developer kit is simply the engine itself packaged with a game of sorts that provides help in getting a new project set up by modifying the included scripts. There simply seems to be no part of the engine that is designed to run without the rest of it running as well.

In experimenting with different products built with Ren’Py, I’ve had to make changes to some combination of the engine itself and my code in order to bootstrap the engine state to a point where the AST can be successfully unpickled. Suffice to say, this has hampered my progress somewhat, and led me to consider slightly different avenues of attack.

The most promising of these would involve a semi-custom unpickler which avoids instantiating actual Ren’Py objects; the only data that need be preserved is the structural information, rather than the many hooks into engine state that are also included in the pickle serialization. Further continuation of this project is likely to take this approach to deserialization.

# GStreamer's playbin, threads and queueing

I’ve been working on a project that uses GStreamer to play back audio files in an automatically-determined order. My implementation uses a playbin, which is nice and easy to use. I had some issues getting it to continue playback on reaching the end of a file, though.

According to the documentation for the about-to-finish signal,

This signal is emitted when the current uri is about to finish. You can set the uri and suburi to make sure that playback continues.

This signal is emitted from the context of a GStreamer streaming thread.

Because I wanted to avoid blocking a streaming thread under the theory that doing so might interrupt playback (the logic in determining what to play next hits external resources so may take some time), my program simply forwarded that message out to be handled in the application’s main thread by posting a message to the pipeline’s bus.

Now, this approach appeared to work, except it didn’t start playing the next URI, and the pipeline never changed state- it was simply wedged. Turns out that you must assign to the uri property from the same thread, otherwise it doesn’t do anything.

Fortunately, it turns out that blocking that streaming thread while waiting for data isn’t an issue (determined by experiment by simply blocking the thread for a while before setting the uri).