Our main focus for the past months has been on improving the existing tools. This technical article talks about some of the steps we took to improve both the quality and reliability of some of the DevMine tools and more specifically about the improvements made on crawld and repotool.

Back in the last months of 2014, our focus on crawld has mainly been on the crawlers part. The simple explanation is that the fetcher part was good enough for the amount of data we had back then. We knew some parts needed improvements like, for instance, the fetcher not being able to update a git repository in a detached head state. However, performance was generally not an issue. This hold true until ght2dm came into the game.

This spring, with ght2dm, we started importing data from the GHTorrent project. Suddenly, from the mere tens of thousands repositories metadata we had, we ended up having millions. It is one thing to clone and update a few thousands repositories, it is another when it comes to hundreds of thousands.

Performance improvements

Use tar archives when storing source code repositories

As a first step, we cloned approximately 300’000 source code repositories. This was a huge bump from the ~80’000 we had clone so far and we started having some issues with the file system. For instance, we had no way to tell how large was the directory in which we clone the repositories. Why? Because there was already so many files that even after running for a week, du(1) would not return a result… Something clearly had to be done…

At this point, we decided that storing too many files was not an option. Hence, the decision was made to archive all source code repositories in tar format. This meant that all the language parsers, crawld and repotool had to be adapted to the change. For the language parsers, the change was fairly easy: they simply have to be able to read the content of a tar archive. However, it was another story for repotool and something even more challenging for crawld.

repotool uses git2go, a Go binding to libgit2, a C library which implements the git protocol. Since it does not work on tar archives, the only way to get commits information is by extracting the archive, at least the .git directory of the git repository, because this is where repository information is stored. This means that repotool has to extract a tar archive before being able to read the repository information it looks for and, of course, delete the extracted content afterwards.

In the same vein, crawld has to extract a repository archive before being able to perform an update. Of course, if the tar option is activated, it has to re-create the tar archive after the operation. Upon new repository clone, it has to create a tar archive as well.

So this meant lots of changes but in the end, all the work was well worth it. du(1) is now quick to calculate the size of the clone directory even with more than 10TB of data. Having 1 file per repository instead of tens of thousands is really a game changer. We also noticed a considerable speedup when parsing source code from repositories. Indeed, only one open/close file operation is now required per repository in order to parse the code. This changes everything…

On the other hand, having to deal with tar archives brings a little overhead to crawld and repotool operations. One of the next paragraph explains how we dealt with this matter.

Parallelize the tasks

Until recently, only two goroutines were spawned by crawld: one to crawl GitHub repositories, users and organizations metadata and another one to fetch git repositories. As there is no hard dependency between two different repositories, there is no reason not to use several goroutines for the fetching part. So now, the user has the possibility to set the number of goroutines to use to fetch repositories. This is especially an improvement now that there is an induced overhead when storing source code in tar archives.

In the past, we used to use a script which was iterating over the repositories giving repositories path location to a number of goroutines which were spawning repotool processes to collect commits information. It used to be decent enough in that it took about 2 weeks to collect ~15mio commits and ~150mio commit diff deltas from the ~80’000 repositories and store them into the database. This way of doing allowed repotool to run in parallel but at the cost of spawning a lot of processes.

Avoid spawning processes

crawld used to spawn git processes to clone and update repositories. This worked okay for a moment but it is not viable when you need to scale. Consider that a process is spawned each time we do a clone or update operation on a repository, that throw-away process creation is costly and that we now have millions of repositories to deal with. Hence, we decided to use libgit2 via git2go which we were already using with repotool with satisfying results.

Using this library allows us to take some shortcuts on repository update operation. At first, one might think that it shall mimic the git pull command, ie doing the equivalent of git fetch && git merge FETCH_HEAD. However, repositories, in our context, are only meant to be read from. Hence, there shall never be anything to actually merge. So the update operation only fetches changes from remote and fast-forwards locally which avoids doing an actual merge.

In the previous section, I mentioned that we were using a script to run several instances of repotool in parallel. Now, this means spawning hundreds of thousands of processes to collect commits information from locally stored repositories. Again, this used to work fine with a limited number of repositories. Now that we have ~750’000 git repositories clone (and that it is just small fraction of what we aim at collecting), it is a problem. Hence, we split repotool into two binaries, one to output information as JSON which works like repotool always has, and another one, repotool-db, which is responsible for inserting information into the database. Now, instead of spawning costly process, it uses goroutine which is way lighter. Also, we decided that inserting commit diff deltas was too much and we simply do not collect them into the database anymore. repotool-db is still capable of doing it but it is slower and generates a very large quantity of data. We were also able to speed things up by inserting commits information in batches of 1mio per transaction, having a routine which takes care of that, instead of one transaction per commit. By not storing commit diff deltas, we are also now able to disable foreign keys constraints and indexes which speed things up. In order to associate commits information with repositories and users, we also used to query the database 1 time per project to retrieve the repository ID and two times per commit to get the author and committer IDs. You can guess that this was impacting performances by a lot. Instead now, all repositories and users IDs are now queried once when repotool-db starts and stored in a hashmap for a O(1) access time.

Use a ramdisk

Storing source code repositories as tar archives has several advantages as I mentioned in a previous section. This does however bring some overhead for the repositories update operation. Actually, crawld now appeared to be limited by disk I/Os, without a surprise. Something had to be done and the ideal solution for this case is to use a ramdisk. The only thing we care to store is only the tar archive of the repositories after all. So crawld has been modified to use a temporary directory, which can be specified by the user, to extract tar archives in order to update the repositories. After the update, repositories are simply re-tar’ed in their original location. There is no reasons not to clone repositories in ramdisk as well and create the tar archive on main storage afterwards. The only problem which may arise by doing so is if the repository does not fit in size in the ramdisk. Most of repositories are not very large so crawld’s behavior is to attempt at cloning in the ramdisk and fallback to cloning directly on main storage if that reveals to be impossible. By doing so, we uncovered a bug in libgit2 where attempting to clone a repository in a not large enough partition leads to a crash because of a SIGBUS signal raised from an attempt at mapping data somewhere it is not allowed to. Fortunately for us, Carlos Martín Nieto, one of the main libgit2 developer was very quick to address that issue.

repotool also uses a ramdisk when repositories are stored as tar archives. Since it has to untar them in order to be able to collect commits information, this is done in a ramdisk. Note that only the .git folder is extracted since this is all that is required to collect the information. Of course, this temporary directory is simply removed after the operation. Again, doing this operation in RAM speeds things up considerably.

Reliability improvements

Take the right decision when errors are raised

Handling errors correctly is essential, especially with tools that are supposed to run continuously for long period of times. However, it is not always clear at first what the right decision is to handle some errors.

crawld used to skip a repository when it was not able to update it. Now, when this happens, unless it is due to a network error, it deletes it from disk and re-clones it. Not doing this when the error is network related is very important because sometimes repositories are deleted from remote and we do not want to delete our copy without any possibility to get it back afterwards.

Restart where interrupted after a crash

Even with all our efforts to make the tools stable, crashes happen. They may be related to a library error or an unexpected non-frequent condition, fact is that they do happen. When the tool has been running for a few days, you are usually quite sad if when checking if it still is running and you note that it has stopped running and, moreover, that this means it has to start over from the start again.

This is why crawld now remembers, using a file, the ID of the last successfully processed repository. Hence, after a crash or having been stopped on a voluntary basis, it can resume its operations where it stopped.

Use an error rate based throttler

When a tool runs continuously for very long period of time, like crawld, you usually simply deal with an error and log it. This is usually fine but think about how the tool would behave if the storage space is full or the network is down? Yes, crawld would record a lot of errors and try to keep on going… This is obviously not a desired behavior. Our take on this problem was to implement an error rate based throttler. What it does now is that each error happening is not only dealt with and logged but it is also recorded by the error rate based throttler. When the error rates gets too high, and all the parameters can be adjusted by the user, which means that something really wrong is probably happening, it automatically throttles all routines for a predefined period of time. In consequence, crawld pauses operations and when the administrator resolves the underlying problem, such as freeing space on the storage device or restoring the network, all operations are resumed.


In this article, I mentioned several steps taken to improve performances and reliability of crawld and repotool. Nothing much can be measured with regard to stability improvements but I can simply say that things are now way better. However, to assess my performance improvement claims, I shall provide some numbers.

The repotool changes allow us to now insert ~20-25mio commits per day on our machine (currently a server with an Intel Xeon CPU E5-2630L v2 @ 2.40GHz processor, 128GB or ECC RAM and 20TB of storage in a 6 times 4TB RAID 5 configuration), which is a big improvement comparing to the ~15mio in two weeks previously. To be completely fair, we do not insert commit diff deltas anymore but we gained a huge speedup nonetheless.

As for crawld, we have no real numbers to compare with. However, I can say that we are now able to collect up to 8 times more data in a single day than we used to collect in a week. With our server and infrastructure, we are now able to clone/update ~6-8TB of repositories data per day.