My Hackathon Projects At Datadog

The only limit is your imagination.


It’s been 3 years since I joined Datadog and I had the chance to participate in 5 hackathon projects. They were a great experience and I learned a lot from them! That’s why I want to share my experience with you, to give you an idea of what can an engineer build in a short period. And perhaps give you some inspiration to do the same.

What does a hackathon look like at Datadog?

A hackathon at Datadog lasts for 2 days. It happens once or twice per year. You can use this time to implement something that you want, either individually or collectively. There are many kinds of projects: a new feature in the existing platform, a new tool, a side project that you want to work on, a new framework that you want to explore, etc. It’s not necessarily related to professional projects, you can also work on a personal one. At the end of the hackathon, there is a 2-minute presentation where you can show the results to other engineers.

Now you have a better understanding of the context, let’s continue to see the projects that I participated in in the past.

Auto-completion For Python Scripts

The first hackathon project that I did was adding auto-completion for some internal Python scripts, built for handling operations for the logs team. It happened in February 2020. At that time, I was in the logs storage team, where we had a lot of operations to do every day. Those operations were related to multiple domains: Elasticsearch, Kafka, MongoDB, etc. Therefore, it involved many scripts and each of those has multiple options and values. It’s hard to remember what are the right options or values. To make things easier, I wanted to add an auto-completion feature so that we can use a tab to let the system show the options or the list of values that are related to the current option. This was built on top of argparse.

Below, you can find a more detailed and technical version.

The scripts are built in Python using the library argparse. When calling the script, we parse the arguments provided to the script and use them as a dictionary. There is another library called argcomplete which provides easy, extensible command line tab completion of arguments for your Python script. Thanks to argcomplete, we can find out the list of available options, or the list of values related to a given option. For example, when creating a new Elasticsearch cluster, you need to specify the data center, the cloud provider, the name of the Kubernetes cluster, etc. Also, you may want to let the data center or Kubernetes cluster be completed automatically.

To achieve auto-completion, we need to:

  1. Install auto-completion. Register the completion when installing the Python scripts. It means creating a Bash script for completion and hooking it into the installation file (in our case, it’s a Makefile). Inside the Bash script, we need to iterate each command and call the register-python-argcomplete to register them; or use the global completion.
  2. Add completer to argument parser. We need to create a completer and add it to the argument parser to provide completions. argcomplete contains a list of completers, such as ChoicesCompleter to complete values as an enumeration.
  3. Making the completion meaningful and optional. We need to ensure that the completion is meaningful to the user. For example, giving the right list of data centers, the right list of Kubernetes clusters, the right tags, etc. Also, we need to make the completion optional so that people enable them if they want to, but we don’t force anyone.
  4. Add tests. To set up the tests, we need to add some environment variables and simulate user behavior (mainly pressing tab) in the tests and ensure that the script behaves correctly. The simulation is mainly done by creating a parser and running the completer on the user’s behavior.
  5. Other tasks. There are also some minor tasks related to dependencies, test setup, and documentation.

In the end, I gave up the project… 😅 I completed items 1, 2, 4, 5 during the Hackathon, but item 3 “Making the completion meaningful and optional” was hard. Making the completion meaningful means I need to have the source of truth of the staging and production environment defined in the source code; or querying some external services in real-time, which has additional performance concerns (auto-completion needs to be fast), or requires additional setup (such as kubectl-port-forward). I didn’t have much time to continue after the hackathon and we had reduced significantly our manual operations after switching to Kubernetes. So I decided to stop continuing on it.

Completable Future And Vavr

For the second Hackathon in July 2022, I didn’t find out anything too interesting to work on, so I picked some refactoring at the last minute. It was about Completable Future in Java. However, one day later, my colleague convinced me that I should try something cooler, so I started to work on Vavr integration, an object-functional library.

Completable Future

If you are familiar with the Completable Future in Java, you probably know that there are two notions: Completion Stage and Completable Future. Completion Stage is an interface, which allows you to write code in an asynchronous way, e.g. thenApply(...), thenCompose(...), to describe the relationship between multiple stages. On the other hand, Completable Future is the implementation for interfaces: Completion Stage and Future. On top of the completion stages, it contains all the APIs to manipulate future, such as join(), get(), which makes it more powerful.

My refactoring was to remove all the unnecessary usage of Completable Future and replace them with Completion Stage. By doing so, it allows us to:

  • Make it more explicit that we don’t want to complete or fail the future.
  • Avoid additional calls of method toCompletableFuture(), which enables interoperability among different implementations of this interface by providing a common conversion type.

Now I re-think this initiative, I won’t do this again. Because:

  • The impact of the change was low but it requires a lot of work. I needed to create 10 PRs (one per group of services) and merge them individually to avoid having too many conflicts to handle.
  • The regression happens quite soon because people don’t know why we should use the Completion Stage and we don’t have any rule in the CI or IDE to enforce this. On the other side, giving the possibility to developers to manipulate futures (join(), get()) is not dangerous, we never had a problem with this as everyone uses the APIs carefully.


In the 2nd day of the hackathon, I moved to something else. I wanted to demonstrate the usage of Java object-functional library vavr, which brings many concepts of Scala into the Java world. In my hackathon, I tried to integrate Vavr into an existing service. I demonstrated multiple data types: streams, map, tuple, optional, either; demonstrated support of JSON serialization; I discussed interoperability, performance, migration plan, etc. However, the project was rejected by our director and some other developers because using the library in the critical path may have a performance impact; also, the Vavr APIs are pretty invasive due to their names, which makes integration hard as we cannot distinguish the normal Java API and the vavr API only using the short name (List, Set, Map, …).

I tried my best to convince people that it’s cool to use Vavr but unfortunately, it’s rejected. That’s also why I stopped contributing to Vavr as I don’t find any use-cases in the near future.

Datadog Instant

In the 3rd hackathon in August 2022, I worked with a colleage on a frontend project called “Datadog Instant”. This is an experimental project proposed by my colleage, which allows users to have instant recommendations in the Log Explorer in Datadog, like what Google proposes in their search bar (see screenshot below). The idea is to provide more relevant results to the user based on the search history; complete a word instantly without waiting, and bring information to the user rather than asking them to look into the “saved views” or other locations. Nobody wants to enter the same query again and again.

Google instant recommendations

There were several aspects to manage:

  • Data structure. We need to store users’ historical data to provide personalized suggestions. This is done using a trie, a type of k-wary search tree. It allows us to persist words or sentences as characters or keys, the number of occurrences in the history, and finally to recompute the words during completion. For the hackathon, we persisted the trie in the local storage of the browser.
  • Frontend. We need to intercept multiple events when the user presses keys on their keyword, especially the A-Z keys and the TAB key. When the event happened, we need to either read the trie or update the content of the search bar. As we were backend developers and we didn’t know the frontend very well, we use the old-school jQuery to handle the logic.

This was the first time that I worked with someone else on a Hackathon project and it was cool! Compared to a solo project, we were able to discuss, validate or reject some ideas, share tasks, and review. Now, this proof of concept (POC) is picked up by the Events UI team and will probably become the reality in the future! Thanks to Arnaud, I also learned how to plan and distribute tasks so that we can work more efficiently as a team.

Temporal Java Worker

In April 2022, I worked with another colleague on workflows. At Datadog, we use the workflow engine Temporal to handle internal deployments and running workflows for different usages. All the existing code is written in Go. It works well for most of the team. However, some teams use Java. Using Go means having an additional barrier to benefit from the existing workflow engine, such as learning the language, re-building domain models and existing features, managing source code in two different repositories, etc. The goal of the Hackathon is to try building a Temporal Java worker using the Temporal Java SDK.

During the Hackathon, I worked with my teammate Julien to make this work. More precisely, we had to:

  • Create a Temporal worker. This was the most obvious part as we can handle can follow the official Java samples and the previous PoC done by other teams. It also meant creating a Maven module, making configuration work, modifying the CI so that we can release the application, etc.
  • Make the authentication work. The communication to the Temporal Server is done in HTTPS and requires authentication using OAuth and JWT, so we needed to find a way to do that in Java. It implies fetching the certificate, finding the right role, providing the token in the header, etc.

We made the entire logic work at the end (a bit after the Hackathon). Thanks to this experience, we figured out the solution to many issues and make it possible to plan the work with much better visibility. Although we didn’t have a chance to move forward right now, there is a good chance that we will do that again soon.

Query Troubleshooting Assistant

In November 2022, I made a query troubleshooting assistant (QTA), a small static web page which allows administrators to troubleshoot query problems. Different services have different facets and it leads to confusion and slowness in the troubleshooting process. This tool unifies the user input and provides mappings between user input and service outputs, which allows users to troubleshoot more efficiently. For example, it asks you to provide information related to customer, storage location, sources, request ID or other information, and then suggests different outputs as URLs for you. The facet mappings between the input and output is handled by the tool. You can also share the result with others because all parameters are reflected as query parameters.

Lessons Learned

What did I learn across these projects?

  1. You don’t have to know everything to get started. Do not be afraid of starting something you don’t know very well. There are chances that you can learn that quickly, or get help from your teammates. In my case, it was the Datadog Instant project. I didn’t know much about jQuery, but I had experience with Vue.js, so I learned that during the hackathon.
  2. Write a plan before writing code. If you write down your plan, there is a much better chance that you will succeed in this game. It helps you to clarify the needs, your goal, the milestones to achieve that, the known constraints, the available resources, etc. It gives better visibility and expectation and also helps distribute works across participants.
  3. Work on something impactful. If your project is really useful for the company (for the product, for teammates, …), there is a much higher chance that your work won’t be lost after the Hackathon.
  4. An incomplete project is not a failure. The most important thing is to learn and explore, it’s ok to fail. Even a failed project can provide much information for further discussion and decisions.


Today I shared the previous Hackathon projects with you in this article. I showed the 5 projects that I did: auto-completion for Python scripts; the completable features refactoring and Vavr integration; the Datadog Instant, providing Google-like instant recommendations in the search bar; the Temporal Java worker to write workflows in Java; and finally the query troubleshooting assistant. I also shared some lessons learned from these projects. Interested to know more? You can subscribe to the feed of my blog, follow me on Twitter or GitHub. Hope you enjoy this article, see you the next time!