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Datagrok was designed to connect millions of users with millions of datasets, data sources, algorithms, scripts, and applications. It should be able to handle such volumes, and it should do it fast and without cluttering the UI.

Below are some scaling-related approaches that we took. Some of them are related to the infrastructure, others to the application, and some to the ecosystem we are building.



By keeping metadata separately from data and utilizing scalable storage for both of them (Postgres for metadata, and S3 for data) Datagrok is capable of storing many datasets, both in terms of numbers and volume.

By utilizing our proprietary in-memory data engine on both the client and server sides, we can transfer datasets between tiers a lot faster than other systems.

The recommended metadata engine for enterprises is RDS.

Application server

Our application server is Dart-based and uses asynchronous coding techniques similar to Node.js. It serves a lot of clients simultaneously (see stress testing). Most scientific computations are off-loaded to the special "compute" machines with autoscaling enabled. Nevertheless, some tasks performed on the app server are still computationally-intensive (for example, parsing CSV files), so our app server is also multithreaded in addition to running asynchronous code. It takes advantage of the modern multi-core architecture.

As can be seen from the stress testing, app server scaling won't be needed for most of the workloads in the enterprise. However, this still could be done if such a need arises.

Data storage

The recommended data storage engine for enterprises is S3, which is inherently scalable.


CVM is used for scientific computations. For high loads, it should be deployed with the auto-scaling mode enabled. See Compute Virtual Machine for details.


The web-based application can interactively work with datasets consisting of tens of millions of rows or columns, entirely on the client, in the browser. To do that, a radical break from the traditional web-based applications was required.

Big datasets

In order to work with big datasets right in the browser, we developed our proprietary in-memory database.


While some popular charting libraries like D3 are great and do produce nice-looking results, we are hitting their limits quite soon once we start working with sizable datasets. First of all, it is not feasible to keep more than 10,000 objects in the browser's DOM tree. Second, since they were not designed for working with big datasets, even the amount of memory they allocate is sizable (usually, the data needs to be in JSON format, which is suboptimal).

To address that, we had to bite the bullet and implement several high-performance visualizations from scratch. All performance-critical viewers use immediate-mode canvas-based rendering, thus solving the DOM issue. Moreover, they use our high-performance data engine. The data is not only efficiently stored, but viewers allocate no additional memory since they work directly with the data engine. Of course, that required a lot of work and meticulous engineering, but the result is definitely worth it.


As the program grows, so does the complexity. Eventually, even a program with the well-thought plugin-based architecture becomes unwieldy, and users get lost in the hundreds of different menus and thousands of options.

We had to come up with a solution that is not only highly extensible but also has a built-in mechanism for restricting the feature creep and keeping the UI clean. We addressed these seemingly self-contradictory requirements by designing a solution that combines first-class support for functions with the data augmentation mechanism.

In the end, we came up with a solution that not only satisfied the initial requirements, but provides a solid foundation for evolving the ecosystem on a global scale:

  • First-class functions, reflectable, with metadata on parameters
  • Support for multiple languages
  • Multiple dynamic backends for functions
  • Everything is a function (db queries, web services, predictive models, etc)
  • Flexible way for packaging, deploying, and targeting functions
  • Dynamic, asynchronous loading of functions
  • Applicable actions are suggested based on the current context. Out of thousands of functions available in the repository, only a handful of relevant ones are being suggested to the user.

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