How Tiled Fits into the Ecosystem

Design Principles

Many institutions are building tools for structured data access. Tiled’s particular approach to this problem is tuned to the needs its users. Our users are opinionated scientists with highly varied data scales, access patterns, and conventions. From their requirements flow Tiled’s design.

  • Be unopinionated about the format(s) data is stored in or accessed in. There will be never “one format to rule them all” across all scientific domains of interest to Tiled. Standardize on common structures not formats. Transcode data upon request into the format most convenient to the user.

  • Meet all users where they are. Support both traditional data storage (directories of files with metadata in filenames) and forward-looking ones involving databases. Provide a smooth path to enable users to explore more powerful data storage and access solutions without disrupting their existing workflows.

  • Use web technologies and standards to make data available to users in a way that is secure and easily interoperable with both legacy and emerging data consumers. These include data science tools, web portals, and traditional file-based software.

Example Use Cases

  • I want to access and share URLs that point to specific slices of my data.

  • I want to keep messy, brittle I/O code out of my data analysis code. I want a Python API (and maybe someday APIs in other languages, like Julia) that gives me numpy/pandas/xarray/whatever directly.

  • I have files in Format A but my data analysis program needs than in Format B.

  • I have files in Format A and I write my data analysis program to work against Format A. Later I want to {move, rename, switch to Format B, move from file-based storage to blob storage} and I don’t want to break my code. I rely on Tiled to provide it the same as always.

  • Some of my users just want files; other users want to work in numpy and not think about files. The second group can use Tiled sitting on top of those files, and the first group can continue as they were.

  • I want to transition from a file-based workflow to a database-backed workflow some day, and I don’t want all my analysis scirpts to break.

What Tiled is Not

  • Tiled is not a file-based data access service like Globus. File-based servers do not “see inside” the files and therefore cannot generally support semantic partial access, as in “Download columns A and C only” or “Download the middle frame of this image time series so I can see if it’s the one I’m interested in.”

  • Tiled is a not a storage format like TileDB or Zarr. It is expected to work well with those formats because they enable efficient chunk-based access, but it is designed to work with less modern formats as well, to accept the data “as is” and convert it on the fly upon request.

  • Tiled is not a data analysis and visualization platform like Plotly Chart Studio, but it can be used to serve data into them.

  • Tiled is not an application server like Vaex. Tiled offers no compute; it is focused only on I/O.

  • Tiled is a not a server for specific data format like Xpublish or JupyterLab HDF5, though it may be possible to build similar things with Tiled easily these formats and others.


The originating authors of Tiled have worked on several previous, related projects.

  • PIMS (2013, JHU Leheny Lab), or Python Image Sequence, abstracts over a variety of image time series formats with a lazy-loading, numpy-slicable Python object. It has some goals in common with Dask Array but it is tightly focused on image time series, which lends it ceratin advantages for that use case. PIMS is still maintained and widely used, including as a dependency of dask-image.

  • Databroker (2015, Brookhaven National Laboratory) provides searchable dict-like “catalogs” of data with entries that ultimately provide N-dimensional or tablular data as PIMS objects or pandas DataFrames respectively. It emphasizes support for the Bluesky “Document Model” for streaming data.

  • Intake (2017, Anaconda Inc.) is similar to Databroker in its goals and feature set—searchable catalogs of data that ultimately provide standard SciPy data structures—but its intended scope is broader than Databroker’s, not being tied to the Bluesky “Document Model” in particular. In 2018–2020, Databroker was refactored to become a set of Intake drivers.

Intake also has a prototype of an HTTP server, which has not yet been fully developed into a robust tool. Attempts to rework that server led to the conclusion that Intake’s architecture and API privilege ergonomic interactive use and direct access from Python. The changes necessary to use it as library code effectively within a performant service or larger application would be largely subtractive, in tension with its use as a user-facing interactive exploratory tool.

Which brought us to Tiled (2021, Brookhaven National Laboratory with the Bluesky Collaboration). Tiled is designed with HTTP-based access as the driving use case, targeting Python clients, browser-based clients, and clients in other languages. Tiled is compatible with PIMS for reading image series. Tiled has search capability and some other features drawn from Databroker. Tiled has a node abstraction similar to Intake’s Catalog, with small but important differences to suit the server–client interaction. (It is hoped that Tiled Nodes and Intake Catalogs can be made interoperable in the near future.) Finally, Tiled has an Adapter abstraction that is something like an amalgam of PIMS Readers and Intake’s DataSources.

In summary:

  • Tiled can use PIMS internally to efficiently slice and read image series.

  • Tiled can probably be made interoperate with Intake objects.

  • Databroker will evolve to be a thin wrapper around Tiled, adding concepts and capabilities specific to the Bluesky “Document Model”.