localForage VS ideogram

Not really an answer but have you looked into https://github.com/localForage/localForage

I may not be pointing you in the right direction but, please read about [IndexDB storage](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB\_API) method and packages like [localForage](https://localforage.github.io/localForage/) that use it under the hood.

LocalForage: this is a wrapper around IndexedDB, WebSQL, LocalStorage, and SessionStorage with a way to define more interfaces (called drivers) *for additional storage solutions. It does a great job handling all your serialization needs, it is **asynchronous **and handles a large set of data types. Its **API resembles Web Storage API* and it is ridiculously simple to learn.

Depending on what kind of storage requirements you have, LocalForage might be a good fit. It provides a unified API to access WebSQL, indexedDB or localStorage.

I find localForage is pretty helpful for this stuff https://github.com/localForage/localForage

localForage (https://localforage.github.io/localForage/) mirrors localStorage syntax, but uses IndexedDB in the background. If that's not supported, it uses WebSQL and then LocalStorage

This is why we have localForage.

> Where are the polished, powerful design tools for biology

User interfaces for biology have drastically improved over the last 10 years.

Domain-specific tools like genome browsers, protein viewers, or phylogenetic explorers [1-3] almost all look and feel a lot better than they did in 2012.

The biggest exception here is UCSC Genome Browser, which has an old-school design and web technology stack. That said, it's steadily added features over the years, has substantially sleekened UX in its periphery, and remains widely used.

There are also bespoke visual design resources for biology applications that are good and getting better, like BioRender and PhyloPic [4-5]. There are multi-tiered packages like Dash Bio that wrap biology components together. There's Blender biology community, too!

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1. Genome browsers and components: https://jbrowse.org/jb2/, https://www.ncbi.nlm.nih.gov/genome/gdv, https://igv.org/app, https://eweitz.github.io/ideogram

2. Protein viewers: https://pymol.org/, https://nglviewer.org/ngl/

3. Phylogenetic explorers: https://clades.nextstrain.org/

4. https://biorender.com/

5. http://phylopic.org/

6. https://github.com/plotly/dash-bio, https://dash.gallery/Portal/?search=[Pharma]

I'm more interested in read speeds than write speeds. I have about 2 MB of data that I fetch, then parsed and transformed into a heavily nested object for easy look-up by various types of keys.

In my brief experiment, it was 12% faster to read from the web Cache API, re-parse and re-transform that heavily nested object than to read the fully transformed object via IndexedDB. That surprised me! My understanding is that IndexedDB does a structured clone as part of the read, which I suspect is the main cause of slowness of IndexedDB relative to the Cache API approach in my use case.

Related commits to reproduce that finding are in [1], specifically [2].

[1] https://github.com/eweitz/ideogram/pull/285

I created Ideogram.js, a JavaScript library for chromosome visualization [1]. Ideogram supports drawing and animating genome-wide datasets, enabling a variety of genomic views [3].

[1] https://github.com/eweitz/ideogram

[2] https://eweitz.github.io/ideogram