Jesse Mitchell @TotalCoder
Hey! I'm Jesse! I'm a bit of a total tech geek. Developer of https://t.co/WT39abMS4t jessemitchell.me Mansfield, TX Joined February 2012-
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@cramforce I worked on an experiment for something similar a while back: pineapple.js.org Though it’s kinda meant to be more declarative
@houlihan_rick We re-architected our largest collection to be native. I still use your 2019 DynamoDB talk as a training material for my team, even when discussing relational database design. It's important to high throughput tables to account for access patterns... ;)
@houlihan_rick I wanted to tweet at you a couple years back, but felt it might come off inflammatory 😅 A couple years back, we built a similar shim for our codebase to migrate from Mongo to Postgres. Overnight, our queries were 10x more responsive, and we reduced costs by $60K/mo.
@houlihan_rick To be fair; many of the legacy services migrated that used the shim were written by resume builders, who didn't bother learning document modeling patterns, so of course SQL-patterns moved to a SQL database was going to perform significantly better.
@therealchreke github.com/orgs/json-logi… Example discussion.
@therealchreke This is partly the intent of JSON Logic / the JSON Logic Core initiative. Developing a universal syntax tree with built in instructions that are designed to be safe even if the definition comes from an untrusted source
@itsrickyszn @VicVijayakumar Caveat: I did cheat a little with the shim; while most of the code remained unchanged & I could just swap imports, I made a minor modification to opt into array subsets. So instead of “tags”: “hi” you’d use “$.tags”: “hi”… It made it simpler to improve the query gen & indexes.
@itsrickyszn @VicVijayakumar We migrated our 50TiB Mongo DB to Postgres. Reduced expenses by about 40k/mo. Main tables were rewritten, but I ended up developing a shim for 70+ “legacy” collections to rewrite mongo queries to Postgres… It ended up yielding significant performance gains on those.
@itsrickyszn @VicVijayakumar To be fair to Mongo, not all of it was its fault. It was an OLAP workload which mongo wasn’t well suited for, and the data models we inherited were not NoSQL optimized. So when the tables went through the shim, they naturally fit the SQL model & indexes better.
@thdxr How are you planning to make your steaks? You might consider blasting them with a blow torch! They’re not pricey and you’ll feel cool doing it 😎 I recommend strategic dabs of butter — and usually I just sear them in a cast iron, flipping them when there’s a good crust.
@jarredsumner This gives users similar capabilities in a similar LoC, without adapting any standard behaviors. If Bun doesn't already, it *could* be nice if it built out a Map implementation (get / has / set) that can intelligently hoist values into columns. Then this pattern is easy
@jarredsumner Tbh; I think it'd be better for developers to compose memoize against the fetch function, to make it easy to sub out the cache mechanism (Redis, SQLite, etc) & have a general pattern that applies well to anything.
@thdxr Happy to help! If there's anything more I can do to help, feel free to open an issue: github.com/TotalTechGeek/… Or I'm happy to communicate here as well :)
@thdxr Here's an example using `build`, which optimizes the logic and gives you a function to call. It also shows an example of using a value passed in:
@thdxr If you'd be interested... I have some experiments with grammars built on top of JSON Logic, which should help with keeping the runtime portable / flexible. On its own, this grammar isn't a published spec, but it does built upon a common one.
@thdxr Grammar here, to be played with in Peggy.js Sandbox gist.github.com/TotalTechGeek/…
@thdxr Another experiment with the grammar
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