Supabase’s Post

IndexedDB is a powerful low-level API that allows for efficient handling of significant amounts of structured data right on the client side, including files and blobs. It's a hybrid between SQL and NoSQL databases—transactional like SQL and object-oriented like NoSQL. In my latest post, I dive deep into: - Creating and managing IndexedDB databases - Performing CRUD operations - Converting operations to asynchronous functions for enhanced convenience If you're looking to upgrade your web storage game, check out the full article here: https://lnkd.in/d4PdTsTq #WebDevelopment #IndexedDB #JavaScript #WebStorage

#RFC Alert: Proposing an inverted index for tag columns in the #MitoEngine to boost filtering and performance alongside the Primary Keys' Min-Max index. Dive deeper：https://lnkd.in/gmzXpfK2 #opensource #database #PerformanceEnhancement

Automatically keeping data in sync across servers and clients is essential for many realtime applications. This is a hard distributed systems problem! In our latest blog post, Zak Knill from the Realtime team looks at four existing solutions to realtime data synchronization - their different technical designs and the tradeoffs they make: 🔍 Supabase 🔍 Liveblocks 🔍 ElectricSQL 🔍 Replicache He then compares these to the approach taken with Ably LiveSync - now available as a public alpha release. Read the blog to learn more: https://hubs.la/Q02xfcJY0

We recently launched LiveSync (https://lnkd.in/entT7gNm), our new product to enable developers to keep their frontend apps in sync with their database in a platform and framework agnostic way. Zak Knill, one of the lead engineers on this impressive work has posted his thoughts on how this stacks up against seemingly similar products from Supabase, Liveblocks, ElectricSQL and Replicache. As ever, the answer is not one is necessarily better than the others, but rather better based on your use case. #realtime #datasync #localfirst

Lock-free compaction. Why not? EDIT: I talked to the main maintainers of Postgres and they were divided between "this is impossible" and "this is useless"...I wonder where did I hear this before 🤔? So anyways I talked last week about lock-free vacuum. Since then I almost finished creating a plugin that works on all #JetBrains products. It hooks to the internal database tool and displays the internals of the database and some stats. I would use this for my next phase: auto-vacuum with compaction. The thing is, after reading the code a million times, I still don't understand why lock-free (or minimum locking) is such a big problem! Is it that hard to lazily move tuples from one page to the other after defragmenting it lazily? I even made (a super simple equation) to calculate how much disk space can be reclaimed, and created a full list of the advantages including maintaining clustered indexes. This would of course be followed by benchmarks. Here is the WIP plugin btw https://lnkd.in/eJPRv6DQ which should be released this week. Like I don't even trust ChatGPT at all but I kept on trying to find reasons for not doing it but I couldn't find any! Such a trivial change that can bring a huge advantage. I just hope that somebody could find me a reason for why it wouldn't work. #postgres #rdbms #mvcc #dba #vacuum #jetbrains #intellij

I would say this one got my attention: GraphQL Query Complexity Analysis (https://lnkd.in/gk22eyWt)

Progress MarkLogic is a great place to manage data, but we have to get it there first. I just published the first of a few planned posts on Apache NiFi and Progress MarkLogic working together. https://lnkd.in/e_3GgE2M

I'm getting back to blogging. My first topic: the interaction between Progress MarkLogic and Apache NiFi. #marklogic #apachenifi #bigdata

Imagine if every database came with just the storage mechanics and you had to implement your own custom SQL engine from scratch each time you used a DB. It would be horrible, unscalable, and unproductive, right? The same holds true for #GraphQL. Handwriting a GraphQL backend is akin to writing your own SQL engine. Ideally, all data sources should come with their own GraphQL interface—a direction that Tailcall is rapidly achieving. Check out our latest blog on why the future of GraphQL is *automatic* and *by default* https://lnkd.in/dCrYB5Bi

An enriching resource I came across: GraphQL Query Complexity Analysis (https://lnkd.in/gzJZnSZ7)