Macrometa claims it can run your microservices as functions and container running concurrently across 175 cities globally with shared durable state if you use their Global Data Network (GDN) as a globally distributed back-end platform.
They provide a global data mesh with a KVs store, a JSON Doc DB, Graphs, pub/Sub, Event Stream Processing, Complex Event Processing, Search along with edge functions and containers. Used by large enterprise customers like Verizon and twilio as well startup and fast growing SAAS players like DevCycle and Resulticks.
Hi Ignoramus - founder and CEO of Macrometa here - regret that our first attempt at explaining our consistency model caused confusion last year. Here's a link to the research paper that describes our architecture and consistency model.
We got accepted in High Performance Transaction systems last year for the innovations around CRDTs for strong eventual consistency (SEC) with low read and write latencies.
Im trying to figure out how to provide a simple light weight way for fly.io users to use our global DB in their apps. It would allow a full stack to run at the edge with the compute on fly.io and the data on Macrometa either directly on fly.io or a nearby PoP (same city). Will update
tl/dr - Barker is a twitter clone that runs its backend across 25 global data centers. The post explains the motivations and challenges that inspired the developer to build barker and how he leveraged Macrometa - a geo distributed platform's document database, graph database and streams to build the app. Source code provided.
Macrometa offers a free developer platform for building apps and APIs like barker at www.macrometa.co
Hi gatherhunterer
Great feedback - I'll add a few links on to the docs on architecture and things like consistency of data (short answer is this a geo distributed data platform with consistency gaurantees).
Thanks also for the great point you made about "altering source of truth" - we will look into how best to provide those gaurantees as well. Our underlying data structures are an event source on an append only log - so we can go back to see the provenance of changes to make sure there was no cheating but we dont expose that externally.
A repl.it repo with a python example of a simple database app (an address book) that stores and queries data from Macrometa's serverless geo-distributed database running in California, Virgina, Germany and India.
TL/DR: Macrometa is a geo-distributed CRDT database and serverless function run time with the following capabilities
1. 25 global locations - across AWS, GCP, telecom and CDN PoPs
2. Local Write and Read performance - your query executes at the closest location to the request
3. Strong consistency guarantees (Strict ACID like for reads after writes to same loction - time bound staleness for reads to different location after write to a specific location)
4. Automatic Concurrent conflict handling - only changes to the same field of a JSON document can have conflicts and a winner is picked globally for the conflict. Changes to different fields in the JSON doc are merged.
5. Multi model - Key/Value, JSON Doc, Graph, Stream, Time series etc
6. Automatic REST API generation for queries to collections - write a query and expose it as a REST end point - easier than graphQL - can mutate data.
7. Write serverless functions to be triggered by changes to Data in streams or database
8. Free 4 region developer account - no strings attached - https://www.macrometa.co/start
Hi PKMishra - Paid premium tiers will be introduced in July. You can request "whisper pricing" which is discounted by opening a free developer account (https://www.macrometa.co/start ) and using chat to talk to JP . He can send you the information you need.
The TLDR
Macrometa is a geo-distributed, real-time, streaming DB with a multi-modal interface (K/V, Doc, Streams, Graph, Time Series). Unlike centralized eventually consistent DBs, Macrometa is a natively geo-replicated DB that uses CRDTs to replicate with better consistency guarantees than eventually consistent DBs.
The DB incorporates streams and streams processing as a first-class database primitive and lets you build low latency, high volume data pipelines that are event-driven and reactive.
You bring up a great point. One absolutely can use us as a firebase alternative. We have a few customers using us with lambda and container services directly on their favorite cloud provider in just 2 locations for high availability of their apps.
The edge computing position is intentional in that we think the edge is a way to build globally distributed apps and apis. Our goal is to make as simple to build and run an app in 25 regions as it is to build an app against say firebase or dynamodb.
They provide a global data mesh with a KVs store, a JSON Doc DB, Graphs, pub/Sub, Event Stream Processing, Complex Event Processing, Search along with edge functions and containers. Used by large enterprise customers like Verizon and twilio as well startup and fast growing SAAS players like DevCycle and Resulticks.