Understanding TTFB Latency in DJango - Seems absurdly slow after DB optimizations even locally

This page summarizes the projects mentioned and recommended in the original post on /r/django

Stream - Scalable APIs for Chat, Feeds, Moderation, & Video.
Stream helps developers build engaging apps that scale to millions with performant and flexible Chat, Feeds, Moderation, and Video APIs and SDKs powered by a global edge network and enterprise-grade infrastructure.
getstream.io
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InfluxDB – Built for High-Performance Time Series Workloads
InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
www.influxdata.com
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  1. django-silk

    Silky smooth profiling for Django

    you're using DRF, which DDT doesn't work well with. DDT is more for template rendered pages, not API responses. Have you brought in Django silk for monitoring this?

  2. Stream

    Stream - Scalable APIs for Chat, Feeds, Moderation, & Video. Stream helps developers build engaging apps that scale to millions with performant and flexible Chat, Feeds, Moderation, and Video APIs and SDKs powered by a global edge network and enterprise-grade infrastructure.

    Stream logo
  3. join-monster

    A GraphQL to SQL query execution layer for query planning and batch data fetching.

    GraphQL could be efficiently translated into SQL and things certainly try, but the only thing close to a "bulletproof" implementation I found was Join Monster (https://github.com/join-monster/join-monster) in NodeJS-land and even that I think is now mostly abandoned. GraphQL as a system was built assuming random-access to data stores is ~free because that's what Facebook has, but the rest of us don't :)

  4. graphql-core

    A Python 3 port of the GraphQL.js reference implementation of GraphQL.

  5. graphql-js

    A reference implementation of GraphQL for JavaScript

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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