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dataloader
DataLoader is a generic utility to be used as part of your application's data fetching layer to provide a consistent API over various backends and reduce requests to those backends via batching and caching.
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PostgreSQL
Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
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SurveyJS
Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App. With SurveyJS form UI libraries, you can build and style forms in a fully-integrated drag & drop form builder, render them in your JS app, and store form submission data in any backend, inc. PHP, ASP.NET Core, and Node.js.
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Sequelize
Feature-rich ORM for modern Node.js and TypeScript, it supports PostgreSQL (with JSON and JSONB support), MySQL, MariaDB, SQLite, MS SQL Server, Snowflake, Oracle DB (v6), DB2 and DB2 for IBM i.
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Redis
Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Knex
A query builder for PostgreSQL, MySQL, CockroachDB, SQL Server, SQLite3 and Oracle, designed to be flexible, portable, and fun to use.
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This is where you setup things like batching and also solve N+1 problems with something like a dataloader, setup caching with something like Redis or even an LRU cache to act as a proxy for the frequently accessed data whenever and wherever possible, optimizing the network chatter by using something like persisted queries, optimize your resolvers by retrieving as much data as possible from the parent resolvers, setting up pagination to limit the results returned, setting up things like query complexity to control the level of nesting and computation performed, rate-limiting in the gateway to avoid things like DDOS and so on.
To do this, try treating your resolvers as just entities which do an operation and respond back with data given a set of inputs (similar to what you would typically do with a REST API). So, try setting up your backend/datasources from which you would want to serve the data (be it a database like Postgres or Mongo with or without an ORM like Prisma, Knex or Sequelize, or even an underlying resource like a REST API maybe with something like GraphQL Mesh or Graph databases like Dgraph) and also your resolvers to process the data as you see fit, adding your business logic on top and return back the fields as needed by the resolvers. This is the point where you replace the mocked data with data from the backend.
To do this, try treating your resolvers as just entities which do an operation and respond back with data given a set of inputs (similar to what you would typically do with a REST API). So, try setting up your backend/datasources from which you would want to serve the data (be it a database like Postgres or Mongo with or without an ORM like Prisma, Knex or Sequelize, or even an underlying resource like a REST API maybe with something like GraphQL Mesh or Graph databases like Dgraph) and also your resolvers to process the data as you see fit, adding your business logic on top and return back the fields as needed by the resolvers. This is the point where you replace the mocked data with data from the backend.
Testing plays a major role especially when building scalable systems which have to be reliable even with a huge stream of changes which might affect it over time. And this is no exception when you work with GraphQL as well. You can setup automated tests, integration tests and so on as you normally would to improve the confidence people have on the system. And there are lot of libraries which facilitate the same as well like Mocha, Jest, AVA and so on taking a lot of the burden away from you.
To do this, try treating your resolvers as just entities which do an operation and respond back with data given a set of inputs (similar to what you would typically do with a REST API). So, try setting up your backend/datasources from which you would want to serve the data (be it a database like Postgres or Mongo with or without an ORM like Prisma, Knex or Sequelize, or even an underlying resource like a REST API maybe with something like GraphQL Mesh or Graph databases like Dgraph) and also your resolvers to process the data as you see fit, adding your business logic on top and return back the fields as needed by the resolvers. This is the point where you replace the mocked data with data from the backend.
This is where you setup things like batching and also solve N+1 problems with something like a dataloader, setup caching with something like Redis or even an LRU cache to act as a proxy for the frequently accessed data whenever and wherever possible, optimizing the network chatter by using something like persisted queries, optimize your resolvers by retrieving as much data as possible from the parent resolvers, setting up pagination to limit the results returned, setting up things like query complexity to control the level of nesting and computation performed, rate-limiting in the gateway to avoid things like DDOS and so on.
Testing plays a major role especially when building scalable systems which have to be reliable even with a huge stream of changes which might affect it over time. And this is no exception when you work with GraphQL as well. You can setup automated tests, integration tests and so on as you normally would to improve the confidence people have on the system. And there are lot of libraries which facilitate the same as well like Mocha, Jest, AVA and so on taking a lot of the burden away from you.
To do this, try treating your resolvers as just entities which do an operation and respond back with data given a set of inputs (similar to what you would typically do with a REST API). So, try setting up your backend/datasources from which you would want to serve the data (be it a database like Postgres or Mongo with or without an ORM like Prisma, Knex or Sequelize, or even an underlying resource like a REST API maybe with something like GraphQL Mesh or Graph databases like Dgraph) and also your resolvers to process the data as you see fit, adding your business logic on top and return back the fields as needed by the resolvers. This is the point where you replace the mocked data with data from the backend.
Testing plays a major role especially when building scalable systems which have to be reliable even with a huge stream of changes which might affect it over time. And this is no exception when you work with GraphQL as well. You can setup automated tests, integration tests and so on as you normally would to improve the confidence people have on the system. And there are lot of libraries which facilitate the same as well like Mocha, Jest, AVA and so on taking a lot of the burden away from you.
To do this, try treating your resolvers as just entities which do an operation and respond back with data given a set of inputs (similar to what you would typically do with a REST API). So, try setting up your backend/datasources from which you would want to serve the data (be it a database like Postgres or Mongo with or without an ORM like Prisma, Knex or Sequelize, or even an underlying resource like a REST API maybe with something like GraphQL Mesh or Graph databases like Dgraph) and also your resolvers to process the data as you see fit, adding your business logic on top and return back the fields as needed by the resolvers. This is the point where you replace the mocked data with data from the backend.