dataloader
gRPC
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dataloader | gRPC | |
---|---|---|
47 | 201 | |
12,632 | 40,685 | |
0.5% | 0.9% | |
3.3 | 9.9 | |
about 1 month ago | 6 days ago | |
JavaScript | C++ | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dataloader
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Delving into the Black Magic of GraphQL DataLoader! 🌌✨
When I began working with GraphQL, I had concerns about the N+1 query problem. In my research, I came across the DataLoader pattern and its implementation on GitHub. While I explored various examples of its usage, I still struggled to grasp how it operates internally. Join me in delving a bit deeper into GraphQL DataLoader! :)
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How to use DataLoader with Mercurius GraphQL
DataLoader: it is the standard solution to N+1 problem.
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Best Practices in Testing GraphQL APIs
Additionally, you can use DataLoader or similar tools to optimize data fetching and avoid over-fetching or under-fetching data. Ultimately, performance and load tests ensure that your GraphQL API delivers optimal performance, meets response time expectations, and provides a smooth experience for users, even under heavy loads.
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Migrating Netflix to GraphQL Safely
The most common practice is to turn N+1 into 1+1 using dataloaders (https://github.com/graphql/dataloader for JS, there are equivalents for most implementations). The N resolvers invoke a single batched loader which receives a list of keys and returns a list of values.
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SQL vs. NoSQL - cutting through the Tech Twitter noise
Let's take Payload, for example. Surprise, surprise. We have a relationship field, and it can store IDs to other related documents which are seamlessly merged in when you retrieve documents from the DB. We leverage the dataloader pattern to batch together all "populations" required for a given query, returning them all super fast and with as few separate queries to the DB as possible. We actually even outperform SQL-based frameworks quite a bit. In a purely relational test, we were 3x faster than Directus and 7x faster than Strapi while both were running Postgres, and we were on MongoDB.
- NoSQL vs. SQL - cutting through the Tech Twitter noise with a real-world use case
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We Ditched REST and Went with GraphQL: Here’s Why
Also, have a look at Facebook's Dataloader[0].
[0] https://github.com/graphql/dataloader
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Implementing logger with metadata
In the next article, I'm going to implement a GraphQL server with dataloader using the tools we introduced.
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Typesafe, (almost) Zero Cost Dependency Injection in TypeScript
The one example of using Scoped dependency that comes to my mind, it's HTTP request level caching for libs like dataloader.
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GraphQL Trades Complexity
you would fetch these 1000 rows via dataloader that batches all requests for this relation to a single query... solving the n+1 issue
gRPC
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Golang: out-of-box backpressure handling with gRPC, proven by a Grafana dashboard
gRPC, built on HTTP/2, inherently supports flow control. The server can push updates, but it must also respect flow control signals from the client, ensuring that it doesn't send data faster than what the client can handle.
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Reverse Engineering Protobuf Definitions from Compiled Binaries
Yes, grpc_cli tool uses essentially the same mechanism except implemented as a grpc service rather than as a stubby service. The basic principle of both is implementing the C++ proto library's DescriptorDatabase interface with cached recursive queries of (usually) the server's compiled in FileDescriptorProtos.
See also https://github.com/grpc/grpc/blob/master/doc/server-reflecti...
The primary difference between what grpc does and what stubby does is that grpc uses a stream to ensure that the reflection requests all go to the same server to avoid incompatible version skew and duplicate proto transmissions. With that said, in practice version skew is rarely a problem for grpc_cli style "issue a single RPC" usecases: even if requests do go to two or more different versions of a binary that might have incompatible proto graphs, it is very common for the request and response and RPC to all be in the same proto file so you only need to make one RPC in the first place unless you're using an extension mechanism like proto2 extensions or google.protobuf.Any.
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Delving Deeper: Enriching Microservices with Golang with CloudWeGo
While gRPC and Apache Thrift have served the microservice architecture well, CloudWeGo's advanced features and performance metrics set it apart as a promising open source solution for the future.
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gRPC Name Resolution & Load Balancing on Kubernetes: Everything you need to know (and probably a bit more)
The loadBalancingConfig is what we use in order to decide which policy to go for (round_robin in this case). This JSON representation is based on a protobuf message, then why does the name resolver returns it in the JSON format? The main reason is that loadBalancingConfig is a oneof field inside the proto message and so it can not contain values unknown to the gRPC if used in the proto format. The JSON representation does not have this requirement so we can use a custom loadBalancingConfig .
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Dart on the Server: Exploring Server-Side Dart Technologies in 2024
The Dart implementation of gRPC which puts mobile and HTTP/2 first. It's built and maintained by the Dart team. gRPC is a high-performance RPC (remote procedure call) framework that is optimized for efficient data transfer.
- Usando Spring Boot RestClient
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How to Build & Deploy Scalable Microservices with NodeJS, TypeScript and Docker || A Comprehesive Guide
gRPC is a high-performance, open-source RPC (Remote Procedure Call) framework initially developed by Google. It uses Protocol Buffers for serialization and supports bidirectional streaming.
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Actual SSH over HTTPS
In general, tunneling through HTTP2 turns out to be a great choice. There is a RPC protocol built on top of HTTP2: gRPC[1].
This is because HTTP2 is great at exploiting a TCP connection to transmit and receive multiple data structures concurrently - multiplexing.
There may not be a reason to use HTTP3 however, as QUIC already provides multiplexing.
I expect that in the future most communications will be over encrypted HTTP2 and QUIC simply because middleware creators can not resist to discriminate.
[1] <https://grpc.io>
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Why gRPC is not natively supported by Browsers
Even in the https://grpc.io blog says this
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SGSG (Svelte + Go + SQLite + gRPC) - open source application
gRPC
What are some alternatives?
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.
ZeroMQ - ZeroMQ core engine in C++, implements ZMTP/3.1
react-relay - Relay is a JavaScript framework for building data-driven React applications.
Apache Thrift - Apache Thrift
Knex - A query builder for PostgreSQL, MySQL, CockroachDB, SQL Server, SQLite3 and Oracle, designed to be flexible, portable, and fun to use.
Cap'n Proto - Cap'n Proto serialization/RPC system - core tools and C++ library
jest - Delightful JavaScript Testing.
zeroRPC - zerorpc for python
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.
rpclib - rpclib is a modern C++ msgpack-RPC server and client library
BatchLoader - :zap: Powerful tool for avoiding N+1 DB or HTTP queries
nanomsg - nanomsg library