Alternatives to gRPC logo

Alternatives to gRPC

GraphQL, RabbitMQ, Kafka, REST, and MQTT are the most popular alternatives and competitors to gRPC.
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What is gRPC and what are its top alternatives?

gRPC is a modern open source high performance RPC framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking...
gRPC is a tool in the Remote Procedure Call (RPC) category of a tech stack.
gRPC is an open source tool with 31.3K GitHub stars and 8.1K GitHub forks. Here’s a link to gRPC's open source repository on GitHub

Top Alternatives to gRPC

  • GraphQL

    GraphQL

    GraphQL is a data query language and runtime designed and used at Facebook to request and deliver data to mobile and web apps since 2012. ...

  • RabbitMQ

    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • Kafka

    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • REST

    REST

    An architectural style for developing web services. A distributed system framework that uses Web protocols and technologies. ...

  • MQTT

    MQTT

    It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium. ...

  • SignalR

    SignalR

    SignalR allows bi-directional communication between server and client. Servers can now push content to connected clients instantly as it becomes available. SignalR supports Web Sockets, and falls back to other compatible techniques for older browsers. SignalR includes APIs for connection management (for instance, connect and disconnect events), grouping connections, and authorization. ...

  • Protobuf

    Protobuf

    Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler. ...

  • JSON-RPC

    JSON-RPC

    It is a very simple protocol, defining only a few data types and commands. It allows for notifications (data sent to the server that does not require a response) and for multiple calls to be sent to the server which may be answered out of order. ...

gRPC alternatives & related posts

GraphQL logo

GraphQL

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A data query language and runtime
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PROS OF GRAPHQL
  • 69
    Schemas defined by the requests made by the user
  • 62
    Will replace RESTful interfaces
  • 59
    The future of API's
  • 47
    The future of databases
  • 12
    Self-documenting
  • 11
    Get many resources in a single request
  • 5
    Ask for what you need, get exactly that
  • 4
    Query Language
  • 3
    Evolve your API without versions
  • 3
    Type system
  • 2
    GraphiQL
  • 2
    Ease of client creation
  • 2
    Fetch different resources in one request
  • 2
    Easy setup
  • 1
    Good for apps that query at build time. (SSR/Gatsby)
  • 1
    Backed by Facebook
  • 1
    Easy to learn
  • 1
    "Open" document
  • 1
    Better versioning
  • 1
    Standard
  • 1
    1. Describe your data
  • 1
    Fast prototyping
CONS OF GRAPHQL
  • 3
    Hard to migrate from GraphQL to another technology
  • 3
    More code to type.
  • 1
    Works just like any other API at runtime
  • 1
    Takes longer to build compared to schemaless.

related GraphQL posts

Shared insights
on
Node.js
GraphQL
MongoDB

I just finished the very first version of my new hobby project: #MovieGeeks. It is a minimalist online movie catalog for you to save the movies you want to see and for rating the movies you already saw. This is just the beginning as I am planning to add more features on the lines of sharing and discovery

For the #BackEnd I decided to use Node.js , GraphQL and MongoDB:

  1. Node.js has a huge community so it will always be a safe choice in terms of libraries and finding solutions to problems you may have

  2. GraphQL because I needed to improve my skills with it and because I was never comfortable with the usual REST approach. I believe GraphQL is a better option as it feels more natural to write apis, it improves the development velocity, by definition it fixes the over-fetching and under-fetching problem that is so common on REST apis, and on top of that, the community is getting bigger and bigger.

  3. MongoDB was my choice for the database as I already have a lot of experience working on it and because, despite of some bad reputation it has acquired in the last months, I still believe it is a powerful database for at least a very long list of use cases such as the one I needed for my website

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Nick Rockwell
SVP, Engineering at Fastly · | 42 upvotes · 1.6M views

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

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RabbitMQ logo

RabbitMQ

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Open source multiprotocol messaging broker
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PROS OF RABBITMQ
  • 226
    It's fast and it works with good metrics/monitoring
  • 79
    Ease of configuration
  • 57
    I like the admin interface
  • 49
    Easy to set-up and start with
  • 20
    Durable
  • 18
    Intuitive work through python
  • 18
    Standard protocols
  • 10
    Written primarily in Erlang
  • 7
    Simply superb
  • 6
    Completeness of messaging patterns
  • 3
    Reliable
  • 3
    Scales to 1 million messages per second
  • 2
    Distributed
  • 2
    Supports AMQP
  • 2
    Better than most traditional queue based message broker
  • 1
    High performance
  • 1
    Reliability
  • 1
    Clusterable
  • 1
    Inubit Integration
  • 1
    Clear documentation with different scripting language
  • 1
    Great ui
  • 1
    Runs on Open Telecom Platform
  • 1
    Better routing system
  • 1
    Supports MQTT
CONS OF RABBITMQ
  • 9
    Too complicated cluster/HA config and management
  • 6
    Needs Erlang runtime. Need ops good with Erlang runtime
  • 5
    Configuration must be done first, not by your code
  • 4
    Slow

related RabbitMQ posts

James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.2M views
Shared insights
on
Celery
RabbitMQ
at

As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

#MessageQueue

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Tim Abbott
Shared insights
on
RabbitMQ
Python
Redis
at

We've been using RabbitMQ as Zulip's queuing system since we needed a queuing system. What I like about it is that it scales really well and has good libraries for a wide range of platforms, including our own Python. So aside from getting it running, we've had to put basically 0 effort into making it scale for our needs.

However, there's several things that could be better about it: * It's error messages are absolutely terrible; if ever one of our users ends up getting an error with RabbitMQ (even for simple things like a misconfigured hostname), they always end up needing to get help from the Zulip team, because the errors logs are just inscrutable. As an open source project, we've handled this issue by really carefully scripting the installation to be a failure-proof configuration (in this case, setting the RabbitMQ hostname to 127.0.0.1, so that no user-controlled configuration can break it). But it was a real pain to get there and the process of determining we needed to do that caused a significant amount of pain to folks installing Zulip. * The pika library for Python takes a lot of time to startup a RabbitMQ connection; this means that Zulip server restarts are more disruptive than would be ideal. * It's annoying that you need to run the rabbitmqctl management commands as root.

But overall, I like that it has clean, clear semanstics and high scalability, and haven't been tempted to do the work to migrate to something like Redis (which has its own downsides).

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Kafka logo

Kafka

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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
  • 120
    High-throughput
  • 114
    Distributed
  • 86
    Scalable
  • 79
    High-Performance
  • 64
    Durable
  • 35
    Publish-Subscribe
  • 18
    Simple-to-use
  • 14
    Open source
  • 10
    Written in Scala and java. Runs on JVM
  • 6
    Message broker + Streaming system
  • 4
    Avro schema integration
  • 2
    Suport Multiple clients
  • 2
    Robust
  • 2
    KSQL
  • 2
    Partioned, replayable log
  • 1
    Fun
  • 1
    Extremely good parallelism constructs
  • 1
    Simple publisher / multi-subscriber model
  • 1
    Flexible
CONS OF KAFKA
  • 27
    Non-Java clients are second-class citizens
  • 26
    Needs Zookeeper
  • 7
    Operational difficulties
  • 2
    Terrible Packaging

related Kafka posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 1.9M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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John Kodumal

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

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REST logo

REST

20
143
0
A software architectural style
20
143
+ 1
0
PROS OF REST
  • 2
    Popularity
CONS OF REST
    Be the first to leave a con

    related REST posts

    MQTT logo

    MQTT

    311
    337
    4
    A machine-to-machine Internet of Things connectivity protocol
    311
    337
    + 1
    4
    PROS OF MQTT
    • 2
      Varying levels of Quality of Service to fit a range of
    • 1
      Very easy to configure and use with open source tools
    • 1
      Lightweight with a relatively small data footprint
    CONS OF MQTT
    • 1
      Easy to configure in an unsecure manner

    related MQTT posts

    SignalR logo

    SignalR

    367
    902
    94
    A new library for ASP.NET developers that makes developing real-time web functionality easy.
    367
    902
    + 1
    94
    PROS OF SIGNALR
    • 20
      Supports .NET server
    • 14
      Real-time
    • 12
      Free
    • 11
      WebSockets
    • 11
      Fallback to SSE, forever frame, long polling
    • 7
      Simple
    • 7
      JSON
    • 5
      Open source
    • 4
      Cool
    • 3
      Ease of use
    CONS OF SIGNALR
    • 1
      Requires jQuery
    • 1
      Expertise hard to get
    • 1
      Weak iOS and Android support

    related SignalR posts

    Shared insights
    on
    gRPC
    SignalR
    .NET

    We need to interact from several different Web applications (remote) to a client-side application (.exe in .NET Framework, Windows.Console under our controlled environment). From the web applications, we need to send and receive data and invoke methods to client-side .exe on javascript events like users onclick. SignalR is one of the .Net alternatives to do that, but it adds overhead for what we need. Is it better to add SignalR at both client-side application and remote web application, or use gRPC as it sounds lightest and is multilingual?

    SignalR or gRPC are always sending and receiving data on the client-side (from browser to .exe and back to browser). And web application is used for graphical visualization of data to the user. There is no need for local .exe to send or interact with remote web API. Which architecture or framework do you suggest to use in this case?

    See more
    Protobuf logo

    Protobuf

    379
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    0
    Google's data interchange format
    379
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    + 1
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    PROS OF PROTOBUF
      Be the first to leave a pro
      CONS OF PROTOBUF
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        related Protobuf posts

        JSON-RPC logo

        JSON-RPC

        26
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        0
        A remote procedure call protocol encoded in JSON
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        PROS OF JSON-RPC
          Be the first to leave a pro
          CONS OF JSON-RPC
            Be the first to leave a con

            related JSON-RPC posts