Alternatives to Consul logo

Alternatives to Consul

etcd, Zookeeper, SkyDNS, Ambassador, and Redis are the most popular alternatives and competitors to Consul.
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What is Consul and what are its top alternatives?

Consul is a tool for service discovery and configuration. Consul is distributed, highly available, and extremely scalable.
Consul is a tool in the Open Source Service Discovery category of a tech stack.
Consul is an open source tool with 18.4K GitHub stars and 3.2K GitHub forks. Here’s a link to Consul's open source repository on GitHub

Consul alternatives & related posts

etcd logo

etcd

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A distributed consistent key-value store for shared configuration and service discovery
etcd logo
etcd
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Consul logo
Consul
SkyDNS logo

SkyDNS

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Distributed service for announcement and discovery of services
SkyDNS logo
SkyDNS
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Consul logo
Consul
Ambassador logo

Ambassador

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Open source, Kubernetes-native API Gateway for Microservices built on Envoy
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    Ambassador logo
    Ambassador
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    Consul logo
    Consul
    Redis logo

    Redis

    16.5K
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    An in-memory database that persists on disk
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    Redis
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    Consul

    related Redis posts

    Robert Zuber
    Robert Zuber
    CTO at CircleCI · | 22 upvotes · 421.1K views
    atCircleCICircleCI
    MongoDB
    MongoDB
    PostgreSQL
    PostgreSQL
    Redis
    Redis
    GitHub
    GitHub
    Amazon S3
    Amazon S3

    We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

    As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

    When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

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    Thierry Schellenbach
    Thierry Schellenbach
    CEO at Stream · | 17 upvotes · 184.6K views
    atStreamStream
    Redis
    Redis
    Cassandra
    Cassandra
    RocksDB
    RocksDB
    #InMemoryDatabases
    #DataStores
    #Databases

    1.0 of Stream leveraged Cassandra for storing the feed. Cassandra is a common choice for building feeds. Instagram, for instance started, out with Redis but eventually switched to Cassandra to handle their rapid usage growth. Cassandra can handle write heavy workloads very efficiently.

    Cassandra is a great tool that allows you to scale write capacity simply by adding more nodes, though it is also very complex. This complexity made it hard to diagnose performance fluctuations. Even though we had years of experience with running Cassandra, it still felt like a bit of a black box. When building Stream 2.0 we decided to go for a different approach and build Keevo. Keevo is our in-house key-value store built upon RocksDB, gRPC and Raft.

    RocksDB is a highly performant embeddable database library developed and maintained by Facebook’s data engineering team. RocksDB started as a fork of Google’s LevelDB that introduced several performance improvements for SSD. Nowadays RocksDB is a project on its own and is under active development. It is written in C++ and it’s fast. Have a look at how this benchmark handles 7 million QPS. In terms of technology it’s much more simple than Cassandra.

    This translates into reduced maintenance overhead, improved performance and, most importantly, more consistent performance. It’s interesting to note that LinkedIn also uses RocksDB for their feed.

    #InMemoryDatabases #DataStores #Databases

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    related Kubernetes posts

    Yshay Yaacobi
    Yshay Yaacobi
    Software Engineer · | 29 upvotes · 519.9K views
    atSolutoSoluto
    Docker Swarm
    Docker Swarm
    .NET
    .NET
    F#
    F#
    C#
    C#
    JavaScript
    JavaScript
    TypeScript
    TypeScript
    Go
    Go
    Visual Studio Code
    Visual Studio Code
    Kubernetes
    Kubernetes

    Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

    Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

    After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 20 upvotes · 1.1M views
    atUber TechnologiesUber Technologies
    Jaeger
    Jaeger
    Python
    Python
    Java
    Java
    Node.js
    Node.js
    Go
    Go
    C++
    C++
    Kubernetes
    Kubernetes
    JavaScript
    JavaScript
    OpenShift
    OpenShift
    C#
    C#
    Apache Spark
    Apache Spark

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

    Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

    https://eng.uber.com/distributed-tracing/

    (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

    Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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    Istio logo

    Istio

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    Open platform to connect, manage, and secure microservices, by Google, IBM, and Lyft
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    Istio
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    Consul logo
    Consul

    related Istio posts

    Anas MOKDAD
    Anas MOKDAD
    Software Architect · | 2 upvotes · 165.3K views
    Kong
    Kong
    Istio
    Istio

    As for the new support of service mesh pattern by Kong, I wonder how does it compare to Istio?

    See more
    Eureka logo

    Eureka

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    AWS Service registry for resilient mid-tier load balancing and failover.
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    Eureka
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    Consul logo
    Consul
    Keepalived logo

    Keepalived

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    A routing software written in C
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      Keepalived
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      Consul logo
      Consul
      Serf logo

      Serf

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      Service orchestration and management tool
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        Serf
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        Consul logo
        Consul
        SmartStack logo

        SmartStack

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        Automated service discovery and registration framework
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        SmartStack
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        Consul
        Libraries.io logo

        Libraries.io

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        Discover open source packages, modules and frameworks you can use in your code
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          Libraries.io logo
          Libraries.io
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          Consul logo
          Consul