Alternatives to Consul logo

Alternatives to Consul

etcd, Zookeeper, SkyDNS, Ambassador, and Kubernetes 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 27.9K GitHub stars and 4.4K GitHub forks. Here’s a link to Consul's open source repository on GitHub

Top Alternatives to Consul

  • etcd
    etcd

    etcd is a distributed key value store that provides a reliable way to store data across a cluster of machines. It’s open-source and available on GitHub. etcd gracefully handles master elections during network partitions and will tolerate machine failure, including the master. ...

  • Zookeeper
    Zookeeper

    A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. All of these kinds of services are used in some form or another by distributed applications. ...

  • SkyDNS
    SkyDNS

    SkyDNS is a distributed service for announcement and discovery of services. It leverages Raft for high-availability and consensus, and utilizes DNS queries to discover available services. This is done by leveraging SRV records in DNS, with special meaning given to subdomains, priorities and weights (more info here: http://blog.gopheracademy.com/skydns). ...

  • Ambassador
    Ambassador

    Map services to arbitrary URLs in a single, declarative YAML file. Configure routes with CORS support, circuit breakers, timeouts, and more. Replace your Kubernetes ingress controller. Route gRPC, WebSockets, or HTTP. ...

  • Kubernetes
    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • Redis
    Redis

    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. ...

  • Istio
    Istio

    Istio is an open platform for providing a uniform way to integrate microservices, manage traffic flow across microservices, enforce policies and aggregate telemetry data. Istio's control plane provides an abstraction layer over the underlying cluster management platform, such as Kubernetes, Mesos, etc. ...

  • Eureka
    Eureka

    Eureka is a REST (Representational State Transfer) based service that is primarily used in the AWS cloud for locating services for the purpose of load balancing and failover of middle-tier servers. ...

Consul alternatives & related posts

etcd logo

etcd

303
412
24
A distributed consistent key-value store for shared configuration and service discovery
303
412
+ 1
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PROS OF ETCD
  • 11
    Service discovery
  • 6
    Fault tolerant key value store
  • 2
    Secure
  • 2
    Bundled with coreos
  • 1
    Consol integration
  • 1
    Privilege Access Management
  • 1
    Open Source
CONS OF ETCD
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    Zookeeper logo

    Zookeeper

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    43
    Because coordinating distributed systems is a Zoo
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    PROS OF ZOOKEEPER
    • 11
      High performance ,easy to generate node specific config
    • 8
      Java
    • 8
      Kafka support
    • 5
      Spring Boot Support
    • 3
      Supports extensive distributed IPC
    • 2
      Curator
    • 2
      Used in ClickHouse
    • 2
      Supports DC/OS
    • 1
      Used in Hadoop
    • 1
      Embeddable In Java Service
    CONS OF ZOOKEEPER
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      Shared insights
      on
      ZookeeperZookeeperHAProxyHAProxy
      at

      Early 2013

      In early 2013, Airbnb tackled the problem of service discovery and load balancing in the context of a service oriented architecture (SOA) by building and releasing an open source tool called SmartStack. SmartStack is built on two other open source tools created by Airbnb called Nerve and Synapse.

      Nerve is a service registration daemon that performs health checks that “creates ephemeral nodes in Zookeeper which contain information about the address/port combos for a backend available to serve requests for a particular service.”

      Synapse is a transparent service discovery framework for connecting an SOA that reads the information in Zookeeper for available backends, and then uses that information to configure a local HAProxy process, which then routes requests between clients and services.

      See more
      SkyDNS logo

      SkyDNS

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      23
      2
      Distributed service for announcement and discovery of services
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      23
      + 1
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      PROS OF SKYDNS
      • 2
        Srv discovery for etcd
      CONS OF SKYDNS
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        Ambassador logo

        Ambassador

        75
        187
        4
        Open source, Kubernetes-native API Gateway for Microservices built on Envoy
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        187
        + 1
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        PROS OF AMBASSADOR
        • 3
          Edge-proxy
        • 1
          Kubernetes friendly configuration
        CONS OF AMBASSADOR
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          Kubernetes logo

          Kubernetes

          58.7K
          50.8K
          677
          Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
          58.7K
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          677
          PROS OF KUBERNETES
          • 164
            Leading docker container management solution
          • 128
            Simple and powerful
          • 106
            Open source
          • 76
            Backed by google
          • 58
            The right abstractions
          • 25
            Scale services
          • 20
            Replication controller
          • 11
            Permission managment
          • 9
            Supports autoscaling
          • 8
            Cheap
          • 8
            Simple
          • 6
            Self-healing
          • 5
            No cloud platform lock-in
          • 5
            Promotes modern/good infrascture practice
          • 5
            Open, powerful, stable
          • 5
            Reliable
          • 4
            Scalable
          • 4
            Quick cloud setup
          • 3
            Cloud Agnostic
          • 3
            Captain of Container Ship
          • 3
            A self healing environment with rich metadata
          • 3
            Runs on azure
          • 3
            Backed by Red Hat
          • 3
            Custom and extensibility
          • 2
            Sfg
          • 2
            Gke
          • 2
            Everything of CaaS
          • 2
            Golang
          • 2
            Easy setup
          • 2
            Expandable
          CONS OF KUBERNETES
          • 16
            Steep learning curve
          • 15
            Poor workflow for development
          • 8
            Orchestrates only infrastructure
          • 4
            High resource requirements for on-prem clusters
          • 2
            Too heavy for simple systems
          • 1
            Additional vendor lock-in (Docker)
          • 1
            More moving parts to secure
          • 1
            Additional Technology Overhead

          related Kubernetes posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10M views

          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

          See more
          Ashish Singh
          Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

          To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

          Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

          We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

          Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

          Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

          #BigData #AWS #DataScience #DataEngineering

          See more
          Redis logo

          Redis

          58.3K
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          Open source (BSD licensed), in-memory data structure store
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          PROS OF REDIS
          • 886
            Performance
          • 542
            Super fast
          • 513
            Ease of use
          • 444
            In-memory cache
          • 324
            Advanced key-value cache
          • 194
            Open source
          • 182
            Easy to deploy
          • 164
            Stable
          • 155
            Free
          • 121
            Fast
          • 42
            High-Performance
          • 40
            High Availability
          • 35
            Data Structures
          • 32
            Very Scalable
          • 24
            Replication
          • 22
            Great community
          • 22
            Pub/Sub
          • 19
            "NoSQL" key-value data store
          • 16
            Hashes
          • 13
            Sets
          • 11
            Sorted Sets
          • 10
            NoSQL
          • 10
            Lists
          • 9
            Async replication
          • 9
            BSD licensed
          • 8
            Bitmaps
          • 8
            Integrates super easy with Sidekiq for Rails background
          • 7
            Keys with a limited time-to-live
          • 7
            Open Source
          • 6
            Lua scripting
          • 6
            Strings
          • 5
            Awesomeness for Free
          • 5
            Hyperloglogs
          • 4
            Transactions
          • 4
            Outstanding performance
          • 4
            Runs server side LUA
          • 4
            LRU eviction of keys
          • 4
            Feature Rich
          • 4
            Written in ANSI C
          • 4
            Networked
          • 3
            Data structure server
          • 3
            Performance & ease of use
          • 2
            Dont save data if no subscribers are found
          • 2
            Automatic failover
          • 2
            Easy to use
          • 2
            Temporarily kept on disk
          • 2
            Scalable
          • 2
            Existing Laravel Integration
          • 2
            Channels concept
          • 2
            Object [key/value] size each 500 MB
          • 2
            Simple
          CONS OF REDIS
          • 15
            Cannot query objects directly
          • 3
            No secondary indexes for non-numeric data types
          • 1
            No WAL

          related Redis posts

          Russel Werner
          Lead Engineer at StackShare · | 32 upvotes · 2.2M views

          StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

          Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

          #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

          See more
          Simon Reymann
          Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.2M views

          Our whole DevOps stack consists of the following tools:

          • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
          • Respectively Git as revision control system
          • SourceTree as Git GUI
          • Visual Studio Code as IDE
          • CircleCI for continuous integration (automatize development process)
          • Prettier / TSLint / ESLint as code linter
          • SonarQube as quality gate
          • Docker as container management (incl. Docker Compose for multi-container application management)
          • VirtualBox for operating system simulation tests
          • Kubernetes as cluster management for docker containers
          • Heroku for deploying in test environments
          • nginx as web server (preferably used as facade server in production environment)
          • SSLMate (using OpenSSL) for certificate management
          • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
          • PostgreSQL as preferred database system
          • Redis as preferred in-memory database/store (great for caching)

          The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

          • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
          • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
          • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
          • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
          • Scalability: All-in-one framework for distributed systems.
          • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
          See more
          Istio logo

          Istio

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          1.5K
          54
          Open platform to connect, manage, and secure microservices, by Google, IBM, and Lyft
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          PROS OF ISTIO
          • 14
            Zero code for logging and monitoring
          • 9
            Service Mesh
          • 8
            Great flexibility
          • 5
            Resiliency
          • 5
            Powerful authorization mechanisms
          • 5
            Ingress controller
          • 4
            Easy integration with Kubernetes and Docker
          • 4
            Full Security
          CONS OF ISTIO
          • 16
            Performance

          related Istio posts

          Shared insights
          on
          IstioIstioDaprDapr

          At my company, we are trying to move away from a monolith into microservices led architecture. We are now stuck with a problem to establish a communication mechanism between microservices. Since, we are planning to use service meshes and something like Dapr/Istio, we are not sure on how to split services between the two. Service meshes offer Traffic Routing or Splitting whereas, Dapr can offer state management and service-service invocation. At the same time both of them provide mLTS, Metrics, Resiliency and tracing. How to choose who should offer what?

          See more
          Anas MOKDAD
          Shared insights
          on
          KongKongIstioIstio

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

          See more
          Eureka logo

          Eureka

          288
          774
          69
          AWS Service registry for resilient mid-tier load balancing and failover.
          288
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          + 1
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          PROS OF EUREKA
          • 21
            Easy setup and integration with spring-cloud
          • 9
            Web ui
          • 8
            Monitoring
          • 8
            Health checking
          • 7
            Circuit breaker
          • 6
            Netflix battle tested components
          • 6
            Service discovery
          • 4
            Open Source
          CONS OF EUREKA
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