Alternatives to etcd logo

Alternatives to etcd

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

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.
etcd is a tool in the Open Source Service Discovery category of a tech stack.
etcd is an open source tool with 37.9K GitHub stars and 8.1K GitHub forks. Here’s a link to etcd's open source repository on GitHub

Top Alternatives to etcd

  • Consul

    Consul

    Consul is a tool for service discovery and configuration. Consul is distributed, highly available, and extremely scalable. ...

  • 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. ...

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • MongoDB

    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

  • Cassandra

    Cassandra

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. ...

  • Vault

    Vault

    Vault is a tool for securely accessing secrets. A secret is anything that you want to tightly control access to, such as API keys, passwords, certificates, and more. Vault provides a unified interface to any secret, while providing tight access control and recording a detailed audit log. ...

  • Memcached

    Memcached

    Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering. ...

  • Kafka

    Kafka

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

etcd alternatives & related posts

Consul logo

Consul

1K
1.3K
204
A tool for service discovery, monitoring and configuration
1K
1.3K
+ 1
204
PROS OF CONSUL
  • 59
    Great service discovery infrastructure
  • 35
    Health checking
  • 27
    Distributed key-value store
  • 25
    Monitoring
  • 23
    High-availability
  • 11
    Web-UI
  • 10
    Token-based acls
  • 6
    Gossip clustering
  • 5
    Dns server
  • 2
    Not Java
  • 1
    Docker integration
CONS OF CONSUL
    Be the first to leave a con

    related Consul posts

    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.

    See more

    Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.

    Apps
    • Web: a mix of JavaScript/ES6 and React.
    • Desktop: And Electron to ship it as a desktop application.
    • Android: a mix of Java and Kotlin.
    • iOS: written in a mix of Objective C and Swift.
    Backend
    • The core application and the API written in PHP/Hack that runs on HHVM.
    • The data is stored in MySQL using Vitess.
    • Caching is done using Memcached and MCRouter.
    • The search service takes help from SolrCloud, with various Java services.
    • The messaging system uses WebSockets with many services in Java and Go.
    • Load balancing is done using HAproxy with Consul for configuration.
    • Most services talk to each other over gRPC,
    • Some Thrift and JSON-over-HTTP
    • Voice and video calling service was built in Elixir.
    Data warehouse
    • Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
    Etc
    See more
    Zookeeper logo

    Zookeeper

    624
    804
    42
    Because coordinating distributed systems is a Zoo
    624
    804
    + 1
    42
    PROS OF ZOOKEEPER
    • 11
      High performance ,easy to generate node specific config
    • 8
      Kafka support
    • 8
      Java
    • 5
      Spring Boot Support
    • 3
      Supports extensive distributed IPC
    • 2
      Supports DC/OS
    • 2
      Used in ClickHouse
    • 1
      Curator
    • 1
      Embeddable In Java Service
    • 1
      Used in Hadoop
    CONS OF ZOOKEEPER
      Be the first to leave a con

      related Zookeeper posts

      Redis logo

      Redis

      42.9K
      32K
      3.9K
      An in-memory database that persists on disk
      42.9K
      32K
      + 1
      3.9K
      PROS OF REDIS
      • 875
        Performance
      • 535
        Super fast
      • 511
        Ease of use
      • 441
        In-memory cache
      • 321
        Advanced key-value cache
      • 190
        Open source
      • 179
        Easy to deploy
      • 163
        Stable
      • 153
        Free
      • 120
        Fast
      • 40
        High-Performance
      • 39
        High Availability
      • 34
        Data Structures
      • 32
        Very Scalable
      • 23
        Replication
      • 20
        Great community
      • 19
        Pub/Sub
      • 17
        "NoSQL" key-value data store
      • 14
        Hashes
      • 12
        Sets
      • 10
        Sorted Sets
      • 9
        Lists
      • 8
        BSD licensed
      • 8
        NoSQL
      • 7
        Async replication
      • 7
        Integrates super easy with Sidekiq for Rails background
      • 7
        Bitmaps
      • 6
        Open Source
      • 6
        Keys with a limited time-to-live
      • 5
        Strings
      • 5
        Lua scripting
      • 4
        Awesomeness for Free!
      • 4
        Hyperloglogs
      • 3
        outstanding performance
      • 3
        Runs server side LUA
      • 3
        Networked
      • 3
        LRU eviction of keys
      • 3
        Written in ANSI C
      • 3
        Feature Rich
      • 3
        Transactions
      • 2
        Data structure server
      • 2
        Performance & ease of use
      • 1
        Existing Laravel Integration
      • 1
        Automatic failover
      • 1
        Easy to use
      • 1
        Object [key/value] size each 500 MB
      • 1
        Simple
      • 1
        Channels concept
      • 1
        Scalable
      • 1
        Temporarily kept on disk
      • 1
        Dont save data if no subscribers are found
      • 0
        Jk
      CONS OF REDIS
      • 14
        Cannot query objects directly
      • 2
        No secondary indexes for non-numeric data types
      • 1
        No WAL

      related Redis posts

      Robert Zuber

      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.

      See more

      I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

      We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

      Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

      We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

      Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

      See more
      MongoDB logo

      MongoDB

      64.2K
      53.5K
      4.1K
      The database for giant ideas
      64.2K
      53.5K
      + 1
      4.1K
      PROS OF MONGODB
      • 824
        Document-oriented storage
      • 591
        No sql
      • 546
        Ease of use
      • 465
        Fast
      • 406
        High performance
      • 256
        Free
      • 215
        Open source
      • 179
        Flexible
      • 142
        Replication & high availability
      • 109
        Easy to maintain
      • 41
        Querying
      • 37
        Easy scalability
      • 36
        Auto-sharding
      • 35
        High availability
      • 31
        Map/reduce
      • 26
        Document database
      • 24
        Easy setup
      • 24
        Full index support
      • 15
        Reliable
      • 14
        Fast in-place updates
      • 13
        Agile programming, flexible, fast
      • 11
        No database migrations
      • 7
        Easy integration with Node.Js
      • 7
        Enterprise
      • 5
        Enterprise Support
      • 4
        Great NoSQL DB
      • 3
        Aggregation Framework
      • 3
        Support for many languages through different drivers
      • 3
        Drivers support is good
      • 2
        Schemaless
      • 2
        Easy to Scale
      • 2
        Fast
      • 2
        Awesome
      • 2
        Managed service
      • 1
        Consistent
      CONS OF MONGODB
      • 5
        Very slowly for connected models that require joins
      • 3
        Not acid compliant
      • 1
        Proprietary query language

      related MongoDB posts

      Jeyabalaji Subramanian

      Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

      We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

      Based on the above criteria, we selected the following tools to perform the end to end data replication:

      We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

      We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

      In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

      Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

      In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

      See more
      Robert Zuber

      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.

      See more
      Cassandra logo

      Cassandra

      3.2K
      3.1K
      492
      A partitioned row store. Rows are organized into tables with a required primary key.
      3.2K
      3.1K
      + 1
      492
      PROS OF CASSANDRA
      • 114
        Distributed
      • 95
        High performance
      • 80
        High availability
      • 74
        Easy scalability
      • 52
        Replication
      • 26
        Multi datacenter deployments
      • 26
        Reliable
      • 8
        OLTP
      • 7
        Open source
      • 7
        Schema optional
      • 2
        Workload separation (via MDC)
      • 1
        Fast
      CONS OF CASSANDRA
      • 2
        Reliability of replication
      • 1
        Updates

      related Cassandra posts

      Thierry Schellenbach
      Shared insights
      on
      RedisRedisCassandraCassandraRocksDBRocksDB
      at

      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

      See more
      Umair Iftikhar
      Technical Architect at Vappar · | 3 upvotes · 135.1K views

      Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

      My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

      See more
      Vault logo

      Vault

      603
      621
      68
      Secure, store, and tightly control access to tokens, passwords, certificates, API keys, and other secrets in modern computing
      603
      621
      + 1
      68
      PROS OF VAULT
      • 16
        Secure
      • 11
        Very easy to set up and use
      • 11
        Variety of Secret Backends
      • 8
        Dynamic secret generation
      • 5
        AuditLog
      • 3
        Leasing and Renewal
      • 3
        Privilege Access Management
      • 2
        Open Source
      • 2
        Consol integration
      • 2
        Handles secret sprawl
      • 2
        Variety of Auth Backends
      • 2
        Easy to integrate with
      • 1
        Multicloud
      CONS OF VAULT
        Be the first to leave a con

        related Vault posts

        Memcached logo

        Memcached

        5.7K
        4K
        469
        High-performance, distributed memory object caching system
        5.7K
        4K
        + 1
        469
        PROS OF MEMCACHED
        • 137
          Fast object cache
        • 128
          High-performance
        • 90
          Stable
        • 65
          Mature
        • 33
          Distributed caching system
        • 11
          Improved response time and throughput
        • 3
          Great for caching HTML
        • 2
          Putta
        CONS OF MEMCACHED
        • 2
          Only caches simple types

        related Memcached posts

        Julien DeFrance
        Principal Software Engineer at Tophatter · | 16 upvotes · 2.4M views

        Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

        I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

        For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

        Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

        Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

        Future improvements / technology decisions included:

        Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

        As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

        One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

        See more
        Kir Shatrov
        Engineering Lead at Shopify · | 15 upvotes · 576.5K views

        At Shopify, over the years, we moved from shards to the concept of "pods". A pod is a fully isolated instance of Shopify with its own datastores like MySQL, Redis, Memcached. A pod can be spawned in any region. This approach has helped us eliminate global outages. As of today, we have more than a hundred pods, and since moving to this architecture we haven't had any major outages that affected all of Shopify. An outage today only affects a single pod or region.

        As we grew into hundreds of shards and pods, it became clear that we needed a solution to orchestrate those deployments. Today, we use Docker, Kubernetes, and Google Kubernetes Engine to make it easy to bootstrap resources for new Shopify Pods.

        See more
        Kafka logo

        Kafka

        15.6K
        14.7K
        573
        Distributed, fault tolerant, high throughput pub-sub messaging system
        15.6K
        14.7K
        + 1
        573
        PROS OF KAFKA
        • 122
          High-throughput
        • 116
          Distributed
        • 87
          Scalable
        • 81
          High-Performance
        • 65
          Durable
        • 36
          Publish-Subscribe
        • 19
          Simple-to-use
        • 15
          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 · 2M 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

        See more
        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.

        See more