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 helps manage and maintain configurations, metadata, and state across a cluster of machines. It is commonly used in distributed systems for storing critical data and ensuring consistency. Key features of etcd include strong consistency, high availability, and support for distributed transactions. However, some limitations of etcd include potential performance issues with large data sets and complex configuration requirements.

  1. Consul: Consul is a service mesh solution that includes a key-value store similar to etcd. It provides service discovery, health checking, and distributed key-value storage. Pros of Consul include support for multi-datacenter deployments and a user-friendly UI. Cons include higher resource usage compared to etcd.

  2. ZooKeeper: ZooKeeper is a centralized service for maintaining configuration information, naming, and synchronization. It is known for its high performance and reliability. Pros of Zookeeper include strong consistency guarantees and a robust ecosystem. Cons include complex architecture and a steeper learning curve than etcd.

  3. HashiCorp Vault: HashiCorp Vault is a secrets management tool that also provides a key-value storage engine. It offers encryption keys, passwords, and other sensitive data protection. Pros of Vault include secure data storage and dynamic secrets management. Cons include a higher focus on security over performance compared to etcd.

  4. NATS: NATS is a high-performance messaging system that includes a distributed key-value store called JetStream. It is known for its simplicity and speed. Pros of NATS include low latency and scalability. Cons include lack of advanced features compared to etcd.

  5. Redis: Redis is an in-memory data structure store that can be used as a distributed key-value store. It is popular for caching and messaging use cases. Pros of Redis include high performance and versatility. Cons include data persistence limitations compared to etcd.

  6. Distributed Redis: Distributed Redis is a sharding proxy for Redis that allows for horizontal scaling. It provides partitioning and routing of keys across multiple Redis nodes. Pros of Distributed Redis include scalability and fault tolerance. Cons include added complexity compared to etcd.

  7. Cassandra: Cassandra is a distributed NoSQL database that can be used as a key-value store. It is designed for high availability and scalability. Pros of Cassandra include linear scalability and fault tolerance. Cons include complexity in setting up clusters compared to etcd.

  8. BoltDB: BoltDB is a pure Go key/value store inspired by etcd. It is known for its simplicity and high performance. Pros of BoltDB include fast read and write operations. Cons include limited features compared to etcd.

  9. Badger: Badger is an embeddable key-value database written in Go. It is optimized for SSDs and provides ACID transactions. Pros of Badger include fast data access and efficient disk usage. Cons include limited distributed capabilities compared to etcd.

  10. RocksDB: RocksDB is an embedded key-value store optimized for fast storage systems. It is developed by Facebook and provides efficient data compression and performance. Pros of RocksDB include high throughput and low latency. Cons include lack of built-in distributed features compared to etcd.

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), 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. ...

  • 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

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A tool for service discovery, monitoring and configuration
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PROS OF CONSUL
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    Great service discovery infrastructure
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    Health checking
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    Distributed key-value store
  • 26
    Monitoring
  • 23
    High-availability
  • 12
    Web-UI
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    Token-based acls
  • 6
    Gossip clustering
  • 5
    Dns server
  • 4
    Not Java
  • 1
    Docker integration
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    Javascript
CONS OF CONSUL
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    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
    Shared insights
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    ConsulConsulElixirElixirErlangErlang
    at

    Postmates built a tool called Bazaar that helps onboard new partners and handles several routine tasks, like nightly emails to merchants alerting them about items that are out of stock.

    Since they ran Bazaar across multiple instances, the team needed to avoid sending multiple emails to their partners by obtaining lock across multiple hosts. To solve their challenge, they created and open sourced ConsulMutEx, and an Elixir module for acquiring and releasing locks with Consul and other backends.

    It works with Consul’s KV store, as well as other backends, including ets, Erlang’s in-memory database.

    See more
    Zookeeper logo

    Zookeeper

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    Because coordinating distributed systems is a Zoo
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    PROS OF ZOOKEEPER
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      High performance ,easy to generate node specific config
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      Java
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      Kafka support
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      Spring Boot Support
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      Supports extensive distributed IPC
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      Curator
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      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
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      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
      Redis logo

      Redis

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      PROS OF REDIS
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        Performance
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        Super fast
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        Ease of use
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        In-memory cache
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        Advanced key-value cache
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        Open source
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        Easy to deploy
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        Stable
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        Free
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        Fast
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        High-Performance
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        High Availability
      • 35
        Data Structures
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        Very Scalable
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        Replication
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        Great community
      • 22
        Pub/Sub
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        "NoSQL" key-value data store
      • 16
        Hashes
      • 13
        Sets
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        Sorted Sets
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        NoSQL
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        Lists
      • 9
        Async replication
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        BSD licensed
      • 8
        Bitmaps
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        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
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        Automatic failover
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        Easy to use
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        Temporarily kept on disk
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        Scalable
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        Existing Laravel Integration
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        Channels concept
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        Object [key/value] size each 500 MB
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        Simple
      CONS OF REDIS
      • 15
        Cannot query objects directly
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        No secondary indexes for non-numeric data types
      • 1
        No WAL

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      Russel Werner
      Lead Engineer at StackShare · | 32 upvotes · 2.5M 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

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      Simon Reymann
      Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M 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.
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      MongoDB logo

      MongoDB

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      PROS OF MONGODB
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        Document-oriented storage
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        No sql
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        Ease of use
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        Fast
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        High performance
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        Free
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        Open source
      • 180
        Flexible
      • 145
        Replication & high availability
      • 112
        Easy to maintain
      • 42
        Querying
      • 39
        Easy scalability
      • 38
        Auto-sharding
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        High availability
      • 31
        Map/reduce
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        Document database
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        Easy setup
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        Full index support
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        Reliable
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        Fast in-place updates
      • 14
        Agile programming, flexible, fast
      • 12
        No database migrations
      • 8
        Easy integration with Node.Js
      • 8
        Enterprise
      • 6
        Enterprise Support
      • 5
        Great NoSQL DB
      • 4
        Support for many languages through different drivers
      • 3
        Schemaless
      • 3
        Aggregation Framework
      • 3
        Drivers support is good
      • 2
        Fast
      • 2
        Managed service
      • 2
        Easy to Scale
      • 2
        Awesome
      • 2
        Consistent
      • 1
        Good GUI
      • 1
        Acid Compliant
      CONS OF MONGODB
      • 6
        Very slowly for connected models that require joins
      • 3
        Not acid compliant
      • 1
        Proprietary query language

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      Shared insights
      on
      Node.jsNode.jsGraphQLGraphQLMongoDBMongoDB

      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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      Vaibhav Taunk
      Team Lead at Technovert · | 31 upvotes · 4M views

      I am starting to become a full-stack developer, by choosing and learning .NET Core for API Development, Angular CLI / React for UI Development, MongoDB for database, as it a NoSQL DB and Flutter / React Native for Mobile App Development. Using Postman, Markdown and Visual Studio Code for development.

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      Cassandra logo

      Cassandra

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      A partitioned row store. Rows are organized into tables with a required primary key.
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      PROS OF CASSANDRA
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        Distributed
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        High performance
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        High availability
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        Easy scalability
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        Replication
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        Reliable
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        Multi datacenter deployments
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        Schema optional
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        OLTP
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        Open source
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        Workload separation (via MDC)
      • 1
        Fast
      CONS OF CASSANDRA
      • 3
        Reliability of replication
      • 1
        Size
      • 1
        Updates

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      Thierry Schellenbach
      Shared insights
      on
      GolangGolangPythonPythonCassandraCassandra
      at

      After years of optimizing our existing feed technology, we decided to make a larger leap with 2.0 of Stream. While the first iteration of Stream was powered by Python and Cassandra, for Stream 2.0 of our infrastructure we switched to Go.

      The main reason why we switched from Python to Go is performance. Certain features of Stream such as aggregation, ranking and serialization were very difficult to speed up using Python.

      We’ve been using Go since March 2017 and it’s been a great experience so far. Go has greatly increased the productivity of our development team. Not only has it improved the speed at which we develop, it’s also 30x faster for many components of Stream. Initially we struggled a bit with package management for Go. However, using Dep together with the VG package contributed to creating a great workflow.

      Go as a language is heavily focused on performance. The built-in PPROF tool is amazing for finding performance issues. Uber’s Go-Torch library is great for visualizing data from PPROF and will be bundled in PPROF in Go 1.10.

      The performance of Go greatly influenced our architecture in a positive way. With Python we often found ourselves delegating logic to the database layer purely for performance reasons. The high performance of Go gave us more flexibility in terms of architecture. This led to a huge simplification of our infrastructure and a dramatic improvement of latency. For instance, we saw a 10 to 1 reduction in web-server count thanks to the lower memory and CPU usage for the same number of requests.

      #DataStores #Databases

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      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

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      Vault logo

      Vault

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      Secure, store, and tightly control access to tokens, passwords, certificates, API keys, and other secrets in modern computing
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      PROS OF VAULT
      • 17
        Secure
      • 13
        Variety of Secret Backends
      • 11
        Very easy to set up and use
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        Dynamic secret generation
      • 5
        AuditLog
      • 3
        Privilege Access Management
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        Leasing and Renewal
      • 2
        Easy to integrate with
      • 2
        Open Source
      • 2
        Consol integration
      • 2
        Handles secret sprawl
      • 2
        Variety of Auth Backends
      • 1
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      CONS OF VAULT
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        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 8.7M views

        Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

        It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

        I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

        We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

        If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

        The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

        Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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        Joseph Irving
        DevOps Engineer at uSwitch · | 8 upvotes · 20.1K views

        At uSwitch we use Vault to generate short lived database credentials for our applications running in Kubernetes. We wanted to move from an environment where we had 100 dbs with a variety of static passwords being shared around to a place where each pod would have credentials that only last for its lifetime.

        We chose vault because:

        • It had built in Kubernetes support so we could use service accounts to permission which pods could access which database.

        • A terraform provider so that we could configure both our RDS instances and their vault configuration in one place.

        • A variety of database providers including MySQL/PostgreSQL (our most common dbs).

        • A good api/Go -sdk so that we could build tooling around it to simplify development worfklow.

        • It had other features we would utilise such as PKI

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        Memcached logo

        Memcached

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        High-performance, distributed memory object caching system
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        PROS OF MEMCACHED
        • 139
          Fast object cache
        • 129
          High-performance
        • 91
          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

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        Kir Shatrov
        Engineering Lead at Shopify · | 17 upvotes · 1.2M 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
        Julien DeFrance
        Principal Software Engineer at Tophatter · | 16 upvotes · 3.2M 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.

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        Kafka

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        607
        Distributed, fault tolerant, high throughput pub-sub messaging system
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        607
        PROS OF KAFKA
        • 126
          High-throughput
        • 119
          Distributed
        • 92
          Scalable
        • 86
          High-Performance
        • 66
          Durable
        • 38
          Publish-Subscribe
        • 19
          Simple-to-use
        • 18
          Open source
        • 12
          Written in Scala and java. Runs on JVM
        • 9
          Message broker + Streaming system
        • 4
          KSQL
        • 4
          Avro schema integration
        • 4
          Robust
        • 3
          Suport Multiple clients
        • 2
          Extremely good parallelism constructs
        • 2
          Partioned, replayable log
        • 1
          Simple publisher / multi-subscriber model
        • 1
          Fun
        • 1
          Flexible
        CONS OF KAFKA
        • 32
          Non-Java clients are second-class citizens
        • 29
          Needs Zookeeper
        • 9
          Operational difficulties
        • 5
          Terrible Packaging

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        Nick Rockwell
        SVP, Engineering at Fastly · | 46 upvotes · 3.5M 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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        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

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