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InfluxDB
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InfluxDB vs Memcached: What are the differences?

InfluxDB: An open-source distributed time series database with no external dependencies. InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.; Memcached: High-performance, distributed memory object caching system. 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.

InfluxDB and Memcached belong to "Databases" category of the tech stack.

"Time-series data analysis" is the primary reason why developers consider InfluxDB over the competitors, whereas "Fast object cache" was stated as the key factor in picking Memcached.

InfluxDB and Memcached are both open source tools. It seems that InfluxDB with 16.7K GitHub stars and 2.38K forks on GitHub has more adoption than Memcached with 8.99K GitHub stars and 2.6K GitHub forks.

According to the StackShare community, Memcached has a broader approval, being mentioned in 755 company stacks & 267 developers stacks; compared to InfluxDB, which is listed in 119 company stacks and 39 developer stacks.

What is InfluxDB?

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

What is 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.
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    What are some alternatives to InfluxDB and Memcached?
    TimescaleDB
    TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.
    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 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.
    Prometheus
    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    See all alternatives
    Decisions about InfluxDB and Memcached
    Node.js
    Node.js
    Python
    Python
    MySQL
    MySQL
    Memcached
    Memcached
    nginx
    nginx
    RabbitMQ
    RabbitMQ
    Redis
    Redis
    Django
    Django
    Tornado
    Tornado
    Varnish
    Varnish
    HAProxy
    HAProxy

    Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.

    See more
    Kir Shatrov
    Kir Shatrov
    Production Engineer at Shopify · | 12 upvotes · 78.3K views
    atShopifyShopify
    Rails
    Rails
    MySQL
    MySQL
    Memcached
    Memcached
    Redis
    Redis

    As is common in the Rails stack, since the very beginning, we've stayed with MySQL as a relational database, Memcached for key/value storage and Redis for queues and background jobs.

    In 2014, we could no longer store all our data in a single MySQL instance - even by buying better hardware. We decided to use sharding and split all of Shopify into dozens of database partitions.

    Sharding played nicely for us because Shopify merchants are isolated from each other and we were able to put a subset of merchants on a single shard. It would have been harder if our business assumed shared data between customers.

    The sharding project bought us some time regarding database capacity, but as we soon found out, there was a huge single point of failure in our infrastructure. All those shards were still using a single Redis. At one point, the outage of that Redis took down all of Shopify, causing a major disruption we later called “Redismageddon”. This taught us an important lesson to avoid any resources that are shared across all of 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.

    See more
    Kir Shatrov
    Kir Shatrov
    Production Engineer at Shopify · | 13 upvotes · 146.1K views
    atShopifyShopify
    Docker
    Docker
    Kubernetes
    Kubernetes
    Google Kubernetes Engine
    Google Kubernetes Engine
    MySQL
    MySQL
    Redis
    Redis
    Memcached
    Memcached

    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
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Heroku
    Heroku
    Ruby
    Ruby
    Rails
    Rails
    Amazon RDS for PostgreSQL
    Amazon RDS for PostgreSQL
    MariaDB
    MariaDB
    Microsoft SQL Server
    Microsoft SQL Server
    Amazon RDS
    Amazon RDS
    AWS Lambda
    AWS Lambda
    Python
    Python
    Redis
    Redis
    Memcached
    Memcached
    AWS Elastic Load Balancing (ELB)
    AWS Elastic Load Balancing (ELB)
    Amazon Elasticsearch Service
    Amazon Elasticsearch Service
    Amazon ElastiCache
    Amazon ElastiCache

    We initially started out with Heroku as our PaaS provider due to a desire to use it by our original developer for our Ruby on Rails application/website at the time. We were finding response times slow, it was painfully slow, sometimes taking 10 seconds to start loading the main page. Moving up to the next "compute" level was going to be very expensive.

    We moved our site over to AWS Elastic Beanstalk , not only did response times on the site practically become instant, our cloud bill for the application was cut in half.

    In database world we are currently using Amazon RDS for PostgreSQL also, we have both MariaDB and Microsoft SQL Server both hosted on Amazon RDS. The plan is to migrate to AWS Aurora Serverless for all 3 of those database systems.

    Additional services we use for our public applications: AWS Lambda, Python, Redis, Memcached, AWS Elastic Load Balancing (ELB), Amazon Elasticsearch Service, Amazon ElastiCache

    See more
    StackShare Editors
    StackShare Editors
    Prometheus
    Prometheus
    Chef
    Chef
    Consul
    Consul
    Memcached
    Memcached
    Hack
    Hack
    Swift
    Swift
    Hadoop
    Hadoop
    Terraform
    Terraform
    Airflow
    Airflow
    Apache Spark
    Apache Spark
    Kubernetes
    Kubernetes
    gRPC
    gRPC
    HHVM (HipHop Virtual Machine)
    HHVM (HipHop Virtual Machine)
    Presto
    Presto
    Kotlin
    Kotlin
    Apache Thrift
    Apache Thrift

    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
    Julien DeFrance
    Julien DeFrance
    Principal Software Engineer at Tophatter · | 16 upvotes · 502K views
    atSmartZipSmartZip
    Rails
    Rails
    Rails API
    Rails API
    AWS Elastic Beanstalk
    AWS Elastic Beanstalk
    Capistrano
    Capistrano