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Azure Functions vs Memcached: What are the differences?

What is Azure Functions? Listen and react to events across your stack. Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

What is 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.

Azure Functions and Memcached are primarily classified as "Serverless / Task Processing" and "Databases" tools respectively.

"Pay only when invoked" is the top reason why over 7 developers like Azure Functions, while over 133 developers mention "Fast object cache" as the leading cause for choosing Memcached.

Memcached is an open source tool with 8.93K GitHub stars and 2.6K GitHub forks. Here's a link to Memcached's open source repository on GitHub.

According to the StackShare community, Memcached has a broader approval, being mentioned in 750 company stacks & 264 developers stacks; compared to Azure Functions, which is listed in 27 company stacks and 21 developer stacks.

- No public GitHub repository available -

What is Azure Functions?

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

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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Why do developers choose Azure Functions?
Why do developers choose Memcached?

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      What are some alternatives to Azure Functions and Memcached?
      AWS Lambda
      AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.
      Serverless
      Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.
      Cloud Functions for Firebase
      Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.
      Google Cloud Functions
      Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running
      Apex
      Apex is a small tool for deploying and managing AWS Lambda functions. With shims for languages not yet supported by Lambda, you can use Golang out of the box.
      See all alternatives
      Decisions about Azure Functions and Memcached
      HAProxy
      HAProxy
      Varnish
      Varnish
      Tornado
      Tornado
      Django
      Django
      Redis
      Redis
      RabbitMQ
      RabbitMQ
      nginx
      nginx
      Memcached
      Memcached
      MySQL
      MySQL
      Python
      Python
      Node.js
      Node.js

      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 · 56K views
      atShopifyShopify
      Redis
      Redis
      Memcached
      Memcached
      MySQL
      MySQL
      Rails
      Rails

      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.

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      Kir Shatrov
      Kir Shatrov
      Production Engineer at Shopify · | 13 upvotes · 106.7K views
      atShopifyShopify
      Memcached
      Memcached
      Redis
      Redis
      MySQL
      MySQL
      Google Kubernetes Engine
      Google Kubernetes Engine
      Kubernetes
      Kubernetes
      Docker
      Docker

      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.

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      Kestas Barzdaitis
      Kestas Barzdaitis
      Entrepreneur & Engineer · | 12 upvotes · 60.8K views
      atCodeFactorCodeFactor
      Google Cloud Functions
      Google Cloud Functions
      Azure Functions
      Azure Functions
      AWS Lambda
      AWS Lambda
      Docker
      Docker
      Google Compute Engine
      Google Compute Engine
      Microsoft Azure
      Microsoft Azure
      Amazon EC2
      Amazon EC2
      CodeFactor.io
      CodeFactor.io
      Kubernetes
      Kubernetes
      #SAAS
      #IAAS
      #Containerization
      #Autoscale
      #Startup
      #Automation
      #Machinelearning
      #AI
      #Devops

      CodeFactor being a #SAAS product, our goal was to run on a cloud-native infrastructure since day one. We wanted to stay product focused, rather than having to work on the infrastructure that supports the application. We needed a cloud-hosting provider that would be reliable, economical and most efficient for our product.

      CodeFactor.io aims to provide an automated and frictionless code review service for software developers. That requires agility, instant provisioning, autoscaling, security, availability and compliance management features. We looked at the top three #IAAS providers that take up the majority of market share: Amazon's Amazon EC2 , Microsoft's Microsoft Azure, and Google Compute Engine.

      AWS has been available since 2006 and has developed the most extensive services ant tools variety at a massive scale. Azure and GCP are about half the AWS age, but also satisfied our technical requirements.

      It is worth noting that even though all three providers support Docker containerization services, GCP has the most robust offering due to their investments in Kubernetes. Also, if you are a Microsoft shop, and develop in .NET - Visual Studio Azure shines at integration there and all your existing .NET code works seamlessly on Azure. All three providers have serverless computing offerings (AWS Lambda, Azure Functions, and Google Cloud Functions). Additionally, all three providers have machine learning tools, but GCP appears to be the most developer-friendly, intuitive and complete when it comes to #Machinelearning and #AI.

      The prices between providers are competitive across the board. For our requirements, AWS would have been the most expensive, GCP the least expensive and Azure was in the middle. Plus, if you #Autoscale frequently with large deltas, note that Azure and GCP have per minute billing, where AWS bills you per hour. We also applied for the #Startup programs with all three providers, and this is where Azure shined. While AWS and GCP for startups would have covered us for about one year of infrastructure costs, Azure Sponsorship would cover about two years of CodeFactor's hosting costs. Moreover, Azure Team was terrific - I felt that they wanted to work with us where for AWS and GCP we were just another startup.

      In summary, we were leaning towards GCP. GCP's advantages in containerization, automation toolset, #Devops mindset, and pricing were the driving factors there. Nevertheless, we could not say no to Azure's financial incentives and a strong sense of partnership and support throughout the process.

      Bottom line is, IAAS offerings with AWS, Azure, and GCP are evolving fast. At CodeFactor, we aim to be platform agnostic where it is practical and retain the flexibility to cherry-pick the best products across providers.

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      Michal Nowak
      Michal Nowak
      Co-founder at Evojam · | 7 upvotes · 62.4K views
      atEvojamEvojam
      Azure Functions
      Azure Functions
      Firebase
      Firebase
      AWS Lambda
      AWS Lambda
      Serverless
      Serverless

      In a couple of recent projects we had an opportunity to try out the new Serverless approach to building web applications. It wasn't necessarily a question if we should use any particular vendor but rather "if" we can consider serverless a viable option for building apps. Obviously our goal was also to get a feel for this technology and gain some hands-on experience.

      We did consider AWS Lambda, Firebase from Google as well as Azure Functions. Eventually we went with AWS Lambdas.

      PROS
      • No servers to manage (obviously!)
      • Limited fixed costs – you pay only for used time
      • Automated scaling and balancing
      • Automatic failover (or, at this level of abstraction, no failover problem at all)
      • Security easier to provide and audit
      • Low overhead at the start (with the certain level of knowledge)
      • Short time to market
      • Easy handover - deployment coupled with code
      • Perfect choice for lean startups with fast-paced iterations
      • Augmentation for the classic cloud, server(full) approach
      CONS
      • Not much know-how and best practices available about structuring the code and projects on the market
      • Not suitable for complex business logic due to the risk of producing highly coupled code
      • Cost difficult to estimate (helpful tools: serverlesscalc.com)
      • Difficulty in migration to other platforms (Vendor lock⚠️)
      • Little engineers with experience in serverless on the job market
      • Steep learning curve for engineers without any cloud experience

      More details are on our blog: https://evojam.com/blog/2018/12/5/should-you-go-serverless-meet-the-benefits-and-flaws-of-new-wave-of-cloud-solutions I hope it helps 🙌 & I'm curious of your experiences.

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

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

      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 · 374.5K views
      atSmartZipSmartZip
      Amazon DynamoDB
      Amazon DynamoDB
      Ruby
      Ruby
      Node.js
      Node.js
      AWS Lambda
      AWS Lambda
      New Relic
      New Relic
      Amazon Elasticsearch Service
      Amazon Elasticsearch Service
      Elasticsearch
      Elasticsearch
      Superset
      Superset
      Amazon Quicksight
      Amazon Quicksight
      Amazon Redshift
      Amazon Redshift
      Zapier
      Zapier
      Segment
      Segment
      Amazon CloudFront
      Amazon CloudFront
      Memcached
      Memcached
      Amazon ElastiCache
      Amazon ElastiCache
      Amazon RDS for Aurora
      Amazon RDS for Aurora
      MySQL
      MySQL
      Amazon RDS
      Amazon RDS
      Amazon S3
      Amazon S3
      Docker
      Docker
      Capistrano
      Capistrano
      AWS Elastic Beanstalk
      AWS Elastic Beanstalk
      Rails API
      Rails API
      Rails
      Rails
      Algolia
      Algolia

      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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      Yonas Beshawred
      Yonas Beshawred
      CEO at StackShare · | 9 upvotes · 26K views
      atStackShareStackShare
      Memcached
      Memcached
      Heroku
      Heroku
      Amazon ElastiCache
      Amazon ElastiCache
      Rails
      Rails
      PostgreSQL
      PostgreSQL
      MemCachier
      MemCachier
      #RailsCaching
      #Caching

      We decided to use MemCachier as our Memcached provider because we were seeing some serious PostgreSQL performance issues with query-heavy pages on the site. We use MemCachier for all Rails caching and pretty aggressively too for the logged out experience (fully cached pages for the most part). We really need to move to Amazon ElastiCache as soon as possible so we can stop paying so much. The only reason we're not moving is because there are some restrictions on the network side due to our main app being hosted on Heroku.

      #Caching #RailsCaching

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      Tim Nolet
      Tim Nolet
      Founder, Engineer & Dishwasher at Checkly · | 5 upvotes · 20.3K views
      atChecklyHQChecklyHQ
      Node.js
      Node.js
      Google Cloud Functions
      Google Cloud Functions
      Azure Functions
      Azure Functions
      Amazon CloudWatch
      Amazon CloudWatch
      Serverless
      Serverless
      AWS Lambda
      AWS Lambda

      AWS Lambda Serverless Amazon CloudWatch Azure Functions Google Cloud Functions Node.js

      In the last year or so, I moved all Checkly monitoring workloads to AWS Lambda. Here are some stats:

      • We run three core functions in all AWS regions. They handle API checks, browser checks and setup / teardown scripts. Check our docs to find out what that means.
      • All functions are hooked up to SNS topics but can also be triggered directly through AWS SDK calls.
      • The busiest function is a plumbing function that forwards data to our database. It is invoked anywhere between 7000 and 10.000 times per hour with an average duration of about 179 ms.
      • We run separate dev and test versions of each function in each region.

      Moving all this to AWS Lambda took some work and considerations. The blog post linked below goes into the following topics:

      • Why Lambda is an almost perfect match for SaaS. Especially when you're small.
      • Why I don't use a "big" framework around it.
      • Why distributed background jobs triggered by queues are Lambda's raison d'être.
      • Why monitoring & logging is still an issue.

      https://blog.checklyhq.com/how-i-made-aws-lambda-work-for-my-saas/

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      Interest over time
      Reviews of Azure Functions and Memcached
      Review ofAzure FunctionsAzure Functions

      Poor developer experience

      How developers use Azure Functions and Memcached
      Avatar of Reactor Digital
      Reactor Digital uses MemcachedMemcached

      As part of the cacheing system within Drupal.

      Memcached mainly took care of creating and rebuilding the REST API cache once changes had been made within Drupal.

      Avatar of Casey Smith
      Casey Smith uses MemcachedMemcached

      Distributed cache exposed through Google App Engine APIs; use to stage fresh data (incoming and recently processed) for faster access in data processing pipeline.

      Avatar of The Independent
      The Independent uses MemcachedMemcached

      Memcache caches database results and articles, reducing overall DB load and allowing seamless DB maintenance during quiet periods.

      Avatar of Yonas B.
      Yonas B. uses Azure FunctionsAzure Functions

      I used Azure functions as part of an integration service when creating a bulk insert module in azure.

      Avatar of eXon Technologies
      eXon Technologies uses MemcachedMemcached

      Used to cache most used files for our clients. Connected with CloudFlare Railgun Optimizer.

      Avatar of ScholaNoctis
      ScholaNoctis uses MemcachedMemcached

      Memcached is used as a simple page cache across the whole application.

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