AWS Lambda vs Memcached

Overview

Memcached

Stacks7.9K

Followers5.7K

Votes473

GitHub Stars14.0K

Forks3.3K

AWS Lambda

Stacks26.0K

Followers18.8K

Votes432

AWS Lambda vs Memcached: What are the differences?

Cost: One key difference between AWS Lambda and Memcached is the cost. With AWS Lambda, you only pay for the actual compute time and resources used, whereas with Memcached, you typically need to provision and manage your own infrastructure, resulting in higher upfront costs.
Scalability: Another difference is the ability to scale. AWS Lambda is highly scalable, automatically provisioning resources based on demand, whereas with Memcached, you need to manually configure and manage your own scaling infrastructure.
Compute Model: AWS Lambda operates on a serverless compute model, where you can run code without having to provision or manage servers. On the other hand, Memcached is an in-memory caching system, used to store frequently accessed data in memory to improve performance.
Managed Service: AWS Lambda is a managed service provided by Amazon, meaning that Amazon takes care of the underlying infrastructure and operations. With Memcached, you are responsible for setting up, configuring, and maintaining the caching infrastructure yourself.
Flexibility: AWS Lambda supports multiple programming languages and can be used to execute a wide variety of tasks, making it more flexible. Memcached, on the other hand, is primarily focused on caching and does not provide the same level of flexibility in terms of task execution.
Integration: AWS Lambda integrates well with other existing AWS services, enabling you to easily build serverless architectures. Memcached, on the other hand, is a standalone caching system and may require additional integration efforts to work with other services.

In summary, AWS Lambda is a serverless compute service that offers cost-effective scalability, flexible task execution, and integration with other AWS services. On the other hand, Memcached is an in-memory caching system that requires manual scalability management and may incur higher upfront costs due to infrastructure provisioning.

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Advice on Memcached, AWS Lambda

Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceon

Heroku

AWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

Developer Experience trumps everything.
AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

Pure, uncut, self hosted Kubernetes — way too much complexity
Managed Kubernetes in various flavors — still too much complexity
Zeit — Maybe, but no Docker support
Elastic Beanstalk — Maybe, bit old but does the job
Heroku
Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

Developer Experience / DX 🤓
Ops Experience / OX 🐂 (?)
Cost 💵
Lock in 🔐

Read the full post linked below for all details

357k views357k

Comments

Detailed Comparison

Memcached	AWS Lambda
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.	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.
-	Extend other AWS services with custom logic;Build custom back-end services;Completely Automated Administration;Built-in Fault Tolerance;Automatic Scaling;Integrated Security Model;Bring Your Own Code;Pay Per Use;Flexible Resource Model
Statistics
GitHub Stars 14.0K	GitHub Stars -
GitHub Forks 3.3K	GitHub Forks -
Stacks 7.9K	Stacks 26.0K
Followers 5.7K	Followers 18.8K
Votes 473	Votes 432
Pros & Cons
Pros 139 Fast object cache 129 High-performance 91 Stable 65 Mature 33 Distributed caching system Cons 2 Only caches simple types	Pros 129 No infrastructure 83 Cheap 70 Quick 59 Stateless 47 No deploy, no server, great sleep Cons 7 Cant execute ruby or go 3 Compute time limited 1 Can't execute PHP w/o significant effort

What are some alternatives to Memcached, AWS Lambda?

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.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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.

Related Comparisons

AWS Lambda vs Memcached: What are the differences?

Cost: One key difference between AWS Lambda and Memcached is the cost. With AWS Lambda, you only pay for the actual compute time and resources used, whereas with Memcached, you typically need to provision and manage your own infrastructure, resulting in higher upfront costs.
Scalability: Another difference is the ability to scale. AWS Lambda is highly scalable, automatically provisioning resources based on demand, whereas with Memcached, you need to manually configure and manage your own scaling infrastructure.
Compute Model: AWS Lambda operates on a serverless compute model, where you can run code without having to provision or manage servers. On the other hand, Memcached is an in-memory caching system, used to store frequently accessed data in memory to improve performance.
Managed Service: AWS Lambda is a managed service provided by Amazon, meaning that Amazon takes care of the underlying infrastructure and operations. With Memcached, you are responsible for setting up, configuring, and maintaining the caching infrastructure yourself.
Flexibility: AWS Lambda supports multiple programming languages and can be used to execute a wide variety of tasks, making it more flexible. Memcached, on the other hand, is primarily focused on caching and does not provide the same level of flexibility in terms of task execution.
Integration: AWS Lambda integrates well with other existing AWS services, enabling you to easily build serverless architectures. Memcached, on the other hand, is a standalone caching system and may require additional integration efforts to work with other services.

AWS Lambda vs Memcached

Overview