Redis vs Sidekiq: What are the differences?
Developers describe Redis as "An in-memory database that persists on disk". 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. On the other hand, Sidekiq is detailed as "Simple, efficient background processing for Ruby". Sidekiq uses threads to handle many jobs at the same time in the same process. It does not require Rails but will integrate tightly with Rails 3/4 to make background processing dead simple.
Redis belongs to "In-Memory Databases" category of the tech stack, while Sidekiq can be primarily classified under "Background Processing".
"Performance", "Super fast" and "Ease of use " are the key factors why developers consider Redis; whereas "Simple", "Efficient background processing" and "Scalability" are the primary reasons why Sidekiq is favored.
Redis and Sidekiq are both open source tools. Redis with 37.1K GitHub stars and 14.3K forks on GitHub appears to be more popular than Sidekiq with 9.66K GitHub stars and 1.66K GitHub forks.
According to the StackShare community, Redis has a broader approval, being mentioned in 3239 company stacks & 1732 developers stacks; compared to Sidekiq, which is listed in 346 company stacks and 77 developer stacks.
What is Redis?
What is Sidekiq?
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delayed_job is a great Rails background job library for new projects, as it only uses what you already have: a relational database. We happily used it during the company’s first two years.
But it started to falter as our web and database transactions significantly grew. Our app interacted with users via SMS texts sent inside background jobs. Because the delayed_job daemon ran every couple seconds, this meant that users often waited several long seconds before getting text replies, which was not acceptable. Moreover, job processing was done inside AWS Elastic Beanstalk web instances, which were already under stress and not meant to handle jobs.
We needed a fast background job system that could process jobs in near real-time and integrate well with AWS. Sidekiq is a fast and popular Ruby background job library, but it does not leverage the Elastic Beanstalk worker architecture, and you have to maintain a Redis instance.
We ended up choosing active-elastic-job, which seamlessly integrates with worker instances and Amazon SQS. SQS is a fast queue and you don’t need to worry about infrastructure or scaling, as AWS handles it for you.
We noticed significant performance gains immediately after making the switch.
I'm building a new process management tool. I decided to build with Rails as my backend, using Sidekiq for background jobs. I chose to work with these tools because I've worked with them before and know that they're able to get the job done. They may not be the sexiest tools, but they work and are reliable, which is what I was optimizing for. For data stores, I opted for PostgreSQL and Redis. Because I'm planning on offering dashboards, I wanted a SQL database instead of something like MongoDB that might work early on, but be difficult to use as soon as I want to facilitate aggregate queries.
We use Sidekiq to process millions of Ruby background jobs a day under normal loads. We sometimes process more than that when running one-off backfill tasks.
With so many jobs, it wouldn't really make sense to use delayed_job, as it would put our main database under unnecessary load, which would make it a bottleneck with most DB queries serving jobs and not end users. I suppose you could create a separate DB just for jobs, but that can be a hassle. Sidekiq uses a separate Redis instance so you don't have this problem. And it is very performant!
I also like that its free version comes "batteries included" with:
- A web monitoring UI that provides some nice stats.
- An API that can come in handy for one-off tasks, like changing the queue of certain already enqueued jobs.
Sidekiq is a pleasure to use. All our engineers love it!
In 2010 we made the very difficult decision to entirely re-engineer our existing monolithic LAMP application from the ground up in order to address some growing concerns about it's long term viability as a platform.
Full application re-write is almost always never the answer, because of the risks involved. However the situation warranted drastic action as it was clear that the existing product was going to face severe scaling issues. We felt it better address these sooner rather than later and also take the opportunity to improve the international architecture and also to refactor the database in. order that it better matched the changes in core functionality.
PostgreSQL was chosen for its reputation as being solid ACID compliant database backend, it was available as an offering AWS RDS service which reduced the management overhead of us having to configure it ourselves. In order to reduce read load on the primary database we implemented an Elasticsearch layer for fast and scalable search operations. Synchronisation of these indexes was to be achieved through the use of Sidekiq's Redis based background workers on Amazon ElastiCache. Again the AWS solution here looked to be an easy way to keep our involvement in managing this part of the platform at a minimum. Allowing us to focus on our core business.
Rails ls was chosen for its ability to quickly get core functionality up and running, its MVC architecture and also its focus on Test Driven Development using RSpec and Selenium with Travis CI providing continual integration. We also liked Ruby for its terse, clean and elegant syntax. Though YMMV on that one!
Unicorn was chosen for its continual deployment and reputation as a reliable application server, nginx for its reputation as a fast and stable reverse-proxy. We also took advantage of the Amazon CloudFront CDN here to further improve performance by caching static assets globally.
We tried to strike a balance between having control over management and configuration of our core application with the convenience of being able to leverage AWS hosted services for ancillary functions (Amazon SES , Amazon SQS Amazon Route 53 all hosted securely inside Amazon VPC of course!).
Whilst there is some compromise here with potential vendor lock in, the tasks being performed by these ancillary services are no particularly specialised which should mitigate this risk. Furthermore we have already containerised the stack in our development using Docker environment, and looking to how best to bring this into production - potentially using Amazon EC2 Container Service
We migrated from Amazon SQS + Shoryuken to Sidekiq in order to have at-most-once delivery out of the box and more flexibility.
The UI builtin Rails makes it smoother for development and QA. Through the sidekiq rails engine we can easily see & understand which job is/was/will be executed, and even get some stats for free. Compared to SQS, we lose in scalability (need to manage the underlying Redis instance) but this is not so critical right now for our business size and the PROs clearly outweigh the CONs. Plugins allow to easily add distributed CRON scheduled jobs in there for almost free, and this is a core feature for us, so we no longer need to maintain a "scheduler" instance and we make our CRON jobs more resilient. The Sidekiq UI can easily be tweaked and for instance we have added a column that translates the CRON syntax into a human readable string, so it's easy for our Q/A to check whether the job is scheduled appropriately.
We still use Amazon SQS for some other apps, but no longer for our main Rails app.
We decided to use AWS Lambda for several serverless tasks such as
- Managing AWS backups
- Processing emails received on Amazon SES and stored to Amazon S3 and notified via Amazon SNS, so as to push a message on our Redis so our Sidekiq Rails workers can process inbound emails
- Pushing some relevant Amazon CloudWatch metrics and alarms to Slack
I use Redis because, based on the case studies I have reviewed, it appears to be the most performant cache database for my Django projects. The ease of configuration and deployment is also a big plus.
Using both higher level view caching as well as low-level QuerySet caching with Redis has allowed me to improve HTTP request times by an order of magnitude.
Redis is a good caching tool for a cluster, but our application had performance issues while using Aws Elasticache Redis since some page had 3000 cache hits per a page load and Redis just couldn't quickly process them all in once + latency and object deseialization time - page load took 8-9 seconds. We create a custom hybrid caching based on Redis and EhCache which worked great for our goals. Check it out on github, it's called HybriCache - https://github.com/batir-akhmerov/hybricache.
Redis is used for storing all ephemeral (that's data you don't necessarily want to store permanently) user data, such as mapping of session IDs (stored in cookies) to current session variables at Cloudcraft.co. The many datastructures supported by Redis also makes it an excellent caching and realtime statistics layer. It doesn't hurt that the author, Antirez, is the nicest guy ever! These days, I would be really hard pressed to find any situation where I would pick something like Memcached over Redis.
Trello uses Redis for ephemeral data that needs to be shared between server processes but not persisted to disk. Things like the activity level of a session or a temporary OpenID key are stored in Redis, and the application is built to recover gracefully if any of these (or all of them) are lost. We run with allkeys-lru enabled and about five times as much space as its actual working set needs, so Redis automatically discards data that hasn’t been accessed lately, and reconstructs it when necessary.
The UI has message inbox that is sent a message when you get a new badge, receive a message, significant event, etc. Done using WebSockets and is powered by redis. Redis has 2 slaves, SQL has 2 replicas, tag engine has 3 nodes, elastic has 3 nodes - any other service has high availability as well (and exists in both data centers).
Redis makes certain operations very easy. When I need a high-availability store, I typically look elsewhere, but for rapid development with the ability to land on your feet in prod, Redis is great. The available data types make it easy to build non-trivial indexes that would require complex queries in postgres.
I use Redis for cacheing, data storage, mining and augmentation, proprietary distributed event system for disparate apps and services to talk to each other, and more. Redis has some very useful native data types for tracking, slicing and dicing information.
We turn to Sidekiq when we need to run background jobs in a Rails app, which we do for just about every Rails app we write. We especially like the ops tools that come with Sidekiq, which make it easy to monitor and maintain.
Background processing of Pushover push notifications to admins when sales occur, payments processing via Pin Payments, Campaign Monitor transaction email sending, and Intercom event API posting.
Sidekiq is used extensively for a multitude of background jobs, everything from audio/video post-processing to sending push notifications.