What is Sidekiq and what are its top alternatives?
Sidekiq is a popular open-source job scheduler for Ruby that uses Redis to manage the job queue. It offers features such as worker resilience, scheduling, web interface, and active job support. However, one limitation of Sidekiq is that it requires a Redis server to function properly.
- Resque: Resque is a task queuing system built on Redis, known for its simple setup and maintenance. Key features include job prioritization, multiple queues, and failure handling. Pros include high reliability and flexibility, while cons include lack of real-time monitoring.
- Delayed::Job: Delayed::Job is a database-backed job scheduler for Ruby applications. It offers a simple and robust way to handle background jobs. Pros include easy setup and tight integration with Rails, while cons include potential database bloat.
- Que: Que is a high-performance job queue for Ruby applications that uses PostgreSQL. Key features include job persistence in the database and multi-threaded worker support. Pros include low overhead and transactional job processing, while cons include limited support for other databases.
- Sidekiq Pro: Sidekiq Pro is the commercial version of Sidekiq with additional features such as job batch processing, priority queues, and job tagging. Pros include enhanced functionality and professional support, while cons include the need for a paid license.
- Sneakers: Sneakers is a fast and simple background processing framework for Ruby applications using RabbitMQ. Key features include worker concurrency control, dead-letter handling, and batch processing. Pros include high performance and RabbitMQ integration, while cons include limited Redis support.
- GoodJob: GoodJob is a multithreaded, Postgres-based background job queuing system for Ruby on Rails applications. It offers features such as ActiveJob compatibility, job scheduling, and real-time monitoring. Pros include simplicity and scalability, while cons include limited database support.
- Queuery: Queuery is a simple, Redis-based job queue library for Ruby applications. It focuses on ease of use and minimal configuration while providing essential job queuing functionality. Pros include lightweight and easy integration, while cons include limited advanced features.
- Shoryuken: Shoryuken is a concurrent Amazon SQS client for Ruby that enables efficient background job processing. Key features include auto-scaling, pre-fetching, and dead-letter handling. Pros include high scalability and Amazon SQS integration, while cons include limited support for other messaging systems.
- Sucker Punch: Sucker Punch is a simple, single-threaded background processing library for Ruby applications. It is designed for lightweight jobs that do not require complex queuing systems. Pros include simplicity and zero-configuration setup, while cons include limited scalability and reliability.
- Backburner: Backburner is a multi-threaded and single-threaded job processing library for Ruby applications that supports beanstalkd and other queuing systems. Key features include job priorities, delayed jobs, and job scheduling. Pros include flexibility and queuing system support, while cons include potential complexity in setup and maintenance.
Top Alternatives to Sidekiq
- Resque
Background jobs can be any Ruby class or module that responds to perform. Your existing classes can easily be converted to background jobs or you can create new classes specifically to do work. Or, you can do both. ...
- Celery
Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. ...
- RabbitMQ
RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...
- delayed_job
Delayed_job (or DJ) encapsulates the common pattern of asynchronously executing longer tasks in the background. It is a direct extraction from Shopify where the job table is responsible for a multitude of core tasks. ...
- Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...
- Amazon SQS
Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use. ...
- Faktory
Redis -> Sidekiq == Faktory -> Faktory. Faktory is a server daemon which provides a simple API to produce and consume background jobs. Jobs are a small JSON hash with a few mandatory keys. ...
- 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. ...
Sidekiq alternatives & related posts
Resque
- Free5
- Scalable3
- Easy to use on heroku1
related Resque posts
- Task queue99
- Python integration63
- Django integration40
- Scheduled Task30
- Publish/subsribe19
- Various backend broker8
- Easy to use6
- Great community5
- Workflow5
- Free4
- Dynamic1
- Sometimes loses tasks4
- Depends on broker1
related Celery posts
As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.
Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.
#MessageQueue
Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.
We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.
- It's fast and it works with good metrics/monitoring234
- Ease of configuration79
- I like the admin interface59
- Easy to set-up and start with50
- Durable21
- Intuitive work through python18
- Standard protocols18
- Written primarily in Erlang10
- Simply superb8
- Completeness of messaging patterns6
- Scales to 1 million messages per second3
- Reliable3
- Distributed2
- Supports MQTT2
- Better than most traditional queue based message broker2
- Supports AMQP2
- Clusterable1
- Clear documentation with different scripting language1
- Great ui1
- Inubit Integration1
- Better routing system1
- High performance1
- Runs on Open Telecom Platform1
- Delayed messages1
- Reliability1
- Open-source1
- Too complicated cluster/HA config and management9
- Needs Erlang runtime. Need ops good with Erlang runtime6
- Configuration must be done first, not by your code5
- Slow4
related RabbitMQ posts
As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.
Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.
#MessageQueue
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.
- Easy to get started3
- Reliable2
- Doesn't require Redis1
related delayed_job posts
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.
#BackgroundProcessing
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!
- High-throughput126
- Distributed119
- Scalable92
- High-Performance86
- Durable66
- Publish-Subscribe38
- Simple-to-use19
- Open source18
- Written in Scala and java. Runs on JVM12
- Message broker + Streaming system9
- KSQL4
- Avro schema integration4
- Robust4
- Suport Multiple clients3
- Extremely good parallelism constructs2
- Partioned, replayable log2
- Simple publisher / multi-subscriber model1
- Fun1
- Flexible1
- Non-Java clients are second-class citizens32
- Needs Zookeeper29
- Operational difficulties9
- Terrible Packaging5
related Kafka posts
The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.
Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).
At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.
For more info:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
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.
- Easy to use, reliable62
- Low cost40
- Simple28
- Doesn't need to maintain it14
- It is Serverless8
- Has a max message size (currently 256K)4
- Triggers Lambda3
- Easy to configure with Terraform3
- Delayed delivery upto 15 mins only3
- Delayed delivery upto 12 hours3
- JMS compliant1
- Support for retry and dead letter queue1
- D1
- Has a max message size (currently 256K)2
- Proprietary2
- Difficult to configure2
- Has a maximum 15 minutes of delayed messages only1
related Amazon SQS posts
We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.
To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas
To build #Webapps we decided to use Angular 2 with RxJS
#Devops - GitHub , Travis CI , Terraform , Docker , Serverless
In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.
While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.
In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.
#ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService
- Worker language agnostic2
- Simple service API1
related Faktory posts
- Performance886
- Super fast542
- Ease of use513
- In-memory cache444
- Advanced key-value cache324
- Open source194
- Easy to deploy182
- Stable164
- Free155
- Fast121
- High-Performance42
- High Availability40
- Data Structures35
- Very Scalable32
- Replication24
- Great community22
- Pub/Sub22
- "NoSQL" key-value data store19
- Hashes16
- Sets13
- Sorted Sets11
- NoSQL10
- Lists10
- Async replication9
- BSD licensed9
- Bitmaps8
- Integrates super easy with Sidekiq for Rails background8
- Keys with a limited time-to-live7
- Open Source7
- Lua scripting6
- Strings6
- Awesomeness for Free5
- Hyperloglogs5
- Transactions4
- Outstanding performance4
- Runs server side LUA4
- LRU eviction of keys4
- Feature Rich4
- Written in ANSI C4
- Networked4
- Data structure server3
- Performance & ease of use3
- Dont save data if no subscribers are found2
- Automatic failover2
- Easy to use2
- Temporarily kept on disk2
- Scalable2
- Existing Laravel Integration2
- Channels concept2
- Object [key/value] size each 500 MB2
- Simple2
- Cannot query objects directly15
- No secondary indexes for non-numeric data types3
- No WAL1
related Redis posts
We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.
As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).
When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.
I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.
We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.
Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis for cache and other time sensitive operations.
We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.
Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.