Alternatives to Starling logo

Alternatives to Starling

Authy, Sparrow, Kafka, RabbitMQ, and Amazon SQS are the most popular alternatives and competitors to Starling.
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What is Starling and what are its top alternatives?

Starling is a powerful but simple messaging server that enables reliable distributed queuing with an absolutely minimal overhead. It speaks the MemCache protocol for maximum cross-platform compatibility. Any language that speaks MemCache can take advantage of Starling's queue facilities.
Starling is a tool in the Message Queue category of a tech stack.
Starling is an open source tool with 467 GitHub stars and 64 GitHub forks. Here’s a link to Starling's open source repository on GitHub

Top Alternatives to Starling

  • Authy

    Authy

    We make the best rated Two-Factor Authentication smartphone app for consumers, a Rest API for developers and a strong authentication platform for the enterprise. ...

  • Sparrow

    Sparrow

    Sparrow keeps messages in memory, but persists them to disk, using Sqlite, when the queue is shutdown. ...

  • Kafka

    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • RabbitMQ

    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • Amazon SQS

    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. ...

  • Celery

    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. ...

  • ActiveMQ

    ActiveMQ

    Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License. ...

  • MQTT

    MQTT

    It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium. ...

Starling alternatives & related posts

Authy logo

Authy

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138
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The easiest way to add Two-Factor Authentication to any website or app.
140
138
+ 1
1
PROS OF AUTHY
  • 1
    Google Authenticator-compatible
  • 0
    1
CONS OF AUTHY
  • 2
    Terrible UI on mobile

related Authy posts

Sparrow logo

Sparrow

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9
0
A really fast lightweight queue written in Ruby that speaks memcache
4
9
+ 1
0
PROS OF SPARROW
    Be the first to leave a pro
    CONS OF SPARROW
      Be the first to leave a con

      related Sparrow posts

      Kafka logo

      Kafka

      15.6K
      14.7K
      573
      Distributed, fault tolerant, high throughput pub-sub messaging system
      15.6K
      14.7K
      + 1
      573
      PROS OF KAFKA
      • 122
        High-throughput
      • 116
        Distributed
      • 87
        Scalable
      • 81
        High-Performance
      • 65
        Durable
      • 36
        Publish-Subscribe
      • 19
        Simple-to-use
      • 15
        Open source
      • 10
        Written in Scala and java. Runs on JVM
      • 6
        Message broker + Streaming system
      • 4
        Avro schema integration
      • 2
        Suport Multiple clients
      • 2
        Robust
      • 2
        KSQL
      • 2
        Partioned, replayable log
      • 1
        Fun
      • 1
        Extremely good parallelism constructs
      • 1
        Simple publisher / multi-subscriber model
      • 1
        Flexible
      CONS OF KAFKA
      • 27
        Non-Java clients are second-class citizens
      • 26
        Needs Zookeeper
      • 7
        Operational difficulties
      • 2
        Terrible Packaging

      related Kafka posts

      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2M views

      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:

      #DataScience #DataStack #Data

      See more
      John Kodumal

      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.

      See more
      RabbitMQ logo

      RabbitMQ

      14.7K
      12.8K
      518
      Open source multiprotocol messaging broker
      14.7K
      12.8K
      + 1
      518
      PROS OF RABBITMQ
      • 229
        It's fast and it works with good metrics/monitoring
      • 79
        Ease of configuration
      • 58
        I like the admin interface
      • 50
        Easy to set-up and start with
      • 20
        Durable
      • 18
        Intuitive work through python
      • 18
        Standard protocols
      • 10
        Written primarily in Erlang
      • 8
        Simply superb
      • 6
        Completeness of messaging patterns
      • 3
        Scales to 1 million messages per second
      • 3
        Reliable
      • 2
        Better than most traditional queue based message broker
      • 2
        Distributed
      • 2
        Supports AMQP
      • 1
        Inubit Integration
      • 1
        Supports MQTT
      • 1
        Runs on Open Telecom Platform
      • 1
        High performance
      • 1
        Reliability
      • 1
        Clusterable
      • 1
        Clear documentation with different scripting language
      • 1
        Great ui
      • 1
        Better routing system
      • 1
        Delayed messages
      CONS OF RABBITMQ
      • 9
        Too complicated cluster/HA config and management
      • 6
        Needs Erlang runtime. Need ops good with Erlang runtime
      • 5
        Configuration must be done first, not by your code
      • 4
        Slow

      related RabbitMQ posts

      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 1.3M views
      Shared insights
      on
      CeleryCeleryRabbitMQRabbitMQ
      at

      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

      See more
      Yogesh Bhondekar
      Co-Founder at weconnect.chat · | 15 upvotes · 93.7K views

      Hi, I am building an enhanced web-conferencing app that will have a voice/video call, live chats, live notifications, live discussions, screen sharing, etc features. Ref: Zoom.

      I need advise finalizing the tech stack for this app. I am considering below tech stack:

      • Frontend: React
      • Backend: Node.js
      • Database: MongoDB
      • IAAS: #AWS
      • Containers & Orchestration: Docker / Kubernetes
      • DevOps: GitLab, Terraform
      • Brokers: Redis / RabbitMQ

      I need advice at the platform level as to what could be considered to support concurrent video streaming seamlessly.

      Also, please suggest what could be a better tech stack for my app?

      #SAAS #VideoConferencing #WebAndVideoConferencing #zoom #stack

      See more
      Amazon SQS logo

      Amazon SQS

      1.9K
      1.6K
      166
      Fully managed message queuing service
      1.9K
      1.6K
      + 1
      166
      PROS OF AMAZON SQS
      • 60
        Easy to use, reliable
      • 39
        Low cost
      • 27
        Simple
      • 13
        Doesn't need to maintain it
      • 8
        It is Serverless
      • 4
        Has a max message size (currently 256K)
      • 3
        Easy to configure with Terraform
      • 3
        Triggers Lambda
      • 3
        Delayed delivery upto 15 mins only
      • 3
        Delayed delivery upto 12 hours
      • 1
        JMS compliant
      • 1
        Support for retry and dead letter queue
      • 1
        D
      CONS OF AMAZON SQS
      • 2
        Has a max message size (currently 256K)
      • 2
        Proprietary
      • 2
        Difficult to configure
      • 1
        Has a maximum 15 minutes of delayed messages only

      related Amazon SQS posts

      Praveen Mooli
      Engineering Manager at Taylor and Francis · | 14 upvotes · 2M views

      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

      See more
      Tim Specht
      ‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 620.9K views

      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

      See more
      Celery logo

      Celery

      1.3K
      1.3K
      265
      Distributed task queue
      1.3K
      1.3K
      + 1
      265
      PROS OF CELERY
      • 94
        Task queue
      • 61
        Python integration
      • 37
        Django integration
      • 29
        Scheduled Task
      • 18
        Publish/subsribe
      • 6
        Easy to use
      • 6
        Various backend broker
      • 5
        Great community
      • 4
        Workflow
      • 4
        Free
      • 1
        Dynamic
      CONS OF CELERY
      • 4
        Sometimes loses tasks
      • 1
        Depends on broker

      related Celery posts

      James Cunningham
      Operations Engineer at Sentry · | 18 upvotes · 1.3M views
      Shared insights
      on
      CeleryCeleryRabbitMQRabbitMQ
      at

      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

      See more
      Pulkit Sapra

      Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.

      See more
      ActiveMQ logo

      ActiveMQ

      447
      1.1K
      76
      A message broker written in Java together with a full JMS client
      447
      1.1K
      + 1
      76
      PROS OF ACTIVEMQ
      • 18
        Easy to use
      • 14
        Open source
      • 13
        Efficient
      • 10
        JMS compliant
      • 6
        High Availability
      • 5
        Scalable
      • 3
        Support XA (distributed transactions)
      • 3
        Persistence
      • 2
        Distributed Network of brokers
      • 1
        Highly configurable
      • 1
        Docker delievery
      • 0
        RabbitMQ
      CONS OF ACTIVEMQ
      • 1
        Support
      • 1
        Low resilience to exceptions and interruptions
      • 1
        Difficult to scale

      related ActiveMQ posts

      I want to choose Message Queue with the following features - Highly Available, Distributed, Scalable, Monitoring. I have RabbitMQ, ActiveMQ, Kafka and Apache RocketMQ in mind. But I am confused which one to choose.

      See more
      Naushad Warsi
      software developer at klingelnberg · | 1 upvote · 610.8K views
      Shared insights
      on
      ActiveMQActiveMQRabbitMQRabbitMQ

      I use ActiveMQ because RabbitMQ have stopped giving the support for AMQP 1.0 or above version and the earlier version of AMQP doesn't give the functionality to support OAuth.

      If OAuth is not required and we can go with AMQP 0.9 then i still recommend rabbitMq.

      See more
      MQTT logo

      MQTT

      351
      387
      4
      A machine-to-machine Internet of Things connectivity protocol
      351
      387
      + 1
      4
      PROS OF MQTT
      • 2
        Varying levels of Quality of Service to fit a range of
      • 1
        Very easy to configure and use with open source tools
      • 1
        Lightweight with a relatively small data footprint
      CONS OF MQTT
      • 1
        Easy to configure in an unsecure manner

      related MQTT posts