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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Beanstalkd vs Redis

Beanstalkd vs Redis

OverviewComparisonAlternatives

Overview

Redis
Redis
Stacks61.9K
Followers46.5K
Votes3.9K
GitHub Stars42
Forks6
Beanstalkd
Beanstalkd
Stacks111
Followers161
Votes74

Beanstalkd vs Redis: What are the differences?

Introduction

Beanstalkd and Redis are both widely used message queue systems, but there are key differences between the two.

  1. Storage: Beanstalkd is primarily a simple queue, with the messages being stored in memory until they are processed by a worker. Redis, on the other hand, is a data structure server that supports various data types, including lists and sets, and the messages can be stored in memory or on disk.

  2. Message Priority: Beanstalkd does not have built-in support for message priority. All messages have equal priority and are processed in a first-in-first-out manner. In Redis, messages can be assigned different priorities using sorted sets, allowing for more complex message processing and ordering.

  3. Message Persistence: Beanstalkd does not provide persistent storage by default. If the server goes down, the messages in the queue are lost. Redis, on the other hand, provides options for configuring persistence, allowing messages to be stored on disk and surviving server restarts.

  4. Pub/Sub System: Redis has a built-in publish/subscribe system, allowing messages to be broadcasted to multiple subscribers. Beanstalkd does not have native support for pub/sub, although it can be emulated using multiple queues and workers.

  5. Atomic Operations: Redis supports atomic operations on data structures, allowing multiple operations to be executed as a single unit. This makes it useful for building complex message processing workflows. Beanstalkd, however, does not support atomic operations, and each operation is executed individually.

  6. Message Timeout: Beanstalkd allows messages to have a timeout value, after which they are automatically released back into the queue for reprocessing. Redis does not have a built-in timeout mechanism for messages, and handling message timeouts requires additional logic.

In Summary, Beanstalkd is a simple queue with no message priority, limited persistence, and no native pub/sub support. Redis, on the other hand, is a data structure server with support for message priority, persistence, pub/sub, atomic operations, and more complex message handling capabilities.

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Detailed Comparison

Redis
Redis
Beanstalkd
Beanstalkd

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.

Beanstalks's interface is generic, but was originally designed for reducing the latency of page views in high-volume web applications by running time-consuming tasks asynchronously.

Statistics
GitHub Stars
42
GitHub Stars
-
GitHub Forks
6
GitHub Forks
-
Stacks
61.9K
Stacks
111
Followers
46.5K
Followers
161
Votes
3.9K
Votes
74
Pros & Cons
Pros
  • 888
    Performance
  • 542
    Super fast
  • 514
    Ease of use
  • 444
    In-memory cache
  • 324
    Advanced key-value cache
Cons
  • 15
    Cannot query objects directly
  • 3
    No secondary indexes for non-numeric data types
  • 1
    No WAL
Pros
  • 23
    Fast
  • 12
    Free
  • 12
    Does one thing well
  • 9
    Scalability
  • 8
    Simplicity

What are some alternatives to Redis, Beanstalkd?

Sidekiq

Sidekiq

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.

Hazelcast

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Aerospike

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

SAP HANA

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

VoltDB

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Hangfire

Hangfire

It is an open-source framework that helps you to create, process and manage your background jobs, i.e. operations you don't want to put in your request processing pipeline. It supports all kind of background tasks – short-running and long-running, CPU intensive and I/O intensive, one shot and recurrent.

Resque

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.

Tarantool

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

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