DistributedLog vs Kestrel

Overview

Kestrel

Stacks37

Followers58

Votes0

DistributedLog

Stacks21

Followers39

Votes0

DistributedLog vs Kestrel: What are the differences?

DistributedLog: High-performance replicated log service, by Twitter. DistributedLog (DL) is a high-performance, replicated log service, offering durability, replication and strong consistency as essentials for building reliable distributed systems; Kestrel: Simple, distributed message queue system. Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM.

DistributedLog and Kestrel can be categorized as "Message Queue" tools.

Some of the features offered by DistributedLog are:

High Performance
Durable and Consistent
Efficient Fan-in and Fan-out

On the other hand, Kestrel provides the following key features:

Written by Robey Pointer
Starling clone written in Scala (a port of Starling from Ruby to Scala)
Queues are stored in memory, but logged on disk

DistributedLog and Kestrel are both open source tools. Kestrel with 2.8K GitHub stars and 326 forks on GitHub appears to be more popular than DistributedLog with 2.25K GitHub stars and 283 GitHub forks.

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Detailed Comparison

Kestrel	DistributedLog
Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM.	DistributedLog (DL) is a high-performance, replicated log service, offering durability, replication and strong consistency as essentials for building reliable distributed systems.
Written by Robey Pointer;Starling clone written in Scala (a port of Starling from Ruby to Scala);Queues are stored in memory, but logged on disk	High Performance;Durable and Consistent;Efficient Fan-in and Fan-out;Various Workloads;Multi Tenant;Layered Architecture
Statistics
Stacks 37	Stacks 21
Followers 58	Followers 39
Votes 0	Votes 0

What are some alternatives to Kestrel, DistributedLog?

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

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.

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.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

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.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.