Stacks3.2K

Followers2.6K

Votes460

simplehttp

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Followers4

Votes0

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Kafka vs simplehttp: What are the differences?

What is Kafka? Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

What is simplehttp? A family of libraries and daemons built upon libevent that make high performance HTTP servers simple and straightforward to write. .

Kafka and simplehttp can be primarily classified as "Message Queue" tools.

Kafka and simplehttp are both open source tools. It seems that Kafka with 12.7K GitHub stars and 6.81K forks on GitHub has more adoption than simplehttp with 480 GitHub stars and 73 GitHub forks.

What is Kafka?

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

What is simplehttp?

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Why do developers choose Kafka?

Why do developers choose simplehttp?

97
High-throughput
93
Distributed
75
Scalable
67
High-Performance
59
Durable
29
Publish-Subscribe
16
Simple-to-use

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What are the cons of using Kafka?

What are the cons of using simplehttp?

12
Needs Zookeeper
10
Non-Java clients are second-class citizens
2
Operational difficulties

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What companies use Kafka?

What companies use simplehttp?

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What tools integrate with Kafka?

What tools integrate with simplehttp?

No integrations found

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What are some alternatives to Kafka and simplehttp?

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.

RabbitMQ

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

Amazon Kinesis

Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Akka

Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM.

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Decisions about Kafka and simplehttp

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Reviews of Kafka and simplehttp

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How developers use Kafka and simplehttp

Pinterest uses

Kafka

Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.