research-blog | A Ghost Theme that is similar to Distill.pub 's style | Theme library

by sdan JavaScript Version: 2.1 License: MIT

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kandi X-RAY | research-blog Summary

research-blog is a JavaScript library typically used in User Interface, Theme applications. research-blog has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

A Ghost Theme that is similar to Distill.pub's style.

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research-blog has a low active ecosystem.

It has 4 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 12 months.

research-blog has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of research-blog is 2.1

Quality

research-blog has no bugs reported.

Security

research-blog has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

research-blog is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

research-blog releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

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research-blog Key Features

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research-blog Examples and Code Snippets

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Community Discussions

Trending Discussions on research-blog

What 's difference for the latency between Storm and Spark Streaming when dealing with tuples\messages?

QUESTION

What 's difference for the latency between Storm and Spark Streaming when dealing with tuples\messages?

Asked 2017-May-30 at 04:21

1,Based on the description below, Both Storm and Spark Streaming dealing with the messages/tuples in batch or small/micro batch? https://storm.apache.org/releases/2.0.0-SNAPSHOT/Trident-tutorial.html

2,If the answer for the above question is yes, it means both technologies have the delay when dealing with the messages/tuples ? If that's the case why I heard often that latency for the Storm is better then Spark Streaming ,such as the below article? https://www.ericsson.com/research-blog/data-knowledge/apache-storm-vs-spark-streaming/

3,From the Trident-tutorial it describes that : "Generally the size of those small batches will be on the order of thousands or millions of tuples, depending on your incoming throughput." So what's the really size of the small batch? thousands or millions of tuples?If it is , how Storm can keep the short latency?

https://storm.apache.org/releases/2.0.0-SNAPSHOT/Trident-tutorial.html

...

ANSWER

Answered 2017-May-30 at 04:21

Storm's core api tries to process an event as it arrives. Its an event at a time processing model which can result in very low latencies.

Storm's Trident is a micro batching model built on top of the storm's core apis for providing exactly-once guarantees. Spark streaming is also based on micro batching and comparable to trident in terms of latencies.

So if one is looking for extremely low latency processing Storm's core api would be the way to go. However this guarantees only at-least once processing and theres a chance of receiving duplicate events in case of failures and the application is expected to handle this.

Take a look at the streaming benchmark from yahoo [1], that can provide more insights.

[1] https://yahooeng.tumblr.com/post/135321837876/benchmarking-streaming-computation-engines-at

Source https://stackoverflow.com/questions/44233693

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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