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Learning ELK Stack

You're reading from   Learning ELK Stack Build mesmerizing visualizations, analytics, and logs from your data using Elasticsearch, Logstash, and Kibana

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781785887154
Length 206 pages
Edition 1st Edition
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Author (1):
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Saurabh Chhajed Saurabh Chhajed
Author Profile Icon Saurabh Chhajed
Saurabh Chhajed
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Toc

Prevention of data loss


Data loss prevention is critical for a production system, as monitoring and debugging is largely dependent on each and every log event to be present in the system; otherwise, whole analytics or the debugging system will fail, and we end up losing some of the important events in our system.

Data loss can be prevented using a message broker in front of the Logstash indexers. Message brokers, such as Redis, prove to be useful when dealing with a large stream of data, as Logstash may slow down while indexing data to Elasticsearch. Redis can help in these situations where it can buffer the data while Logstash is busy indexing to Elasticsearch. It also adds a layer of resiliency where if indexing fails, events are held in a queue instead of getting lost. ZeroMQ, RabbitMQ, AMQP can also be used as a broker in place of Redis.

For example, the following architecture can be useful:

ELK Architecture with message broker

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