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Python Microservices Development

You're reading from   Python Microservices Development Build, test, deploy, and scale microservices in Python

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785881114
Length 340 pages
Edition 1st Edition
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Table of Contents (20) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Introduction
1. Understanding Microservices FREE CHAPTER 2. Discovering Flask 3. Coding, Testing, and Documenting - the Virtuous Cycle 4. Designing Runnerly 5. Interacting with Other Services 6. Monitoring Your Services 7. Securing Your Services 8. Bringing It All Together 9. Packaging and Running Runnerly 10. Containerized Services 11. Deploying on AWS 12. What Next?

Summary


In this chapter, we've seen how to add some instrumentation in our microservices and at the web server level. We've also learned how to set up Graylog to centralize and use all the generated logs and performance metrics.

Graylog uses Elasticsearch to store all the data, and that choice offers fantastic search features that will make your life easier to look for what's going on. The ability to add alerts is also useful for being notified when something's wrong. But deploying Graylog should be considered carefully. An Elastic Search cluster is heavy to run and maintain once it has a lot of data.

For your metrics, time-series based systems such as ;InfluxDB (open source) from InfluxData (https://www.influxdata.com/) is a faster and lightweight alternative. But it's not meant to store raw logs and exceptions.

So if you just care about performance metrics and exceptions, maybe a good solution would be to use a combination of tools: Sentry for your exceptions and InfluxDB for tracking performances...

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