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Observability

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Microservices for the Enterprise

Abstract

Collecting data is cheap, but not having it when you need it can be expensive. In March 2016, Amazon was down for 20 minutes and the estimated revenue loss was $3.75 million. Also in January 2017, there was a system outage at Delta Airlines, which caused cancellation of more than 170 flights and resulted in an estimated loss of $8.5 million. In both cases if we had the right level of data collected, we could have predicted such behavior or could have recovered from it as soon as it has happened by identifying the root cause. The more information we have, the better decisions we can make.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Observability

  2. 2.

    https://cloud.spring.io/spring-cloud-sleuth/

  3. 3.

    https://storage.googleapis.com/pub-tools-public-publication-data/pdf/36356.pdf

  4. 4.

    https://www.slf4j.org/

  5. 5.

    https://zipkin.io/

  6. 6.

    https://www.jaegertracing.io/

  7. 7.

    http://opentracing.io/

  8. 8.

    https://docs.docker.com/config/containers/logging/fluentd/

  9. 9.

    Stdout (standard output) is the default file descriptor where a process can write output.

  10. 10.

    https://www.fluentd.org/plugins/all#input-output

  11. 11.

    https://www.elastic.co/guide/en/logstash/

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© 2018 Kasun Indrasiri and Prabath Siriwardena

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Indrasiri, K., Siriwardena, P. (2018). Observability. In: Microservices for the Enterprise. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3858-5_13

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