Course Introduction & Intro to Logging
Google Cloud Logging
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Logging is very important today given the volume and variety of data we deal with across different customer use cases. This course will enable you to take a more proactive approach towards identifying faults and crashes in your applications through the effective use of Google Cloud Logging. As a result, you will learn how to delegate the operational overhead to GCP through automated logging tools, resulting in a more productive operational pipeline for your team and organization.
Through this course, you will equip yourself with the required skills for streaming log data to Google Cloud Logging service and use metrics to understand your system's behavior. The course will start with an introduction to the Cloud Logging Service and then demonstrate how to stream logs using Cloud Logging Agent and the Python client library.
To get the most out of this course, you should already have an understanding of application logging.
This course is suited for anyone interested in logging using Google Cloud Platform (GCP) Cloud Logging.
- Source code for this course: https://github.com/cloudacademy/Managing-Application-Logs-and-Metrics-on-GCP
- Google Cloud fluentd source code: https://github.com/GoogleCloudPlatform/google-fluentd
- Google Cloud fluentd additional configurations: https://github.com/GoogleCloudPlatform/fluentd-catch-all-config
- Google Cloud fluentd output plugin configuration: https://cloud.google.com/logging/docs/agent/logging/configuration#cloud-fluentd-config
- Package release history: https://pypi.org/project/google-cloud-logging/#history
- Metrics Explorer pricing: https://cloud.google.com/stackdriver/pricing#metrics-chargeable
Hello, and welcome back. So far, we have learned to stream log messages from our application to GCP. So now let's talk about generating monitoring metrics from this data. Log-based metrics are created from the content of the log data. For example, a metric can show the number of log entries containing a particular error message or generated by a particular application. We can use this metric to create charts in cloud monitoring and create alert policies.
There are two types of log-based metrics, system-defined log-based metrics and user-defined log-based metrics. System-defined log based metrics are predefined by Google for use by all GCP projects. For example, compute.googleapis.com/firewall/dropped_packets_count These metrics provides the count of incoming bytes dropped by the firewall. User-defined log-based metrics are created by a user within a GCP project based on their requirement. For example, counting the log entries based on a custom match filter. From the pricing perspective at the time of recording this video, system-defined log-based metrics are non-chargeable and user-defined log-based metrics are chargeable. For the latest pricing, please refer the Google Cloud documentation linked below in the description. Now let's move to the next section to see the log-based metrics in action.
Pradeep Bhadani is an IT Consultant with over nine years of experience and holds various certifications related to AWS, GCP, and HashiCorp. He is recognized as HashiCorp Ambassador and GDE (Google Developers Expert) in Cloud for his knowledge and contribution to the community.
He has extensive experience in building data platforms on the cloud and as well as on-premises through the use of DevOps strategies & automation. Pradeep is skilled at delivering technical concepts helping teams and individuals to upskill on the latest technologies.