One thing you may have discovered with Heroku logging is that making sense of data within your log files can be time-consuming. When you’re running large numbers of applications which process tons of data each day, the sheer volume of data that appears in the logs can seem overwhelming.
Managing Massive Log OutputÂ
Due to the sheer amount of data that is gathered, it’s important to find ways of making the process a little bit easier, limit information to what is useful and organize what you have into digestible pieces.
Parsing with Drains
Using Logplex and log drains can help you filter out large chunks of your logs. Syslog drains will help you move any system messages to another server for archiving purposes. HTTPS drains can help you better take control of your system messaging for sending it to third-party apps for analysis.Â
By assigning clear indicators that identify the application, system or API that creates a log, these can be streamed into channels that can be better understood in relation to their given purposes.Â
Log Retention Period
While we may think that more information is always good, there is a limit to the amount that we can process. Some log data becomes meaningless or redundant after a certain length of time. It is wise to set realistic log retention periods on data based on business needs.
Limit Verbosity
Don’t log everything. Turning on debugging is appropriate during development and if there is a specific problem that needs investigating, but in most cases, these shouldn’t be stored.Â
Complexity in Stream Patterns
The challenge with large amounts of data is that there may be subtle differences between successful activity and erratic behavior. The details may be small, and essentially invisible to the naked eye unless examined carefully.Â
Use Regex for Identifying Patterns
Through the use of log analytics tools, you can set these to pick up specified regex patterns to subtle changes or variations in your logs that can provide meaningful insights.Â
Different Log Data Needs
You may have people with different needs who wish to make use of the information you gain in your Heroku logs. While you have data that is of interest to developers and QA engineers, there are other tech-savvy users who may wish to gain business intelligence (BI) from the massive output of data, to be able to identify key performance indicators (KPI).
Analytics Tools
Using intelligent add-on analytics applications can help by filtering and sorting information into usable chunks that can be understood by users in different roles. By accurately filtering, you can provide intelligent real-time alerts for a specific application or system behavior to those who may wish to make use of it.Â
Application Issue Forecasting
One of the best potential uses of your detailed Heroku logs involves the opportunities to spot patterns of problems that have occurred in the past and use them to predict problems that may occur in the future.
As an expert, you may be able to spot patterns in your logs, however, even the smartest and most attentive engineers will not be able to manually define every scenario worth detecting.
Occasionally patterns will occur that are only detectable through the rote process of comparing and contrasting working behavior with non-working behavior. Often, the subtleties are too small for a human to be able to notice the difference.
Machine Learning Assistance
Naturally, utilizing ML-powered techniques can help catch the unknowns. By investing in ML, using some of the better libraries or choosing a Heroku logging add-on with these capabilities, you can monitor your applications and infrastructure with less effort.Â
Securing Sensitive Data
With the rapid pace of CI/CD, a large amount of activity regularly flows through your pipelines from development into production. Throughout this process, it’s possible for sensitive data to start appearing in logs. While your teams may be diligent about keeping control over OAuth credentials, security keys, authentication tokens and more, nobody is perfect. Sensitive security credentials can accidentally make their way onto production servers. Sensitive customer data can also accidentally find its way into public logs if mistakes are made.
Credential Detection and Security Alerts
Logs can serve as a saving point for the identification of sensitive information. Using a parsing engine, these can be immediately redacted for compliant log management. By monitoring your log files for any patterns which could include secret keys or other potential security breaches, you’ll be able to continue ingesting critical log data without increasing business risk.
Heroku Private Spaces
With Heroku Enterprise, you can make use of Heroku Private Spaces which are an isolated network of private apps and data services, which will help you keep sensitive data separate from public interference. If you are handling a large amount of sensitive customer data, ensuring that any information gets filtered into these places will help you remain compliant with GDPR and other privacy-related regulations.  Â
Conclusion
Heroku can be a fantastic solution for monitoring the activity which goes on in your cloud clusters. However, unless you have solid analytical tools in place, you may find yourself spending a lot of time wondering how to make use of this sea of data that has been collected. Consider using an add-on as an analytic layer to help spot patterns, organize data better and create smart alerts to make sure your operations continue to run smoothly.
