Ensuring that systems are available, performant, and secure is dependent upon maintaining and monitoring the application and managing it. Checking the status of applications, logs, metrics and configurations is a very, very time-consuming task for IT teams. Python scripting offers a good way to automate and improve many application monitoring and maintenance tasks.

Python is a general-purpose, interpreted language that is designed to be readable and very fast to develop. Since Python has large standard libraries, a vast amount of community support, and can integrate with many technologies, it is a good language to use to write scripts to monitor and maintain applications.

In this article, we will discuss how Python can be used to monitor and maintain key applications. We will also examine the best practices for writing a maintainable Python script and show you some ways of using Python for application performance monitoring, log analytics and automation.

Benefits of Using Python for Scripting

There are several key reasons why Python is an excellent choice for scripting application monitoring and maintenance tasks:

• Easy to learn. The learning curve of Python is gentler than that of lower-level system languages. It has a clear and concise syntax and semantics that are intended for readability. This also makes Python scripts easier to write and maintain.
• Quick prototyping. Python is interpreted and dynamic, and it has a large library with lots of support for rapid iterative development. Prototyping scripts can be done quickly, and the scripts can evolve as needed.
• Portable. Python runs on all major operating systems. Scripts written on one OS can generally be reused on others with little to no modification.
• Scalable. Python transparently handles very high workloads without needing to change code. Scripts can scale up to handle increasing monitoring and maintenance work.
• Extensive libraries. Python includes extensive standard libraries and third-party packages for virtually any task, including system/IT automation, monitoring, log analysis, APIs, machine learning, and much more.
• Interoperability. Python provides native integration and libraries for interfacing with many data stores, protocols, APIs, applications, and tools. This is extremely useful for monitoring heterogeneous environments.

In summary, Python makes it far easier and faster to develop, prototype and maintain custom scripts for application monitoring and maintenance. And Python scales seamlessly as needs grow. If you're interested in diving deeper, you can learn more through this insightful article on automation in application management.

Best Practices for Maintainable Python Scripts

A good side of Python is its flexibility, but it also encourages developers to develop creativity in style and structure. This can make it difficult to maintain Python scripts in the long term if the original developers are not available.

Here are some key best practices that can dramatically improve maintainability of Python scripts:

1. Modularity. The code should be expressed in small, logical, self-contained modules which do one thing at a time. Implementation details should be hidden behind modules, and the interfaces should be clear.
2. Naming conventions. Names of all code constructs should be clear and self-documenting with PEP8 style guidelines. Avoid single-letter variables. Use underscores and capitalization to make the code more readable.
3. Comments. Explain the approach, complex logic, external dependencies and the reasons behind structural decisions with judicious comments. Don't comment on obvious code.
4. Error handling. Check input data validity. Catch and handle exceptions appropriately. Use logging to capture errors rather than fail silently .istent: PEP guidelines code.
5. Version control. This maintains all in-version system Git code control. This histoPreservesrves supports auditing and enables rolling back problematic changes.
6. Reusability. Structure code to maximize reuse. Use parameters/configs rather than hard-coding values. Design code to be imported as modules.
7. Testing. Create unit and integration tests covering key functionality. Run tests before deploying updates. Tests allow safer refactoring over time.
8. Documentation. Document at module, class, and function levels. Include usage, parameters, return values, exceptions raised etc. Keep documentation current.
9. Simplicity. Avoid premature optimization and over-engineering. Start simple. Based on usage, refactor to enhance robustness only as needed.

In addition to the extensive standard libraries, Python comes with a large number of third-party packages for virtually any task, including system/IT automation, monitoring, log analysis, APIs, machine learning, and more.

Using Python for Application Performance Monitoring

Application performance monitoring (APM) is important to verify that the system fulfills performance and scalability requirements. APM does real-time visibility over operational metrics such as request rates, response times, error rates etc. However, native APM agent capabilities are often limited, especially for custom applications. Python scripting fills these gaps by extending monitoring as needed.

In order to appreciate some of the usage of Python scripts to enhance the performance monitoring of your application, here are a few examples.

Custom Metrics

The platform metrics tracked by APM systems include CPU usage, memory, network I/O etc. However, to monitor the application accurately, custom metrics like order rate, concurrent sessions, cache hit ratio etc., are required. Python scripts can periodically extract such metrics from application logs, databases, and  APIs and push them to the APM system.

Synthetic Transactions

APM systems passively monitor real user transactions. Synthetic transactions should, however, be used to monitor critical user journeys actively. Python scripts can automate sophisticated synthetic transactions with business logic, data setup, delays and validation checks.

Alert Enhancements

Standard metrics are simply detected with native APM alerts when a threshold is breached. Python scripts can enhance the ability to aggregate metrics, perform multi-condition checks, create seasonal profiles, implement intelligent alert suppression techniques like state flapping detection, and integrate alerting with ITSM/messaging systems for notifications.

Correlation Analysis

While APM charts show metric values over time, they don’t indicate the causes of the issues. Python automation can implement temporal correlation analysis to detect and alert on probable causes, such as code releases automatically, config changes, etc., that chronologically align with performance changes.

Capacity Planning

Insufficient capacity causes most performance issues. Python scripts can capture live workload metrics such as peak concurrent users, average response times, etc., store historical demand data, and forecast future capacity requirements using linear regression and auto-recommending capacity changes.

In summary, Python scripting delivers powerful APM enhancements that are difficult to achieve using APM system capabilities alone.

Using Python for Log Analysis 

An application's logs provide valuable operational insights, but manually parsing myriad log files is infeasible. Python empowers IT teams to analyze logs at scale easily to identify issues, optimize systems, and strengthen security.

Here are some examples of using Python for log analytics:

Real-time Parsing

Python can be used to ingest real-time streaming (omah_log in our case). Log data can be streamed to Python to parse, filter and route to the appropriate analytics systems (Elasticsearch and Splunk). Log processing pipelines are implemented in scripts as a series of steps to extract fields, handle formatting invariances, mask out sensitive data, and add external metadata.

Operational Analytics

Application logs can be aggregated in Python scripts to produce key trends like error rates, response times, and so forth, along with anomaly detection algorithms that are run to automatically detect abnormal events, which can then be used to perform data-driven troubleshooting as well as capacity planning.

Intrusion Detection

Correlating events between application and network logs is necessary for security analytics as it is needed to discover sophisticated threats and zero-day attacks that can not be discovered from individual logs. Python automation frameworks streamline implementation, such as Security Information and Event Management (SIEM) pipelines.

Log Retention

Log volumes grow exponentially and require careful retention policies to balance compliance, analytics value and storage costs. Python scripts codify such policies and handle compression, archiving, and deletion of aged logs in on-prem/cloud storage per defined lifecycles.

Audit Trails

However, many applications do not have native support for detailed audit trailing of admin actions, access attempts, configuration changes, etc., which are necessary for security, compliance and forensics. Python scripts are used to capture, normalize,e and persist in the full suite of audit event streams.

In summary, Python is invaluable for unlocking operational intelligence and security insights from application logs in ways that are difficult to scale otherwise.

Using Python for IT Process Automation

Maintaining applications involves considerable repetitive manual work across domains, such as deployments, patching, backups, integrity checks and more. Python helps overcome human bottlenecks through comprehensive IT process automation.

Let's explore some examples:

Application Deployments

Python scripts are used to increase CI/CD pipeline capabilities, including dynamically creating configs, executing pre/post deployment validations, using highly sophisticated deployment strategies (canary, blue green etc), integrating with ITSM ticketing and automatically rolling back failed deployments.

Configuration Drift

They are caused by drifting application configurations over time. Periodically, Python automation snaps production configurations, compares them to versioned baselines, detects changes, and notifies (or automatically remediates) them.

Integrity Checks

File integrity monitoring is essential to detect malicious or accidental modifications to binaries, configs and web assets. Python scripts help implement robust integrity checks that integrate with cloud APIs, Git, build tools, and other tools to track authorized changes.

Backups and Cloning

Python assists legacy and custom applications lacking native cloud-native backup/restore capabilities by scripting mechanisms to snapshot, backup, clone and restore application volumes on demand or by schedule in environments like VMware.

Housekeeping Tasks

When it comes to helping to keep systems healthy, python codifies the tedious housekeeping tasks of cleaning up temporary files or logs, archiving or deleting old data, refreshing test data, recycling resources, updating DNS or SSL certificates, and checking availability on schedules or events.

In summary, Python enables easily automating wide-ranging IT operations and maintenance workflows to minimize disruptions, strengthen security, and boost productivity.

Conclusion

Application monitoring and maintenance are very laborious, tedious, error-prone and hard-to-scale tasks that require a significant amount of human effort. Python provides comprehensive automation to overcome these challenges through an ideal scripting solution.

Python allows you to extend APM and log analytics systems by extracting custom metrics, performing synthetic checks, finding anomalies, and more. On the IT infrastructure side, it also excels at automating deployments, configuration management, integrity checks, backups and other similar tasks.

In order for this to be a sustainable project, Python scripting best practices need to be adopted, such as modular design, rigorous testing and thorough documentation; however, libraries of well-structured Python scripts multiply force, increasing efficiency gains over time.

Python scripting finally provides significant value to application monitoring by providing faster operational insights and systematizing maintenance. By using Python, IT teams can concentrate on innovation and not on repetitive tasks. True, the future of Python is very bright in simplifying application management!