Best Practices for Effective Log Management

Can following log management best practices help organizations with their overall observability, as well as troubleshooting issues and security analytics?

Absolutely.

In addition, following log management best practices can provide significant competitive advantages when it comes to understanding your users. Centralized log management can help your team accelerate time to insights, and make changes to your applications that improve the user experience.

In this week’s blog, you’ll discover eight log management best practices that can help your team optimize customer experiences and capitalize on your full revenue potential, while avoiding common logging mistakes.

8 Application Logging Best Practices

1. Implement Structured Logging
2. Build Meaning and Context into Log Messages
3. Avoid Logging Non-essential or Sensitive Information
4. Capture Logs from Diverse Sources
5. Aggregate and Centralize Your Logs Collected
6. Index Logs for Querying and Analytics
7. Monitor Logs and Configure Real-Time Alerts
8. Optimize Your Log Retention Policy

1. Implement Structured Logging

The traditional way of logging is to write event logs as plain text into a log file. The problem with this method is that plain text logs are an unstructured data format, which means they can’t easily be filtered or queried to extract insights.

As an alternative to traditional logging, organizations should implement structured logging and write their logs in a format like JSON or XML that’s easier to parse, analyze and query. Logs written in JSON are easily readable by both humans and machines, and structured JSON logs are easily tabularized to enable filtering and queries.

Structured logging saves time, accelerates insight development, and helps organizations maximize the value of their log data as they optimize their applications and infrastructure.

Watch this quick demo to see how ChaosSearch handles JSON logs:

2. Build Meaning and Context into Log Messages

Log messages should include meaningful information about the event that triggered the log, as well as additional context that can help analysts understand what happened, find correlations with other events, and diagnose potential issues that require further investigation.

Meaningful logs are descriptive and detailed, providing DevSecOps teams with useful information that can help streamline the diagnostic process when an error occurs.

Valuable context for log messages can include fields like:

• Timestamps - Knowing the exact date and time that an event occurred allows analysts to filter and query for other events that happened in the same time frame.
• User Request Identifiers - Requests from client browsers to the web server have a unique identifier code that may be included in logs for events triggered by the request.
• Unique Identifiers - Organizations assign unique identifiers for individual users, products, user sessions, pages, shopping carts, and more. These data points can be written into event logs, providing valuable context and insight into the state of the application when the event occurred.

3. Avoid Logging Non-essential or Sensitive Information

Deciding what to include in log messages is just as important as determining what can be left out. Logging non-essential information that doesn’t help with diagnostics or root cause analysis results in increased time-to-insights, log levels, and costs.

It’s also important to avoid logging sensitive information, especially proprietary data, application source codes, and personally identifiable information (PII) that may be covered by data privacy and security regulations or standards like the European GDPR, HIPAA, or PCI DSS.

Organizations can optimize customer experiences by logging data from individual user sessions, but instead of logging the user’s name and email with each event, we recommend assigning each User/Session a unique identifier that conceals their identity while still enabling analysts to effectively correlate events by session or user.

Read: How to Drive Observability Cost Savings without Sacrifices

4. Capture Logs from Diverse Sources

As IT environments grow in complexity, DevOps teams have the potential to capture logs from tens or even hundreds of different sources. For cloud native teams, serverless log management presents its own set of challenges, including the sheer volume of log data generated. And while not all of these logs may be deemed essential, capturing the right logs can provide meaningful data and valuable context when it comes to detecting and diagnosing errors.

Organizations should think about capturing logs from:

• Infrastructure Devices - Logs from switches, routers, and network access points can help digital retailers diagnose misconfiguration issues that might be causing slow-downs for their customers.
• Security Devices - Security log analytics is essential during peak events, such as traffic spikes. Logs from firewalls and intrusion detection systems enable SecOps teams to quickly detect and respond to security concerns before they result in costly unplanned downtime.
• Web Servers - Web server logs are essential for capturing information about how users interact with your digital properties. They can help both DevOps and marketing teams optimize the customer experience by understanding when users visit the site, where they come from, and the actions they take upon arrival.
• Applications - Logs from payment gateways, analytics tools, databases, and mobile apps can help DevOps teams pinpoint errors for rapid resolution.
• Cloud Infrastructure - The logs generated by cloud infrastructure and services can help DevOps teams gain insight into cloud service availability and performance, resource allocation, and latency issues.

When it comes to optimizing results, organizations should focus their logging efforts on operations that are closely tied to revenue and customer experience, including the shopping cart, checkout process, email registration system, and authentication.

5. Aggregate and Centralize Your Logs Collected

Log data is generated at many different points in the IT infrastructure, but it must be aggregated in a centralized location before it can be used effectively for data analysis.

As IT systems generate logs, your log aggregator tool (e.g. Logstash, Graylog, etc.) should automatically ingest those logs and ship them out of the production environment and into a centralized location (e.g. public cloud storage, or a log management tool). Some teams centralize logs in popular observability platforms like Datadog, but may find themselves constrained by costs or Datadog log management challenges. These teams may find it more cost-effective to aggregate logs in cloud object storage, such as Amazon S3.

Aggregating and centralizing log data gives developer teams the ability to investigate security or application performance issues without having to manually extract, organize, and prepare log data from potentially hundreds of different sources. This can be particularly effective for serverless log management in AWS, where there is a high volume of logs collected.

6. Index Logs for Querying and Analytics

As enterprise IT environments increase in complexity, they generate massive volumes of log data that can take a long time to query. Indexing your logs creates a new data representation that’s optimized for query efficiency, enabling enterprise DevOps and data teams to more readily solve problems and extract value from their logs.

DevOps teams may choose log indexing engines like Elasticsearch or Apache Solr to index their logs, but these engines may encounter performance issues or Devops data retention trade-offs when analyzing logs at scale.

Shameless plug: ChaosSearch’s proprietary Chaos Index® technology indexes logs directly in Amazon S3 and Google Cloud Storage with up to 95% data compression, enabling text, relational, and ML queries that help organizations get the most value from their log data.

Read: Best Practices for Modern Enterprise Data Management in Multi-Cloud World

7. Monitor Logs and Configure Real-Time Alerts

When the stakes are high, issues in the production environment need to be discovered and addressed right away. That’s never more true than during peak traffic surges, when even a few minutes of unplanned service interruption can result in thousands of dollars in lost revenue.

DevSecOps teams can configure their log management systems or SIEM tools to monitor the stream of ingested logs and alert on known errors or anomalous events that could signal a security incident or application performance issue.

Alerts can be routed directly to the mobile phones and/or Slack accounts of incident response teams, enabling rapid detection, diagnosis, and resolution of errors, and minimizing their impact on the customer journey.

8. Optimize Your Log Retention Policy

Enterprises should set different retention policies for different types of logs, depending on their unique needs and circumstances.

In some cases, preserving logs for the long-term is required to comply with local data protection regulations. You may also want to retain certain logs past the standard 90-day retention period to support long-term analysis of application performance or user behaviors.

Organizations can use historical logs and trend data to anticipate traffic spikes, forecast the number of expected users, and optimize their architecture, systems, and staffing to deliver the best possible customer experience during peak demand periods.

Future-proof with Database Logging Best Practices

Hopefully these eight tips will help you plan for log data spikes during peak times for your industry. And as you think through a long-term strategy for log analytics, consider partnering with ChaosSearch to give your SRE team peace of mind.

The ChaosSearch database logging platform enables log analytics at scale, with less toil and at lower cost, while taking advantage of all the reliability and security that comes with the cloud.

ChaosSearch is the best database for logging, indexing logs directly in your Amazon S3 or Google Cloud Storage buckets, preserving every detail of your log data with up to 95% compression, no data movement, and low cost of ownership. The platform enables multi-API data access, making your logs available for text search, relational (SQL) analytics, and machine learning queries using the tools your team already knows and loves (for instance, Kibana).

Companies ranging from financial services giants like Equifax to gaming companies like Cloud Imperium Games use ChaosSearch to detect and investigate errors that impact the customer journey, forecast peak demand times using historical log data, and analyze user session logs to improve overall customer experience.

You’re welcome to give the platform a try to see how ChaosSearch can help you future-proof your business.