Observability Tools for Development Teams: A Practical Guide
Observability is a crucial component of building powerful applications. It provides the engineering teams with essential insights into a system’s performance, health, and behavior, enabling quicker issue resolution based on real-time feedback.
Selecting the right tools is a key part of implementing observability. Without a well-established observability infrastructure, your application could be at risk of downtime or significant disruptions, which can result in a poor customer experience and lost revenue.
This practical guide has been designed to help you construct your observability infrastructure. It will help you understand the different types of observability tools and platforms, their advantages, use cases, and how to effectively integrate them into your software development processes.
The importance of observability tools in development
As observability-driven development (ODD) gains momentum, teams are exploring innovative ways to utilize observability tools right from the early phases of development.
While observability has traditionally been a primary focus for Operations and System Reliability Engineers (SRE), developers are now adopting these practices to address code-related issues as well. This shift represents a broader integration of observability into the entire software development lifecycle.
How developers use observability tools
Traditionally, operations teams have relied on observability tools to ensure the smooth operation of applications. They focus on monitoring logs, metrics, and traces to maintain performance and detect any irregularities or threats.
On the other hand, developers are increasingly using observability tools for a variety of purposes, including:
- Identifying and optimizing problematic code
- Gaining deeper insights into API behavior
- Understanding how code changes can impact the application’s behavior before and after deployment
- Proactively testing for potential issues and validating dependencies between components
This shift in the use of observability tools by developers reflects their broader integration into the development process.
The observability-driven development paradigm shift
Observability-Driven Development (ODD) encourages a proactive approach to system visibility and reliability by integrating observability tools into the development process from the very beginning.
This shift in focus aims to:
- Enhance System Reliability: ODD helps ensure that applications are designed for easy monitoring and diagnosis, increasing system reliability not just after deployment but throughout the entire development lifecycle.
- Collaboration: It fosters better collaboration and workflows between development and operations teams. By using observability tools, both teams can work together to identify and resolve issues, creating a more efficient and productive development environment.
- Predictive Insights: ODD enables developers to make informed predictions about system behavior. This includes predicting anomalies, identifying components that may fail, and estimating future resource requirements. These insights allow teams to plan and address potential issues before they become critical, enhancing the overall robustness of the application.
Types of observability solutions
The toolkit you create should match your observability requirements, the intricacy of your system, and your financial resources. Multiple solutions are intended to aid with observability, but the distinctions between them may not be immediately obvious.
To begin, let’s examine some of these differences.
Single-point tools versus observability platforms
Single-purpose tools are designed to focus on specific aspects of the system or particular parts of the application.
Usually, monitoring covers three types of data:
- Metrics — Metrics tools gather quantitative data to monitor resource utilization and system response times.
- Logs — Logging tools record system events, errors, warnings, and informational messages, assisting in troubleshooting, auditing, and debugging.
- Traces — Distributed tracing tools provide the flow of requests and transactions, pinpointing bottlenecks, dependencies, and latency issues.
However, modern systems have become increasingly complex, with millions of lines of code and countless interconnected components running in containerized environments and across various cloud services. Engineering teams often require multiple tools to conduct thorough monitoring.
With the growth of observability platforms, this telemetry data is gathered and squeezed into a unified source of truth. These platforms integrate multiple observability features, providing a comprehensive view of the system’s status.
Understanding the difference between application observability and data observability
Another differentiation to consider regarding observability technologies is the distinction between application and data observability.
Application observability tools assist DevOps teams in preventing downtime and offer developers valuable insights for comprehending, troubleshooting, and enhancing software systems. Typical applications include managing incidents, analyzing root causes, and resolving issues.
On the other hand, data observability tools have a more specific focus. They examine data tables to evaluate data volumes, structures, and distribution, supplying Data Engineers with insights into data quality and trust levels.
Features to consider in application observability solutions
Application observability tools and platforms offer a range of features. When considering your choices, it’s important to consider the capabilities required to enhance visibility into performance and infrastructure.
Centralized dashboards and visualizations
Observability is based on the ability to interpret real-time information from the application. Centralized dashboards and visualization choices reduce the need to sift through vast amounts of data to extract insights and establish connections.
Alerting and notifications
As a software engineer, receiving timely alerts for critical issues is indispensable, and it’s important to avoid getting overwhelmed by irrelevant information. Application observability tools should offer robust alerting capabilities, allowing you to establish rules and thresholds for sending notifications when needed.
Automated anomaly detection
For a complex architecture with data originating from various sources, having infrastructure that enables anomaly detection is required.
Observability tools utilize Machine Learning algorithms to automatically detect and notify you of unusual or abnormal behavior.
Automated service instrumentation
Integrating services to capture the necessary logs, traces, and metrics can be a time-consuming and intricate task. It often requires advanced configuration. It’s advisable to explore tools that automate this process, simplifying the setup and maintenance of your observability infrastructure.
Traceability across services
When your applications are distributed or rely on microservices, it’s important to ensure that your selected tools provide distributed tracing features to monitor requests across services. Trace visualization enables you to follow the complete path of requests and identify the precise locations of failures.
Popular observability tools & platforms
Many vendors offer a wide array of observability solutions, encompassing Application Performance Management (APM) software, distributed tracing, log analytics, and data visualizations.
Full-stack observability platforms like AppDynamics, Datadog, Dynatrace, and New Relic provide comprehensive APM monitoring that covers both infrastructure and applications, some even extending their support to databases and network monitoring. These platforms offer integration with various data sources and container orchestration systems.
You might also want to explore open-source alternatives like Prometheus, Jaeger, OpenTelemetry, or OpenSearch.
In the realm of log analytics and management, tools like Loggly and Sumo Logic enable the collection, centralization, and querying of log data, along with advanced analysis, alerting, and visualization features.
As for data visualization, Grafana stands out as a versatile dashboarding tool, seamlessly compatible with a wide range of data sources, including Raspberry Pi devices and services like Google Sheets, enabling the creation of informative and customizable dashboards.
Additionally, it’s worth mentioning that major cloud providers such as AWS, Google Cloud, and Azure offer their own built-in observability services. AWS CloudWatch and Google Cloud Monitoring are examples of these cloud-native observability tools.
Use cases and benefits for businesses and engineering teams
Application observability and the associated tools introduce a fresh perspective on software development and maintenance. It extends beyond simply addressing issues reactively and shifts towards proactively enhancing performance and continually refining the system through data-driven insights.
Improving customer experience
Observability solutions offer valuable insights into the areas that require optimization, ultimately enhancing the user experience.
In the quest to introduce customer experience innovations, such as VR shopping experiences and AI-powered products, the observability of customer experience becomes a critical element in maintaining the reliability and user-friendliness of these technologies.
Optimizing cost and performance
System failures come at a high cost, ranging from the resources expended in identifying the root cause of incidents to the damage inflicted on a brand’s reputation.
A rich observability infrastructure allows organizations to reduce these expenses. Moreover, it enables more precise predictions of storage and computing resource requirements, helping eliminate wasteful expenses.
Enhancing modern application security
When security and IT teams lack cooperation, defending systems against attacks becomes significantly more challenging. Full-stack observability tools offer a common ground by better integrating runtime application security into CI/CD pipelines, both in development and production settings.
By coordinating observability-driven development with security measures, engineering teams can enhance their responsiveness to exploits and threats. This synergy provides a stronger defense against potential security breaches.
Gaining better control over complex systems
Cloud-native technologies and distributed infrastructures, such as microservices and containerized components, are now the standard. However, these stacks bring new challenges, including the substantial volume of data they generate and the increased complexity of their management.
Observability plays a vital role in enabling engineering teams to filter through this complexity and gain a clear understanding of how performance issues are affecting key metrics. It facilitates a more precise estimation of the impact of these challenges on the system.
How leading companies use observability solutions
Puma detects order issues in minutes leading to $10k per hour in boosted revenue
Puma, with its extensive history in sports apparel sales, operates more than 45 eCommerce websites. Like any eCommerce vendor, dealing with failed orders can pose a significant challenge, resulting in dissatisfied customers and missed sales opportunities.
Puma successfully utilized an application observability solution to quicken the identification and resolution of order-related problems. This faster incident investigation led to a remarkable reduction in response time, down to just 15 minutes, and a substantial revenue increase to $10,000 per hour.
Kmart Australia ensures 99.9% uptime through Application Performance Monitoring
Kmart Australia, one of the largest retail department store chains in the country, ventured on a digital transformation journey. They adopted an APM observability solution, which significantly enhanced their visibility and control over their infrastructure and systems.
This move resulted in an impressive accomplishment — a consistent 99.9% uptime maintained over a six-month period. This substantial uptime improvement enabled them to efficiently handle high-demand events like Black Friday and holiday shopping periods.
Key steps for setting up your observability infrastructure
Once you’ve chosen an observability tool or platform, the next step is to implement and seamlessly integrate it into your existing workflows. This process demands meticulous planning to ensure that you can maximize the benefits of your investment.
1. Get clear on metrics and data collection strategy
Observability allows you to monitor a wide range of factors, from bandwidth utilization to the energy consumption of cooling systems for efficient operation.
To start, define what you intend to track and the sources from which this information will originate. Examine the various data sources, including applications, servers, databases, and network devices, and ensure that your infrastructure is equipped to accommodate these monitoring requirements.
This initial step is critical for setting up an effective observability system.
2. Integrate observability tools in your tech stack
For a successful implementation of your chosen observability tools, you must instrument your applications to begin generating the desired observability data. This may include using libraries or SDKs to capture relevant metrics and events, although some observability solutions offer automated instrumentation.
A robust observability toolkit should offer effortless integration with popular programming languages and frameworks, cloud service providers, and container orchestration systems. This ensures a smooth and effective data collection process for monitoring and analysis.
3. Define team responsibilities and workflows
The size and complexity of your software project will determine whether you assign an individual or a team to manage your observability environment and ensure its smooth operation.
Smaller organizations with small teams or relatively straightforward architectures may rely on one or two software developers who also handle these tools.
However, as organizations and projects expand, the responsibility for observability often transitions to specialized roles, such as Observability Managers or Site Reliability Engineers, to address the growing needs and intricacies of observability management.
4. Configure real-time monitoring and alerting
As a software engineer, you don’t want to miss potential incidents or threats. On the flip side, sorting through a flood of alerts, some of which may not require a response, can lead to monitoring fatigue.
An integral aspect of implementing observability is the accurate configuration of monitoring and alerting systems.
Automate alert responses by setting appropriate thresholds, and ensure your observability tools are configured to capture data accurately. This helps strike a balance between staying vigilant and preventing alert overload.
5. Ensure your observability infrastructure is scalable
As data volumes, system complexity, and traffic grow, your observability solution should possess the capacity to adapt. Implementing auto-scaling for your cloud infrastructure enables automatic adjustments to allocated resources based on demand.
For further insights, you can explore our article detailing how we assisted a partner fintech company in reducing monthly infrastructure expenses by up to 50% through effective auto-scaling.
Want to build highly observable applications?
Observability tools provide engineering teams with the capability to monitor system performance throughout the development lifecycle, ultimately helping with the creation of more powerful applications.
In this approach, observability becomes an integral component of the process of developing and deploying high-quality code, assisting in the identification of the root causes of issues.
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