Serverless applications are intricate, distributed architectures of functions and API calls, and are enclosed within ephemeral containers. Since serverless architecture segregates the code from the infrastructure that is running it, traditional observability practices and tools are not competent enough to identify and handle the technical challenges associated with a serverless architecture. In such a scenario, there is a need for specialized tools to verify the performance of a serverless system, to make sure that customers are provided with the right service at the right SLA, that errors are being tracked and handled as required, and that logic and business flows are correctly in place.
Achieving Serverless-Observability
An observable system allows the internal state of all its components to be externally observable. This is usually achieved through instrumentation, which enables you to ask various questions regarding the working of your software. If the functions you’ve written and the services they are using aren’t well-instrumented, it will be difficult to know what’s going on with your applications. Consequently, the first step toward achieving serverless-observability is generating your system’s observable data, namely, logs, metrics, and tracers.Â
Logs are an immutable and verbose representation of discrete events that have happened over time, and are used for debugging, auditing, and analyzing system behavior. Metrics provide time-series measurements specific to the application/environment (e.g., CPU, memory metrics), module/layer (e.g., cache, DynamoDB metrics), or domain (e.g., user metrics). Tracers provide end-to-end visibility into requests throughout the entire processing chain. Tracers are used to identify the location of performance bottlenecks, detect which components lead to errors, and debug the entire request flow for domain-level issues.Â
Manual instrumentation is tedious and error-prone; however, one way to make instrumentation easier is through open-source tools such as OpenTelemetryOpens a new window . This tool features a set of provider-independent libraries and APIs that can be utilized to instrumentate your serverless applications.
The next step is to present observability data cohesively. ZipkinOpens a new window and JaegerOpens a new window are two of the best tools for open-source tracing visualization and can aid you in presenting readable formats of generated traces and metrics. Even though instrumenting your serverless functions helps improve visibility into the system’s health, extremely high data volume sent to the observability system might complicate debugging and affect client-side latency.
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Addressing the Challenges
Cloud Provider Monitoring Tools
To aggregate metrics and logs through monitoring and management services, you can use cloud vendor consoles such as Amazon’s AWS CloudWatchOpens a new window and Google’s Cloud Operations SuiteOpens a new window . Amazon’s CloudWatch provides a metrics repository for monitoring AWS cloud resources and the applications that run on AWS and presents the data it collects as a comprehensive dashboard. AWS CloudWatch also lets you put your custom metrics into the repository and then retrieve statistics based on those metrics.Â
Google’s Cloud Operations Suite provides real-time log management and analyses, thereby giving you the ability to monitor, troubleshoot, and improve application performance. It includes a wide variety of tools for monitoring and debugging GCP and Lambda applications, along with a query language for identifying trends and uncovering patterns. However, these tools alone do not provide a complete understanding of connections across different log entries. The more complex the distributed system, the more difficult understanding connections between the components will be.
Log Streaming to an External Service
A single-pane view log metrics across distributed systems is facilitated by SplunkOpens a new window or Solarwinds’ LogglyOpens a new window , which are well-known log aggregation platforms. These platforms also analyze data to generate baseline performance profiles useful in detecting suspected anomalies and alerting for the same. Developers can generate logs for these tools using any log aggregation service and run related queries and searches as well.Â
In the event of the failure of a function, not only can the corresponding log be searched, but logs related to other functions can also be found. However, similar to cloud provider monitoring tools, even log aggregation platforms are unable to provide sufficient understanding of any relationship between an event and a trigger, which, in the end, makes troubleshooting an extremely difficult and tedious task.
Function-Level Monitoring
Tools meant for function-level monitoring, such as AWS X-RayOpens a new window , allow the automatic integration and instrumentation of Lambda functions, thereby giving an end-to-end functional-level view of requests while they move through systems. With X-Ray’s end-to-end tracing capabilities, you can analyze how Lambda functions and the connected services are performing. You would also be able to pinpoint and troubleshoot the root cause of any performance issue or error and view a map of your application’s underlying components.
The main drawback of a function-level monitoring tool is that it only measures the metrics of functions and only on an individual basis. For example, function-level monitoring can detect if a particular function is experiencing a disproportionately high number of cold starts, resulting in its latency. However, it cannot see application-level issues, like when a user has abandoned a shopping cart due to a transaction taking too much time. As numerous moving parts need to be tracked, function-level monitoring is unable to provide business flow insights.
Automated Distributed Tracing Solutions
Achieving application-level observability will require distributed tracing. When a user interacts with an application, the request across all the components involved is tracked with distributed tracing. For this purpose, you can choose from several third-party distributed tracing platforms. While some platforms automatically identify issues and send alerts, others will require you to search for events manually. In other words, some platforms automatically discover system components and their interdependencies, while others will need you to instrument your code manually. Some platforms will go beyond just your code and provide insights into the whole system. Some platforms come with a single-pane console, and some will need you to aggregate information from other sources, for example, the dashboard of your cloud provider.
Learn More: The Hottest Trend in the Cloud Evergreen: Amazon Lambda Leads in Serverless Computing
Here are 6 popular monitoring platforms that developers can use to get a centralized view across their distributed serverless systems.
6 Best Tools to Bridge the Serverless-Observability Gap
DashbirdOpens a new window
Dashbird is a serverless monitoring platform that works on top of AWS CloudWatch. Databird automatically collects data from your applications, providing a centralized hub for performance tracking and alerts. The platform actively analyzes logs and metrics for any errors and changes in application performance.
EpsagonOpens a new window
This is a serverless monitoring and troubleshooting platform based on the concept of distributed tracing. It automatically discovers the components of your serverless system and the relationships between them, thereby providing actionable insights into logic and business flows. Besides root cause analysis, Epsagon uses advanced artificial intelligence (AI) methods and predicts any issue before it occurs.
LumigoOpens a new window
Lumigo is a serverless monitoring platform that provides a ‘Visual System Map’ with simple filters to ensure full observability with traces, logs, and metrics of a specific transaction in one place. It uses machine learning and heuristic analysis to alert you to issues that are likely to impact the lifecycle of serverless applications.
New RelicOpens a new window
New Relic is a full-stack observability platform that monitors Lambda-based event-driven architectures, including infrastructure and digital experience. It provides developers with real-time metrics, customizable alerts, tracing, filtered searches, and profiling and serverless monitoring app capabilities, along with proactive incident detection.
Splunk Observability SuiteOpens a new window
The Splunk Observability Suite is an end-to-end observability platform for serverless applications that offers tracing and automated incident response. Splunk provides insights into cloud-native applications and microservices architecture with real-time visibility and performance monitoring. It includes a tightly integrated user experience, enabling seamless and context-rich workflows for monitoring, troubleshooting, and investigation. It provides end-to-end visibility based on open ingestion and correlation of all data, including metrics, traces, and logs. It also includes a streaming analytics engine to detect issues in real-time and AI-driven analytics to provide actionable insights from your data.
ThundraOpens a new window
Thundra is an end-to-end serverless-observability platform that provides distributed system monitoring without increasing execution time latency and works on top of AWS X-Ray. Its drop-in libraries, integrated with the AWS Lambda execution layer, enable fully automated observability, facilitating the automatic instrumentation of your functions at each point in their lifecycle. It uses machine learning to understand application behavior patterns to identify and blacklist anomalies automatically.
Closing the Serverless Observability Gap
Serverless-observability throws up unique challenges for developers. CloudWatch and X-Ray, which are cloud provider tools, give a good start in achieving observability. However, they come with various limitations and can be costly if you are designing a completely observable system. You can use open-source tools to acquire a complete picture of your application, but they turn out to be labor-intensive if implemented at scale.
Third-party tools, such as those listed above, will take you a step ahead in closing the serverless-observability gap, facilitating the perfect mix of manual and automated observation techniques. This will allow developers to spend less time on writing instrumentation and more time on writing functional code.
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