Traces
Last updated
Last updated
Traces are a powerful , providing granular insights into microservice interactions. Traditionally, they were hard to implement, requiring coordination of multiple teams and constant code changes, making this critical aspect very challenging to maintain.
groundcover's eBPF sensor disrupts the famous tradeoff, empowering developers to gain full visibility into their applications, effortlessly and without any code changes.
The platform supports two kinds of traces:
All services that took part in the interaction (both client and server)
Accessed resource
Full payloads, including:
All headers
All query parameters
All bodies - for both the request and response
These can be ingested into the platform, allowing to leverage already existing instrumentation to create a single pane of glass for all of your traces.
Traces are stored in groundcover's ClickHouse deployment, ensuring top notch performance on every scale.
groundcover further disrupts the customary traces experience by reinventing the concept of sampling. This innovation differs between the different types of traces:
These are generated by using 100% of the data, always processing every request being made, on every scale. However, the groundcover platform utilizes smart sampling to only store a fraction of the traces, while still generating an accurate picture. In general, sampling is performed according to these rules:
Requests with unusually high or low latencies, measured per resource
Requests which returned an error response (e.g 500 status code for HTTP)
"Normal" requests which form the baseline for each resource
These traces are always ingested fully - meaning, no sampling is applied to traces which have been generated elsewhere and ingested by the platform.
When integrating 3rd-party traces, it is often wise to configure some sampling mechanism according to the specific use case.
Each trace is enriched with additional information to give as much context as possible for the service which generated the trace. This includes:
Container information - image, environment variables, pod name
Logs generated by the service around the time of the trace
Kubernetes events relevant to the service
CPU and Memory utilization of the service and the node it is scheduled on
One of the advantages of ingesting 3rd-party traces is the ability to leverage their distributed tracing feature. groundcover natively displays the full trace for ingested traces in the Traces page.
Trace Attributes enable advanced filtering and search capabilities. groundcover support attributes across all trace types. This encompasses a diverse range of protocols such as HTTP, MongoDB, PostgreSQL, and others, as well as varied sources including eBPF or manual instrumentations (for example - OpenTelemetry).
Each attribute can be effortlessly integrated into your filters and search queries. You can add them directly from the trace side-panel with a simple click or input them manually into the search bar.
Example: If you want to filter all HTTP traces that contain the path "/products". The query would be formatted as: @http.path:"/products". For a comprehensive guide on the query syntax, see Syntax table below.
Trace Tags enable advanced filtering and search capabilities. groundcover support tags across all trace types. This encompasses a diverse range of protocols such as HTTP, MongoDB, PostgreSQL, and others, as well as varied sources including eBPF or manual instrumentations (for example - OpenTelemetry).
Tags are powerful metadata components, structured as key-value pairs. They offer insightful information about the resource generating the span, like: container.image.name ,host.name and more.
Tags include metadata enriched by the our sensor and additional metadata if provided by manual instrumentations (such as OpenTelemetry traces) . Utilizing these Tags enhances understanding and context of your traces, allowing for more comprehensive analysis and easier filtering by the relevant information.
Each tag can be effortlessly integrated into your filters and search queries. You can add them directly from the trace side-panel with a simple click or input them manually into the search bar.
Example: If you want to filter all traces from mysql containers - The query would be formatted as: container.image.name:mysql. For a comprehensive guide on the query syntax, see Syntax table below.
The Trace Explorer integrates dynamic filters and a versatile search functionality, to enhance your trace data analysis. You can filter out traces using specific criteria, including trace-status, workload, namespace and more, as well as limit your search to a specific time range.
Tracing can be customized in several ways:
These traces are automatically generated for every service in your stack. They are available out-of-the-box and within seconds of installation. These traces always include critical information such as:
For more details about ingesting 3rd party traces, see the .
Lastly, is utilized to make the sampling decisions on the node itself, without having to send or save any redundant traces.
Certain aspects of our sampling algorithm are configurable - read more .
of the resource around the time of the trace
groundcover enriches your original traces and generates meaningful metadata as key-value pairs. This metadata includes critical information, such as protocol type, http.path, db.statement, and similar attributes, aligning with OTel conventions. Furthermore, groundcover seamlessly incorporates this metadata from spans received through supported manual instrumentations. For an in-depth understanding of attributes in OTel, please refer to (external link to OpenTelemtry website).
groundcover natively supports setting up log pipelines using This allow for full flexibility in the processing and manipulation of traces being collected - parsing additional patterns by regex, renaming attributes, and many more.
groundcover allows full control over the retention of your traces. to learn more.
