Dynatrace PurePath doesn’t just trace requests; it reconstructs the entire distributed transaction, even across technologies that have never "talked" to each other before.
Let’s see it in action. Imagine a user clicking "Add to Cart" on an e-commerce site. This single click triggers a cascade:
- Browser: JavaScript initiates an AJAX call to the
/cart/addendpoint. - Load Balancer: Directs the request to one of many web server instances.
- Web Server (Nginx): Receives the request and forwards it to a Java application server.
- Java Application (Spring Boot):
- Validates the request.
- Calls a user service (likely a separate microservice) to get user details.
- Calls a product service to get product information.
- Updates the cart in a Redis cache.
- Persists the cart to a PostgreSQL database.
- Publishes an event to Kafka for downstream processing (e.g., inventory update).
- Kafka: The message is published.
- Inventory Service (Python): Consumes the Kafka message and decrements inventory in its own database.
A traditional APM tool might show you the Nginx logs, the Java application’s method timings, or the Redis cache hit. PurePath stitches all of this together into a single, waterfall-like view. You see the Nginx request, the Java service calls, the Redis interaction, the PostgreSQL query, and the Kafka publish, all correlated by a single transaction ID, and crucially, with the actual time spent in each component.
The core problem PurePath solves is the "black box" nature of modern distributed systems. When a user reports a slow "Add to Cart" experience, the problem could be anywhere: network latency, a misbehaving microservice, a slow database query, or even an issue in the browser’s JavaScript. Pinpointing the bottleneck requires visibility across all these layers.
PurePath achieves this through a combination of bytecode instrumentation (for Java, .NET, etc.), kernel-level network monitoring, and intelligent log correlation. For technologies it can’t instrument directly (like pure JavaScript frontends or certain network appliances), it uses sophisticated techniques to infer and link events. For example, it can correlate an AJAX request from the browser with the incoming request on the web server by matching timing and URL patterns, even if no explicit tracing headers are present initially. When tracing headers are present (like traceparent from W3C Trace Context), it leverages them to link spans across services.
The key to understanding PurePath is realizing it’s not just about what happened, but when and where. Each segment in the PurePath view represents a discrete unit of work. For a Java service, this could be a method call. For a database, it’s the execution of a query. For network traffic, it’s the time spent transferring data between hosts. Dynatrace automatically injects context (like transaction IDs and span IDs) into network payloads where possible or infers them based on timing and context. This allows it to reconstruct the entire flow, even if services don’t explicitly pass tracing information.
What most people miss is how PurePath handles "uninstrumented" or "dark" technologies. If you have a legacy C++ service or a proprietary message queue that Dynatrace can’t directly instrument with its agents, PurePath can still trace through it. It observes the network traffic entering and leaving that component. By correlating the timestamp and payload of the incoming request with the outgoing request, it can accurately measure the time spent within that opaque component, even without an agent installed. This means you can see, for instance, that a request spent 500ms inside a "black box" service, and then drill down into the network traffic and logs associated with that specific timeframe to investigate further, rather than just seeing a "gap" in your trace.
The next concept to explore is how PurePath leverages AI (Davis) to automatically analyze these traces and pinpoint the root cause of performance degradation.