# Cloud Profiler overview
Understanding the performance of production systems is notoriously difficult.
Attempting to measure performance in test environments usually fails to
replicate the pressures on a production system. Microbenchmarking parts
of your application is sometimes feasible, but it also typically fails to
replicate the workload and behavior of a production system.
Continuous profiling of production systems is an effective way to discover
where resources like CPU cycles and memory are consumed as a service operates
in its working environment. But profiling adds an additional load on the
production system: in order to be an acceptable way to discover patterns
of resource consumption, the additional load of profiling must be small.
Cloud Profiler is a statistical, low-overhead profiler that
continuously gathers CPU usage and memory-allocation information from your
production applications. It attributes that information to the source code
that generated it, helping you identify the parts of your application that are
consuming the most resources, and otherwise illuminating your applications
performance characteristics.
## Types of profiling available
Cloud Profiler supports different types of profiling based on
the language in which a program is written. The following table summarizes
the supported profile types by language:
| Profile type | Go | Java | Node.js | Python |
|---|---|---|---|---|
| CPU time | Y | Y | | Y |
| Heap | Y | Y | Y | |
| Allocated heap | Y | | | |
| Contention | Y | | | |
| Threads | Y | | | |
| Wall time | | Y | Y | Y |
For complete information on the language requirements and any restrictions,
see the language's how-to page.
For more information about these profile types, see
[Profiling concepts](https://docs.cloud.google.com/profiler/docs/concepts-profiling.md.txt).
## Supported configurations
When you instrument your application to capture profile data, you include a
language-specific
[profiling agent](https://docs.cloud.google.com/profiler/docs/about-profiler.md.txt#profiling_agent).
The following table summarizes the supported environments:
| Environments | Go | Java | Node.js | Python |
|---|---|---|---|---|
| Compute Engine | Y | Y | Y | Y |
| Google Kubernetes Engine | Y | Y | Y | Y |
| App Engine flexible environment | Y | Y | Y | Y |
| App Engine standard environment | Y | Y | Y | Y |
| Managed Service for Apache Spark | | Y | | |
| Dataflow | | Y | | Y |
| Outside of Google Cloud | Y | Y | Y | Y |
The following table summarizes the supported operating systems:
| Operating systems | Go | Java | Node.js | Python |
|---|---|---|---|---|
| Linux `glibc` implementation of the standard C library | Y | Y | Y | Y |
| Linux `musl` implementation of the standard C library | Y | Y (Alpha) | Y | Y (Alpha) |
## Performance impact
Cloud Profiler creates a single **profile** by collecting profiling data,
usually for 10 seconds, every 1 minute for a single instance of the configured
service in a single Compute Engine zone. If, for example, your
GKE
service runs 10 replicas of a pod, then, in a 10-minute period,
roughly 10 profiles are created, and each pod is profiled
approximately once. The profiling period is randomized, so there is
variation. See [Profile collection](https://docs.cloud.google.com/profiler/docs/concepts-profiling.md.txt#collection) for more information.
The overhead of the CPU and heap allocation profiling at the time of the
data collection is less than 5 percent. Amortized over the execution time
and across multiple replicas of a service, the overhead is commonly less
than 0.5 percent, making it an affordable option for always-on profiling
in production systems.
## Components
Cloud Profiler consists of the profiling agent, which collects
the data, and a console interface on Google Cloud, which lets you
view and analyze the data collected by the agent.
### Profiling agent
You install the agent on the virtual machines where your application
runs. The agent typically comes as a library that you attach to your
application when you run it. The agent collects profiling data as the app runs.
For information on running the Cloud Profiler agent, see:
- [Profiling Go applications](https://docs.cloud.google.com/profiler/docs/profiling-go.md.txt)
- [Profiling Java applications](https://docs.cloud.google.com/profiler/docs/profiling-java.md.txt)
- [Profiling Node.js applications](https://docs.cloud.google.com/profiler/docs/profiling-nodejs.md.txt)
- [Profiling Python applications](https://docs.cloud.google.com/profiler/docs/profiling-python.md.txt)
- [Profiling applications running outside Google Cloud](https://docs.cloud.google.com/profiler/docs/profiling-external.md.txt)
### Profiler interface
After the agent has collected some profiling data, you can use the
Profiler interface to see how the statistics for CPU and memory
usage correlate with areas of your application.
The profile data is retained for 30 days, so you
can analyze performance data for periods up to the last
30 days. Profiles can be downloaded for long-term
storage.
## Quotas and limits
For information on viewing and managing your Profiler quotas,
see [Quotas and limits](https://docs.cloud.google.com/profiler/quotas.md.txt).
## Data security
Cloud Profiler is a VPC Service Controls supported service. For more
information, see
[VPC Service Controls documentation](https://docs.cloud.google.com/vpc-service-controls/docs.md.txt).
## Analyzing data
After Profiler has collected data, you can view and analyze
this data using the Profiler interface.
In the Google Cloud console, go to the **Profiler** page:
[Go to **Profiler**](https://console.cloud.google.com/profiler)
You can also find this page by using the search bar.