Expand description
Amazon Managed Service for Prometheus is a serverless, Prometheus-compatible monitoring service for container metrics that makes it easier to securely monitor container environments at scale. With Amazon Managed Service for Prometheus, you can use the same open-source Prometheus data model and query language that you use today to monitor the performance of your containerized workloads, and also enjoy improved scalability, availability, and security without having to manage the underlying infrastructure.
For more information about Amazon Managed Service for Prometheus, see the Amazon Managed Service for Prometheus User Guide.
Amazon Managed Service for Prometheus includes two APIs.
- Use the Amazon Web Services API described in this guide to manage Amazon Managed Service for Prometheus resources, such as workspaces, rule groups, and alert managers.
- Use the Prometheus-compatible API to work within your Prometheus workspace.
§Getting Started
Examples are available for many services and operations, check out the examples folder in GitHub.
The SDK provides one crate per AWS service. You must add Tokio
as a dependency within your Rust project to execute asynchronous code. To add aws-sdk-amp
to
your project, add the following to your Cargo.toml file:
[dependencies]
aws-config = { version = "1.1.7", features = ["behavior-version-latest"] }
aws-sdk-amp = "1.67.0"
tokio = { version = "1", features = ["full"] }
Then in code, a client can be created with the following:
use aws_sdk_amp as amp;
#[::tokio::main]
async fn main() -> Result<(), amp::Error> {
let config = aws_config::load_from_env().await;
let client = aws_sdk_amp::Client::new(&config);
// ... make some calls with the client
Ok(())
}
See the client documentation for information on what calls can be made, and the inputs and outputs for each of those calls.
§Using the SDK
Until the SDK is released, we will be adding information about using the SDK to the Developer Guide. Feel free to suggest additional sections for the guide by opening an issue and describing what you are trying to do.
§Getting Help
- GitHub discussions - For ideas, RFCs & general questions
- GitHub issues - For bug reports & feature requests
- Generated Docs (latest version)
- Usage examples
§Crate Organization
The entry point for most customers will be Client
, which exposes one method for each API
offered by Amazon Prometheus Service. The return value of each of these methods is a “fluent builder”,
where the different inputs for that API are added by builder-style function call chaining,
followed by calling send()
to get a Future
that will result in
either a successful output or a SdkError
.
Some of these API inputs may be structs or enums to provide more complex structured information.
These structs and enums live in types
. There are some simpler types for
representing data such as date times or binary blobs that live in primitives
.
All types required to configure a client via the Config
struct live
in config
.
The operation
module has a submodule for every API, and in each submodule
is the input, output, and error type for that API, as well as builders to construct each of those.
There is a top-level Error
type that encompasses all the errors that the
client can return. Any other error type can be converted to this Error
type via the
From
trait.
The other modules within this crate are not required for normal usage.
Modules§
- client
- Client for calling Amazon Prometheus Service.
- config
- Configuration for Amazon Prometheus Service.
- error
- Common errors and error handling utilities.
- meta
- Information about this crate.
- operation
- All operations that this crate can perform.
- primitives
- Primitives such as
Blob
orDateTime
used by other types. - types
- Data structures used by operation inputs/outputs.
- waiters
- Supporting types for waiters.
Structs§
Enums§
- Error
- All possible error types for this service.