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Version: 3.6.5

Resoto Metrics

Resoto Metrics (resotometrics) takes Resoto Core graph data and runs aggregation functions on the data.

The aggregated metrics are then exposed in a Prometheus-compatible format.

Resoto Metrics Docker image
somecr.io/someengineering/resotometrics:3.6.5

Usage

Once started resotometrics will register for generate_metrics core events. When such an event is received it will generate Resoto metrics and provide them at the /metrics endpoint.

A Prometheus config could look like this:

scrape_configs:
- job_name: "resotometrics"
static_configs:
- targets: ["localhost:9955"]

Options

OptionDescriptionDefault
--subscriber-id <SUBSCRIBER_ID>Unique subscriber IDresoto.worker
--psk <PSK>Pre-shared key
--verbose, -vVerbose logging
--quietOnly log errors
--resotocore-uri <RESOTOCORE_URI>Resoto Core URIhttps://localhost:8900
--override CONFIG_OVERRIDE [<CONFIG_OVERRIDE> ...]Override config attribute(s)
--ca-cert <CA_CERT>Path to custom CA certificate file
--cert <CERT>Path to custom certificate file
--cert-key <CERT_KEY>Path to custom certificate key file
--cert-key-pass <CERT_KEY_PASS>Passphrase for certificate key file
--no-verify-certsTurn off certificate verification

Environment Variables

CLI options can also be set via environment variables. The environment variable name is the same as the option name, but in uppercase with the prefix RESOTOMETRICS_ and dashes replaced by underscores.

For example, --verbose would become RESOTOMETRICS_VERBOSE=true.

Details

Resoto Core supports aggregated queries to produce metrics. The common library resotolib defines a number of base resources that are common to a lot of cloud proviers, like say compute instances, subnets, routers, load balancers, and so on. All of those ship with a standard set of metrics specific to each resource.

For example, instances have CPU cores and memory, so they define default metrics for those attributes. Right now metrics are hard coded and read from the base resources, but future versions of Resoto will allow you to define your own metrics in resotocore and have resotometrics export them.

Example

Enter the following command into Resoto Shell:

> search aggregate(/ancestors.cloud.reported.name as cloud, /ancestors.account.reported.name as account, /ancestors.region.reported.name as region, instance_type as type : sum(1) as instances_total, sum(instance_cores) as cores_total, sum(instance_memory*1024*1024*1024) as memory_bytes): is(instance)

If your graph contains any compute instances the resulting output will look something like this

---
group:
cloud: aws
account: someengineering-platform
region: us-west-2
type: m5.2xlarge
instances_total: 6
cores_total: 24
memory_bytes: 96636764160
---
group:
cloud: aws
account: someengineering-platform
region: us-west-2
type: m5.xlarge
instances_total: 8
cores_total: 64
memory_bytes: 257698037760
---
group:
cloud: gcp
account: someengineering-dev
region: us-west1
type: n1-standard-4
instances_total: 12
cores_total: 48
memory_bytes: 193273528320

Let us dissect the above command:

  • aggregate(/ancestors.cloud.reported.name as cloud, /ancestors.account.reported.name as account, /ancestors.region.reported.name as region, instance_type as type aggregate the instance metrics by cloud, account, and region name as well as instance_type (think GROUP_BY in SQL).
  • sum(1) as instances_total, sum(instance_cores) as cores_total, sum(instance_memory*1024*1024*1024) as memory_bytes): sum up the total number of instances, number of instance cores and memory. The later is stored in GB and here we convert it to bytes as is customary in Prometheus exporters.
  • is(instance) search all the resources that inherit from base kind instance. This would be compute instances like aws_ec2_instance or gcp_instance.

Taking It One Step Further

> search aggregate(/ancestors.cloud.reported.name as cloud, /ancestors.account.reported.name as account, /ancestors.region.reported.name as region, instance_type as type : sum(/ancestors.instance_type.reported.ondemand_cost) as instances_hourly_cost_estimate): is(instance) and instance_status = running

Outputs something like

---
group:
cloud: gcp
account: maestro-229419
region: us-central1
type: n1-standard-4
instances_hourly_cost_estimate: 0.949995

What did we do here? We told Resoto to find all resource of type compute instance (is(instance)) with a status of running and then merge the result with ancestors (parents and parent parents) of type cloud, account, region and now also instance_type.

Let us look at two things here. First, in the previous example we already aggregated by instance_type. However this was the string attribute called instance_type that is part of every instance resource and contains strings like m5.xlarge (AWS) or n1-standard-4 (Google Cloud).

Example

> search is(instance) | tail -1 | format {kind} {name} {instance_type}
# highlight-next-line
aws_ec2_instance i-039e06bb2539e5484 t2.micro

What we did now was ask Resoto to go up the graph and find the directly connected resource of kind instance_type.

An instance_type resource looks something like this

> search is(instance_type) | tail -1 | dump
​reported:
​ kind: aws_ec2_instance_type
​ id: t2.micro
​ tags: {}
​ name: t2.micro
​ instance_type: t2.micro
​ instance_cores: 1
​ instance_memory: 1
​ ondemand_cost: 0.0116
​ ctime: '2021-09-28T13:10:08Z'

As you can see, the instance type resource has a float attribute called ondemand_cost which is the hourly cost a cloud provider charges for this particular type of compute instance. In our aggregation query we now sum up the hourly cost of all currently running compute instances and export them as a metric named instances_hourly_cost_estimate. If we now export this metric into a timeseries DB like Prometheus we are able to plot our instance cost over time.

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Some Engineering Inc.