# Metrics Export
source: https://docs.chalk.ai/docs/metricexport

## Export Chalk metrics to other monitoring systems.

Chalk's online dashboard provides a simple way to view
metrics about performance of your feature pipelines.
However, you may wish to export these metrics from Chalk
into other observability tools so that you can view
your Chalk-related data alongside data from other
systems you maintain.

### Exporting metrics

Chalk tracks various time series metrics that measure the latency and throughput of resolvers and
streaming pipelines.

Chalk uses TimescaleDB to store these metrics. You can use any
OpenMetrics-compatible collector to collect metrics about the execution of your feature pipelines
from Chalk. Examples include:

- Prometheus
- OpenMetrics (Datadog)
- NewRelic
- Stackdriver Prometheus

### Available metrics

The table below summarizes the metrics that are available for export.
The headers in the table are the exported metric name followed by the
OpenMetrics metric type
(gauge,
histogram,
summary, or
counter).

Provides information about the time it takes to compute a resolver.

The name of the resolver, for example, my.company.get_user

Whether this latency represents the median, 75th percentile, 95th percentile, or 99th percentile
of the latency

The type of the resolver - online, offline, or stream.

Provides information about the time it takes to execute an online query.

The name of the query, for example, eligibility_query_v2. Queries without names are
labeled "Unnamed"

Whether this latency represents the median, 75th percentile, 95th percentile, or 99th percentile
of the latency

Provides information about the time it takes to execute a cron run.

The name of the resolver executed by the cron run, for example, my.company.get_user

Whether this latency represents the median, 75th percentile, 95th percentile, or 99th percentile
of the latency

Provides information about the number of times a feature was computed.

The name of the feature, for example, user.age

The status of the computed feature (success or failure)

The context in which the feature was generated

Provides information about the number of times a resolver was computed. This metric informs the number of
times that resolvers are being called and the context in which they are called, for example in a cron
run as part of a scheduled job or in inference as part of a query plan.

The name of the resolver, for example, my.company.get_user

The status of the resolver run (success or failure)

The context in which the resolver ran

The type of the resolver - online, offline, or stream.

Provides information about the number of times a cron run was executed. This metric is useful for
monitoring the status of resolver runs that are scheduled or triggered via API to load data into
the online and/or offline store.

The name of the resolver executed by the cron run, for example, my.company.get_user

The status of the cron run (success or failure)

Provides information about the number of features computed by cron written to online / offline store. This
metric is useful for monitoring resolver runs that are scheduled or triggered via API to
load data into the online and/or offline store.

The name of the resolver executed by the cron run, for example, my.company.get_user

Whether the features were written to online or offline store.

Provides statistical information about the value of features.

The name of the feature, for example, user.age

Whether this value represents the median, 75th percentile, 95th percentile, or 99th percentile
of the feature value

Provides information about the number of times an online query was executed.

The name of the query, for example, eligibility_query_v2. Queries without names are
labeled "Unnamed"

The status of the query (success or failure)

The active deployment version. This gauge will always have a value of 1 for active deployments.
A gauge of this kind is sometimes called an Info metric.

The ID of the deployment.

The response counts by HTTP response code.

The ID of the environment.

The human-readable name of the environment (for example, prod or staging).

The maximum container CPU utilization percentage observed over the recent lookback window, grouped by
Chalk service.

The ID of the environment.

The human-readable name of the environment (for example, prod or staging).

The Chalk service reporting the metric. http is the HTTP query server,
grpc is the gRPC query server, and branch is the branch server.

The maximum container memory usage in bytes observed over the recent lookback window, grouped by Chalk
service.

The ID of the environment.

The human-readable name of the environment (for example, prod or staging).

The Chalk service reporting the metric. http is the HTTP query server,
grpc is the gRPC query server, and branch is the branch server.

The maximum number of distinct replicas observed over the recent lookback window, grouped by Chalk
service.

The ID of the environment.

The human-readable name of the environment (for example, prod or staging).

The Chalk service reporting the metric. http is the HTTP query server,
grpc is the gRPC query server, and branch is the branch server.

The current max_ingested_timestamp in UNIX epoch time for resolvers.

The ID of the resolver.