An exporter for converting BigQuery results into Prometheus metrics.
Prometheus collects the current status of a system as reported by an exporter. Prometheus then associates the values collected with a timestamp of the time of collection.
NOTE: there is no way to associate historical values with timestamps in the the past with this exporter!
So, the results of queries run by prometheus-bigquery-exporter should represent a meaningful value at a fixed point in time relative to the time the query is made, e.g. total number of tests in a 5 minute window 1 hour ago.
The prometheus-bigquery-exporter accepts arbitrary BQ queries. However, the query results must be structured in a predictable way for the exporter to successfully interpret and convert it into prometheus metrics.
Metric names are derived from the query file name and query value columns.
The bigquery-exporter identifies value columns by looking for column names
that match the pattern: value([.+]). All characters in the matching group
([.+]) are appended to the metric prefix taken from the query file name.
For example:
- Filename:
bq_ndt_test.sql - Metric prefix:
bq_ndt_test - Column name:
value_count - Final metric:
bq_ndt_test_count
Value columns are required (at least one):
value([.+])- every query must define a result "value". Values must be integers or floats. For a query to return multiple values, prefix each with "value" and define unique suffixes.
Label columns are optional:
- If there is more than one result row, then the query must also define labels to distinguish each value. Every column name that is not "value" will create a label on the resulting metric. For example, results with two columns, "machine" and "value" would create metrics with labels named "machine" and values from the results for that row.
Labels must be strings:
- There is no limit on the number of labels, but you should respect the prometheus best practices by limiting label value cardinality.
Duplicate metrics are an error:
- If the query returns multiple rows that are not distinguished by the set of labels for each row.
The following query creates a label and groups by each label.
-- Example data in place of an actual table of values.
WITH example_data as (
SELECT "a" as label, 5 as widgets
UNION ALL
SELECT "b" as label, 2 as widgets
UNION ALL
SELECT "b" as label, 3 as widgets
)
SELECT
label, SUM(widgets) as value
FROM
example_data
GROUP BY
label-
Save the sample query to a file named "bq_example.sql".
-
Start the exporter:
prometheus-bigquery-exporter -gauge-query bq_example.sql
-
Visit http://localhost:9348/metrics and you will find metrics like:
bq_example{label="a"} 5 bq_example{label="b"} 5 ...
Typical deployments will be in Kubernetes environment, like GKE.
# Change to the example directory.
cd example
# Deploy the example query as a configmap and example k8s deployment.
./deploy.shTo run the bigquery exporter locally (e.g. with a new query) you can build and run locally.
Use the following steps:
- Build the docker image.
docker build -t bqx-local -f Dockerfile .- Authenticate using your Google account. Both steps are necessary, the first to run gcloud commands (which uses user credentials), the second to run the bigquery exporter (which uses application default credentials).
gcloud auth login
gcloud auth application-default login- Run the image, with fowarded ports and access to gcloud credentials.
docker run -p 9348:9348 --rm \
-v $HOME/.config/gcloud:/root/.config/gcloud \
-v $PWD:/queries -it bqx-local \
-project=$GCLOUD_PROJECT \
-gauge-query=/queries/example/config/bq_example.sql
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