Export Prometheus metrics generated from SQL queries.
Project description
# query-exporter - Export Prometheus metrics from SQL queries
[](https://pypi-hypernode.com/pypi/query-exporter)
[](https://travis-ci.org/albertodonato/query-exporter)
[](https://codecov.io/gh/albertodonato/query-exporter)
`query-exporter` is a [Prometheus](https://prometheus.io/) exporter which
allows collecting metrics from database queries, at specified time intervals.
It currently supports [PostgreSQL](https://www.postgresql.org/) as a backend
database.
Each query can be run on multiple databases, and update multiple metrics.
The application is called with a configuration file that looks like this:
```yaml
databases:
db1:
dsn: dbname=sampledb1
db2:
dsn: dbname=sampledb2
metrics:
metric1:
type: gauge
description: A sample gauge
metric2:
type: summary
description: A sample summary
metric3:
type: histogram
description: A sample histogram
buckets: [10, 20, 50, 100, 1000]
queries:
query1:
interval: 30
databases: [db1]
metrics: [metric1]
sql: SELECT random() * 100
query2:
interval: 1m
databases: [db1, db2]
metrics: [metric2, metric3]
sql: SELECT random() * 1000, random() * 10000
```
The `metrics` list in the query configuration must match values returned by the
query defined in `sql`.
The `interval` value is interpreted as seconds if no suffix is specified; valid
suffix are `s`, `m`, `h`, `d`. Only integer values can be specified.
Queries will usually return a single row, but multiple rows are supported, and
each row will cause an update of the related metrics.
For the configuration above, exported metrics look like this:
```
# HELP metric1 A sample gauge
# TYPE metric1 gauge
metric1{database="db1"} 13.8291064184159
# HELP metric2 A sample summary
# TYPE metric2 summary
metric2_count{database="db1"} 1.0
metric2_sum{database="db1"} 889.48124460876
metric2_count{database="db2"} 1.0
metric2_sum{database="db2"} 665.63375480473
# HELP metric3 A sample histogram
# TYPE metric3 histogram
metric3_bucket{database="db1",le="10.0"} 0.0
metric3_bucket{database="db1",le="20.0"} 0.0
metric3_bucket{database="db1",le="50.0"} 0.0
metric3_bucket{database="db1",le="100.0"} 0.0
metric3_bucket{database="db1",le="1000.0"} 0.0
metric3_bucket{database="db1",le="+Inf"} 1.0
metric3_count{database="db1"} 1.0
metric3_sum{database="db1"} 9988.39943669736
metric3_bucket{database="db2",le="10.0"} 0.0
metric3_bucket{database="db2",le="20.0"} 0.0
metric3_bucket{database="db2",le="50.0"} 0.0
metric3_bucket{database="db2",le="100.0"} 0.0
metric3_bucket{database="db2",le="1000.0"} 0.0
metric3_bucket{database="db2",le="+Inf"} 1.0
metric3_count{database="db2"} 1.0
metric3_sum{database="db2"} 9923.82999043912
```
Metrics are automatically tagged with the `database` label so that indipendent
series are generated for each database.
[](https://pypi-hypernode.com/pypi/query-exporter)
[](https://travis-ci.org/albertodonato/query-exporter)
[](https://codecov.io/gh/albertodonato/query-exporter)
`query-exporter` is a [Prometheus](https://prometheus.io/) exporter which
allows collecting metrics from database queries, at specified time intervals.
It currently supports [PostgreSQL](https://www.postgresql.org/) as a backend
database.
Each query can be run on multiple databases, and update multiple metrics.
The application is called with a configuration file that looks like this:
```yaml
databases:
db1:
dsn: dbname=sampledb1
db2:
dsn: dbname=sampledb2
metrics:
metric1:
type: gauge
description: A sample gauge
metric2:
type: summary
description: A sample summary
metric3:
type: histogram
description: A sample histogram
buckets: [10, 20, 50, 100, 1000]
queries:
query1:
interval: 30
databases: [db1]
metrics: [metric1]
sql: SELECT random() * 100
query2:
interval: 1m
databases: [db1, db2]
metrics: [metric2, metric3]
sql: SELECT random() * 1000, random() * 10000
```
The `metrics` list in the query configuration must match values returned by the
query defined in `sql`.
The `interval` value is interpreted as seconds if no suffix is specified; valid
suffix are `s`, `m`, `h`, `d`. Only integer values can be specified.
Queries will usually return a single row, but multiple rows are supported, and
each row will cause an update of the related metrics.
For the configuration above, exported metrics look like this:
```
# HELP metric1 A sample gauge
# TYPE metric1 gauge
metric1{database="db1"} 13.8291064184159
# HELP metric2 A sample summary
# TYPE metric2 summary
metric2_count{database="db1"} 1.0
metric2_sum{database="db1"} 889.48124460876
metric2_count{database="db2"} 1.0
metric2_sum{database="db2"} 665.63375480473
# HELP metric3 A sample histogram
# TYPE metric3 histogram
metric3_bucket{database="db1",le="10.0"} 0.0
metric3_bucket{database="db1",le="20.0"} 0.0
metric3_bucket{database="db1",le="50.0"} 0.0
metric3_bucket{database="db1",le="100.0"} 0.0
metric3_bucket{database="db1",le="1000.0"} 0.0
metric3_bucket{database="db1",le="+Inf"} 1.0
metric3_count{database="db1"} 1.0
metric3_sum{database="db1"} 9988.39943669736
metric3_bucket{database="db2",le="10.0"} 0.0
metric3_bucket{database="db2",le="20.0"} 0.0
metric3_bucket{database="db2",le="50.0"} 0.0
metric3_bucket{database="db2",le="100.0"} 0.0
metric3_bucket{database="db2",le="1000.0"} 0.0
metric3_bucket{database="db2",le="+Inf"} 1.0
metric3_count{database="db2"} 1.0
metric3_sum{database="db2"} 9923.82999043912
```
Metrics are automatically tagged with the `database` label so that indipendent
series are generated for each database.
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