Monitoring v1

Important

Installing Prometheus and Grafana is beyond the scope of this project. We assume they are correctly installed in your system. However, for experimentation we provide instructions in Part 4 of the Quickstart.

Monitoring Instances

For each PostgreSQL instance, the operator provides an exporter of metrics for Prometheus via HTTP, on port 9187, named metrics. The operator comes with a predefined set of metrics, as well as a highly configurable and customizable system to define additional queries via one or more ConfigMap or Secret resources (see the "User defined metrics" section below for details).

Important

Starting from version 1.11, EDB Postgres for Kubernetes already installs by default a set of predefined metrics in a ConfigMap called default-monitoring.

Info

You can inspect the exported metrics by following the instructions in the "How to inspect the exported metrics" section below.

All monitoring queries that are performed on PostgreSQL are:

atomic (one transaction per query)
executed with the pg_monitor role
executed with application_name set to cnp_metrics_exporter
executed as user postgres

Please refer to the "Predefined Roles" section in PostgreSQL documentation for details on the pg_monitor role.

Queries, by default, are run against the main database, as defined by the specified bootstrap method of the Cluster resource, according to the following logic:

using initdb: queries will be run by default against the specified database in initdb.database, or app if not specified
using recovery: queries will be run by default against the specified database in recovery.database, or postgres if not specified
using pg_basebackup: queries will be run by default against the specified database in pg_basebackup.database, or postgres if not specified

The default database can always be overridden for a given user-defined metric, by specifying a list of one or more databases in the target_databases option.

Prometheus/Grafana

If you are interested in evaluating the integration of EDB Postgres for Kubernetes with Prometheus and Grafana, you can find a quick setup guide in Part 4 of the quickstart

Prometheus Operator example

A specific PostgreSQL cluster can be monitored using the Prometheus Operator's resource PodMonitor. A PodMonitor correctly pointing to a Cluster can be automatically created by the operator by setting .spec.monitoring.enablePodMonitor to true in the Cluster resource itself (default: false).

Important

Any change to the PodMonitor created automatically will be overridden by the Operator at the next reconciliation cycle, in case you need to customize it, you can do so as described below.

To deploy a PodMonitor for a specific Cluster manually, you can just define it as follows, changing it as needed:

apiVersion: monitoring.coreos.com/v1
kind: PodMonitor
metadata:
  name: cluster-example
spec:
  selector:
    matchLabels:
      "k8s.enterprisedb.io/cluster": cluster-example
  podMetricsEndpoints:
  - port: metrics

Important

Make sure you modify the example above with a unique name as well as the correct cluster's namespace and labels (we are using cluster-example).

Important

Label postgresql, used in previous versions of this document, is deprecated and will be removed in the future. Please use the label k8s.enterprisedb.io/cluster instead to select the instances.

Predefined set of metrics

Every PostgreSQL instance exporter automatically exposes a set of predefined metrics, which can be classified in two major categories:

PostgreSQL related metrics, starting with cnp_collector_*, including:
- number of WAL files and total size on disk
- number of .ready and .done files in the archive status folder
- requested minimum and maximum number of synchronous replicas, as well as the expected and actually observed values
- number of distinct nodes accommodating the instances
- timestamps indicating last failed and last available backup, as well as the first point of recoverability for the cluster
- flag indicating if replica cluster mode is enabled or disabled
- flag indicating if a manual switchover is required
- flag indicating if fencing is enabled or disabled
Go runtime related metrics, starting with go_*

Below is a sample of the metrics returned by the localhost:9187/metrics endpoint of an instance. As you can see, the Prometheus format is self-documenting:

# HELP cnp_collector_collection_duration_seconds Collection time duration in seconds
# TYPE cnp_collector_collection_duration_seconds gauge
cnp_collector_collection_duration_seconds{collector="Collect.up"} 0.0031393

# HELP cnp_collector_collections_total Total number of times PostgreSQL was accessed for metrics.
# TYPE cnp_collector_collections_total counter
cnp_collector_collections_total 2

# HELP cnp_collector_fencing_on 1 if the instance is fenced, 0 otherwise
# TYPE cnp_collector_fencing_on gauge
cnp_collector_fencing_on 0

# HELP cnp_collector_nodes_used NodesUsed represents the count of distinct nodes accommodating the instances. A value of '-1' suggests that the metric is not available. A value of '1' suggests that all instances are hosted on a single node, implying the absence of High Availability (HA). Ideally this value should match the number of instances in the cluster.
# TYPE cnp_collector_nodes_used gauge
cnp_collector_nodes_used 3

# HELP cnp_collector_last_collection_error 1 if the last collection ended with error, 0 otherwise.
# TYPE cnp_collector_last_collection_error gauge
cnp_collector_last_collection_error 0

# HELP cnp_collector_manual_switchover_required 1 if a manual switchover is required, 0 otherwise
# TYPE cnp_collector_manual_switchover_required gauge
cnp_collector_manual_switchover_required 0

# HELP cnp_collector_pg_wal Total size in bytes of WAL segments in the '/var/lib/postgresql/data/pgdata/pg_wal' directory  computed as (wal_segment_size * count)
# TYPE cnp_collector_pg_wal gauge
cnp_collector_pg_wal{value="count"} 9
cnp_collector_pg_wal{value="slots_max"} NaN
cnp_collector_pg_wal{value="keep"} 32
cnp_collector_pg_wal{value="max"} 64
cnp_collector_pg_wal{value="min"} 5
cnp_collector_pg_wal{value="size"} 1.50994944e+08
cnp_collector_pg_wal{value="volume_max"} 128
cnp_collector_pg_wal{value="volume_size"} 2.147483648e+09

# HELP cnp_collector_pg_wal_archive_status Number of WAL segments in the '/var/lib/postgresql/data/pgdata/pg_wal/archive_status' directory (ready, done)
# TYPE cnp_collector_pg_wal_archive_status gauge
cnp_collector_pg_wal_archive_status{value="done"} 6
cnp_collector_pg_wal_archive_status{value="ready"} 0

# HELP cnp_collector_replica_mode 1 if the cluster is in replica mode, 0 otherwise
# TYPE cnp_collector_replica_mode gauge
cnp_collector_replica_mode 0

# HELP cnp_collector_sync_replicas Number of requested synchronous replicas (synchronous_standby_names)
# TYPE cnp_collector_sync_replicas gauge
cnp_collector_sync_replicas{value="expected"} 0
cnp_collector_sync_replicas{value="max"} 0
cnp_collector_sync_replicas{value="min"} 0
cnp_collector_sync_replicas{value="observed"} 0

# HELP cnp_collector_up 1 if PostgreSQL is up, 0 otherwise.
# TYPE cnp_collector_up gauge
cnp_collector_up{cluster="cluster-example"} 1

# HELP cnp_collector_postgres_version Postgres version
# TYPE cnp_collector_postgres_version gauge
cnp_collector_postgres_version{cluster="cluster-example",full="15.3"} 15.3

# HELP cnp_collector_last_failed_backup_timestamp The last failed backup as a unix timestamp
# TYPE cnp_collector_last_failed_backup_timestamp gauge
cnp_collector_last_failed_backup_timestamp 0

# HELP cnp_collector_last_available_backup_timestamp The last available backup as a unix timestamp
# TYPE cnp_collector_last_available_backup_timestamp gauge
cnp_collector_last_available_backup_timestamp 1.63238406e+09

# HELP cnp_collector_first_recoverability_point The first point of recoverability for the cluster as a unix timestamp
# TYPE cnp_collector_first_recoverability_point gauge
cnp_collector_first_recoverability_point 1.63238406e+09

# HELP cnp_collector_lo_pages Estimated number of pages in the pg_largeobject table
# TYPE cnp_collector_lo_pages gauge
cnp_collector_lo_pages{datname="app"} 0
cnp_collector_lo_pages{datname="postgres"} 78

# HELP cnp_collector_wal_buffers_full Number of times WAL data was written to disk because WAL buffers became full. Only available on PG 14+
# TYPE cnp_collector_wal_buffers_full gauge
cnp_collector_wal_buffers_full{stats_reset="2023-06-19T10:51:27.473259Z"} 6472

# HELP cnp_collector_wal_bytes Total amount of WAL generated in bytes. Only available on PG 14+
# TYPE cnp_collector_wal_bytes gauge
cnp_collector_wal_bytes{stats_reset="2023-06-19T10:51:27.473259Z"} 1.0035147e+07

# HELP cnp_collector_wal_fpi Total number of WAL full page images generated. Only available on PG 14+
# TYPE cnp_collector_wal_fpi gauge
cnp_collector_wal_fpi{stats_reset="2023-06-19T10:51:27.473259Z"} 1474

# HELP cnp_collector_wal_records Total number of WAL records generated. Only available on PG 14+
# TYPE cnp_collector_wal_records gauge
cnp_collector_wal_records{stats_reset="2023-06-19T10:51:27.473259Z"} 26178

# HELP cnp_collector_wal_sync Number of times WAL files were synced to disk via issue_xlog_fsync request (if fsync is on and wal_sync_method is either fdatasync, fsync or fsync_writethrough, otherwise zero). Only available on PG 14+
# TYPE cnp_collector_wal_sync gauge
cnp_collector_wal_sync{stats_reset="2023-06-19T10:51:27.473259Z"} 37

# HELP cnp_collector_wal_sync_time Total amount of time spent syncing WAL files to disk via issue_xlog_fsync request, in milliseconds (if track_wal_io_timing is enabled, fsync is on, and wal_sync_method is either fdatasync, fsync or fsync_writethrough, otherwise zero). Only available on PG 14+
# TYPE cnp_collector_wal_sync_time gauge
cnp_collector_wal_sync_time{stats_reset="2023-06-19T10:51:27.473259Z"} 0

# HELP cnp_collector_wal_write Number of times WAL buffers were written out to disk via XLogWrite request. Only available on PG 14+
# TYPE cnp_collector_wal_write gauge
cnp_collector_wal_write{stats_reset="2023-06-19T10:51:27.473259Z"} 7243

# HELP cnp_collector_wal_write_time Total amount of time spent writing WAL buffers to disk via XLogWrite request, in milliseconds (if track_wal_io_timing is enabled, otherwise zero). This includes the sync time when wal_sync_method is either open_datasync or open_sync. Only available on PG 14+
# TYPE cnp_collector_wal_write_time gauge
cnp_collector_wal_write_time{stats_reset="2023-06-19T10:51:27.473259Z"} 0

# HELP cnp_last_error 1 if the last collection ended with error, 0 otherwise.
# TYPE cnp_last_error gauge
cnp_last_error 0

# HELP go_gc_duration_seconds A summary of the pause duration of garbage collection cycles.
# TYPE go_gc_duration_seconds summary
go_gc_duration_seconds{quantile="0"} 5.01e-05
go_gc_duration_seconds{quantile="0.25"} 7.27e-05
go_gc_duration_seconds{quantile="0.5"} 0.0001748
go_gc_duration_seconds{quantile="0.75"} 0.0002959
go_gc_duration_seconds{quantile="1"} 0.0012776
go_gc_duration_seconds_sum 0.0035741
go_gc_duration_seconds_count 13

# HELP go_goroutines Number of goroutines that currently exist.
# TYPE go_goroutines gauge
go_goroutines 25

# HELP go_info Information about the Go environment.
# TYPE go_info gauge
go_info{version="go1.20.5"} 1

# HELP go_memstats_alloc_bytes Number of bytes allocated and still in use.
# TYPE go_memstats_alloc_bytes gauge
go_memstats_alloc_bytes 4.493744e+06

# HELP go_memstats_alloc_bytes_total Total number of bytes allocated, even if freed.
# TYPE go_memstats_alloc_bytes_total counter
go_memstats_alloc_bytes_total 2.1698216e+07

# HELP go_memstats_buck_hash_sys_bytes Number of bytes used by the profiling bucket hash table.
# TYPE go_memstats_buck_hash_sys_bytes gauge
go_memstats_buck_hash_sys_bytes 1.456234e+06

# HELP go_memstats_frees_total Total number of frees.
# TYPE go_memstats_frees_total counter
go_memstats_frees_total 172118

# HELP go_memstats_gc_cpu_fraction The fraction of this program's available CPU time used by the GC since the program started.
# TYPE go_memstats_gc_cpu_fraction gauge
go_memstats_gc_cpu_fraction 1.0749468700447189e-05

# HELP go_memstats_gc_sys_bytes Number of bytes used for garbage collection system metadata.
# TYPE go_memstats_gc_sys_bytes gauge
go_memstats_gc_sys_bytes 5.530048e+06

# HELP go_memstats_heap_alloc_bytes Number of heap bytes allocated and still in use.
# TYPE go_memstats_heap_alloc_bytes gauge
go_memstats_heap_alloc_bytes 4.493744e+06

# HELP go_memstats_heap_idle_bytes Number of heap bytes waiting to be used.
# TYPE go_memstats_heap_idle_bytes gauge
go_memstats_heap_idle_bytes 5.8236928e+07

# HELP go_memstats_heap_inuse_bytes Number of heap bytes that are in use.
# TYPE go_memstats_heap_inuse_bytes gauge
go_memstats_heap_inuse_bytes 7.528448e+06

# HELP go_memstats_heap_objects Number of allocated objects.
# TYPE go_memstats_heap_objects gauge
go_memstats_heap_objects 26306

# HELP go_memstats_heap_released_bytes Number of heap bytes released to OS.
# TYPE go_memstats_heap_released_bytes gauge
go_memstats_heap_released_bytes 5.7401344e+07

# HELP go_memstats_heap_sys_bytes Number of heap bytes obtained from system.
# TYPE go_memstats_heap_sys_bytes gauge
go_memstats_heap_sys_bytes 6.5765376e+07

# HELP go_memstats_last_gc_time_seconds Number of seconds since 1970 of last garbage collection.
# TYPE go_memstats_last_gc_time_seconds gauge
go_memstats_last_gc_time_seconds 1.6311727586032727e+09

# HELP go_memstats_lookups_total Total number of pointer lookups.
# TYPE go_memstats_lookups_total counter
go_memstats_lookups_total 0

# HELP go_memstats_mallocs_total Total number of mallocs.
# TYPE go_memstats_mallocs_total counter
go_memstats_mallocs_total 198424

# HELP go_memstats_mcache_inuse_bytes Number of bytes in use by mcache structures.
# TYPE go_memstats_mcache_inuse_bytes gauge
go_memstats_mcache_inuse_bytes 14400

# HELP go_memstats_mcache_sys_bytes Number of bytes used for mcache structures obtained from system.
# TYPE go_memstats_mcache_sys_bytes gauge
go_memstats_mcache_sys_bytes 16384

# HELP go_memstats_mspan_inuse_bytes Number of bytes in use by mspan structures.
# TYPE go_memstats_mspan_inuse_bytes gauge
go_memstats_mspan_inuse_bytes 191896

# HELP go_memstats_mspan_sys_bytes Number of bytes used for mspan structures obtained from system.
# TYPE go_memstats_mspan_sys_bytes gauge
go_memstats_mspan_sys_bytes 212992

# HELP go_memstats_next_gc_bytes Number of heap bytes when next garbage collection will take place.
# TYPE go_memstats_next_gc_bytes gauge
go_memstats_next_gc_bytes 8.689632e+06

# HELP go_memstats_other_sys_bytes Number of bytes used for other system allocations.
# TYPE go_memstats_other_sys_bytes gauge
go_memstats_other_sys_bytes 2.566622e+06

# HELP go_memstats_stack_inuse_bytes Number of bytes in use by the stack allocator.
# TYPE go_memstats_stack_inuse_bytes gauge
go_memstats_stack_inuse_bytes 1.343488e+06

# HELP go_memstats_stack_sys_bytes Number of bytes obtained from system for stack allocator.
# TYPE go_memstats_stack_sys_bytes gauge
go_memstats_stack_sys_bytes 1.343488e+06

# HELP go_memstats_sys_bytes Number of bytes obtained from system.
# TYPE go_memstats_sys_bytes gauge
go_memstats_sys_bytes 7.6891144e+07

# HELP go_threads Number of OS threads created.
# TYPE go_threads gauge
go_threads 18

Note

cnp_collector_postgres_version is a GaugeVec metric containing the Major.Minor version of Postgres (either PostgreSQL or EPAS). The full semantic version Major.Minor.Patch can be found inside one of its label field named full.

Note

cnp_collector_first_recoverability_point and cnp_collector_last_available_backup_timestamp will be zero until your first backup to the object store. This is separate from the WAL archival.

User defined metrics

This feature is currently in beta state and the format is inspired by the queries.yaml file (release 0.12) of the PostgreSQL Prometheus Exporter.

Custom metrics can be defined by users by referring to the created Configmap/Secret in a Cluster definition under the .spec.monitoring.customQueriesConfigMap or customQueriesSecret section as in the following example:

apiVersion: postgresql.k8s.enterprisedb.io/v1
kind: Cluster
metadata:
  name: cluster-example
  namespace: test
spec:
  instances: 3

  storage:
    size: 1Gi

  monitoring:
    customQueriesConfigMap:
      - name: example-monitoring
        key: custom-queries

The customQueriesConfigMap/customQueriesSecret sections contain a list of ConfigMap/Secret references specifying the key in which the custom queries are defined. Take care that the referred resources have to be created in the same namespace as the Cluster resource.

Note

If you want ConfigMaps and Secrets to be automatically reloaded by instances, you can add a label with key k8s.enterprisedb.io/reload to it, otherwise you will have to reload the instances using the kubectl cnp reload subcommand.

Important

When a user defined metric overwrites an already existing metric the instance manager prints a json warning log, containing the message:Query with the same name already found. Overwriting the existing one. and a key queryName containing the overwritten query name.

Example of a user defined metric

Here you can see an example of a ConfigMap containing a single custom query, referenced by the Cluster example above:

apiVersion: v1
kind: ConfigMap
metadata:
  name: example-monitoring
  namespace: test
  labels:
    k8s.enterprisedb.io/reload: ""
data:
  custom-queries: |
    pg_replication:
      query: "SELECT CASE WHEN NOT pg_is_in_recovery()
              THEN 0
              ELSE GREATEST (0,
                EXTRACT(EPOCH FROM (now() - pg_last_xact_replay_timestamp())))
              END AS lag,
              pg_is_in_recovery() AS in_recovery,
              EXISTS (TABLE pg_stat_wal_receiver) AS is_wal_receiver_up,
              (SELECT count(*) FROM pg_stat_replication) AS streaming_replicas"

      metrics:
        - lag:
            usage: "GAUGE"
            description: "Replication lag behind primary in seconds"
        - in_recovery:
            usage: "GAUGE"
            description: "Whether the instance is in recovery"
        - is_wal_receiver_up:
            usage: "GAUGE"
            description: "Whether the instance wal_receiver is up"
        - streaming_replicas:
            usage: "GAUGE"
            description: "Number of streaming replicas connected to the instance"

A list of basic monitoring queries can be found in the default-monitoring.yaml file that is already installed in your EDB Postgres for Kubernetes deployment (see "Default set of metrics").

Example of a user defined metric running on multiple databases

If the target_databases option lists more than one database the metric is collected from each of them.

Database auto-discovery can be enabled for a specific query by specifying a shell-like pattern (i.e., containing *, ? or []) in the list of target_databases. If provided, the operator will expand the list of target databases by adding all the databases returned by the execution of SELECT datname FROM pg_database WHERE datallowconn AND NOT datistemplate and matching the pattern according to path.Match() rules.

Note

The * character has a special meaning in yaml, so you need to quote ("*") the target_databases value when it includes such a pattern.

It is recommended that you always include the name of the database in the returned labels, for example using the current_database() function as in the following example:

some_query: |
  query: |
    SELECT
     current_database() as datname,
     count(*) as rows
    FROM some_table
  metrics:
    - datname:
        usage: "LABEL"
        description: "Name of current database"
    - rows:
        usage: "GAUGE"
        description: "number of rows"
  target_databases:
    - albert
    - bb
    - freddie

This will produce in the following metric being exposed:

cnp_some_query_rows{datname="albert"} 2
cnp_some_query_rows{datname="bb"} 5
cnp_some_query_rows{datname="freddie"} 10

Here is an example of a query with auto-discovery enabled which also runs on the template1 database (otherwise not returned by the aforementioned query):

some_query: |
  query: |
    SELECT
     current_database() as datname,
     count(*) as rows
    FROM some_table
  metrics:
    - datname:
        usage: "LABEL"
        description: "Name of current database"
    - rows:
        usage: "GAUGE"
        description: "number of rows"
  target_databases:
    - "*"
    - "template1"

The above example will produce the following metrics (provided the databases exist):

cnp_some_query_rows{datname="albert"} 2
cnp_some_query_rows{datname="bb"} 5
cnp_some_query_rows{datname="freddie"} 10
cnp_some_query_rows{datname="template1"} 7
cnp_some_query_rows{datname="postgres"} 42

Structure of a user defined metric

Every custom query has the following basic structure:

<MetricName>:
      query: "<SQLQuery>"
      metrics:
        - <ColumnName>:
            usage: "<MetricType>"
            description: "<MetricDescription>"

Here is a short description of all the available fields:

<MetricName>: the name of the Prometheus metric
- query: the SQL query to run on the target database to generate the metrics
- primary: whether to run the query only on the primary instance
- master: same as primary (for compatibility with the Prometheus PostgreSQL exporter's syntax - deprecated)
- runonserver: a semantic version range to limit the versions of PostgreSQL the query should run on (e.g. ">=11.0.0" or ">=12.0.0 <=15.0.0")
- target_databases: a list of databases to run the query against, or a shell-like pattern to enable auto discovery. Overwrites the default database if provided.
- metrics: section containing a list of all exported columns, defined as follows:
  - <ColumnName>: the name of the column returned by the query
    - usage: one of the values described below
    - description: the metric's description
    - metrics_mapping: the optional column mapping when usage is set to MAPPEDMETRIC

The possible values for usage are:

Column Usage Label	Description
`DISCARD`	this column should be ignored
`LABEL`	use this column as a label
`COUNTER`	use this column as a counter
`GAUGE`	use this column as a gauge
`MAPPEDMETRIC`	use this column with the supplied mapping of text values
`DURATION`	use this column as a text duration (in milliseconds)
`HISTOGRAM`	use this column as a histogram

Please visit the "Metric Types" page from the Prometheus documentation for more information.

Output of a user defined metric

Custom defined metrics are returned by the Prometheus exporter endpoint (:9187/metrics) with the following format:

cnp_<MetricName>_<ColumnName>{<LabelColumnName>=<LabelColumnValue> ... } <ColumnValue>

Note

LabelColumnName are metrics with usage set to LABEL and their Value

Considering the pg_replication example above, the exporter's endpoint would return the following output when invoked:

# HELP cnp_pg_replication_in_recovery Whether the instance is in recovery
# TYPE cnp_pg_replication_in_recovery gauge
cnp_pg_replication_in_recovery 0
# HELP cnp_pg_replication_lag Replication lag behind primary in seconds
# TYPE cnp_pg_replication_lag gauge
cnp_pg_replication_lag 0
# HELP cnp_pg_replication_streaming_replicas Number of streaming replicas connected to the instance
# TYPE cnp_pg_replication_streaming_replicas gauge
cnp_pg_replication_streaming_replicas 2
# HELP cnp_pg_replication_is_wal_receiver_up Whether the instance wal_receiver is up
# TYPE cnp_pg_replication_is_wal_receiver_up gauge
cnp_pg_replication_is_wal_receiver_up 0

Default set of metrics

The operator can be configured to automatically inject in a Cluster a set of monitoring queries defined in a ConfigMap or a Secret, inside the operator's namespace. You have to set the MONITORING_QUERIES_CONFIGMAP or MONITORING_QUERIES_SECRET key in the "operator configuration", respectively to the name of the ConfigMap or the Secret; the operator will then use the content of the queries key.

Any change to the queries content will be immediately reflected on all the deployed Clusters using it.

The operator installation manifests come with a predefined ConfigMap, called postgresql-operator-default-monitoring, to be used by all Clusters. MONITORING_QUERIES_CONFIGMAP is by default set to postgresql-operator-default-monitoring in the operator configuration.

If you want to disable the default set of metrics, you can:

disable it at operator level: set the MONITORING_QUERIES_CONFIGMAP/MONITORING_QUERIES_SECRET key to "" (empty string), in the operator ConfigMap. Changes to operator ConfigMap require an operator restart.
disable it for a specific Cluster: set .spec.monitoring.disableDefaultQueries to true in the Cluster.

Important

The ConfigMap or Secret specified via MONITORING_QUERIES_CONFIGMAP/MONITORING_QUERIES_SECRET will always be copied to the Cluster's namespace with a fixed name: postgresql-operator-default-monitoring. So that, if you intend to have default metrics, you should not create a ConfigMap with this name in the cluster's namespace.

Differences with the Prometheus Postgres exporter

EDB Postgres for Kubernetes is inspired by the PostgreSQL Prometheus Exporter, but presents some differences. In particular, the cache_seconds field is not implemented in EDB Postgres for Kubernetes' exporter.

Monitoring the operator

The operator internally exposes Prometheus metrics via HTTP on port 8080, named metrics.

Info

You can inspect the exported metrics by following the instructions in the "How to inspect the exported metrics" section below.

Currently, the operator exposes default kubebuilder metrics, see kubebuilder documentation for more details.

Prometheus Operator example

The operator deployment can be monitored using the Prometheus Operator by defining the following PodMonitor resource:

apiVersion: monitoring.coreos.com/v1
kind: PodMonitor
metadata:
  name: postgresql-operator-controller-manager
spec:
  selector:
    matchLabels:
      app.kubernetes.io/name: cloud-native-postgresql
  podMetricsEndpoints:
    - port: metrics

How to inspect the exported metrics

In this section we provide some basic instructions on how to inspect the metrics exported by a specific PostgreSQL instance manager (primary or replica) or the operator, using a temporary pod running curl in the same namespace.

Note

In the example below we assume we are working in the default namespace, alongside with the PostgreSQL cluster. Please feel free to adapt this example to your use case, by applying basic Kubernetes knowledge.

Create the curl.yaml file with this content:

apiVersion: v1
kind: Pod
metadata:
  name: curl
spec:
  containers:
  - name: curl
    image: curlimages/curl:7.84.0
    command: ['sleep', '3600']

Then create the pod:

kubectl apply -f curl.yaml

In case you want to inspect the metrics exported by an instance, you need to connect to port 9187 of the target pod. This is the generic command to be run (make sure you use the correct IP for the pod):

kubectl exec -ti curl -- curl -s <pod_ip>:9187/metrics

For example, if your PostgreSQL cluster is called cluster-example and you want to retrieve the exported metrics of the first pod in the cluster, you can run the following command to programmatically get the IP of that pod:

POD_IP=$(kubectl get pod cluster-example-1 --template '{{.status.podIP}}')

And then run:

kubectl exec -ti curl -- curl -s ${POD_IP}:9187/metrics

In case you want to access the metrics of the operator, you need to point to the pod where the operator is running, and use TCP port 8080 as target.

At the end of the inspection, please make sure you delete the curl pod:

kubectl delete -f curl.yaml

Auxiliary resources

Important

These resources are provided for illustration and experimentation, and do not represent any kind of recommendation for your production system

In the doc/src/samples/monitoring/ directory you will find a series of sample files for observability. Please refer to Part 4 of the quickstart section for context:

kube-stack-config.yaml: a configuration file for the kube-stack helm chart installation. It ensures that Prometheus listens for all PodMonitor resources.
prometheusrule.yaml: a PrometheusRule with alerts for EDB Postgres for Kubernetes. NOTE: this does not include inter-operation with notification services. Please refer to the Prometheus documentation.
podmonitor.yaml: a PodMonitor for the EDB Postgres for Kubernetes Operator deployment.
grafana-configmap.yaml: a ConfigMap containing the definition of the sample EDB Postgres for Kubernetes Dashboard. Note the labels in the definition, which ensure that the Grafana deployment will find the ConfigMap.

In addition, we provide the "raw" sources for the Grafana dashboard and the Prometheus alert rules, for your reference:

alerts.yaml: Prometheus rules with alerts
grafana-dashboard.json: the EDB Postgres for Kubernetes dashboard as a native Grafana JSON.

Note that, for the configuration of kube-prometheus-stack, other fields and settings are available over what we provide in kube-stack-config.yaml.

You can execute helm show values prometheus-community/kube-prometheus-stack to view them. For further information, please refer to the kube-prometheus-stack page.

Monitoring on OpenShift

Starting on Openshift 4.6 there is a complete monitoring stack called "Monitoring for user-defined projects" which can be enabled by cluster administrators. Cloud Native PostgreSQL will automatically create a PodMonitor object if the option spec.monitoring.enablePodMonitor of the Cluster definition is set to true.

To enable cluster wide user-defined monitoring you must first create a ConfigMap with the name cluster-monitoring-config in the openshift-monitoring namespace/project with the following content:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |
    enableUserWorkload: true

If the ConfigMap already exists, just add the variable enableUserWorkload: true.

Important

This will enable the monitoring for the whole cluster, if it is needed only for one namespace/project please refer to the official Red Hat documentation or talk with your cluster administrator.

After that, just create the proper PodMonitor in the namespace/project with something similar to this:

apiVersion: monitoring.coreos.com/v1
kind: PodMonitor
metadata:
  name: cluster-sample
spec:
  selector:
    matchLabels:
      postgresql: cluster-sample
  podMetricsEndpoints:
    - port: metrics

Note

We currently don’t use ServiceMonitor because our service doesn’t define a port pointing to the metrics. If we added a metric port this could expose sensitive data.