Operator Capability Levels
This section provides a summary of the capabilities implemented by CloudNativePG, classified using the "Operator SDK definition of Capability Levels" framework.
Based on the Operator Capability Levels model, You can expect a "Level V - Auto Pilot" set of capabilities from the CloudNativePG Operator.
Each capability level is associated with a certain set of management features the operator offers:
- Basic Install
- Seamless Upgrades
- Full Lifecycle
- Deep Insights
- Auto Pilot
We consider this framework as a guide for future work and implementations in the operator.
Level 1 - Basic Install
Capability level 1 involves installation and configuration of the operator. This category includes usability and user experience enhancements, such as improvements in how you interact with the operator and a PostgreSQL cluster configuration.
We consider Information Security part of this level.
Operator deployment via declarative configuration
The operator is installed in a declarative way using a Kubernetes manifest
which defines 4 major
PostgreSQL cluster deployment via declarative configuration
A PostgreSQL cluster (operand) is defined using the
Cluster custom resource
in a fully declarative way. The PostgreSQL version is determined by the
operand container image defined in the CR, which is automatically fetched
from the requested registry. When deploying an operand, the operator also
automatically creates the following resources:
Override of operand images through the CRD
The operator is designed to support any operand container image with
By default, the operator uses the latest available minor
version of the latest stable major version supported by the PostgreSQL
Community and published on ghcr.io.
You can use any compatible image of PostgreSQL supporting the
primary/standby architecture directly by setting the
attribute in the CR. The operator also supports
to access private container registries, as well as digests in addition to
tags for finer control of container image immutability.
Labels and annotations
The operator can be configured to support inheritance of labels and annotations that are defined in a cluster's metadata, with the goal to improve organizations of CloudNativePG deployment in your Kubernetes infrastructure.
Self-contained instance manager
Instead of relying on an external tool such as Patroni or Stolon to
coordinate PostgreSQL instances in the Kubernetes cluster pods, the operator
injects the operator executable inside each pod, in a file named
/controller/manager. The application is used to control the underlying
PostgreSQL instance and to reconcile the pod status with the instance itself
based on the PostgreSQL cluster topology. The instance manager also starts a
web server that is invoked by the
kubelet for probes. Unix signals invoked
kubelet are filtered by the instance manager and, where appropriate,
forwarded to the
postgres process for fast and controlled reactions to
external events. The instance manager is written in Go and has no external
Storage is a critical component in a database workload. Taking advantage of
Kubernetes native capabilities and resources in terms of storage, the
operator gives you enough flexibility to choose the right storage for your
workload requirements, based on what the underlying Kubernetes environment
can offer. This implies choosing a particular storage class in
a public cloud environment or fine-tuning the generated PVC through a
PVC template in the CR's
cnp-bench open source
project can be used to benchmark both the storage and the database prior to
The operator automatically detects replicas in a cluster
through a single parameter called
instances. If set to
1, the cluster
comprises a single primary PostgreSQL instance with no replica. If higher
1, the operator manages
instances -1 replicas, including high
availability through automated failover and rolling updates through
The operator is designed to manage a PostgreSQL cluster with a single
database. The operator transparently manages access to the database through
three Kubernetes services automatically provisioned and managed for read-write,
read, and read-only workloads.
Using the convention over configuration approach, the operator creates a
app, by default owned by a regular Postgres user with the
same name. Both the database name and the user name can be specified if
Although no configuration is required to run the cluster, you can customize
both PostgreSQL run-time configuration and PostgreSQL Host-Based
Authentication rules in the
postgresql section of the CR.
Pod Security Policies
For InfoSec requirements, the operator does not require privileged mode for any container and enforces read only root filesystem to guarantee containers immutability for both the operator and the operand pods. It also explicitly sets the required security contexts.
affinity section enables fine-tuning of how pods and related
resources such as persistent volumes are scheduled across the nodes of a
Kubernetes cluster. In particular, the operator supports:
- pod affinity and anti-affinity
- node selector
- taints and tolerations
Command line interface
CloudNativePG does not have its own command line interface.
It simply relies on the best command line interface for Kubernetes,
by providing a plugin called
cnpg to enhance and simplify your PostgreSQL
cluster management experience.
Current status of the cluster
The operator continuously updates the status section of the CR with the
observed status of the cluster. The entire PostgreSQL cluster status is
continuously monitored by the instance manager running in each pod: the
instance manager is responsible for applying the required changes to the
controlled PostgreSQL instance to converge to the required status of
the cluster (for example: if the cluster status reports that pod
-1 is the
-1 needs to promote itself while the other pods need to follow
-1). The same status is used by the
cnpg plugin for
kubectl to provide
Operator's certification authority
The operator automatically creates a certification authority for itself. It creates and signs with the operator certification authority a leaf certificate to be used by the webhook server, to ensure safe communication between the Kubernetes API Server and the operator itself.
Cluster's certification authority
The operator automatically creates a certification authority for every PostgreSQL
cluster, which is used to issue and renew TLS certificates for clients' authentication,
including streaming replication standby servers (instead of passwords).
Support for a custom certification authority for client certificates is
available through secrets: this also includes integration with cert-manager.
Certificates can be issued with the
cnpg plugin for
The operator transparently and natively supports TLS/SSL connections to encrypt client/server communications for increased security using the cluster's certification authority. Support for custom server certificates is available through secrets: this also includes integration with cert-manager.
Certificate authentication for streaming replication
The operator relies on TLS client certificate authentication to authorize streaming replication connections from the standby servers, instead of relying on a password (and therefore a secret).
Continuous configuration management
The operator enables you to apply changes to the
Cluster resource YAML
section of the PostgreSQL configuration and makes sure that all instances
are properly reloaded or restarted, depending on the configuration option.
Current limitation: changes with
ALTER SYSTEM are not detected, meaning
that the cluster state is not enforced.
CloudNativePG supports the installation of clusters with the PostGIS open source extension for geographical databases, one of the most popular extensions for PostgreSQL.
Basic LDAP authentication for PostgreSQL
The operator allows you to configure LDAP authentication for your PostgreSQL clients, using either the simple bind or search+bind mode, as described in the "PostgreSQL documentation: LDAP authentication" section.
Multiple installation methods
The operator can be installed through a Kubernetes manifest via
apply, to be used in a traditional Kubernetes installation in public
and private cloud environments. Additionally, a Helm Chart for the operator is
Convention over configuration
The operator supports the convention over configuration paradigm, deciding
standard default values while allowing you to override them and customize
them. You can specify a deployment of a PostgreSQL cluster using
Cluster CRD in a couple of YAML code lines.
Level 2 - Seamless Upgrades
Capability level 2 is about enabling updates of the operator and the actual workload, in our case PostgreSQL servers. This includes PostgreSQL minor release updates (security and bug fixes normally) as well as major online upgrades.
Upgrade of the operator
You can upgrade the operator seamlessly as a new deployment. A change in the operator does not require a change in the operand - thanks to the instance manager's injection. The operator can manage older versions of the operand.
CloudNativePG also supports in-place updates of the instance manager following an upgrade of the operator: in-place updates do not require a rolling update - and subsequent switchover - of the cluster.
Upgrade of the managed workload
The operand can be upgraded using a declarative configuration approach as
part of changing the CR and, in particular, the
imageName parameter. The
operator prevents major upgrades of PostgreSQL while making it possible to go
in both directions in terms of minor PostgreSQL releases within a major
version (enabling updates and rollbacks).
In the presence of standby servers, the operator performs rolling updates
starting from the replicas by dropping the existing pod and creating a new
one with the new requested operand image that reuses the underlying storage.
Depending on the value of the
primaryUpdateStrategy, the operator proceeds
with a switchover before updating the former primary (
unsupervised) or waits
for the user to manually issue the switchover procedure (
supervised) via the
cnpg plugin for
Which setting to use depends on the business requirements as the operation
might generate some downtime for the applications, from a few seconds to
minutes based on the actual database workload.
Display cluster availability status during upgrade
At any time, convey the cluster's high availability status, for example,
Setting up primary,
Creating a new replica,
Cluster in healthy state,
Switchover in progress,
Upgrading cluster, etc.
Level 3 - Full Lifecycle
Capability level 3 requires the operator to manage aspects of business continuity and scalability. Disaster recovery is a business continuity component that requires that both backup and recovery of a database work correctly. While as a starting point, the goal is to achieve RPO < 5 minutes, the long term goal is to implement RPO=0 backup solutions. High Availability is the other important component of business continuity that, through PostgreSQL native physical replication and hot standby replicas, allows the operator to perform failover and switchover operations. This area includes enhancements in:
- control of PostgreSQL physical replication, such as synchronous replication, (cascading) replication clusters, and so on;
- connection pooling, to improve performance and control through a connection pooling layer with pgBouncer.
The operator has been designed to provide application-level backups using PostgreSQL’s native continuous backup technology based on physical base backups and continuous WAL archiving. Specifically, the operator currently supports only backups on object stores (AWS S3 and S3-compatible, Azure Blob Storage, Google Cloud Storage, and gateways like MinIO).
WAL archiving and base backups are defined at the cluster level, declaratively,
backup parameter in the cluster definition, by specifying
an S3 protocol destination URL (for example, to point to a specific folder in
an AWS S3 bucket) and, optionally, a generic endpoint URL. WAL archiving,
a prerequisite for continuous backup, does not require any further
action from the user: the operator will automatically and transparently set
archive_command to rely on
barman-cloud-wal-archive to ship WAL
files to the defined endpoint. Users can decide the compression algorithm,
as well as the number of parallel jobs to concurrently upload WAL files
in the archive. In addition to that
Instance Manager automatically checks
the correctness of the archive destination, by performing
command before beginning to ship the very first set of WAL files.
You can define base backups in two ways: on-demand (through the
custom resource definition) or scheduled (through the
customer resource definition, using a cron-like syntax). They both rely on
barman-cloud-backup for the job (distributed as part of the application
container image) to relay backups in the same endpoint, alongside WAL files.
barman-cloud-backup are distributed in
the application container image under GNU GPL 3 terms.
Full restore from a backup
The operator enables you to bootstrap a new cluster (with its settings)
starting from an existing and accessible backup taken using
barman-cloud-backup. Once the bootstrap process is completed, the operator
initiates the instance in recovery mode and replays all available WAL files
from the specified archive, exiting recovery and starting as a primary.
Subsequently, the operator will clone the requested number of standby instances
from the primary.
CloudNativePG supports parallel WAL fetching from the archive.
Point-In-Time Recovery (PITR) from a backup
The operator enables you to create a new PostgreSQL cluster by recovering an existing backup to a specific point-in-time, defined with a timestamp, a label or a transaction ID. This capability is built on top of the full restore one and supports all the options available in PostgreSQL for PITR.
Zero Data Loss clusters through synchronous replication
Achieve Zero Data Loss (RPO=0) in your local High Availability CloudNativePG
cluster through quorum based synchronous replication support. The operator provides
two configuration options that control the minimum and maximum number of
expected synchronous standby replicas available at any time. The operator will
react accordingly, based on the number of available and ready PostgreSQL
instances in the cluster, through the following formula for the quorum (
1 <= minSyncReplicas <= q <= maxSyncReplicas <= readyReplicas
Define a cross Kubernetes cluster topology of PostgreSQL clusters, by taking
advantage of PostgreSQL native streaming and cascading replication.
replica option, you can setup an independent cluster to be
continuously replicating data from another PostgreSQL source of the same major
version: such a source can be anywhere, as long as a direct streaming
connection via TLS is allowed from the two endpoints.
Moreover, the source can be even outside Kubernetes, running in a physical or
Replica clusters can be created from a recovery object store (backup in Barman
Cloud format) or via streaming through
pg_basebackup. Both WAL file shipping
and WAL streaming are allowed.
Replica clusters dramatically improve the business continuity posture of your
PostgreSQL databases in Kubernetes, spanning over multiple datacenters and
opening up for hybrid and multi-cloud setups (currently, manual switchover
across data centers is required, while waiting for Kubernetes federation
Liveness and readiness probes
The operator defines liveness and readiness probes for the Postgres
Containers that are then invoked by the kubelet. They are mapped respectively
/readyz endpoints of the web server managed
directly by the instance manager.
The liveness probe is based on the
pg_isready executable, and the pod is
considered healthy with exit codes 0 (server accepting connections normally)
and 1 (server is rejecting connections, for example during startup). The
readiness probe issues a simple query (
;) to verify that the server is
ready to accept connections.
The operator supports rolling deployments to minimize the downtime and, if a PostgreSQL cluster is exposed publicly, the Service will load-balance the read-only traffic only to available pods during the initialization or the update.
Scale up and down of replicas
The operator allows you to scale up and down the number of instances in a
PostgreSQL cluster. New replicas are automatically started up from the
primary server and will participate in the cluster's HA infrastructure.
The CRD declares a "scale" subresource that allows the user to use the
kubectl scale command.
Maintenance window and PodDisruptionBudget for Kubernetes nodes
The operator creates a
PodDisruptionBudget resource to limit the number of
concurrent disruptions to one primary instance. This configuration prevents the
maintenance operation from deleting all the pods in a cluster, allowing the
specified number of instances to be created. The PodDisruptionBudget will be
applied during the node draining operation, preventing any disruption of the
While this strategy is correct for Kubernetes Clusters where
storage is shared among all the worker nodes, it may not be the best solution
for clusters using Local Storage or for clusters installed in a private
cloud. The operator allows you to specify a Maintenance Window and
configure the reaction to any underlying node eviction. The
in the maintenance window section enables to specify the strategy to be used:
allocate new storage in a different PVC for the evicted instance or wait
for the underlying node to be available again.
Fencing is the process of protecting the data in one, more, or even all instances of a PostgreSQL cluster when they appear to be malfunctioning. When an instance is fenced, the PostgreSQL server process is guaranteed to be shut down, while the pod is kept running. This makes sure that, until the fence is lifted, data on the pod is not modified by PostgreSQL and that the file system can be investigated for debugging and troubleshooting purposes.
Reuse of Persistent Volumes storage in Pods
When the operator needs to create a pod that has been deleted by the user or
has been evicted by a Kubernetes maintenance operation, it reuses the
PersistentVolumeClaim if available, avoiding the need
to re-clone the data from the primary.
CPU and memory requests and limits
The operator allows administrators to control and manage resource usage by
the cluster's pods, through the
resources section of the manifest. In
limits values can be set for both CPU and RAM.
Connection pooling with PgBouncer
CloudNativePG provides native support for connection pooling with PgBouncer, one of the most popular open source connection poolers for PostgreSQL. From an architectural point of view, the native implementation of a PgBouncer connection pooler introduces a new layer to access the database which optimizes the query flow towards the instances and makes the usage of the underlying PostgreSQL resources more efficient. Instead of connecting directly to a PostgreSQL service, applications can now connect to the PgBouncer service and start reusing any existing connection.
Level 4 - Deep Insights
Capability level 4 is about observability: in particular, monitoring, alerting, trending, log processing. This might involve the use of external tools such as Prometheus, Grafana, Fluent Bit, as well as extensions in the PostgreSQL engine for the output of error logs directly in JSON format.
CloudNativePG has been designed to provide everything that is needed to easily integrate with industry-standard and community accepted tools for flexible monitoring and logging.
Prometheus exporter with configurable queries
The instance manager provides a pluggable framework and, via its own web server
listening on the
metrics port (9187), exposes an endpoint to export metrics
for the Prometheus monitoring and alerting tool.
The operator supports custom monitoring queries defined as
Secret objects using a syntax that is compatible with the
postgres_exporter for Prometheus.
CloudNativePG provides a set of basic monitoring queries for
PostgreSQL that can be integrated and adapted to your context.
The [cnp-sandbox project] is an open source Helm chart that demonstrates
how to integrate CloudNativePG with Prometheus and Grafana, by providing
some basic metrics and an example of dashboard.
Standard output logging of PostgreSQL error messages in JSON format
Every log message is delivered to standard output in JSON format, with the first level
definition of the timestamp, the log level and the type of log entry, such as
postgres for the canonical PostgreSQL error message channel.
As a result, every Pod managed by CloudNativePG can be easily and directly
integrated with any downstream log processing stack that supports JSON as source
Real-time query monitoring
CloudNativePG transparently and natively supports:
- the essential
pg_stat_statementsextension, which enables tracking of planning and execution statistics of all SQL statements executed by a PostgreSQL server.
auto_explainextension, which provides a means for logging execution plans of slow statements automatically, without having to manually run
EXPLAIN(helpful for tracking down un-optimized queries).
CloudNativePG allows database and security administrators, auditors, and operators to track and analyze database activities using PGAudit (for PostgreSQL). Such activities flow directly in the JSON log and can be properly routed to the correct downstream target using common log brokers like Fluentd.
Record major events as expected by the Kubernetes API, such as creating resources,
removing nodes, upgrading, and so on. Events can be displayed through
kubectl describe and
kubectl get events command.
Level 5 - Auto Pilot
Capability level 5 is focused on automated scaling, healing and tuning - through the discovery of anomalies and insights that emerged from the observability layer.
Automated Failover for self-healing
In case of detected failure on the primary, the operator will change the
status of the cluster by setting the most aligned replica as the new target
primary. As a consequence, the instance manager in each alive pod will
initiate the required procedures to align itself with the requested status of
the cluster, by either becoming the new primary or by following it.
In case the former primary comes back up, the same mechanism will avoid a
split-brain by preventing applications from reaching it, running
the server and restarting it as a standby.
Automated recreation of a standby
In case the pod hosting a standby has been removed, the operator initiates the procedure to recreate a standby server.