Cloud Native Database Support

MinervaDB Inc. • Cloud Native Database Engineering
Cloud Native Database Systems Support from MinervaDB Inc.
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Cloud Native Database Support, Kubernetes SRE and 24/7 Performance Engineering

MinervaDB provides vendor-neutral cloud native database support for engineering teams that run PostgreSQL, MySQL, MariaDB, MongoDB, Redis, ClickHouse and Kafka on Kubernetes and on managed cloud data services. This page is deliberately technical. It explains what actually changes when a stateful database moves onto a container orchestrator, which controls decide durability and latency, how operators reconcile a failed primary, and how our engineers diagnose the failure modes that only appear once storage, scheduling and replication are separated by an API. If you are evaluating partners for cloud native database support, read the sections below as a competency checklist for cloud native database support. Nothing here is aspirational: every pattern is drawn from production incident work, migrations off virtual machines, and capacity reviews for platforms measured in petabytes and millions of transactions per hour.

Contents

Cloud Native Database Support Starts With Reference Architecture

A cloud native database is not simply a database in a container. It is a declarative system in which desired state lives in Git, a controller reconciles that state continuously, storage is attached over an API, and every network path is a policy decision. The topology below is the shape we design, review and support most often. 
       GITOPS / DEVELOPERS                   KUBERNETES CONTROL PLANE
  +--------------------------+          +------------------------------+
  | Git repo = desired state |   API    |  kube-apiserver  +  etcd     |
  | Helm / Kustomize / Argo  |--------->|  scheduler | controller-mgr  |
  +--------------------------+          +---------------+--------------+
                                                        | watch + reconcile
                                        +---------------v--------------+
                                        |     DATABASE OPERATOR        |
                                        |  CRDs: Cluster, Backup,      |
                                        |  Restore, Pooler, Schedule   |
                                        +---------------+--------------+
                                                        |
   ZONE-a                    ZONE-b                     |            ZONE-c
 +--------------+        +--------------+                        +--------------+
 |  pod db-0    | stream |  pod db-1    | stream                 |  pod db-2    |
 |  PRIMARY  RW |<------>|  REPLICA  RO |<---------------------->|  REPLICA  RO |
 |  PVC-0       |        |  PVC-1       |                        |  PVC-2       |
 +------+-------+        +------+-------+                        +------+-------+
        |                       |                                      |
    CSI driver              CSI driver                             CSI driver
        |                       |                                      |
 [ zonal block vol ]     [ zonal block vol ]                   [ zonal block vol ]

  SERVICES    rw-svc  -> endpoint follows the current primary only
              ro-svc  -> healthy, non-lagging replicas
  ARCHIVE     WAL / binlog / oplog streamed continuously to object storage
  POLICY      NetworkPolicy + mTLS between every tier, no flat pod network
Three rules govern every cluster our cloud native database support engineers sign off. First, replicas of the same dataset never share a failure domain, which means anti-affinity by zone and by node, not merely by pod. Second, the write endpoint is an abstraction owned by the operator, never a hard-coded pod address, so promotion is invisible to applications that reconnect correctly. Third, the archive is external to the cluster, because a backup that lives on the same CSI storage class as the primary is not a backup.

What Cloud Native Actually Changes for Database Support

Teams engaging us for cloud native database support often assume Kubernetes is a thinner hypervisor. It is not. The scheduler is free to evict, the node may disappear, the volume is a network resource, and the control loop will fight any manual change you make. The table below is the mental model we teach during onboarding.
Concern Virtual machine estate Cloud native platform Consequence for the DBA
Identity Hostname is stable for years Pod is disposable; StatefulSet ordinal is the stable identity Never bind config, certificates or monitoring to a pod IP
Storage Local NVMe or SAN LUN PVC bound to a zonal volume through a CSI driver Latency, IOPS ceilings and attach time become schedulable properties
Failover Runbook plus a human, or Pacemaker Operator reconciliation with leader election Fencing correctness matters more than promotion speed
Config change Edit the file, reload Change the custom resource, controller rolls the fleet Drift is reverted automatically; out-of-band edits are lost
Capacity Resize the VM Requests, limits, PVC expansion, node pool shape Throttling and OOM kills replace simple saturation
Upgrade In-place, one host at a time Rolling update ordered by the controller Minor version skew windows must be planned, not discovered
Our PostgreSQL on Kubernetes practice and our Apache Kafka support team share the same operating principle: the orchestrator is authoritative, so every operational habit that assumes a mutable server has to be rewritten before the first production cutover.

Kubernetes Operators, Reconciliation and Cloud Native Database Support

An operator is a controller that encodes cloud native database support expertise as software. It watches a custom resource, compares it with the observed world, and issues the smallest set of actions that closes the gap. Understanding this loop is the single most useful thing a DBA can learn when moving to cloud native database support, because every cloud native database support decision is expressed through it. 
   DESIRED STATE (custom resource)          OBSERVED STATE (live cluster)
   +-----------------------------+          +----------------------------+
   | spec:                       |          | 2 pods Ready               |
   |   instances: 3              |          | 1 pod CrashLoopBackOff     |
   |   version: 17.2             |          | running version 17.1       |
   |   storage: 2Ti              |          | PVC size 1Ti               |
   |   backup: every 15 min      |          | last archive 41 min ago    |
   +--------------+--------------+          +-------------+--------------+
                  |                                       |
                  +---------------> DIFF <----------------+ | +-------------v--------------+ | OPERATOR ACTIONS | | roll pod with new image | | expand PVC via CSI | | promote healthiest replica| | repoint rw Service | | re-arm archive timer | +-------------+--------------+ | apply -> observe -> diff -> apply   (forever)
Two operational consequences follow immediately. Manual intervention is temporary, because anything you change by hand is reverted on the next pass unless you also change the resource that describes it. And an operator upgrade is a production change, because the reconciliation logic itself is the thing deciding whether your primary is fenced correctly. We pin operator versions, read the release notes for reconciliation behaviour, and rehearse upgrades in a staging cluster that has the same storage class as production. Choosing an operator is an architectural cloud native database support decision rather than a preference. We evaluate candidates against a fixed rubric: does it fence before it promotes, does it support point-in-time recovery to a timestamp, can it perform a minor-version rolling upgrade without a full restore, does it expose replication lag as a first-class condition, and is the project governed openly. The Kubernetes operator pattern documentation describes the mechanism; the rubric is what keeps you from adopting an operator that automates the wrong thing very reliably.

StatefulSets, PersistentVolumeClaims and the CSI Storage Stack

Every durability guarantee a cloud native database makes is ultimately a claim about what happened when it called fsync, and cloud native database support begins with proving that claim. On Kubernetes that call travels through more layers than most teams realise, and each layer can silently change the performance envelope. 
  LAYER                      WHAT IT CONTROLS                       TUNING LEVER
  ------------------------------------------------------------------------------
  database engine            shared buffers, WAL/redo writer,       checkpoint pacing,
                             checkpointer, background flush         wal_compression
  ------------------------------------------------------------------------------
  container filesystem       ext4 / xfs journal behaviour           mount options,
                             page cache inside the cgroup           noatime, discard
  ------------------------------------------------------------------------------
  PersistentVolumeClaim      capacity, access mode RWO,             StorageClass choice,
                             expansion policy                       allowVolumeExpansion
  ------------------------------------------------------------------------------
  CSI driver                 attach / detach, snapshot, resize      driver version,
                             topology constraints                   volumeBindingMode
  ------------------------------------------------------------------------------
  cloud block device         provisioned IOPS, throughput cap,      volume type and size,
                             zone affinity                          burst vs baseline
  ------------------------------------------------------------------------------
  node                       queue depth, NIC bandwidth shared      instance family,
                             with every other pod                   dedicated node pool
A StatefulSet gives each replica a stable ordinal identity and a PVC that survives rescheduling, which is exactly what a database needs. It does not give you a free lunch: because a zonal volume can only attach to a node in its own zone, a pod whose zone is out of capacity will stay Pending rather than move. We therefore treat storage topology as part of the availability design, not as an afterthought, and we set volumeBindingMode: WaitForFirstConsumer so the scheduler and the volume agree on placement before anything is provisioned. The Kubernetes persistent volume documentation covers the primitives; production behaviour is dominated by the driver and the volume class you pick.

Storage decisions that change database latency

Symptom Likely cause in a cloud native stack Remedy we apply
Commit latency spikes every few hours Burst credit exhaustion on a baseline-IOPS volume Move to provisioned IOPS class, size volume for throughput not capacity
Checkpoint storms stall queries Write cache flushed against a throughput-capped device Spread checkpoints, raise volume throughput, separate WAL volume
Pod stuck Pending after node loss Zonal volume cannot attach in the surviving zone Zone-aware replica placement plus streaming, never volume-level failover
Slow restore from snapshot Lazy-loaded blocks hydrated on first read Pre-warm, or restore from archive stream instead of snapshot
Disk full despite retention policy Archive failing silently, WAL/binlog retained for safety Alert on archive age, not on archive errors alone

High Availability, Fencing and Automated Failover

Automated failover is where cloud native database support earns its fee, so we treat it as the first thing to audit. A promotion that happens too quickly, without fencing the old writer, produces two primaries and a data reconciliation problem that no backup can fully undo. The sequence below is the behaviour we require from any operator we support. 
  t+0.0s   node hosting the primary stops reporting; kubelet lease expires
  t+~5s    operator health probe fails N consecutive checks (not just one)
  t+~8s    FENCE FIRST: rw Service endpoints emptied, writer connections cut,
           old primary is marked unschedulable and prevented from restarting
  t+~10s   candidate selection: replica with the highest received position
           and lowest apply lag wins; ties broken by zone diversity policy
  t+~12s   promotion issued; a new timeline / epoch begins so divergent
           history from the old primary can never be replayed by accident
  t+~14s   rw Service endpoint repointed; clients reconnect on a new socket
  t+~60s   old primary rejoins as a replica after rewind or full re-clone

  FAILURE TO FENCE = SPLIT BRAIN. Speed is worthless without exclusion.
Cloud native database support game days measure three separate numbers, and they are not the same number. Detection time is how long the platform takes to believe the primary is gone. Promotion time is how long the new writer takes to accept its first transaction. Recovery time as experienced by the application includes DNS or endpoint propagation, connection pool reconnection and retry backoff, and it is usually two to five times longer than the promotion figure quoted in a vendor datasheet. Our 24/7 emergency DBA coverage exists because the gap between those numbers is where outages live.

Service Routing, Connection Pooling and Client Behaviour

Most "the database went down" tickets that reach our cloud native database support desk are really connection-layer tickets. The database recovered in twelve seconds; the application took nine minutes because a pool held dead sockets and a retry policy backed off exponentially without a cap. 
  application pods
        |
        |  (1) resolve rw-svc.namespace.svc.cluster.local
        v
  +-------------+        Service is a virtual IP, not a proxy with memory.
  |  rw Service |        On failover its ENDPOINT changes; existing TCP
  +------+------+        sessions are NOT migrated, they are broken.
         |
         v
  +---------------------+     pooling tier absorbs reconnect storms,
  |  connection pooler  |     caps backend connections, and gives you
  |  (pgbouncer / proxy)|     one place to pause traffic during a switchover
  +----------+----------+
             |
             v
     +----------------+     max_connections is a memory budget, not a
     | primary pod    |     capacity plan. Every backend costs RAM inside
     +----------------+     the same cgroup limit that can OOM-kill you.

  CLIENT CHECKLIST   short socket timeouts | bounded retry with jitter |
                     fail fast on stale endpoint | separate ro and rw pools |
                     health check that runs a real query, not just a TCP dial
Cloud native database support standards here are simple: we insist on a pooling tier for any cluster with more than a few hundred client connections, we cap total backends well below the memory limit of the container, and we require applications to distinguish read and write endpoints so that a promotion drains only the write path. Where a service mesh is already in place, we use it for mutual TLS and outlier detection rather than for retries, because a mesh that retries a non-idempotent write is a data-integrity hazard.

Resource Requests, Limits, QoS and CPU Throttling

The most common performance regression we see in cloud native database support work has nothing to do with the database engine. It is CPU throttling caused by a limit that looked generous on a spreadsheet. A database that briefly needs eight cores to flush a checkpoint will be throttled hard by a two-core quota, and the symptom presents as random latency rather than as CPU saturation.
Setting What it really does MinervaDB guidance for stateful workloads
CPU request Scheduling weight and guaranteed share under contention Size to the steady-state working set, never below observed p95
CPU limit Hard quota enforced per 100 ms period; excess is throttled Usually omit it, or set it far above request; monitor throttled periods
Memory request Scheduling and eviction protection Set equal to the limit to obtain Guaranteed QoS class
Memory limit Hard ceiling; breach means the process is killed, not slowed Buffer pool plus work memory plus connections plus 25 percent headroom
Huge pages Reduces page table overhead for large shared memory areas Enable for large buffer pools; must be reserved on the node first
Priority class and PDB Eviction ordering and voluntary disruption budget High priority class, PDB that never allows quorum loss
In cloud native database support reviews we check this first, because page cache is charged to the container cgroup, which means a database that relies on the operating system cache can be evicted for using memory that it would happily give back. We size the engine cache explicitly, leave verified headroom, and alert on throttling counters rather than on average CPU. The Kubernetes resource management guide defines the mechanics; the sizing judgement is what we bring.

Backups, Point-in-Time Recovery and Object Storage

Snapshots are convenient, and on their own they are not a recovery strategy, which is why cloud native database support has to start with recoverability. A volume snapshot captures a moment; a database needs a continuous archive so it can be restored to the instant before a bad deployment. Our standard for cloud native database support combines both, and treats restore rehearsal as the only evidence that either works. 
  CONTINUOUS ARCHIVE                       PERIODIC BASE
  primary --> WAL / binlog / oplog         volume snapshot or full backup
      |        shipped every N seconds          taken every 24h
      v                                              |
  +---------------------------+                      |
  |  object storage bucket    | <--------------------+ | versioned + object lock | separate account / project, | lifecycle to cold tier | separate credentials, cross-region copy +-------------+-------------+ | v RESTORE PATH base image ->  replay archive to target timestamp  ->  verify
                                                              |
   VERIFY MEANS: row counts, checksum sample, application smoke test,
   and a recorded RTO. An unverified restore is an untested assumption.
Cloud native database support requires the archive destination to sit in a different security boundary from the cluster, with object lock or immutability enabled, because ransomware and a misapplied manifest have the same effect on a bucket that a cluster service account can delete. Recovery objectives are written down as numbers, rehearsed quarterly, and tied to the retention window that the archive can actually satisfy.

Observability for Cloud Native Database Support

Observability for cloud native database support needs two correlated views: what the engine is doing, and what the platform is doing to the engine. A slow query dashboard that cannot show you that the pod was throttled or that the volume hit its throughput ceiling will send you chasing the wrong cause.
Layer Signals we alert on Why it matters
Engine Commit latency percentiles, replication lag in bytes and seconds, lock waits, checkpoint duration, connection saturation Direct user-visible impact
Container CPU throttled periods, memory working set versus limit, OOM kill count, restart count Explains latency that the engine cannot explain
Storage Volume IOPS and throughput against provisioned ceiling, queue depth, attach and detach events Identifies the ceiling before it becomes an outage
Control plane Operator reconcile errors, custom resource conditions, pending pods, PDB blocks, node lease expiry Predicts failover and upgrade problems
Data protection Age of last successful archive segment, last verified restore, snapshot success The only metrics that describe recoverability
For cloud native database support, retention matters as much as collection. Post-incident analysis needs at least thirteen months of low-resolution history to distinguish a genuine regression from seasonal growth, and high-resolution samples for the days around an event. We also record every operator reconcile alongside engine metrics, because a latency change that begins exactly when a rolling update started is not a coincidence.

Multi-Zone and Multi-Region Topologies

In cloud native database support, zonal redundancy protects against a data centre failure and costs little more than careful placement. Regional redundancy protects against a region failure and changes your consistency model, your latency budget and your bill. We help teams choose deliberately rather than by default. 
  PATTERN A  single region, three zones          synchronous quorum
  +--------+   +--------+   +--------+           RPO = 0
  | zone a |   | zone b |   | zone c |           RTO = seconds
  | PRIMARY|<->| SYNC   |<->| SYNC   |           commit waits for a peer
  +--------+   +--------+   +--------+           protects: zone loss

  PATTERN B  primary region + async standby region
  region-1 (rw)                    region-2 (ro / DR)
  [ 3-zone quorum ] === async ===> [ standby cluster, own quorum ]
  RPO = replication lag at failure   RTO = minutes, promotion is a decision
  protects: region loss, blast-radius of a bad control-plane change

  PATTERN C  active-active / multi-writer
  region-1 (rw) <== conflict resolution ==> region-2 (rw)
  RPO ~ 0 per region, but WRITE CONFLICTS BECOME AN APPLICATION PROBLEM.
  Adopt only when the data model has natural partitioning or CRDT semantics.

  DECIDE WITH: what does one hour of downtime cost, and what does one
  lost transaction cost? Those two numbers select the pattern for you.
Whichever pattern applies, cloud native database support requires an independent control plane per region. A standby that depends on the primary region's cluster API, secret store or CI system is not a disaster recovery site, it is a replica with extra latency. We validate that a region can be promoted with its own credentials, its own image registry mirror and its own archive copy. Our cloud platform teams for AWS, Google Cloud and Microsoft Azure handle the provider-specific parts of that design.

Security Hardening: RBAC, Secrets, mTLS and Network Policy

Container platforms default to convenience, so security is an explicit workstream in every cloud native database support engagement. A production data platform has to be moved deliberately to least privilege, and every one of the controls below has appeared as a finding in audits we have run.
Control Common weak default Hardened target
Network reachability Flat pod network, any pod can dial the database port Default-deny NetworkPolicy, explicit allow per namespace and port
Transport TLS terminated at the edge, plaintext inside the cluster Mutual TLS end to end with short-lived, automatically rotated certificates
Credentials Long-lived passwords in a Secret, never rotated External secret manager, workload identity, scheduled rotation with no downtime
Cluster permissions Broad ClusterRole bound to the operator service account Namespaced roles, no wildcard verbs, exec into database pods audited and alerted
Pod posture Root filesystem writable, privileged escalation allowed Restricted Pod Security Standard, read-only root, dropped capabilities, seccomp
Images Mutable tags pulled from the internet Digest-pinned images from a private registry with signature verification
Data at rest Provider default key Customer-managed keys, encrypted archive, separate key custody from cluster admin
Auditability is part of every cloud native database support deliverable. We make sure that every privileged action against a database, including an interactive shell into a pod, produces a durable log entry outside the cluster that is retained for the period your regulator expects.

FinOps: The Real Cost Model of Databases on Kubernetes

Consolidating databases onto Kubernetes usually reduces cost, but not for the reason most cloud native database support business cases claim. The saving comes from bin-packing and from removing per-instance licensing and per-instance idle capacity, not from the orchestrator itself. Cost discipline requires knowing which line items actually move. 
  MONTHLY COST OF A DATABASE PLATFORM  (relative weight, typical estate)

  compute (node pools)      ############################   45-55 %
  block storage             ##################             25-30 %
  storage IOPS / throughput ########                        5-12 %
  cross-zone data transfer  ######                          4-10 %   <-- silent
  backup + archive storage  ####                            3-6 %
  observability pipeline    ###                             2-5 %    <-- silent
  control plane fees        #                               1-2 %

  LEVERS THAT ACTUALLY WORK
   1. right-size requests from observed p95, then bin-pack the node pool
   2. buy committed / reserved capacity for the steady baseline only
   3. tier cold archive data and stop paying hot rates for audit history
   4. keep chatty replicas zone-aware to cut cross-zone transfer
   5. sample and aggregate metrics; cardinality is a real budget line
   6. consolidate small clusters into namespaced tenants with quotas

  LEVERS THAT LOOK GOOD AND HURT
   x. shrinking memory limits until the buffer pool thrashes
   x. spot / preemptible nodes for quorum members
   x. cutting replicas to two and losing the ability to lose one
Cloud native database support should be auditable, so we publish the arithmetic behind every recommendation so finance and engineering can argue about the same numbers. Where a managed cloud database service is genuinely cheaper for a given workload, we say so; vendor neutrality is the reason clients let us review both options. Our cloud database optimization and FinOps practice runs these reviews as a standalone engagement.

Failure Modes Our Cloud Native Database Support Team Diagnoses

Observed failure Underlying mechanism Correction
Two writers accepted transactions during a failover Promotion issued before the old primary was fenced or its endpoint removed Fence-first policy, endpoint gating, timeline or epoch enforcement
Cluster healthy, application timing out Stale connection pool holding sockets to a promoted-away pod Bounded socket timeouts, pool eviction on error, real query health checks
Random multi-second query latency CPU quota throttling during checkpoint or vacuum Remove or widen the limit, alert on throttled periods, isolate node pool
Pod OOM-killed with plenty of node memory free Cgroup limit reached including page cache and per-connection memory Explicit cache sizing, Guaranteed QoS, connection cap at the pooler
Replica never catches up after a node replacement Archive gap plus insufficient retention forces a full re-clone Retention sized to clone duration, replication slots with safety limits
Rolling upgrade stalled halfway PodDisruptionBudget blocked eviction while quorum was already degraded Correct PDB arithmetic, pre-flight health gate before each roll
Restore succeeded, data was wrong Recovery target set to a segment boundary rather than a timestamp Timestamp-based PITR, mandatory post-restore verification suite
Storage costs doubled with no traffic change Orphaned PVCs and snapshots left by failed reconciles Reclaim policy review, lifecycle rules, scheduled orphan reporting

Migrating From Virtual Machines to Kubernetes

A database migration onto a container platform is a change of operating model rather than a lift and shift, and our cloud native database support team so our cloud native database support plan keeps the rollback cheap for as long as possible. The path below is the one we run for clients moving PostgreSQL, MySQL, MariaDB or MongoDB estates. 
  PHASE 0  ASSESS      inventory, versions, extensions, SLOs, licence exposure,
                       storage profile, connection topology, compliance scope
     |
  PHASE 1  DESIGN      operator selection, storage class, zone layout, sizing,
                       archive design, security baseline, rollback criteria
     |
  PHASE 2  BUILD       non-production cluster from Git, restore real data,
                       load test at production concurrency, break things on purpose
     |
  PHASE 3  REHEARSE    game day: kill a node, fill a disk, throttle a volume,
                       corrupt an archive, promote a region. Measure every RTO.
     |
  PHASE 4  REPLICATE   live replication from the legacy primary into the new
                       cluster; both sides observable side by side for weeks
     |
  PHASE 5  CUTOVER     drain writers at the pooler, verify zero lag, promote,
                       repoint, keep the legacy primary warm as rollback target
     |
  PHASE 6  OPERATE     handover runbooks, alert tuning, quarterly game days,
                       capacity review, operator upgrade calendar

  ROLLBACK REMAINS AVAILABLE UNTIL PHASE 6 IS SIGNED OFF.
Cloud native database support does not treat cutover as the finish line. The first four weeks of cloud native database support after migration produce the most useful tuning data of the whole project, because it is the only period in which the same workload has run on both architectures with comparable instrumentation.

MinervaDB Cloud Native Database Support Catalogue and SLAs

MinervaDB cloud native database support is engine-agnostic and platform-agnostic. We support self-managed Kubernetes, every major managed Kubernetes service, and managed cloud database services where the workload belongs there instead. Engagements are staffed by engineers who have carried a pager for these systems.
Tier Coverage First response Best for
Advisory Business hours, scheduled architecture and review sessions 1 business day Teams designing their first cloud native data platform
Production 24 x 7 for severity 1 and 2, proactive health reviews 30 minutes for severity 1 Revenue-bearing workloads already running on Kubernetes
Mission critical 24 x 7 x 365, named engineers, quarterly game days, upgrade calendar 15 minutes for severity 1 Payments, trading, healthcare, telemetry and regulated platforms
Project Fixed-scope engagement with defined deliverables By agreement Migrations, operator adoption, DR builds, cost and security audits

What every cloud native database support tier includes

  • Architecture review of the operator, storage class, zone layout and failover policy.
  • Recoverability audit covering archive continuity, immutability and a measured restore.
  • Performance engineering across the engine, container, storage and client layers.
  • Security baseline against least-privilege RBAC, network policy, mTLS and secret rotation.
  • Written runbooks and handover so your team can operate the platform without us.
Engine-specific work continues under the same cloud native database support agreement through our PostgreSQL support, MySQL support, MariaDB support, MongoDB support and analytics and data warehousing teams, and day-to-day operations can be delegated to MinervaDB remote DBA services. If you would prefer a single commercial relationship for the whole estate, our enterprise database support programme wraps all of it.

How a cloud native database support engagement starts

  WEEK 0     discovery: clusters, operators, versions, storage classes, SLOs
     |
  WEEK 1     instrumented baseline: latency, lag, throttling, IOPS ceilings,
             archive age, restore timing, security posture
     |
  WEEK 2     findings report with ranked, costed remediation and a rollback plan
     |
  WEEK 3+    implementation alongside your engineers, then continuous support
             with quarterly game days and an operator upgrade calendar
Talk to a MinervaDB cloud native database engineer to schedule a platform review. The Cloud Native Computing Foundation landscape moves quickly, and we track it so that your platform decisions age well.

Frequently Asked Questions

Is running a production database on Kubernetes actually safe?

Yes. Cloud native database support makes it safe when the operator fences before it promotes, storage is zone-aware, and recovery is rehearsed. The platform is not the risk; unverified assumptions about failover and restore are. We measure both before signing off a production cutover.

Do I need an operator, or can I use a StatefulSet directly?

In cloud native database support terms, a bare StatefulSet gives you stable identity and storage but no promotion logic, no archive management and no rolling upgrade safety. For anything beyond a development cluster we use an operator, chosen against a written rubric rather than by popularity.

Which operator do you recommend?

Our cloud native database support practice is deliberately vendor-neutral and recommends per workload. The decision turns on fencing behaviour, point-in-time recovery to a timestamp, upgrade mechanics, how replication lag is surfaced, and the governance of the project. We will support the operator you already run.

Should some databases stay on managed cloud services instead?

Often, yes, and honest cloud native database support says so. Small, low-change workloads with no unusual extension requirements are frequently cheaper and safer on a managed service. Cloud native database support means choosing the right runtime for each workload, not moving everything onto Kubernetes.

How much cost saving is realistic?

Most estates that enter cloud native database support carry twenty to forty percent of avoidable spend, concentrated in oversized requests, over-provisioned storage throughput, cross-zone transfer and metric cardinality. We quantify it during the baseline week rather than promising a figure in advance.

What causes split brain on Kubernetes and how do you prevent it?

In cloud native database support reviews we look for split brain risk, which happens when a new primary is promoted while the old one can still accept writes. Prevention is fencing before promotion, endpoint gating at the service layer, and a timeline or epoch marker that makes divergent history unreplayable.

Can you support us if we already have a platform team?

That is our most common cloud native database support engagement. We work alongside your platform and SRE engineers as the database specialists, contribute to your Git repositories and runbooks, and stay on the escalation path for severity 1 incidents.

Which engines and platforms do you cover?

Cloud native database support covers PostgreSQL, MySQL, MariaDB, MongoDB, SQL Server, Redis, ClickHouse, Kafka and the major cloud database services, on self-managed Kubernetes and on every major managed Kubernetes offering, in single-region, multi-region and hybrid topologies.

How quickly can you help during an outage?

Mission critical cloud native database support clients get a named engineer within fifteen minutes for severity 1. If you are in an incident now, use our 24/7 emergency DBA channel rather than the standard enquiry form.