Kubernetes Production Readiness Self-Assessment

Think you’re ready for production? Ensuring that your Weaviate cluster is production-ready requires careful planning, configuration, and ongoing maintenance. Ensuring that you have a stable, reliable deployment requires you to think of your ending at the beginning. This guide provides you with introspective questions to assess readiness and identify any potential gaps before moving your workloads into production.

tip

If you do identify gaps within your deployment, be sure to reach out to your SE (solutions engineer) who can help steer you on the path to production success!

High Availability and Resilience

• Are your clusters deployed across multiple availability zones (AZs) or regions to prevent downtime?
• Are you running Weaviate in a highly available setup with a 3-node minimum?
• Have you configured your schema to use a replication factor of 3 to ensure copies of data are available during node outage?
• Are replicas deployed across multiple nodes for redundancy?
• Is your control plane highly available?
• Is your application fault-tolerant without your control plane?
• Are there automatic node repair or self-healing mechanisms in place?
• Have failover scenarios been tested to validate resilience?
• Are you utilizing Weaviate’s backup capabilities for disaster recovery?
  • How often are these mechanisms tested?
  • Has the ability to recover from a node failure or database corruption been tested?
• Have you thought about the retention period of backups?
  • How do you clean up any out-of-date backups?
• Are rolling updates performed to avoid downtime?
• Are canary deployments implemented to safely test new releases?
• Do you have development or test environments to safely test changes?

Data Ingestion and Query Performance

• Is there a strategy for handling heavy ingestion loads?
• Has the percentage of resources for indexing vs querying applications been specified?
• Is there a defined strategy for data deduplication and cleanup before ingestion?
• How frequently is data added, updated, or deleted?
  • Is data updated in place or mostly append-only
  • How often do deletion operations trigger garbage collection?
• Have you implemented a scheduling strategy for large ingestion jobs?
• Have you tested query performance under load?
  • Is query performance monitored using Prometheus or Grafana?
• Have replica shards been deployed for load balancing and failover support?

Resource Management

• Have you considered your data’s consumption pattern(s)?
  • Has your memory allocation been right-sized to match workload demand?
  • Has your storage/compute allocation also been right-sized to match workload demand?
  • Is there a process to delete old or unused objects?
• Have multiple replicas been configured to balance read-heavy workloads?
• Has the proper storage class been selected for your needs?
  • Does your storage class support volume expansion so that you can support growth over time?
• Is the data within your cluster properly backed up, including the persistent storage?
• Is the sharding strategy aligned with the size and access patterns of the dataset?
• Is GOMEMLIMIT properly configured for memory management?
  • Is GOMEMLIMIT set based on available system memory to prevent excessive garbage collection pauses?
• Have you considered vector quantization techniques to reduce memory requirements?

Tenant State Management

• Are you implementing multi-tenancy?
  • Are there limits or quotas per tenant to avoid noisy neighbor issues?
• Is there a strategy for offloading inactive tenant data?

Security

• Are the components of your cluster communicating via SSL/TLS and trusted certificates?
• Is the “principle of least privilege” being followed?
• Are your container security defaults set properly?
• Is access to your cluster strictly limited?
• Has RBAC been implemented to restrict access?
• Have network policies been implemented to limit pod-to-pod communication?
• Are secrets secured with K8s Secrets or a vault solution?
• Do you have a process for when secrets are exposed, when access is lost to a key or certificate, and when secrets need to be rotated?

Monitoring and Observability

• Is logging implemented?
  • Are the collected logs stored centrally?
• Is metric collection enabled using Prometheus (or Alloy, DataDog, or another monitoring platform)?
• Are health and performance metrics being visualized in Grafana?
• Are alerts configured for events?

Evaluate these key areas to build a highly available, resilient, and efficient deployment that will scale to meet your business needs. By ensuring that these self-assessment questions have been addressed, you can proactively identify potential risks and maximize the reliability of your deployment.

Questions and feedback

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