GCP deployment guidelines

Recommended instance types

As a general guideline, we recommend:

• ARM-based CPU.
• A 1:8 ratio of vCPU to GiB memory.
• At least a 2:1 ratio of GiB local instance storage to GiB memory when using swap.

When operating on GCP in production, we recommend the Arm-based C4A high-memory series. Both C4A and C4 offer local SSDs only on their -lssd machine variants, which bundle a fixed number of Titanium SSD disks.

C4A is not available in every region. Where it is unavailable, use the x86-based C4 high-memory series instead.

To maintain the recommended disk-to-RAM ratio for your machine type, see Number of local SSDs to determine the number of local SSDs to use.

See also Locally attached NVMe storage.

Number of local SSDs

Each local SSD in GCP provides 375GB of storage. Use the appropriate number of local SSDs to ensure your total disk space is at least twice the amount of RAM in your machine type for optimal Materialize performance.

C4A and C4 bundle a fixed number of Titanium SSD disks in each -lssd machine variant. The count is not configurable, but every high-memory -lssd variant satisfies the 2:1 disk-to-RAM ratio:

For other machine series, the local SSD count is configurable but may only support predefined values. To determine the valid number of local SSDs to attach for your machine type, see the GCP documentation.

Locally-attached NVMe storage

Configuring swap on nodes to use locally-attached NVMe storage allows Materialize to spill to disk when operating on datasets larger than main memory. This setup can provide significant cost savings and provides a more graceful degradation rather than OOMing. Network-attached storage (like EBS volumes) can significantly degrade performance and is not supported.

Swap support

The Materialize Terraform module supports configuring swap out of the box.

CPU affinity

It is strongly recommended to enable the Kubernetes static CPU management policy. This ensures that each worker thread of Materialize is given exclusively access to a vCPU. Our benchmarks have shown this to substantially improve the performance of compute-bound workloads.

Recommended metadata database sizing

Self-managed Materialize uses an external PostgreSQL metadata database to store its catalog and to coordinate the state of the objects it keeps up to date. Every durable object that updates continuously (materialized views, sources, sinks, and tables) produces a steady stream of small writes to the metadata database. Metadata-database load therefore scales with the number of continuously-updating objects, not with the volume of data flowing through them.

Safe operating point

The primary factor that dictates the size of the metadata database is the number of durable objects Materialize keeps continuously fresh (materialized views, sources, sinks, and tables). Data volume, the query rate against Materialize, and cluster size do not materially change metadata database load. For example, a larger cluster running the same number of materialized views places roughly the same load on the metadata database.

It is recommended that you size the metadata database so that its steady-state CPU stays below 60%. The headroom between ~60% and full utilization provides capacity to absorb everyday load variance, background database maintenance, and Materialize zero-downtime upgrades.

Cloud SQL machine types

For the Cloud SQL for PostgreSQL instance that backs the metadata database, we recommend:

• The Enterprise Plus edition with a performance-optimized (N-series) machine type, which provides the 1:8 vCPU-to-memory ratio recommended for the metadata database. Avoid shared-core machine types (db-f1-micro, db-g1-small) in production.
• A regional (highly available) configuration for production.

TLS

When running with TLS in production, run with certificates from an official Certificate Authority (CA) rather than self-signed certificates.

Upgrading guideline

Whe upgrading:

• Always check the version-specific upgrade notes.

• Always upgrade the operator first and ensure version compatibility between the operator and the Materialize instance you are upgrading to.

• Always upgrade your Materialize instances after upgrading the operator to ensure compatibility.

Node pool resizing

The VM type of a Kubernetes node pool is immutable on EKS, AKS, and GKE, so changing it triggers a destroy + create that fails while Materialize pods are still running on the pool. The supported pattern is to add a second pool with the new VM type, roll out the Materialize instance so new pods land on it, and then drop the old pool.

For the full procedure, see Resize node pools.