Home  /  Concepts

Clusters

Overview

Clusters are pools of compute resources (CPU, memory, and scratch disk space) for running your workloads.

The following operations require compute resources in Materialize, and so need to be associated with a cluster:

Resource isolation

Clusters provide resource isolation. Each cluster provisions dedicated compute resources and can fail independently from other clusters.

Workloads on different clusters are strictly isolated from one another. That is, a given workload has access only to the CPU, memory, and scratch disk of the cluster that it is running on. All workloads on a given cluster compete for access to that cluster’s compute resources.

Fault tolerance

The replication factor of a cluster determines the number of replicas provisioned for the cluster. Each replica of the cluster provisions a new pool of compute resources to perform exactly the same work on exactly the same data.

Provisioning more than one replica for a cluster improves fault tolerance. Clusters with multiple replicas can tolerate failures of the underlying hardware that cause a replica to become unreachable. As long as one replica of the cluster remains available, the cluster can continue to maintain dataflows and serve queries.

NOTE:

  • Each replica incurs cost, calculated as cluster size * replication factor per second. See Usage & billing for more details.

  • Increasing the replication factor does not increase the cluster’s work capacity. Replicas are exact copies of one another: each replica must do exactly the same work as all the other replicas of the cluster(i.e., maintain the same dataflows and process the same queries). To increase the capacity of a cluster, you must increase its size.

Materialize automatically assigns names to replicas (e.g., r1, r2). You can view information about individual replicas in the Materialize console and the system catalog.

Availability guarantees

When provisioning replicas,

  • For clusters sized up to and including 3200cc, Materialize guarantees that all provisioned replicas in a cluster are distributed across the underlying cloud provider’s availability zones.

  • For clusters sized above 3200cc, even distribution of replicas across availability zones cannot be guaranteed.

Cluster sizing

When creating a cluster, you must choose its size (e.g., 25cc, 50cc, 100cc), which determines its resource allocation (CPU, memory, and scratch disk space) and cost. The appropriate size for a cluster depends on the resource requirements of your workload. Larger clusters have more compute resources available and can therefore process data faster and handle larger data volumes.

As your workload changes, you can resize a cluster. Depending on the type of objects in the cluster, this operation might incur downtime. See Resizing downtime for more details.

💡 Tip: To gauge the performance and utilization of your clusters, use the Environment Overview page in the Materialize Console.

Best practices

The following provides some general guidelines for clusters. See also Operational guidelines.

Three-tier architecture in production

In production, use a three-tier architecture, if feasible.

Image of the 3-tier architecture: Source cluster(s), Compute/Transform cluster(s), Serving cluster(s)

A three-tier architecture consists of:

Tier Description
Source cluster(s)

A dedicated cluster(s) for sources.

In addition, for upsert sources:

  • Consider separating upsert sources from your other sources. Upsert sources have higher resource requirements (since, for upsert sources, Materialize maintains each key and associated last value for the key as well as to perform deduplication). As such, if possible, use a separate source cluster for upsert sources.

  • Consider using a larger cluster size during snapshotting for upsert sources. Once the snapshotting operation is complete, you can downsize the cluster to align with the steady-state ingestion.
Compute/Transform cluster(s)

A dedicated cluster(s) for compute/transformation:

  • Materialized views to persist the results in durable storage. Results of materialized views are available across all clusters.

  • Indexes, only as needed, to make transformation fast (such as possibly indexes on join keys).

💡 Tip: From the compute/transformation clusters, do not create indexes on the materialized views for the purposes of serving the view results. Instead, use the serving cluster(s) when creating indexes to serve the results.
Serving cluster(s)

A dedicated cluster(s) for serving queries, including indexes on the materialized views. Indexes are local to the cluster in which they are created.

Additional Considerations:

  • Clusters containing sinks can only have a replication factor of 0 or 1.

Benefits of a three-tier architecture include:

See also Operational guidelines.

Alternatives

Alternatively, if a three-tier architecture is not feasible or unnecessary due to low volume or a non-production setup, a two cluster or a single cluster architecture may suffice.

See Appendix: Alternative cluster architectures for details.

Use production clusters for production workloads only

Use production cluster(s) for production workloads only. That is, avoid using production cluster(s) to run development workloads or non-production tasks.

Consider hydration requirements

During hydration, materialized views require memory proportional to both the input and output. When estimating required resources, consider both the hydration cost and the steady-state cost.

Back to top ↑