Optimize hydration requirements

View as Markdown

Hydration primarily impacts memory usage, and its speed scales with cluster size. This guide covers strategies to reduce the memory a cluster needs during hydration, speed hydration up, or avoid triggering it in the first place.

Isolate hydration-heavy objects

Use a dedicated cluster for sources.
In addition, use a dedicated cluster for upsert sources; i.e., do not co-locate with append-only Kafka sources or CDC database sources.
- Keeping append-only Kafka sources and CDC database sources (PostgreSQL, MySQL, and SQL Server sources) on a separate cluster isolates ingestion from possible OOM loops caused by memory-heavy objects such as Kafka upsert sources.
- Note: PostgreSQL, MySQL, and SQL Server sources run on a single replica, the oldest, and remain there until that replica is removed. As such, the use of a burst replica (through AUTO SCALING STRATEGY (ON HYDRATION)) has no impact on these single-replica sources.
Distribute materialized views and indexes across multiple clusters. Each cluster’s replicas hydrate their objects independently, which distributes the memory required for hydration, lets objects on different clusters hydrate in parallel, and limits how much must re-hydrate when any one replica restarts.

Limit concurrent hydration

By default, a replica hydrates up to 4 dataflows at a time. Lowering this limit spreads out the memory spikes from hydrating many objects at once, at the cost of a longer total hydration time; raising it can shorten total hydration time at the cost of a higher peak. Contact our team if you’d like to tune this for your cluster.

Reuse arrangements across consumers

If multiple objects in the same cluster consume the same view, add an index to that view before creating the consumers. Consumers in that cluster can reuse the indexed arrangement instead of each building equivalent in-memory state, which can reduce both memory usage during hydration and steady-state memory.

Index reuse is limited to the cluster the index is on, and the index must exist before its consumers are created for the optimizer to reuse it.
For a view with only one consumer, an index generally adds memory instead of saving it.

Split large materialized views

For a very large materialized view, consider splitting it into several smaller materialized views, for example by a partition key such as customer, region, or date range. Smaller materialized views can hydrate as separate dataflows, which can bound peak memory compared with hydrating one very large materialized view.

This helps most when a cluster runs only a few large materialized views, where a single view’s hydration spike can dictate the cluster size. A cluster with many materialized views already hydrates them as separate dataflows and gets this benefit naturally.
A re-plan or replacement of one split view affects only that portion of the data. A replica restart still re-hydrates all views on the replica, though in smaller units.
If the split views share expensive computation, put that computation in a common indexed view first, creating the index before creating the split views. Otherwise, each split view may rebuild its own copy of the shared work, increasing total memory.
Queries must target or combine the split views.

Reduce arrangement memory

For multi-way joins (more than two inputs), a delta join keeps no intermediate results in memory, unlike a differential join. Index all the join keys so the optimizer can choose a delta join.
If your workload has columns with a small set of often-repeated values (for example, status strings or enum-like labels), dictionary compression can reduce the memory those arrangements use, at the cost of CPU and slower hydration.
Avoid setting RETAIN HISTORY on indexes: an index’s arrangement holds its full retained history in memory, not just the storage layer. Configure history retention on a materialized view instead.

Provision extra capacity while hydrating

Add an AUTO SCALING STRATEGY (ON HYDRATION) to your cluster with memory-heavy objects. With this strategy, Materialize automatically provisions an extra, larger replica (a burst replica) while the cluster has unhydrated objects, then removes it once a steady-size replica catches up. You pay for the burst replica while it is provisioned, but not at steady state.

If a steady-size replica runs out of memory during hydration, resize the cluster. During the resize, the cluster continues to serve from the burst replica.

Avoid unnecessary full-cluster hydration

When changing a materialized view or index, or forcing dependents to re-plan (for example, after dropping an index and recreating the dependents), build the new version to the side to avoid downtime:

A blue/green deployment hydrates the new version alongside the old and cuts over when hydrated, with no serving gap. Note that blue/green requires sources and sinks to live on dedicated clusters that are excluded from the swap. For more information, see blue/green deployment.
For a single materialized view, creating and hydrating a replacement materialized view (public preview) and replacing the existing view in place may be simpler, but briefly reduces freshness. The replacement materialized view can be either on the same or different cluster.

NOTE: A slim deployment that redeploys only changed objects onto a copy of a cluster only exercises those objects' hydration spike, not the full cluster’s combined spike. A cluster that passes a slim deployment can still fail to hydrate on the next full restart or upgrade. Use a blue/green deployment in production to validate hydration for the whole cluster.

NOTE: The burst-replica and blue/green strategies run extra replicas alongside the existing ones, as do a resize or a zero-downtime upgrade. During the overlap, the cluster temporarily uses additional resources. Account for the additional cost and, on self-managed deployments, the additional capacity required.