We recently released a major architectural enhancement that fundamentally changes how resource-intensive populate operations work in Tinybird. With compute-compute separation for optimizing complex compute workloads, Tinybird now handles even the most demanding data transformations and backfills without impacting your primary workloads or requiring temporary cluster scaling more compute resources.
What is Compute-Compute Separation for Populates?
Populates are critical operations in Tinybird; they perform complex data transformations and backfills. They execute INSERT INTO queries combined with materialized views transformations to move and transform missing data from one table to another after adding a materialized view.
In the past, these operations ran on existing compute infrastructure, competing for resources with your live queries, API requests, and analytics workloads. With compute-compute separation, we've fundamentally changed this approach.
Why Compute-Compute Separation matters
Eliminates resource contention
Heavy populate operations no longer compete with your production queries for CPU cycles, memory, or I/O bandwidth. Your dashboards, real-time analytics, and user-facing applications maintain consistent response times even during massive data backfills involving billions of rows.
Cost efficiency
A key advantage of compute-compute separation is the ability to avoid over-provisioning. In traditional setups, ensuring readiness for occasional heavy populate operations often meant scaling infrastructure beyond what was needed for daily workloads—resulting in wasted capacity and cost.
With compute-compute separation, we spin up the exact resources required only when you need them. This ephemeral, right-sized provisioning model reduces costs and aligns your infrastructure usage with actual demand, all without compromising performance or reliability.
Enhanced reliability
Isolating populate operations eliminates the risk of failure due to resource exhaustion. Populate operations have highly variable resource requirements - complex aggregations can consume tens of gigabytes of memory while intensive transformations may saturate all available CPU cores.
With dedicated replicas, these operations can scale to their full resource needs without causing out-of-memory errors, query timeouts, or performance degradation in your production environment.
How Compute-Compute Separation works in Tinybird
When you initiate a populate operation leveraging compute-compute separation for ClickHouse®, Tinybird's orchestration layer automatically provisions dedicated ClickHouse® instances. These instances are configured specifically for the data transformation task at hand, with appropriate memory, CPU, and storage configurations.
The populate process then runs through its standard workflow—executing the INSERT INTO operations, managing null tables, and applying materialized view transformations—but with dedicated resources and complete isolation from your primary compute environment.
Once the operation completes successfully, the temporary instances are automatically decommissioned, ensuring you only pay for actual usage time.
This means your populate operations get dedicated, right-sized compute resources while your primary workloads continue running uninterrupted.
Availability and opt-in
This powerful new capability is available to all clients through an opt-in model. We believe in giving you control over when and how you adopt new features, especially architectural changes that affect your data operations.
Contact your account team or reach out through our support channels to enable compute-compute separation for your populate operations. We'll work with you to ensure a smooth transition and optimal configuration for your use cases.
What’s next
Compute-compute separation for populates represents our commitment to providing infrastructure that scales with your needs while optimizing for both performance and cost. By isolating resource-intensive operations, we're empowering you to handle larger datasets, more complex transformations, and more frequent backfills without architectural constraints.
