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Before moving data from D1 into Durable Objects, list the queries that use it and identify which room or other entity owns each record. Queries confined to one owner may fit that object’s storage; queries that span owners need a separate design, because one Durable Object cannot directly query another object’s private storage.
Why query scope changes the migration decision
D1 is a SQL database. Durable Objects are uniquely addressed, stateful compute instances with attached storage, suited to coordinating activity around an entity such as a chat room, collaborative document, game, or notification stream. Cloudflare describes these as different levels of abstraction: D1 offers database-management features, while Durable Objects give application code more control over routing and behavior. See Cloudflare’s D1 and Durable Objects comparison and Durable Objects overview.
Each Durable Object’s attached storage belongs to that unique object. Its storage is strongly consistent, but another object cannot directly read it. That means a query that currently joins or aggregates records from several rooms cannot simply run across all their object databases. Before moving those records, decide how the feature will obtain its cross-room view. Cloudflare documents the storage boundary in SQLite-backed Durable Object Storage.
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Create one row per query or query family, including operations hidden inside ORM methods, scheduled work, and background jobs. The inventory is an engineering aid, not a Cloudflare-provided migration checklist.
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- Feature and caller: name the application feature and the Worker route, scheduled task, or job that issues the query.
- Query: record the SQL or ORM operation, tables touched, filters, joins, sorting, and aggregation.
- Data shape: note whether it reads or writes, expected rows returned or changed, and relevant indexes.
- Workload: estimate frequency, burst behavior, concurrency, and whether the operation needs a transaction.
- Ownership: identify the room, user, document, match, or other entity that could own each record.
- Scope: mark whether the operation touches one proposed owner or combines multiple owners.
For example, “load the latest messages for room 42” may be room-local if all relevant messages belong to that room. “Show unread counts across every room for this user” crosses room ownership if those counts live in separate room objects. The SQL alone does not settle the question: map the records it touches to the proposed ownership boundary.
Classify each query before choosing a destination
Object-local reads and writes
These use data owned by one room or entity. They are candidates for that owner’s object storage when the object is also the natural authority for the feature. Confirm that the object can serve the required reads, writes, and transaction semantics; a local query is a potential fit, not an automatic reason to move the data.
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Cross-object reads and writes
These combine data owned by multiple objects. Choose an explicit pattern before migration: fan out to objects, maintain a derived summary, use another shared data store, change the feature’s read path, or keep the relevant data in D1. Compare the consequences for freshness, consistency, latency, and operational complexity. There is no built-in cross-object SQL query implied by Durable Object storage.
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Relational and reporting queries
Queries that depend on flexible joins, broad filtering, or analytics across many entities may be more costly to redesign than to leave in a SQL database. Include reporting and administrative use, not just user-facing request paths, in this decision.
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Coordination state
Durable Objects are a natural candidate when multiple clients need one authoritative state boundary for a room or other coordinated entity. Cloudflare lists chat, collaborative editing, multiplayer interactions, and live notifications among the use cases. That fit does not imply that unrelated or broadly relational application data should move with the coordinated state.
Compare the responsibilities you would be taking on
| Decision area | D1 | Durable Objects with SQLite storage |
|---|---|---|
| Query scope | SQL database for relational queries; assess the actual schema and query needs. | Storage is attached to one unique object; cross-owner access needs an application-level design. |
| Ownership and routing | Application queries a D1 database through its Worker binding. | A front-end Worker routes requests to a uniquely addressed object, which owns its state. |
| Management features | Cloudflare describes schema management, data import/export, and query insights. | A lower-level building block with more routing and behavior responsibility in application code. |
| SQL and recovery | Use D1’s SQL and migration interfaces. | SQLite-backed Durable Objects provide SQL and point-in-time recovery APIs; the SQL API is limited to SQLite-backed classes. |
These product distinctions are described in Cloudflare’s D1 and Durable Objects comparison, Durable Object storage guidance, and SQLite storage API documentation. Cloudflare says SQL query pricing and limits are intended to be identical between D1 and SQLite in Durable Objects; that does not make their APIs, routing, or management responsibilities interchangeable. Verify current limits, availability, and billing for your plan and workload.
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Account for D1’s documented query guidance
Cloudflare’s D1 FAQ, checked in 2026, describes an indexed lookup such as finding a name by ID as taking “less than a millisecond” of SQL duration as a rough guideline, not a latency guarantee. It describes writes such as INSERT or UPDATE as taking “several milliseconds,” depending on rows written. Treat these figures as Cloudflare guidance, not application-level end-to-end performance promises. The FAQ also says one Worker invocation can open up to six simultaneous D1 connections. See the D1 FAQ.
For large changes, Cloudflare recommends batching UPDATE or DELETE work. Its FAQ gives roughly 1,000 rows at a time as an example and warns that a single query affecting hundreds of thousands of rows or hundreds of megabytes may exceed limits. The appropriate batch size depends on the operation and current platform constraints; capture indexes, result sizes, and concurrency rather than assuming a move to object storage will solve a query-shape problem. See the D1 FAQ for the guidance.
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Use the inventory to stage a safer move
- Capture the baseline. Record the current schema and the complete query inventory, including ORM-generated queries, scheduled tasks, and background jobs. Cloudflare documents SQL migration files and configuration in D1 migrations.
- Assign proposed owners. Group records by the Durable Object that would own them. Mark every query that crosses those ownership groups, including reports and secondary views.
- Choose a design for each cross-owner feature. Decide whether it will use fan-out, a maintained summary, a different shared store, a changed read path, or D1. Write down the acceptable freshness and consistency, and the expected latency and operational work.
- Prototype a representative object. Check that Worker routes reach the intended object and compare its read and write behavior with the existing D1 behavior. Verify that results preserve the feature’s required semantics, not merely that the data can be loaded.
- Plan data movement and recovery separately. Specify export, load, verification, and rollback steps. Use batches for large D1 changes. Cloudflare documents D1 migrations and Durable Object storage separately; its documentation does not establish a turnkey command to convert D1 data into Durable Objects. See D1 migrations and SQLite-backed Durable Object Storage.
When to keep data in D1
Keeping data in D1 is a reasonable choice when important features depend on joins or broad queries across the records you would otherwise split among objects, or when the management features of a SQL database outweigh the value of per-entity coordination. You can also split responsibilities: keep relational or reporting data in D1 while a Durable Object owns the state that needs coordination for one room or entity. Decide from the complete query inventory rather than moving every table because one feature is a good fit for an object.
Quick Recap
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