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A high-volume outbox has to do more than save events reliably: its claim query must keep working as the pending queue grows, while row size, transaction-log activity, and broker failures stay manageable. In a case study by Robson Kades, one Azure SQL Database Business Critical system reportedly processed about 3 million events a day and held about 45 million outbox rows. The central lesson is to measure the actual workload before changing its hot path: a rewrite that looked cheaper in an estimated plan performed much worse in measured CPU.
What problem does the outbox solve?
A service that commits a business change to a database and then separately publishes an event can fail between those two actions. The database may show the change even though the broker never received its event. An outbox avoids that dual-write gap by saving the business change and its event record in the same database transaction. A worker later reads committed, unhandled records, publishes them, and updates their delivery state.
Microsoft’s architecture guidance describes this transactional outbox pattern, and a Microsoft Azure SQL engineering post describes using stored procedures to make business-table changes and write corresponding outbox rows. Operation type and message ID can help downstream routing and ordering. Neither description means the broker publication is part of the database transaction: the worker still has to manage delivery and recovery.
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Delivery is not automatically exactly once
In Kades’s implementation, the worker claims and updates records in a database transaction, then publishes outside that transaction. This avoids keeping database locks open while waiting on network or broker calls, but creates a failure window: the process can stop after the claim commits and before publication succeeds or failure handling runs. The case study does not establish exactly-once delivery.
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Plan for retries and duplicates. Give events a stable identifier, make consumers idempotent for that identifier, and define how abandoned claims become eligible for recovery. If event order matters—for example, a consumer must see an aggregate’s creation before its update—preserve or enforce that order explicitly. Microsoft’s separate Cosmos DB example uses transactional batches, a change feed, and Service Bus; it illustrates the broader pattern, but it is not the relational Azure SQL implementation described here.
What did the reported workload look like?
Kades reports about 3 million events per day and about 45 million rows at steady state on Azure SQL Database Business Critical. These are observations from one system, not a capacity guarantee or an independently reproduced benchmark. The article’s date line says “Sep 16” without a year, so its figures should not be treated as a current Azure service limit.
The article estimates that roughly 1.3 KB per row across 45 million rows amounts to about 58 GB. It compares that footprint with 41.5 GB of stated engine memory for the described 8-vCore Business Critical configuration, and notes that buffer-pool capacity is lower still. That arithmetic explains why a large outbox can become a storage and access-path concern, not merely a matter of finding a faster SQL expression.
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It also estimates lifecycle log generation—insert, status change, and purge delete—at roughly four times the payload size. That is an estimate tied to the described schema and lifecycle, not a general multiplier for every outbox. Azure SQL documents both data I/O governance and transaction-log rate governance; the applicable limits depend on service level and hardware series, and log throttling can appear as a LOG_RATE_GOVERNOR wait.
How does the worker claim records without scanning an ever-growing backlog?
Use a pending-row access path
The described hot-path index is a filtered nonclustered index on event type and ID, with aggregate ID and payload included, filtered to rows in pending status. This keeps the claim path focused on eligible records rather than making it repeatedly search the whole retained table. The author reports that the application initially used a clustered scan instead of this intended index.
The case study attributes that behavior to a combination of plan-cache differences involving connection SET options and parameterized status predicates that made it harder for the optimizer to prove that the filtered index applied. For this particular query and index design, the author recommends literal status predicates in the relevant native queries. Check the actual execution plan and the application’s connection options rather than assuming the same cause or remedy applies to another schema.
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In the author’s diagnosis, ANSI_WARNINGS ON affects the functional interpretation of ARITHABORT on modern compatibility levels, while ARITHABORT remains part of the plan-cache key. Setting ARITHABORT ON aligned the application connection with the behavior observed in SQL Server Management Studio in that environment; it is not a universal fix for scans.
Resume with keyset pagination
The worker uses keyset pagination rather than OFFSET. With an offset, the database may have to read and discard an increasing number of earlier rows as the backlog grows. A keyset query can seek after the last processed ID, keeping the work tied more closely to the next page rather than the queue’s accumulated history.
The reported worker caps a cycle at 20 rounds of up to 100 events each. That limits a single scheduler cycle to at most 2,000 events, rather than allowing a large backlog to occupy a scheduler thread and database connection indefinitely. The cap is a fairness and resource-control choice; it does not, by itself, guarantee a particular drain time.
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Coordinate concurrent workers with care
For each event type, the worker queries up to 100 pending events and claims them using SQL Server locking hints translated from Hibernate pessimistic locking: UPDLOCK, ROWLOCK, and READPAST. The intent is for concurrent workers to skip rows already locked by another worker and claim different records.
These hints express the design’s concurrency strategy, not a promise that contention or lock escalation can never occur. Validate behavior under the real transaction shape, indexes, and worker count. Keep the claim transaction short, and make the recovery policy for claims interrupted before publication explicit.
Why did UPDATE…OUTPUT lose to a two-step claim?
Kades tried replacing the original select-and-update claim with a single UPDATE ... FROM ... OUTPUT statement, expecting one statement to be more efficient. In the article’s environment, the estimated plan showed a cost of 0.06, but measurements from sys.dm_exec_query_stats reported the following CPU per 100-row claim:
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| Claim approach | Reported CPU per 100-row claim | What the case study reports |
|---|---|---|
UPDATE ... FROM ... OUTPUT |
142.84 ms | The rewrite showed an eager spool for Halloween protection; concurrent READPAST behavior also undermined the optimizer’s TOP row goal. |
| Original SELECT plus updates | Approximately 2.9 ms | The measured CPU was much lower in this workload, despite the rewrite’s attractive estimated plan. |
Those measurements are author-reported observations for one workload, not a general rule that a two-step claim is always faster. The practical takeaway is to test the work the database actually performs. Compare CPU and I/O under representative concurrency, using tools such as sys.dm_exec_query_stats and SET STATISTICS IO, and inspect the actual plan rather than choosing a rewrite from estimated cost alone.
What should you check when the table and payload grow?
Reduce row width only when the data stays intact
The article reports that the application sent payloads as VARCHAR although the column was NVARCHAR, prompting consideration of changing the column to VARCHAR to reduce row and log bytes. Such a conversion can silently replace characters that the target encoding cannot represent. Before changing the column, test the real stored values against the chosen target code page and the application’s language requirements; do not infer safety just because a sample or test environment converted cleanly. Kades reports zero lossy rows in the test environment used for the article, which does not establish that another deployment’s data is safe to convert.
Account for SQL and broker limits as separate boundaries
Database I/O and transaction-log generation can be governed independently, while the broker imposes its own message and batch-size limits. Kades’s article advises respecting the configured Service Bus message and batch sizes, but those limits vary by tier and protocol and may change. Verify the current limit for the deployed tier and protocol before setting payload or batch sizes; do not treat a figure from a particular configuration as universal.
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Which implementation choices are requirements, and which are specific to this system?
The article lists Java 25, Spring Boot 4.1, Hibernate 7, the Microsoft SQL Server JDBC driver, and Azure SQL Database Business Critical as its stack. It also describes virtual threads for an I/O-bound scheduler, explicit graceful shutdown, small connection-pool settings, JDBC batching, and keyset-based work. These are choices from the reported implementation, not prerequisites for the outbox pattern.
- Keep transaction boundaries deliberate: commit the business change and outbox insert together; keep broker calls outside the claim transaction if holding database locks during network waits is unacceptable.
- Design delivery recovery: decide how a claimed but unpublished record is retried, and make consumers tolerate duplicate delivery.
- Make backlog cost predictable: use an index and paging strategy aligned with the pending-row predicate and ordering key, then inspect the plan under application connection settings.
- Measure before replacing SQL: compare CPU, I/O, waits, and behavior under concurrency; an estimated-plan advantage is not a measured result.
- Budget storage and logs: include payload width, state changes, cleanup, and the database’s applicable I/O and log governance in capacity planning.
- Limit work per cycle: set a batch and cycle cap that fits the scheduler and connection budget, and tune it from observed service time and backlog behavior.
What does this case study establish?
It shows one way to keep a relational outbox’s polling cost bounded as retained rows accumulate: seek by key, index the pending subset, limit each cycle, and verify query changes with actual measurements. It also shows why delivery semantics, row width, logging, and connection settings belong in the design alongside SQL syntax. The reported throughput and CPU figures describe Kades’s environment; they are not a cross-tier benchmark or a forecast for another workload.
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