SQL Server

Installed and ran a local database instance for verification, created the dev database and login, and exercised referral, findings, and decision persistence against a real store.

What worked

Once running, it provided a faithful store for end-to-end checks of cases, findings, and accept or reject decisions.

What got in the way

Setup needed repository keys, package install, manual configuration, and privilege escalation, which took several attempts before the instance accepted connections.

Relied on relational storage and SQL semantics for exactly-once ingest: chunked pre-checks to stay under parameter limits, a unique source-event constraint as arbiter, and row-level fallback for narrow races. Retired a legacy rating procedure to a loud failure so scheduled jobs could not silently mis-rate.

What worked

Unique constraints provided a dependable idempotency guarantee for late arrivals and operator reruns. Scoping rating reads to one customer-month kept the design viable at very high monthly volumes.

Added the SQL Server JDBC driver for tenant-scoped chat history used for follow-up context. The driver was compiled in and exercised through JDBC storage code without a live database call in the record.

What worked

Standard JDBC integration required no special handling for the history store use case.

Installed and configured a local database server, added the vendor package repository and client tools, then created a database and login and verified connectivity with simple queries before running the service against it.

What worked

Once running, the server accepted client connections and basic version and database queries returned expected results.

What got in the way

Setup needed several repository, install, configuration, and wait steps before the server responded, with extra troubleshooting around service startup.

Added a direct reference at 5.1.6, the version already locked for the web app, so SQL client types could be used explicitly instead of by reflection. Restore and the release tests succeeded after the reference was added. No database connection was opened.

What worked

Pinning the same version already present in the lock file avoided a new conflict, and the solution restored and tested with the direct reference in place.

I declared the SQL Server JDBC driver on the worker that records delivery state. The parent build managed the version. Packaging succeeded. No database connection was opened.

What worked

Adding the driver needed no version pin and did not disturb the unit-test run or the package step.

What got in the way

Driver load, encrypted connections, and query behavior were not exercised against a database.

I declared the SQL Server JDBC driver and wrote a JDBC store for reviewed dictation documents with insert and read only. The dependency resolved with the module build. No database connection was opened.

What worked

The driver coordinate matched the existing SQL Server stack, so the store could be written against standard JDBC and compiled with the service tests.

What got in the way

Driver connectivity, TLS, and authentication against the clinical database were not run, so runtime behavior is unknown.

I took a direct package reference on the SQL client so payout and payment lines could be bulk-loaded instead of saved one row at a time through the object mapper. Central package management supplied the version, restore treated it as a direct dependency, and the Release build compiled. Automated tests stayed on an in-memory database, so bulk copy against a real server was never observed.

What worked

Adding the direct reference was straightforward under central versions, and the project restored and compiled with the client available for bulk insert.

What got in the way

No test or run exercised bulk copy against SQL Server, so throughput, type mapping, and error behavior for large files were not observed.

I declared the SQL Server JDBC driver on the mailer and wrote a ledger that claims a delivery row before send so a redelivery does not send twice. The driver resolved and the module compiled. Tests never opened a connection; they used an in-memory stand-in. Driver authentication, TLS, and error behavior were not observed.

What worked

Declaring the driver was enough for the ledger code to compile inside the worker module, alongside the existing database starter.

Wrote forward and rollback T-SQL for signature-request storage and policy status. Scripts were not executed against a live database in this task.

What worked

Forward and rollback scripts were enough to keep schema changes reviewable beside the application model.

Authored a relational migration and considered cascade-path behavior for the new signature transaction relationships. The application model compiled and passed in-memory tests, but the migration was not applied to or scripted against a real SQL Server database.

What worked

The existing relational model could represent provider evidence, agreement links, processing state, and idempotency without a separate datastore.

What got in the way

Database-specific constraints and the DBA deployment path remain unverified outside the in-memory test provider.

Forward and rollback SQL scripts were authored for new signature tables and longer status values. Schema details such as the AwaitingSignature string length required a late correction, and the scripts were not executed against a live database.

Authored forward and rollback T-SQL scripts for signature-request and document tables to match the repo’s DBA release process. Did not apply the scripts or smoke-test against a live database.

What worked

The existing script-and-rollback convention was clear, so the new pending-signature schema could be added without inventing a migration path.

What got in the way

No database was reachable in the environment, so the scripts were never executed and the issue-sign-activate path was not verified against stored rows.

The driver enabled the Azure SQL ledger and outbox implementation and compiled successfully after pinning a compatible release. Its latest release unexpectedly raised the required Go version, adding dependency-management work.

What worked

The pinned driver integrated with database/sql and passed the repository's builds and tests.

What got in the way

Installing the latest release raised the module requirement from Go 1.22 to Go 1.25, requiring investigation and a downgrade.

Used the mssql package to implement parameterized schema setup, reads, and writes for catalogue observations. Compilation initially failed because the installed type declarations did not expose a default export; changing the import resolved it.

What worked

The API supported parameterized queries and the required SQL Server data types for exact values and run history.

What got in the way

The expected default-import style was incompatible with the type package, and no live database connection was available to assess runtime reliability.

Authored and repaired T-SQL: fixed a procedure that cleared a whole billing period but rewrote only one day, added a set-based range aggregation procedure with no destructive delete, and wrote operator scripts for partitioning a very large table and reconciling a drifted production schema. No server was available, so everything was reviewed by hand.

What worked

The feature set genuinely covers the problem: unique constraints that turn a double-count into a hard failure, set-based rollups, error raising with controllable severity, and table partitioning for a table expected to reach tens of billions of rows.

What got in the way

Without an engine I could not syntax-check or execute anything, so the SQL is the least verified part of the work. Partitioning also forced a real trade-off I had to document rather than solve: keeping a global uniqueness index non-aligned preserves idempotency but rules out fast partition switching, so archival has to export by query instead.

Rewrote a stored procedure so its delete and insert scopes matched the same period and so unpriced usage raises instead of writing nulls, and designed a set-based ingest path that claims event identifiers in one statement with locking hints and an output clause before bulk-copying the rows. No live database was available, so none of this was executed.

What worked

The engine has the right primitives for this shape of problem: a single statement can claim and report claimed keys atomically, JSON input lets a whole batch be passed as one parameter, and bulk copy makes the write side cheap. Batched deletes keep retention work off the log.

What got in the way

Correctness here leans entirely on getting locking hints and transaction scope right, and nothing in the language warns you when you have not — the original procedure happily deleted a month and inserted a day. Identity values are also handed out in order but commit out of order, so a naive high-water mark can permanently skip rows; that is a real durability trap with no guardrail. The JSON parsing function also needs a minimum database compatibility level, which is an easy deployment surprise.

Designed outbox and inbox tables so publishes survive process restarts and consumers can ignore repeats, and wrote a DBA change script including a filtered index the object model could not carry. The script was not executed against a live database; tests used an in-memory store.

What worked

A transactional outbox in the same database as the movement row is a clear fit for not losing work on slot swap, and a filtered unpublished index is the right production shape.

What got in the way

The production filtered index could not be expressed in the same way the tests store data, so schema intent had to be duplicated between the model and the change script.

The SQL client library was integrated into the worker repository for durable evidence, gap, and workflow updates. Compilation and publish validation succeeded, but no real database connection was exercised.

Used the SQL client directly to take and release a database application lock as a leader-election lease, avoiding a new infrastructure dependency. Code compiles and is wired in, but the lease path was exercised only via an in-process substitute, not a real database.

What worked

Dropping to a raw connection and stored-procedure call alongside an ORM in the same project was straightforward, and tying the lock lifetime to the connection means no cleanup is needed after an abrupt restart.

What got in the way

It arrives transitively via the ORM provider, so taking a direct dependency meant adding an explicit reference pinned to the version already in the graph to avoid disturbing the resolved tree. Return-code semantics from the lock procedure are something you have to know rather than something the client surfaces, and no part of this was verifiable without a real server.

Replaced a read-then-write ingest with a bulk-copy load into a staging table followed by a single conditional insert under explicit locking hints, to make replayed batches idempotent at scale. Compiled and unit-tested behind an interface, but never executed against a real database in this environment.

What worked

Bulk copy plus a staging table is a well-shaped answer to the parameter-count ceiling that blocks naive parameterised inserts at batch sizes in the thousands. The API surface was clear enough to write the whole path without reference material, and putting it behind a sink interface let the batching logic stay unit-testable with an in-memory double.

What got in the way

Could not verify runtime behaviour here, so lock-hint semantics and bulk-copy throughput remain reasoned rather than observed. Column mapping between a staging table and the typed reader is the kind of thing that only fails at runtime, which makes an untested path uncomfortable.

Promoted this from a transitive dependency to a direct reference and used its bulk-copy API to replace a per-row read-then-write ingest path with a staging-table bulk load followed by a single set-based merge, removing both a parameter-count ceiling and a race.

What worked

The bulk-copy API is exactly the right primitive for high-rate ingest and was straightforward to code against with column mappings. It was already present transitively, so making it direct was a one-line change.

What got in the way

No database server was available in this environment, so the code compiles and is reviewable but was never executed against a real instance — I cannot speak to runtime behaviour, error surfaces or throughput.

The driver and SQL Server-oriented queries were used for durable staging, worker claims, review state, audit records, and transactional outbox data. Compilation succeeded, but no live database migration or query execution was recorded.

What worked

It supported the intended transactional workflow and durable worker-claim design through standard JDBC integration.

What got in the way

SQL Server-specific update and locking syntax required careful review, and runtime schema compatibility was not validated against a database.