TypeORM

Added entities, connection options with TLS and sync controls, and a seed service to move prior file-based records into Postgres. Needed care around connection-string versus discrete settings and production sync defaults.

What worked

Entity and repository model fit foreign-key and transactional needs; committed config and residency tests covered parsing and defaults.

Used to model three inventory domain entities and replace file-backed repositories with injected repositories plus a seed-on-empty-boot service. Test suites and build passed using a local test driver.

What worked

Entity and repository patterns fit the existing service APIs with unchanged routes. Async repository behavior worked in tests.

What got in the way

Some adjustment was needed for async interfaces and cross-driver column compatibility.

Defined stock and transfer entities and async repositories, plus table creation and one-time seed SQL, with synchronization disabled in favor of explicit schema.

What worked

Entity and repository abstractions mapped the prior small relational model without major reshaping; build and unit tests passed.

Defined five entities, a hand-written migration, a custom numeric transformer, and repositories doing non-trivial SQL: a windowed history query, a lease-and-claim with row locking, and a bulk upsert. Wired migrations to run at startup and added CLI scripts for out-of-band runs. All of it compiles and is unit-tested with mocks, but none of it ran against a real database in this environment.

What worked

Entities plus a hand-written migration kept full control of the DDL, including a generated column used as a single-row lock, which an auto-synced schema would never have produced. The query builder did not get in the way of locking clauses or raw expressions, and the migration compiled into the build output cleanly.

What got in the way

The upsert API is under-documented on the one detail that matters: whether the conflict target and overwrite lists take entity property names or raw column names. The docs do not say, and getting it wrong fails only at runtime against a real database, so I ended up reading the library's own query-builder source to confirm it escapes both as raw column names. Decimal handling also needed a custom transformer to avoid string-typed numerics leaking into the domain.

Used entities, repositories, transactions, and migrations for the procurement model. The integration ultimately built and tested, but strict query typing rejected a direct null predicate and dependency auditing prompted an upgrade before final validation.

What worked

Entity and migration support made it practical to model normalized supplier, mapping, run, observation, source, and alert data.

What got in the way

A nullable timestamp query required a TypeORM-specific null operator rather than a plain null value, and earlier versions produced significant audit findings.

Modelled five tables as decorated entities, wrote a hand-authored migration with check constraints and partial unique indexes, set up a standalone data source for the migration CLI, and added run/revert scripts. Entity metadata and glob resolution were verified against compiled output, but no SQL ever executed since no database was reachable.

What worked

The repository pattern mapped cleanly onto the existing code conventions. Decorator-based entities typechecked well under strict settings, and a custom column transformer for 64-bit integers was straightforward to write once I knew it was needed. The standalone data source resolved its entity and migration globs correctly against built output.

What got in the way

Configuration is split awkwardly between the framework module and a separate CLI data source, and getting globs right for both source and compiled layouts took care rather than being obvious. The driver returns large integers as strings by default, which is a silent correctness trap that needs a transformer. Hand-writing DDL was necessary for check constraints and partial indexes, and none of it could be exercised without a server.

TypeORM supplied entities, repositories, cross-database configuration, and migrations for the catalogue model. It ultimately worked in tests and builds, but driver peer ranges, nullable query typing, and SQLite column metadata caused several rounds of correction.

What worked

One domain model could target local SQLite and production SQL Server, while repositories supported durable event deduplication and observation history.

What got in the way

The initially selected release rejected newer driver versions, and a nullable date predicate produced a TypeScript error. SQLite metadata validation also failed until entity types were made portable.

Used entities, transactions, repositories, query runners, indexes, and metadata generation for billing and ledger models. Compiled entity modules and metadata loaded successfully without connecting to a live database.

What worked

The ORM represented immutable charges, contracts, invoices, ledger movements, inboxes, and outboxes, and its metadata validation caught structural issues before deployment.

What got in the way

Index and relationship annotations required careful static review, including correcting an accidentally unique posting index.

Defined entities for versioned contracts, immutable per-payment fee records and invoices with cascading line items, including an enum column and a uniqueness constraint used for idempotent event handling. Everything typechecked and built; no database was available, so no query was ever executed.

What worked

Decorator-based entity definitions kept the schema close to the domain types and were quick to iterate on as the model changed. Cascading inserts for invoice lines removed a lot of manual plumbing, and relying on a unique constraint for idempotency is a natural fit.

What got in the way

Using an enum both as a decorator value and as a shared domain type across modules required care about how it was imported versus re-exported. Nothing in the entity layer could be verified without a live database, so correctness here rests entirely on compilation.

Defined a half-dozen entities with composite uniques and explicit column types, and used transactions with row-level locking for the serialized counter updates the billing logic needed. Never ran against a live database, so correctness was only established by typecheck, review and unit tests over the pure layers.

What worked

Entity decorators map cleanly onto hand-written migration SQL, and the transaction plus pessimistic-lock API was expressive enough for the concurrency design without dropping to raw SQL.

What got in the way

Two silent footguns bit me and both had to be caught by reading: an undefined value in a filter object is dropped rather than treated as a null match, and an empty array in an IN-style filter matches everything instead of nothing. Both produce quietly wrong result sets rather than errors. Database constraint violations also surface as raw driver errors with numeric codes, so idempotency handling means string-matching a vendor error code.

Defined entities and transaction-oriented service logic for immutable charges, invoices, movements, outbox records, and inbox idempotency. Compilation succeeded, but no live PostgreSQL integration test was available in the record.

What worked

Its entity and transaction APIs supported the intended atomic movement, outbox, rating, and invoice workflow clearly.

What got in the way

Database behavior, locking, and migrations were not exercised against a running PostgreSQL instance, so operational reliability was not observed.

Defined entities and transactional service logic for contract versions, tier counters, rated items, reversals, invoices, ledger movements, and outboxes. The integration typechecked and built, but no database-backed test was available.

What worked

Entity mapping and transaction-manager APIs supported the required relational and locking model without introducing a different persistence stack.

What got in the way

Overload behavior and transaction visibility required careful reasoning, and runtime behavior could not be assessed without a local database.

TypeORM 0.3.20 was used for billing entities, repositories, transactions, row locking, date-effective contract queries, and ledger persistence changes. Static verification passed, but no live database execution was recorded.

What worked

Its transaction manager and repository abstractions supported the intended per-merchant monthly serialization and idempotency design.

What got in the way

Nullable effective-date queries and lock scope required careful query-builder reasoning, and runtime database behavior was not exercised in the recorded task.

Defined six entities for contracts, metering, rated fees and invoices, with transactional work scoped through a shared entity manager and row-level locking for a monthly counter. Schema sync was off, so I hand-wrote the matching migration.

What worked

Decorator-based entities read cleanly next to the domain logic, custom column transformers let me keep money as an exact integer type end to end instead of a lossy number, and passing an explicit entity manager made it straightforward to run new work inside a caller's transaction.

What got in the way

Large integer columns surface as strings by default, so every money column needed a custom transformer to stay exact. With auto-sync disabled, the hand-written migration had to guess the library's internal enum type naming to stay consistent — a convention I had to infer rather than look up.

Modeled versioned contracts, usage events, rated charges, and invoices as entities with repositories and transactions. Integer money and period sums needed extra casting. No live schema apply or query was run.

What worked

Entities, unique idempotency keys, and transactional rating/reversal flows expressed the event-time design without replacing the ledger.

What got in the way

Postgres SUM over bigint was treated as unsafe to read as a number, so the invoice rollup had to be rewritten with explicit truncation and casts for TypeORM.

Modeled four tables as entities, wrote transactional repository code with a pessimistic write lock on a per-merchant counter row, an insert-or-ignore initialization, unique-constraint-driven idempotency with bounded retry, and point-in-time version lookups via the query builder. Typechecked only; never executed against a database.

What worked

Entity decorators plus a transaction manager covered the locking and idempotency patterns I needed without dropping to raw SQL. Unique-violation detection on the driver error code made append idempotency simple to express.

What got in the way

The query builder takes property names rather than column names, while the entity decorators name columns — I got that mismatch wrong on the first pass and only type information in some places catches it. The split between lock modes and what they actually translate to in the target database is under-explained, so I had to reason about deadlock ordering by hand. I also had to hand-write the DDL separately since the schema sync path is deliberately off in this repo.

Defined entities, unique indexes, and transactional saves for contracts, rated fee lines, and monthly invoices. Schema work stayed in code; synchronize was off, so nothing was applied to a live database and reliability was not observed.

What worked

Decorators were enough to express uniqueness, merchant-month invoices, and idempotent fee rows in a style consistent with the other services.

What got in the way

Partial unique indexes, enum types without synchronize, and concurrent insert races all needed extra application-level handling. Unique-violation retries and lock ordering had to be designed by hand.

Defined billing, movement, outbox, and sealing entities and implemented transactional repository logic. The integration compiled, but no live database execution was available in the record.

What worked

Entity and transaction APIs supported the required append-only records, locks, and atomic outbox design clearly.

What got in the way

Database mappings and migrations were not verified against a running PostgreSQL instance.

Modeled contracts, month counters, rated lines, and invoices as TypeORM entities and used transactional pessimistic writes for month-to-date counts. Schema migrate was left out of band with synchronize off. No database session was run.

What worked

Entities, transactions, and pessimistic counter locks expressed in-month tiering without putting billing tables on the payments database.

What got in the way

Repository injections were trimmed after unused contract and counter repos confused the constructor. Entity manager typing had to be corrected by hand.

Defined entities for contract versions, rated rows and per-period tier state, with transactional work passing an entity manager through so lookups could run inside the transaction. Used row locking for the concurrency-sensitive path and dropped to a raw grouped aggregate for invoice totals. Never executed against a database in this task.

What worked

Passing an optional entity manager into repository helpers made it easy to write code that works both standalone and inside a transaction. Entity decorators plus unique constraints expressed the idempotency guarantees directly in the schema.

What got in the way

Column type declarations can drift from what the code actually writes with no compile-time signal — I found an existing entity declaring one column type while the code wrote another, which would only fail at runtime. Aggregates also return large integer sums as strings, so totals need explicit coercion or they concatenate; that is easy to get silently wrong.

Defined several new entities for contracts, rated transactions, adjustments and a per-period counter, and refactored an existing write path to accept an optional entity manager so the new rating logic could post inside its own transaction. Used row-level locking on the counter to make concurrent at-least-once deliveries safe.

What worked

Threading an optional entity manager through the existing write path was a clean way to compose a new transaction around code that previously owned its own. Entity decorators plus the compiler caught a column type that was too narrow for the values every writer was actually passing.

What got in the way

Schema changes are applied out of band here, so entity definitions alone do not create tables — I had to hand-write the DDL in a doc to match the decorators, which is duplicated truth waiting to drift. Everything beyond typechecking is unverified: no database was available, so transaction rollback, the lock under contention and idempotent replay were never exercised.

Modeled versioned contracts, immutable charges, and period counters as entities, and designed transactional rating with a write lock on the current period row. Relied on existing service patterns. Did not run against a live database.

What worked

Entity, repository, and transaction patterns already in the workspace were enough to design idempotent rating and period counters without inventing a new data layer.

What got in the way

Schema changes were left to the same out-of-band process as the other services, so there was no migration path to verify in this task.

Defined new entities for contracts, running period counters, append-only fee ratings, invoices and invoice lines, plus a reversal entity in an existing service, and wrote the transactional paths — row-locked reads inside transactions to keep concurrent partial reversals and tier counters correct.

What worked

Decorator-based entity definitions matched the existing services' style closely enough that the new code read as native. Transaction scoping with a managed entity manager, and pessimistic locking on the row that guards a running counter, were expressible directly and clearly. Repository injection made the services easy to construct in tests.

What got in the way

The repository and entity-manager surface is wide and partly overlapping, which made building a faithful in-memory fake for tests more work than expected; I ended up simplifying a parent/child relation and injecting a dedicated child repository purely so the test double did not have to emulate cascade-on-save. I never ran any of it against a real database in this task, so persistence behavior is unverified — only the shapes typecheck.