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The estate's transaction logging wrote to blob storage through a dedicated logging function - which consumers located through the same Key Vault registry the logger belongs to. Discovery consuming discovery, the transaction-id thread that made it useful, and the bootstrap questions self-reference always raises. Part 6 of Service Discovery Without a Service Mesh.
One static registration method wires the whole discovery subsystem into an Azure Functions host, and one CI pipeline packs it as a private NuGet package. The lifetime choices in those few lines quietly decide the caching semantics of the entire estate - and a duplicate registration teaches how DI forgives. Part 5 of Service Discovery Without a Service Mesh.
Above raw resolution sits a convention layer - an abstract endpoint base class that derives its Key Vault lookup key from its own type name, a template method separating the call from the interpretation, and a generic proxy that routes by name across every endpoint the DI container knows. Part 4 of Service Discovery Without a Service Mesh.
Azure Functions want their key in an x-functions-key header; Logic App workflows want a SAS signature in the query string. How a discovery record's type field became a calling-convention discriminator, and how one small HTTP service hid that difference from every caller in the estate. Part 3 of Service Discovery Without a Service Mesh.
The resolution path of a Key Vault-backed discovery client - three fail-fast environment variables, DefaultAzureCredential with a user-assigned managed identity, a name composed as service-plus-environment, a tolerant JSON parser, and a process-lifetime cache with a bug I shipped and honest trade-offs I chose. Part 2 of Service Discovery Without a Service Mesh.
Dozens of Azure Functions and Logic Apps need each other's URLs and keys, and there is no Kubernetes, no sidecar, no mesh to hand them out. How a small .NET library turned one Key Vault and a naming convention into a working discovery layer - the consumer side of a registry made of secrets. Part 1 of a new series.
Retention as a first-class column: how ArchiveKit stamps ExpiresAt at archive time from a TtlDays attribute, hard-deletes expired rows in bounded batches, and runs the whole thing on Lambda with the hosted services switched off. Also, the rows TTL doesn't clean yet. Part 8 of the Archive-on-Delete with ArchiveKit series.
The read side of archive-on-delete: querying archives through promoted typed columns, digging into jsonb payloads for everything else, polling snapshots with a 202 pattern, and an honest accounting of the restore path ArchiveKit doesn't have yet. Part 9 wraps up the Archive-on-Delete with ArchiveKit series.
ArchiveKit's snapshot service assembles a full JSON tree of a deleted object graph using nothing but archive rows - the primary data is already gone. Job tickets, a four-state machine, retries with a terminal Failed state, and the single-table assembly limitation I still owe a fix for. Part 7 of the Archive-on-Delete with ArchiveKit series.
Correlation ids and cascade archiving in ArchiveKit - how one Guid per save turns scattered archive rows into a reconstructable object graph, why cascade happens through the EF change tracker rather than the database, and two honest limits: a naive root heuristic and a CascadeArchive flag the interceptor doesn't read yet. Part 6 of the Archive-on-Delete with ArchiveKit series.
How ArchiveKit wires itself up - the [Archivable] attribute's three knobs, an assembly scanner that fails fast at startup instead of quietly at delete time, the static registry behind every lookup, and the AddArchiveKit configuration surface. Plus an honest note about the source generator that doesn't exist yet. Part 5 of the Archive-on-Delete with ArchiveKit series.
The storage design behind ArchiveKit - typed queryable columns and a jsonb payload from a single write, why the archive's EntityId is an index but deliberately not a foreign key, and the reflective model configuration that builds every archive table from one method call. Part 4 of the Archive-on-Delete with ArchiveKit series.
Why ArchiveKit's inline archive limit defaults to exactly 14, how EF Core's MaxBatchSize turns archiving into arithmetic, and an honest inventory of every deletion path the interceptor cannot see - synchronous saves, ExecuteDelete, and raw SQL. Part 3 of the Archive-on-Delete with ArchiveKit series.
The anatomy of ArchiveKit's SaveChangesInterceptor - detecting soft-deleted entities in the change tracker, building archive rows with zero extra database reads, and the one line of state-flip ordering that keeps EF from cascade-deleting the archive you just wrote. Part 2 of the Archive-on-Delete with ArchiveKit series.
Hard deletes destroy the audit trail; soft deletes clutter live tables forever. Introducing ArchiveKit, a .NET library that intercepts deletions and stores immutable archive snapshots with TTL policies and cascade archiving.
Archive tables are meant to empty out - a background worker migrates their rows into the current table while live traffic reads and write-through updates race it. This part walks the EF backfill's insert-then-conditional-delete choreography and the timestamp that keeps it honest. Part 9 of the Entity Versioning with VersionKit series.
Static registries, Activator-created migrators, a Postgres accent, a flag that does nothing, and no test project - the closing part of the VersionKit series itemises every trade-off in the library, says when you should not use it, and sketches what comes next. Part 10 of the Entity Versioning with VersionKit series.
A point lookup that might hit any of three tables, a list query that should span them all, and writes that must keep a registry honest - this part walks VersionKit's version-aware reader and writer, and the generated UNION view that makes every schema era queryable as if it were current. Part 8 of the Entity Versioning with VersionKit series.
JSON payloads can migrate lazily because the reader controls the shape - relational rows cannot, because the table is the shape. VersionKit's EF Core answer is table-per-version - archive Orders as Orders_v2 on a schema bump, track every row's home in a registry, and let old rows stay old until something moves them. Part 7 of the Entity Versioning with VersionKit series.
Lazy migration makes deploys instant but taxes every read of old data. VersionKit's backfill service rewrites stale payloads in scheduled batches until the store converges on the current version - in a host, or from a serverless function. Part 6 of the Entity Versioning with VersionKit series.
Splitting a name field, converting a string to a bool, deriving a flag from a total - some migrations are code, not metadata. This part covers VersionKit's three escape hatches and the sharp edges each one carries. Part 5 of the Entity Versioning with VersionKit series.
At startup VersionKit reflects over your model assembly and compiles property attributes into an executable pipeline - one migration step per version pair, five operation types, all of them deliberately idempotent. Part 3 of the Entity Versioning with VersionKit series.
Every stored payload carries a __version stamp; every read walks it up one step at a time. This part dissects VersionKit's lazy migration pipeline - the MigrationRunner API, the JsonElement traps System.Text.Json sets for dictionary code, and reading history on purpose. Part 4 of the Entity Versioning with VersionKit series.
Rename, move, add, remove, type change - VersionKit's attribute surface covers five kinds of schema evolution. This part walks the whole declarative API, the merge rules behind stacked attributes, and what the attributes deliberately refuse to express. Part 2 of the Entity Versioning with VersionKit series.
Nine parts of architecture posts lie by omission unless the tenth is the ledger. What earned its keep — isolation as data, MaxTenants = 1, the schema-keyed model cache — and the honest list of shortcuts: plaintext connection strings, cache windows, reflection, and a fan-out loop that still says TODO.
