feature: MinIO Data Lake Writer (Parquet, Partitioned)
This commit is contained in:
voltsrage
@@ -4,7 +4,7 @@ A production-quality clinical backend built with ASP.NET Core 8, PostgreSQL, Apa
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## Domain Model — How It Maps to a Real Clinical System
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In a hospital, a patient presents for care and an encounter is opened. Bedside monitors and lab systems post observations continuously against that encounter. A rules engine evaluates each observation against configured thresholds and flags abnormal values as clinical alerts. Clinicians acknowledge and resolve alerts. If a critical alert goes unacknowledged for five minutes, the system escalates to the on-call backup. All events flow through Kafka so the Elasticsearch dashboard, sepsis engine, and data lake each consume the same stream independently.
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In a hospital, a patient presents for care and an encounter is opened. Bedside monitors and lab systems post observations continuously against that encounter. A rules engine evaluates each observation against configured thresholds and flags abnormal values as clinical alerts. Clinicians acknowledge and resolve alerts. If a critical alert goes unacknowledged for five minutes, the system escalates to the on-call backup. All events flow through Kafka so the Elasticsearch dashboard, sepsis engine, and data lake writer consume the same stream independently.
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```
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Patient ─────────────────────────── one patient = one MRN, many lifetime encounters
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@@ -55,7 +55,7 @@ An `OutboxEvent` is written in the same transaction as any observation or alert,
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- **Elasticsearch CQRS Projection** — `EsIndexerService` consumer group upserts `patient_encounters` documents, appends to the `observations` index, and updates `openAlertCount` on alert events; patient/encounter search; per-encounter observation trend (hourly avg/min/max); alert volume summary by department and severity; population query (numeric range aggregation across all patients)
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- **Sepsis Early Warning Engine** — `SepsisEngineService` Kafka consumer evaluates SIRS criteria (temperature, heart rate, respiratory rate, WBC) per encounter using Redis keys with a 30-minute TTL sliding window; on ≥2 active criteria, inserts a `SEPSIS_WARNING / CRITICAL` alert idempotently (`INSERT WHERE NOT EXISTS`)
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- **RabbitMQ Notification Workers** — `NotificationPublisherService` reads `alert.generated` from Kafka and publishes paging jobs to `alerts.paging.queue`; `PagingWorkerService` sends the page and waits for acknowledgment; if no ack arrives before timeout it NACKs to `alerts.paging.dlq` with `x-message-ttl = 300000ms`; if the host is stopping, in-flight paging messages are NACKed with `requeue=true` so they are retried after restart and do not false-escalate; `EscalationWorkerService` pages the on-call backup and sets alert status to `escalated`; `DischargeSummaryWorkerService` reads `encounter.status.changed`, generates a discharge summary, and stores it in MinIO under `/discharge-summaries/{encounterId}/summary.pdf`
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- **Data Lake Writer** — Kafka consumer writing partitioned Parquet files to MinIO (`/observations/`, `/alerts/`, `/encounters/` by date); flush policy: 1,000 events or 5 minutes, whichever comes first; columnar format for 10-year regulatory retention
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- **Data Lake Writer (Phase 9 - in progress)** — `DataLakeWriterService` Kafka consumer buffers `observation.recorded`, `alert.generated`, and `encounter.status.changed` events, flushes partitioned Parquet files to MinIO (`/observations/`, `/alerts/`, `/encounters/` by date), and commits offsets after successful uploads
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- **Reconciliation Jobs** — three scheduled checks: (1) unacknowledged CRITICAL alerts older than 30 minutes, (2) pending orders without results after 4 hours, (3) active inpatients with no observation in 2 hours; each finding creates a `reconciliation_alerts` row and publishes to RabbitMQ
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- **Standard Envelope** — all responses use a consistent `{ success, statusCode, data, error }` wrapper; validation errors use the same shape; `ApiBehaviorOptions` overridden so model validation also produces the standard envelope
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- **Observability** — Serilog structured logging enriched with `correlationId`, `encounterId`, `patientId` on alert paths; Seq sink (`http://localhost:5345`); Prometheus (`http://localhost:9101`) scrapes `GET /metrics`; Grafana dashboards (`http://localhost:3101`, admin/admin) for clinical metrics including `alerts_unacknowledged_gauge`; per-request correlation IDs in request logs and response headers
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@@ -87,6 +87,7 @@ IHostedServices (background):
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PagingWorkerService → RabbitMQ paging.queue → log page → NACK on timeout (or requeue on shutdown)
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EscalationWorkerService → RabbitMQ escalation.queue → update alert status
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DischargeSummaryWorkerService → RabbitMQ discharge.queue → MinIO PDF
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DataLakeWriterService → Kafka (data-lake-writer) → Parquet files in MinIO
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ReconciliationScheduler → three scheduled safety checks → reconciliation_alerts + RabbitMQ
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```
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@@ -182,6 +183,14 @@ VigilCareClinicalAPI/
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│ └── RabbitMqTopologyProvisioner.cs # Declares exchange, queues, DLQ bindings on startup
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├── Storage/
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│ └── MinioClientFactory.cs
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├── DataLake/
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│ ├── DataLakeOptions.cs # Flush thresholds and bucket settings
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│ ├── DataLakeWriterService.cs # consumer group: data-lake-writer; Kafka → Parquet → MinIO
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│ └── ParquetFileBuilder.cs # Topic row models → Parquet byte arrays
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├── Models/Records/
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│ ├── Observation/ObservationRow.cs # Parquet row contract for observation events
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│ ├── Alert/AlertRow.cs # Parquet row contract for alert events
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│ └── Encounter/EncounterStatusRow.cs # Parquet row contract for encounter status events
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├── Data/
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│ ├── AppDbContext.cs # EF Core context — entity configs, indexes, constraints
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│ ├── Configurations/ # IEntityTypeConfiguration per entity
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@@ -213,7 +222,9 @@ tests/
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├── SirsDetectorTests.cs # Redis SIRS state SET/DEL/MGET logic
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├── SirsEvaluatorTests.cs # Per-code criterion evaluation
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├── NotificationPipelineTests.cs # RabbitMQ topology, DLQ routing
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└── ReconciliationTests.cs # Three reconciliation checks, deduplication, RabbitMQ publish
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├── ReconciliationTests.cs # Three reconciliation checks, deduplication, RabbitMQ publish
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├── ObservabilityPhase8Tests.cs # /metrics families and correlation header behavior
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└── DataLakePhase9Tests.cs # Kafka → MinIO Parquet flow and schema checks
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```
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---
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@@ -817,4 +828,4 @@ Observation history uses cursor pagination on `(recorded_at DESC, id DESC)`. Off
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| 6 | RabbitMQ exchange and queue topology; `NotificationPublisherService`; `PagingWorkerService`; DLQ escalation (`EscalationWorkerService`); discharge summary (`DischargeSummaryWorkerService` → MinIO); integration tests | Done |
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| 7 | Reconciliation scheduler — unacknowledged critical alerts, stale pending orders, disconnected monitors; `reconciliation_alerts` table; RabbitMQ publish; integration tests | Done |
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| 8 | Prometheus metrics (`GET /metrics`); Grafana dashboards; eight application metric families | In progress |
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| 9 | Data lake writer — Kafka consumer group `data-lake-writer`; Parquet flush to MinIO | Planned |
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| 9 | Data lake writer — Kafka consumer group `data-lake-writer`; Parquet flush to MinIO; integration tests (`DataLakePhase9Tests`) | In progress |
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@@ -0,0 +1,249 @@
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using System.Net.Http.Json;
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using System.Text.Json;
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using Microsoft.EntityFrameworkCore;
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using Microsoft.Extensions.DependencyInjection;
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using Microsoft.Extensions.Options;
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using Minio.DataModel.Args;
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using Parquet;
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using StackExchange.Redis;
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public class DataLakePhase9Tests : IClassFixture<ApiFixture>
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{
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private readonly ApiFixture _fixture;
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private readonly HttpClient _http;
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private readonly MinioOptions _minioOpts;
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public DataLakePhase9Tests(ApiFixture fixture)
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{
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_fixture = fixture;
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_http = fixture.CreateClient();
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_minioOpts = fixture.Services
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.GetRequiredService<IOptions<MinioOptions>>().Value;
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}
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[Fact]
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public async Task ObservationEvents_FlushesToParquetInMinIO()
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{
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await EnsureThresholdsAsync();
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var patientId = await CreatePatientAsync();
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var encounterId = await CreateActiveEncounterAsync(patientId);
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for (var i = 0; i < 3; i++)
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{
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var resp = await _http.PostAsJsonAsync(
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$"/api/v1/encounters/{encounterId}/observations",
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new BatchIngestRequest(new List<IngestObservationRequest>
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{
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new("HEART_RATE", 72 + i, "bpm", ObservationSource.Device, DateTimeOffset.UtcNow, null)
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}));
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resp.EnsureSuccessStatusCode();
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}
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await Task.Delay(TimeSpan.FromSeconds(20));
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var keys = await ListMinioObjectsAsync("observations/");
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Assert.True(keys.Count > 0,
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$"No Parquet files found under observations/ in MinIO bucket '{_minioOpts.BucketName}'.");
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Assert.All(keys, k => Assert.EndsWith(".parquet", k));
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}
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[Fact]
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public async Task AlertEvents_FlushesToParquetInMinIO()
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{
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await EnsureThresholdsAsync();
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for (var i = 0; i < 3; i++)
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{
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var pid = await CreatePatientAsync();
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var eid = await CreateActiveEncounterAsync(pid);
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var resp = await _http.PostAsJsonAsync(
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$"/api/v1/encounters/{eid}/observations",
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new BatchIngestRequest(new List<IngestObservationRequest>
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{
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new("POTASSIUM_MEQ_L", 2.1m, "mEq/L", ObservationSource.Device, DateTimeOffset.UtcNow, null)
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}));
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resp.EnsureSuccessStatusCode();
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}
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await Task.Delay(TimeSpan.FromSeconds(20));
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var keys = await ListMinioObjectsAsync("alerts/");
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Assert.True(keys.Count > 0,
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$"No Parquet files found under alerts/ in MinIO bucket '{_minioOpts.BucketName}'.");
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}
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[Fact]
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public async Task EncounterStatusEvents_FlushesToParquetInMinIO()
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{
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await EnsureThresholdsAsync();
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for (var i = 0; i < 3; i++)
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{
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var pid = await CreatePatientAsync();
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_ = await CreateActiveEncounterAsync(pid);
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}
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await Task.Delay(TimeSpan.FromSeconds(20));
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var keys = await ListMinioObjectsAsync("encounters/");
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Assert.True(keys.Count > 0,
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$"No Parquet files found under encounters/ in MinIO bucket '{_minioOpts.BucketName}'.");
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}
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[Fact]
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public async Task ObservationParquetFile_ContainsCorrectColumns()
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{
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await EnsureThresholdsAsync();
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var patientId = await CreatePatientAsync();
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var encounterId = await CreateActiveEncounterAsync(patientId);
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for (var i = 0; i < 3; i++)
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{
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var resp = await _http.PostAsJsonAsync(
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$"/api/v1/encounters/{encounterId}/observations",
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new BatchIngestRequest(new List<IngestObservationRequest>
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{
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new("TEMP_C", 37.5m, "°C", ObservationSource.Device, DateTimeOffset.UtcNow, null)
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}));
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resp.EnsureSuccessStatusCode();
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}
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await Task.Delay(TimeSpan.FromSeconds(20));
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var keys = await ListMinioObjectsAsync("observations/");
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Assert.True(keys.Count > 0);
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var bytes = await DownloadMinioObjectAsync(keys[0]);
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Assert.True(bytes.Length > 0);
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using var ms = new MemoryStream(bytes);
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using var reader = await ParquetReader.CreateAsync(ms);
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var columnNames = reader.Schema.DataFields.Select(f => f.Name).ToHashSet();
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Assert.Contains("observation_id", columnNames);
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Assert.Contains("encounter_id", columnNames);
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Assert.Contains("observation_code", columnNames);
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Assert.Contains("value", columnNames);
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Assert.Contains("kafka_partition", columnNames);
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Assert.Contains("kafka_offset", columnNames);
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}
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private async Task<List<string>> ListMinioObjectsAsync(string prefix)
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{
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var client = MinioClientFactory.Build(_minioOpts);
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var keys = new List<string>();
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var listArgs = new ListObjectsArgs()
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.WithBucket(_minioOpts.BucketName)
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.WithPrefix(prefix)
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.WithRecursive(true);
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await foreach (var item in client.ListObjectsEnumAsync(listArgs))
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{
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keys.Add(item.Key);
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}
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return keys;
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}
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private async Task<byte[]> DownloadMinioObjectAsync(string objectKey)
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{
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var client = MinioClientFactory.Build(_minioOpts);
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using var ms = new MemoryStream();
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await client.GetObjectAsync(new GetObjectArgs()
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.WithBucket(_minioOpts.BucketName)
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.WithObject(objectKey)
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.WithCallbackStream(stream => stream.CopyTo(ms)));
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return ms.ToArray();
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}
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private async Task<Guid> CreatePatientAsync()
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{
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var resp = await _http.PostAsJsonAsync("/api/v1/patients", new
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{
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firstName = "DataLake",
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lastName = "Test",
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dateOfBirth = "1980-11-12",
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gender = "Female",
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});
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resp.EnsureSuccessStatusCode();
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var body = await resp.Content.ReadFromJsonAsync<JsonDocument>();
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return body!.RootElement.GetProperty("data").GetProperty("id").GetGuid();
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}
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private async Task<Guid> CreateActiveEncounterAsync(Guid patientId)
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{
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var resp = await _http.PostAsJsonAsync(
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$"/api/v1/patients/{patientId}/encounters",
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new
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{
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encounterType = "INPATIENT",
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department = "ICU",
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attendingPhysician = "Dr. Osei",
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admittedAt = DateTimeOffset.UtcNow,
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});
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resp.EnsureSuccessStatusCode();
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var body = await resp.Content.ReadFromJsonAsync<JsonDocument>();
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var encounterId = body!.RootElement.GetProperty("data").GetProperty("id").GetGuid();
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// Encounter may be created as Scheduled depending on fixture seed path; move it
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// to Active if required, but tolerate Conflict when already Active.
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var activateResp = await _http.PatchAsJsonAsync(
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$"/api/v1/encounters/{encounterId}/status",
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new { status = "Active" });
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if (activateResp.StatusCode != System.Net.HttpStatusCode.Conflict)
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activateResp.EnsureSuccessStatusCode();
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return encounterId;
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}
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private async Task EnsureThresholdsAsync()
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{
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using var scope = _fixture.Services.CreateScope();
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var db = scope.ServiceProvider.GetRequiredService<AppDbContext>();
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await EnsureThresholdAsync(db, "HEART_RATE", "Heart Rate", "bpm", 30, 50, 100, 150);
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await EnsureThresholdAsync(db, "POTASSIUM_MEQ_L", "Serum Potassium", "mEq/L", 2.5m, 3.5m, 5.0m, 6.5m);
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await EnsureThresholdAsync(db, "TEMP_C", "Temperature", "°C", 34m, 36m, 37.8m, 40m);
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await db.SaveChangesAsync();
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var redis = scope.ServiceProvider.GetRequiredService<IConnectionMultiplexer>();
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var cache = redis.GetDatabase(1);
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await cache.StringSetAsync("threshold:HEART_RATE",
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"""{"ObservationCode":"HEART_RATE","CriticalLow":30,"WarningLow":50,"WarningHigh":100,"CriticalHigh":150}""");
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await cache.StringSetAsync("threshold:POTASSIUM_MEQ_L",
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"""{"ObservationCode":"POTASSIUM_MEQ_L","CriticalLow":2.5,"WarningLow":3.5,"WarningHigh":5.0,"CriticalHigh":6.5}""");
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await cache.StringSetAsync("threshold:TEMP_C",
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"""{"ObservationCode":"TEMP_C","CriticalLow":34,"WarningLow":36,"WarningHigh":37.8,"CriticalHigh":40}""");
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}
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private static async Task EnsureThresholdAsync(
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AppDbContext db,
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string code,
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string displayName,
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string unit,
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decimal criticalLow,
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decimal warningLow,
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decimal warningHigh,
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decimal criticalHigh)
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{
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var existing = await db.AlertThresholds.FirstOrDefaultAsync(t => t.ObservationCode == code);
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if (existing is not null) return;
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db.AlertThresholds.Add(new AlertThreshold
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{
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Id = Guid.NewGuid(),
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ObservationCode = code,
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DisplayName = displayName,
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Unit = unit,
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CriticalLow = criticalLow,
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WarningLow = warningLow,
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WarningHigh = warningHigh,
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CriticalHigh = criticalHigh,
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CreatedAt = DateTimeOffset.UtcNow
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});
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}
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}
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@@ -0,0 +1,17 @@
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public sealed class DataLakeOptions
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{
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public const string Section = "DataLake";
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// Maximum events buffered before a forced flush. At ~17 obs/sec steady state
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// this fires approximately once per minute. Set low in tests (3–5) to avoid
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// waiting for real traffic.
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public int FlushCount { get; init; } = 1_000;
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// Maximum age of the oldest buffered event before a time-based flush is forced.
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// Production: 300 seconds (5 minutes). Tests: 10 seconds.
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public int FlushIntervalSeconds { get; init; } = 300;
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// MinIO bucket. The discharge summary worker from Phase 6 uses the same bucket
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// under a different prefix — all VigilCare data stays in one bucket.
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public string BucketName { get; init; } = "vigilcare";
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}
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@@ -0,0 +1,302 @@
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using System.Text.Json;
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using Confluent.Kafka;
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using Microsoft.Extensions.Options;
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using Minio.DataModel.Args;
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public sealed class DataLakeWriterService : BackgroundService
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{
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private readonly KafkaOptions _kafkaOptions;
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private readonly DataLakeOptions _opts;
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private readonly MinioOptions _minioOpts;
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private readonly ILogger<DataLakeWriterService> _logger;
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// Buffer key: identifies one Parquet file-to-be.
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// Events sharing a topic, date, and Kafka partition land in the same file.
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private record BufferKey(string Topic, string DatePath, int Partition);
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private record BufferedEvent(string Payload, long Offset);
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private readonly Dictionary<BufferKey, List<BufferedEvent>> _buffer = new();
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// Track the highest offset per topic-partition for post-flush commit.
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private readonly Dictionary<TopicPartition, TopicPartitionOffset> _highWatermarks = new();
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public DataLakeWriterService(
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IOptions<KafkaOptions> kafkaOptions,
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IOptions<DataLakeOptions> opts,
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IOptions<MinioOptions> minioOpts,
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ILogger<DataLakeWriterService> logger)
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{
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_kafkaOptions = kafkaOptions.Value;
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_opts = opts.Value;
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_minioOpts = minioOpts.Value;
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_logger = logger;
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}
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protected override async Task ExecuteAsync(CancellationToken ct)
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{
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var consumerConfig = new ConsumerConfig
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{
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BootstrapServers = _kafkaOptions.BootstrapServers,
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GroupId = "data-lake-writer",
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AutoOffsetReset = AutoOffsetReset.Earliest,
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EnableAutoCommit = false,
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};
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using var consumer = new ConsumerBuilder<string, string>(consumerConfig).Build();
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consumer.Subscribe(new[]
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{
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_kafkaOptions.Topics.ObservationRecorded,
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_kafkaOptions.Topics.AlertGenerated,
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_kafkaOptions.Topics.EncounterStatusChanged,
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});
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_logger.LogInformation(
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"DataLakeWriterService started. FlushCount={FlushCount} FlushIntervalSeconds={FlushInterval}",
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_opts.FlushCount, _opts.FlushIntervalSeconds);
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var lastFlush = DateTimeOffset.UtcNow;
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try
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{
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while (!ct.IsCancellationRequested)
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{
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ConsumeResult<string, string>? result;
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try
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{
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result = consumer.Consume(TimeSpan.FromMilliseconds(500));
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}
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catch (ConsumeException ex)
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{
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_logger.LogError(ex, "DataLakeWriter consume error");
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continue;
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}
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|
||||
if (result is not null)
|
||||
AddToBuffer(result);
|
||||
|
||||
var totalBuffered = _buffer.Values.Sum(v => v.Count);
|
||||
var shouldFlushCount = totalBuffered >= _opts.FlushCount;
|
||||
var shouldFlushTime = DateTimeOffset.UtcNow - lastFlush
|
||||
>= TimeSpan.FromSeconds(_opts.FlushIntervalSeconds);
|
||||
|
||||
if ((shouldFlushCount || shouldFlushTime) && totalBuffered > 0)
|
||||
{
|
||||
await FlushAsync(consumer, ct);
|
||||
lastFlush = DateTimeOffset.UtcNow;
|
||||
}
|
||||
}
|
||||
}
|
||||
finally
|
||||
{
|
||||
// Final flush on shutdown so buffered events are not lost.
|
||||
if (_buffer.Values.Sum(v => v.Count) > 0)
|
||||
{
|
||||
try { await FlushAsync(consumer, ct); }
|
||||
catch (Exception ex)
|
||||
{
|
||||
_logger.LogError(ex, "DataLakeWriter shutdown flush failed — some events may be re-read on next start");
|
||||
}
|
||||
}
|
||||
consumer.Close();
|
||||
}
|
||||
}
|
||||
|
||||
private void AddToBuffer(ConsumeResult<string, string> result)
|
||||
{
|
||||
var datePath = ExtractDatePath(result.Topic, result.Message.Value);
|
||||
var key = new BufferKey(result.Topic, datePath, result.Partition.Value);
|
||||
|
||||
if (!_buffer.TryGetValue(key, out var list))
|
||||
{
|
||||
list = new List<BufferedEvent>();
|
||||
_buffer[key] = list;
|
||||
}
|
||||
list.Add(new BufferedEvent(result.Message.Value, result.Offset.Value));
|
||||
|
||||
// Track highest offset per topic-partition for post-flush commit.
|
||||
var tp = new TopicPartition(result.Topic, result.Partition);
|
||||
_highWatermarks[tp] = new TopicPartitionOffset(tp, result.Offset + 1);
|
||||
}
|
||||
|
||||
private async Task FlushAsync(IConsumer<string, string> consumer, CancellationToken ct)
|
||||
{
|
||||
var filesWritten = 0;
|
||||
|
||||
foreach (var (key, events) in _buffer)
|
||||
{
|
||||
if (events.Count == 0) continue;
|
||||
|
||||
try
|
||||
{
|
||||
var firstOffset = events.Min(e => e.Offset);
|
||||
var objectKey = BuildObjectKey(key, firstOffset);
|
||||
var bytes = await BuildParquetAsync(key.Topic, events, key.Partition);
|
||||
|
||||
await UploadToMinioAsync(objectKey, bytes, ct);
|
||||
filesWritten++;
|
||||
|
||||
_logger.LogInformation(
|
||||
"[DATA-LAKE] Wrote {Count} events → {ObjectKey} ({Bytes} bytes)",
|
||||
events.Count, objectKey, bytes.Length);
|
||||
}
|
||||
catch (Exception ex)
|
||||
{
|
||||
// Log and continue — a failed file for one key must not prevent other
|
||||
// keys from flushing. The uncommitted offsets will cause reprocessing.
|
||||
_logger.LogError(ex, "[DATA-LAKE] Failed to write file for key {Key}", key);
|
||||
}
|
||||
}
|
||||
|
||||
// Commit only after all files are uploaded.
|
||||
// Events for any key that failed above will be re-read on next startup.
|
||||
if (_highWatermarks.Any())
|
||||
{
|
||||
consumer.Commit(_highWatermarks.Values);
|
||||
_logger.LogInformation(
|
||||
"[DATA-LAKE] Committed offsets for {PartitionCount} partitions after flushing {FileCount} files",
|
||||
_highWatermarks.Count, filesWritten);
|
||||
}
|
||||
|
||||
_buffer.Clear();
|
||||
_highWatermarks.Clear();
|
||||
}
|
||||
|
||||
private async Task<byte[]> BuildParquetAsync(
|
||||
string topic, List<BufferedEvent> events, int partition)
|
||||
{
|
||||
if (topic == _kafkaOptions.Topics.ObservationRecorded)
|
||||
{
|
||||
var rows = events.Select(e => ParseObservationRow(e, partition)).ToList();
|
||||
return await ParquetFileBuilder.BuildObservationsAsync(rows);
|
||||
}
|
||||
if (topic == _kafkaOptions.Topics.AlertGenerated)
|
||||
{
|
||||
var rows = events.Select(e => ParseAlertRow(e, partition)).ToList();
|
||||
return await ParquetFileBuilder.BuildAlertsAsync(rows);
|
||||
}
|
||||
if (topic == _kafkaOptions.Topics.EncounterStatusChanged)
|
||||
{
|
||||
var rows = events.Select(e => ParseEncounterRow(e, partition)).ToList();
|
||||
return await ParquetFileBuilder.BuildEncountersAsync(rows);
|
||||
}
|
||||
throw new InvalidOperationException($"Unknown topic: {topic}");
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Object key and date partition helpers
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
// File path: observations/2025/01/15/partition-0-offset-0000001000.parquet
|
||||
// The date comes from the event timestamp, not the wall clock.
|
||||
// Events from the same encounter that cross midnight are written into the date
|
||||
// bucket matching their recorded_at timestamp — consistent with how Athena and
|
||||
// Spark partition-prune by event time, not ingest time.
|
||||
private string BuildObjectKey(BufferKey key, long firstOffset)
|
||||
{
|
||||
var folder = key.Topic switch
|
||||
{
|
||||
var t when t == _kafkaOptions.Topics.ObservationRecorded => "observations",
|
||||
var t when t == _kafkaOptions.Topics.AlertGenerated => "alerts",
|
||||
var t when t == _kafkaOptions.Topics.EncounterStatusChanged => "encounters",
|
||||
_ => "unknown",
|
||||
};
|
||||
return $"{folder}/{key.DatePath}/partition-{key.Partition}-offset-{firstOffset:D10}.parquet";
|
||||
}
|
||||
|
||||
private string ExtractDatePath(string topic, string payload)
|
||||
{
|
||||
try
|
||||
{
|
||||
var doc = JsonDocument.Parse(payload);
|
||||
var ts = topic switch
|
||||
{
|
||||
var t when t == _kafkaOptions.Topics.ObservationRecorded => doc.RootElement.GetProperty("recordedAt").GetDateTimeOffset(),
|
||||
var t when t == _kafkaOptions.Topics.AlertGenerated => doc.RootElement.GetProperty("triggeredAt").GetDateTimeOffset(),
|
||||
var t when t == _kafkaOptions.Topics.EncounterStatusChanged => doc.RootElement.GetProperty("changedAt").GetDateTimeOffset(),
|
||||
_ => DateTimeOffset.UtcNow,
|
||||
};
|
||||
return $"{ts.Year:D4}/{ts.Month:D2}/{ts.Day:D2}";
|
||||
}
|
||||
catch
|
||||
{
|
||||
// Malformed payload: use today so the event is not lost.
|
||||
var now = DateTimeOffset.UtcNow;
|
||||
return $"{now.Year:D4}/{now.Month:D2}/{now.Day:D2}";
|
||||
}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// Payload deserializers — each reads only the fields needed for the Parquet row.
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
private static ObservationRow ParseObservationRow(BufferedEvent e, int partition)
|
||||
{
|
||||
var d = JsonDocument.Parse(e.Payload).RootElement;
|
||||
return new ObservationRow(
|
||||
ObservationId : d.GetProperty("observationId").GetString() ?? "",
|
||||
EncounterId : d.GetProperty("encounterId").GetString() ?? "",
|
||||
PatientId : d.GetProperty("patientId").GetString() ?? "",
|
||||
Mrn : d.TryGetProperty("mrn", out var mrn) ? mrn.GetString() ?? "" : "",
|
||||
ObservationCode : d.GetProperty("observationCode").GetString() ?? "",
|
||||
Value : d.GetProperty("value").GetDouble(),
|
||||
Unit : d.GetProperty("unit").GetString() ?? "",
|
||||
Source : d.GetProperty("source").GetString() ?? "",
|
||||
RecordedAt : d.GetProperty("recordedAt").GetString() ?? "",
|
||||
KafkaPartition : partition,
|
||||
KafkaOffset : e.Offset
|
||||
);
|
||||
}
|
||||
|
||||
private static AlertRow ParseAlertRow(BufferedEvent e, int partition)
|
||||
{
|
||||
var d = JsonDocument.Parse(e.Payload).RootElement;
|
||||
return new AlertRow(
|
||||
AlertId : d.GetProperty("alertId").GetString() ?? "",
|
||||
EncounterId : d.GetProperty("encounterId").GetString() ?? "",
|
||||
PatientId : d.GetProperty("patientId").GetString() ?? "",
|
||||
AlertType : d.GetProperty("alertType").GetString() ?? "",
|
||||
Severity : d.GetProperty("severity").GetString() ?? "",
|
||||
Details : d.GetProperty("details").GetString() ?? "",
|
||||
TriggeredAt : d.GetProperty("triggeredAt").GetString() ?? "",
|
||||
KafkaPartition : partition,
|
||||
KafkaOffset : e.Offset
|
||||
);
|
||||
}
|
||||
|
||||
private static EncounterStatusRow ParseEncounterRow(BufferedEvent e, int partition)
|
||||
{
|
||||
var d = JsonDocument.Parse(e.Payload).RootElement;
|
||||
return new EncounterStatusRow(
|
||||
EncounterId : d.GetProperty("encounterId").GetString() ?? "",
|
||||
PatientId : d.GetProperty("patientId").GetString() ?? "",
|
||||
PreviousStatus : d.GetProperty("previousStatus").GetString() ?? "",
|
||||
NewStatus : d.GetProperty("newStatus").GetString() ?? "",
|
||||
ChangedAt : d.GetProperty("changedAt").GetString() ?? "",
|
||||
KafkaPartition : partition,
|
||||
KafkaOffset : e.Offset
|
||||
);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// MinIO upload
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
private async Task UploadToMinioAsync(string objectKey, byte[] bytes, CancellationToken ct)
|
||||
{
|
||||
var client = MinioClientFactory.Build(_minioOpts);
|
||||
var bucket = _opts.BucketName;
|
||||
|
||||
var exists = await client.BucketExistsAsync(
|
||||
new BucketExistsArgs().WithBucket(bucket), ct);
|
||||
if (!exists)
|
||||
await client.MakeBucketAsync(new MakeBucketArgs().WithBucket(bucket), ct);
|
||||
|
||||
await client.PutObjectAsync(new PutObjectArgs()
|
||||
.WithBucket(bucket)
|
||||
.WithObject(objectKey)
|
||||
.WithStreamData(new MemoryStream(bytes))
|
||||
.WithObjectSize(bytes.Length)
|
||||
.WithContentType("application/octet-stream"),
|
||||
ct);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,102 @@
|
||||
using Parquet;
|
||||
using Parquet.Data;
|
||||
using Parquet.Schema;
|
||||
|
||||
public static class ParquetFileBuilder
|
||||
{
|
||||
public static async Task<byte[]> BuildObservationsAsync(IReadOnlyList<ObservationRow> rows)
|
||||
{
|
||||
var schema = new ParquetSchema(
|
||||
new DataField<string>("observation_id"),
|
||||
new DataField<string>("encounter_id"),
|
||||
new DataField<string>("patient_id"),
|
||||
new DataField<string>("mrn"),
|
||||
new DataField<string>("observation_code"),
|
||||
new DataField<double>("value"),
|
||||
new DataField<string>("unit"),
|
||||
new DataField<string>("source"),
|
||||
new DataField<string>("recorded_at"),
|
||||
new DataField<int>("kafka_partition"),
|
||||
new DataField<long>("kafka_offset")
|
||||
);
|
||||
|
||||
using var ms = new MemoryStream();
|
||||
using (var writer = await ParquetWriter.CreateAsync(schema, ms))
|
||||
using (var rg = writer.CreateRowGroup())
|
||||
{
|
||||
var f = schema.DataFields;
|
||||
await rg.WriteColumnAsync(new DataColumn(f[0], rows.Select(r => r.ObservationId).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[1], rows.Select(r => r.EncounterId).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[2], rows.Select(r => r.PatientId).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[3], rows.Select(r => r.Mrn).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[4], rows.Select(r => r.ObservationCode).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[5], rows.Select(r => r.Value).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[6], rows.Select(r => r.Unit).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[7], rows.Select(r => r.Source).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[8], rows.Select(r => r.RecordedAt).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[9], rows.Select(r => r.KafkaPartition).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[10], rows.Select(r => r.KafkaOffset).ToArray()));
|
||||
}
|
||||
return ms.ToArray();
|
||||
}
|
||||
|
||||
public static async Task<byte[]> BuildAlertsAsync(IReadOnlyList<AlertRow> rows)
|
||||
{
|
||||
var schema = new ParquetSchema(
|
||||
new DataField<string>("alert_id"),
|
||||
new DataField<string>("encounter_id"),
|
||||
new DataField<string>("patient_id"),
|
||||
new DataField<string>("alert_type"),
|
||||
new DataField<string>("severity"),
|
||||
new DataField<string>("details"),
|
||||
new DataField<string>("triggered_at"),
|
||||
new DataField<int>("kafka_partition"),
|
||||
new DataField<long>("kafka_offset")
|
||||
);
|
||||
|
||||
using var ms = new MemoryStream();
|
||||
using (var writer = await ParquetWriter.CreateAsync(schema, ms))
|
||||
using (var rg = writer.CreateRowGroup())
|
||||
{
|
||||
var f = schema.DataFields;
|
||||
await rg.WriteColumnAsync(new DataColumn(f[0], rows.Select(r => r.AlertId).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[1], rows.Select(r => r.EncounterId).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[2], rows.Select(r => r.PatientId).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[3], rows.Select(r => r.AlertType).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[4], rows.Select(r => r.Severity).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[5], rows.Select(r => r.Details).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[6], rows.Select(r => r.TriggeredAt).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[7], rows.Select(r => r.KafkaPartition).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[8], rows.Select(r => r.KafkaOffset).ToArray()));
|
||||
}
|
||||
return ms.ToArray();
|
||||
}
|
||||
|
||||
public static async Task<byte[]> BuildEncountersAsync(IReadOnlyList<EncounterStatusRow> rows)
|
||||
{
|
||||
var schema = new ParquetSchema(
|
||||
new DataField<string>("encounter_id"),
|
||||
new DataField<string>("patient_id"),
|
||||
new DataField<string>("previous_status"),
|
||||
new DataField<string>("new_status"),
|
||||
new DataField<string>("changed_at"),
|
||||
new DataField<int>("kafka_partition"),
|
||||
new DataField<long>("kafka_offset")
|
||||
);
|
||||
|
||||
using var ms = new MemoryStream();
|
||||
using (var writer = await ParquetWriter.CreateAsync(schema, ms))
|
||||
using (var rg = writer.CreateRowGroup())
|
||||
{
|
||||
var f = schema.DataFields;
|
||||
await rg.WriteColumnAsync(new DataColumn(f[0], rows.Select(r => r.EncounterId).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[1], rows.Select(r => r.PatientId).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[2], rows.Select(r => r.PreviousStatus).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[3], rows.Select(r => r.NewStatus).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[4], rows.Select(r => r.ChangedAt).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[5], rows.Select(r => r.KafkaPartition).ToArray()));
|
||||
await rg.WriteColumnAsync(new DataColumn(f[6], rows.Select(r => r.KafkaOffset).ToArray()));
|
||||
}
|
||||
return ms.ToArray();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,11 @@
|
||||
public sealed record AlertRow(
|
||||
string AlertId,
|
||||
string EncounterId,
|
||||
string PatientId,
|
||||
string AlertType,
|
||||
string Severity,
|
||||
string Details,
|
||||
string TriggeredAt,
|
||||
int KafkaPartition,
|
||||
long KafkaOffset
|
||||
);
|
||||
@@ -0,0 +1,9 @@
|
||||
public sealed record EncounterStatusRow(
|
||||
string EncounterId,
|
||||
string PatientId,
|
||||
string PreviousStatus,
|
||||
string NewStatus,
|
||||
string ChangedAt,
|
||||
int KafkaPartition,
|
||||
long KafkaOffset
|
||||
);
|
||||
@@ -0,0 +1,13 @@
|
||||
public sealed record ObservationRow(
|
||||
string ObservationId,
|
||||
string EncounterId,
|
||||
string PatientId,
|
||||
string Mrn,
|
||||
string ObservationCode,
|
||||
double Value,
|
||||
string Unit,
|
||||
string Source,
|
||||
string RecordedAt,
|
||||
int KafkaPartition,
|
||||
long KafkaOffset
|
||||
);
|
||||
@@ -55,6 +55,9 @@ try
|
||||
builder.Services.Configure<ReconciliationJobOptions>(
|
||||
builder.Configuration.GetSection(ReconciliationJobOptions.Section));
|
||||
|
||||
builder.Services.Configure<DataLakeOptions>(
|
||||
builder.Configuration.GetSection(DataLakeOptions.Section));
|
||||
|
||||
builder.Services.AddScoped<IPatientService, PatientService>();
|
||||
builder.Services.AddScoped<IEncounterService, EncounterService>();
|
||||
builder.Services.AddScoped<IAlertThresholdService, AlertThresholdService>();
|
||||
@@ -84,6 +87,7 @@ try
|
||||
builder.Services.AddHostedService<AlertsUnacknowledgedCollector>();
|
||||
builder.Services.AddHostedService<OutboxPendingCollector>();
|
||||
builder.Services.AddHostedService<KafkaConsumerLagCollector>();
|
||||
builder.Services.AddHostedService<DataLakeWriterService>();
|
||||
|
||||
builder.Services.AddControllers()
|
||||
.AddJsonOptions(opts =>
|
||||
|
||||
@@ -18,6 +18,7 @@
|
||||
</PackageReference>
|
||||
<PackageReference Include="Minio" Version="6.0.3" />
|
||||
<PackageReference Include="Npgsql.EntityFrameworkCore.PostgreSQL" Version="8.0.4" />
|
||||
<PackageReference Include="Parquet.Net" Version="4.24.0" />
|
||||
<PackageReference Include="prometheus-net.AspNetCore" Version="8.2.1" />
|
||||
<PackageReference Include="RabbitMQ.Client" Version="6.8.1" />
|
||||
<PackageReference Include="Serilog.AspNetCore" Version="10.0.0" />
|
||||
|
||||
@@ -13,5 +13,9 @@
|
||||
},
|
||||
"RabbitMq": {
|
||||
"PagingAckTimeoutMs": 5000
|
||||
},
|
||||
"DataLake": {
|
||||
"FlushCount": 3,
|
||||
"FlushIntervalSeconds": 10
|
||||
}
|
||||
}
|
||||
|
||||
@@ -74,5 +74,9 @@
|
||||
"UnacknowledgedAlertThresholdMinutes": 30,
|
||||
"PendingOrderThresholdHours": 4,
|
||||
"NoObservationThresholdHours": 2
|
||||
},
|
||||
"DataLake": {
|
||||
"FlushCount": 1000,
|
||||
"FlushIntervalSeconds": 300
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,87 @@
|
||||
```markdown
|
||||
# Decision: Data Lake Architecture — Parquet on MinIO
|
||||
|
||||
## Context
|
||||
|
||||
Healthcare regulations in most jurisdictions require medical records to be retained
|
||||
for 7–25 years. A medium hospital with 200 concurrent inpatients generates roughly
|
||||
17 observations per second at steady state. Over 10 years that is approximately
|
||||
5 billion observation rows. Keeping this in PostgreSQL would require continuous
|
||||
partitioning and archival work; running population health queries against it would
|
||||
compete with live ingest writes.
|
||||
|
||||
## Decision
|
||||
|
||||
A separate data lake tier handles long-term retention. The Kafka `data-lake-writer`
|
||||
consumer group reads all three topics and writes Parquet files to MinIO under
|
||||
date-partitioned prefixes.
|
||||
|
||||
## Why Parquet over JSON or CSV
|
||||
|
||||
Parquet is columnar. A population health query — "give me all heart rate values for
|
||||
ICU patients in 2024" — reads only the `observation_code` and `value` columns without
|
||||
deserializing `encounter_id`, `patient_id`, `unit`, `source`, or `recorded_at`.
|
||||
|
||||
The repetition of `observation_code` values across millions of rows (thousands of rows
|
||||
all with `HEART_RATE`) compresses at 5–10× vs JSON via dictionary encoding. At 10-year
|
||||
scale this difference is measured in terabytes of storage cost.
|
||||
|
||||
CSV is row-oriented like JSON and does not support schema evolution — adding a new
|
||||
column requires rewriting every historical file.
|
||||
|
||||
## Why MinIO (S3-compatible) over PostgreSQL or Elasticsearch for archive
|
||||
|
||||
PostgreSQL is the operational write layer. Running 10-year-scale queries on the same
|
||||
instance that serves live ingest introduces contention. Adding partitioning and tiered
|
||||
storage to PostgreSQL adds operational complexity without solving the fundamental
|
||||
problem: it is still a row store.
|
||||
|
||||
Elasticsearch is optimized for search and aggregations, not for full-table scans or
|
||||
columnar projections. Storing 5 billion observation rows in Elasticsearch would require
|
||||
enormous index memory and produce no benefit for the analytics pattern that justifies
|
||||
the archive (population health over multi-year windows).
|
||||
|
||||
MinIO is S3-compatible. The files it stores can be queried directly by DuckDB
|
||||
(single analyst), Apache Spark (cluster analytics), and AWS Athena (serverless queries
|
||||
over S3) without any data movement. Switching from MinIO to S3 in production requires
|
||||
changing one endpoint URL.
|
||||
|
||||
## Why not Apache Flink or Spark Streaming for the lake writer
|
||||
|
||||
At a single-hospital scale — 50 obs/sec peak, 6 Kafka partitions — the consumer lag
|
||||
from a simple .NET BackgroundService with in-memory buffering and a 5-minute flush is
|
||||
negligible. The overhead of deploying and operating a Flink cluster adds operational
|
||||
cost that is not justified by the throughput.
|
||||
|
||||
The scale inflection point: approximately 10,000+ events/sec sustained, or the need
|
||||
for exactly-once semantics at the storage layer. Below that, the simple consumer is
|
||||
correct. Above it, Flink's checkpoint-based exactly-once delivery to Parquet
|
||||
(via its FileSystem sink) becomes worth the operational cost.
|
||||
|
||||
## Flush policy rationale
|
||||
|
||||
**Count-based flush (1,000 events):** Bounds memory. At 17 obs/sec steady state this
|
||||
fires approximately once per minute. At 50 obs/sec peak it fires every 20 seconds.
|
||||
|
||||
**Time-based flush (5 minutes):** Bounds latency. At low load (night shift, few
|
||||
monitors active), the count threshold might not fire for hours. A 5-minute ceiling
|
||||
means the most recent data is always queryable within 5 minutes of arriving in Kafka.
|
||||
|
||||
**At-least-once delivery:** Kafka offsets are committed only after the Parquet file
|
||||
is successfully uploaded. A process crash between buffering and uploading produces
|
||||
duplicate rows on the next startup — the same observation appears in two files with
|
||||
different `kafka_offset` values. For a regulatory archive this is acceptable.
|
||||
Downstream queries can deduplicate on `observation_id`. Data loss is not acceptable;
|
||||
duplicates are.
|
||||
|
||||
## Date partition structure
|
||||
|
||||
Files are partitioned by the **event timestamp** from the payload, not by wall clock
|
||||
time at consumption. An observation `recorded_at 2025-01-14T23:59:59Z` consumed at
|
||||
`2025-01-15T00:01:00Z` is written to `observations/2025/01/14/...`.
|
||||
|
||||
This matches how Athena and Spark apply partition pruning: a query with
|
||||
`WHERE recorded_at BETWEEN '2025-01-14' AND '2025-01-14'` reads only the
|
||||
`observations/2025/01/14/` prefix. Partitioning by ingest time instead would cause
|
||||
the query to miss that row.
|
||||
```
|
||||
Reference in New Issue
Block a user