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Data Pump","maintainers":[{"name":"jbiskur","email":"jbiskur@gmail.com"},{"name":"suuunly","email":"suuunly@gmail.com"}],"readme":"# Flowcore Data Pump Client\n\nA reliable, high-performance TypeScript client for streaming and processing events from the Flowcore platform. Built for\nreal-time event processing with automatic retry, buffering, and state management.\n\n[![NPM Version](https://img.shields.io/npm/v/@flowcore/data-pump)](https://www.npmjs.com/package/@flowcore/data-pump)\n\n## Simple Example Setup\n\n```typescript\nimport { FlowcoreDataPump } from \"@flowcore/data-pump\"\n\nconst dataPump = FlowcoreDataPump.create({\n  // The identity must have permission to list buckets and read events.\n  auth: {\n    apiKey: process.env.FLOWCORE_API_KEY!,\n  },\n  dataSource: {\n    tenant: \"your-tenant-name\",\n    dataCore: \"your-data-core\",\n    flowType: \"your-flow-type\",\n    eventTypes: [\"event-type-1\", \"event-type-2\"],\n  },\n  stateManager: {\n    getState: () => loadState(),\n    setState: (state) => saveState(state),\n  },\n  processor: {\n    concurrency: 10, // Batch size passed to one handler invocation\n    handler: async (events) => {\n      for (const event of events) {\n        await processIdempotently(event)\n      }\n    },\n  },\n  notifier: { type: \"websocket\" },\n  bufferSize: 500,\n  logger: {\n    debug: (msg) => console.log(`[DEBUG] ${msg}`),\n    info: (msg) => console.log(`[INFO] ${msg}`),\n    warn: (msg) => console.warn(`[WARN] ${msg}`),\n    error: (msg) => console.error(`[ERROR] ${msg}`),\n  },\n})\n\n// The callback form runs in the background and enables fetch-loop self-healing.\nawait dataPump.start((error) => {\n  if (error) console.error(\"Data Pump stopped with an error\", error)\n})\n```\n\n## Installation\n\n### Node.js\n\n```bash\nnpm install @flowcore/data-pump\n```\n\n```typescript\nimport { FlowcoreDataPump } from \"@flowcore/data-pump\"\n```\n\n## Key Concepts\n\n### **Time Buckets**\n\nEvents are organized in **hourly time buckets** using the format `yyyyMMddHH0000`:\n\n```\n20240315140000 = March 15, 2024, 14:00 (2 PM)\n20240315150000 = March 15, 2024, 15:00 (3 PM)\n20240315160000 = March 15, 2024, 16:00 (4 PM)\n```\n\n**Why time buckets matter:**\n\n- **Precise positioning**: Resume from any hour in your event history\n- **Efficient queries**: Flowcore can quickly locate events within time ranges\n- **Catch-up processing**: Process months of historical data in sequence\n- **Debugging**: Jump to specific time periods when issues occurred\n\n### **State Management**\n\nThe pump persists a conservative processing frontier using a time bucket and event ID:\n\n```typescript\n// Current position in event stream\n{\n  timeBucket: \"20240315140000\",\n  eventId: \"abc-123-def-456\"\n}\n```\n\nThe fetch position can run ahead of this persisted frontier. The saved event ID is the last event in source order whose\nprocessing is complete with no unfinished event before it. The source resumes **after** that ID. If later events finish\nfirst, the pump holds their completion in memory until the earlier gap closes. After a crash, the pump may replay events\nwhose completion was not durably checkpointed. Consumers must use `eventId` as a durable idempotency key.\n\nThis corrects the older interpretation that stored the first unfinished event. Existing saved cursors are not rewritten:\nif an older version already stored an unfinished event ID, upgrading cannot recover the event that the exclusive resume\nboundary will skip. Reset or rewind a cursor only through an explicit operator decision; the pump does not reset it\nautomatically.\n\n### **Event Lifecycle & Processing Modes**\n\nUnderstanding how events flow through the system:\n\n#### **Push Mode Flow (Automatic)**\n\n```\nFetch → Buffer → Reserve → Process → ✅ Acknowledge (or ❌ Retry)\n         ↑                    ↑                 ↑\n    You configure         Pump handles     You write business logic\n```\n\n#### **Pull Mode Flow (Manual)**\n\n```\nFetch → Buffer → YOU Reserve → YOU Process → YOU Acknowledge/Fail\n         ↑           ↑              ↑              ↑\n    Pump handles   You control   You control   You control\n```\n\n### **Buffer Management**\n\nLocal **in-memory event queue** between fetching and processing:\n\n```typescript\nBuffer: [Event1, Event2, Event3, Event4, Event5]\n         ↑                              ↑\n    Processing these              Fetching more\n```\n\n**Handles key scenarios:**\n\n- **Backpressure**: When processing is slower than event arrival rate\n- **Batch processing**: Group multiple events for efficient processing\n- **Flow control**: Automatic throttling based on buffer capacity\n- **Memory protection**: Prevents unlimited memory growth during slow processing\n\nThe fetch window is bounded by the checkpoint queue, not only by events still in the delivery buffer. One unfinished event\ntherefore applies backpressure after `bufferSize` fetched events are retained behind it. This is intentional: continuing to\nfetch would grow the out-of-order completion frontier without bound. Monitor `checkpointQueueDepth` in pulses or\n`flowcore_data_pump_checkpoint_queue_depth_gauge` to distinguish this state from an idle empty buffer.\n\n### **Live vs Historical Processing**\n\nTwo fundamental processing patterns:\n\n#### **Live Mode**\n\n- **When**: `stateManager.getState()` returns `null`\n- **Behavior**: Start in the current hour after a time-based event ID representing now, then process newly stored events\n- **Use case**: Production event processing, real-time analytics\n\nReturning `null` does not replay events from earlier in the current hour. Supply an explicit state for a backfill or when\ncreating a projection from retained history.\n\n#### **Historical Mode**\n\n- **When**: `stateManager.getState()` returns `{ timeBucket, eventId }`\n- **Behavior**: Process events from specific point in time\n- **Use case**: Backfill data, debugging, data migration, replaying scenarios\n\n### **Batch Size and Application Concurrency**\n\nIn version 0.22.x, `processor.concurrency` controls how many events are reserved and passed to one handler invocation. It\ndoes not start that many handler invocations in parallel:\n\n```typescript\nprocessor: {\n  concurrency: 5,\n  handler: async (events) => {\n    // This batch contains up to five events.\n    // Add parallelism here only when ordering and dependencies allow it.\n    await Promise.all(events.map(processIdempotently))\n  }\n}\n```\n\nThe batch is acknowledged only after the complete handler resolves. If some events commit before a later event throws,\nthe whole batch can be delivered again.\n\n### **Failure Handling and Retries**\n\nHandler retries are driven by the acknowledgment timeout:\n\n```\nReserve → Handler throws → Reservation times out → Reopen → Reserve again\n```\n\n`achknowledgeTimeoutMs` is a fixed delay; per-event redelivery does not use exponential backoff. With the default\n`maxRedeliveryCount: 3`, an event can be reserved four times: the initial delivery plus three redeliveries. Set the value\nto `-1` only when unlimited redelivery is intentional.\n\nExponential backoff from one to thirty seconds is used for failed fetch loops, process loops, leader pumps, and cluster\nreconnections.\n\n## Usage Patterns\n\n### Push Mode (Automatic Lifecycle Management)\n\n**Best for most use cases** - You focus on business logic, the pump handles everything else automatically.\n\n- **You handle**: Writing your event processing logic\n- **Pump handles**: Reserve → Process → Acknowledge → Retry on failures\n- **Use when**: Standard event processing with simple error handling\n\n```typescript\nconst dataPump = FlowcoreDataPump.create({\n  auth: {\n    /* auth config */\n  },\n  dataSource: {\n    /* data source config */\n  },\n  stateManager: {\n    /* state management */\n  },\n  processor: {\n    concurrency: 5,\n    handler: async (events) => {\n      for (const event of events) {\n        await processIdempotently(event)\n      }\n      // The complete batch is acknowledged after this handler resolves.\n      // If it throws, the reservation reopens after achknowledgeTimeoutMs.\n    },\n    failedHandler: async (failedEvents) => {\n      // Handle events that permanently failed after all retries\n      await logFailedEvents(failedEvents)\n    },\n  },\n  bufferSize: 1000,\n  maxRedeliveryCount: 3,\n})\n\nawait dataPump.start((error) => {\n  if (error) console.error(\"Data Pump stopped\", error)\n})\n```\n\n### Pull Mode (Manual Lifecycle Control)\n\n**For advanced scenarios** - You control reservation and acknowledgment manually.\n\n- **You handle**: Reserve → Process → Acknowledge, leave reserved for retry, or fail terminally\n- **Pump provides**: Raw event access and buffer management\n- **Use when**: Complex error handling, partial batch failures, or custom acknowledgment logic\n\n```typescript\nconst dataPump = FlowcoreDataPump.create({\n  auth: {\n    /* auth config */\n  },\n  dataSource: {\n    /* data source config */\n  },\n  stateManager: {\n    /* state management */\n  },\n  // ❌ No processor = manual mode\n})\n\nawait dataPump.start((error) => {\n  if (error) console.error(\"Data Pump stopped\", error)\n})\n\n// You manually control the entire event lifecycle\nwhile (dataPump.isRunning) {\n  const events = await dataPump.reserve(10)\n\n  for (const event of events) {\n    try {\n      await processIdempotently(event)\n      await dataPump.acknowledge([event.eventId])\n    } catch (error) {\n      console.warn(\"Leaving event reserved for timeout-based redelivery\", event.eventId, error)\n    }\n  }\n}\n```\n\n`fail(eventIds)` is terminal: it removes matching events from the buffer and invokes `failedHandler` when configured. It\ndoes **not** schedule a retry. Leave a reservation unresolved to make it eligible for redelivery after the acknowledgment\ntimeout.\n\nTerminal failure callbacks are notifications, not a transactional dead-letter guarantee. The pump advances the terminal\nevent's checkpoint before waiting for `failedHandler` or `onFinalyFailed`; a callback rejection is still returned/logged,\nbut it does not put the event back into the buffer. Stopping while a callback is in flight does not cancel it; the terminal\ncompletion and state write were already scheduled, so a late callback cannot complete an item from a later generation. If\ndurable dead-letter storage is required, make the terminal decision only after that storage contract is satisfied, or use\nan idempotent external recovery workflow.\n\n### Which Mode Should You Use?\n\n| Scenario                        | Recommended Mode | Why                                                        |\n| ------------------------------- | ---------------- | ---------------------------------------------------------- |\n| **Simple event processing**     | **Push Mode**    | Just write business logic, pump handles everything else    |\n| **Standard error handling**     | **Push Mode**    | Automatic retries and failure handling work for most cases |\n| **Getting started**             | **Push Mode**    | Much simpler to set up and understand                      |\n| **Complex error handling**      | **Pull Mode**    | Need to handle some events succeeding while others fail    |\n| **Conditional acknowledgments** | **Pull Mode**    | Business logic determines which events to acknowledge      |\n| **Custom retry strategies**     | **Pull Mode**    | Need more control than simple retry count                  |\n| **Transaction integration**     | **Pull Mode**    | Need to coordinate with database transactions              |\n\n## Authentication\n\n### API Key Authentication\n\n```typescript\nauth: {\n  apiKey: process.env.FLOWCORE_API_KEY!\n}\n```\n\nCurrent `fc_{id}_{secret}` keys contain their key ID. `apiKeyId` remains available for old keys but is deprecated. The\nidentity needs IAM permission to list time buckets and read events for the selected resources.\n\n### OIDC/Bearer Token Authentication\n\n```typescript\nimport { oidcClient } from \"@flowcore/sdk-oidc-client\"\n\nconst oidc = oidcClient({\n  clientId: \"your-client-id\",\n  clientSecret: \"your-client-secret\",\n})\n\nauth: {\n  getBearerToken: () => oidc.getToken().then((token) => token.accessToken)\n}\n```\n\n## State Management\n\nThe state manager stores a conservative processing frontier. It supports recovery, but it does not provide exactly-once\nprocessing. A crash between a business side effect and acknowledgment can cause replay, so handlers must be idempotent.\n\n### Understanding State\n\n**State Format:**\n\n```typescript\ninterface FlowcoreDataPumpState {\n  timeBucket: string // Format: \"yyyyMMddHH0000\" (e.g., \"20240101120000\")\n  eventId?: string | undefined // Optional: specific event ID to resume from\n  // eventId doesn't have to be the id of an actual event. Event Ids are timestamps that have been converted to UUIDs.\n  // You can use the TimeUuid class to convert between timestamps and event IDs.\n}\n```\n\n**Return Values:**\n\n- `null` → Start in the current hour after an event ID representing the current time\n- `{ timeBucket, eventId }` → Start from **specific position** (historical processing)\n\n### Precise Positioning with TimeUuid\n\nFlowcoreDataPump includes utilities for converting between timestamps and event IDs for precise positioning:\n\n```typescript\nimport { TimeUuid } from \"@flowcore/time-uuid\"\n\n// Generate event ID from specific timestamp\nconst eventId = TimeUuid.fromDate(new Date(\"2024-01-01T12:30:00Z\")).toString()\n\n// Start processing from timestamp (doesn't need to match existing event)\nconst stateManager = {\n  getState: () => ({\n    timeBucket: \"20240101120000\", // Hour bucket: 2024-01-01 12:00\n    eventId, // Start from first event AFTER 12:30:00\n  }),\n  setState: (state) => {\n    const timestamp = state.eventId ? TimeUuid.fromString(state.eventId).getDate() : undefined\n    console.log(\"Saved processing frontier\", state.timeBucket, timestamp?.toISOString())\n  },\n}\n// Other useful TimeUuid methods:\nconst now = TimeUuid.now().toString() // Current timestamp as UUID\nconst date = TimeUuid.fromString(eventId).getDate() // Extract Date from UUID\nconst timestamp = date.getTime() // Unix timestamp\n```\n\n**Use cases:**\n\n- **Precise replay**: Start from any timestamp within an hour (finds next available event)\n- **Debugging**: Convert event IDs back to readable timestamps\n- **Monitoring**: Track processing progress with human-readable times\n- **Coordination**: Synchronize multiple instances to specific points\n- **Gap handling**: Works even when no events exist at exact timestamp\n\n### Memory State Manager (Development)\n\n**Best for**: Local development, testing, non-critical applications\n\n```typescript\nlet currentState = null; // Start in live mode\n\n// Or start from specific time:\n// let currentState = {\n//   timeBucket: \"20240101000000\",  // January 1, 2024 00:00\n//   eventId: undefined             // Start from first event in that hour\n// };\n\nstateManager: {\n  getState: () => currentState,\n  setState: (state) => {\n    currentState = state;\n    console.log(`Processed up to: ${state.timeBucket} - ${state.eventId}`);\n  }\n}\n```\n\n**⚠️ Limitations:**\n\n- State lost on process restart\n- No crash recovery\n- Cannot share state between instances\n\n### File-based State Manager (Single Instance)\n\n**Best for**: Single instance deployments, simple persistence needs\n\n```typescript\nimport { existsSync, readFileSync, writeFileSync } from \"node:fs\"\n\nstateManager: {\n  getState: () => {\n    if (!existsSync(\"pump-state.json\")) return null\n    return JSON.parse(readFileSync(\"pump-state.json\", \"utf8\"))\n  },\n  setState: (state) => {\n    writeFileSync(\"pump-state.json\", JSON.stringify(state, null, 2))\n  }\n}\n```\n\n**✅ Benefits:**\n\n- Survives process restarts\n- Simple file-based persistence\n- No database dependency\n\n**⚠️ Limitations:**\n\n- Single instance only\n- File system dependency\n- No atomic updates\n\n### Database State Manager (Production)\n\n**Best for**: Production systems, multi-instance deployments, mission-critical applications\n\n```sql\n-- Example table schema\nCREATE TABLE flowcore_pump_state (\n  id VARCHAR(50) PRIMARY KEY,       -- Logical consumer identifier\n  time_bucket VARCHAR(14) NOT NULL, -- \"yyyyMMddHH0000\"\n  event_id VARCHAR(255),            -- Conservative processing frontier\n  updated_at TIMESTAMP DEFAULT NOW()\n);\n```\n\n```typescript\nstateManager: {\n  getState: async () => {\n    try {\n      const result = await db.query(\n        'SELECT time_bucket, event_id FROM flowcore_pump_state WHERE id = ?',\n        ['main']\n      );\n\n      if (result.length === 0) {\n        console.log('No previous state found, starting in live mode');\n        return null;\n      }\n\n      const state = {\n        timeBucket: result[0].time_bucket,\n        eventId: result[0].event_id\n      };\n      console.log('Resuming from database state:', state);\n      return state;\n\n    } catch (error) {\n      console.error('Failed to load state from database:', error);\n      throw error; // Do not silently jump to \"now\" when durable state is unavailable.\n    }\n  },\n\n  setState: async (state) => {\n    try {\n      await db.query(`\n        INSERT INTO flowcore_pump_state (id, time_bucket, event_id, updated_at)\n        VALUES (?, ?, ?, NOW())\n        ON DUPLICATE KEY UPDATE\n          time_bucket = VALUES(time_bucket),\n          event_id = VALUES(event_id),\n          updated_at = NOW()\n      `, ['main', state.timeBucket, state.eventId]);\n\n    } catch (error) {\n      console.error('CRITICAL: Failed to save state to database:', error);\n      throw error; // Stop processing if we can't save progress\n    }\n  }\n}\n```\n\n**✅ Benefits:**\n\n- Survives crashes and restarts\n- Supports shared state for cluster mode\n- Atomic updates with transactions\n- Can be backed up with your database\n- Enables coordinated horizontal scaling\n\n**⚠️ Considerations:**\n\n- Database dependency\n- Network latency on state updates\n- Requires error handling strategy\n\n### Independent Consumers and Shared Clusters\n\nUse separate state keys when instances intentionally process different event selections as independent consumers:\n\n```typescript\nconst consumerId = process.env.CONSUMER_ID!;\n\nstateManager: {\n  getState: async () => {\n    const result = await db.query(\n      'SELECT time_bucket, event_id FROM flowcore_pump_state WHERE id = ?',\n      [consumerId]\n    );\n    return result[0] || null;\n  },\n  setState: async (state) => {\n    await db.query(\n      'INSERT OR REPLACE INTO flowcore_pump_state (id, time_bucket, event_id) VALUES (?, ?, ?)',\n      [consumerId, state.timeBucket, state.eventId]\n    );\n  }\n}\n```\n\nDo not use separate state rows for replicas that are meant to share one workload. Use `FlowcoreDataPumpCluster` with one\nshared durable state manager so only the elected leader fetches and advances the logical checkpoint.\n\nPersist every state update unless your storage adapter can prove that coalescing writes cannot advance beyond unfinished\nwork. Skipping arbitrary checkpoint writes can increase replay and make the stored frontier misleading.\n\nCheckpoint writes are serialized. A `setState` call that never settles blocks all later checkpoint persistence, including\nafter `restart()`. State adapters must apply their own bounded I/O timeout and reject on timeout. The pump intentionally\ndoes not race or detach a timed-out write because a late older write could otherwise overwrite a newer frontier.\n\n### Choosing a State Manager\n\n| Scenario                        | Recommended             | Reason                             |\n| ------------------------------- | ----------------------- | ---------------------------------- |\n| **Local development**           | Memory                  | Fast iteration, no setup           |\n| **Testing/CI**                  | Memory                  | Clean state per test run           |\n| **Single instance, simple**     | File-based              | Persistence without DB complexity  |\n| **Production, single instance** | Database                | Reliability and backup integration |\n| **Independent consumers**       | Database, separate keys | Separate selections and positions  |\n| **Shared worker cluster**       | Database + cluster mode | One elected fetcher and checkpoint |\n| **Mission-critical**            | Database + Monitoring   | Full observability stack           |\n\n## Notification Methods\n\n### WebSocket (Default)\n\nReal-time notifications via Flowcore's notification service:\n\n```typescript\nnotifier: {\n  type: \"websocket\"\n}\n```\n\nWebSocket waits have a 20-second safety timeout. An event, connection error, abort signal, or timeout releases the wait so\nthe fetch loop can query durable storage again. Each cycle creates a fresh client, allowing the pump to recover from a\nhung or half-open connection.\n\n### NATS\n\nFor distributed systems with message queues:\n\n```typescript\nnotifier: {\n  type: \"nats\",\n  servers: [\"nats://localhost:4222\", \"nats://backup:4222\"]\n}\n```\n\n### Polling\n\nSimple polling mechanism:\n\n```typescript\nnotifier: {\n  type: \"poller\",\n  intervalMs: 1000\n}\n```\n\n> **Version 0.22.x caveat:** The implementation currently uses `Math.min(intervalMs, 1000)`. Values above one second\n> therefore still wake after one second. Account for the API traffic until this behavior is corrected.\n\nNotifications only wake the pump. Flowcore event history and the persisted state remain the durable recovery mechanism.\n\n## Cluster Mode\n\n`FlowcoreDataPumpCluster` scales handler execution while keeping one logical fetcher and one shared checkpoint. Every\nreplica registers with a coordinator and participates in lease election. The leader runs `FlowcoreDataPump`; other\ninstances process batches distributed by the leader.\n\nCluster mode requires a durable state manager and a user-provided `FlowcoreDataPumpCoordinator`. The package defines the\ncoordinator contract but does not ship a production implementation:\n\n```typescript\ninterface FlowcoreDataPumpCoordinator {\n  acquireLease(instanceId: string, key: string, ttlMs: number): Promise<boolean>\n  renewLease(instanceId: string, key: string, ttlMs: number): Promise<boolean>\n  releaseLease(instanceId: string, key: string): Promise<void>\n  register(instanceId: string, address: string): Promise<void>\n  heartbeat(instanceId: string): Promise<void>\n  unregister(instanceId: string): Promise<void>\n  getInstances(staleThresholdMs: number): Promise<\n    Array<{\n      instanceId: string\n      address: string\n    }>\n  >\n}\n```\n\nLease acquisition must be atomic. Renewal must succeed only for the current holder, and `getInstances()` must omit stale\nregistrations. The `integration/app` directory contains a PostgreSQL reference used by the Kubernetes integration suite.\n\n### NATS Distribution\n\nSetting `notifier.type` to `nats` also selects NATS request/reply for cluster event distribution:\n\n```typescript\nimport { FlowcoreDataPumpCluster } from \"@flowcore/data-pump\"\n\nconst cluster = new FlowcoreDataPumpCluster({\n  auth: { apiKey: process.env.FLOWCORE_API_KEY! },\n  dataSource: {\n    tenant: \"acme\",\n    dataCore: \"commerce\",\n    flowType: \"order.0\",\n    eventTypes: [\"order.placed.0\"],\n  },\n  stateManager: sharedStateManager,\n  coordinator: postgresCoordinator,\n  notifier: {\n    type: \"nats\",\n    servers: [process.env.NATS_URL!],\n  },\n  clusterKey: \"orders-projection-v1\",\n  workerConcurrency: 10,\n  processor: {\n    handler: async (events) => {\n      for (const event of events) await processIdempotently(event)\n    },\n  },\n})\n\nawait cluster.start()\n```\n\nAll replicas join the `data-pump-workers` queue group, including the leader. A distribution request waits up to 30\nseconds for a reply. If NATS distribution fails, the leader runs the handler locally. A worker may have committed before\nits reply was lost, so this fallback preserves at-least-once rather than exactly-once behavior.\n\n`clusterKey` scopes the NATS subject. In 0.22.x the internal leader lease key is fixed to\n`flowcore-data-pump-leader`; it is not scoped by `clusterKey`. Namespace coordinator storage when unrelated logical\nclusters share a database.\n\n### WebSocket Distribution\n\nAll non-NATS cluster configurations use a WebSocket mesh. Each replica must:\n\n1. Host a WebSocket server and pass accepted connections to `cluster.handleConnection()`.\n2. Configure an `advertisedAddress` reachable by every peer.\n3. Register and heartbeat through the shared coordinator.\n\nThe leader discovers live workers every ten seconds and sends complete batches round-robin. Connections use ping/pong\nhealth checks, and each delivery has a 30-second acknowledgment timeout. If no worker is available, the leader processes\nlocally.\n\nThe default lease TTL is 30 seconds, renewal interval is 10 seconds, and heartbeat interval is 5 seconds. Tune those\nvalues together. Cluster mode exposes the automatic processor model only; it does not expose `reserve()`,\n`acknowledge()`, `fail()`, or `restart()`.\n\nStop a cluster gracefully so it can release its lease and unregister:\n\n```typescript\nprocess.on(\"SIGTERM\", async () => {\n  await cluster.stop()\n  await db.end()\n  process.exit(0)\n})\n```\n\n## ⚙️ Configuration Reference\n\n| Option                  | Type                              | Default      | Description                                                                                                                                                               |\n| ----------------------- | --------------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| `auth`                  | `FlowcoreDataPumpAuth`            | **Required** | Authentication configuration (API key or Bearer token)                                                                                                                    |\n| `dataSource`            | `FlowcoreDataPumpDataSource`      | **Required** | Data source configuration (tenant, dataCore, flowType, eventTypes)                                                                                                        |\n| `stateManager`          | `FlowcoreDataPumpStateManager`    | **Required** | State persistence configuration                                                                                                                                           |\n| `bufferSize`            | `number`                          | `1000`       | Maximum events to buffer in memory                                                                                                                                        |\n| `maxRedeliveryCount`    | `number`                          | `3`          | Redeliveries after the initial attempt; `-1` disables the cap                                                                                                             |\n| `achknowledgeTimeoutMs` | `number`                          | `5000`       | Fixed timeout before an unresolved reservation reopens (spelling preserved by the public API)                                                                             |\n| `includeSensitiveData`  | `boolean`                         | `false`      | Include sensitive data in events                                                                                                                                          |\n| `processor`             | `FlowcoreDataPumpProcessor`       | `undefined`  | Automatic processing configuration; `concurrency` is the handler batch size in 0.22.x                                                                                     |\n| `notifier`              | `FlowcoreDataPumpNotifierOptions` | `websocket`  | Notification method configuration                                                                                                                                         |\n| `logger`                | `FlowcoreLogger`                  | `undefined`  | Custom logger implementation                                                                                                                                              |\n| `stopAt`                | `Date`                            | `undefined`  | Stop processing at specific date (for historical processing)                                                                                                              |\n| `baseUrlOverride`       | `string`                          | `undefined`  | Override Flowcore API base URL                                                                                                                                            |\n| `noTranslation`         | `boolean`                         | `false`      | Treat tenant, data core, flow type, and event type values as IDs and skip translation                                                                                     |\n| `directMode`            | `boolean`                         | `false`      | Enables direct API execution mode, bypassing intermediary gateways; recommended for dedicated Flowcore clusters to reduce latency (often used with `noTranslation: true`) |\n| `pulse`                 | `object`                          | `undefined`  | Periodically send pump position, buffer, counters, and uptime to a Flowcore control-plane endpoint                                                                        |\n\n## 🔧 API Reference\n\n### FlowcoreDataSource Methods\n\nThe `FlowcoreDataSource` class provides several useful methods for historical processing and data exploration. This can\nbe used to replay events from the beginning or a specific time in the State Manager.\n\n#### Time Bucket Management\n\n```typescript\n// Get all available time buckets for your event types\nconst timeBuckets = await dataSource.getTimeBuckets()\nconsole.log(`Found ${timeBuckets.length} time buckets`)\nconsole.log(`First: ${timeBuckets[0]}, Last: ${timeBuckets[timeBuckets.length - 1]}`)\n\n// Get the next time bucket after a specific one\nconst nextBucket = await dataSource.getNextTimeBucket(\"20240101120000\")\n\n// Get the closest time bucket to a specific time (forward or backward)\nconst closestBucket = await dataSource.getClosestTimeBucket(\"20240101150000\") // Forward\nconst previousBucket = await dataSource.getClosestTimeBucket(\"20240101150000\", true) // Backward\n```\n\n#### Direct Event Access\n\n```typescript\n// Get events directly from a specific state\nconst events = await dataSource.getEvents(\n  { timeBucket: \"20240101120000\", eventId: \"some-event-id\" },\n  100, // amount\n  undefined, // toEventId (optional)\n  undefined, // cursor (optional)\n  false, // includeSensitiveData\n)\n\nconsole.log(`Retrieved ${events.events.length} events`)\n```\n\n#### Resource Information\n\n```typescript\n// Access configured names\nconsole.log(dataSource.tenant) // \"your-tenant-name\"\nconsole.log(dataSource.dataCore) // \"your-data-core\"\nconsole.log(dataSource.flowType) // \"your-flow-type\"\nconsole.log(dataSource.eventTypes) // [\"event-type-1\", \"event-type-2\"]\n\n// Get translated IDs (useful for debugging or direct API calls)\nconst tenantId = await dataSource.getTenantId()\nconst dataCoreId = await dataSource.getDataCoreId()\nconst flowTypeId = await dataSource.getFlowTypeId()\nconst eventTypeIds = await dataSource.getEventTypeIds()\n```\n\n### FlowcoreDataPump Methods\n\nThe `FlowcoreDataPump` provides control methods for both push and pull modes:\n\n#### Pump Control\n\n```typescript\n// Check if pump is running\nif (dataPump.isRunning) {\n  console.log(\"Pump is running\")\n}\n\n// Blocking: resolves when the fetch loop stops and rejects on a fetch error.\nawait dataPump.start()\n\n// Background/self-healing: fetch errors retry with exponential backoff.\nawait dataPump.start((error) => {\n  if (error) console.error(\"Data Pump stopped\", error)\n})\n\n// Stop immediately. The in-memory buffer is cleared rather than drained.\ndataPump.stop()\n\n// Restart from a specific position. The bucket must contain exactly 14 digits.\ndataPump.restart({\n  timeBucket: \"20240101120000\",\n  eventId: \"specific-event-id\",\n})\n```\n\n#### Pause and Resume Delivery\n\n`pause()` stops delivery to the processor without stopping the pump. Use it to hold events back while a downstream\nsystem is repaired, then continue exactly where you left off.\n\n```typescript\ndataPump.pause()\nconsole.log(dataPump.isPaused) // true\n\n// ... repair the downstream system ...\n\ndataPump.resume() // delivery continues from the same position\n```\n\nWhile paused:\n\n- The fetch loop keeps running and fills the buffer to `bufferSize`, then applies normal backpressure.\n- The buffer and the cursor are retained. Nothing is lost and nothing is skipped.\n- The pulse emitter keeps reporting, with `paused: true`, so the control plane still sees a live pump.\n- A batch already inside the handler finishes and acknowledges, so the checkpoint stays accurate.\n\nBoth calls are idempotent. The pause flag is sticky across `restart()` and `stop()`/`start()`, so a repositioned or\nbounced pump stays paused until `resume()` is called.\n\nTo restore a pause that you store somewhere durable, pass it at construction rather than calling `pause()` after\n`start()`. The pump would otherwise deliver events in the gap between the two calls:\n\n```typescript\nconst dataPump = FlowcoreDataPump.create({\n  // ...\n  paused: await myStore.isPaused(), // born paused, nothing escapes on the way up\n})\n```\n\nTwo limits to know:\n\n- A paused pump holds up to `bufferSize` events in memory.\n- `pause()` only governs the processor. It is ignored on a pump created without one, and it logs a warning, because\n  `reserve()` in pull mode would still hand out events and the `paused` pulse flag would be a false claim.\n\nIn cluster mode, call `pause()` and `resume()` on the `FlowcoreDataPumpCluster`, not on the pump. The flag is held on\nthe cluster and re-applied to the pump each new leader builds, so a failover does not silently resume delivery. The\nflag is in memory only — persist it in your coordinator or control plane and pass it back through the `paused` option\nif it must survive a rolling deploy.\n\nRestart clears the current buffer and refreshes available time buckets. In 0.22.x, the optional\n`restart(state, stopAt)` argument updates the option but does not rebuild the internal stop boundary. Create a new pump\nwhen changing `stopAt`.\n\n#### Pull Mode Methods (Manual Processing)\n\n```typescript\nconst events = await dataPump.reserve(10) // Mark 10 events as reserved for processing\n\nawait dataPump.acknowledge(events.map((e) => e.eventId))\n\n// Terminal: removes these events. This does not retry them.\nawait dataPump.fail([\"event-id-1\", \"event-id-2\"])\n\n// Called only after timeout-based redelivery exceeds maxRedeliveryCount.\ndataPump.onFinalyFailed(async (failedEvents) => {\n  console.log(`${failedEvents.length} events permanently failed`)\n})\n```\n\n`onFinalyFailed` is the current public spelling.\n\n## Pulse Status Reporting\n\nThe optional pulse emitter reports pump status to a Flowcore control-plane endpoint:\n\n```typescript\nconst dataPump = FlowcoreDataPump.create({\n  // ...required options...\n  pulse: {\n    url: process.env.FLOWCORE_CONTROL_PLANE_URL!,\n    pathwayId: process.env.FLOWCORE_PATHWAY_ID!,\n    sourceId: process.env.PUMP_SOURCE_ID,\n    intervalMs: 30_000,\n    successLogLevel: \"debug\",\n    failureLogLevel: \"warn\",\n  },\n})\n```\n\nPulses include the current bucket and event ID, live status, buffer depth and reserved count, payload bytes, cumulative\npulled, acknowledged and failed counts, checkpoint-queue depth, and uptime. The first pulse is randomly staggered within the interval so replicas\ndo not all report at once. Pulse failures are logged and do not stop processing. `pulse.url` is independent of\n`baseUrlOverride`.\n\n## Monitoring & Metrics\n\nThe data pump exposes Prometheus-compatible metrics:\n\n```typescript\nimport { dataPumpPromRegistry } from \"@flowcore/data-pump\"\n\n// Express.js example\napp.get(\"/metrics\", async (req, res) => {\n  res.set(\"Content-Type\", dataPumpPromRegistry.contentType)\n  res.end(await dataPumpPromRegistry.metrics())\n})\n```\n\n### Available Metrics\n\n- `flowcore_data_pump_buffer_events_gauge` - Events in buffer\n- `flowcore_data_pump_buffer_reserved_events_gauge` - Reserved events\n- `flowcore_data_pump_buffer_size_bytes_gauge` - Buffer size in bytes\n- `flowcore_data_pump_checkpoint_queue_depth_gauge` - Events retained by the contiguous checkpoint frontier\n- `flowcore_data_pump_events_acknowledged_counter` - Successfully processed events\n- `flowcore_data_pump_events_failed_counter` - Failed events\n- `flowcore_data_pump_events_pulled_size_bytes_counter` - Data throughput\n- `flowcore_data_pump_sdk_commands_counter` - API calls to Flowcore\n- `flowcore_data_pump_cluster_active_workers_gauge` - Connected cluster workers\n- `flowcore_data_pump_cluster_leader_status_gauge` - `1` on the elected leader\n- `flowcore_data_pump_cluster_events_distributed_counter` - Events sent to workers\n- `flowcore_data_pump_cluster_worker_acks_counter` - Successful worker batch acknowledgments\n- `flowcore_data_pump_cluster_worker_fails_counter` - Failed worker batch deliveries\n\nPump event metrics use `tenant`, `data_core`, `flow_type`, and `event_type` labels. The SDK command counter uses only\n`command`. Cluster metrics currently have no tenant or event labels and are process-local.\n","readmeFilename":"README.md"}