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TypeScript library to simplify working with DynamoDB","maintainers":[{"name":"kysumi","email":"maunderscott@gmail.com"}],"readme":"# dyno-table\n\n> Stop hand-writing `pk`/`sk` strings and raw `KeyConditionExpression` syntax. Use typed query and update builders with compile-time checks on your item shapes instead.\n\n[![npm version](https://img.shields.io/npm/v/dyno-table.svg)](https://www.npmjs.com/package/dyno-table)\n[![npm downloads](https://img.shields.io/npm/dm/dyno-table.svg)](https://www.npmjs.com/package/dyno-table)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![TypeScript](https://img.shields.io/badge/TypeScript-4.0%2B-blue?logo=typescript)](https://www.typescriptlang.org/)\n\n## Why dyno-table?\n\nWithout dyno-table, querying a GSI means remembering which index holds what and hand-building the expression:\n\n```ts\nawait client.send(new QueryCommand({\n  TableName: \"dinosaurs\",\n  IndexName: \"gsi1\",\n  KeyConditionExpression: \"gsi1pk = :pk\",\n  ExpressionAttributeValues: { \":pk\": `DIET#${diet}` },\n}));\n```\n\nWith dyno-table, that index is a named method:\n\n```ts\nawait dinoRepo.query.getDinosaursByDiet({ diet: \"carnivore\" }).execute();\n```\n\n- `getDinosaursByDiet()` replaces the `gsi1` / `KeyConditionExpression` you'd otherwise have to look up\n- Zod, ArkType, Valibot, or any Standard Schema library validates every write before it hits DynamoDB\n- Item shapes, keys, and query inputs get checked at compile time instead of failing at runtime\n\n## Quick start\n\n```bash\nnpm install dyno-table @aws-sdk/client-dynamodb @aws-sdk/lib-dynamodb\n```\n\n```ts\nimport { z } from \"zod\";\nimport { createIndex, createQueries, defineCollection, defineEntity } from \"dyno-table/entity\";\n\n// Define your dinosaur schema\nconst dinosaurSchema = z.object({\n  id: z.string(),\n  species: z.string(),\n  period: z.enum([\"triassic\", \"jurassic\", \"cretaceous\"]),\n  diet: z.enum([\"herbivore\", \"carnivore\", \"omnivore\"]),\n  discoveryYear: z.number(),\n  weight: z.number(),\n});\n\nconst createQuery = createQueries<z.infer<typeof dinosaurSchema>>();\n\n// Create your entity with indexes for efficient queries\nconst DinosaurEntity = defineEntity({\n  name: \"Dinosaur\",\n  schema: dinosaurSchema,\n  primaryKey: createIndex()\n    .input(z.object({ id: z.string() }))\n    .partitionKey(({ id }) => `DINO#${id}`)\n    .sortKey(() => \"PROFILE\"),\n  indexes: {\n    byDiet: createIndex()\n      .input(dinosaurSchema)\n      .partitionKey(({ diet }) => `DIET#${diet}`)\n      .sortKey(({ species }) => species),\n  },\n  queries: {\n    getDinosaursByDiet: createQuery\n      .input<{ diet: \"herbivore\" | \"carnivore\" | \"omnivore\" }>()\n      .query(({ input, entity }) =>\n        entity.query({ pk: `DIET#${input.diet}` }).useIndex(\"byDiet\")\n      ),\n  },\n});\n\n// Create the repository\nconst dinoRepo = DinosaurEntity.createRepository(table);\n\n// Create a T-Rex\nconst tRex = await dinoRepo.create({\n  id: \"t-rex-1\",\n  species: \"Tyrannosaurus Rex\",\n  period: \"cretaceous\",\n  diet: \"carnivore\",\n  discoveryYear: 1905,\n  weight: 8000,\n}).execute();\n\n// Find all carnivores (query via index)\nconst carnivores = await dinoRepo.query\n  .getDinosaursByDiet({ diet: \"carnivore\" })\n  .execute();\n```\n\nYou now have a type-safe, validated database with semantic queries.\n\n---\n\n## Feature overview\n\n### Entity pattern (recommended)\nUse for most application code: schema validation, generated keys, and semantic query names instead of hand-written `pk`/`sk` strings. dyno-table validates every write against your schema before it hits DynamoDB.\n\n```ts\n// Get specific dinosaur\nconst { item: tRex } = await dinoRepo.get({ id: \"t-rex-1\" }).execute();\n\n// Semantic queries\nconst cretaceousDinos = await dinoRepo.query\n  .getDinosaursByPeriod({ period: \"cretaceous\" })\n  .execute();\n```\n**[Complete Entity Guide →](docs/entities.md)**\n\n### Entity collections\nUse when several entity types share the same GSI and you want to query it in one shot (e.g. \"everything at this location\"). Results come back grouped by entity type, so downstream code reads `page.Dinosaur` / `page.Warehouse` rather than one mixed array.\n\n```ts\nconst pages = defineCollection({\n  entities: { Dinosaur: DinosaurEntity, Warehouse: WarehouseEntity },\n  indexName: \"GSI1\",\n})\n  .createReader(table)\n  .query({ pk: \"LOCATION#WELLINGTON\" })\n  .paginate();\n\nfor await (const page of pages) {\n  console.log(page.Dinosaur, page.Warehouse);\n}\n```\n\n`paginate()` streams grouped pages. `execute()` streams individual configured items; its `toArray()` returns one grouped result.\n\n**[Collection Guide →](docs/collections.md)**\n\n### Direct table operations\nUse when you need raw `pk`/`sk` control or something the entity layer doesn't model. You own key construction yourself and skip schema validation.\n\n```ts\n// Direct DynamoDB access with query\nconst carnivoresInCretaceous = await table\n  .query({ pk: \"PERIOD#cretaceous\" })\n  .filter(op => op.eq(\"diet\", \"carnivore\"))\n  .execute();\n```\n**[Table Operations Guide →](docs/table-query-builder.md)**\n\n### Vector search\n\nUse a configured DynamoDB vector index without dropping to raw SDK expressions. Results retain service rank and score; entity and collection searches scope candidates before TopK.\n\n```ts\nconst result = await table\n  .searchVectors<Product>(\"ProductEmbedding\", { vector: embedding, topK: 10, partition: \"Electronics\" })\n  .filter(op => op.eq(\"status\", \"ACTIVE\"))\n  .select([\"productId\", \"title\"])\n  .execute();\n```\n\n**[Vector Search Guide →](docs/vector-search.md)**\n\n### Advanced querying & filtering\nUse `.filter()` for business logic DynamoDB's key conditions can't express. It's applied after the read, so it narrows what's *returned*, not what's read. It doesn't reduce RCU cost the way a tighter key condition or index would.\n\n```ts\n// Find large herbivores from Jurassic period using query + filter\nconst conditions = await dinoRepo.query\n  .getDinosaursByDiet({ diet: \"herbivore\" })\n  .filter(op => op.and(\n    op.eq(\"period\", \"jurassic\"),\n    op.gt(\"weight\", 3000)\n  ))\n  .execute();\n```\n**[Advanced Queries Guide →](docs/query-builder.md)**\n\n### Batch operations\nUse when you're reading or writing many known keys at once (up to 100 reads / 25 writes per batch). Batches don't support conditions and aren't atomic. Reach for `.transaction()` instead when operations must all succeed or all fail together.\n\n```ts\nconst batch = table.batchBuilder();\n\n// Queue reads\n[{ id: \"t-rex-1\" }, { id: \"triceratops-1\" }, { id: \"stegosaurus-1\" }]\n  .forEach(key => dinoRepo.get(key).withBatch(batch));\n\n// Queue writes; reads and writes can share one batch\ncarnivores.forEach(dino => dinoRepo.create(dino).withBatch(batch));\n\nconst { reads } = await batch.execute();\nconst dinos = reads.itemsByType.Dinosaur;\n```\n**[Batch Operations Guide →](docs/batch-operations.md)**\n\n### Transactions\nUse when multiple writes must succeed or fail together (ACID). dyno-table caps transactions at 25 operations. For bulk work that doesn't need atomicity, batch operations are cheaper.\n\n```ts\n// Atomic dinosaur discovery\nawait table.transaction(async (tx) => {\n  dinoRepo.create(newDinosaur).withTransaction(tx);\n  researchRepo.update({ id: \"paleontologist-1\" }, {})\n    .add(\"discoveriesCount\", 1)\n    .withTransaction(tx);\n});\n```\n**[Transactions Guide →](docs/transactions.md)**\n\n### Pagination & memory management\nStream (`for await`) when you just need to process results one at a time and want flat memory usage. Use `.paginate(pageSize)` when you control page boundaries yourself, e.g. returning one page per API response. Only call `.toArray()` when you already know the result set is small.\n\n```ts\n// Stream large datasets (memory efficient)\nconst allCarnivores = await dinoRepo.query\n  .getDinosaursByDiet({ diet: \"carnivore\" })\n  .execute();\nfor await (const dino of allCarnivores) {\n  await processDiscovery(dino); // Process one at a time\n}\n\n// Paginated results\nconst paginator = dinoRepo.query\n  .getDinosaursByDiet({ diet: \"herbivore\" })\n  .paginate(50);\nwhile (paginator.hasNextPage()) {\n  const page = await paginator.getNextPage();\n  console.log(`Processing ${page.items.length} herbivores...`);\n}\n```\n**[Pagination Guide →](docs/pagination.md)**\n\n### Schema validation\nUse whichever validation library your project already has. dyno-table works with anything implementing the Standard Schema interface, not just Zod.\n\n```ts\n// Zod (included)\nconst dinoSchema = z.object({\n  species: z.string().min(3),\n  weight: z.number().positive(),\n});\n\n// ArkType\nconst dinoSchema = type({\n  species: \"string>2\",\n  weight: \"number>0\",\n});\n\n// Valibot\nconst dinoSchema = v.object({\n  species: v.pipe(v.string(), v.minLength(3)),\n  weight: v.pipe(v.number(), v.minValue(1)),\n});\n```\n**[Standard Schema Support →](docs/entities.md#standard-schema-support)**\n\n### Migrations\nUse for backfills or data-movement scripts that run against a live table. Every run is a dry run until you pass `{ apply: true }`, and an applied run resumes from its last completed page instead of restarting.\n\n```ts\nimport { MigrationManager } from \"dyno-table/migration\";\n\nconst manager = new MigrationManager({\n  repos: { orders: orderRepo },\n  migrationRepo: migrationCheckpointRepo,\n});\n\nmanager.createMigration(\"backfill-order-totals\", async ({ repos, cursor }) => {\n  for await (const order of cursor(repos.orders.scan(), { pageSize: 100 })) {\n    await repos.orders.update({ id: order.id }, { total: computeTotal(order) }).execute();\n  }\n});\n\n// Dry run by default, no writes happen until you opt in\nawait manager.run(\"backfill-order-totals\");\nawait manager.run(\"backfill-order-totals\", { apply: true });\n\n// Run all pending migrations\nawait manager.runAll({ apply: true })\n```\nChoose a page size that bounds how much work an interrupted page may repeat.\n\n**[Migrations Guide →](docs/migration.md)**\n\n### Performance optimization\nReach for an index whenever you know the access pattern in advance. It's always cheaper than a scan. Reach for `.segments(n)` only when `.query()` isn't an option (no known partition key) and a sequential scan is the actual bottleneck. It parallelizes the scan across a table, but each segment issues its own concurrent request, so a high segment count can throttle a provisioned table.\n\n```ts\n// Use indexes for fast lookups\nconst jurassicCarnivores = await dinoRepo.query\n  .getDinosaursByPeriodAndDiet({\n    period: \"jurassic\",\n    diet: \"carnivore\"\n  })\n  .useIndex(\"period-diet-index\")\n  .execute();\n\n// Split a full-table scan across parallel segments\nconst allDinos = await table.scan().segments(4).toArray();\n```\n**[Table Operations Guide →](docs/table-query-builder.md)**\n\n### Observability\nPass `plugins` to `Table` to get an `onRequestStart`/`onRequestEnd` callback around every physical DynamoDB request — for logging, APM spans, or counting request volume, the same way SQL libraries expose query logging. Each plugin is independent, so logging, tracing, and metrics can be registered side by side.\n\n```ts\nconst table = new Table({\n  client, tableName: \"Dinosaurs\", indexes: { partitionKey: \"pk\", sortKey: \"sk\" },\n  plugins: [{\n    name: \"logger\",\n    onRequestStart: (e) => console.log(`→ ${e.operation} [${e.entityNames.join(\", \") || \"table\"}]`),\n    onRequestEnd: (e) => console.log(`← ${e.operation} in ${e.durationMs}ms`),\n  }],\n});\n```\n\nHooks may be async and are awaited. Hook failures are isolated from the DynamoDB operation and can be reported through the plugin's `onError` callback. For request-local tracing state, snapshots, and full lifecycle behavior, see the **[Observability Guide →](docs/observability.md)**.\n\n---\n\n## Documentation\n\n### Getting started\n- **[Quick Start Tutorial →](docs/quick-start.md)** - Install, define an entity, run your first query\n- **[Installation Guide →](docs/installation.md)** - Setup and configuration\n- **[Your First Entity →](docs/first-entity.md)** - Define fields, keys, and indexes step by step\n\n### Core concepts\n- **[Entity vs Table →](docs/entity-vs-table.md)** - When to use the entity layer vs. raw table operations\n- **[Key Design Patterns →](docs/key-patterns.md)** - Partition and sort keys, single-table design\n\n### Features\n- **[Query Building →](docs/query-builder.md)** - Complex queries and filtering\n- **[Standard Schema Support →](docs/entities.md#standard-schema-support)** - Zod, ArkType, Valibot validation\n- **[Transactions →](docs/transactions.md)** - ACID operations\n- **[Batch Operations →](docs/batch-operations.md)** - Read or write up to 100/25 items per call\n- **[Pagination →](docs/pagination.md)** - Streaming, page-based, and cursor-based result handling\n- **[Entity Collections →](docs/collections.md)** - Group shared-index query pages by entity type\n- **[Table Operations →](docs/table-query-builder.md)** - Direct table access, scans, parallel scan segments\n- **[Observability →](docs/observability.md)** - Hook every request for logging, tracing, and APM spans\n\n### Advanced topics\n- **[Error Handling →](docs/error-handling.md)** - Error classes, type guards, and AWS error unwrapping\n- **[Migrations →](docs/migration.md)** - Backfill scripts with dry-run safety and resumability\n\n### Examples\n- **[Entity Example App →](examples/entity-example)** - Full runnable project with entities and repositories\n- **[Entity Collections →](examples/collection-example.ts)** - Grouping shared-index query results by entity type\n- **[Single-Table GSI Design →](examples/gsi-example.ts)** - Multiple entity types on shared indexes\n- **[Multiple GSIs →](examples/multi-example.ts)** - Working with more than one global secondary index\n\n---\n\n## Links\n\n- **[Documentation](docs/)** - Complete guides and references\n- **[Issues](https://github.com/Kysumi/dyno-table/issues)** - Report bugs or request features\n- **[Discussions](https://github.com/Kysumi/dyno-table/discussions)** - Ask questions and share ideas\n- **[NPM](https://www.npmjs.com/package/dyno-table)** - Package information\n","readmeFilename":"README.md"}