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Supabase Storage JS SDK

JavaScript SDK to interact with Supabase Storage, including file storage and vector embeddings.

Guides · Reference Docs · TypeDoc

Features

• File Storage: Upload, download, list, move, and delete files
• Access Control: Public and private buckets with fine-grained permissions
• Signed URLs: Generate time-limited URLs for secure file access
• Image Transformations: On-the-fly image resizing and optimization
• Vector Embeddings: Store and query high-dimensional embeddings with similarity search
• Analytics Buckets: Iceberg table-based buckets optimized for analytical queries and data processing

Quick Start Guide

Installing the module

npm install @supabase/storage-js

Connecting to the storage backend

There are two ways to use the Storage SDK:

Option 1: Via Supabase Client (Recommended)

If you're already using @supabase/supabase-js, access storage through the client:

import { createClient } from '@supabase/supabase-js'

const supabase = createClient('https://<project_ref>.supabase.co', '<your-anon-key>')

// Access storage
const storage = supabase.storage

// Access different bucket types
const regularBucket = storage.from('my-bucket')
const vectorBucket = storage.vectors.from('embeddings-bucket')
const analyticsBucket = storage.analytics // Analytics API

Option 2: Standalone StorageClient

For applications that only need storage functionality:

import { StorageClient } from '@supabase/storage-js'

const STORAGE_URL = 'https://<project_ref>.supabase.co/storage/v1'
const SERVICE_KEY = '<service_role>' //! service key, not anon key

const storageClient = new StorageClient(STORAGE_URL, {
  apikey: SERVICE_KEY,
  Authorization: `Bearer ${SERVICE_KEY}`,
})

// Access different bucket types
const regularBucket = storageClient.from('my-bucket')
const vectorBucket = storageClient.vectors.from('embeddings-bucket')
const analyticsBucket = storageClient.analytics // Analytics API

When to use each approach:

• Use supabase.storage when working with other Supabase features (auth, database, etc.)
• Use new StorageClient() for storage-only applications or when you need fine-grained control

Understanding Bucket Types

Supabase Storage supports three types of buckets, each optimized for different use cases:

1. Regular Storage Buckets (File Storage)

Standard buckets for storing files, images, videos, and other assets.

// Create regular storage bucket
const { data, error } = await storageClient.createBucket('my-files', {
  public: false,
})

// Upload files
await storageClient.from('my-files').upload('avatar.png', file)

Use cases: User uploads, media assets, documents, backups

2. Vector Buckets (Embeddings Storage)

Specialized buckets for storing and querying high-dimensional vector embeddings.

// Create vector bucket
await storageClient.vectors.createBucket('embeddings-prod')

// Create index and insert vectors
const bucket = storageClient.vectors.from('embeddings-prod')
await bucket.createIndex({
  indexName: 'documents',
  dimension: 1536,
  distanceMetric: 'cosine',
})

Use cases: Semantic search, AI-powered recommendations, similarity matching

See full Vector Embeddings documentation below

3. Analytics Buckets

Specialized buckets using Apache Iceberg table format, optimized for analytical queries and large-scale data processing.

// Create analytics bucket
await storageClient.analytics.createBucket('analytics-data')

// List analytics buckets
const { data, error } = await storageClient.analytics.listBuckets()

// Delete analytics bucket
await storageClient.analytics.deleteBucket('analytics-data')

Use cases: Time-series data, analytical queries, data lakes, large-scale data processing, business intelligence

See full Analytics Buckets documentation below

Handling resources

Handling Storage Buckets

• Create a new Storage bucket:

  const { data, error } = await storageClient.createBucket(
    'test_bucket', // Bucket name (must be unique)
    { public: false } // Bucket options
  )

• Retrieve the details of an existing Storage bucket:

  const { data, error } = await storageClient.getBucket('test_bucket')

• Update a new Storage bucket:

  const { data, error } = await storageClient.updateBucket(
    'test_bucket', // Bucket name
    { public: false } // Bucket options
  )

• Remove all objects inside a single bucket:

  const { data, error } = await storageClient.emptyBucket('test_bucket')

• Delete an existing bucket (a bucket can't be deleted with existing objects inside it):

  const { data, error } = await storageClient.deleteBucket('test_bucket')

• Retrieve the details of all Storage buckets within an existing project:

  const { data, error } = await storageClient.listBuckets()

Handling Files

• Upload a file to an existing bucket:

  const fileBody = ... // load your file here
  
  const { data, error } = await storageClient.from('bucket').upload('path/to/file', fileBody)

  Note:
  The path in data.Key is prefixed by the bucket ID and is not the value which should be passed to the download method in order to fetch the file.
  To fetch the file via the download method, use data.path and data.bucketId as follows:

  const { data, error } = await storageClient.from('bucket').upload('/folder/file.txt', fileBody)
  // check for errors
  const { data2, error2 } = await storageClient.from(data.bucketId).download(data.path)

  Note: The upload method also accepts a map of optional parameters. For a complete list see the Supabase API reference.

• Download a file from an exisiting bucket:

  const { data, error } = await storageClient.from('bucket').download('path/to/file')

• List all the files within a bucket:

  const { data, error } = await storageClient.from('bucket').list('folder')

  Note: The list method also accepts a map of optional parameters. For a complete list see the Supabase API reference.

• Replace an existing file at the specified path with a new one:

  const fileBody = ... // load your file here
  
  const { data, error } = await storageClient
    .from('bucket')
    .update('path/to/file', fileBody)

  Note: The upload method also accepts a map of optional parameters. For a complete list see the Supabase API reference.

• Move an existing file:

  const { data, error } = await storageClient
    .from('bucket')
    .move('old/path/to/file', 'new/path/to/file')

• Delete files within the same bucket:

  const { data, error } = await storageClient.from('bucket').remove(['path/to/file'])

• Create signed URL to download file without requiring permissions:

  const expireIn = 60
  
  const { data, error } = await storageClient
    .from('bucket')
    .createSignedUrl('path/to/file', expireIn)

• Retrieve URLs for assets in public buckets:

  const { data, error } = await storageClient.from('public-bucket').getPublicUrl('path/to/file')

Analytics Buckets

Supabase Storage provides specialized analytics buckets using Apache Iceberg table format, optimized for analytical workloads and large-scale data processing. These buckets are designed for data lake architectures, time-series data, and business intelligence applications.

What are Analytics Buckets?

Analytics buckets use the Apache Iceberg open table format, providing:

• ACID transactions for data consistency
• Schema evolution without data rewrites
• Time travel to query historical data
• Efficient metadata management for large datasets
• Optimized for analytical queries rather than individual file operations

When to Use Analytics Buckets

Use analytics buckets for:

• Time-series data (logs, metrics, events)
• Data lake architectures
• Business intelligence and reporting
• Large-scale batch processing
• Analytical workloads requiring ACID guarantees

Use regular storage buckets for:

• User file uploads (images, documents, videos)
• Individual file management
• Content delivery
• Simple object storage needs

Quick Start

You can access analytics functionality through the analytics property on your storage client:

Via Supabase Client

import { createClient } from '@supabase/supabase-js'

const supabase = createClient('https://your-project.supabase.co', 'your-anon-key')

// Access analytics operations
const analytics = supabase.storage.analytics

// Create an analytics bucket
const { data, error } = await analytics.createBucket('analytics-data')
if (error) {
  console.error('Failed to create analytics bucket:', error.message)
} else {
  console.log('Created bucket:', data.name)
}

Via StorageClient

import { StorageClient } from '@supabase/storage-js'

const storageClient = new StorageClient('https://your-project.supabase.co/storage/v1', {
  apikey: 'YOUR_API_KEY',
  Authorization: 'Bearer YOUR_TOKEN',
})

// Access analytics operations
const analytics = storageClient.analytics

// Create an analytics bucket
await analytics.createBucket('analytics-data')

API Reference

Create Analytics Bucket

Creates a new analytics bucket using Iceberg table format:

const { data, error } = await analytics.createBucket('my-analytics-bucket')

if (error) {
  console.error('Error:', error.message)
} else {
  console.log('Created bucket:', data)
}

Returns:

{
  data: {
    id: string
    type: 'ANALYTICS'
    format: string
    created_at: string
    updated_at: string
  } | null
  error: StorageError | null
}

List Analytics Buckets

Retrieves all analytics buckets in your project with optional filtering and pagination:

const { data, error } = await analytics.listBuckets({
  limit: 10,
  offset: 0,
  sortColumn: 'created_at',
  sortOrder: 'desc',
  search: 'prod',
})

if (data) {
  console.log(`Found ${data.length} analytics buckets`)
  data.forEach((bucket) => {
    console.log(`- ${bucket.id} (created: ${bucket.created_at})`)
  })
}

Parameters:

• limit?: number - Maximum number of buckets to return
• offset?: number - Number of buckets to skip (for pagination)
• sortColumn?: 'id' | 'name' | 'created_at' | 'updated_at' - Column to sort by
• sortOrder?: 'asc' | 'desc' - Sort direction
• search?: string - Search term to filter bucket names

Returns:

{
  data: AnalyticBucket[] | null
  error: StorageError | null
}

Example with Pagination:

// Fetch first page
const firstPage = await analytics.listBuckets({
  limit: 100,
  offset: 0,
  sortColumn: 'created_at',
  sortOrder: 'desc',
})

// Fetch second page
const secondPage = await analytics.listBuckets({
  limit: 100,
  offset: 100,
  sortColumn: 'created_at',
  sortOrder: 'desc',
})

Delete Analytics Bucket

Deletes an analytics bucket. The bucket must be empty before deletion.

const { data, error } = await analytics.deleteBucket('old-analytics-bucket')

if (error) {
  console.error('Failed to delete:', error.message)
} else {
  console.log('Bucket deleted:', data.message)
}

Returns:

{
  data: { message: string } | null
  error: StorageError | null
}

Note: A bucket cannot be deleted if it contains data. You must empty the bucket first.

Error Handling

Analytics buckets use the same error handling pattern as the rest of the Storage SDK:

const { data, error } = await analytics.createBucket('my-bucket')

if (error) {
  console.error('Error:', error.message)
  console.error('Status:', error.status)
  console.error('Status Code:', error.statusCode)
  // Handle error appropriately
}

Throwing Errors

You can configure the client to throw errors instead of returning them:

const analytics = storageClient.analytics
analytics.throwOnError()

try {
  const { data } = await analytics.createBucket('my-bucket')
  // data is guaranteed to be present
  console.log('Success:', data)
} catch (error) {
  if (error instanceof StorageApiError) {
    console.error('API Error:', error.statusCode, error.message)
  }
}

TypeScript Types

The library exports TypeScript types for analytics buckets:

import type { AnalyticBucket, BucketType, StorageError } from '@supabase/storage-js'

// AnalyticBucket type
interface AnalyticBucket {
  id: string
  type: 'ANALYTICS'
  format: string
  created_at: string
  updated_at: string
}

Common Patterns

Checking if a Bucket Exists

async function bucketExists(bucketName: string): Promise<boolean> {
  const { data, error } = await analytics.listBuckets({
    search: bucketName,
  })

  if (error) {
    console.error('Error checking bucket:', error.message)
    return false
  }

  return data?.some((bucket) => bucket.id === bucketName) ?? false
}

Creating Bucket with Error Handling

async function ensureAnalyticsBucket(bucketName: string) {
  // Try to create the bucket
  const { data, error } = await analytics.createBucket(bucketName)

  if (error) {
    // Check if bucket already exists (conflict error)
    if (error.statusCode === '409') {
      console.log(`Bucket '${bucketName}' already exists`)
      return { success: true, created: false }
    }

    // Other error occurred
    console.error('Failed to create bucket:', error.message)
    return { success: false, error }
  }

  console.log(`Created new bucket: '${bucketName}'`)
  return { success: true, created: true, data }
}

Listing All Buckets with Pagination

async function getAllAnalyticsBuckets() {
  const allBuckets: AnalyticBucket[] = []
  let offset = 0
  const limit = 100

  while (true) {
    const { data, error } = await analytics.listBuckets({
      limit,
      offset,
      sortColumn: 'created_at',
      sortOrder: 'desc',
    })

    if (error) {
      console.error('Error fetching buckets:', error.message)
      break
    }

    if (!data || data.length === 0) {
      break
    }

    allBuckets.push(...data)

    // If we got fewer results than the limit, we've reached the end
    if (data.length < limit) {
      break
    }

    offset += limit
  }

  return allBuckets
}

Vector Embeddings

Supabase Storage provides built-in support for storing and querying high-dimensional vector embeddings, powered by S3 Vectors. This enables semantic search, similarity matching, and AI-powered applications without needing a separate vector database.

Note: Vector embeddings functionality is available in @supabase/storage-js v2.76 and later.

Features

• Vector Buckets: Organize vector indexes into logical containers
• Vector Indexes: Define schemas with configurable dimensions and distance metrics
• Batch Operations: Insert/update/delete up to 500 vectors per request
• Similarity Search: Query for nearest neighbors using cosine, euclidean, or dot product distance
• Metadata Filtering: Store and filter vectors by arbitrary JSON metadata
• Pagination: Efficiently scan large vector datasets
• Parallel Scanning: Distribute scans across multiple workers for high throughput
• Cross-platform: Works in Node.js, browsers, and edge runtimes

Quick Start

You can access vector functionality in three ways, depending on your use case:

Option 1: Via Supabase Client (Most Common)

If you're using the full Supabase client:

import { createClient } from '@supabase/supabase-js'

const supabase = createClient('https://your-project.supabase.co', 'your-anon-key')

// Access vector operations through storage
const vectors = supabase.storage.vectors

// Create a vector bucket
await vectors.createBucket('embeddings-prod')

// Create an index
const bucket = vectors.from('embeddings-prod')
await bucket.createIndex({
  indexName: 'documents-openai',
  dataType: 'float32',
  dimension: 1536,
  distanceMetric: 'cosine',
})

// Insert vectors
const index = bucket.index('documents-openai')
await index.putVectors({
  vectors: [
    {
      key: 'doc-1',
      data: { float32: [0.1, 0.2, 0.3 /* ...1536 dimensions */] },
      metadata: { title: 'Introduction', category: 'docs' },
    },
  ],
})

// Query similar vectors
const { data, error } = await index.queryVectors({
  queryVector: { float32: [0.15, 0.25, 0.35 /* ...1536 dimensions */] },
  topK: 5,
  returnDistance: true,
  returnMetadata: true,
})

if (data) {
  data.matches.forEach((match) => {
    console.log(`${match.key}: distance=${match.distance}`)
    console.log('Metadata:', match.metadata)
  })
}

Option 2: Via StorageClient

If you're using the standalone StorageClient for storage operations, access vectors through the vectors property:

import { StorageClient } from '@supabase/storage-js'

const storageClient = new StorageClient('https://your-project.supabase.co/storage/v1', {
  apikey: 'YOUR_API_KEY',
  Authorization: 'Bearer YOUR_TOKEN',
})

// Access vector operations
const vectors = storageClient.vectors

// Use the same API as shown in Option 1
await vectors.createBucket('embeddings-prod')
const bucket = vectors.from('embeddings-prod')
// ... rest of operations

Option 3: Standalone Vector Client

For vector-only applications that don't need regular file storage operations:

import { StorageVectorsClient } from '@supabase/storage-js'

// Initialize standalone vector client
const vectorClient = new StorageVectorsClient('https://your-project.supabase.co/storage/v1', {
  headers: { Authorization: 'Bearer YOUR_TOKEN' },
})

// Use the same API as shown in Option 1
await vectorClient.createBucket('embeddings-prod')
const bucket = vectorClient.from('embeddings-prod')
// ... rest of operations

When to use each approach:

• Option 1: When using other Supabase features (auth, database, realtime)
• Option 2: When working with both file storage and vectors
• Option 3: For dedicated vector-only applications without file storage

API Reference

Client Initialization

const vectorClient = new StorageVectorsClient(url, options?)

Options:

• headers?: Record<string, string> - Custom HTTP headers (e.g., Authorization)
• fetch?: Fetch - Custom fetch implementation

Vector Buckets

Vector buckets are top-level containers for organizing vector indexes.

Create Bucket

const { data, error } = await vectorClient.createBucket('my-bucket')

Get Bucket

const { data, error } = await vectorClient.getBucket('my-bucket')
console.log('Created at:', new Date(data.vectorBucket.creationTime! * 1000))

List Buckets

const { data, error } = await vectorClient.listBuckets({
  prefix: 'prod-',
  maxResults: 100,
})

// Pagination
if (data?.nextToken) {
  const next = await vectorClient.listBuckets({ nextToken: data.nextToken })
}

Delete Bucket

// Bucket must be empty (all indexes deleted first)
const { error } = await vectorClient.deleteBucket('my-bucket')

Vector Indexes

Vector indexes define the schema for embeddings including dimension and distance metric.

Create Index

const bucket = vectorClient.from('my-bucket')

await bucket.createIndex({
  indexName: 'my-index',
  dataType: 'float32',
  dimension: 1536,
  distanceMetric: 'cosine', // 'cosine' | 'euclidean' | 'dotproduct'
  metadataConfiguration: {
    nonFilterableMetadataKeys: ['raw_text', 'internal_id'],
  },
})

Distance Metrics:

• cosine - Cosine similarity (normalized dot product)
• euclidean - Euclidean distance (L2 norm)
• dotproduct - Dot product similarity

Get Index

const { data, error } = await bucket.getIndex('my-index')
console.log('Dimension:', data?.index.dimension)
console.log('Distance metric:', data?.index.distanceMetric)

List Indexes

const { data, error } = await bucket.listIndexes({
  prefix: 'documents-',
  maxResults: 100,
})

Delete Index

// Deletes index and all its vectors
await bucket.deleteIndex('my-index')

Vector Operations

Insert/Update Vectors (Upsert)

const index = vectorClient.from('my-bucket').index('my-index')

await index.putVectors({
  vectors: [
    {
      key: 'unique-id-1',
      data: {
        float32: [
          /* 1536 numbers */
        ],
      },
      metadata: {
        title: 'Document Title',
        category: 'technical',
        page: 1,
      },
    },
    // ... up to 500 vectors per request
  ],
})

Limitations:

• 1-500 vectors per request
• Vectors must match index dimension
• Keys must be unique within index

Get Vectors by Key

const { data, error } = await index.getVectors({
  keys: ['doc-1', 'doc-2', 'doc-3'],
  returnData: true, // Include embeddings
  returnMetadata: true, // Include metadata
})

data?.vectors.forEach((v) => {
  console.log(v.key, v.metadata)
})

Query Similar Vectors (ANN Search)

const { data, error } = await index.queryVectors({
  queryVector: {
    float32: [
      /* 1536 numbers */
    ],
  },
  topK: 10,
  filter: {
    category: 'technical',
    published: true,
  },
  returnDistance: true,
  returnMetadata: true,
})

// Results ordered by similarity
data?.matches.forEach((match) => {
  console.log(`${match.key}: distance=${match.distance}`)
})

Filter Syntax: The filter parameter accepts arbitrary JSON for metadata filtering. Non-filterable keys (configured at index creation) cannot be used in filters but can still be returned.

List/Scan Vectors

// Simple pagination
let nextToken: string | undefined
do {
  const { data } = await index.listVectors({
    maxResults: 500,
    nextToken,
    returnMetadata: true,
  })

  console.log('Batch:', data?.vectors.length)
  nextToken = data?.nextToken
} while (nextToken)

// Parallel scanning (4 workers)
const workers = [0, 1, 2, 3].map(async (segmentIndex) => {
  const { data } = await index.listVectors({
    segmentCount: 4,
    segmentIndex,
    returnMetadata: true,
  })
  return data?.vectors || []
})

const results = await Promise.all(workers)
const allVectors = results.flat()

Limitations:

• maxResults: 1-1000 (default: 500)
• segmentCount: 1-16
• Response may be limited by 1MB size

Delete Vectors

await index.deleteVectors({
  keys: ['doc-1', 'doc-2', 'doc-3'],
  // ... up to 500 keys per request
})

Error Handling

The library uses a consistent error handling pattern:

const { data, error } = await vectorClient.createBucket('my-bucket')

if (error) {
  console.error('Error:', error.message)
  console.error('Status:', error.status)
  console.error('Code:', error.statusCode)
}

Error Codes

Throwing Errors

You can configure the client to throw errors instead:

const vectorClient = new StorageVectorsClient(url, options)
vectorClient.throwOnError()

try {
  const { data } = await vectorClient.createBucket('my-bucket')
  // data is guaranteed to be present
} catch (error) {
  if (error instanceof StorageVectorsApiError) {
    console.error('API Error:', error.statusCode)
  }
}

Advanced Usage

Scoped Clients

Create scoped clients for cleaner code:

// Bucket-scoped operations
const bucket = vectorClient.from('embeddings-prod')
await bucket.createIndex({
  /* ... */
})
await bucket.listIndexes()

// Index-scoped operations
const index = bucket.index('documents-openai')
await index.putVectors({
  /* ... */
})
await index.queryVectors({
  /* ... */
})

Custom Fetch

Provide a custom fetch implementation:

import { StorageVectorsClient } from '@supabase/storage-js'

const vectorClient = new StorageVectorsClient(url, {
  fetch: customFetch,
  headers: {
    /* ... */
  },
})

Batch Processing

Process large datasets in batches:

async function insertLargeDataset(vectors: VectorObject[]) {
  const batchSize = 500

  for (let i = 0; i < vectors.length; i += batchSize) {
    const batch = vectors.slice(i, i + batchSize)
    await index.putVectors({ vectors: batch })
    console.log(`Inserted ${i + batch.length}/${vectors.length}`)
  }
}

Float32 Validation

Ensure vectors are properly normalized to float32:

import { normalizeToFloat32 } from '@supabase/storage-js'

const vector = normalizeToFloat32([0.1, 0.2, 0.3 /* ... */])

Type Definitions

The library exports comprehensive TypeScript types:

import type {
  VectorBucket,
  VectorIndex,
  VectorData,
  VectorObject,
  VectorMatch,
  VectorMetadata,
  DistanceMetric,
  ApiResponse,
  StorageVectorsError,
} from '@supabase/storage-js'

Development

This package is part of the Supabase JavaScript monorepo. To work on this package:

Building

Build Scripts Overview

The storage-js package uses multiple build scripts to generate different module formats for various JavaScript environments:

Running Builds

Complete Build (Recommended)

# From the monorepo root
npx nx build storage-js

This command executes the full build pipeline:

1. Cleans - Removes any existing build artifacts
2. Formats - Ensures consistent code formatting
3. Builds CommonJS - For Node.js environments (dist/main/)
4. Builds ES Modules - For modern bundlers (dist/module/)
5. Builds UMD - For browser script tags (dist/umd/)

Development Build with Watch Mode

# Continuously rebuild on file changes (from monorepo root)
npx nx build storage-js --watch

Individual Build Targets

For specific build outputs during development:

# Build CommonJS only (Node.js)
npx nx build:main storage-js

# Build ES Modules only (Modern bundlers)
npx nx build:module storage-js

# Build UMD only (Browser/CDN)
npx nx build:umd storage-js

Other Useful Commands

# Clean build artifacts
npx nx clean storage-js

# Format code
npx nx format storage-js

# Type checking
npx nx typecheck storage-js

# Generate documentation
npx nx docs storage-js

Build Outputs Explained

CommonJS (dist/main/)

• Used by: Node.js applications, older build tools
• Entry point: dist/main/index.js
• Module format: require() and module.exports
• TypeScript definitions: Included

ES Modules (dist/module/)

• Used by: Modern bundlers (Webpack, Rollup, Vite)
• Entry point: dist/module/index.js
• Module format: import and export
• TypeScript definitions: dist/module/index.d.ts
• Benefits: Tree-shaking, better static analysis

UMD (dist/umd/)

• Used by: Browser <script> tags, CDNs
• Entry point: dist/umd/supabase.js
• Global variable: window.supabase
• Size: Larger (includes all dependencies)
• Usage example:

  <script src="https://unpkg.com/@supabase/storage-js/dist/umd/supabase.js"></script>
  <script>
    const { StorageClient } = window.supabase
  </script>

Package Exports

The package.json exports are configured to provide the right format for each environment:

{
  "main": "dist/main/index.js",
  "module": "dist/module/index.js",
  "types": "dist/module/index.d.ts",
  "jsdelivr": "dist/umd/supabase.js",
  "unpkg": "dist/umd/supabase.js"
}

• main → Node.js environments (CommonJS format)
• module → Modern bundlers like Webpack, Vite, Rollup (ES Modules)
• types → TypeScript type definitions
• jsdelivr/unpkg → CDN usage via <script> tags (UMD format)

Testing

Important: The storage-js tests require a local test infrastructure running in Docker. This is NOT the same as a regular Supabase instance - it's a specialized test setup with its own storage API, database, and Kong gateway.

Prerequisites

1. Docker must be installed and running
2. Port availability - The following ports must be free:
   • 5432 (PostgreSQL database)
   • 5050 (Storage API - sometimes 5000 conflicts macOS AirPlay conflict)
   • 8000 (Kong API Gateway)
   • 50020 (imgproxy for image transformations)

Note: If port 5000 conflicts with macOS AirPlay Receiver, the docker-compose.yml has been configured to use port 5050 instead.

Test Scripts Overview

Running Tests

Option 1: Complete Test Run (Recommended)

This handles everything automatically - starting infrastructure, running tests, and cleaning up:

# From monorepo root
npx nx test:storage storage-js

This command will:

1. Stop any existing test containers
2. Build and start fresh test infrastructure
3. Wait for services to be ready
4. Run all Jest tests with coverage
5. Clean up all containers after tests complete

Option 2: Manual Infrastructure Management

Useful for development when you want to run tests multiple times without restarting Docker:

# Step 1: Start the test infrastructure
# From root
npx nx test:infra storage-js
# This starts: PostgreSQL, Storage API, Kong Gateway, and imgproxy

# Step 2: Run tests (can run multiple times)
npx nx test:suite storage-js

# Step 3: When done, clean up the infrastructure
npx nx test:clean storage-js

Option 3: Development Mode

For actively developing and debugging tests:

# Start infrastructure once (from root)
npx nx test:infra storage-js

# Run tests in watch mode
npx nx test:suite storage-js --watch

# Clean up when done
npx nx test:clean storage-js

Test Infrastructure Details

The test infrastructure (infra/docker-compose.yml) includes:

• PostgreSQL Database (port 5432)

  • Initialized with storage schema and test data
  • Contains bucket configurations and permissions

• Storage API (port 5050, internal 5000)

  • Supabase Storage service for handling file operations
  • Configured with test authentication keys

• Kong Gateway (port 8000)

  • API gateway that routes requests to storage service
  • Handles authentication and CORS

• imgproxy (port 50020)

  • Image transformation service for on-the-fly image processing

Common Issues and Solutions

Understanding Test Failures

• StorageUnknownError with "request failed": Infrastructure not running
• Port binding errors: Ports are already in use by other services
• Snapshot failures: Expected test data has changed - review and update snapshots if needed

What About Supabase CLI?

No, you don't need supabase start or a regular Supabase instance for these tests. The storage-js tests use their own specialized Docker setup that's lighter and focused specifically on testing the storage SDK. This test infrastructure:

• Is completely independent from any Supabase CLI projects
• Uses fixed test authentication keys
• Has predictable test data and bucket configurations
• Runs faster than a full Supabase stack
• Doesn't interfere with your local Supabase development projects

Contributing

We welcome contributions! Please see our Contributing Guide for details on how to get started.

For major changes or if you're unsure about something, please open an issue first to discuss your proposed changes.

2.76.1 (2025-10-21)

🩹 Fixes

• repo: add missing tslib dependency to core packages (#1789)

❤️ Thank You

• Katerina Skroumpelou @mandarini

2.76.0 (2025-10-20)

🚀 Features

• realtime: realtime explicit REST call (#1751)
• realtime: enhance RealtimeChannel type (#1747)
• storage: storage vectors and analytics in storage-js (#1752)

🩹 Fixes

• functions: missing body when Content-Type header supplied by dev (#1758)
• functions: add application/pdf response parsing to FunctionsClient (#1757)
• realtime: manipulate URLs using URL object (#1769)
• repo: convert postbuild to explicit codegen (#1778)
• storage: correct list v2 types to correctly match data returned from api (#1761)
• storage: use backward compatible return type in download function (#1750)
• storage: api types (#1784)

❤️ Thank You

• Fabrizio @fenos
• Filipe Cabaço @filipecabaco
• Guilherme Souza
• Katerina Skroumpelou @mandarini
• Lenny @itslenny

2.75.1 (2025-10-17)

🩹 Fixes

• storage: use backward compatible return type in download function (#1750)

❤️ Thank You

• Lenny @itslenny

2.75.0 (2025-10-09)

🚀 Features

• postgrest: add embeded functions type inference (#1632)

❤️ Thank You

• Andrew Valleteau @avallete

2.74.0 (2025-10-07)

🚀 Features

• auth: add deprecation notice to onAuthStateChange with async function (#1580)
• auth: add OAuth 2.1 client admin endpoints (#1582)
• docs: explicitly mark options as optional (#1622)
• realtime: add support to configure Broadcast Replay (#1623)
• release: enable trusted publishing (#1592)
• storage: add support for sorting to list v2 (#1606)

🩹 Fixes

• storage: remove trailing slash from baseUrl normalization (#1589)

❤️ Thank You

• Cemal Kılıç @cemalkilic
• Doğukan Akkaya
• Eduardo Gurgel
• Etienne Stalmans @staaldraad
• Lenny @itslenny
• Stojan Dimitrovski @hf
• Taketo Yoshida