Object Storage Configuration

This page describes how to properly configure object storage for BYOC Agent deployments.

We highly recommend running the WarpStream Agent with a dedicated bucket for isolation; however, the WarpStream Agent will only write/read data under the warpstream prefix.

The WarpStream Agent manages all data in the object storage warpstream directory. It is extremely important that you allow it to do so alone and never delete files from the warpstream directory manually. Manually deleting files in the warpstream directory will effectively "brick" a virtual cluster and require that it be recreated from scratch.

Bucket URL Construction

The bucketURL flag is the URL of the object storage bucket that the WarpStream Agent should write to. See the table below for how to configure it for different object store implementations.

Note that the WarpStream Agents will automatically write all of their data to a top-level warpstream prefix in the bucket. In addition, each cluster will write its data to a cluster-specific prefix (derived from the cluster ID) within the warpstream prefix so multiple WarpStream clusters can co-exist within the same object storage bucket without issue.

An S3 bucket with 16 different cluster prefixes under the top-level warpstream prefix.

AWS S3

Format: s3://$BUCKET_NAME?region=$BUCKET_REGION

Example: s3://my_warpstream_bucket_123?region=us-east-1

The WarpStream Agent embeds the official AWS Golang SDK V2 so authentication/authorization with the specified S3 bucket can be handled in any of the expected ways, like using a shared credentials file, environment variables, or simply running the Agents in an environment with an appropriate IAM role with Write/Read/Delete/List permissions on the S3 bucket.

If you want to use an AssumeRole provider to authenticate, you can add the WARPSTREAM_BUCKET_ASSUME_ROLE_ARN_DEFAULT environment variable to your Agent. For example:

WARPSTREAM_BUCKET_ASSUME_ROLE_ARN_DEFAULT=arn:aws:iam::103069001423:role/YourRoleName

GCP GCS

Format: gs://$BUCKET_NAME

Example: gs://my_warpstream_bucket_123

The WarpStream Agent embeds the official GCP Golang SDK so authentication/authorization with the storage bucket can be handled in any of the expected ways.

Azure Blob Storage

Format: azblob://$CONTAINER_NAME

Example: azblob://my_warpstream_container_123

The WarpStream Agent embeds the official Azure Golang SDK which expects the AZURE_STORAGE_ACCOUNT environment variable to be set, along with one of the two following environment variables: AZURE_STORAGE_KEY or AZURE_STORAGE_SAS_TOKEN.

Memory

For testing and local development only. All data will be lost once the Agent shuts down.

Example: mem://my_memory_bucket

File

For testing and local development only. The file store implementation is not robust.

Format: file://$PATH_TO_DIRECTORY

Example: file:///tmp/warpstream_tmp_123

S3-compatible Object Stores (MinIO, R2, Oracle Cloud, Tigris, etc)

If you're using an "S3 compatible" object store that is not actually S3, like MinIO, R2 or Oracle Cloud Object Store then you'll need to provide credentials manually as environment variables and force the S3 client to construct the URL using the "path style":

MinIO

If you have a MinIO docker container running locally on your machine on port 9000, you can run the Agent like this after creating an Access Key in the MinIO UI:

AWS_ACCESS_KEY_ID="wKghTMkQrFqszshHJcop" \
AWS_SECRET_ACCESS_KEY="MpMO9GFMaoIFFYd8cZi5gyk5SAjwleEbkZOSxIXv" \
warpstream demo \
-bucketURL "s3://warpstream-minio-bucket?s3ForcePathStyle=true&endpoint=http://127.0.0.1:9000"

The MinIO team has a more detailed integration guide on their website as well.

R2

1. Create an account with Cloudflare.

2. Create an R2 bucket.

3. Create an R2 access token.

AWS_ACCESS_KEY_ID="XXX" \
AWS_SECRET_ACCESS_KEY="XXX" \
warpstream demo -bucketURL "s3://warpstream-demo-for-fun?s3ForcePathStyle=true&region=auto&endpoint=https://XXX.r2.cloudflarestorage.com" 

Note that if you run multiple WarpStream Agents this way in non-demo mode, then by default they need to be running on the same internal network. The reason for this is that if the Agents believe they're all running in the same "availability zone", they will attempt to form a distributed cache with each other to reduce R2 API GET requests.

However, if you wish to run multiple Agents in separate networks / regions, but still allow them to function as a single "Kafka Cluster", assign each one a dedicated availability zone.

For example, Agent 1:

WARPSTREAM_AVAILABILITY_ZONE="personal_laptop_chicago" \
AWS_ACCESS_KEY_ID="XXX" \
AWS_SECRET_ACCESS_KEY="XXX" \
warpstream agent -bucketURL "s3://warpstream-demo-for-fun?s3ForcePathStyle=true&region=auto&endpoint=https://XXX.r2.cloudflarestorage.com"

Agent 2:

WARPSTREAM_AVAILABILITY_ZONE="personal_laptop_nashville" \
AWS_ACCESS_KEY_ID="XXX" \
AWS_SECRET_ACCESS_KEY="XXX" \
warpstream agent -bucketURL "s3://warpstream-demo-for-fun?s3ForcePathStyle=true&region=auto&endpoint=https://XXX.r2.cloudflarestorage.com"

This signals to each Agent that they should not attempt to communicate with each other directly over the local network, and that each one should behave as if it were running in a different availability zone. However, data will still be able to be streamed from Chicago to Nashville (or vice versa) because the Agents will use R2 as "the network".

The net result of this is a "multi-region" Cluster that can read and write all topics/partitions from multiple regions at the same time.

Using a Bucket Prefix

If you want the WarpStream Agents to store data in a specific prefix in the bucket, you can add the prefix as a query argument to the bucket URL. The prefix must terminate with a "/". For example:

s3://my_warpstream_bucket_123?region=us-east-1&prefix=my_prefix/

Bucket Configuration

The WarpStream bucket should not have a configured object retention policy. WarpStream will manage the lifecycle of the objects, including deleting objects that have been compacted or have expired due to retention. If you must configure a retention policy on your bucket, make sure it is significantly longer than the longest retention of any topic/stream in any of your Virtual Clusters to avoid data loss.

We recommend configuring a lifecycle policy for cleaning up aborted multi-part uploads. This will prevent failed file uploads from the WarpStream Agent from accumulating in the bucket forever and increasing your storage costs. Below is a sample Terraform configuration for various different cloud providers:

AWS

resource "aws_s3_bucket" "warpstream_bucket" {
  bucket = "my-warpstream-bucket-123"

  tags = {
    Name        = "my-warpstream-bucket-123"
    Environment = "staging"
  }
}

resource "aws_s3_bucket_metric" "warpstream_bucket_metrics" {
 bucket = aws_s3_bucket.warpstream_bucket.id
 name   = "EntireBucket"
}

resource "aws_s3_bucket_lifecycle_configuration" "warpstream_bucket_lifecycle" {
  bucket = aws_s3_bucket.warpstream_bucket.id

  # Automatically cancel all multi-part uploads after 7d so we don't accumulate an infinite
  # number of partial uploads.
  rule {
    id     = "7d multi-part"
    status = "Enabled"
    abort_incomplete_multipart_upload {
      days_after_initiation = 7
    }
  }
  
  # No other lifecycle policy. The WarpStream Agent will automatically clean up and
  # deleted expired files.
}

GCP

resource "google_storage_bucket" "warpstream_bucket" {
  name     = "my-warpstream-bucket-123"
  location = "$REGION"

  labels = {
    Name        = "my-warpstream-bucket-123"
    Environment = "staging"
  }
  
  lifecycle_rule {
    condition {
      age = 7
    }
    action {
      type = "AbortIncompleteMultipartUpload"
    }
  }
}

Bucket Permissions

In addition to configuring the WarpStream buckets, you'll also need to make sure the Agent containers have the appropriate permissions to interact with the bucket.

AWS

Specifically, the Agents need permission to perform the following operations:

• PutObject

  • To create new files.

• GetObject

  • To read existing files.

• DeleteObject

  • So the Agents can enforce retention and cleanup of pre-compaction files.

• ListBucket

  • So the Agents can enforce retention and cleanup of pre-compaction files.

Below is an example Terraform configuration for an AWS IAM policy document that provides WarpStream with the appropriate permissions to access a dedicated S3 bucket:

data "aws_iam_policy_document" "warpstream_s3_policy_document" {
  statement {
    sid     = "AllowS3"
    effect  = "Allow"
    actions = [
      "s3:PutObject",
      "s3:GetObject",
      "s3:DeleteObject",
      "s3:ListBucket"
    ]
    resources = [
      "arn:aws:s3:::my-warpstream-bucket-123",
      "arn:aws:s3:::my-warpstream-bucket-123/*"
    ]
  }
}

GCP

The easiest way to configure bucket access in GCP is with the roles/storage.objectUser role like so:

resource "google_storage_bucket_iam_policy" "warpstream_bucket_iam" {
  bucket     = "my-warpstream-bucket-123"

  policy_data = jsonencode({
    bindings = [
      {
        role    = "roles/storage.objectUser"
        members = [
          "user:$USER"
        ]
      }
    ]
  })
}

However, if you need more granular permission sets, then WarpStream requires at least the following:

• storage.objects.create

• storage.objects.delete

• storage.objects.get

• storage.objects.list

• storage.multipartUploads.*

Migrating Between Object Storage Buckets

If you need to migrate a WarpStream cluster from one object storage bucket to another, follow these steps:

1. Make sure that the Agents have permission to perform operations on both the old bucket and the new bucket.

2. Deploy the Agents with the bucketURL flag set to the new bucket instead of the old one. This will cause the Agents to write all new files (both for ingestion and compaction) to the new bucket while still allowing them to read historical data from the old bucket.

3. Wait until there are no more data files under the warpstream prefix in the old bucket.