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@adambenhassen adambenhassen commented Nov 17, 2025

Description

Implements a resilient OTLP trace pipeline architecture by splitting the monolithic OTEL collector into separate ingress and egress components with Redpanda as a buffering layer. This provides better scalability, fault tolerance, and performance isolation.

Architecture Changes

  • Before: Client -> OTEL Collector -> ClickHouse
  • After: Client -> Ingress Collectors -> Redpanda -> Egress Collectors -> ClickHouse

What's New

Split Collector Architecture

  • Ingress Collectors: Handle authentication, receive OTLP data, forward to Redpanda
    • Includes HiveAuth extension for authentication
    • Processes attributes and applies memory limits
    • Exports to Kafka with Snappy compression
  • Egress Collectors: Consume from Redpanda, batch, send to ClickHouse
    • No authentication needed (internal only)
    • Batch processing for efficient ClickHouse inserts
    • Dedicated to ClickHouse export operations

Redpanda Message Buffer

  • Acts as durable buffer between ingress and egress
  • Provides backpressure handling and fault tolerance
  • 10 partitions for parallel processing
  • 30-day retention for trace replay capability
  • 10MB max message size for large traces

@adambenhassen adambenhassen marked this pull request as draft November 17, 2025 00:25
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Summary of Changes

Hello @adambenhassen, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly upgrades the OpenTelemetry trace ingestion pipeline by introducing Redpanda as an intermediary buffering layer. This architectural shift, along with splitting the OTel collector into dedicated ingress and egress services, aims to improve the system's resilience, scalability, and performance. The changes are backed by detailed benchmarking that informed optimal memory and queue configurations, ensuring reliable trace processing even when downstream services experience bottlenecks.

Highlights

  • Redpanda Buffering Layer: Introduced Redpanda as a robust buffering layer for OpenTelemetry traces, enhancing data durability and decoupling ingestion from processing.
  • Split OpenTelemetry Collectors: The OpenTelemetry collector architecture has been refactored into distinct ingress and egress components. The ingress collector receives traces and forwards them to Redpanda, while the egress collector consumes from Redpanda and exports to ClickHouse.
  • Comprehensive Benchmarking: Extensive benchmarking was conducted to optimize OpenTelemetry collector memory configuration and stability. This led to the adoption of fixed MiB memory limits and file-based sending queues, significantly improving reliability and throughput, especially under high load and ClickHouse backpressure.
  • Enhanced Observability: Added a new custom statsviz extension to the OpenTelemetry collector, providing real-time visualization of Go runtime metrics for better performance monitoring and debugging.
  • Development Environment Updates: The docker-compose.dev.yml configuration was updated to reflect the new split collector setup, include Redpanda topic provisioning, and use a newer Redpanda image. Go versions for collector extensions were also upgraded.
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github-actions bot commented Nov 17, 2025

📚 Storybook Deployment

The latest changes are available as preview in: https://pr-7275.hive-storybook.pages.dev

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Code Review

This pull request introduces a significant architectural change by adding a Redpanda buffering layer between two separate OpenTelemetry collectors for ingress and egress. This is a solid approach to improve the reliability and scalability of the trace ingestion pipeline. The changes include splitting the collector, updating configurations, and adding a comprehensive benchmark report which is excellent for documenting the design decisions. My review focuses on ensuring the new configuration aligns with the benchmark findings and that the supporting scripts are robust. I've identified a critical inconsistency in the egress collector configuration compared to the benchmark results, a potential race condition in the development environment setup, a minor formatting issue in the documentation, and an issue with the load test script that could lead to unrealistic test results.

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github-actions bot commented Nov 20, 2025

💻 Website Preview

The latest changes are available as preview in: https://pr-7275.hive-landing-page.pages.dev

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github-actions bot commented Nov 21, 2025

🐋 This PR was built and pushed to the following Docker images:

Targets: build

Platforms: linux/amd64

Image Tag: 4dfce45af893b95d8056fa38534dbc91b4fbdb75

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            "name": "context"
          },
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            "name": "dist"
          },
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            "name": "dockerfile"
          },
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            "name": "shared"
          }
        ]
      },
      "environment": {
        "platform": "linux/amd64",
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  "containerimage.descriptor": {
    "mediaType": "application/vnd.docker.distribution.manifest.v2+json",
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},
"webhooks": {
  "buildx.build.provenance": {
    "builder": {
      "id": ""
    },
    "buildType": "https://mobyproject.org/buildkit@v1",
    "materials": [
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    "invocation": {
      "configSource": {
        "entryPoint": "services.dockerfile"
      },
      "parameters": {
        "frontend": "dockerfile.v0",
        "args": {
          "build-arg:HEALTHCHECK_CMD": "wget --spider -q http://127.0.0.1:${PORT}/_readiness",
          "build-arg:IMAGE_DESCRIPTION": "The webhooks ingestor service of the GraphQL Hive project.",
          "build-arg:IMAGE_TITLE": "graphql-hive/webhooks",
          "build-arg:PORT": "3005",
          "build-arg:RELEASE": "4dfce45af893b95d8056fa38534dbc91b4fbdb75",
          "build-arg:SERVICE_DIR_NAME": "@hive/webhooks",
          "context:dist": "local:dist",
          "context:shared": "local:shared",
          "frontend.caps": "moby.buildkit.frontend.contexts+forward",
          "local-sessionid:context": "erbt6mdib7kief85j20vcis6v",
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}
}

availabilityOnEveryNode: true,
port: 4318,
memoryLimit: args.environment.podsConfig.tracingCollector.memoryLimit,
memoryLimit: '512Mi',
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should we keep this configurable by env?

args: [
'redpanda',
'start',
'--overprovisioned',
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Redpanda expects a dev system to be an overprovisioned environment. Based on a Seastar option, setting overprovisioned disables thread affinity, zeros idle polling time, and disables busy-poll for disk I/O.

https://docs.redpanda.com/current/reference/rpk/rpk-redpanda/rpk-redpanda-mode/#production-mode

We want to make sure this is disabled in production

],
livenessProbe: {
httpGet: {
path: '/v1/status/ready',
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for most of our services we use _health. I'm not sure if we want to keep this consistent or if we don't mind...

clusterIP: 'None',
selector: labels,
ports: [
{ name: 'kafka', port: 9092, targetPort: 9092 as any },
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why do we need the any casts ?

telemetry:
logs:
level: DEBUG
level: INFO
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Is this just for testing or is there a reason to use info logs?

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2 participants