How Canva Collects 25 Billion Events a Day

Canva's event collection system processes 25 billion events daily, leveraging a scalable architecture with Kafka, Flink, and S3. The system prioritizes reliability, low latency, and cost-efficiency while handling diverse event types from global users. Key optimizations include batching, compression, and intelligent routing to balance performance and resource usage.

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Core Technical Concepts/Technologies

• Event Streaming: Kafka for high-throughput data ingestion
• Stream Processing: Flink for real-time event aggregation/enrichment
• Storage: S3 for cost-effective long-term retention
• Batching/Compression: Protocol Buffers (Protobuf) and Snappy for efficiency
• Load Balancing: Regional routing to minimize latency

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Main Points

• Scale Challenges:

  • 25B events/day (~300k events/sec peak) with sub-second latency requirements
  • Events vary in size (1KB–10KB) and type (e.g., clicks, edits, collaborations)

• Architecture:

  1. Client SDKs: Lightweight collectors batch events (5s/100KB thresholds) with Protobuf+Snappy compression.
  2. Ingestion Layer: Regional Kafka clusters handle traffic spikes; auto-scaling via Kubernetes.
  3. Processing: Flink jobs enrich/aggregate events (e.g., sessionization) in real time.
  4. Storage: Processed data lands in S3 (Parquet format) via hourly partitions for analytics.

• Optimizations:

  • Batching: Reduces network overhead (e.g., 100KB batches cut TCP handshake costs).
  • Regional Proximity: Clients route to nearest AWS region (us-east-1, ap-southeast-2, etc.).
  • Dead-Letter Queues: Handle malformed events without blocking pipelines.

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Technical Specifications

• Kafka Configuration:
  • 6-node clusters per region, 32 vCPUs/node, 64GB RAM
  • Retention: 7 days (hot storage), 30 days (cold via S3)
• Flink Jobs:
  • Checkpointing every 10s for fault tolerance
  • Parallelism tuned per event type (e.g., 32–128 tasks)

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Key Takeaways

1. Batching is critical for high-volume event systems to reduce network/processing overhead.
2. Regional routing improves latency and reliability for global user bases.
3. Protocol Buffers + Snappy offer an optimal balance of size and speed for serialization.
4. Separation of hot/cold storage (Kafka → S3) balances cost and accessibility.

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Limitations & Future Work

• Cold Start Latency: Flink recovery from checkpoints can delay processing after failures.
• Schema Evolution: Protobuf requires careful versioning for backward compatibility.
• Exploration Areas: Testing Arrow format for analytics queries on S3 data.