Define a canonical event schema with required envelope fields (event_name, event_version, session_id, player_id, client_timestamp, server_timestamp, platform) and a typed payload object per event type.
Collect events client-side into an in-memory queue; flush the queue to the collection endpoint on a time interval (e.g., every 30 seconds) or when the queue reaches a size threshold, whichever comes first.
On the collection endpoint (e.g., an HTTP ingest service), validate incoming event batches against the schema registry; reject or quarantine malformed events before forwarding to the pipeline.
Route validated events to a streaming platform (e.g., Kafka, Azure Event Hubs, AWS Kinesis) using a topic-per-event-type or a single topic with event_name as a partition key for consumer filtering.
Deploy stream processors (e.g., Apache Flink, Spark Structured Streaming, or cloud-native equivalents) to aggregate real-time metrics (concurrent players, match start rate) and write to a time-series store.
Sink raw events to cold storage (e.g., Parquet files in object storage) for batch analytics and experiment analysis; partition by date and event_name for efficient query performance.
Known gotchas
Client clocks drift and can be set arbitrarily; always record a server_timestamp on ingestion and use it for ordering, storing client_timestamp only for latency analysis.
Schema evolution without versioning breaks downstream consumers; use a schema registry with compatibility enforcement (backward or full compatibility) and increment event_version on breaking changes.
Batching reduces ingest cost but increases data latency; tune batch size and flush interval based on the freshness SLA for live dashboards versus cost tolerance for cold analytics.
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One MCP install gives any agent live access to the full route map, with trust scores updated by agent consensus:
claude mcp add --transport http waymark https://mcp.waymark.network/mcp