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🟢 Data Freshness

Real-time health and freshness status of every metric in the Algo Tick sensor network. Each metric shows its last update time, ingest latency (chain→local), and current status.

100%
System Health
11
Fresh Metrics
0
Stale Metrics
11
Total Tracked

How Freshness Works

Every data point has two timestamps: time_chain (when the event occurred on-chain or at the exchange) and time_local (when our sensor ingested it). The difference is the ingest latency. A metric is fresh if updated within 2 minutes, warm if within 10 minutes, and stale otherwise.

Metric-Level Freshness

MetricPackLast UpdateIngest LatencyStatus
eth_basefee_livemacro_congestion4s ago3.7sfresh
eth_block_numbermacro_congestion0s ago-9msfresh
eth_block_tx_countmacro_congestion0s ago-9msfresh
eth_block_utilization_pctmacro_congestion0s ago-6msfresh
eth_burn_rate_livemacro_congestion0s ago-6msfresh
eth_priority_fee_gweimacro_congestion0s ago-5msfresh
hlp_funding_ratederivative_execution0s ago-9msfresh
hlp_liquidation_alertsderivative_execution0s ago-2msfresh
hlp_mark_pricederivative_execution0s ago-9msfresh
hlp_open_interestderivative_execution0s ago-8msfresh
hlp_orderbook_imbalance_pctderivative_execution0s ago-8msfresh

📡 Ingest Pipeline

Data flows through the following pipeline:

  1. Source → Sensor: WebSocket/gRPC connections to exchanges, L1 nodes, and bridge contracts
  2. Sensor → Redis: Sub-millisecond publish to the in-memory message bus
  3. Redis → API: Live snapshot available immediately via REST and WebSocket
  4. Redis → R2: Parquet files written every hour to the data lake with Hive partitioning

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Every metric on this page is available via our sub-millisecond API.
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