Monitoring data from various sources is essential for businesses to make informed decisions. A reliable monitor can reconstruct the same normalized state from a known snapshot plus accepted events. Here are the key steps involved in this process:
- Read the current terms, access restrictions, licensing rules, and official documentation to understand the data source.
- Discover events and markets through an unauthenticated endpoint when available to gather information.
- Select native event, market, and outcome identifiers returned by the discovery response for further processing.
- Fetch a REST snapshot and save the complete raw payload with an ingestion timestamp, adhering to the source’s terms and retention rules.
- Normalize identifiers, lifecycle state, timestamps, and decimal fields through a venue adapter for consistency.
- Subscribe to a WebSocket only after the REST snapshot, using authentication when necessary, and retain any initial stream snapshot before deltas.
- Validate sequence, schema, source time, and namespaced record IDs for every message received.
- Persist accepted events append-only and reconcile the materialized state with periodic REST snapshots.
- Store wording, rules, labels, and the resolution source with the market record for reference.
- Continue polling or listening until the venue reaches a terminal state, such as settled, voided, or canceled, then display the record in a local log or dashboard.
Reconciliation and replay are crucial processes in maintaining the integrity of stored data. Reconciliation replaces diverged local state with a fresh official snapshot, while replay applies the same adapter transformations to recorded messages without live writes, ensuring the stored state remains auditable.
Here is an example of how a monitor can rebuild the state from saved snapshots and accepted events:
# Executable local fixtures. No network or account access.
from decimal import Decimal
initial_snapshot = {
"venue": "example",
"event_id_native": "event-7",
"market_id_native": "market-3",
"outcome_id_native": "yes",
"sequence": 40,
"price_type": "best_bid",
"price_decimal": "0.60",
"size_decimal": "125",
"book_time_ms": 1788134399000,
"source_event_at_ms": 1788134400000,
"observed_at_ms": 1788134400100,
}
recovery_snapshot = {
**initial_snapshot,
"sequence": 42,
"price_decimal": "0.615",
"book_time_ms": 1788134402000,
"source_event_at_ms": 1788134402000,
"observed_at_ms": 1788134402100,
}
messages = [
{
**initial_snapshot,
"sequence": 41,
"price_decimal": "0.61",
"book_time_ms": 1788134401000,
"source_event_at_ms": 1788134401000,
"observed_at_ms": 1788134401100,
},
{
**initial_snapshot,
"sequence": 43,
"price_decimal": "0.62",
"book_time_ms": 1788134403000,
"source_event_at_ms": 1788134403000,
"observed_at_ms": 1788134403100,
},
]
saved_snapshots = iter([initial_snapshot, recovery_snapshot])
rebuild_triggers = []
# Code continues with loading saved snapshots, applying deltas, and monitoring sequence boundaries
It is important to note that the example provided focuses on reconstructing the state based on saved snapshots and accepted events. Production credentials and live-order endpoints are not included in this scope. A monitor should be able to record alerts or data-quality exceptions without converting them into transactions.
By following these steps and best practices, businesses can ensure that their data monitoring processes are efficient, accurate, and reliable.