Websocket definition and what it means in practice
Websocket is a network protocol that keeps a connection open between a client and a server so they can exchange messages in both directions with low overhead. For automation, this usually means a software system can receive frequent updates (for example, quotes or status messages) and send commands back without repeatedly opening new connections.
When evaluating Websocket, separate stable mechanics (how the protocol and your client typically behave) from variable conditions (provider infrastructure, network quality, server load, and the market environment). Stable mechanics help you reason about correctness; variable conditions determine whether it works in real usage.
A due-diligence checklist (control-checklist)
1) AFV: assumptions, failure, verification
Start by writing assumptions. For each message you rely on, state: what the message represents, how you will detect it arrived, and what you do when it doesn’t.
Then identify failure modes (AFVinkpunten):
- Dropped or delayed messages: confirm what happens under load and whether there are gaps.
- Out-of-order delivery or duplicate events: define how you will handle repeats and ordering.
- State desynchronization: if you build a local view, verify whether you can resync.
Finally, specify a verification method: log raw frames, compare with server-provided sequence info (if available), and run tests that simulate disconnects and throttling.
2) Message semantics: types, schema, and ordering
Check official documentation for the message schema and message types. You should verify at least:
- Whether messages include timestamps and sequence numbers (or equivalent ordering markers).
- Whether the server sends initial snapshots before deltas/updates.
- How your system should interpret “heartbeat” or “subscription” acknowledgements.
A ready-to-use test is: subscribe, capture messages for a fixed window, and confirm your parser and state machine handle every documented field and event type.
3) Reliability behavior: reconnect, backoff, and gaps
A material limitation is that networks and servers are not guaranteed to be perfectly available. Verify how Websocket behaves during:
- connection drops,
- temporary outages,
- rate limiting,
- authentication failures,
- and server restarts.
You should confirm whether the provider offers a way to recover missed updates (for example, resubscribe plus snapshot, or a gap-recovery mechanism). Without this, your local state can become stale while your system continues operating.
4) Throughput and latency expectations (and what to measure)
Instead of assuming performance, measure it. Even if you see “fast” updates in one test, throughput may degrade under higher activity or at peak times.
Check whether the documentation describes limits such as maximum subscription counts, message size, or rate policies. Then measure in your own environment:
- end-to-end latency from receipt to processing completion,
- CPU and memory impact of parsing,
- and how processing time affects your ability to keep up.
If you cannot measure, you should assume your system may fall behind and start accumulating latency.
5) Security and access control
Verify authentication requirements and token handling rules from the provider’s official materials. Check:
- how credentials are sent,
- whether tokens expire,
- and how the system should react to “unauthorized” errors.
Also validate that your implementation treats sensitive data carefully in logs (avoid storing secrets in plain text).
6) Evidence of correctness: logs and auditability
Require evidence that you can reconstruct what happened. Practically, this means:
- storing raw message captures (at least for test runs),
- keeping structured logs tied to sequence/order markers,
- and recording reconnect and resubscribe events.
This is your “bewijs of document” checklist: you prove correctness by comparing expected behavior (per documentation) with observed behavior (your tests).
Limitations and risks to expect (with at least one concrete failure mode)
A common failure mode is stale state after a disconnect. Example assumption: your client builds a local representation of order/quote data based on incremental updates. If the connection drops and you reconnect without a documented resync mechanism, you may continue using an incomplete or outdated state.
Other risks to plan for:
- Parsing and schema drift: undocumented fields or changes can break your parser.
- Timing assumptions: timestamps from different systems may not align; your logic must not assume perfect clock sync.
- Non-predictive history: even if behavior looked stable historically, it does not establish future message reliability.