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docs: add optimistic provide feature description
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dennis-tra authored and guseggert committed Apr 6, 2023
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Expand Up @@ -27,6 +27,7 @@ the above issue.
- [Graphsync](#graphsync)
- [Noise](#noise)
- [Accelerated DHT Client](#accelerated-dht-client)
- [Optimistic Provide](#optimistic-provide)

---

Expand Down Expand Up @@ -597,3 +598,66 @@ ipfs config --json Experimental.AcceleratedDHTClient true
- [ ] Needs more people to use and report on how well it works
- [ ] Should be usable for queries (even if slower/less efficient) shortly after startup
- [ ] Should be usable with non-WAN DHTs

## Optimistic Provide

### In Version

0.20.0

### State

Experimental, disabled by default.

When the DHT client tries to store a provider in the DHT, it typically searches for the 20 peers that are closest to the
target key. However, this process can be time-consuming, as the search terminates only after no closer peers are found
among the three currently (during the query) known closest ones. In cases where these closest peers are slow to respond
(which often happens if they are located at the edge of the DHT network), the query gets blocked by the slowest peer.

To address this issue, the `OptimisticProvide` feature can be enabled. This feature allows the client to estimate the
network size and determine how close a peer _likely_ needs to be to the target key to be within the 20 closest peers.
While searching for the closest peers in the DHT, the client will _optimistically_ store the provider record with peers
and abort the query completely when the set of currently known 20 closest peers are also _likely_ the actual 20 closest
ones. This heuristic approach can significantly speed up the process, resulting in a speed improvement of 2x to >10x.

When it is enabled:

- DHT provide operations should complete much faster than with it disabled
- This can be tested with commands such as `ipfs routing provide`

**Tradeoffs**

There are now the classic client, the accelerated DHT client, and optimistic provide that improve the provider process.
There are different trade-offs with all of them. The accelerated DHT client is still faster to provide large amounts
of provider records at the cost of high resource requirements. Optimistic provide doesn't have the high resource
requirements but might not choose optimal peers and is not as fast as the accelerated client, but still much faster
than the classic client.

**Caveats:**

1. Providing optimistically requires a current network size estimation. This estimation is calculated through routing
table refresh queries and is only available after the daemon has been running for some time. If there is no network
size estimation available the client will transparently fall back to the classic approach.
2. The chosen peers to store the provider records might not be the actual closest ones. Measurements showed that this
is not a problem.
3. The optimistic provide process returns already after 15 out of the 20 provider records were stored with peers. The
reasoning here is that one out of the remaining 5 peers are very likely to time out and delay the whole process. To
limit the number of in-flight async requests there is the second `OptimisticProvideJobsPoolSize` setting. Currently,
this is set to 60. This means that at most 60 parallel background requests are allowed to be in-flight. If this
limit is exceeded optimistic provide will block until all 20 provider records are written. This is still 2x faster
than the classic approach but not as fast as returning early which yields >10x speed-ups.
4. Since the in-flight background requests are likely to time out, they are not consuming many resources and the job
pool size could probably be much higher.

For more information, see: https://protocollabs.notion.site/Optimistic-Provide-2c79745820fa45649d48de038516b814

### How to enable

```
ipfs config --json Experimental.OptimisticProvide true
```

### Road to being a real feature

- [ ] Needs more people to use and report on how well it works
- [ ] Should prove at least equivalent availability of provider records as the classic approach

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