Crates.io | tower-abci |
lib.rs | tower-abci |
version | 0.18.0 |
source | src |
created_at | 2022-12-05 22:09:40.167839 |
updated_at | 2024-11-18 21:18:57.971492 |
description | A `tower`-based interface to Tendermint's ABCI |
homepage | https://github.com/penumbra-zone/tower-abci |
repository | https://github.com/penumbra-zone/tower-abci |
max_upload_size | |
id | 730614 |
size | 162,601 |
An interface for ABCI built on Tower's Service
abstraction.
ABCI is the interface between Tendermint (a consensus engine for BFT
replication of a state machine), and an arbitrary application (the state
machine to be replicated). The ABCI interface consists of a set of requests
and responses the consensus engine makes to drive the application state.
Tower is a library of modular components for building networking clients
and servers. Tower defines a core abstraction, the Service
trait,
which represents an asynchronous function with backpressure, and then
provides combinators that allow generic composition of additional behavior,
e.g., timeouts, buffering, load-shedding, rate-limiting, instrumentation,
etc.
This crate uses Tower to define an asynchronous ABCI interface. It has two parts:
An ABCI server, which listens for connections and forwards ABCI requests
to one of four user-provided Service
s, each responsible for processing
one category of requests (consensus, mempool, info, or snapshot).
Middleware that splits a single Service
implementing all of ABCI
into four cloneable component services, each implementing one category of
requests. The component services use message-passing to share access to the
main service, which processes requests with the following category-based
prioritization:
ConsensusRequest
s sent to the Consensus
service;MempoolRequest
s sent to the Mempool
service;SnapshotRequest
s sent to the Snapshot
service;InfoRequest
s sent to the Info
service.Because the ABCI server takes one service per category, users can apply Tower
layers to the services they pass to the ABCI Server
to add
category-specific behavior, such as load-shedding, buffering, etc.
These parts can be combined in different ways to provide different points on the tradeoff curve between implementation complexity and performance:
At the lowest level of complexity, application developers can implement an
ABCI application entirely synchronously. To do this, they implement
Service<Request>
so that Service::call
performs request processing and
returns a ready future. Then they use split::service
to create four
component services that share access to their application, and use those to
construct the ABCI Server
. The application developer does not need to
manage synchronization of shared state between different clones of their
application, because there is only one copy of their application.
At the next level of complexity, application developers can implement an
ABCI application partially synchronously. As before, they implement
Service<Request>
to create a single ABCI application, but instead of
processing all requests in the body of Service::call
, they can defer
processing of some requests by immediately returning a future that will be
executed on the caller's task. Although all requests are still received by
the application task, not all request processing needs to happen on the
application task.
At this level the developer must pay closer attention to utilising Tower
layers to control the concurrency of the individual services mentioned above.
In particular the Consensus
service should be wrapped with
ServiceBuilder::concurrency_limit
of 1 to avoid a potential reordering of
consensus message effects caused by concurrent execution, as well as
ServiceBuilder::buffer
to avoid any deadlocks in message handling in Connection
due to the limited concurrency.
At the highest level of complexity, application developers can implement
multiple distinct Service
s and manually control synchronization of shared
state between them, then use these to construct the ABCI Server
.
Because these use the same interfaces in different ways, application developers can move gradually along this curve according to their performance requirements, starting with a synchronous application, then refactoring it to do some processing asynchronously, then doing more processing asynchronously, then splitting out one standalone service, then using entirely distinct services, etc.