Braft, also known as Baidu Braft, is an open-source C++ implementation of the Raft consensus algorithm. Developed within Baidu, it is designed to help engineers build highly available distributed systems that require reliable coordination, replicated data, and fault tolerance. The project is built around the Raft consensus protocol and uses brpc as a key dependency for communication between distributed nodes.
Braft is intended for applications that need to maintain consistent state across multiple servers. Rather than requiring developers to implement consensus mechanisms from scratch, it provides a library that handles important distributed-systems operations, including leader election, log replication, recovery, and membership management.
The project is particularly relevant to developers working on distributed storage systems, database infrastructure, metadata services, and other applications in which service availability and consistency are important.
Understanding the Raft Consensus Algorithm
To understand Braft, it helps to understand Raft. Raft is a consensus algorithm that enables a group of computers, called nodes, to agree on a sequence of operations even when some nodes experience failures or become temporarily unavailable.
Distributed systems frequently store or process information across multiple machines. These machines must coordinate their actions so that they do not independently apply conflicting updates. Without a reliable coordination mechanism, different nodes could maintain inconsistent versions of the same data.
Raft addresses this problem through a structured approach to leadership, log replication, and commitment.
A Raft group generally operates with three important concepts:
- Leader: The node responsible for coordinating updates and replicating log entries.
- Followers: Nodes that receive replicated entries from the leader and participate in the consensus process.
- Candidate: A node that seeks leadership during an election when the existing leader is unavailable or a new election is required.
In a typical cluster, a leader receives operations, records them in a log, and replicates them to other nodes. An operation becomes committed when the protocol’s requirements are satisfied, including agreement from a majority of the cluster. The participating state machines can then apply committed operations in a consistent order.
Braft implements this consensus model in C++, providing developers with an established foundation for building distributed applications.
Key Features of Baidu Braft
1. Leader Election
Leader election is a central feature of Braft. A distributed system needs a reliable way to select a node responsible for coordinating updates.
If the current leader becomes unavailable, the remaining nodes can initiate an election to select a replacement, provided the cluster can satisfy the protocol’s voting requirements.
This mechanism helps distributed applications continue operating when individual servers fail. It also reduces the need for developers to create their own leader-selection logic.
2. Log Replication
Braft supports the replication of operations across participating nodes. The leader coordinates the replication of log entries so that committed operations can be applied consistently by the replicated state machines.
Log replication is important for applications such as distributed databases, storage systems, and coordination services. By maintaining replicated logs, these systems can preserve a consistent sequence of operations across multiple machines.
3. Recovery and Fault Tolerance
Distributed systems must account for server restarts, network interruptions, and other failures. Braft includes replication and recovery capabilities intended to help nodes recover and rejoin normal operation.
Its fault-tolerance features support applications that need to remain reliable despite individual node failures. However, the actual availability of a deployment depends on factors such as cluster configuration, network connectivity, storage reliability, and the number of functioning nodes.
A consensus library cannot guarantee uninterrupted service under every failure condition. For example, a cluster that loses its ability to communicate with a majority of its members may be unable to commit new operations.
4. Snapshots and Log Compaction
As a distributed application processes operations, its log can grow over time. Retaining every historical entry indefinitely can increase storage requirements and recovery costs.
Braft supports snapshots and log compaction to help manage this growth. A snapshot captures a representation of the state machine’s state at a particular point. Once the relevant snapshot and recovery requirements are satisfied, older log entries can potentially be compacted.
These mechanisms help manage persistent data and support the recovery of replicas without requiring the entire operation history to be replayed in every situation.
5. Cluster Membership Management
Distributed applications sometimes need to change the composition of a cluster by adding or removing participating nodes.
Braft includes membership-management capabilities to support changes to the group of consensus participants. Such operations must be handled carefully because the participating nodes need to maintain the protocol’s safety guarantees during configuration changes.
This functionality is useful when infrastructure needs to expand, servers need to be replaced, or a distributed service must adapt to changing operational requirements.
6. Concurrent Replication
Braft is designed to support concurrent replication and demanding distributed workloads. Its implementation aims to balance performance with the correctness requirements of consensus.
Concurrency is particularly important for applications that manage multiple replicated groups or process many operations simultaneously. Developers must still evaluate actual throughput and latency under their own workloads rather than assuming that every deployment will achieve the same performance.
Applications and Use Cases of Braft
Baidu’s project documentation identifies several categories of systems in which Braft can be used, including storage systems, SQL storage infrastructure, distributed transactions, and metadata services.
Distributed Storage Systems
Storage platforms may replicate data or coordinate operations across multiple servers. Braft can provide the consensus layer needed to coordinate updates and maintain consistent state among participating replicas.
Potential applications include key-value storage, block storage, object storage, and distributed file-related infrastructure.
Database Infrastructure
Database systems often require reliable coordination when multiple machines participate in storing or managing data. Braft can help developers implement consensus-backed components for database infrastructure.
Its documented use cases include highly available MySQL clusters, NewSQL systems, and distributed transaction-related infrastructure. Braft itself should not be confused with a complete database: it supplies consensus functionality that can be incorporated into a larger application.
Metadata and Coordination Services
Distributed storage and computing platforms frequently maintain metadata describing resources, configurations, ownership, and service state.
Because incorrect or conflicting metadata can affect an entire system, these services benefit from consistent replicated state. Braft can be used to build highly available metadata services and other coordination components.
High-Availability Services
Applications that depend on multiple servers can use consensus mechanisms to coordinate critical operations and recover from certain node failures.
Braft is therefore relevant to engineers developing distributed services where reliable state replication and coordinated decision-making are essential.
How Braft Fits into a Distributed Application
Braft is a library rather than a complete, ready-to-run distributed application. Developers integrate it into their own systems and implement the application-specific logic.
A typical integration involves several components:
- Application service: Receives client requests and defines the operations the application needs to perform.
- Braft consensus group: Coordinates leadership, log replication, and consensus among participating nodes.
- Replicated state machine: Applies committed operations to maintain the application’s state.
- Persistent storage: Stores the information needed for logs, Raft metadata, and snapshots.
- Communication layer: Enables nodes to exchange messages and coordinate distributed operations.
Braft is built around brpc, Baidu’s C++ RPC framework. Its documentation describes how Braft services can be integrated into a brpc server. Developers must also implement the application-specific state machine and configure storage and cluster settings appropriately.
The precise architecture depends on the application being developed and the requirements of its distributed environment.
Getting Started with Braft
Developers can explore Braft through its public source repository and official documentation. The project includes source code, examples, tests, build configuration, and documentation covering its core functionality.
The documented setup process includes building the brpc dependency and compiling Braft with CMake. The repository also describes installation through the vcpkg package manager.
Example applications include:
- Counter: Demonstrates a replicated integer value that can be incremented.
- Atomic: Demonstrates an integer supporting exchange and compare-and-exchange operations.
- Block: Demonstrates a block-device-related example that supports concurrent random reads and writes.
These examples help developers understand how the library can be integrated into an application and how operations can be coordinated across a group of servers.
Before adopting Braft, developers should review the current repository instructions, dependency requirements, supported build environment, and configuration guidance. Production deployments also require appropriate monitoring, testing, backup and recovery procedures, and operational planning.
Benefits and Considerations
Braft offers several potential benefits for developers building distributed infrastructure.
Established consensus implementation: It provides an implementation of Raft, reducing the need to develop consensus logic independently.
C++ integration: It is suitable for applications whose core components are written in C++.
Distributed-system capabilities: Leader election, replication, recovery, snapshots, and membership management provide building blocks for highly available services.
Open-source availability: Its source code and Apache 2.0 licence allow developers to inspect and evaluate the implementation under the licence’s terms.
Nevertheless, Braft introduces considerations that should be assessed before deployment. Developers need to understand Raft’s quorum requirements, configure persistent storage correctly, handle application-level errors, and test failure scenarios. Integration also requires familiarity with C++, the library’s interfaces, and its dependencies.
The availability and performance of a finished system depend on the implementation as a whole, not solely on the consensus library.
Conclusion
Braft is Baidu’s open-source C++ implementation of the Raft consensus algorithm, designed to help developers build reliable and highly available distributed systems. By providing capabilities such as leader election, log replication, recovery, snapshots, and cluster membership management, it offers a foundation for applications that need coordinated and consistent state across multiple servers.
Its potential use cases include distributed storage, database infrastructure, metadata services, and other consensus-dependent components. For developers working with C++ and distributed systems, Braft is a project worth exploring when a proven consensus implementation is needed as part of a larger application.