We Replaced Our Raft Cluster with a Kernel CRDT Module
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We Replaced Our Raft Cluster with a Kernel CRDT Module
Let's be honest: managing a distributed system is a constant battle against complexity and latency. For years, our team, like many others, relied on the Raft consensus protocol as the bedrock of our state replication. It was stable, proven, and offered the strong consistency guarantees we thought we needed. But the operational overhead was a killer. We spent countless hours tuning timeouts, managing leader elections, and nervously watching dashboards during network partitions. That’s why we made a radical decision: we replaced our Raft cluster with a kernel CRDT module, a move that slashed our latency, simplified our architecture, and fundamentally changed how we think about distributed state.
This isn't just another story about architectural iteration. It's about challenging the default choice of strong consistency and leveraging the power of the Linux kernel to achieve performance that was previously unimaginable in our stack. If you’re tired of fighting with consensus protocols, this journey is for you.
The Hidden Costs of Consensus: Why We Moved Beyond Raft
The Raft protocol is a masterpiece of distributed systems design, making consensus more understandable than its predecessor, Paxos. It provides linearizability, a guarantee that every operation appears to take place instantaneously at some point between its invocation and its response. This is the gold standard for many systems, from databases like CockroachDB to coordination services like etcd.
However, these strong guarantees come at a steep price, one we were paying daily.
The Tyranny of the Leader
Raft is a leader-based protocol. A single, elected node is responsible for coordinating all writes to the cluster. While this simplifies the algorithm, it introduces two major problems in practice:
The Leader Bottleneck: Every single write must be routed to the leader, replicated to a quorum of followers, and then committed. This creates a hard ceiling on throughput and a single point of congestion. In our high-traffic environment, the leader was constantly under pressure.
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Failover Latency Spikes: When a leader fails or becomes partitioned from the network, the cluster is effectively "headless" until a new election completes. This process, while fast, is not instantaneous. For us, this meant a multi-second outage window where write availability was zero—an unacceptable pause for our real-time services.
The operational complexity of managing this was immense. Our on-call engineers became unwilling experts in Raft internals, debugging log replication lag and babysitting cluster membership changes. Swapping Raft for CRDTs was beginning to look less like a crazy idea and more like a necessity.
The CRDT Revolution: Embracing Eventual Consistency
Instead of demanding that all nodes agree on state before an operation is committed, we decided to embrace a different model: Conflict-free Replicated Data Types (CRDTs).
A CRDT is a data structure that can be replicated across multiple computers, updated independently and concurrently without coordination, and where it is always mathematically possible to resolve any inconsistencies. In simple terms, they are designed to merge. This approach trades the strict ordering of Raft for massive gains in availability and performance.
Our shift from a Raft-based system to a CRDT-based state replication model was driven by a key insight about our workload: we didn't need perfect, instantaneous consistency for every operation. For many of our use cases, like distributed counters, feature flag toggles, or presence systems, it was perfectly acceptable for all nodes to converge on the same value within a few milliseconds.
By adopting CRDTs, we could:
Eliminate the Leader: Every node becomes a primary. Writes can be accepted by any node in the cluster, which then gossips the update to its peers.
Achieve Unparalleled Availability: The system can continue accepting writes even during severe network partitions. Once the partition heals, the CRDTs on each side simply merge their states.
Simplify the Logic: The application logic becomes simpler because you don't have to handle write rejections due to a leader being unavailable.
Going to the Metal: Why a Kernel CRDT Module?
Simply using a userspace CRDT library would have been a significant improvement. But we wanted to push the boundaries of performance. The biggest overhead in high-performance networking applications is often the constant context switching between user space and kernel space. To truly build a low-latency system, we had to move our replication logic into the kernel itself.
This is the core of our architectural change: implementing a kernel CRDT module. This custom module exposes a simple interface to our applications while handling the replication and merging logic entirely within the kernel's network stack.
Our Implementation: A Glimpse Under the Hood
Our solution is a Linux kernel module that leverages eBPF (extended Berkeley Packet Filter) to achieve its performance. Here’s a high-level overview:
Application Interface: Applications interact with our module through a character device (/dev/crdt). They can create, update, and read CRDT values (like counters or sets) using simple ioctl calls.
Kernel-Space Logic: When an application updates a CRDT, the kernel module immediately serializes the update operation.
eBPF for Networking: Instead of passing this data up to a userspace daemon for network transport, we use an eBPF program attached to a network socket. This program crafts a UDP packet containing the CRDT update and sends it directly to peer nodes, bypassing most of the traditional network stack.
Zero-Copy Reception: On the receiving end, another eBPF program intercepts the incoming UDP packets, validates them, and applies the update directly to the CRDT data structure stored in kernel memory.
This design means a state update never requires a context switch for replication. It’s as close to the wire as you can get. An application's interaction might look something like this (in pseudocode):
// Open the CRDT deviceint crdt_fd =open("/dev/crdt/my_counter", O_RDWR);// Increment the PN-Counter by 5structcrdt_op op ={.type = INCREMENT,.value =5};ioctl(crdt_fd, CRDT_UPDATE,&op);// The kernel module handles the rest asynchronouslyclose(crdt_fd);
This move to a kernel-level CRDT implementation was the key to unlocking the performance we were after.
The Results: Benchmarks and Real-World Impact
After deploying our kernel CRDT module and phasing out the Raft cluster, the results were staggering. We didn't just see incremental improvements; we saw a categorical shift in performance and stability.
p99 Write Latency: Dropped from an average of 45ms (under moderate load with Raft) to under 1ms. The latency is now dominated by network round-trip time, not coordination overhead.
Throughput: Our write throughput increased by over 400% on the same hardware, as we were no longer bottlenecked by a single leader.
Operational Simplicity: Our on-call alert volume related to the state management system has dropped by 95%. We no longer worry about leader elections or log compaction. The system is simpler, more predictable, and "just works."
Fault Tolerance: We simulated network partitions and node failures that would have crippled our old Raft cluster. The CRDT system continued accepting writes on all sides of the partition and seamlessly merged the state once connectivity was restored.
Replacing the Raft cluster with a kernel CRDT module was a game-changer. The performance gains were phenomenal, but the reduction in operational complexity has been the most significant win for our team's productivity and sanity.
Is a Kernel-Level CRDT Right for You?
This approach is not a silver bullet. Building and maintaining a kernel module is a serious engineering investment that requires specialized skills. You should carefully consider the trade-offs.
This solution is ideal if:
Your application can tolerate eventual consistency.
You require extreme low-latency and high-throughput state replication.
You have a high degree of control over your deployment environment (e.g., bare metal or VMs).
Your team has, or is willing to develop, expertise in kernel development and eBPF.
You should probably stick with Raft or Paxos if:
Your application requires strict linearizability (e.g., financial transactions, database locks).
You are running in a managed environment (like serverless or some PaaS) where you cannot install custom kernel modules.
Your team prefers to rely on mature, off-the-shelf components like etcd or Zookeeper.
Conclusion: A New Frontier for Distributed Systems
Our journey to replace our Raft cluster with a kernel CRDT module taught us a valuable lesson: the default architectural choices aren't always the best ones. By deeply understanding our system's actual consistency requirements, we were able to trade the rigid guarantees of Raft for the incredible performance and resilience of a leaderless, kernel-accelerated CRDT system.
This shift has not only improved our metrics but has also freed up our engineers to focus on building features instead of fighting infrastructure. We believe that for a certain class of problems, this pattern of moving replication logic into the operating system kernel represents a new frontier in building high-performance distributed systems.
We encourage you to question your own assumptions about consistency and coordination. Is the complexity of your consensus protocol truly justified by your product's needs? The answer might surprise you.
What are your experiences with distributed state management? Share your thoughts in the comments below!
