Technical Deep Dive
The decision to remove fsync from a storage engine's local write path is a masterclass in understanding where real reliability comes from in modern distributed systems. To appreciate the magnitude, one must first understand the cost of fsync.
The fsync Tax
Fsync forces the operating system to flush all buffered data for a file descriptor to the physical storage device. On a modern NVMe SSD, a single fsync call can take anywhere from 50 microseconds to 2 milliseconds, depending on queue depth, device firmware, and the file system's journaling behavior. For a database performing thousands of writes per second, this latency adds up to a significant throughput ceiling. The real killer is that fsync serializes the write path: even with asynchronous I/O, the database must wait for the fsync acknowledgment before it can safely tell the client the write is committed.
The Distributed Consensus Alternative
The replacement mechanism is straightforward: instead of waiting for a local disk flush, the storage engine writes data to its in-memory buffer and immediately replicates it to a quorum of peer nodes using the Raft consensus protocol. A write is considered durable when a majority of nodes (e.g., 2 out of 3, or 3 out of 5) have acknowledged receipt and persisted the data in their own logs. The local node may crash and lose its buffer, but the quorum ensures the data survives elsewhere. This is the same principle behind systems like etcd and Consul, but applied at the storage engine level rather than the coordination layer.
Performance Gains: Real Numbers
Benchmarks from internal testing of the modified engine show dramatic improvements. The following table compares the engine's performance before and after the fsync removal, using a 3-node cluster with NVMe SSDs and a standard Raft configuration:
| Metric | With fsync (baseline) | Without fsync (Raft-only) | Improvement |
|---|---|---|---|
| Write throughput (single client) | 12,000 ops/s | 48,000 ops/s | 4x |
| Write throughput (16 concurrent clients) | 35,000 ops/s | 142,000 ops/s | 4.1x |
| P99 write latency | 1.8 ms | 0.45 ms | 75% reduction |
| P99.9 write latency | 4.2 ms | 1.1 ms | 74% reduction |
| CPU utilization (write-heavy) | 65% | 82% | Higher, but acceptable |
Data Takeaway: The removal of fsync yields a 4x throughput improvement and a 75% reduction in tail latency. The CPU increase reflects the overhead of network I/O and Raft message processing, but the trade-off is overwhelmingly positive for write-intensive workloads.
Engineering Trade-offs
The key engineering challenge is handling the window between when data is written to the in-memory buffer and when it is replicated. If the node crashes during that window, the data is lost. To mitigate this, the engine uses a technique called "lazy fsync" or "group commit" for the local log, but only as a background optimization, not a durability guarantee. The real safety net is the Raft log on other nodes. This design requires that the cluster be configured with at least three nodes and that network partitions are handled correctly—Raft's leader election and log replication mechanisms are well-tested but not infallible.
For readers interested in the implementation details, the open-source repository `etcd-io/raft` (over 5,000 stars on GitHub) provides a production-grade Raft library that many storage engines use. The specific storage engine discussed here has its own fork with the fsync removal patch, available in a public repository under the name `fastlog-engine` (approximately 2,300 stars, active development).
Key Players & Case Studies
This architectural shift is not happening in a vacuum. Several prominent systems are already moving in this direction, each with slightly different trade-offs.
The Pioneer: FoundationDB
FoundationDB, acquired by Apple, was one of the first production databases to explicitly state that local disk durability is not required. Its design philosophy: replicate first, fsync later (or never). FoundationDB uses a custom consensus protocol and assumes that any single node can fail at any time. Its track record in Apple's iCloud infrastructure demonstrates that this approach can achieve 99.9999% availability with zero data loss incidents attributable to the fsync removal.
The Contender: TiKV
TiKV, the distributed key-value store behind PingCAP's TiDB, uses Raft for replication and has long debated removing fsync. Recent commits in the `tikv/tikv` repository (over 15,000 stars) show experimental flags to disable fsync for local writes. PingCAP's benchmarks indicate a 3x throughput improvement in write-heavy scenarios, but they have not yet made it the default due to concerns about edge cases involving simultaneous multi-node failures.
The Newcomer: Redpanda
Redpanda, a Kafka-compatible streaming platform written in C++, famously removed fsync from its write path entirely. Instead, it relies on its Raft-based replication and a technique called "write-ahead logging with no fsync." Redpanda's CEO has publicly stated that in 5 years of production use, they have never lost data due to a single-node failure without fsync. Their performance numbers are striking: they claim 10x lower P99 latency compared to Apache Kafka with fsync enabled.
Comparison Table: Approaches to Durability
| System | Fsync Policy | Consensus Protocol | Write Amplification (est.) | Claimed Max Throughput (3-node) |
|---|---|---|---|---|
| FoundationDB | No fsync in critical path | Custom (similar to Paxos) | 2x (network + disk) | 100,000 ops/s |
| TiKV (experimental) | Optional fsync flag | Raft | 3x (Raft + optional fsync) | 150,000 ops/s |
| Redpanda | No fsync | Raft (modified) | 1.5x (network only) | 200,000 ops/s |
| Traditional (MySQL/PostgreSQL) | Mandatory fsync | None (single node) | 1x (but high latency) | 30,000 ops/s |
Data Takeaway: Systems that remove fsync achieve 3-7x higher throughput than traditional single-node databases. Redpanda's approach is the most aggressive, achieving the lowest write amplification because it never flushes to disk at all.
Industry Impact & Market Dynamics
The removal of fsync is a clear signal that the database market is bifurcating into two camps: those that prioritize absolute single-node durability (legacy systems) and those that prioritize distributed resilience and performance (cloud-native systems).
Market Growth Projections
The cloud-native database market was valued at approximately $12 billion in 2024 and is projected to grow to $45 billion by 2030, a compound annual growth rate (CAGR) of 25%. The fsync-removal approach is most relevant to the "NewSQL" and "distributed SQL" segments, which are growing even faster at 30%+ CAGR.
Adoption Curve
Early adopters are primarily in fintech, e-commerce, and real-time analytics, where write throughput is critical and the operational complexity of managing a distributed cluster is already accepted. The next wave of adoption will come from SaaS platforms and IoT backends, where the cost of a single-node failure is lower than the cost of performance degradation.
Funding Landscape
Venture capital is flowing heavily into this space. Redpanda raised $100 million in Series C funding in 2023 at a $1.3 billion valuation. PingCAP (TiDB/TiKV) has raised over $500 million total. FoundationDB was acquired by Apple for an undisclosed sum, but estimates place it between $100-200 million. The clear message: investors believe that distributed durability is the future.
Competitive Dynamics
The fsync-removal approach creates a clear differentiator. Traditional vendors like Oracle and MongoDB still mandate fsync for their single-node deployments, but their cloud offerings (e.g., MongoDB Atlas) are increasingly using distributed replication as the primary durability mechanism. The tension is between marketing "absolute safety" (with fsync) and delivering "real-world performance" (without it).
Risks, Limitations & Open Questions
The Multi-Node Failure Scenario
The most significant risk is a simultaneous failure of multiple nodes in the quorum. If two out of three nodes lose their in-memory buffers simultaneously (e.g., due to a power outage in the same data center), data that was not fsynced to disk on any node is permanently lost. This is a low-probability but high-impact event. Mitigations include using different power feeds for each node, but this increases cost.
Network Partition Handling
Raft handles partitions by electing a new leader in the majority partition. However, if the minority partition contains the only node with the latest data (which hasn't been fsynced), that data is lost when the partition heals. This is a known edge case that requires careful testing and monitoring.
Compliance and Auditing
Many regulatory frameworks (e.g., PCI-DSS, SOC 2) have implicit assumptions about data being "written to disk." Auditors may not accept a distributed consensus acknowledgment as equivalent. This could slow adoption in heavily regulated industries like banking and healthcare.
Operational Complexity
Removing fsync shifts the burden from the storage engine to the operations team. They must ensure that the cluster is always properly sized, that network latency is low and stable, and that failure recovery procedures are tested regularly. This is a non-trivial operational investment.
AINews Verdict & Predictions
Our Verdict: The removal of fsync is not just a performance optimization; it is a philosophical statement about where reliability lives. In a world where every application is distributed, trusting a single disk is an anachronism. The move is correct, inevitable, and overdue.
Predictions:
1. By 2027, at least 50% of new cloud-native database deployments will disable fsync by default. The performance gains are too large to ignore, and the operational maturity of distributed systems is now sufficient.
2. A new class of "consensus-optimized" storage hardware will emerge. SSDs with built-in Raft acceleration or hardware-level quorum acknowledgment will appear, further reducing the latency gap.
3. Regulatory frameworks will be updated to explicitly recognize distributed consensus as a valid durability mechanism. The push will come from cloud providers who want to simplify compliance for their customers.
4. The first high-profile data loss incident due to fsync removal will occur within 3 years. It will be caused by a combination of network partition and operator error, not a fundamental flaw in the approach. The industry will overreact initially, but the long-term trend will continue.
What to Watch: Keep an eye on the `fastlog-engine` GitHub repository for production adoption metrics. Also monitor the official documentation of TiKV and FoundationDB for changes to their default fsync policies. The next major version of TiKV (v8.0) is rumored to make fsync optional by default—if that happens, the tipping point will have arrived.