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To build a small HDFS-style distributed file system in Go, separate metadata decisions from block storage: a coordinator tracks files, blocks, and storage-node health; storage nodes persist and serve blocks; and clients ask the coordinator for a plan before transferring file data directly to storage nodes. That is an architectural adaptation of HDFS, not Hadoop wire compatibility. Start with one coordinator, one storage node, immutable files, and a single writer; add replication and stronger metadata availability only after the basic write, read, and recovery behavior is testable.
What the system needs to do
A distributed filesystem is more than a way to split a file across disks. It must keep a consistent account of which blocks make up each file, where those blocks live, whether a write is complete, and what to do when a node or connection fails.
HDFS provides a useful architectural reference. Its NameNode manages the filesystem namespace and block mappings, while DataNodes store and serve blocks. The client’s user data does not pass through the NameNode in the documented design. DataNodes send heartbeats to signal that they are functioning and block reports to describe the blocks they hold. The design below borrows those roles without claiming to implement HDFS itself.
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Three roles, with distinct responsibilities
- Metadata coordinator: owns directory and file metadata, ordered block IDs, block locations, replication targets, and node-liveness state. It answers metadata and placement requests; it should not be in the file-data path.
- Storage node: writes and reads block data on local disk, checks block integrity, reports its inventory, and performs replication or deletion only when authorized.
- Client: resolves a path through the coordinator, then transfers blocks directly to or from storage nodes.
This split keeps large data transfers from making the coordinator a proxy bottleneck. The coordinator still matters to file operations: it decides which blocks belong to a file and which nodes may serve or receive them.
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Choose a small, explicit metadata model
Represent the facts needed to find and validate a file, and make incomplete writes distinguishable from committed files. HDFS documentation establishes the namespace, block, and mapping concepts; the exact schema here is a project design choice.
- File: stable file identity, path, ordered block IDs, and a lifecycle state such as pending or committed.
- Block: stable block ID, byte length, and checksum.
- Replica: the storage-node identities that report a copy of a block, along with enough status to distinguish usable locations from stale or unavailable ones.
- Storage node: identity, last-seen liveness information, and reported inventory.
Keep the order of block IDs in file metadata. A set of block locations alone cannot reconstruct the byte order of a file. Treat a path as namespace metadata rather than as a storage address: blocks should be identified independently so that a path change does not require renaming data on every node.
Define the state transitions before adding concurrency. For example, a file can move from pending to committed only after the chosen write policy is satisfied and the coordinator has recorded the final block mapping. Specify what happens to pending metadata and orphaned blocks after an interrupted write; do not silently expose a partially uploaded file as complete.
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The coordinator should return a plan, not accept the block payload itself. This is the key flow that preserves the separation between control-plane metadata and user-data transfer.
Writing a file
- Create: the client asks the coordinator to create a path. The coordinator checks namespace rules and returns a pending file identity and a write plan.
- Choose blocks: the client divides the input into blocks according to the project’s configured block-size policy and obtains a destination or replication plan for each block. Do not present a block size as an HDFS default unless the implementation actually adopts a documented HDFS configuration.
- Transfer: the client sends block data to the selected storage node, or to the first node in a replication pipeline if the project implements one. Storage nodes persist the block and compute or verify its length and checksum.
- Acknowledge: each node returns an acknowledgement only after the persistence condition defined by the project has been met. State whether success means local write completion, synchronized data, or another explicit condition; do not leave “written” ambiguous.
- Commit: after required acknowledgements arrive, the client asks the coordinator to commit. The coordinator records the block order, lengths, checksums, and replica locations, then makes the file visible as committed.
These steps are a suggested project protocol, not a claim that they reproduce HDFS’s internal RPC sequence. For a first version, immutable files and one writer at a time are sensible scope limits. HDFS documentation describes write-once files with one writer at a time, while current-generation architecture also allows append and truncate exceptions. Supporting those exceptions or concurrent writers requires additional rules for ordering, partial updates, and recovery.
Reading a file
- The client asks the coordinator to resolve the path and receives committed file metadata, ordered block IDs, lengths, checksums, and available locations.
- For each block in order, the client selects an available replica and fetches the data directly from its storage node.
- The client verifies the received block against its expected length and checksum before assembling it into the file stream.
- If a transfer fails or a checksum does not match, the client retries from another reported replica when one is available. It should report failure rather than return corrupted or incomplete content if no valid copy can be read.
Checksums and retry behavior are implementation recommendations for this project design. The architecture reference establishes the block and location model, not a particular Go client protocol or checksum algorithm.
Define replication and acknowledgements as policy
HDFS allows applications to choose replication factor and block size per file. Its NameNode makes replication decisions and monitors DataNodes through heartbeats and block reports. Replica placement is a policy with reliability, availability, and network-use trade-offs, not just a copy count.
Before implementing replication, decide what “write succeeded” means. If a file requires multiple replicas, the project must specify how many successful acknowledgements are necessary before commit. It must also define whether nodes receive a block directly from the client or forward it to the next node in a pipeline. A pipeline can reduce repeated client uploads, but creates extra failure and acknowledgement cases. The coordinator should record the policy outcome, not infer success merely because a client disconnected after sending data.
A fixed number of copies does not guarantee availability under every failure pattern. Copies placed in the same machine, power domain, or network segment can fail together. HDFS documentation discusses rack-aware placement; a local educational cluster may have no rack topology, but the design should still distinguish replica count from failure-domain diversity.
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Plan for node loss, retries, and restart recovery
Distributed behavior becomes meaningful when operations are interrupted. Specify each case as a state transition and test it, rather than relying on a happy-path demo.
- Heartbeat expires: mark a node unavailable for new placement or reads according to a defined timeout policy. A timeout is evidence that the coordinator has not heard from the node; it does not prove that every block on it is permanently lost.
- Missing replicas: compare committed block metadata with inventories from live nodes. When a block falls below its target, schedule an authorized copy from a surviving replica and update metadata after the replacement is confirmed.
- Node restarts: have the node report its local inventory. Reconcile that report with coordinator state before treating its blocks as valid locations. Define what happens to unknown local blocks and metadata entries that refer to missing data.
- Client loses connection before commit confirmation: let the client query the operation or file state using a stable request or file identifier. It must be possible to distinguish a committed write from a pending one without blindly creating duplicates.
- Duplicate requests: make create, commit, replication, and deletion operations idempotent where practical. Repeating a request should not create conflicting metadata or accidentally remove a valid replica.
- Stale replicas: issue deletion only after the coordinator has decided the copy is no longer needed and the node can verify the target block identity. Avoid deleting based only on an old inventory snapshot.
- Disk or network errors: return explicit failures, apply bounded retries where appropriate, and preserve enough state to recover or clean up an incomplete operation.
Replication repairs and stale-copy cleanup are control-plane tasks with data-plane effects. Make their authorization and completion visible in the protocol so a delayed or repeated command cannot silently corrupt the replica set.
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A single coordinator is the clearest first milestone, but it is a control-plane failure point. If it stops, storage nodes may still contain blocks, yet clients cannot reliably resolve paths or obtain current placement decisions. Persisting coordinator state improves restart recovery; it does not make the coordinator highly available while it is down.
Best Value
| Scope | What it gives you | What it does not give you |
|---|---|---|
| One coordinator with restart persistence | A simpler metadata state machine and a practical way to test recovery after a process restart. | Availability during coordinator failure; a durable local state file alone is not replicated metadata. |
| Replicated metadata with consensus | A path to coordinated metadata decisions across replicas, if the state machine and durable log are correctly integrated. | Automatic filesystem correctness just by adding a Raft library. Transport, persistence, snapshots or recovery, membership, and the relationship between metadata commits and block operations still need design. |
The etcd Raft Go package describes Raft as a replicated-state-machine protocol and leaves network transport and disk I/O to its users. Users must persist required entries before sending messages and apply committed log entries to application state. A filesystem using consensus therefore needs a durable metadata state machine, peer transport, membership handling, recovery behavior, and safe coordination between metadata commits and block operations. Consensus does not make the storage nodes’ block writes atomic with the metadata log.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep Go concurrency and cancellation understandable
Use context.Context to carry cancellation and deadlines through request handlers, RPC calls, replication work, and storage operations. Derived contexts can propagate cancellation, and Go contexts are safe for simultaneous use by multiple goroutines. A goroutine started for a request or background task should have a clear cancellation path and release resources when it exits.
Choose an ownership model for mutable metadata before writing handlers. One option is a coordinator loop that owns state and accepts requests over channels. Another is shared state protected by carefully scoped mutexes. Go’s Effective Go guidance states, “Do not communicate by sharing memory; instead, share memory by communicating,” but that is guidance rather than a prohibition on mutexes. Either model can work if updates, reads, and state transitions have clear synchronization rules.
Make disk and network operations fallible in the interfaces. Pass contexts where cancellation is meaningful; return errors that distinguish missing blocks, checksum failures, timeouts, and storage failures; and define retry and idempotency behavior at the protocol boundary. These are design recommendations for a distributed service, not requirements imposed by the Go context documentation.
Build in stages and validate failure behavior
- Model metadata and local storage: define file, block, replica, and pending-write state, plus an interface for writing and reading a local block.
- Prove the basic path: run one coordinator and one storage node. Store a file split across blocks, then read it through client-to-node transfers rather than routing payloads through the coordinator.
- Add node inventory and liveness: start multiple storage nodes, implement heartbeats and block reports, and make the coordinator’s view of node state observable.
- Add replication and commit rules: choose a replication target and acknowledgement policy, then test that a file is not committed before that policy is met.
- Exercise failure cases: test node loss, node restart, duplicate operations, interrupted uploads, disk errors, checksum mismatch, and coordinator restart.
- Consider metadata replication: only after the single-coordinator state machine and its recovery behavior are understandable should you add consensus and peer coordination.
Use the Go toolchain’s go test ./... to run package tests across the module. Unit tests can cover metadata transitions and idempotency deterministically; integration tests should start multiple nodes or processes and exercise failures between protocol steps. Those integration scenarios are project validation recommendations, not a test methodology prescribed by the Go testing documentation.
Do not describe the result as durable, fast, or production-ready without measurements and failure testing. A teaching implementation can demonstrate the architecture while omitting requirements such as security, operations, upgrades, and compatibility that a production system would need.
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