Move the consistent hash ring implementation (FNV-1a, virtual nodes, bisect lookup) from code to docs/design/consistent-hash-ring.md as a forward-looking reference for future scalability work. The current rebalancer uses weight-proportional distribution (simpler, exact splits, no hash variance). The ring algorithm is documented with test vectors, stability properties, and a comparison table for when the ring approach becomes advantageous (large clusters, decentralized routing, cross-language determinism). hash_ring.py retains: RING_SIZE, bucket_of(), RingNode, NoAvailableNodeError (all actively used by router and rebalancer).
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Consistent Hash Ring — Reference Design
Status: Reference (not currently in the hot path) Date: 2026-03-30
Overview
This document describes a consistent hash ring algorithm evaluated during
the design of the direct HTTP transport routing system. The current
implementation uses weight-proportional bucket assignment with a
donor/recipient rebalancing algorithm (see direct-http-transport.md).
The consistent hash ring is documented here as a reference for future
scalability work — if the cluster grows beyond the point where the
weight-proportional approach is sufficient, the ring provides a
proven alternative with stronger stability guarantees.
When to consider the ring approach
The current weight-proportional seeding + donor/recipient rebalancer works well when:
- Cluster size is moderate (< 50 nodes)
- Nodes join/leave infrequently
- The rebalancer runs centrally (in the console)
The consistent hash ring becomes advantageous when:
- Cluster size grows large (50+ nodes) and frequent membership changes cause the donor/recipient algorithm to churn
- Decentralized routing is needed (each node computes the ring locally, no central console required)
- Cross-language determinism is important (multiple implementations must agree on the same assignment without sharing state)
Algorithm
Hash function: FNV-1a (32-bit)
def fnv1a_32(data: bytes) -> int:
"""FNV-1a 32-bit hash.
Basis: 0x811C9DC5, Prime: 0x01000193.
XOR each byte, then multiply by prime (masked to 32 bits).
"""
h = 0x811C9DC5
for b in data:
h ^= b
h = (h * 0x01000193) & 0xFFFFFFFF
return h
Known test vectors:
fnv1a_32(b"")=0x811C9DC5(basis value)fnv1a_32(b"foobar")=0xBF9CF968
Cross-language implementations:
- Python: loop above (no dependencies)
- Go: same algorithm with
uint32arithmetic - TypeScript: same algorithm with
>>> 0for unsigned 32-bit
Virtual nodes
Each physical node with weight w gets w * 150 virtual positions on a
16-bit ring (65536 positions). Virtual node i of physical node N is
placed at:
position = fnv1a_32(f"{N.node_id}:{i}".encode()) % 65536
With 150 vnodes per unit weight:
- 2 equal-weight nodes: ~50/50 split (measured: 38-62% range due to hash variance, stddev ~3% with large vnode counts)
- 3 nodes at weights 2:1:1: ~50/25/25 (within 10% tolerance)
Lookup
def owner(bucket: int) -> str:
"""O(log n) bisect-right walk to find the next virtual node clockwise."""
idx = bisect_right(positions, bucket)
if idx >= len(positions):
idx = 0 # wrap around
return vnode_map[positions[idx]]
Stability properties
The consistent hash ring guarantees:
- Node addition: adding a node moves at most
1/Nof buckets (where N is the new node count). Other nodes' buckets are unaffected. - Node removal: only the removed node's buckets are reassigned. Buckets owned by surviving nodes don't move.
- Determinism: same membership list always produces the same ring. No coordination needed between processes.
Full assignment precomputation
def assignments() -> list[tuple[int, str]]:
"""Compute all 65536 bucket-to-node mappings."""
return [(b, owner(b)) for b in range(65536)]
This produces a complete assignment table that can be loaded into a flat array for O(1) request-time lookup. The ring itself is never consulted on the hot path.
Data structures
@dataclass(frozen=True, slots=True)
class RingNode:
node_id: str
url: str
weight: int = 1
class HashRing:
"""Immutable consistent hash ring. Thread-safe (no mutable state)."""
def __init__(self, nodes: Sequence[RingNode], vnodes_per_unit: int = 150):
# Validate no duplicate node_ids
# Build sorted array of (position, node_id) tuples
# positions[i] = fnv1a_32(f"{node_id}:{i}".encode()) % RING_SIZE
def owner(self, bucket: int) -> RingNode | None:
# bisect_right + wrap
@property
def version(self) -> int:
# Deterministic hash of membership: fnv1a_32 of sorted node_id:weight pairs
def assignments(self) -> list[tuple[int, str]]:
# Precompute all 65536 bucket assignments
Comparison with current approach
| Aspect | Weight-proportional (current) | Consistent hash ring |
|---|---|---|
| Seeding | Exact weight split, deterministic | Hash-based, ~3% variance |
| Node addition | Donor/recipient moves only excess | Ring moves ~1/N buckets |
| Node removal | Dead buckets → most underloaded | Ring redistributes to clockwise neighbors |
| Cross-node churn | Zero (only donor→recipient) | Zero (ring stability guarantee) |
| Decentralized | No (needs central rebalancer) | Yes (each node computes locally) |
| Complexity | Simple weight arithmetic | Virtual node construction + bisect |
Test vectors
For cross-language implementation validation:
{
"fnv1a_32": [
{"input": "", "output": 2166136261},
{"input": "foobar", "output": 3215766888}
],
"bucket_of": [
{"ws_id": "a3f100000000000000000000000000000", "bucket": 41969},
{"ws_id": "00000000000000000000000000000000", "bucket": 0},
{"ws_id": "ffff0000000000000000000000000000", "bucket": 65535}
],
"ring_single_node": {
"nodes": [{"node_id": "n1", "weight": 1}],
"vnodes_per_unit": 150,
"expected_n1_buckets": 65536
}
}