Once data or cache entries are spread across several machines, every request has to answer one question: which machine holds this key? The obvious answer, hashing the key and taking the result modulo the number of machines, works until the number of machines changes. Then almost every key moves at once. Consistent hashing is a family of techniques that keeps most keys where they are when machines join or leave.
\nIt comes up in interviews whenever you shard a database, partition a distributed cache, or design something like a key-value store. The primer mentions it in passing, both as a way to cut rebalancing work in sharding and for routing in the query cache design. This chapter fills in how it works and where it falls short.
","headings":[]},{"id":"the-problem-with-hash-mod-n","title":"The problem with hash mod N","html":"With N servers, the simple scheme is server = hash(key) mod N. A good hash spreads keys evenly and the lookup is a single division.
Now add a server. A key stays put only if hash(key) mod N equals hash(key) mod (N+1), and for a uniform hash that happens for only about 1 in N+1 keys. Going from 4 servers to 5, about 20% of keys stay where they were and 80% move. Going from 10 to 11, about 91% move. Removing a server, or having one crash, is just as bad.
Ideally, adding a fifth server would move only the fifth of keys that the new server should now own, and nothing else.
\nWhat all that movement costs depends on what you're storing:
\nConsistent hashing, introduced by Karger and colleagues in 1997 for web caching (work that led to Akamai), hashes servers and keys into the same space. Picture the hash range bent into a circle. Each server sits at the position given by hashing its name, and a key belongs to the first server clockwise from the key's position.
\nA small example makes it concrete. Use a toy ring numbered 0 to 99 (real rings use 0 to 2^64 − 1) and three servers that hash to positions 10, 45 and 80:
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n| Server | Position | Owns positions |
|---|---|---|
| North | 10 | 81–99 and 0–10 (wrapping past the top) |
| East | 45 | 11–45 |
| West | 80 | 46–80 |
So a key hashing to 22 lives on East, 61 on West, and 93 wraps around to North. Now see what a change costs:
\nOn average, adding or removing one of N servers moves about 1/N of the keys, which is the minimum possible.
\nThe lookup is cheap. Keep the server positions in a sorted array, binary-search for the first position greater than the key's hash, and wrap to the start if you run off the end. That's O(log N), and the array is small enough to keep on every client.
\nThe plain ring has two problems:
\nThe fix for both problems is to give each physical server many positions on the ring, called virtual nodes or tokens. Hash \"A#0\", \"A#1\", … \"A#99\" instead of just \"A\", and every one of those positions points back to server A.
Here is the whole idea in Python, including the replica walk explained in the next section:
\nimport bisect, hashlib\n\ndef h(s: str) -> int:\n return int.from_bytes(hashlib.md5(s.encode()).digest()[:8], \"big\")\n\nclass Ring:\n def __init__(self, nodes, vnodes=100):\n self.points = sorted((h(f\"{n}#{i}\"), n) for n in nodes for i in range(vnodes))\n self.hashes = [p for p, _ in self.points]\n\n def owners(self, key: str, replicas: int = 1) -> list[str]:\n start = bisect.bisect_right(self.hashes, h(key)) # first point clockwise\n found = []\n for j in range(len(self.points)): # walk on, wrapping around\n node = self.points[(start + j) % len(self.points)][1]\n if node not in found: # skip a node's other vnodes\n found.append(node)\n if len(found) == replicas:\n break\n return foundVirtual nodes have costs too. The ring table grows to N × V entries, and every virtual node is a separate range to stream, repair and track. Cassandra used 256 tokens per node by default for years; version 4.0 lowered the default to 16 and paired it with an allocation algorithm that picks token positions for balance instead of at random, because hundreds of tokens per node made repair and streaming slower. The Dynamo paper describes a similar journey: Amazon moved from random tokens to splitting the ring into a fixed number of equal partitions and handing those out to nodes, which made balance predictable and data easier to move.
","headings":[{"id":"virtual-nodes","title":"Virtual nodes","depth":3}]},{"id":"replication-along-the-ring","title":"Replication along the ring","html":"The ring also tells you where to put copies. To store three copies of a key, start at the key's position and walk clockwise, taking the first three distinct physical servers you meet. Dynamo calls this list the key's preference list.
\nThe word \"distinct\" matters. With virtual nodes, the next few positions clockwise might all belong to the same machine, and three copies on one machine protect against nothing. The walk skips positions whose physical server is already in the list, which is what the if node not in found line above does. Real systems go further and spread replicas across racks or data centers; Cassandra's NetworkTopologyStrategy does this.
Replication along the ring pairs naturally with quorum reads and writes (read and write quorums that overlap), with read repair, and with Dynamo-style hinted handoff, where a write meant for a down replica is held by the next server on the ring until the replica returns. Those belong to replication and consistency more than to hashing; design a key-value store puts them together.
","headings":[{"id":"replication-along-the-ring","title":"Replication along the ring","depth":3}]},{"id":"hot-keys","title":"Hot keys","html":"Consistent hashing balances keys, not load. If one key gets a hundred times more traffic than the rest, such as a celebrity's profile or a flash-sale product, it lands on one server no matter how many virtual nodes you add. More nodes don't help either, because the hot key still maps to exactly one place.
\nCommon fixes, each with a cost:
\ncounter:42#0 through counter:42#9, which hash to different servers, and sum them on read. This suits counters and append-only data. Reads get more expensive, and you need to know in advance which keys are hot.hash-balance-factor setting. It suits load balancing, where any server can handle any request but you'd prefer the one with a warm cache.The ring isn't the only way to get minimal movement.
\nRendezvous hashing, also called highest random weight hashing (Thaler and Ravishankar, 1996), skips the ring. For each key, compute hash(key, server) for every server and pick the server with the highest score. Removing a server only moves the keys for which it scored highest. Balance is good without virtual nodes, and taking the top few scores gives you replica placement for free. The catch is that every lookup touches every server: O(N) work. That's fine for tens of servers and wasteful for thousands.
Jump consistent hash (Lamping and Veach, Google, 2014) is a few lines of arithmetic that map a 64-bit key and a bucket count N to a bucket number from 0 to N−1. It needs no memory, runs in O(log N) time and balances almost perfectly. The limitation is that buckets are numbered: you can grow or shrink only at the end. If bucket 3 of 10 disappears, you can't simply remove it. That suits storage where each \"bucket\" is a logical shard with its own replicas, so a machine failure is handled below the hashing layer. It doesn't suit a pool of cache servers where any one of them can die.
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n| Method | Lookup cost | State to keep | Balance | Remove any server? | Good fit |
|---|---|---|---|---|---|
| hash mod N | O(1) | none | good | moves almost all keys | fixed-size clusters |
| Ring + virtual nodes | O(log NV) | N × V positions | good with enough V | yes | Dynamo-style stores, cache clients |
| Rendezvous | O(N) | server list | good | yes | small server sets, picking replicas |
| Jump hash | O(log N) | none | near perfect | only the last bucket | numbered, replicated shards |
Who uses what:
\nhash … consistent directive uses ketama, and HAProxy has hash-type consistent.About 10 of every 11 keys now map to a different server, so roughly 91% of lookups miss at once, and the hit rate stays low until the caches refill. Nearly all of that traffic falls through to the database, which was sized for the old hit rate and may fall over. With a consistent hashing ring, only about 1/11 of keys would move.
\nAll of its range goes to the next server clockwise, which suddenly carries roughly two-thirds of the traffic while the third server's load doesn't change. With 100 virtual nodes per server, the failed server's ranges are scattered around the ring, so its load splits roughly evenly between the two survivors.
\nWith virtual nodes, consecutive ring positions can belong to the same machine. Without the skip, \"three replicas\" could mean three copies on one server, and losing that server would lose the data. Skipping repeats guarantees distinct machines. Production systems also skip to spread across racks or zones.
\nNo. Both rebalance how keys are spread, but this one key still maps to exactly one server. Put a short-lived in-process cache in front of the shared tier so most reads never leave the application server, or spread reads across the key's replicas. If the traffic is writes, such as a view counter, split the key into several sub-keys and sum them on read.
\nWhen your buckets are logical shards numbered 0 to N−1 that never vanish individually, because each shard is itself replicated, and you only grow by adding shards at the end. Then jump hash gives near-perfect balance with no table to store or sync. For a pool of cache servers where any one can fail, use a ring or rendezvous hashing instead.
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