Scaling a High-traffic Rate Limiting Stack With Redis Cluster

Redis is the often unspoken workhorse of production. It’s not often used as a primary data store, but it has a sweet spot in storing and accessing ephemeral data whose loss can be tolerated – metrics, session state, caching – and it does so fast, providing not only optimal performance, but efficient algorithms on a useful set of built-in data structures. It’s one of the most common staples in the modern technology stack.

Stripe’s rate limiters are built on top of Redis, and until recently, they ran on a single very hot instance of Redis. The server had followers in place for failover, but at any given time, one node was handling every operation.

You have to admire a system where this is even possible. Various sources claim that Redis can handle a million operations a second on one node – we weren’t doing that many, but we were doing a lot. Every rate limiting check requires multiple Redis commands to run, and every API request passes through many rate limiters. One node was handling on the scale of tens to hundreds of thousands of operations per second 1.

The node’s total saturation was leading to us seeing an ambient level of failures happening around the clock. Our services were written to tolerate Redis unavailability so most of the time this was okay, but the severity of the problem would escalate under certain conditions. We eventually solved it by migrating to a 10-node Redis Cluster. Impact on performance was negligible, and we now have an easy knob to turn for horizontal scalability.

The before and after error cliff 2:

Errors subsiding after a transition to Redis Cluster.

The limits of operation

Before replacing a system, it’s worth understanding the cause and effect that led the original to fail.

A property of Redis that’s worth understanding is that it’s a single-threaded program. This isn’t strictly true anymore because background threads handle some operations like object deletion, but it’s practically true in that all executing operations block on access to a single flow control point. It’s relatively easy to understand how this came about – Redis’ guarantee around the atomicity of any given operation (be it a single command, MULTI, or EXEC) stems from the fact that it’s only executing one of the them at a time. Even so, there are some obvious parallelism opportunities, and notes in the FAQ suggest that the intention is to start investigating a more threaded design beyond 5.0.

That single-threaded model was indeed our bottleneck. You could log onto the original node and see a single core pegged at 100% usage.

Intersecting failures

Even operating right at maximum capacity, we found Redis to degrade quite gracefully. The main manifestation was an increased rate of baseline connectivity errors as observed from the nodes talking to Redis – in order to be tolerant of a malfunctioning Redis they were constrained with aggressive connect and read timeouts (~0.1 seconds), and couldn’t establish a connection of execute an operation within that time when dealing with an overstrained host.

Although not optimal, the situation was okay most of the time. It only became a real problem came in when we were targeted with huge surges of illegitimate traffic (i.e., orders of magnitude over allowed limits) from legitimate users who could authenticate successfully and run expensive operations on the underlying database. That’s expensive in the relative sense – even returning a set of objects from a list endpoint is far more expensive than denying the request with a 401 because its authentication wasn’t valid, or with a 429 because it’s over limit. These surges are often the result of users building and running high-concurrency scraping programs.

These traffics spikes would lead to a proportional increase in error rate, and much of that traffic would be allowed through as rate limiters defaulted to allowing requests under error conditions. That would put increased pressure on the backend database, which doesn’t fail as gracefully as Redis when overloaded. It’s prone to seeing partitions becoming almost wholly inoperable and timing out a sizable number of the requests made to them.

Redis Cluster's sharding model

A core design value of Redis is speed, and Redis Cluster is structured so as not to compromise that. Unlike many other distributed models, operations in Redis Cluster aren’t confirming on multiple nodes before reporting a success, and instead look a lot more like a set of independent Redis’ sharing a workload by divvying up the total space of possible work. This sacrifices high availability in favor of keeping operations fast – the additional overhead of running an operation against a Redis Cluster is negligible compared to a standard Redis standalone instance.

The total set of possible keys are divided into 16,384 slots. A key’s slot is calculated with a stable hashing function that all clients know how to do:

HASH_SLOT = CRC16(key) mod 16384

For example, if we wanted to execute GET foo, we’d get the slot number for foo:

HASH_SLOT = CRC16("foo") mod 16384 = 12182

Each node in a cluster will handle some fraction of those total 16,384 slots, with the exact number depending on the number of nodes. Nodes communicate with each other to coordinate slot distribution, availability, and rebalancing.

The set of hash slots spread across nodes in a cluster.

Clients use the CLUSTER family of commands to query a cluster’s state. A common operation is CLUSTER NODES to get a mapping of slots to nodes, the result of which is generally cached locally as long as it stays fresh.

127.0.0.1:30002 master - 0 1426238316232 2 connected 5461-10922
127.0.0.1:30003 master - 0 1426238318243 3 connected 10923-16383
127.0.0.1:30001 myself,master - 0 0 1 connected 0-5460

I’ve simplified the output above, but the important parts are the host addresses in the first column and the numbers in the last. 5461-10922 means that this node handles the range of slots starting at 5461 and ending at 10922.

`MOVED` redirection

If a node in a Redis Cluster receives a command for a key in a slot that it doesn’t handle, it makes no attempt to forward that command elsewhere. Instead, the client is told to try again somewhere else. This comes in the form of a MOVED response with the address of the new target:

GET foo
-MOVED 3999 127.0.0.1:6381

During a cluster rebalancing, slots migrate from one node to another, and MOVED is an important signal that servers use to tell a client its local mappings of slots to nodes are stale.

A slot migrating from one node to another.

Every node knows the current slot mapping, and in theory a node that receives an operation that it can’t handle could ask the right node for the result and forward it back to the client, but sending MOVED instead is a deliberate design choice. It trades of some additional client implementation complexity for fast and deterministic performance. As long as a client’s mappings are fresh, operations are always executed in just one hop. Because rebalancing is relatively rare, the coordination overhead amortized over the cluster’s lifetime is negligible.

Besides MOVED, there are a few other cluster-specific mechanics at work, but I’m going to skip them for brevity. The full specification is a great resource for a more thorough look at how Redis Cluster works.

How clients execute requests

Redis clients need a few extra features to support Redis Cluster, with the most important ones being support for the key hashing algorithm and a scheme to maintain slot to node mappings so that they know where to dispatch commands.

Generally, a client will operate like this:

  1. On startup, connect to a node and get a mapping table with CLUSTER NODES.
  2. Execute commands normally, targeting servers according to key slot and slot mapping.
  3. If MOVED is received, return to 1.

A multi-threaded client can be optimized by having it merely mark the mappings table dirty when receiving MOVED, and have threads executing commands follow MOVED responses with new targets while a background thread refreshes the mappings asynchronously. In practice, even while rebalancing the likelihood is that most slots won’t be moving, so this model allows most commands to continue executing with no overhead.

Localizing multi-key operations with hash tags

It’s common in Redis to run operations that operate on multiple keys through the use of the EVAL command with a custom Lua script. This is an especially important feature for implementing rate limiting, because all the work dispatched via a single EVAL is guaranteed to be atomic. This allows us to correctly calculate remaining quotas even when there are concurrent operations that might conflict.

A distributed model would make this type of multi-key operation difficult. Because the slot of each key is calculated via hash, there’d be no guarantee that related keys would map to the same slot. My keys user123.first_name and user123.last_name, obviously meant to belong together, could end up on two completely different nodes in the cluster. An EVAL that read from both of them wouldn’t be able to run on a single node without an expensive remote fetch from another.

Say for example we have an EVAL operation that concatenates a first and last name to produce a person’s full name:

# Gets the full name of a user
EVAL "return redis.call('GET', KEYS[1]) .. ' ' .. redis.call('GET', KEYS[2])"
    2 "user123.first_name" "user123.last_name"

A sample invocation:

> SET "user123.first_name" William
> SET "user123.last_name" Adama

> EVAL "..." 2 "user123.first_name" "user123.last_name"
"William Adama"

This script wouldn’t run correctly if Redis Cluster didn’t provide a way for it to do so. Luckily it does through the use of hash tags.

The Redis Cluster answer to EVALs that would require cross-node operations is to disallow them (a choice that once again optimizes for speed). Instead, it’s the user’s jobs to ensure that the keys that are part of any particular EVAL map to the same slot by hinting how a key’s hash should be calculated with a hash tag. Hash tags look like curly braces in a key’s name, and they dictate that only the surrounded part of the key is used for hashing.

We’d fix our script above by redefining our keys to only hash their shared user123:

> EVAL "..." 2 "{user123}.first_name" "{user123}.last_name"

And when calculating a slot for one of them:

HASH_SLOT = CRC16("{user123}.first_name") mod 16384
          = CRC16("user123") mod 16384
          = 13438

{user123}.first_name and {user123}.last_name are now guaranteed to map to the same slot, and EVAL operations that contain both of them will be trouble-free. This is only a basic example, but the same concept maps all the way up to a complex rate limiter implementation.

Simple and reliable

Transitioning over to Redis Cluster went remarkably smoothly, with the most difficult part being shoring up one of the Redis Cluster clients for production use. Even to this day, good client support is somewhat spotty, which may be an indication that Redis is fast enough that most people using it can get away with a simple standalone instance. Our error rates took a nosedive, and we’re confident that we have ample runway for continued growth.

Philosophically, there’s a lot to like about Redis Cluster’ design – simple, yet powerful. Especially when it comes to distributed systems, many implementations are exceedingly complicated, and that level of complexity can be catastrophic when encountering a tricky edge in production. Redis Cluster is scalable, and yet with few enough moving parts that even a layperson like myself can wrap their head around what it’s doing. Its design doc is comprehensive, but also approachable.

In the months since setting it up, I haven’t touched it again even once, despite the considerable load it’s under every second of the day. This is a rare quality in production systems, and not found even amongst some of my other favorites like Postgres. We need more building blocks like Redis that do what they’re supposed to, then get out of the way.

Your daily dose of tangentially related photography: Stone at the top of Massive Mountain in Alberta sharding into thin flakes.

1 The exact number of operations per second is left intentionally vague.

2 Notably, we’re not error-free. There are enough operations in flight that some level of intermittent failure is unavoidable.

Article
Scaling a High-traffic Rate Limiting Stack With Redis Cluster

Published
April 26, 2018

Location
San Francisco

Find me on Twitter at @brandur.

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