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Fred Benchmark

Redis includes a benchmarking tool that can be used to measure the throughput of a client/connection pool. This module attempts to reproduce the same process with Tokio and Fred.

The general strategy involves using an atomic global counter and spawning -c Tokio tasks that share -P clients in order to send -n total INCR commands to the server as quickly as possible.

Each of the -c Tokio tasks use a different random key so commands are uniformly distributed across a cluster or replica set.

This strategy also has the benefit of being somewhat representative of an Axum or Actix web server use case where requests run in separate Tokio tasks but share a common client pool.

The benchmark metrics folder contains a tool that can test different combinations of concurrency (-c) and pool size (-P) argv.

Tuning

There are several additional features or performance tuning options that can affect these results. For example:

  • Tracing. Enabling the FF cut throughput by ~20% in my tests.
  • Pipelining. The auto_pipeline feature can dramatically improve throughput in scenarios like this where a client or pool is shared among many Tokio tasks. The original purpose of this tool was to test this particular optimization.
  • Clustering
  • Backpressure settings
  • Network latency
  • Log levels, often indirectly for the same reason as tracing (contention on a pipe, file handle, or socket).
  • The size of the client connection pool.

Callers should take care to consider each of these when deciding on argv values.

This module also includes an optional assert-expected feature flag that adds an assert! call after each INCR command to ensure the response is actually correct.

Tracing

This also shows how to configure the client with tracing enabled against a local Jaeger instance. A docker compose file is included that will run a local Jaeger instance.

docker-compose -f /path/to/fred/tests/docker/compose/jaeger.yml up

Then navigate to http://localhost:16686.

By default, this module does not compile any tracing features, but there are 3 flags that can toggle how tracing is configured.

  • partial-tracing - Enables fred/partial-tracing and emits traces to the local jaeger instance.
  • full-tracing - Enables fred/full-tracing and emits traces to the local jaeger instance.
  • stdout-tracing - Enables fred/partial-tracing and emits traces to stdout.

Docker

Linux+Docker is the best supported option via the ./run.sh script. The Cargo.toml provided here has a comment/toggle around the lines that need to change if callers want to use a remote server.

Callers may have to also change run.sh to enable additional features in docker.

Usage

USAGE:
    fred_benchmark [FLAGS] [OPTIONS] [SUBCOMMAND]

FLAGS:
        --cluster     Whether to assume a clustered deployment.
        --help        Prints help information
    -q, --quiet       Only print the final req/sec measurement.
        --replicas    Whether to use `GET` with replica nodes instead of `INCR` with primary nodes.
    -t, --tls         Enable TLS via whichever build flag is provided.
    -t, --tracing     Whether to enable tracing via a local Jeager instance. See tests/docker-compose.yml to
                      start up a local Jaeger instance.
    -V, --version     Prints version information

OPTIONS:
    -a, --auth <STRING>           The password/key to use. `REDIS_USERNAME` and `REDIS_PASSWORD` can also be used.
    -c, --concurrency <NUMBER>    The number of Tokio tasks used to run commands. [default: 100]
    -n, --commands <NUMBER>       The number of commands to run. [default: 100000]
    -h, --host <STRING>           The hostname of the redis server. [default: 127.0.0.1]
    -P, --pool <NUMBER>           The number of clients in the redis connection pool. [default: 1]
    -p, --port <NUMBER>           The port for the redis server. [default: 6379]
    -u, --unix-sock <PATH>        The path to a unix socket.

SUBCOMMANDS:
    help           Prints this message or the help of the given subcommand(s)
    no-pipeline    Run the test without pipelining [Default].
    pipeline       Run the test with pipelining.

Examples

All the examples below use the following parameters:

  • Clustered deployment via local docker (3 primary nodes with one replica each)
  • No tracing features enabled
  • No TLS
  • 10_000_000 INCR commands with assert-expected enabled
  • 10_000 Tokio tasks
  • 15 clients in the connection pool

With auto_pipeline disabled:

$ ./run.sh --cluster -c 10000 -n 10000000 -P 15 -h redis-cluster-1 -p 30001 -a bar no-pipeline
Performed 10000000 operations in: 27.038434665s. Throughput: 369849 req/sec

With auto_pipeline enabled:

$ ./run.sh --cluster -c 10000 -n 10000000 -P 15 -h redis-cluster-1 -p 30001 -a bar pipeline
Performed 10000000 operations in: 3.728232639s. Throughput: 2682403 req/sec

With auto_pipeline enabled and using GET with replica nodes instead of INCR with primary nodes:

$ ./run.sh --cluster -c 10000 -n 10000000 -P 15 -h redis-cluster-1 -p 30001 -a bar --replicas pipeline
erformed 10000000 operations in: 3.234255482s. Throughput: 3092145 req/sec

Maybe Relevant Specs:

  • 32 CPUs
  • 64 GB memory

redis-rs Comparison

The USE_REDIS_RS environment variable can be toggled to switch the benchmark logic to use redis-rs instead of fred. There's also an info level log line that can confirm this at runtime.

The redis-rs variant uses the same general strategy, but with bb8-redis ( specifically Pool<RedisConnectionManager>) instead of fred::clients::RedisPool. All the other components in the benchmark logic are the same.

Examples

These examples use the following parameters:

  • Centralized deployment via local docker
  • No tracing features enabled
  • No TLS
  • 10_000_000 INCR commands with assert-expected enabled
  • 10_000 Tokio tasks
  • 15 clients in the connection pool
# fred without `auto_pipeline` 
$ ./run.sh -h redis-main -p 6379 -a bar -n 10000000 -P 15 -c 10000 no-pipeline
Performed 10000000 operations in: 52.156700826s. Throughput: 191732 req/sec

# redis-rs via bb8-redis
$ USE_REDIS_RS=1 ./run.sh -h redis-main -p 6379 -a bar -n 10000000 -P 15 -c 10000
Performed 10000000 operations in: 102.953612933s. Throughput: 97131 req/sec

# fred with `auto_pipeline`
$ ./run.sh -h redis-main -p 6379 -a bar -n 10000000 -P 15 -c 10000 pipeline
Performed 10000000 operations in: 5.74236423s. Throughput: 1741553 req/sec