dragonfly/tools/cache_testing.py

#!/usr/bin/env python

import redis
import argparse
from urllib.parse import urlparse
import numpy as np

'''
Run Cache Testing.
This tool performs cache testing for Dragonfly
by calling the `incrby` function on a constrained set
of items, as defined by the user. Additionally, it
distributes the frequency of `incrby` calls for each
item based on a Zipfian distribution (with alpha values
between 0 and 1 being representative of real-life cache
load scenarios)
'''

def rand_zipf_generator(n, alpha, count, pipeline):
    """
    n: The upper bound of the values to generate a zipfian distribution over
    (n = 30 would generate a distribution of given alpha from values 1 to 30)
    alpha: The alpha parameter to be used while creating the Zipfian distribution
    num_samples: The total number of samples to generate over the Zipfian distribution
    This is a generator that yields up to count values using a generator.
    """

    # Calculate Zeta values from 1 to n:
    tmp = np.power( np.arange(1, n+1), -alpha )
    zeta = np.r_[0.0, np.cumsum(tmp)]

    # Store the translation map:
    distMap = [x / zeta[-1] for x in zeta]

    if pipeline == 0:
        # Generate an array of uniform 0-1 pseudo-random values:
        u = np.random.random(count)

        # bisect them with distMap
        v = np.searchsorted(distMap, u)

        samples = [t-1 for t in v]

        for sample in samples:
            yield sample
    else:
        current_count = 0
        while current_count < count:
            # Generate an array of uniform 0-1 pseudo-random values, of the pipeline length:
            u = np.random.random(pipeline)

            # bisect them with distMap
            v = np.searchsorted(distMap, u)

            samples = [t-1 for t in v]
            yield samples

            current_count += len(samples)

def update_stats(r, hits, misses, value_index, total_count):
    """
    A void function that uses terminal control sequences
    to update hit/miss ratio stats for the user
    while the testing tool runs.
    """
    percent_complete = (value_index + 1) / total_count

    # Use the terminal control sequence to move the cursor to the beginning of the line
    print("\r", end="")

    # Print the loading bar and current hit rate
    print("[{}{}] {:.0f}%, current hit rate: {:.6f}%".format("#" * int(percent_complete * 20), " " * int(20 - percent_complete * 20), percent_complete * 100, (hits / (hits + misses)) * 100), end="")

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Cache Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('-c', '--count', type=int, default=100000, help='total number of incrby operations')
    parser.add_argument('-u', '--uri', type=str, default='redis://localhost:6379', help='Redis server URI')
    parser.add_argument('-a', '--alpha', type=int, default=1.0, help='alpha value being used for the Zipf distribution')
    parser.add_argument('-n', '--number', type=int, default=30, help='the number of values to be used in the distribution')
    parser.add_argument('-d', '--length', type=int, default=10, help='the length of the values to be used in the distribution')
    parser.add_argument('-p', '--pipeline', type=int, default=0, help='pipeline size')

    args = parser.parse_args()
    uri = urlparse(args.uri)

    r = redis.StrictRedis(host=uri.hostname, port=uri.port)

    misses = 0
    hits = 0

    distribution_keys_generator = rand_zipf_generator(args.number, args.alpha, args.count, args.pipeline)

    if args.pipeline == 0:
        for idx, key in enumerate(distribution_keys_generator):
            result = r.set(str(key), 'x' * args.length, nx=True)
            if result:
                misses += 1
            else:
                hits += 1
            if idx % 50 == 0:
                update_stats(r, hits, misses, idx, args.count)
    else:
        total_count = 0
        for idx, keys in enumerate(distribution_keys_generator):
            total_count += len(keys)
            p = r.pipeline(transaction=False)
            for key in keys:
                p.set(str(key), 'x' * args.length, nx=True)
            responses = p.execute()
            for resp in responses:
                if resp:
                    misses += 1
                else:
                    hits += 1
            if idx % 20 == 0:
                update_stats(r, hits, misses, total_count, args.count)