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3d610ee2d7
feat(server): json family using json type Signed-off-by: Boaz Sade <boaz@dragonflydb.io> Signed-off-by: Boaz Sade <boaz@dragonflydb.io>
123 lines
3.7 KiB
Python
Executable File
123 lines
3.7 KiB
Python
Executable File
#!/usr/bin/env python
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import multiprocessing
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import time
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import redis
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import sys
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import argparse
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from urllib.parse import urlparse
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import os
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from collections import defaultdict
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import math
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'''
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Run JSON benchmark for 3 commands:
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JSON.SET
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JSON.GET
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JSON.TYPE
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We want to the overall time it takes
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to save and access keys that contains
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JSON values with this benchmark.
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This also verify that the basic functionalities
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for using JSON types work correctly
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'''
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def ping(r):
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r.ping()
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def jsonset(r, i):
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key = "json-{}".format(i)
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r.execute_command('JSON.SET', key, '.', '{"a":123456, "b": "hello", "nested": {"abc": "ffffff", "bfb": null}}')
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def jsonget(r, i):
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key = "json-{}".format(i)
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r.execute_command('JSON.GET', key, '$.a', '$..abc')
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def jsontype(r, i):
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key = "json-{}".format(i)
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r.execute_command('JSON.TYPE', key, '$.a')
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def runWorker(ctx):
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wpid = os.getpid()
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print( '{} '.format(wpid))
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rep = defaultdict(int)
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r = redis.StrictRedis(host=ctx['host'], port=ctx['port'])
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work = ctx['work']
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if ctx['pipeline'] == 0:
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total_count = int(ctx['count'])
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for i in range(0, total_count):
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s0 = time.time()
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jsonset(r, i)
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s1 = time.time() - s0
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bin = int(math.floor(s1 * 1000)) + 1
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rep[bin] += 1
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for i in range(0, total_count):
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s0 = time.time()
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jsonget(r, i)
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s1 = time.time() - s0
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bin = int(math.floor(s1 * 1000)) + 1
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rep[bin] += 1
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for i in range(0, total_count):
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s0 = time.time()
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jsontype(r, i)
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s1 = time.time() - s0
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bin = int(math.floor(s1 * 1000)) + 1
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rep[bin] += 1
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else:
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for i in range(0, ctx['count'], ctx['pipeline']):
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p = r.pipeline()
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s0 = time.time()
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for j in range(0, ctx['pipeline']):
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work(p)
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p.execute()
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s1 = time.time() - s0
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bin = int(math.floor(s1 * 1000)) + 1
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rep[bin] += ctx['pipeline']
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return rep
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='ReJSON Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('-c', '--count', type=int, default=100000, help='total number of operations')
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parser.add_argument('-p', '--pipeline', type=int, default=0, help='pipeline size')
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parser.add_argument('-w', '--workers', type=int, default=8, help='number of worker processes')
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parser.add_argument('-u', '--uri', type=str, default='redis://localhost:6379', help='Redis server URI')
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args = parser.parse_args()
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uri = urlparse(args.uri)
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r = redis.Redis(host=uri.hostname, port=uri.port)
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pool = multiprocessing.Pool(args.workers)
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s0 = time.time()
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ctx = {
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'count': args.count / args.workers,
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'pipeline': args.pipeline,
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'host': uri.hostname,
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'port': uri.port,
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'work': jsonset,
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}
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print ('Starting workers: ')
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p = multiprocessing.Pool(args.workers)
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results = p.map(runWorker, (ctx, ) * args.workers)
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print("")
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sys.stdout.flush()
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s1 = time.time() - s0
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agg = defaultdict(int)
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for res in results:
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for k, v in res.items():
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agg[k] += v
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print()
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count = args.count * 3
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print (f'Count: {args.count}, Workers: {args.workers}, Pipeline: {args.pipeline}')
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print (f'Using hireds: {redis.utils.HIREDIS_AVAILABLE}')
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print (f'Runtime: {round(s1, 2):,} seconds')
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print (f'Throughput: {round(count/s1, 2):,} requests per second')
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for k, v in sorted(agg.items()):
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perc = 100.0 * v / count
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print (f'{perc:.4f}% <= {k:,} milliseconds')
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