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authorFlorian Fischer <florian.fl.fischer@fau.de>2018-07-20 14:17:04 +0200
committerFlorian Fischer <florian.fl.fischer@fau.de>2018-07-20 14:17:04 +0200
commit78098dbe189ae84b38cf6c94cbe06dafd07cb10c (patch)
treea637cc1cc44dd3f0ddce3a47e9887bb546774adb /bench_mysql.py
parent08cdc1d18cd8371b38ef1a6bf663ca32bb951c9e (diff)
downloadallocbench-78098dbe189ae84b38cf6c94cbe06dafd07cb10c.tar.gz
allocbench-78098dbe189ae84b38cf6c94cbe06dafd07cb10c.zip
make results independant and add cmdline options
Diffstat (limited to 'bench_mysql.py')
-rw-r--r--bench_mysql.py17
1 files changed, 11 insertions, 6 deletions
diff --git a/bench_mysql.py b/bench_mysql.py
index f3ce9cf..0627537 100644
--- a/bench_mysql.py
+++ b/bench_mysql.py
@@ -10,6 +10,7 @@ import shutil
import subprocess
from time import sleep
+from benchmark import Benchmark
from common_targets import common_targets
cwd = os.getcwd()
@@ -23,8 +24,9 @@ cmd = ("sysbench oltp_read_only --threads={} --time=10 --max-requests=0 "
server_cmd = "mysqld -h {0}/mysql_test --socket={0}/mysql_test/socket".format(cwd).split(" ")
-class Benchmark_MYSQL():
+class Benchmark_MYSQL( Benchmark ):
def __init__(self):
+ self.file_name = "bench_mysql"
self.name = "MYSQL Stress Benchmark"
self.descrition = """See sysbench documentation."""
self.targets = copy.copy(common_targets)
@@ -32,7 +34,7 @@ class Benchmark_MYSQL():
self.nthreads = range(1, multiprocessing.cpu_count() * 2 + 1)
self.results = {}
-
+
def start_and_wait_for_server(self, env, verbose, log=None):
if not log:
log = os.devnull
@@ -148,15 +150,18 @@ class Benchmark_MYSQL():
return True
def summary(self):
- for target in self.targets:
- y_vals = [0] * len(self.nthreads)
+ nthreads = self.results["args"]["nthreads"]
+ targets = self.results["targets"]
+
+ for target in targets:
+ y_vals = [0] * len(nthreads)
for mid, measures in self.results.items():
if mid[0] == target:
d = []
for m in measures:
d.append(int(m["transactions"]))
- y_vals[mid[1]-1] = np.mean(d)
- plt.plot(self.nthreads, y_vals, label=target, linestyle='-', marker='.')
+ y_vals[mid[1]-nthreads[0]] = np.mean(d)
+ plt.plot(nthreads, y_vals, label=target, linestyle='-', marker='.')
plt.legend()
plt.xlabel("threads")