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performance_search_max_throughput_test.py
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import re
import json
from collections import defaultdict
from typing import Any
# from sdcm.send_email import Email
from performance_regression_test import PerformanceRegressionTest
from sdcm.results_analyze import SearchBestThroughputConfigPerformanceAnalyzer
from sdcm.utils.common import format_timestamp
class MaximumPerformanceSearchTest(PerformanceRegressionTest):
def test_search_best_read_throughput(self): # pylint: disable=too-many-locals
stress_params = {"stress_cmd_tmpl": self.params.get('stress_cmd_r'),
"test_name": "Test read"}
stress_params.update(**self._get_stress_parameters())
self.run_fstrim_on_all_db_nodes()
# run a write workload as a preparation
self.preload_data()
self.wait_no_compactions_running(n=400, sleep_time=120)
self.run_fstrim_on_all_db_nodes()
self.run_search_best_performance(**stress_params)
def test_search_best_write_throughput(self): # pylint: disable=too-many-locals
stress_params = {"stress_cmd_tmpl": self.params.get('stress_cmd_w'),
"test_name": "Test write"}
stress_params.update(**self._get_stress_parameters())
# run a write workload as a preparation
self.preload_data()
self.wait_no_compactions_running(n=400, sleep_time=120)
self.run_fstrim_on_all_db_nodes()
self.run_search_best_performance(**stress_params)
def test_search_best_mixed_throughput(self): # pylint: disable=too-many-locals
stress_params = {"stress_cmd_tmpl": self.params.get('stress_cmd_m'),
"test_name": "Test mixed"}
stress_params.update(**self._get_stress_parameters())
# run a write workload as a preparation
self.preload_data()
self.wait_no_compactions_running(n=400, sleep_time=120)
self.run_fstrim_on_all_db_nodes()
self.run_search_best_performance(**stress_params)
def run_search_best_performance(self, stress_cmd_tmpl: str, # pylint: disable=too-many-arguments,too-many-locals,too-many-statements # noqa: PLR0914
stress_num: int,
stress_num_step: int,
stress_step_duration: str,
start_threads: int,
threads_step: int,
num_loaders_step: int,
max_deviation: float,
test_name: str):
"""Run search best performance result
Test starts with predefined number of loaders,
processes per loader and threads of c-s tool.
after first run we got some throughput which
is set as best result. After that on each
step the number of threads of cs tool increased
on thread_num_step threads. The total threads become bigger.
1. if result on current step is lager than best result + deviation (configured),
then new best throughput result is written and continue
2. if current result is in (best - deviation, best + deviation),
then rerun step with decreased number of cs tool processes per
loader with last number of threads. the total threads in this case
will be in interval ( total threads in previous step, total threads in current step).
if current step with less number of process and same number of threads
give new best results, we back to condition 1.
the number of processes continue to decrease while reach 0.
3. if test configured with loader_num_step = 0, this means
that number of loaders won't be decreased, then test will be stopped.
if test configured with loader_num_step > 1, then, number of loaders
will decreased, number of processes will be set to preconfigured one,
and number of threads per process will be calculated by dividing total
threads of best result on new number of loaders and processes
"""
all_results = []
best_result = {}
original_nodes_list = self.loaders.nodes[:]
origin_stress_num = stress_num
decrease_loaders_num = False
threads = start_threads
self.log.info("Start search best performance")
self.log.debug("Use c-s cmd template %s", stress_cmd_tmpl)
while len(self.loaders.nodes) > 0:
stress_cmd = self._build_stress_command(cmd_tmpl=stress_cmd_tmpl,
threads=threads,
duration=stress_step_duration)
self.log.debug("Run c-s command %s", stress_cmd)
stress_queue = self.run_stress_thread(stress_cmd=stress_cmd,
stress_num=stress_num,
stats_aggregate_cmds=False,)
results = self.get_stress_results(queue=stress_queue, store_results=False)
self.log.debug("Current results %s", results)
current_result = self._calculate_average_stats(results)
current_result.update({
"threads": threads,
"n_loaders": len(self.loaders.nodes),
"n_process": stress_num,
"total_threads": threads * stress_num * len(self.loaders.nodes),
"stress_cmd": stress_cmd})
self.log.debug("Summary results for current step: %s", current_result)
all_results.append(current_result)
if not best_result:
best_result = current_result
self.log.debug("Increase threads on %s", threads_step)
threads += threads_step
continue
deviation = best_result["op rate"] * max_deviation / 100
if current_result["op rate"] > best_result["op rate"] + deviation:
self.log.info("New best throughtput %s", current_result["op rate"])
best_result = current_result
self.log.info("Current throughput: %s larger than best: %s. Increase threads",
current_result["op rate"],
best_result["op rate"])
threads += threads_step
elif (current_result["op rate"] >= best_result["op rate"] - deviation and
current_result["op rate"] <= best_result["op rate"] + deviation):
stress_num -= stress_num_step
if stress_num < 1:
decrease_loaders_num = True
else:
decrease_loaders_num = True
if decrease_loaders_num:
if not num_loaders_step:
self.log.info("Decrease number of stress processes")
stress_num -= stress_num_step
if stress_num < 1:
self.log.info("No processes left for run. Stop test")
break
else:
self.log.info("Decrease number of loaders and back to start num of process")
new_num_of_nodes = len(self.loaders.nodes) - num_loaders_step
if new_num_of_nodes < 1:
self.log.info("No loaders left for run. Stop test")
break
self.loaders.nodes = original_nodes_list[:new_num_of_nodes]
stress_num = origin_stress_num
threads = (best_result["total_threads"] // (len(self.loaders.nodes) * stress_num)) - threads_step
threads = 10 * round(threads / 10) or start_threads
decrease_loaders_num = False
if current_result["op rate"] < best_result["op rate"] / 2:
self.log.warning("Current result less in 2 times than best result. Stop the test")
break
self.loaders.nodes = original_nodes_list[:]
self.log.info("Found best configuration with results %s", best_result)
self.log.info("Write data to files")
filename = f"{self.logdir}/all_stats_result.json"
with open(filename, "w", encoding="utf-8") as fp_json:
json.dump(all_results, fp_json, indent=4)
filename = f"{self.logdir}/best_stat_result.json"
with open(filename, "w", encoding="utf-8") as fp_json:
json.dump(best_result, fp_json, indent=4)
raw_results = {
"best_stat": best_result,
"all_stats": all_results
}
setup_details = {
"scylla_version": self.db_cluster.nodes[0].scylla_version_detailed,
"start_time": format_timestamp(self.start_time),
"instance_type_db": self.params.get("instance_type_db"),
"instance_type_loader": self.params.get("instance_type_loader"),
"prepare_cs_cmd": self.params.get("prepare_write_cmd")
}
analyzer = SearchBestThroughputConfigPerformanceAnalyzer(es_index=self._test_index,
email_recipients=self.params.get("email_recipients"))
analyzer.check_regression(test_name, setup_details=setup_details, test_results=raw_results)
def _get_stress_parameters(self) -> dict[Any, Any]:
return {
"stress_num": int(self.params.get("n_stress_process")),
"stress_num_step": int(self.params.get("stress_process_step")),
"num_loaders_step": int(self.params.get("num_loaders_step")),
"start_threads": int(self.params.get("stress_threads_start_num")),
"threads_step": int(self.params.get("num_threads_step")),
"max_deviation": float(self.params.get("max_deviation")),
"stress_step_duration": self.params.get("stress_step_duration"),
}
def _calculate_average_stats(self, results):
status = defaultdict(list)
# calculate total status for each c-s
for result in results:
for key in result.keys():
try:
status[key].append(float(result.get(key)))
except ValueError as exc:
self.log.warning("Failed to convert value %s. Error: %s", result.get(key), exc)
continue
# get average for latencies
final_result = {}
for key in status:
if key == "op rate":
final_result[key] = sum(status[key])
continue
if not status[key] or key == "errors":
final_result[key] = 0
continue
final_result[key] = round(sum(status[key]) / len(status[key]), 2) or 0
return final_result
@staticmethod
def _calculate_difference(current: float, best: float) -> float:
diff = (current - best) / current * 100
diff = diff if diff > 0 else -1 * diff
return diff
@staticmethod
def _build_stress_command(cmd_tmpl: str, threads: int, duration: str) -> str:
if isinstance(cmd_tmpl, list):
cmd_tmpl = cmd_tmpl[0]
cmd_tmpl = re.sub(r'\sthreads=\d+\s', f' threads={threads} ', cmd_tmpl)
cmd_tmpl = re.sub(r'\sduration=\d+[mhd]\s', f' duration={duration} ', cmd_tmpl)
return cmd_tmpl