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* Prepare next release * Include Docker images for TPC-H in subfolder * More TPC examples * Experiments cleaned * TPC-DS: Some example DDL and query files * TPC-H: Some docs
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| """ | ||
| The clustermanager module | ||
| """ | ||
| __all__ = ["experimentsmanager","tpch"] | ||
| __all__ = ["experimentsmanager","tpch","tpcds"] |
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| """ | ||
| :Date: 2021-02-12 | ||
| :Version: 0.1 | ||
| :Authors: Patrick Erdelt | ||
| Perform TPC-DS inspired benchmarks in a Kubernetes cluster. | ||
| This either profiles the imported data in several DBMS and compares some statistics, or runs the TPC-H queries. | ||
| Optionally monitoring is actived. | ||
| User can choose to detach the componenten of the benchmarking system, so that as much as possible is run inside a Kubernetes (K8s) cluster. | ||
| User can also choose some parameters like number of runs per query and configuration and request some resources. | ||
| """ | ||
| from bexhoma import * | ||
| from dbmsbenchmarker import * | ||
| #import experiments | ||
| import logging | ||
| import urllib3 | ||
| import logging | ||
| import argparse | ||
| import time | ||
| from timeit import default_timer | ||
| import datetime | ||
|
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| urllib3.disable_warnings() | ||
| logging.basicConfig(level=logging.ERROR) | ||
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| def do_benchmark(): | ||
| description = """Perform TPC-DS inspired benchmarks in a Kubernetes cluster. | ||
| This either profiles the imported data in several DBMS and compares some statistics, or runs the TPC-DS queries. | ||
| Optionally monitoring is actived. | ||
| User can choose to detach the componenten of the benchmarking system, so that as much as possible is run inside a Kubernetes (K8s) cluster. | ||
| User can also choose some parameters like number of runs per query and configuration and request some resources. | ||
| """ | ||
| # argparse | ||
| parser = argparse.ArgumentParser(description=description) | ||
| parser.add_argument('mode', help='profile the import of TPC-DS data, or run the TPC-DS queries, or start DBMS and load data, or just start the DBMS', choices=['profiling', 'run', 'start', 'load']) | ||
| parser.add_argument('-cx', '--context', help='context of Kubernetes (for a multi cluster environment), default is current context', default=None) | ||
| parser.add_argument('-e', '--experiment', help='sets experiment code for continuing started experiment', default=None) | ||
| parser.add_argument('-d', '--detached', help='puts most of the experiment workflow inside the cluster', action='store_true') | ||
| parser.add_argument('-m', '--monitoring', help='activates monitoring', action='store_true') | ||
| parser.add_argument('-ms', '--max-sut', help='maximum number of parallel DBMS configurations, default is no limit', default=None) | ||
| parser.add_argument('-dt', '--datatransfer', help='activates datatransfer', action='store_true', default=False) | ||
| parser.add_argument('-md', '--monitoring-delay', help='time to wait [s] before execution of the runs of a query', default=10) | ||
| parser.add_argument('-nr', '--num-run', help='number of runs per query', default=1) | ||
| parser.add_argument('-nc', '--num-config', help='number of runs per configuration', default=1) | ||
| parser.add_argument('-ne', '--num-query-executors', help='comma separated list of number of parallel clients', default="1") | ||
| parser.add_argument('-sf', '--scaling-factor', help='scaling factor (SF)', default=1) | ||
| parser.add_argument('-t', '--timeout', help='timeout for a run of a query', default=180) | ||
| parser.add_argument('-rr', '--request-ram', help='request ram', default='16Gi') | ||
| parser.add_argument('-rc', '--request-cpu', help='request cpus', default='4') | ||
| parser.add_argument('-rct', '--request-cpu-type', help='request node having node label cpu=', default='') | ||
| parser.add_argument('-rg', '--request-gpu', help='request number of gpus', default=1) | ||
| parser.add_argument('-rgt', '--request-gpu-type', help='request node having node label gpu=', default='a100') | ||
| parser.add_argument('-rst', '--request-storage-type', help='request persistent storage of certain type', default=None, choices=[None, '', 'local-hdd', 'shared']) | ||
| parser.add_argument('-rss', '--request-storage-size', help='request persistent storage of certain size', default='10Gi') | ||
| parser.add_argument('-rnn', '--request-node-name', help='request a specific node', default=None) | ||
| args = parser.parse_args() | ||
| # set parameter | ||
| monitoring = args.monitoring | ||
| mode = str(args.mode) | ||
| SF = str(args.scaling_factor) | ||
| timeout = int(args.timeout) | ||
| numRun = int(args.num_run) | ||
| numExperiments = int(args.num_config) | ||
| cpu = str(args.request_cpu) | ||
| memory = str(args.request_ram) | ||
| cpu_type = str(args.request_cpu_type) | ||
| gpu_type = str(args.request_gpu_type) | ||
| gpus = str(args.request_gpu) | ||
| request_storage_type = args.request_storage_type | ||
| request_storage_size = args.request_storage_size | ||
| request_node_name = args.request_node_name | ||
| datatransfer = args.datatransfer | ||
| code = args.experiment | ||
| # set cluster | ||
| cluster = clusters.kubernetes(context=args.context) | ||
| cluster_name = cluster.contextdata['clustername'] | ||
| if args.max_sut is not None: | ||
| cluster.max_sut = int(args.max_sut) | ||
| # set experiment | ||
| if code is None: | ||
| code = cluster.code | ||
| experiment = experiments.tpcds(cluster=cluster, SF=SF, timeout=timeout, detached=True, code=code, numExperiments=numExperiments) | ||
| if mode == 'run': | ||
| # we want all TPC-H queries | ||
| experiment.set_queries_full() | ||
| experiment.set_workload( | ||
| name = 'TPC-H Queries SF='+str(SF), | ||
| info = 'This experiment compares run time and resource consumption of TPC-DS queries in different DBMS.' | ||
| ) | ||
| else: | ||
| # we want to profile the import | ||
| experiment.set_queries_profiling() | ||
| experiment.set_workload( | ||
| name = 'TPC-H Data Profiling SF='+str(SF), | ||
| info = 'This experiment compares imported TPC-DS data sets in different DBMS.' | ||
| ) | ||
| if monitoring: | ||
| # we want to monitor resource consumption | ||
| experiment.set_querymanagement_monitoring(numRun=numRun, delay=10, datatransfer=datatransfer) | ||
| else: | ||
| # we want to just run the queries | ||
| experiment.set_querymanagement_quicktest(numRun=numRun, datatransfer=datatransfer) | ||
| # set resources for dbms | ||
| experiment.set_resources( | ||
| requests = { | ||
| 'cpu': cpu, | ||
| 'memory': memory, | ||
| 'gpu': 0 | ||
| }, | ||
| limits = { | ||
| 'cpu': 0, | ||
| 'memory': 0 | ||
| }, | ||
| nodeSelector = { | ||
| 'cpu': cpu_type, | ||
| 'gpu': '', | ||
| }) | ||
| if request_node_name is not None: | ||
| experiment.set_resources( | ||
| nodeSelector = { | ||
| 'cpu': cpu_type, | ||
| 'gpu': '', | ||
| 'kubernetes.io/hostname': request_node_name | ||
| }) | ||
| # persistent storage | ||
| print(request_storage_type) | ||
| experiment.set_storage( | ||
| storageClassName = request_storage_type, | ||
| storageSize = request_storage_size,#'100Gi', | ||
| keep = False | ||
| ) | ||
| cluster.start_dashboard() | ||
| # add configs | ||
| config = configurations.default(experiment=experiment, docker='MonetDB', configuration='MonetDB-{}'.format(cluster_name), alias='DBMS A', dialect='MonetDB') | ||
| #config = configurations.default(experiment=experiment, docker='MemSQL', configuration='MemSQL-{}'.format(cluster_name), alias='DBMS B', dialect='MonetDB') | ||
| #config = configurations.default(experiment=experiment, docker='MariaDB', configuration='MariaDB-{}'.format(cluster_name), alias='DBMS C') | ||
| config = configurations.default(experiment=experiment, docker='PostgreSQL', configuration='PostgreSQL-{}'.format(cluster_name), alias='DBMS D', dialect='MonetDB') | ||
| #config = configurations.default(experiment=experiment, docker='Citus', configuration='Citus-{}'.format(cluster_name), alias='DBMS E', dialect='OmniSci') | ||
| #config = configurations.default(experiment=experiment, docker='MySQL', configuration='MySQL-{}'.format(cluster_name), alias='DBMS F') | ||
| #config = configurations.default(experiment=experiment, docker='MariaDBCS', configuration='MariaDBCS-{}'.format(cluster_name), alias='DBMS G') | ||
| #config = configurations.default(experiment=experiment, docker='Exasol', configuration='Exasol-{}'.format(cluster_name), alias='DBMS H') | ||
| #config = configurations.default(experiment=experiment, docker='DB2', configuration='DB2-{}'.format(cluster_name), alias='DBMS I') | ||
| #config = configurations.default(experiment=experiment, docker='SAPHANA', configuration='SAPHANA-{}'.format(cluster_name), alias='DBMS J', dialect='MonetDB') | ||
| #config = configurations.default(experiment=experiment, docker='Clickhouse', configuration='Clickhouse-{}'.format(cluster_name), alias='DBMS K') | ||
| #config = configurations.default(experiment=experiment, docker='SQLServer', configuration='SQLServer-{}'.format(cluster_name), alias='DBMS L') | ||
| #config = configurations.default(experiment=experiment, docker='OmniSci', configuration='OmniSci-{}'.format(cluster_name) alias='DBMS M') | ||
| if args.mode == 'start': | ||
| experiment.start_sut() | ||
| elif args.mode == 'load': | ||
| # start all DBMS | ||
| experiment.start_sut() | ||
| # configure number of clients per config = 0 | ||
| list_clients = [] | ||
| # total time of experiment | ||
| experiment.add_benchmark_list(list_clients) | ||
| start = default_timer() | ||
| start_datetime = str(datetime.datetime.now()) | ||
| print("Experiment starts at {} ({})".format(start_datetime, start)) | ||
| # run workflow | ||
| experiment.work_benchmark_list() | ||
| # total time of experiment | ||
| end = default_timer() | ||
| end_datetime = str(datetime.datetime.now()) | ||
| duration_experiment = end - start | ||
| print("Experiment ends at {} ({}): {}s total".format(end_datetime, end, duration_experiment)) | ||
| else: | ||
| # configure number of clients per config | ||
| list_clients = args.num_query_executors.split(",") | ||
| if len(list_clients) > 0: | ||
| list_clients = [int(x) for x in list_clients] | ||
| experiment.add_benchmark_list(list_clients) | ||
| # total time of experiment | ||
| start = default_timer() | ||
| start_datetime = str(datetime.datetime.now()) | ||
| print("Experiment starts at {} ({})".format(start_datetime, start)) | ||
| # run workflow | ||
| experiment.work_benchmark_list() | ||
| # total time of experiment | ||
| end = default_timer() | ||
| end_datetime = str(datetime.datetime.now()) | ||
| duration_experiment = end - start | ||
| print("Experiment ends at {} ({}): {}s total".format(end_datetime, end, duration_experiment)) | ||
| ################## | ||
| experiment.evaluate_results() | ||
| experiment.stop_benchmarker() | ||
| experiment.stop_sut() | ||
| cluster.stop_dashboard() | ||
| cluster.start_dashboard() | ||
| # OOM? exit code 137 | ||
| #experiment.zip() | ||
| exit() |
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| filled*.sql |
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