#!/usr/bin/env python3 # Copyright 2022 Memgraph Ltd. # # Use of this software is governed by the Business Source License # included in the file licenses/BSL.txt; by using this file, you agree to be bound by the terms of the Business Source # License, and you may not use this file except in compliance with the Business Source License. # # As of the Change Date specified in that file, in accordance with # the Business Source License, use of this software will be governed # by the Apache License, Version 2.0, included in the file # licenses/APL.txt. import argparse import collections import copy import fnmatch import inspect import json import math import multiprocessing import random import statistics import sys import datasets import helpers import log import runners WITH_FINE_GRAINED_AUTHORIZATION = "with_fine_grained_authorization" WITHOUT_FINE_GRAINED_AUTHORIZATION = "without_fine_grained_authorization" # Parse options. parser = argparse.ArgumentParser( description="Memgraph benchmark executor.", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( "benchmarks", nargs="*", default="", help="descriptions of benchmarks that should be run; " "multiple descriptions can be specified to run multiple " "benchmarks; the description is specified as " "dataset/variant/group/query; Unix shell-style wildcards " "can be used in the descriptions; variant, group and query " "are optional and they can be left out; the default " "variant is '' which selects the default dataset variant; " "the default group is '*' which selects all groups; the" "default query is '*' which selects all queries", ) parser.add_argument( "--vendor-binary", help="Vendor binary used for benchmarking, by defuault it is memgraph", default=helpers.get_binary_path("memgraph"), ) parser.add_argument( "--vendor-name", default="memgraph", help="Input vendor binary name (memgraph, neo4j)", ) parser.add_argument( "--client-binary", default=helpers.get_binary_path("tests/mgbench/client"), help="Client binary used for benchmarking", ) parser.add_argument( "--num-workers-for-import", type=int, default=multiprocessing.cpu_count() // 2, help="number of workers used to import the dataset", ) parser.add_argument( "--num-workers-for-benchmark", type=int, default=1, help="number of workers used to execute the benchmark", ) parser.add_argument( "--single-threaded-runtime-sec", type=int, default=10, help="single threaded duration of each query", ) parser.add_argument( "--no-load-query-counts", action="store_true", help="disable loading of cached query counts", ) parser.add_argument( "--no-save-query-counts", action="store_true", help="disable storing of cached query counts", ) parser.add_argument( "--export-results", default="", help="file path into which results should be exported", ) parser.add_argument( "--temporary-directory", default="/tmp", help="directory path where temporary data should " "be stored", ) parser.add_argument("--no-properties-on-edges", action="store_true", help="disable properties on edges") parser.add_argument("--bolt-port", default=7687, help="memgraph bolt port") parser.add_argument( "--no-authorization", action="store_false", default=True, help="Run each query with authorization", ) parser.add_argument( "--warmup-run", action="store_true", default=False, help="Run warmup before benchmarks", ) parser.add_argument( "--mixed-workload", nargs="*", type=int, default=[], help="""Define combination that defines the mixed workload. Mixed workload can be run as a single configuration for all groups of queries, Pass the positional arguments as values of what percentage of write/read/update/analytical queries you want to have in your workload. Example: --mixed-workload 1000 20 70 10 0 will execute 1000 queries, 20% write, 70% read, 10% update and 0% analytical. Mixed workload can also be run on each query under some defined load. By passing one more positional argument, you are defining what percentage of that query will be in mixed workload, and this is executed for each query. The rest of the queries will be selected from the appropriate groups Running --mixed-workload 1000 30 0 0 0 70, will execute each query 700 times or 70%, with the presence of 300 write queries from write type or 30%""", ) parser.add_argument("--tail-latency", type=int, default=100, help="Number of queries for the tail latency statistics") parser.add_argument( "--performance-tracking", action="store_true", default=False, help="Flag for runners performance tracking, this logs RES through time and vendor specific performance tracking.", ) args = parser.parse_args() class Workload: def __init__(self, config): config_len = len(config) if config_len == 0: self.name = "Isolated" self.config = config elif config_len >= 5: if sum(config[1:]) != 100: raise Exception( "Please make sure that passed arguments % sum to 100% percent!, passed: ", config, ) if config_len == 5: self.name = "Realistic" self.config = config else: self.name = "Mixed" self.config = config def get_queries(gen, count): # Make the generator deterministic. random.seed(gen.__name__) # Generate queries. ret = [] for i in range(count): ret.append(gen()) return ret def match_patterns(dataset, variant, group, query, is_default_variant, patterns): for pattern in patterns: verdict = [fnmatch.fnmatchcase(dataset, pattern[0])] if pattern[1] != "": verdict.append(fnmatch.fnmatchcase(variant, pattern[1])) else: verdict.append(is_default_variant) verdict.append(fnmatch.fnmatchcase(group, pattern[2])) verdict.append(fnmatch.fnmatchcase(query, pattern[3])) if all(verdict): return True return False def filter_benchmarks(generators, patterns): patterns = copy.deepcopy(patterns) for i in range(len(patterns)): pattern = patterns[i].split("/") if len(pattern) > 5 or len(pattern) == 0: raise Exception("Invalid benchmark description '" + pattern + "'!") pattern.extend(["", "*", "*"][len(pattern) - 1 :]) patterns[i] = pattern filtered = [] for dataset in sorted(generators.keys()): generator, queries = generators[dataset] for variant in generator.VARIANTS: is_default_variant = variant == generator.DEFAULT_VARIANT current = collections.defaultdict(list) for group in queries: for query_name, query_func in queries[group]: if match_patterns( dataset, variant, group, query_name, is_default_variant, patterns, ): current[group].append((query_name, query_func)) if len(current) > 0: filtered.append((generator(variant, args.vendor_name), dict(current))) return filtered def warmup(client): print("Executing warm-up queries") client.execute( queries=[ ("CREATE ();", {}), ("CREATE ()-[:TempEdge]->();", {}), ("MATCH (n) RETURN n LIMIT 1;", {}), ], num_workers=1, ) def tail_latency(vendor, client, func): vendor.start_benchmark("tail_latency") if args.warmup_run: warmup(client) latency = [] iteration = args.tail_latency query_list = get_queries(func, iteration) for i in range(0, iteration): ret = client.execute(queries=[query_list[i]], num_workers=1) latency.append(ret[0]["duration"]) latency.sort() query_stats = { "iterations": iteration, "min": latency[0], "max": latency[iteration - 1], "mean": statistics.mean(latency), "p99": latency[math.floor(iteration * 0.99) - 1], "p95": latency[math.floor(iteration * 0.95) - 1], "p90": latency[math.floor(iteration * 0.90) - 1], "p75": latency[math.floor(iteration * 0.75) - 1], "p50": latency[math.floor(iteration * 0.50) - 1], } print("Query statistics for tail latency: ") print(query_stats) vendor.stop("tail_latency") return query_stats def mixed_workload(vendor, client, dataset, group, queries, workload): num_of_queries = workload.config[0] percentage_distribution = workload.config[1:] if sum(percentage_distribution) != 100: raise Exception( "Please make sure that passed arguments % sum to 100% percent!, passed: ", percentage_distribution, ) s = [str(i) for i in workload.config] config_distribution = "_".join(s) print("Generating mixed workload.") percentages_by_type = { "write": percentage_distribution[0], "read": percentage_distribution[1], "update": percentage_distribution[2], "analytical": percentage_distribution[3], } queries_by_type = { "write": [], "read": [], "update": [], "analytical": [], } for (_, funcname) in queries[group]: for key in queries_by_type.keys(): if key in funcname: queries_by_type[key].append(funcname) for key, percentage in percentages_by_type.items(): if percentage != 0 and len(queries_by_type[key]) == 0: raise Exception( "There is a missing query in group (write, read, update or analytical) for given workload distribution." ) random.seed(config_distribution) # Executing mixed workload for each test if workload.name == "Mixed": for query, funcname in queries[group]: full_workload = [] log.info( "Running query in mixed workload:", "{}/{}/{}".format( group, query, funcname, ), ) base_query = getattr(dataset, funcname) base_query_type = funcname.rsplit("_", 1)[1] if percentages_by_type.get(base_query_type, 0) > 0: continue options = ["write", "read", "update", "analytical", "query"] function_type = random.choices(population=options, weights=percentage_distribution, k=num_of_queries) for t in function_type: # Get the apropropriate functions with same probabilty if t == "query": full_workload.append(base_query()) else: funcname = random.choices(queries_by_type[t], k=1)[0] aditional_query = getattr(dataset, funcname) full_workload.append(aditional_query()) vendor.start_benchmark( dataset.NAME + dataset.get_variant() + "_" + "mixed" + "_" + query + "_" + config_distribution ) if args.warmup_run: warmup(client) ret = client.execute( queries=full_workload, num_workers=args.num_workers_for_benchmark, )[0] usage_workload = vendor.stop( dataset.NAME + dataset.get_variant() + "_" + "mixed" + "_" + query + "_" + config_distribution ) ret["database"] = usage_workload results_key = [ dataset.NAME, dataset.get_variant(), group, query + "_" + config_distribution, WITHOUT_FINE_GRAINED_AUTHORIZATION, ] results.set_value(*results_key, value=ret) else: # Executing mixed workload from groups of queries full_workload = [] options = ["write", "read", "update", "analytical"] function_type = random.choices(population=options, weights=percentage_distribution, k=num_of_queries) for t in function_type: # Get the apropropriate functions with same probabilty funcname = random.choices(queries_by_type[t], k=1)[0] aditional_query = getattr(dataset, funcname) full_workload.append(aditional_query()) vendor.start_benchmark(dataset.NAME + dataset.get_variant() + "_" + workload.name + "_" + config_distribution) if args.warmup_run: warmup(client) ret = client.execute( queries=full_workload, num_workers=args.num_workers_for_benchmark, )[0] usage_workload = vendor.stop( dataset.NAME + dataset.get_variant() + "_" + workload.name + "_" + config_distribution ) mixed_workload = { "count": ret["count"], "duration": ret["duration"], "retries": ret["retries"], "throughput": ret["throughput"], "num_workers": ret["num_workers"], "database": usage_workload, } results_key = [ dataset.NAME, dataset.get_variant(), group, config_distribution, WITHOUT_FINE_GRAINED_AUTHORIZATION, ] results.set_value(*results_key, value=mixed_workload) print(mixed_workload) def get_query_cache_count(vendor, client, func, config_key): cached_count = config.get_value(*config_key) if cached_count is None: print( "Determining the number of queries necessary for", args.single_threaded_runtime_sec, "seconds of single-threaded runtime...", ) # First run to prime the query caches. vendor.start_benchmark("cache") if args.warmup_run: warmup(client) client.execute(queries=get_queries(func, 1), num_workers=1) # Get a sense of the runtime. count = 1 while True: ret = client.execute(queries=get_queries(func, count), num_workers=1) duration = ret[0]["duration"] should_execute = int(args.single_threaded_runtime_sec / (duration / count)) print( "executed_queries={}, total_duration={}, " "query_duration={}, estimated_count={}".format(count, duration, duration / count, should_execute) ) # We don't have to execute the next iteration when # `should_execute` becomes the same order of magnitude as # `count * 10`. if should_execute / (count * 10) < 10: count = should_execute break else: count = count * 10 vendor.stop("cache") # Lower bound for count if count < 20: count = 20 config.set_value( *config_key, value={ "count": count, "duration": args.single_threaded_runtime_sec, }, ) else: print( "Using cached query count of", cached_count["count"], "queries for", cached_count["duration"], "seconds of single-threaded runtime.", ) count = int(cached_count["count"] * args.single_threaded_runtime_sec / cached_count["duration"]) return count # Testing pre commit. # Detect available datasets. generators = {} for key in dir(datasets): if key.startswith("_"): continue dataset = getattr(datasets, key) if not inspect.isclass(dataset) or dataset == datasets.Dataset or not issubclass(dataset, datasets.Dataset): continue queries = collections.defaultdict(list) for funcname in dir(dataset): if not funcname.startswith("benchmark__"): continue group, query = funcname.split("__")[1:] queries[group].append((query, funcname)) generators[dataset.NAME] = (dataset, dict(queries)) if dataset.PROPERTIES_ON_EDGES and args.no_properties_on_edges: raise Exception( 'The "{}" dataset requires properties on edges, ' "but you have disabled them!".format(dataset.NAME) ) # List datasets if there is no specified dataset. if len(args.benchmarks) == 0: log.init("Available queries") for name in sorted(generators.keys()): print("Dataset:", name) dataset, queries = generators[name] print( " Variants:", ", ".join(dataset.VARIANTS), "(default: " + dataset.DEFAULT_VARIANT + ")", ) for group in sorted(queries.keys()): print(" Group:", group) for query_name, query_func in queries[group]: print(" Query:", query_name) sys.exit(0) # Create cache, config and results objects. cache = helpers.Cache() if not args.no_load_query_counts: config = cache.load_config() else: config = helpers.RecursiveDict() results = helpers.RecursiveDict() # Filter out the generators. benchmarks = filter_benchmarks(generators, args.benchmarks) # Run all specified benchmarks. for dataset, queries in benchmarks: workload = Workload(args.mixed_workload) run_config = { "vendor": args.vendor_name, "condition": "hot" if args.warmup_run else "cold", "workload": workload.name, "workload_config": workload.config, } results.set_value("__run_configuration__", value=run_config) log.init("Preparing", dataset.NAME + "/" + dataset.get_variant(), "dataset") dataset.prepare(cache.cache_directory("datasets", dataset.NAME, dataset.get_variant())) # TODO: Create some abstract class for vendors, that will hold this data if args.vendor_name == "neo4j": vendor = runners.Neo4j( args.vendor_binary, args.temporary_directory, args.bolt_port, args.performance_tracking, ) else: vendor = runners.Memgraph( args.vendor_binary, args.temporary_directory, not args.no_properties_on_edges, args.bolt_port, args.performance_tracking, ) client = runners.Client(args.client_binary, args.temporary_directory, args.bolt_port) ret = None usage = None if args.vendor_name == "neo4j": vendor.start_preparation("preparation") print("Executing database cleanup and index setup...") ret = client.execute(file_path=dataset.get_index(), num_workers=args.num_workers_for_import) usage = vendor.stop("preparation") dump_dir = cache.cache_directory("datasets", dataset.NAME, dataset.get_variant()) dump_file, exists = dump_dir.get_file("neo4j.dump") if exists: vendor.load_db_from_dump(path=dump_dir.get_path()) else: vendor.start_preparation("import") print("Importing dataset...") ret = client.execute(file_path=dataset.get_file(), num_workers=args.num_workers_for_import) usage = vendor.stop("import") vendor.dump_db(path=dump_dir.get_path()) else: vendor.start_preparation("import") print("Executing database cleanup and index setup...") ret = client.execute(file_path=dataset.get_index(), num_workers=args.num_workers_for_import) print("Importing dataset...") ret = client.execute(file_path=dataset.get_file(), num_workers=args.num_workers_for_import) usage = vendor.stop("import") # Save import results. import_key = [dataset.NAME, dataset.get_variant(), "__import__"] if ret != None and usage != None: # Display import statistics. print() for row in ret: print( "Executed", row["count"], "queries in", row["duration"], "seconds using", row["num_workers"], "workers with a total throughput of", row["throughput"], "queries/second.", ) print() print( "The database used", usage["cpu"], "seconds of CPU time and peaked at", usage["memory"] / 1024 / 1024, "MiB of RAM.", ) results.set_value(*import_key, value={"client": ret, "database": usage}) else: results.set_value(*import_key, value={"client": "dump_load", "database": "dump_load"}) # Run all benchmarks in all available groups. for group in sorted(queries.keys()): # Running queries in mixed workload if workload.name == "Mixed" or workload.name == "Realistic": mixed_workload(vendor, client, dataset, group, queries, workload) else: for query, funcname in queries[group]: log.info( "Running query:", "{}/{}/{}/{}".format(group, query, funcname, WITHOUT_FINE_GRAINED_AUTHORIZATION), ) func = getattr(dataset, funcname) query_statistics = tail_latency(vendor, client, func) # Query count for each vendor config_key = [ dataset.NAME, dataset.get_variant(), args.vendor_name, group, query, ] count = get_query_cache_count(vendor, client, func, config_key) # Benchmark run. print("Sample query:", get_queries(func, 1)[0][0]) print( "Executing benchmark with", count, "queries that should " "yield a single-threaded runtime of", args.single_threaded_runtime_sec, "seconds.", ) print( "Queries are executed using", args.num_workers_for_benchmark, "concurrent clients.", ) vendor.start_benchmark(dataset.NAME + dataset.get_variant() + "_" + workload.name + "_" + query) if args.warmup_run: warmup(client) ret = client.execute( queries=get_queries(func, count), num_workers=args.num_workers_for_benchmark, )[0] usage = vendor.stop(dataset.NAME + dataset.get_variant() + "_" + workload.name + "_" + query) ret["database"] = usage ret["query_statistics"] = query_statistics # Output summary. print() print("Executed", ret["count"], "queries in", ret["duration"], "seconds.") print("Queries have been retried", ret["retries"], "times.") print("Database used {:.3f} seconds of CPU time.".format(usage["cpu"])) print("Database peaked at {:.3f} MiB of memory.".format(usage["memory"] / 1024.0 / 1024.0)) print("{:<31} {:>20} {:>20} {:>20}".format("Metadata:", "min", "avg", "max")) metadata = ret["metadata"] for key in sorted(metadata.keys()): print( "{name:>30}: {minimum:>20.06f} {average:>20.06f} " "{maximum:>20.06f}".format(name=key, **metadata[key]) ) log.success("Throughput: {:02f} QPS".format(ret["throughput"])) # Save results. results_key = [ dataset.NAME, dataset.get_variant(), group, query, WITHOUT_FINE_GRAINED_AUTHORIZATION, ] results.set_value(*results_key, value=ret) ## If there is need for authorization testing. if args.no_authorization: print("Running query with authorization") vendor.start_benchmark("authorization") client.execute( queries=[ ("CREATE USER user IDENTIFIED BY 'test';", {}), ("GRANT ALL PRIVILEGES TO user;", {}), ("GRANT CREATE_DELETE ON EDGE_TYPES * TO user;", {}), ("GRANT CREATE_DELETE ON LABELS * TO user;", {}), ] ) client = runners.Client( args.client_binary, args.temporary_directory, args.bolt_port, username="user", password="test", ) vendor.stop("authorization") for query, funcname in queries[group]: log.info( "Running query:", "{}/{}/{}/{}".format(group, query, funcname, WITH_FINE_GRAINED_AUTHORIZATION), ) func = getattr(dataset, funcname) query_statistics = tail_latency(vendor, client, func) config_key = [ dataset.NAME, dataset.get_variant(), args.vendor_name, group, query, ] count = get_query_cache_count(vendor, client, func, config_key) vendor.start_benchmark("authorization") if args.warmup_run: warmup(client) ret = client.execute( queries=get_queries(func, count), num_workers=args.num_workers_for_benchmark, )[0] usage = vendor.stop("authorization") ret["database"] = usage ret["query_statistics"] = query_statistics # Output summary. print() print( "Executed", ret["count"], "queries in", ret["duration"], "seconds.", ) print("Queries have been retried", ret["retries"], "times.") print("Database used {:.3f} seconds of CPU time.".format(usage["cpu"])) print("Database peaked at {:.3f} MiB of memory.".format(usage["memory"] / 1024.0 / 1024.0)) print("{:<31} {:>20} {:>20} {:>20}".format("Metadata:", "min", "avg", "max")) metadata = ret["metadata"] for key in sorted(metadata.keys()): print( "{name:>30}: {minimum:>20.06f} {average:>20.06f} " "{maximum:>20.06f}".format(name=key, **metadata[key]) ) log.success("Throughput: {:02f} QPS".format(ret["throughput"])) # Save results. results_key = [ dataset.NAME, dataset.get_variant(), group, query, WITH_FINE_GRAINED_AUTHORIZATION, ] results.set_value(*results_key, value=ret) # Clean up database from any roles and users job vendor.start_benchmark("authorizations") ret = client.execute( queries=[ ("REVOKE LABELS * FROM user;", {}), ("REVOKE EDGE_TYPES * FROM user;", {}), ("DROP USER user;", {}), ] ) vendor.stop("authorization") # Save configuration. if not args.no_save_query_counts: cache.save_config(config) # Export results. if args.export_results: with open(args.export_results, "w") as f: json.dump(results.get_data(), f)