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Extended use-cases to include secondary index ones #87
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@@ -1,8 +1,8 @@ | ||
numpy==1.22.0 | ||
tqdm==4.30.0 | ||
boto3==1.13.24 | ||
tdigest==0.5.2.2 | ||
matplotlib==3.2.1 | ||
common==0.1.2 | ||
python_dateutil==2.8.1 | ||
numpy>=1.22.0 | ||
tqdm>=4.30.0 | ||
boto3>=1.13.24 | ||
tdigest>=0.5.2.2 | ||
matplotlib>=3.2.1 | ||
common>=0.1.2 | ||
python_dateutil>=2.8.1 | ||
urllib3>=1.24.5 # not directly required, pinned by Snyk to avoid a vulnerability |
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import sys | ||
import os | ||
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sys.path.append(os.getcwd() + "/..") |
309 changes: 309 additions & 0 deletions
309
scripts/datagen_redisearch/simple_doc_by_id/ftsb_generate_json_singlevalue_numeric.py
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#!/usr/bin/python3 | ||
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import argparse | ||
import csv | ||
import json | ||
import os | ||
import random | ||
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# package local imports | ||
import sys | ||
import uuid | ||
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import boto3 | ||
from tqdm import tqdm | ||
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sys.path.append(os.getcwd() + "/..") | ||
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from common_datagen import ( | ||
download_url, | ||
generate_setup_json, | ||
compress_files, | ||
generate_inputs_dict_item, | ||
humanized_bytes, | ||
del_non_use_case_specific_keys, | ||
add_key_metric, | ||
upload_dataset_artifacts_s3, | ||
add_deployment_requirements_redis_server_module, | ||
add_deployment_requirements_benchmark_tool, | ||
add_deployment_requirements_utilities, | ||
init_deployment_requirement, | ||
remove_file_if_exists, | ||
) | ||
from pathlib import Path | ||
import string | ||
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def str_to_float_or_zero(entry): | ||
val = 0.0 | ||
try: | ||
val = float(entry) | ||
except ValueError as e: | ||
pass | ||
return val | ||
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def index_or_none(list, value): | ||
index = None | ||
try: | ||
index = list.index(value) | ||
except ValueError: | ||
pass | ||
return index | ||
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def rand_str(minN, maxN): | ||
return "".join( | ||
random.choices( | ||
string.ascii_uppercase + string.digits, k=random.randint(minN, maxN) | ||
) | ||
) | ||
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def rand_arr(minN, maxN): | ||
arr = [] | ||
for x in range(1, random.randint(minN, maxN)): | ||
arr.append(rand_str(3, 10)) | ||
return arr | ||
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def rand_numeric_arr(minN, maxN): | ||
arr = [] | ||
for x in range(1, random.randint(minN, maxN)): | ||
arr.append(get_rand_int_v()) | ||
return arr | ||
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def get_rand_int_v(start_val=-1000, end_val=1000): | ||
return random.randint(start_val, end_val) | ||
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def get_rand_float_v(start_val=-1000.0, end_val=1000.0): | ||
return random.random() * (end_val - start_val) + start_val | ||
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def rand_numeric_float_arr(minN, maxN): | ||
arr = [] | ||
for x in range(1, random.randint(minN, maxN)): | ||
arr.append(get_rand_float_v()) | ||
return arr | ||
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def use_case_csv_row_to_cmd(doc_id): | ||
numeric_int = rand_numeric_arr(1, 10) | ||
numeric_float = rand_numeric_float_arr(1, 10) | ||
doc = {} | ||
for n, v in enumerate(numeric_int): | ||
doc["numericInt{}".format(n + 1)] = v | ||
for n, v in enumerate(numeric_float): | ||
doc["numericFloat{}".format(n + 1)] = v | ||
docid_str = "doc:single:{hash}:{n}".format(hash=uuid.uuid4().hex, n=doc_id) | ||
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cmd = ["WRITE", "W1", 1, "JSON.SET", docid_str, ".", "{}".format(json.dumps(doc))] | ||
return docid_str, cmd | ||
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def human_format(num): | ||
magnitude = 0 | ||
while abs(num) >= 1000: | ||
magnitude += 1 | ||
num /= 1000.0 | ||
# add more suffixes if you need them | ||
return "%.0f%s" % (num, ["", "K", "M", "G", "T", "P"][magnitude]) | ||
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def ft_search_numeric_int(index_name): | ||
val_from = get_rand_int_v(-1000, 500) | ||
val_to = get_rand_int_v(val_from + 1) | ||
condition = "'@numericInt1:[{} {}]".format(val_from, val_to) | ||
for n in range(2, 11): | ||
condition = condition + "|@numericInt{}:[{} {}]".format(n, val_from, val_to) | ||
condition = condition + "'" | ||
return ["READ", "R1", 1, "FT.SEARCH", index_name, condition, "NOCONTENT"] | ||
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def ft_search_numeric_float(index_name): | ||
val_from = get_rand_float_v(-1000.0, 500.0) | ||
val_to = get_rand_float_v(val_from + 1.0) | ||
condition = "'@numericFloat1:[{} {}]".format(val_from, val_to) | ||
for n in range(2, 11): | ||
condition = condition + "|@numericFloat{}:[{} {}]".format(n, val_from, val_to) | ||
condition = condition + "'" | ||
return ["READ", "R2", 1, "FT.SEARCH", index_name, condition, "NOCONTENT"] | ||
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SEARCH_NUMERIC_FLOAT = "FT.SEARCH-SINGLEVALUE-FLOAT" | ||
SEARCH_NUMERIC_INT = "FT.SEARCH-SINGLEVALUE-INT" | ||
"" | ||
choices_str = ",".join([SEARCH_NUMERIC_FLOAT, SEARCH_NUMERIC_INT]) | ||
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if __name__ == "__main__": | ||
parser = argparse.ArgumentParser( | ||
description="RediSearch FTSB data generator.", | ||
formatter_class=argparse.ArgumentDefaultsHelpFormatter, | ||
) | ||
parser.add_argument( | ||
"--project", type=str, default="redisjson", help="the project being tested" | ||
) | ||
parser.add_argument( | ||
"--index-name", | ||
type=str, | ||
default="idx:single", | ||
help="the index name used for search commands", | ||
) | ||
parser.add_argument( | ||
"--seed", | ||
type=int, | ||
default=12345, | ||
help="the random seed used to generate random deterministic outputs", | ||
) | ||
parser.add_argument( | ||
"--query-choices", | ||
type=str, | ||
default=choices_str, | ||
help="comma separated list of queries to produce. one of: {}".format( | ||
choices_str | ||
), | ||
) | ||
parser.add_argument( | ||
"--doc-limit", | ||
type=int, | ||
default=1000000, | ||
help="the total documents to generate to be added in the setup stage", | ||
) | ||
parser.add_argument( | ||
"--total-benchmark-commands", | ||
type=int, | ||
default=1000000, | ||
help="the total commands to generate to be issued in the benchmark stage", | ||
) | ||
parser.add_argument( | ||
"--test-name", | ||
type=str, | ||
default="singlevalue-numeric-json", | ||
help="the name of the test", | ||
) | ||
parser.add_argument( | ||
"--test-description", | ||
type=str, | ||
default="benchmark making usage of POST format as the dump of StackOverflow posts.", | ||
help="the full description of the test", | ||
) | ||
parser.add_argument( | ||
"--upload-artifacts-s3", | ||
default=False, | ||
action="store_true", | ||
help="uploads the generated dataset files and configuration file to public benchmarks.redislabs bucket. Proper credentials are required", | ||
) | ||
parser.add_argument( | ||
"--upload-artifacts-s3-uncompressed", | ||
action="store_true", | ||
help="uploads the generated dataset files and configuration file to public benchmarks.redislabs bucket. Proper credentials are required", | ||
) | ||
parser.add_argument( | ||
"--temporary-work-dir", | ||
type=str, | ||
default="./tmp", | ||
help="The temporary dir to use as working directory for file download, compression,etc... ", | ||
) | ||
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args = parser.parse_args() | ||
use_case_specific_arguments = del_non_use_case_specific_keys(dict(args.__dict__)) | ||
query_choices = args.query_choices.split(",") | ||
total_benchmark_commands = args.total_benchmark_commands | ||
# generate the temporary working dir if required | ||
working_dir = args.temporary_work_dir | ||
Path(working_dir).mkdir(parents=True, exist_ok=True) | ||
seed = args.seed | ||
project = args.project | ||
doc_limit = args.doc_limit | ||
test_name = args.test_name | ||
index_name = args.index_name | ||
description = args.test_description | ||
test_name = "{}-{}".format(human_format(doc_limit), test_name) | ||
s3_bucket_name = "benchmarks.redislabs" | ||
s3_bucket_path = "redisearch/datasets/{}/".format(test_name) | ||
s3_uri = "https://s3.amazonaws.com/{bucket_name}/{bucket_path}".format( | ||
bucket_name=s3_bucket_name, bucket_path=s3_bucket_path | ||
) | ||
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benchmark_output_file = "{test_name}.{project}.commands".format( | ||
test_name=test_name, project=project | ||
) | ||
benchmark_config_file = "{test_name}.{project}.cfg.json".format( | ||
test_name=test_name, project=project | ||
) | ||
bench_fname = "{}.BENCH.csv".format(benchmark_output_file, "__".join(query_choices)) | ||
setup_fname = "{}.SETUP.csv".format(benchmark_output_file) | ||
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## remove previous files if they exist | ||
remove_file_if_exists(benchmark_config_file) | ||
remove_file_if_exists(bench_fname) | ||
remove_file_if_exists(setup_fname) | ||
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used_indices = [] | ||
setup_commands = [] | ||
teardown_commands = [] | ||
key_metrics = [] | ||
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total_writes = 0 | ||
total_reads = 0 | ||
total_updates = 0 | ||
total_deletes = 0 | ||
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json_version = "0.1" | ||
benchmark_repetitions_require_teardown_and_resetup = True | ||
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print("-- Benchmark: {} -- ".format(test_name)) | ||
print("-- Description: {} -- ".format(description)) | ||
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total_docs = 0 | ||
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print("Using random seed {0}".format(args.seed)) | ||
random.seed(args.seed) | ||
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total_docs = 0 | ||
doc_ids = [] | ||
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progress = tqdm(unit="docs", total=doc_limit) | ||
all_csvfile = open(setup_fname, "a", newline="") | ||
all_csv_writer = csv.writer(all_csvfile, delimiter=",") | ||
for row_n in range(0, doc_limit): | ||
docid, cmd = use_case_csv_row_to_cmd(row_n) | ||
all_csv_writer.writerow(cmd) | ||
progress.update() | ||
doc_ids.append(docid) | ||
progress.close() | ||
all_csvfile.close() | ||
progress = tqdm(unit="docs", total=total_benchmark_commands) | ||
all_csvfile = open(bench_fname, "a", newline="") | ||
all_csv_writer = csv.writer(all_csvfile, delimiter=",") | ||
len_docs = len(doc_ids) | ||
row_n = 0 | ||
while row_n < total_benchmark_commands: | ||
doc_id = doc_ids[random.randint(0, len_docs - 1)] | ||
choice = random.choices(query_choices)[0] | ||
if choice == SEARCH_NUMERIC_INT: | ||
cmd = ft_search_numeric_int(index_name) | ||
elif choice == SEARCH_NUMERIC_FLOAT: | ||
cmd = ft_search_numeric_float(index_name) | ||
row_n = row_n + 1 | ||
all_csv_writer.writerow(cmd) | ||
progress.update() | ||
progress.close() | ||
all_csvfile.close() | ||
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if args.upload_artifacts_s3: | ||
artifacts = [setup_fname, bench_fname] | ||
upload_dataset_artifacts_s3(s3_bucket_name, s3_bucket_path, artifacts) | ||
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print("############################################") | ||
print("All artifacts generated.") | ||
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create_cmd = "FT.CREATE {} ON JSON PREFIX 1 doc:single SCHEMA".format(index_name) | ||
for n in range(1, 11): | ||
create_cmd = create_cmd + " $.numericInt{} AS numericInt{} NUMERIC".format(n, n) | ||
create_cmd = create_cmd + " $.numericFloat{} AS numericFloat{} NUMERIC".format( | ||
n, n | ||
) | ||
print("FT.CREATE command:{}".format(create_cmd)) |
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opt.semgrep.python.lang.security.unquoted-csv-writer.unquoted-csv-writer: Found an unquoted CSV writer. This is susceptible to injection. Use
quoting=csv.QUOTE_ALL
.❗❗ 3 similar findings have been found in this PR
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[ 🙁 Not relevant ] - [ 😕 Won't fix ] - [ 😑 Not critical, will fix ] - [ 🙂 Critical, will fix ] - [ 😊 Critical, fixing now ]