blob: e84148ba37d72a53cb857ef620a7bfb7b0a45eb0 [file] [log] [blame]
#!/usr/bin/env impala-python
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
# Used to extract minimum memory data for test_mem_usage_scaling.py from stress test
# runtime info json.
#
# Usage
# =====
# Run the stress test binary search on the 3 node minicluster:
#
# ./tests/stress/concurrent_select.py --tpch-db=tpch_parquet \
# --runtime-info-path=mem_usage_scaling_runtime_info.json --samples 3 \
# --mem_limit_eq_threshold_percent=0.01 --mem_limit_eq_threshold_mb=5 \
# --common-query-options="default_spillable_buffer_size=256k"
#
# Then run this script to extract minimum memory:
#
# ./tests/stress/extract_min_mem.py mem_usage_scaling_runtime_info.json
#
import json
import sys
results = []
with open(sys.argv[1]) as f:
data = json.load(f)
for query_data in data['db_names']['tpch_parquet'].itervalues():
runtime_info = query_data['[]']
# Build up list of query numbers and minimum memory.
results.append((int(runtime_info['name'][1:]),
runtime_info['required_mem_mb_with_spilling']))
results.sort()
print ', '.join(["'Q{0}': {1}".format(num, mem) for num, mem in results])