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from __future__ import absolute_import |
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import re |
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import sys |
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import uuid |
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from datetime import date |
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from datetime import datetime |
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from decimal import Decimal |
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from functools import partial |
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from ..compat import reraise |
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from ..stats import normalize_stats |
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try: |
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import elasticsearch |
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from elasticsearch.serializer import JSONSerializer |
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except ImportError as exc: |
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reraise(ImportError, ImportError("Please install elasticsearch or pytest-benchmark[elasticsearch]", exc.args), |
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sys.exc_info()[2]) |
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class BenchmarkJSONSerializer(JSONSerializer): |
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def default(self, data): |
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if isinstance(data, (date, datetime)): |
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return data.isoformat() |
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elif isinstance(data, Decimal): |
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return float(data) |
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elif isinstance(data, uuid.UUID): |
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return str(data) |
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else: |
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return "UNSERIALIZABLE[%r]" % data |
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def _mask_hosts(hosts): |
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m = re.compile('^([^:]+)://[^@]+@') |
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sub_fun = partial(m.sub, '\\1://***:***@') |
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masked_hosts = list(map(sub_fun, hosts)) |
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return masked_hosts |
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class ElasticsearchStorage(object): |
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def __init__(self, hosts, index, doctype, project_name, logger, |
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default_machine_id=None): |
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self._es_hosts = hosts |
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self._es_index = index |
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self._es_doctype = doctype |
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self._es = elasticsearch.Elasticsearch(self._es_hosts, serializer=BenchmarkJSONSerializer()) |
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self._project_name = project_name |
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self.default_machine_id = default_machine_id |
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self.logger = logger |
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self._cache = {} |
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self._create_index() |
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def __str__(self): |
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return str(self._es_hosts) |
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@property |
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def location(self): |
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return str(self._es_hosts) |
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def query(self): |
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""" |
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Returns sorted records names (ids) that corresponds with project. |
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""" |
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body = { |
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"size": 0, |
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"aggs": { |
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"benchmark_ids": { |
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"terms": { |
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"field": "benchmark_id" |
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} |
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} |
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} |
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} |
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result = self._es.search(index=self._es_index, doc_type=self._es_doctype, body=body) |
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return sorted([record["key"] for record in result["aggregations"]["benchmark_ids"]["buckets"]]) |
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def load(self, id_prefix=None): |
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""" |
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Yield key and content of records that corresponds with project name. |
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""" |
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r = self._search(self._project_name, id_prefix) |
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groupped_data = self._group_by_commit_and_time(r["hits"]["hits"]) |
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result = [(key, value) for key, value in groupped_data.items()] |
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result.sort(key=lambda x: datetime.strptime(x[1]["datetime"], "%Y-%m-%dT%H:%M:%S.%f")) |
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for key, data in result: |
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for bench in data["benchmarks"]: |
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normalize_stats(bench["stats"]) |
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yield key, data |
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def _search(self, project, id_prefix=None): |
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body = { |
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"size": 1000, |
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"sort": [ |
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{ |
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"datetime": { |
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"order": "desc" |
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} |
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} |
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], |
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"query": { |
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"bool": { |
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"filter": { |
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"term": { |
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"commit_info.project": project |
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} |
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} |
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} |
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} |
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} |
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if id_prefix: |
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body["query"]["bool"]["must"] = { |
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"prefix": { |
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"_id": id_prefix |
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} |
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} |
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return self._es.search(index=self._es_index, doc_type=self._es_doctype, body=body) |
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@staticmethod |
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def _benchmark_from_es_record(source_es_record): |
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result = {} |
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for benchmark_key in ("group", "stats", "options", "param", "name", "params", "fullname", "benchmark_id"): |
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result[benchmark_key] = source_es_record[benchmark_key] |
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return result |
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@staticmethod |
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def _run_info_from_es_record(source_es_record): |
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result = {} |
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for run_key in ("machine_info", "commit_info", "datetime", "version"): |
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result[run_key] = source_es_record[run_key] |
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return result |
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def _group_by_commit_and_time(self, hits): |
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result = {} |
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for hit in hits: |
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source_hit = hit["_source"] |
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key = "%s_%s" % (source_hit["commit_info"]["id"], source_hit["datetime"]) |
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benchmark = self._benchmark_from_es_record(source_hit) |
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if key in result: |
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result[key]["benchmarks"].append(benchmark) |
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else: |
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run_info = self._run_info_from_es_record(source_hit) |
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run_info["benchmarks"] = [benchmark] |
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result[key] = run_info |
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return result |
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def load_benchmarks(self, *args): |
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""" |
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Yield benchmarks that corresponds with project. Put path and |
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source (uncommon part of path) to benchmark dict. |
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""" |
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id_prefix = args[0] if args else None |
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r = self._search(self._project_name, id_prefix) |
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for hit in r["hits"]["hits"]: |
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bench = self._benchmark_from_es_record(hit["_source"]) |
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bench.update(bench.pop("stats")) |
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bench["source"] = bench["benchmark_id"] |
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yield bench |
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def save(self, output_json, save): |
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output_benchmarks = output_json.pop("benchmarks") |
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for bench in output_benchmarks: |
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# add top level info from output_json dict to each record |
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bench.update(output_json) |
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benchmark_id = save |
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if self.default_machine_id: |
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benchmark_id = self.default_machine_id + "_" + benchmark_id |
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doc_id = benchmark_id + "_" + bench["fullname"] |
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bench["benchmark_id"] = benchmark_id |
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self._es.index( |
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index=self._es_index, |
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doc_type=self._es_doctype, |
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body=bench, |
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id=doc_id, |
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) |
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# hide user's credentials before logging |
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masked_hosts = _mask_hosts(self._es_hosts) |
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self.logger.info("Saved benchmark data to %s to index %s as doctype %s" % ( |
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masked_hosts, self._es_index, self._es_doctype)) |
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def _create_index(self): |
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mapping = { |
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"mappings": { |
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"benchmark": { |
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"properties": { |
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"commit_info": { |
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"properties": { |
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"dirty": { |
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"type": "boolean" |
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}, |
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"id": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"project": { |
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"type": "string", |
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"index": "not_analyzed" |
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} |
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} |
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}, |
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"datetime": { |
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"type": "date", |
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"format": "strict_date_optional_time||epoch_millis" |
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}, |
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"name": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"fullname": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"version": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"benchmark_id": { |
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"type": "string", |
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"index": "not_analyzed", |
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}, |
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"machine_info": { |
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"properties": { |
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"machine": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"node": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"processor": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"python_build": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"python_compiler": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"python_implementation": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"python_implementation_version": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"python_version": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"release": { |
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"type": "string", |
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"index": "not_analyzed" |
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}, |
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"system": { |
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"type": "string", |
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"index": "not_analyzed" |
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} |
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} |
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}, |
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"options": { |
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"properties": { |
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"disable_gc": { |
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"type": "boolean" |
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}, |
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"max_time": { |
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"type": "double" |
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}, |
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"min_rounds": { |
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"type": "long" |
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}, |
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"min_time": { |
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"type": "double" |
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}, |
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"timer": { |
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"type": "string" |
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}, |
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"warmup": { |
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"type": "boolean" |
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} |
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} |
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}, |
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"stats": { |
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"properties": { |
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"hd15iqr": { |
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"type": "double" |
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}, |
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"iqr": { |
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"type": "double" |
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}, |
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"iqr_outliers": { |
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"type": "long" |
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}, |
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"iterations": { |
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"type": "long" |
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}, |
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"ld15iqr": { |
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"type": "double" |
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}, |
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"max": { |
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"type": "double" |
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}, |
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"mean": { |
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"type": "double" |
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}, |
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"median": { |
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"type": "double" |
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}, |
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"min": { |
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"type": "double" |
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}, |
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"outliers": { |
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"type": "string" |
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}, |
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"q1": { |
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"type": "double" |
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}, |
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"q3": { |
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"type": "double" |
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}, |
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327
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"rounds": { |
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"type": "long" |
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329
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}, |
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"stddev": { |
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331
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"type": "double" |
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332
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}, |
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333
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"stddev_outliers": { |
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334
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"type": "long" |
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335
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}, |
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336
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"ops": { |
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337
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|
|
"type": "double" |
|
338
|
|
|
}, |
|
339
|
|
|
} |
|
340
|
|
|
}, |
|
341
|
|
|
} |
|
342
|
|
|
} |
|
343
|
|
|
} |
|
344
|
|
|
} |
|
345
|
|
|
self._es.indices.create(index=self._es_index, ignore=400, body=mapping) |
|
346
|
|
|
|
ionelmc /
pytest-benchmark
