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import datetime |
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class ElasticsearchStorage(object): |
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def __init__(self, elasticsearch_hosts, elasticsearch_index, elasticsearch_doctype, logger, default_machine_id=None): |
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try: |
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import elasticsearch |
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except ImportError as exc: |
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raise ImportError(exc.args, "Please install elasticsearch or pytest-benchmark[elasticsearch]") |
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self._elasticsearch_hosts = elasticsearch_hosts |
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self._elasticsearch_index = elasticsearch_index |
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self._elasticsearch_doctype = elasticsearch_doctype |
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self._elasticsearch = elasticsearch.Elasticsearch(self._elasticsearch_hosts) |
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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._elasticsearch_hosts) |
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@property |
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def location(self): |
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return str(self._elasticsearch_hosts) |
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def query(self, project): |
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""" |
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Returns sorted records names (ids) that corresponds with globs_or_files. |
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""" |
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return [commit_and_time for commit_and_time, _ in self.load(project)] |
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def load(self, project): |
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""" |
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Yield path and content of records that corresponds with globs_or_files |
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""" |
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r = self._search(project) |
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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.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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yield key, data |
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def _search(self, project): |
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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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return self._elasticsearch.search(index=self._elasticsearch_index, doc_type=self._elasticsearch_doctype, body=body) |
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@staticmethod |
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def _benchmark_from_es_record(source_es_record): |
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return {benchmark_key: source_es_record[benchmark_key] for benchmark_key in ("group", "stats", "options", "param", "name", "params", "fullname")} |
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@staticmethod |
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def _run_info_from_es_record(source_es_record): |
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return {run_key: source_es_record[run_key] for run_key in ("machine_info", "commit_info", "datetime", "version")} |
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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, project): |
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""" |
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Yield benchmarks that corresponds with glob_or_files. Put path and |
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source (uncommon part of path) to benchmark dict. |
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""" |
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r = self._search(project) |
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for hit in r["hits"]["hits"]: |
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yield self._benchmark_from_es_record(hit["_source"]) |
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def save(self, document, document_id): |
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self._elasticsearch.index( |
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index=self._elasticsearch_index, |
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doc_type=self._elasticsearch_doctype, |
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body=document, |
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id=document_id, |
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) |
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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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"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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"rounds": { |
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"type": "long" |
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}, |
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"stddev": { |
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"type": "double" |
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}, |
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"stddev_outliers": { |
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"type": "long" |
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} |
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} |
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}, |
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} |
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} |
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} |
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} |
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self._elasticsearch.indices.create(index=self._elasticsearch_index, ignore=400, body=mapping) |
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ionelmc /
pytest-benchmark
