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https://github.com/codex-storage/bittorrent-benchmarks.git
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113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
import logging
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from collections.abc import Iterator
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from typing import Optional, Tuple, Any, Dict
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from elasticsearch import Elasticsearch
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from benchmarks.logging.sources.sources import LogSource, ExperimentId, NodeId, RawLine
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GROUP_LABEL = "app.kubernetes.io/part-of"
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EXPERIMENT_LABEL = "app.kubernetes.io/instance"
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logger = logging.getLogger(__name__)
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class LogstashSource(LogSource):
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"""Log source for logs stored in Elasticsearch by Logstash. This is typically used when running experiments
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in a Kubernetes cluster."""
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def __init__(
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self,
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client: Elasticsearch,
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structured_only: bool = False,
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chronological: bool = False,
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):
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"""
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@:param client: Elasticsearch client to use for retrieving logs
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@:param structured_only: If True, only return structured log lines (those starting with '>>').
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@:param chronological: If True, return logs in chronological order. This is mostly meant for use
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in testing, and can get quite slow/expensive for large queries.
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"""
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self.client = client
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self.structured_only = structured_only
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self.chronological = chronological
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def experiments(self, group_id: str) -> Iterator[str]:
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"""Retrieves all experiment IDs within an experiment group."""
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query = {
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"size": 0,
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"query": {
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"constant_score": {
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"filter": {"term": {f"pod_labels.{GROUP_LABEL}.keyword": group_id}}
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}
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},
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"aggs": {
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"experiments": {
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"terms": {
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"field": f"pod_labels.{EXPERIMENT_LABEL}.keyword",
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"size": 1000,
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}
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}
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},
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}
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response = self.client.search(index="benchmarks-*", body=query)
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for bucket in response["aggregations"]["experiments"]["buckets"]:
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yield bucket["key"]
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def logs(
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self, group_id: str, experiment_id: Optional[str] = None
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) -> Iterator[Tuple[ExperimentId, NodeId, RawLine]]:
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"""Retrieves logs for either all experiments within a group, or a specific experiment."""
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filters = [{"term": {f"pod_labels.{GROUP_LABEL}.keyword": group_id}}]
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if experiment_id:
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filters.append(
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{"term": {f"pod_labels.{EXPERIMENT_LABEL}.keyword": experiment_id}}
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)
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if self.structured_only:
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filters.append({"match_phrase": {"message": "entry_type"}})
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query: Dict[str, Any] = {"query": {"bool": {"filter": filters}}}
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if self.chronological:
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query["sort"] = [{"@timestamp": {"order": "asc"}}]
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# Scrolls are much cheaper than queries.
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scroll_response = self.client.search(
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index="benchmarks-*", body=query, scroll="2m"
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)
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scroll_id = scroll_response["_scroll_id"]
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try:
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while True:
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hits = scroll_response["hits"]["hits"]
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if not hits:
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break
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for hit in hits:
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source = hit["_source"]
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message = source["message"]
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experiment_id = source["pod_labels"][EXPERIMENT_LABEL]
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node_id = source["pod_name"]
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if (
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not isinstance(experiment_id, str)
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or not isinstance(node_id, str)
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or not isinstance(message, str)
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):
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logger.warning(
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"Skipping log entry with invalid data: %s", source
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)
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continue
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yield experiment_id, node_id, message
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# Get next batch of results
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scroll_response = self.client.scroll(scroll_id=scroll_id, scroll="2m")
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finally:
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# Clean up scroll context
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self.client.clear_scroll(scroll_id=scroll_id)
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