Source code for airflow.providers.elasticsearch.log.es_task_handler

# 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
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations

import inspect
import logging
import sys
import warnings
from collections import defaultdict
from operator import attrgetter
from time import time
from typing import TYPE_CHECKING, Any, Callable, List, Tuple
from urllib.parse import quote, urlparse

# Using `from elasticsearch import *` would break elasticsearch mocking used in unit test.
import elasticsearch
import pendulum
from elasticsearch.exceptions import NotFoundError
from typing_extensions import Literal

from airflow.configuration import conf
from airflow.exceptions import AirflowProviderDeprecationWarning
from airflow.models.dagrun import DagRun
from airflow.providers.elasticsearch.log.es_json_formatter import ElasticsearchJSONFormatter
from airflow.providers.elasticsearch.log.es_response import ElasticSearchResponse, Hit
from airflow.utils import timezone
from airflow.utils.log.file_task_handler import FileTaskHandler
from airflow.utils.log.logging_mixin import ExternalLoggingMixin, LoggingMixin
from airflow.utils.session import create_session

    from datetime import datetime

    from airflow.models.taskinstance import TaskInstance

[docs]LOG_LINE_DEFAULTS = {"exc_text": "", "stack_info": ""}
# Elasticsearch hosted log type
[docs]EsLogMsgType = List[Tuple[str, str]]
# Compatibility: Airflow 2.3.3 and up uses this method, which accesses the # LogTemplate model to record the log ID template used. If this function does # not exist, the task handler should use the log_id_template attribute instead.
[docs]USE_PER_RUN_LOG_ID = hasattr(DagRun, "get_log_template")
[docs]VALID_ES_CONFIG_KEYS = set(inspect.signature(elasticsearch.Elasticsearch.__init__).parameters.keys())
# Remove `self` from the valid set of kwargs VALID_ES_CONFIG_KEYS.remove("self")
[docs]def get_es_kwargs_from_config() -> dict[str, Any]: elastic_search_config = conf.getsection("elasticsearch_configs") kwargs_dict = ( {key: value for key, value in elastic_search_config.items() if key in VALID_ES_CONFIG_KEYS} if elastic_search_config else {} ) # For elasticsearch>8 retry_timeout have changed for elasticsearch to retry_on_timeout # in Elasticsearch() compared to previous versions. # Read more at: if ( elastic_search_config and "retry_timeout" in elastic_search_config and not kwargs_dict.get("retry_on_timeout") ): retry_timeout = elastic_search_config.get("retry_timeout") if retry_timeout is not None: kwargs_dict["retry_on_timeout"] = retry_timeout return kwargs_dict
[docs]class ElasticsearchTaskHandler(FileTaskHandler, ExternalLoggingMixin, LoggingMixin): """ ElasticsearchTaskHandler is a python log handler that reads logs from Elasticsearch. Note that Airflow does not handle the indexing of logs into Elasticsearch. Instead, Airflow flushes logs into local files. Additional software setup is required to index the logs into Elasticsearch, such as using Filebeat and Logstash. To efficiently query and sort Elasticsearch results, this handler assumes each log message has a field `log_id` consists of ti primary keys: `log_id = {dag_id}-{task_id}-{execution_date}-{try_number}` Log messages with specific log_id are sorted based on `offset`, which is a unique integer indicates log message's order. Timestamps here are unreliable because multiple log messages might have the same timestamp. :param base_log_folder: base folder to store logs locally :param log_id_template: log id template :param host: Elasticsearch host name """
[docs] PAGE = 0
[docs] MAX_LINE_PER_PAGE = 1000
[docs] LOG_NAME = "Elasticsearch"
[docs] trigger_should_wrap = True
def __init__( self, base_log_folder: str, end_of_log_mark: str, write_stdout: bool, json_format: bool, json_fields: str, host_field: str = "host", offset_field: str = "offset", host: str = "http://localhost:9200", frontend: str = "localhost:5601", index_patterns: str | None = conf.get("elasticsearch", "index_patterns", fallback="_all"), es_kwargs: dict | None | Literal["default_es_kwargs"] = "default_es_kwargs", *, filename_template: str | None = None, log_id_template: str | None = None, ): es_kwargs = es_kwargs or {} if es_kwargs == "default_es_kwargs": es_kwargs = get_es_kwargs_from_config() host = self.format_url(host) super().__init__(base_log_folder, filename_template) self.closed = False self.client = elasticsearch.Elasticsearch(host, **es_kwargs) # type: ignore[attr-defined] # in airflow.cfg, host of elasticsearch has to be http://dockerhostXxxx:9200 if USE_PER_RUN_LOG_ID and log_id_template is not None: warnings.warn( "Passing log_id_template to ElasticsearchTaskHandler is deprecated and has no effect", AirflowProviderDeprecationWarning, ) self.log_id_template = log_id_template # Only used on Airflow < 2.3.2. self.frontend = frontend self.mark_end_on_close = True self.end_of_log_mark = end_of_log_mark.strip() self.write_stdout = write_stdout self.json_format = json_format self.json_fields = [label.strip() for label in json_fields.split(",")] self.host_field = host_field self.offset_field = offset_field self.index_patterns = index_patterns self.context_set = False self.formatter: logging.Formatter self.handler: logging.FileHandler | logging.StreamHandler # type: ignore[assignment] self._doc_type_map: dict[Any, Any] = {} self._doc_type: list[Any] = [] @staticmethod
[docs] def format_url(host: str) -> str: """ Formats the given host string to ensure it starts with 'http'. Checks if the host string represents a valid URL. :params host: The host string to format and check. """ parsed_url = urlparse(host) # Check if the scheme is either http or https # Handles also the Python 3.9+ case where urlparse understands "localhost:9200" # differently than urlparse in Python 3.8 and below ( if parsed_url.scheme not in ("http", "https"): host = "http://" + host parsed_url = urlparse(host) # Basic validation for a valid URL if not parsed_url.netloc: raise ValueError(f"'{host}' is not a valid URL.") return host
def _render_log_id(self, ti: TaskInstance, try_number: int) -> str: with create_session() as session: dag_run = ti.get_dagrun(session=session) if USE_PER_RUN_LOG_ID: log_id_template = dag_run.get_log_template(session=session).elasticsearch_id else: log_id_template = self.log_id_template try: dag = ti.task.dag except AttributeError: # ti.task is not always set. data_interval = (dag_run.data_interval_start, dag_run.data_interval_end) else: if TYPE_CHECKING: assert dag is not None data_interval = dag.get_run_data_interval(dag_run) if self.json_format: data_interval_start = self._clean_date(data_interval[0]) data_interval_end = self._clean_date(data_interval[1]) execution_date = self._clean_date(dag_run.execution_date) else: if data_interval[0]: data_interval_start = data_interval[0].isoformat() else: data_interval_start = "" if data_interval[1]: data_interval_end = data_interval[1].isoformat() else: data_interval_end = "" execution_date = dag_run.execution_date.isoformat() return log_id_template.format( dag_id=ti.dag_id, task_id=ti.task_id, run_id=getattr(ti, "run_id", ""), data_interval_start=data_interval_start, data_interval_end=data_interval_end, execution_date=execution_date, try_number=try_number, map_index=getattr(ti, "map_index", ""), ) @staticmethod def _clean_date(value: datetime | None) -> str: """ Clean up a date value so that it is safe to query in elasticsearch by removing reserved characters. """ if value is None: return "" return value.strftime("%Y_%m_%dT%H_%M_%S_%f") def _group_logs_by_host(self, logs): grouped_logs = defaultdict(list) for log in logs: key = getattr_nested(log, self.host_field, None) or "default_host" grouped_logs[key].append(log) return grouped_logs def _read_grouped_logs(self): return True def _read( self, ti: TaskInstance, try_number: int, metadata: dict | None = None ) -> tuple[EsLogMsgType, dict]: """ Endpoint for streaming log. :param ti: task instance object :param try_number: try_number of the task instance :param metadata: log metadata, can be used for steaming log reading and auto-tailing. :return: a list of tuple with host and log documents, metadata. """ if not metadata: metadata = {"offset": 0} if "offset" not in metadata: metadata["offset"] = 0 offset = metadata["offset"] log_id = self._render_log_id(ti, try_number) logs = self.es_read(log_id, offset, metadata) logs_by_host = self._group_logs_by_host(logs) next_offset = offset if not logs else attrgetter(self.offset_field)(logs[-1]) # Ensure a string here. Large offset numbers will get JSON.parsed incorrectly # on the client. Sending as a string prevents this issue. # metadata["offset"] = str(next_offset) # end_of_log_mark may contain characters like '\n' which is needed to # have the log uploaded but will not be stored in elasticsearch. metadata["end_of_log"] = False for logs in logs_by_host.values(): if logs[-1].message == self.end_of_log_mark: metadata["end_of_log"] = True cur_ts = if "last_log_timestamp" in metadata: last_log_ts = timezone.parse(metadata["last_log_timestamp"]) # if we are not getting any logs at all after more than N seconds of trying, # assume logs do not exist if int(next_offset) == 0 and cur_ts.diff(last_log_ts).in_seconds() > 5: metadata["end_of_log"] = True missing_log_message = ( f"*** Log {log_id} not found in Elasticsearch. " "If your task started recently, please wait a moment and reload this page. " "Otherwise, the logs for this task instance may have been removed." ) return [("", missing_log_message)], metadata if ( # Assume end of log after not receiving new log for N min, cur_ts.diff(last_log_ts).in_minutes() >= 5 # if max_offset specified, respect it or ("max_offset" in metadata and int(offset) >= int(metadata["max_offset"])) ): metadata["end_of_log"] = True if int(offset) != int(next_offset) or "last_log_timestamp" not in metadata: metadata["last_log_timestamp"] = str(cur_ts) # If we hit the end of the log, remove the actual end_of_log message # to prevent it from showing in the UI. def concat_logs(lines): log_range = (len(lines) - 1) if lines[-1].message == self.end_of_log_mark else len(lines) return "\n".join(self._format_msg(lines[i]) for i in range(log_range)) message = [(host, concat_logs(hosted_log)) for host, hosted_log in logs_by_host.items()] return message, metadata def _format_msg(self, log_line): """Format ES Record to match settings.LOG_FORMAT when used with json_format.""" # Using formatter._style.format makes it future proof i.e. # if we change the formatter style from '%' to '{' or '$', this will still work if self.json_format: try: return self.formatter._style.format( logging.makeLogRecord({**LOG_LINE_DEFAULTS, **log_line.to_dict()}) ) except Exception: pass # Just a safe-guard to preserve backwards-compatibility return log_line.message
[docs] def es_read(self, log_id: str, offset: int | str, metadata: dict) -> list | ElasticSearchResponse: """ Return the logs matching log_id in Elasticsearch and next offset or ''. :param log_id: the log_id of the log to read. :param offset: the offset start to read log from. :param metadata: log metadata, used for steaming log download. """ query: dict[Any, Any] = { "query": { "bool": { "filter": [{"range": {self.offset_field: {"gt": int(offset)}}}], "must": [{"match_phrase": {"log_id": log_id}}], } } } try: max_log_line = self.client.count(index=self.index_patterns, body=query)["count"] # type: ignore except NotFoundError as e: self.log.exception("The target index pattern %s does not exist", self.index_patterns) raise e logs: list[Any] | ElasticSearchResponse = [] if max_log_line != 0: try: query.update({"sort": [self.offset_field]}) res = # type: ignore index=self.index_patterns, body=query, size=self.MAX_LINE_PER_PAGE, from_=self.MAX_LINE_PER_PAGE * self.PAGE, ) logs = ElasticSearchResponse(self, res) except Exception as err: self.log.exception("Could not read log with log_id: %s. Exception: %s", log_id, err) return logs
[docs] def emit(self, record): if self.handler: setattr(record, self.offset_field, int(time() * (10**9))) self.handler.emit(record)
[docs] def set_context(self, ti: TaskInstance) -> None: """ Provide task_instance context to airflow task handler. :param ti: task instance object """ is_trigger_log_context = getattr(ti, "is_trigger_log_context", None) is_ti_raw = getattr(ti, "raw", None) self.mark_end_on_close = not is_ti_raw and not is_trigger_log_context if self.json_format: self.formatter = ElasticsearchJSONFormatter( fmt=self.formatter._fmt, json_fields=[*self.json_fields, self.offset_field], extras={ "dag_id": str(ti.dag_id), "task_id": str(ti.task_id), "execution_date": self._clean_date(ti.execution_date), "try_number": str(ti.try_number), "log_id": self._render_log_id(ti, ti.try_number), }, ) if self.write_stdout: if self.context_set: # We don't want to re-set up the handler if this logger has # already been initialized return self.handler = logging.StreamHandler(stream=sys.__stdout__) self.handler.setLevel(self.level) self.handler.setFormatter(self.formatter) else: super().set_context(ti) self.context_set = True
[docs] def close(self) -> None: # When application exit, system shuts down all handlers by # calling close method. Here we check if logger is already # closed to prevent uploading the log to remote storage multiple # times when `logging.shutdown` is called. if self.closed: return # todo: remove `getattr` when min airflow version >= 2.6 if not self.mark_end_on_close or getattr(self, "ctx_task_deferred", None): # when we're closing due to task deferral, don't mark end of log self.closed = True return # Case which context of the handler was not set. if self.handler is None: self.closed = True return # Reopen the file stream, because FileHandler.close() would be called # first in logging.shutdown() and the stream in it would be set to None. if is None or # type: ignore[attr-defined] = self.handler._open() # type: ignore[union-attr] # Mark the end of file using end of log mark, # so we know where to stop while auto-tailing. self.emit(logging.makeLogRecord({"msg": self.end_of_log_mark})) if self.write_stdout: self.handler.close() sys.stdout = sys.__stdout__ super().close() self.closed = True
[docs] def log_name(self) -> str: """The log name.""" return self.LOG_NAME
[docs] def get_external_log_url(self, task_instance: TaskInstance, try_number: int) -> str: """ Creates an address for an external log collecting service. :param task_instance: task instance object :param try_number: task instance try_number to read logs from. :return: URL to the external log collection service """ log_id = self._render_log_id(task_instance, try_number) scheme = "" if "://" in self.frontend else "https://" return scheme + self.frontend.format(log_id=quote(log_id))
@property def _resolve_nested(self, hit: dict[Any, Any], parent_class=None) -> type[Hit]: """ Resolves nested hits from Elasticsearch by iteratively navigating the `_nested` field. The result is used to fetch the appropriate document class to handle the hit. This method can be used with nested Elasticsearch fields which are structured as dictionaries with "field" and "_nested" keys. """ doc_class = Hit nested_path: list[str] = [] nesting = hit["_nested"] while nesting and "field" in nesting: nested_path.append(nesting["field"]) nesting = nesting.get("_nested") nested_path_str = ".".join(nested_path) if hasattr(parent_class, "_index"): nested_field = parent_class._index.resolve_field(nested_path_str) if nested_field is not None: return nested_field._doc_class return doc_class def _get_result(self, hit: dict[Any, Any], parent_class=None) -> Hit: """ Process a hit (i.e., a result) from an Elasticsearch response and transform it into a class instance. The transformation depends on the contents of the hit. If the document in hit contains a nested field, the '_resolve_nested' method is used to determine the appropriate class (based on the nested path). If the hit has a document type that is present in the '_doc_type_map', the corresponding class is used. If not, the method iterates over the '_doc_type' classes and uses the first one whose '_matches' method returns True for the hit. If the hit contains any 'inner_hits', these are also processed into 'ElasticSearchResponse' instances using the determined class. Finally, the transformed hit is returned. If the determined class has a 'from_es' method, this is used to transform the hit An example of the hit argument: {'_id': 'jdeZT4kBjAZqZnexVUxk', '_index': '.ds-filebeat-8.8.2-2023.07.09-000001', '_score': 2.482621, '_source': {'@timestamp': '2023-07-13T14:13:15.140Z', 'asctime': '2023-07-09T07:47:43.907+0000', 'container': {'id': 'airflow'}, 'dag_id': 'example_bash_operator', 'ecs': {'version': '8.0.0'}, 'execution_date': '2023_07_09T07_47_32_000000', 'filename': '', 'input': {'type': 'log'}, 'levelname': 'INFO', 'lineno': 1144, 'log': {'file': {'path': "/opt/airflow/Documents/GitHub/airflow/logs/ dag_id=example_bash_operator'/run_id=owen_run_run/ task_id=run_after_loop/attempt=1.log"}, 'offset': 0}, 'log.offset': 1688888863907337472, 'log_id': 'example_bash_operator-run_after_loop-owen_run_run--1-1', 'message': 'Dependencies all met for dep_context=non-requeueable ' 'deps ti=<TaskInstance: ' 'example_bash_operator.run_after_loop owen_run_run ' '[queued]>', 'task_id': 'run_after_loop', 'try_number': '1'}, '_type': '_doc'} """ doc_class = Hit dt = hit.get("_type") if "_nested" in hit: doc_class = self._resolve_nested(hit, parent_class) elif dt in self._doc_type_map: doc_class = self._doc_type_map[dt] else: for doc_type in self._doc_type: if hasattr(doc_type, "_matches") and doc_type._matches(hit): doc_class = doc_type break for t in hit.get("inner_hits", ()): hit["inner_hits"][t] = ElasticSearchResponse(self, hit["inner_hits"][t], doc_class=doc_class) # callback should get the Hit class if "from_es" is not defined callback: type[Hit] | Callable[..., Any] = getattr(doc_class, "from_es", doc_class) return callback(hit)
def getattr_nested(obj, item, default): """ Get item from obj but return default if not found. E.g. calling ``getattr_nested(a, 'b.c', "NA")`` will return ``a.b.c`` if such a value exists, and "NA" otherwise. :meta private: """ try: return attrgetter(item)(obj) except AttributeError: return default

Was this entry helpful?