作爲默認值，氣流連接太多連接

Question

我打開氣流並檢查連接，發現連接後面有太多連接。 任何想法如何殺死那些我不使用，或者我想知道最小的conn_id來運行它。

架構

• LocalExecutor（沒有像任何其他代理）
• POSTGRES作爲所述的metadb

然而它列出17個連接。

這裏是連接列表。

這是airflow.cfg。

[core] 
# Thee home folder for airflow, default is ~/airflow 
airflow_home = /usr/src/app 

# The folder where your airflow pipelines live, most likely a 
# subfolder in a code repository 
dags_folder = /usr/src/app/dags 

# The folder where airflow should store its log files. This location 
base_log_folder = /usr/src/app/logs 

# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users 
# must supply a remote location URL (starting with either 's3://...' or 
# 'gs://...') and an Airflow connection id that provides access to the storage 
# location. 
remote_base_log_folder = 
remote_log_conn_id = 
# Use server-side encryption for logs stored in S3 
encrypt_s3_logs = False 
# deprecated option for remote log storage, use remote_base_log_folder instead! 
# s3_log_folder = 

# The executor class that airflow should use. Choices include 
# SequentialExecutor, LocalExecutor, CeleryExecutor 
executor = LocalExecutor 

# The SqlAlchemy connection string to the metadata database. 
# SqlAlchemy supports many different database engine, more information 
# their website 
sql_alchemy_conn = postgresql+psycopg2://airflow:airflow@db/airflow 

# The SqlAlchemy pool size is the maximum number of database connections 
# in the pool. 
sql_alchemy_pool_size = 5 

# The SqlAlchemy pool recycle is the number of seconds a connection 
# can be idle in the pool before it is invalidated. This config does 
# not apply to sqlite. 
sql_alchemy_pool_recycle = 3600 

# The amount of parallelism as a setting to the executor. This defines 
# the max number of task instances that should run simultaneously 
# on this airflow installation 
parallelism = 32 

# The number of task instances allowed to run concurrently by the scheduler 
dag_concurrency = 16 

# Are DAGs paused by default at creation 
dags_are_paused_at_creation = True 

# When not using pools, tasks are run in the "default pool", 
# whose size is guided by this config element 
non_pooled_task_slot_count = 128 

# The maximum number of active DAG runs per DAG 
max_active_runs_per_dag = 16 

# Whether to load the examples that ship with Airflow. It's good to 
# get started, but you probably want to set this to False in a production 
# environment 
load_examples = False 

# Where your Airflow plugins are stored 
plugins_folder = /usr/src/app/plugins 

# Secret key to save connection passwords in the db 
fernet_key = cryptography_not_found_storing_passwords_in_plain_text 

# Whether to disable pickling dags 
donot_pickle = False 

# How long before timing out a python file import while filling the DagBag 
dagbag_import_timeout = 30 


[operators] 
# The default owner assigned to each new operator, unless 
# provided explicitly or passed via `default_args` 
default_owner = Airflow 


[webserver] 
# The base url of your website as airflow cannot guess what domain or 
# cname you are using. This is used in automated emails that 
# airflow sends to point links to the right web server 
base_url = http://localhost:8080 

# The ip specified when starting the web server 
web_server_host = 0.0.0.0 

# The port on which to run the web server 
web_server_port = 8080 

# The time the gunicorn webserver waits before timing out on a worker 
web_server_worker_timeout = 120 

# Secret key used to run your flask app 
secret_key = temporary_key 

# Number of workers to run the Gunicorn web server 
workers = 4 

# The worker class gunicorn should use. Choices include 
# sync (default), eventlet, gevent 
worker_class = sync 

# Expose the configuration file in the web server 
expose_config = true 

# Set to true to turn on authentication: 
# http://pythonhosted.org/airflow/installation.html#web-authentication 
authenticate = False 

# Filter the list of dags by owner name (requires authentication to be enabled) 
filter_by_owner = False 

[email] 
email_backend = airflow.utils.email.send_email_smtp 

[smtp] 
# If you want airflow to send emails on retries, failure, and you want to use 
# the airflow.utils.email.send_email_smtp function, you have to configure an smtp 
# server here 
smtp_host = localhost 
smtp_starttls = True 
smtp_ssl = False 
smtp_user = airflow 
smtp_port = 25 
smtp_password = airflow 
smtp_mail_from = airflow@airflow.com 

[celery] 
# This section only applies if you are using the CeleryExecutor in 
# [core] section above 

# The app name that will be used by celery 
celery_app_name = airflow.executors.celery_executor 

# The concurrency that will be used when starting workers with the 
# "airflow worker" command. This defines the number of task instances that 
# a worker will take, so size up your workers based on the resources on 
# your worker box and the nature of your tasks 
celeryd_concurrency = 16 

# When you start an airflow worker, airflow starts a tiny web server 
# subprocess to serve the workers local log files to the airflow main 
# web server, who then builds pages and sends them to users. This defines 
# the port on which the logs are served. It needs to be unused, and open 
# visible from the main web server to connect into the workers. 
worker_log_server_port = 8793 

# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally 
# a sqlalchemy database. Refer to the Celery documentation for more 
# information. 
broker_url = sqla+mysql://airflow:airflow@localhost:3306/airflow 

# Another key Celery setting 
celery_result_backend = db+mysql://airflow:airflow@localhost:3306/airflow 

# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start 
# it `airflow flower`. This defines the port that Celery Flower runs on 
flower_port = 5555 

# Default queue that tasks get assigned to and that worker listen on. 
default_queue = default 

[scheduler] 
# Task instances listen for external kill signal (when you clear tasks 
# from the CLI or the UI), this defines the frequency at which they should 
# listen (in seconds). 
job_heartbeat_sec = 5 

# The scheduler constantly tries to trigger new tasks (look at the 
# scheduler section in the docs for more information). This defines 
# how often the scheduler should run (in seconds). 
scheduler_heartbeat_sec = 5 

# Statsd (https://github.com/etsy/statsd) integration settings 
# statsd_on = False 
# statsd_host = localhost 
# statsd_port = 8125 
# statsd_prefix = airflow 

# The scheduler can run multiple threads in parallel to schedule dags. 
# This defines how many threads will run. However airflow will never 
# use more threads than the amount of cpu cores available. 
max_threads = 2 

[mesos] 
# Mesos master address which MesosExecutor will connect to. 
master = localhost:5050 

# The framework name which Airflow scheduler will register itself as on mesos 
framework_name = Airflow 

# Number of cpu cores required for running one task instance using 
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>' 
# command on a mesos slave 
task_cpu = 1 

# Memory in MB required for running one task instance using 
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>' 
# command on a mesos slave 
task_memory = 256 

# Enable framework checkpointing for mesos 
# See http://mesos.apache.org/documentation/latest/slave-recovery/ 
checkpoint = False 

# Failover timeout in milliseconds. 
# When checkpointing is enabled and this option is set, Mesos waits 
# until the configured timeout for 
# the MesosExecutor framework to re-register after a failover. Mesos 
# shuts down running tasks if the 
# MesosExecutor framework fails to re-register within this timeframe. 
# failover_timeout = 604800 

# Enable framework authentication for mesos 
# See http://mesos.apache.org/documentation/latest/configuration/ 
authenticate = False 

# Mesos credentials, if authentication is enabled 
# default_principal = admin 
# default_secret = admin 

Answer 1

這些是默認連接。它們不是「運行」的，它們只是設置中的配置記錄。您可以手動刪除它們。