认识airflow
目前在一家互联网金融公司数据部门工作，人员很少，很多大数据的架构没有经验，一切只能自己研究。
做数据分析就得涉及到数据的ETL，OLTP的数据库不能拿来直接分析，所以首先就是数据extraction，老同事用了kettle作为导数工具，用cron定时运行。
不得不说kettle的GUI确实强大，但我却对它没有太多好感。crontab也有缺点，如果数量很庞大，比如上百行定时任务，再加上各种脚本，很不方便维护和升级。
直到在网上发现了airflow这款流程管理工具，Python写的，类似于Luigi, Oozie, Azkaban。目前还在Apache孵化中，社区和gitter都很活跃。
安装很简单直接pip install airflow，然后根据使用目的不同还需要安装其他组件。如果做分布式架构，还需要安装mysql, celery或者Mesos。
安装完成后会在home目录下生成一个airflow文件夹。这样就算安装成功了。如果要做分布式，需要在每一台机器上都安装同样的。
常用概念
- DAG：翻译过来是有向无环图，做过大数据处理的都知道。下面是airflow官方解释
In Airflow, a DAG – or a Directed Acyclic Graph – is a collection of all the tasks you want
to run, organized in a way that reflects their relationships and dependencies. - webserver：这个是airflow的数据可视化页面
- scheduler：流程调度器，负责监控所有任务和dag，并触发任务
- worker：和celery worker概念一样，负责在分布式环境下对任务进行处理
- airflow.cfg：airflow所有配置都在这里了, 需要重点学习
- connections：airflow为你预置了一些数据库和各种服务的连接模板，你只需要在一个集中的位置管理好这些连接，就可以在任意地方使用了
配置airflow.cfg
需要分别对webserver和celery sections进行配置, 直接上我的配置
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225[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /root/airflow
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
dags_folder = /root/airflow/dags
# The folder where airflow should store its log files. This location
base_log_folder = /root/airflow/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 = CeleryExecutor
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
sql_alchemy_conn = mysql://username:password@databasehost:3306/airflow?charset=utf8
# 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 = /root/airflow/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 = gevent
# 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 = smtp.domain.com
smtp_starttls = True
smtp_ssl = False
smtp_user = airflow@domain.com
smtp_port = 25
smtp_password = xt5l3hIyWkqqiEDbufwe
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 = amqp://user:pass@masterhost:5672/airflowvhost
# Another key Celery setting
celery_result_backend = db+mysql://user:pass@databasehost: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 = 1
[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
我用了rabbitmq作为broker, 安装在master机器上。配置文件要在不同机器间保持一致。
初始化并运行
初始化命令：airflow initdb
然后在master上分别运行airflow scheduler, airflow webserver,这时访问浏览器localhost:port就可以看到airflow的可视化页面了