基于DockerCompose部署Airflow多节点集群并实现配置与数据同步-开发者社区-阿里云

集群环境

同样是在Ubuntu 20.04.3 LTS机器上安装Airflow集群，这次我们准备三台同等配置服务器，进行测试，前篇文章中，我们已经在Bigdata1服务器上安装了airflow的所有组件，没看过的可以点击链接先看下之前的文章，现在只需要在其他两个节点安装worker组件即可。

	Bigdata1(A)	Bigdata2(B)	Bigdata3(C)
Webserver	√
Scheduler	√
Worker	√	√	√

在上篇文章中的docker-compose.yml中没有对部署文件以及数据目录进行的分离，这样在后期管理的时候不太方便，因此我们可以把服务停止后，将数据库以及数据目录与部署文件分开

部署文件:docker-compose.yaml/.env 存放在/apps/airflow目录下
MySQL以及配置文件: 放在/data/mysql
airflow数据目录: 放在/data/airflow

这样拆分开就方便后期的统一管理了。

部署worker服务

前期准备

mkdir /data/airflow/{dags,plugins} -pv
mkdir -pv /apps/airflow
mkdir -pv /logs/airflow

worker的部署文件:

---
version: '3'
x-airflow-common:
  &airflow-common
  # In order to add custom dependencies or upgrade provider packages you can use your extended image.
  # Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml
  # and uncomment the "build" line below, Then run `docker-compose build` to build the images.
  image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.2.3}
  # build: .
  environment:
    &airflow-common-env
    AIRFLOW__CORE__EXECUTOR: CeleryExecutor
    AIRFLOW__CORE__SQL_ALCHEMY_CONN: mysql+mysqldb://airflow:aaaa@$${MYSQL_HOST}:3306/airflow #修改MySQL对应的账号和密码
    AIRFLOW__CELERY__RESULT_BACKEND: db+mysql://airflow:aaaa@$${MYSQL_HOST}:3306/airflow #修改MySQL对应的账号和密码
    AIRFLOW__CELERY__BROKER_URL: redis://:xxxx@$${REDIS_HOST}:7480/0 #修改Redis的密码
    AIRFLOW__CORE__FERNET_KEY: ''
    AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
    AIRFLOW__CORE__LOAD_EXAMPLES: 'true'
    AIRFLOW__API__AUTH_BACKEND: 'airflow.api.auth.backend.basic_auth'
    _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
  volumes:
    - /data/airflow/dags:/opt/airflow/dags
    - /logs/airflow:/opt/airflow/logs
    - /data/airflow/plugins:/opt/airflow/plugins
    - /data/airflow/airflow.cfg:/opt/airflow/airflow.cfg
  user: "${AIRFLOW_UID:-50000}:0"

services:
  airflow-worker:
    <<: *airflow-common
    command: celery worker
    healthcheck:
      test:
        - "CMD-SHELL"
        - 'celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
      interval: 10s
      timeout: 10s
      retries: 5
    environment:
      <<: *airflow-common-env
      # Required to handle warm shutdown of the celery workers properly
      # See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation
      DUMB_INIT_SETSID: "0"
    restart: always
    hostname: bigdata-20-194 # 此处设置容器的主机名，便于在flower中查看是哪个worker
    depends_on:
      airflow-init:
        condition: service_completed_successfully

  airflow-init:
    <<: *airflow-common
    entrypoint: /bin/bash
    # yamllint disable rule:line-length
    command:
      - -c
      - |
        function ver() {
          printf "%04d%04d%04d%04d" $${1//./ }
        }
        airflow_version=$$(gosu airflow airflow version)
        airflow_version_comparable=$$(ver $${airflow_version})
        min_airflow_version=2.2.0
        min_airflow_version_comparable=$$(ver $${min_airflow_version})
        if (( airflow_version_comparable < min_airflow_version_comparable )); then
          echo
          echo -e "\033[1;31mERROR!!!: Too old Airflow version $${airflow_version}!\e[0m"
          echo "The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!"
          echo
          exit 1
        fi
        if [[ -z "${AIRFLOW_UID}" ]]; then
          echo
          echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m"
          echo "If you are on Linux, you SHOULD follow the instructions below to set "
          echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
          echo "For other operating systems you can get rid of the warning with manually created .env file:"
          echo "    See: https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#setting-the-right-airflow-user"
          echo
        fi
        one_meg=1048576
        mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))
        cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)
        disk_available=$$(df / | tail -1 | awk '{print $$4}')
        warning_resources="false"
        if (( mem_available < 4000 )) ; then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough memory available for Docker.\e[0m"
          echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))"
          echo
          warning_resources="true"
        fi
        if (( cpus_available < 2 )); then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\e[0m"
          echo "At least 2 CPUs recommended. You have $${cpus_available}"
          echo
          warning_resources="true"
        fi
        if (( disk_available < one_meg * 10 )); then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\e[0m"
          echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))"
          echo
          warning_resources="true"
        fi
        if [[ $${warning_resources} == "true" ]]; then
          echo
          echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m"
          echo "Please follow the instructions to increase amount of resources available:"
          echo "   https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#before-you-begin"
          echo
        fi
        mkdir -p /sources/logs /sources/dags /sources/plugins
        chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins}
        exec /entrypoint airflow version
    # yamllint enable rule:line-length
    environment:
      <<: *airflow-common-env
      _AIRFLOW_DB_UPGRADE: 'true'
      _AIRFLOW_WWW_USER_CREATE: 'true'
      _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}
      _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}
    user: "0:0"
    volumes:
      - .:/sources

  airflow-cli:
    <<: *airflow-common
    profiles:
      - debug
    environment:
      <<: *airflow-common-env
      CONNECTION_CHECK_MAX_COUNT: "0"
    # Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252
    command:
      - bash
      - -c
      - airflow

初始化检测，检查环境是否满足:

cd /apps/ariflow/
echo -e "AIRFLOW_UID=$(id -u)" > .env # 注意，此处一定要保证AIRFLOW_UID是普通用户的UID,且保证此用户有创建这些持久化目录的权限
docker-compose up airflow-init

如果数据库已经存在，初始化检测不影响已有的数据库,接下来就运行airflow-worker服务

docker-compose up -d

接下来，按照同样的方式在bigdata3节点上安装airflow-worker服务就可以了。部署完成之后，就可以通过flower查看broker的状态:

持久化配置文件

大多情况下，使用airflow多worker节点的集群，我们就需要持久化airflow的配置文件，并且将airflow同步到所有的节点上，因此这里需要修改一下docker-compose.yaml中x-airflow-common的volumes,将airflow.cfg通过挂载卷的形式挂载到容器中，配置文件可以在容器中拷贝一份出来，然后在修改;

前期使用的时候，我们需要将docker-compose文件中的一些环境变量的值写入到airflow.cfg文件中，例如以下信息:

[core]
dags_folder = /opt/airflow/dags
hostname_callable = socket.getfqdn
default_timezone = Asia/Shanghai # 修改时区
executor = CeleryExecutor
sql_alchemy_conn = mysql+mysqldb://airflow:aaaa@$${MYSQL_HOST}:3306/airflow
sql_engine_encoding = utf-8
sql_alchemy_pool_enabled = True
sql_alchemy_pool_size = 5
sql_alchemy_max_overflow = 10
sql_alchemy_pool_recycle = 1800
sql_alchemy_pool_pre_ping = True
sql_alchemy_schema =
parallelism = 32
max_active_tasks_per_dag = 16
dags_are_paused_at_creation = True
max_active_runs_per_dag = 16
load_examples = True
load_default_connections = True
plugins_folder = /opt/airflow/plugins
execute_tasks_new_python_interpreter = False
fernet_key =
donot_pickle = True
dagbag_import_timeout = 30.0
dagbag_import_error_tracebacks = True
dagbag_import_error_traceback_depth = 2
dag_file_processor_timeout = 50
task_runner = StandardTaskRunner
default_impersonation =
security =
unit_test_mode = False
enable_xcom_pickling = False
killed_task_cleanup_time = 60
dag_run_conf_overrides_params = True
dag_discovery_safe_mode = True
default_task_retries = 0
default_task_weight_rule = downstream
min_serialized_dag_update_interval = 30
min_serialized_dag_fetch_interval = 10
max_num_rendered_ti_fields_per_task = 30
check_slas = True
xcom_backend = airflow.models.xcom.BaseXCom
lazy_load_plugins = True
lazy_discover_providers = True
max_db_retries = 3
hide_sensitive_var_conn_fields = True
sensitive_var_conn_names =
default_pool_task_slot_count = 128
[logging]
base_log_folder = /opt/airflow/logs
remote_logging = False
remote_log_conn_id =
google_key_path =
remote_base_log_folder =
encrypt_s3_logs = False
logging_level = INFO
fab_logging_level = WARNING
logging_config_class =
colored_console_log = True
colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
task_log_prefix_template =
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
log_processor_filename_template = {{ filename }}.log
dag_processor_manager_log_location = /opt/airflow/logs/dag_processor_manager/dag_processor_manager.log
task_log_reader = task
extra_logger_names =
worker_log_server_port = 8793
[metrics]
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow
statsd_allow_list =
stat_name_handler =
statsd_datadog_enabled = False
statsd_datadog_tags =
[secrets]
backend =
backend_kwargs =
[cli]
api_client = airflow.api.client.local_client
endpoint_url = http://localhost:8080
[debug]
fail_fast = False
[api]
enable_experimental_api = False
auth_backend = airflow.api.auth.backend.deny_all
maximum_page_limit = 100
fallback_page_limit = 100
google_oauth2_audience =
google_key_path =
access_control_allow_headers =
access_control_allow_methods =
access_control_allow_origins =
[lineage]
backend =
[atlas]
sasl_enabled = False
host =
port = 21000
username =
password =
[operators]
default_owner = airflow
default_cpus = 1
default_ram = 512
default_disk = 512
default_gpus = 0
default_queue = default
allow_illegal_arguments = False
[hive]
default_hive_mapred_queue =
[webserver]
base_url = https://devopsman.cn/airflow #自定义airflow域名
default_ui_timezone = Asia/Shanghai # 设置默认的时区
web_server_host = 0.0.0.0
web_server_port = 8080
web_server_ssl_cert =
web_server_ssl_key =
web_server_master_timeout = 120
web_server_worker_timeout = 120
worker_refresh_batch_size = 1
worker_refresh_interval = 6000
reload_on_plugin_change = False
secret_key = emEfndkf3QWZ5zVLE1kVMg==
workers = 4
worker_class = sync
access_logfile = -
error_logfile = -
access_logformat =
expose_config = False
expose_hostname = True
expose_stacktrace = True
dag_default_view = tree
dag_orientation = LR
log_fetch_timeout_sec = 5
log_fetch_delay_sec = 2
log_auto_tailing_offset = 30
log_animation_speed = 1000
hide_paused_dags_by_default = False
page_size = 100
navbar_color = #fff
default_dag_run_display_number = 25
enable_proxy_fix = False
proxy_fix_x_for = 1
proxy_fix_x_proto = 1
proxy_fix_x_host = 1
proxy_fix_x_port = 1
proxy_fix_x_prefix = 1
cookie_secure = False
cookie_samesite = Lax
default_wrap = False
x_frame_enabled = True
show_recent_stats_for_completed_runs = True
update_fab_perms = True
session_lifetime_minutes = 43200
auto_refresh_interval = 3
[email]
email_backend = airflow.utils.email.send_email_smtp
email_conn_id = smtp_default
default_email_on_retry = True
default_email_on_failure = True
[smtp] # 邮箱配置
smtp_host = localhost
smtp_starttls = True
smtp_ssl = False
smtp_port = 25
smtp_mail_from = airflow@example.com
smtp_timeout = 30
smtp_retry_limit = 5
[sentry]
sentry_on = false
sentry_dsn =
[celery_kubernetes_executor]
kubernetes_queue = kubernetes
[celery]
celery_app_name = airflow.executors.celery_executor
worker_concurrency = 16
worker_umask = 0o077
broker_url = redis://:xxxx@$${REDIS_HOST}:7480/0
result_backend = db+mysql://airflow:aaaa@$${MYSQL_HOST}:3306/airflow
flower_host = 0.0.0.0
flower_url_prefix =
flower_port = 5555
flower_basic_auth =
sync_parallelism = 0
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
ssl_active = False
ssl_key =
ssl_cert =
ssl_cacert =
pool = prefork
operation_timeout = 1.0
task_track_started = True
task_adoption_timeout = 600
task_publish_max_retries = 3
worker_precheck = False
[celery_broker_transport_options]
[dask]
cluster_address = 127.0.0.1:8786
tls_ca =
tls_cert =
tls_key =
[scheduler]
job_heartbeat_sec = 5
scheduler_heartbeat_sec = 5
num_runs = -1
scheduler_idle_sleep_time = 1
min_file_process_interval = 30
dag_dir_list_interval = 300
print_stats_interval = 30
pool_metrics_interval = 5.0
scheduler_health_check_threshold = 30
orphaned_tasks_check_interval = 300.0
child_process_log_directory = /opt/airflow/logs/scheduler
scheduler_zombie_task_threshold = 300
catchup_by_default = True
max_tis_per_query = 512
use_row_level_locking = True
max_dagruns_to_create_per_loop = 10
max_dagruns_per_loop_to_schedule = 20
schedule_after_task_execution = True
parsing_processes = 2
file_parsing_sort_mode = modified_time
use_job_schedule = True
allow_trigger_in_future = False
dependency_detector = airflow.serialization.serialized_objects.DependencyDetector
trigger_timeout_check_interval = 15
[triggerer]
default_capacity = 1000
[kerberos]
ccache = /tmp/airflow_krb5_ccache
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab
forwardable = True
include_ip = True
[github_enterprise]
api_rev = v3
[elasticsearch]
host =
log_id_template = {dag_id}-{task_id}-{execution_date}-{try_number}
end_of_log_mark = end_of_log
frontend =
write_stdout = False
json_format = False
json_fields = asctime, filename, lineno, levelname, message
host_field = host
offset_field = offset
[elasticsearch_configs]
use_ssl = False
verify_certs = True
[kubernetes]
pod_template_file =
worker_container_repository =
worker_container_tag =
namespace = default
delete_worker_pods = True
delete_worker_pods_on_failure = False
worker_pods_creation_batch_size = 1
multi_namespace_mode = False
in_cluster = True
kube_client_request_args =
delete_option_kwargs =
enable_tcp_keepalive = True
tcp_keep_idle = 120
tcp_keep_intvl = 30
tcp_keep_cnt = 6
verify_ssl = True
worker_pods_pending_timeout = 300
worker_pods_pending_timeout_check_interval = 120
worker_pods_queued_check_interval = 60
worker_pods_pending_timeout_batch_size = 100
[smart_sensor]
use_smart_sensor = False
shard_code_upper_limit = 10000
shards = 5
sensors_enabled = NamedHivePartitionSensor

修改完成之后，重启一下服务。

docker-compose restart

数据同步

因为airflow使用了三个worker节点，每个节点修改配置，其他节点都要同步，同时DAGS目录以及plugins目录也需要实时进行同步，在scheduler将信息调度到某个节点后，如果找不到对应的DAGS文件，就会报错，因此我们使用lsyncd进行数据实时同步:

apt-get install lsyncd -y

配置节点之间通过公钥连接

ssh-keygen -t rsa -C "airflow-sync" -b 4096 #生成一对名为airflow-sync的密钥
for ip in 100 200;do ssh-copy-id -i ~/.ssh/airflow-sync.pub ${USERNAME}@192.168.0.$ip -P12022;done

然后我们就可以通过私钥访问了其它节点了。

编辑同步的配置文件,lsyncd配置的更多参数学习，可以直达官方文档

settings {
    logfile = "/var/log/lsyncd.log", # 日志文件
    statusFile = "/var/log/lsyncd.status", # 同步状态信息
    pidfile = "/var/run/lsyncd.pid",
    statusInterval = 1,
    nodaemon = false, # 守护进程
    inotifyMode  = "CloseWrite",
    maxProcesses = 1,
    maxDelays = 1,
}
sync {
    default.rsync,
    source = "/data/airflow",
    target = "192.168.0.100:/data/airflow",

    rsync = {
       binary = "/usr/bin/rsync",
       compress = false,
       archive = true,
       owner = true,
       perms = true,
       --delete =  true,
       whole_file = false,
       rsh = "/usr/bin/ssh -p 12022 -l suoper -o StrictHostKeyChecking=no -i /home/username/.ssh/airflow-rsync"
    },
}
sync {
    default.rsync,
    source = "/data/airflow",
    target = "192.168.0.200:/data/airflow",

    rsync = {
       binary = "/usr/bin/rsync",
       compress = false,
       archive = true,
       owner = true,
       perms = true,
       --delete =  true,
       whole_file = false,
       rsh = "/usr/bin/ssh -p 12022 -l suoper -o StrictHostKeyChecking=no -i /home/username/.ssh/airflow-rsync"
    },
}

以上的参数是什么意思，可以访问官网查看，此处是通过rsync的rsh定义ssh命令，能够解决使用了私钥，自定义端口等安全措施的场景，当然你也可以使用配置无密访问，然后使用default.rsync或者default.rsyncssh等进行配置。

配置lsyncd的服务托管

cat << EOF > /etc/systemd/system/lsyncd.service
[Unit]
Description=lsyncd
ConditionFileIsExecutable=/usr/bin/lsyncd

After=network-online.target
Wants=network-online.target

[Service]
StartLimitBurst=10
ExecStart=/usr/bin/lsyncd /etc/lsyncd.conf
Restart=on-failure
RestartSec=120
EnvironmentFile=-/etc/sysconfig/aliyun
KillMode=process
[Install]
WantedBy=multi-user.target
EOF

systemctl daemon-reload
systemctl enable --now lsyncd.service #启动服务并配置开启自启

这样就完成了数据(dags,plugins,airflow.cfg)的同步问题，后期使用CICD场景的时候，便可以直接将dag文件上传到Bigdata1节点上即可，其他两个节点就会自动同步了。如果出现问题，可以通过查看日志进行debug

lsyncd -log all /etc/lsyncd.conf
tail -f /var/log/lsyncd.log

反向代理

如果你需要将airflow放在反向代理之后，如https://lab.mycompany.com/myorg/airflow/你可以通过一下配置完成：

在airflow.cfg中配置base_url

base_url = http://my_host/myorg/airflow
enable_proxy_fix = True

nginx的配置

server {
  listen 80;
  server_name lab.mycompany.com;

  location /myorg/airflow/ {
      proxy_pass http://localhost:8080;
      proxy_set_header Host $http_host;
      proxy_redirect off;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection "upgrade";
  }
}

到这里就基本上完成的airflow分布式调度集群的安装了.看下具体效果如。

Airflow 2.2.3 容器化分布式集群安装

集群环境

部署worker服务

持久化配置文件

数据同步

反向代理

容器服务

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