I'm using Hashicorp vault as a secrets store and installed it via apt repository on Ubuntu 20.04.
After that, I added the root key to access the UI and I'm able to add or delete secrets using UI.
Whenever I'm trying to add or get a secret using the command line, I get the following error :
jarvis#saki:~$ vault kv get secret/vault
Get "https://127.0.0.1:8200/v1/sys/internal/ui/mounts/secret/vault": http: server gave HTTP response to HTTPS client
My vault config looks like this :
# Full configuration options can be found at https://www.vaultproject.io/docs/configuration
ui = true
#mlock = true
#disable_mlock = true
storage "file" {
path = "/opt/vault/data"
}
#storage "consul" {
# address = "127.0.0.1:8500"
# path = "vault"
#}
# HTTP listener
#listener "tcp" {
# address = "127.0.0.1:8200"
# tls_disable = 1
#}
# HTTPS listener
listener "tcp" {
address = "0.0.0.0:8200"
tls_cert_file = "/opt/vault/tls/tls.crt"
tls_key_file = "/opt/vault/tls/tls.key"
}
# Example AWS KMS auto unseal
#seal "awskms" {
# region = "us-east-1"
# kms_key_id = "REPLACE-ME"
#}
# Example HSM auto unseal
#seal "pkcs11" {
# lib = "/usr/vault/lib/libCryptoki2_64.so"
# slot = "0"
# pin = "AAAA-BBBB-CCCC-DDDD"
# key_label = "vault-hsm-key"
# hmac_key_label = "vault-hsm-hmac-key"
#}
I fixed the problem. Though the exception can be common to more than one similar problem, I fixed the problem by exporting the root token generated after running this command :
vault server -dev
The output is like this
...
You may need to set the following environment variable:
$ export VAULT_ADDR='http://127.0.0.1:8200'
The unseal key and root token are displayed below in case you want to
seal/unseal the Vault or re-authenticate.
Unseal Key: 1+yv+v5mz+aSCK67X6slL3ECxb4UDL8ujWZU/ONBpn0=
Root Token: s.XmpNPoi9sRhYtdKHaQhkHP6x
Development mode should NOT be used in production installations!
...
Then just export these variables by running the following commands :
export VAULT_ADDR='http://127.0.0.1:8200'
export VAULT_TOKEN="s.XmpNPoi9sRhYtdKHaQhkHP6x"
Note: Replace "s.XmpNPoi9sRhYtdKHaQhkHP6x" with your token received as output from the above command.
Then run the following command to check the status :
vault status
Again, the error message can be similar for many different problems.
In PowerShell on Windows 10, I was able to set it this way:
$Env:VAULT_ADDR='http://127.0.0.1:8200'
Then
vault status
returned correctly. This was on Vault 1.7.3 in dev mode
You can echo VAULT_ADDR by specifying it on the command line and pressing enter - same as the set line above but omitting the = sign and everything after it
$Env:VAULT_ADDR
Output:
Key Value
--- ----- Seal Type shamir Initialized true Sealed false Total Shares 1 Threshold 1 Version
1.7.3 Storage Type inmem Cluster Name vault-cluster-80649ba2 Cluster ID 2a35e304-0836-2896-e927-66722e7ca488 HA Enabled
false
Try using a new terminal window. This worked for me
Related
I have a basic Gatling script on EC2 instance from which I want to push the results into an Influx database instance. I can successfully run a Gatling script and Influx is also running.
My Gatling configuration is the following:
data {
writers = [console, graphite] # The list of DataWriters to which Gatling write simulation data (currently supported : console, file, graphite)
console {
#light = false # When set to true, displays a light version without detailed request stats
#writePeriod = 5 # Write interval, in seconds
}
file {
#bufferSize = 8192 # FileDataWriter's internal data buffer size, in bytes
}
leak {
#noActivityTimeout = 30 # Period, in seconds, for which Gatling may have no activity before considering a leak may be happening
}
graphite {
light = false # only send the all* stats
host = "ec2-35-181-26-79.eu-west-3.compute.amazonaws.com" # The host where the Carbon server is located
port = 2003 # The port to which the Carbon server listens to (2003 is default for plaintext, 2004 is default for pickle)
protocol = "tcp" # The protocol used to send data to Carbon (currently supported : "tcp", "udp")
rootPathPrefix = "gatling" # The common prefix of all metrics sent to Graphite
bufferSize = 8192 # Internal data buffer size, in bytes
writePeriod = 1 # Write period, in seconds
}
And for Influx, I've setup a Telegraf with the following configuration
[[outputs.influxdb_v2]]
## The URLs of the InfluxDB cluster nodes.
##
## Multiple URLs can be specified for a single cluster, only ONE of the
## urls will be written to each interval.
## urls exp: http://127.0.0.1:8086
urls = ["http://ec2-35-181-26-79.eu-west-3.compute.amazonaws.com:8086"]
## Token for authentication.
token = "$INFLUX_TOKEN"
## Organization is the name of the organization you wish to write to; must exist.
organization = "Test"
## Destination bucket to write into.
bucket = "Test"
[[inputs.socket_listener]]
## URL to listen on
service_address = "tcp://:2003"
data_format = "graphite"
## Content encoding for message payloads, can be set to "gzip" to or
## "identity" to apply no encoding.
# content_encoding = "identity"
templates = [
"gatling.*.*.*.* measurement.simulation.request.status.field",
"gatling.*.users.*.* measurement.simulation.measurement.request.field"
]
With both Telegraf (with this configuration) and Influx running, I don't see any data pushed into the 'Test' bucket. Moreover I don't get any errors that could help me debugging.
Any help would be much appreciated. Thanks.
Everyone, I am using Airflow(v.1.10.12) for one of the project to schedule the Jobs daily. Wanted to execute multiple DAG’s(14) in parallel. Have updated the concurrency parameters in cfg file. However, have observed the behavior that: Scheduler starts the execution of tasks, assigns the tasks to queue but after certain amount of time it pauses and restart after 5 minutes. Due to this behavior the time taken by all Dag is huge and also some of tasks are getting failed. Can someone help me with the understanding why the Scheduler gets halt and if we should modify some parameter or shift to some other Airflow version?
Below is the configuration file
[core]
dags_folder = /home/airflow/dags
base_log_folder = /home/airflow/logs
remote_logging = False
remote_log_conn_id =
remote_base_log_folder =
encrypt_s3_logs = False
logging_level = INFO
fab_logging_level = WARN
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
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
log_processor_filename_template = {{ filename }}.log
dag_processor_manager_log_location = /home/airflow/logs/dag_processor_manager/dag_processor_manager.log
hostname_callable = socket:getfqdn
default_timezone = utc
executor = CeleryExecutor
sql_alchemy_conn = postgresql+psycopg2://devairflow:airflow#localhost:5432/pcfdb
sql_engine_encoding = utf-8
sql_alchemy_pool_enabled = True
sql_alchemy_pool_size = 0
sql_alchemy_max_overflow = -1
# 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. If the number of DB connections is ever exceeded,
# a lower config value will allow the system to recover faster.
sql_alchemy_pool_recycle = 1800
sql_alchemy_pool_pre_ping = True
sql_alchemy_schema =
parallelism = 50
dag_concurrency = 50
dags_are_paused_at_creation = True
max_active_runs_per_dag = 50
load_examples = False
load_default_connections = True
plugins_folder = /home/airflow/plugins
fernet_key = M4dpP6f2Hd5p3N--CxtIoUo9XaSDifA42MPLs1UR7-g=
donot_pickle = False
dagbag_import_timeout = 41460
dag_file_processor_timeout = 60
task_runner = StandardTaskRunner
default_impersonation =
security =
secure_mode = False
unit_test_mode = False
enable_xcom_pickling = True
killed_task_cleanup_time = 60
dag_run_conf_overrides_params = False
worker_precheck = False
dag_discovery_safe_mode = True
default_task_retries = 0
store_serialized_dags = False
min_serialized_dag_update_interval = 30
max_num_rendered_ti_fields_per_task = 100
# On each dagrun check against defined SLAs
check_slas = True
[secrets]
# Full class name of secrets backend to enable (will precede env vars and metastore in search path)
# Example: backend = airflow.contrib.secrets.aws_systems_manager.SystemsManagerParameterStoreBackend
backend =
# The backend_kwargs param is loaded into a dictionary and passed to __init__ of secrets backend class.
# See documentation for the secrets backend you are using. JSON is expected.
# Example for AWS Systems Manager ParameterStore:
# ``{"connections_prefix": "/airflow/connections", "profile_name": "default"}``
backend_kwargs =
[cli]
# In what way should the cli access the API. The LocalClient will use the
# database directly, while the json_client will use the api running on the
# webserver
api_client = airflow.api.client.local_client
# If you set web_server_url_prefix, do NOT forget to append it here, ex:
# ``endpoint_url = http://localhost:8080/myroot``
# So api will look like: ``http://localhost:8080/myroot/api/experimental/...``
endpoint_url = http://localhost:8080
[debug]
# Used only with DebugExecutor. If set to True DAG will fail with first
# failed task. Helpful for debugging purposes.
fail_fast = False
[api]
# How to authenticate users of the API. See
# https://airflow.apache.org/docs/stable/security.html for possible values.
# ("airflow.api.auth.backend.default" allows all requests for historic reasons)
auth_backend = airflow.api.auth.backend.deny_all
[lineage]
# what lineage backend to use
backend =
[atlas]
sasl_enabled = False
host =
port = 21000
username =
password =
[operators]
# The default owner assigned to each new operator, unless
# provided explicitly or passed via ``default_args``
default_owner = airflow
default_cpus = 1
default_ram = 1024
default_disk = 1024
default_gpus = 0
[hive]
# Default mapreduce queue for HiveOperator tasks
default_hive_mapred_queue =
[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
# Default timezone to display all dates in the RBAC UI, can be UTC, system, or
# any IANA timezone string (e.g. Europe/Amsterdam). If left empty the
# default value of core/default_timezone will be used
# Example: default_ui_timezone = America/New_York
default_ui_timezone = UTC
# 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
# Paths to the SSL certificate and key for the web server. When both are
# provided SSL will be enabled. This does not change the web server port.
web_server_ssl_cert =
# Paths to the SSL certificate and key for the web server. When both are
# provided SSL will be enabled. This does not change the web server port.
web_server_ssl_key =
# Number of seconds the webserver waits before killing gunicorn master that doesn't respond
web_server_master_timeout = 41460
# Number of seconds the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 41460
# Number of workers to refresh at a time. When set to 0, worker refresh is
# disabled. When nonzero, airflow periodically refreshes webserver workers by
# bringing up new ones and killing old ones.
worker_refresh_batch_size = 1
# Number of seconds to wait before refreshing a batch of workers.
worker_refresh_interval = 30
# If set to True, Airflow will track files in plugins_folder directory. When it detects changes,
# then reload the gunicorn.
reload_on_plugin_change = False
# Secret key used to run your flask app
# It should be as random as possible
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
# Log files for the gunicorn webserver. '-' means log to stderr.
access_logfile = -
# Log files for the gunicorn webserver. '-' means log to stderr.
error_logfile = -
# Expose the configuration file in the web server
expose_config = True
# Expose hostname in the web server
expose_hostname = True
# Expose stacktrace in the web server
expose_stacktrace = True
# Set to true to turn on authentication:
# https://airflow.apache.org/security.html#web-authentication
authenticate = False
# Filter the list of dags by owner name (requires authentication to be enabled)
filter_by_owner = False
# Filtering mode. Choices include user (default) and ldapgroup.
# Ldap group filtering requires using the ldap backend
#
# Note that the ldap server needs the "memberOf" overlay to be set up
# in order to user the ldapgroup mode.
owner_mode = user
# Default DAG view. Valid values are:
# tree, graph, duration, gantt, landing_times
dag_default_view = tree
# "Default DAG orientation. Valid values are:"
# LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top)
dag_orientation = LR
# Puts the webserver in demonstration mode; blurs the names of Operators for
# privacy.
demo_mode = False
# The amount of time (in secs) webserver will wait for initial handshake
# while fetching logs from other worker machine
log_fetch_timeout_sec = 5
# Time interval (in secs) to wait before next log fetching.
log_fetch_delay_sec = 2
# Distance away from page bottom to enable auto tailing.
log_auto_tailing_offset = 30
# Animation speed for auto tailing log display.
log_animation_speed = 1000
# By default, the webserver shows paused DAGs. Flip this to hide paused
# DAGs by default
hide_paused_dags_by_default = False
# Consistent page size across all listing views in the UI
page_size = 100
# Use FAB-based webserver with RBAC feature
rbac = False
# Define the color of navigation bar
navbar_color = #007A87
# Default dagrun to show in UI
default_dag_run_display_number = 25
# Enable werkzeug ``ProxyFix`` middleware for reverse proxy
enable_proxy_fix = False
# Number of values to trust for ``X-Forwarded-For``.
# More info: https://werkzeug.palletsprojects.com/en/0.16.x/middleware/proxy_fix/
proxy_fix_x_for = 1
# Number of values to trust for ``X-Forwarded-Proto``
proxy_fix_x_proto = 1
# Number of values to trust for ``X-Forwarded-Host``
proxy_fix_x_host = 1
# Number of values to trust for ``X-Forwarded-Port``
proxy_fix_x_port = 1
# Number of values to trust for ``X-Forwarded-Prefix``
proxy_fix_x_prefix = 1
# Set secure flag on session cookie
cookie_secure = False
# Set samesite policy on session cookie
cookie_samesite =
# Default setting for wrap toggle on DAG code and TI log views.
default_wrap = False
# Allow the UI to be rendered in a frame
x_frame_enabled = True
# Send anonymous user activity to your analytics tool
# choose from google_analytics, segment, or metarouter
# analytics_tool =
# Unique ID of your account in the analytics tool
# analytics_id =
# Update FAB permissions and sync security manager roles
# on webserver startup
update_fab_perms = True
# Minutes of non-activity before logged out from UI
# 0 means never get forcibly logged out
force_log_out_after = 0
# The UI cookie lifetime in days
session_lifetime_days = 30
[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 Address
# 192.168.152.213
# SMTP Port
# 25
# User Name
# etf#csopasset.com
smtp_host = *.*.*.*
smtp_starttls = True
smtp_ssl = False
# smtp_user = etf#csopasset.com
# smtp_password = etfGen2013
smtp_port = 25
smtp_mail_from = etf#***.com
[sentry]
# Sentry (https://docs.sentry.io) integration
sentry_dsn =
[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 celery 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
worker_concurrency = 50
# The maximum and minimum concurrency that will be used when starting workers with the
# ``airflow celery worker`` command (always keep minimum processes, but grow
# to maximum if necessary). Note the value should be max_concurrency,min_concurrency
# Pick these numbers based on resources on worker box and the nature of the task.
# If autoscale option is available, worker_concurrency will be ignored.
# http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale
# Example: worker_autoscale = 16,12
# worker_autoscale =
# 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.
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#broker-settings
#roker_url = sqla+mysql://airflow:airflow#localhost:3306/airflow
broker_url = amqp://guest:guest#localhost:5672//
# The Celery result_backend. When a job finishes, it needs to update the
# metadata of the job. Therefore it will post a message on a message bus,
# or insert it into a database (depending of the backend)
# This status is used by the scheduler to update the state of the task
# The use of a database is highly recommended
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings
#result_backend = db+mysql://airflow:airflow#localhost:3306/airflow
result_backend = db+postgresql+psycopg2://devairflow:airflow#localhost:5432/pcfdb
# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it ``airflow flower``. This defines the IP that Celery Flower runs on
flower_host = 0.0.0.0
# The root URL for Flower
# Example: flower_url_prefix = /flower
flower_url_prefix =
# This defines the port that Celery Flower runs on
flower_port = 5555
# Securing Flower with Basic Authentication
# Accepts user:password pairs separated by a comma
# Example: flower_basic_auth = user1:password1,user2:password2
flower_basic_auth =
# Default queue that tasks get assigned to and that worker listen on.
default_queue = default
# How many processes CeleryExecutor uses to sync task state.
# 0 means to use max(1, number of cores - 1) processes.
sync_parallelism = 0
# Import path for celery configuration options
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
# In case of using SSL
ssl_active = False
ssl_key =
ssl_cert =
ssl_cacert =
# Celery Pool implementation.
# Choices include: prefork (default), eventlet, gevent or solo.
# See:
# https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency
# https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html
pool = prefork
# The number of seconds to wait before timing out ``send_task_to_executor`` or
# ``fetch_celery_task_state`` operations.
operation_timeout = 50
[celery_broker_transport_options]
# This section is for specifying options which can be passed to the
# underlying celery broker transport. See:
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options
# The visibility timeout defines the number of seconds to wait for the worker
# to acknowledge the task before the message is redelivered to another worker.
# Make sure to increase the visibility timeout to match the time of the longest
# ETA you're planning to use.
# visibility_timeout is only supported for Redis and SQS celery brokers.
# See:
# http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options
# Example: visibility_timeout = 21600
# visibility_timeout =
[dask]
# This section only applies if you are using the DaskExecutor in
# [core] section above
# The IP address and port of the Dask cluster's scheduler.
cluster_address = 127.0.0.1:8786
# TLS/ SSL settings to access a secured Dask scheduler.
tls_ca =
tls_cert =
tls_key =
[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
# After how much time should the scheduler terminate in seconds
# -1 indicates to run continuously (see also num_runs)
run_duration = -1
# The number of times to try to schedule each DAG file
# -1 indicates unlimited number
num_runs = -1
# The number of seconds to wait between consecutive DAG file processing
processor_poll_interval = 1
# after how much time (seconds) a new DAGs should be picked up from the filesystem
min_file_process_interval = 0
# How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes.
dag_dir_list_interval = 300
# How often should stats be printed to the logs. Setting to 0 will disable printing stats
print_stats_interval = 30
# If the last scheduler heartbeat happened more than scheduler_health_check_threshold
# ago (in seconds), scheduler is considered unhealthy.
# This is used by the health check in the "/health" endpoint
scheduler_health_check_threshold = 30
child_process_log_directory = /home/airflow/logs/scheduler
# Local task jobs periodically heartbeat to the DB. If the job has
# not heartbeat in this many seconds, the scheduler will mark the
# associated task instance as failed and will re-schedule the task.
scheduler_zombie_task_threshold = 1800
# Turn off scheduler catchup by setting this to False.
# Default behavior is unchanged and
# Command Line Backfills still work, but the scheduler
# will not do scheduler catchup if this is False,
# however it can be set on a per DAG basis in the
# DAG definition (catchup)
catchup_by_default = True
# This changes the batch size of queries in the scheduling main loop.
# If this is too high, SQL query performance may be impacted by one
# or more of the following:
# - reversion to full table scan
# - complexity of query predicate
# - excessive locking
# Additionally, you may hit the maximum allowable query length for your db.
# Set this to 0 for no limit (not advised)
max_tis_per_query = 512
# Statsd (https://github.com/etsy/statsd) integration settings
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow
# If you want to avoid send all the available metrics to StatsD,
# you can configure an allow list of prefixes to send only the metrics that
# start with the elements of the list (e.g: scheduler,executor,dagrun)
statsd_allow_list =
# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run.
max_threads = 2
authenticate = False
# Turn off scheduler use of cron intervals by setting this to False.
# DAGs submitted manually in the web UI or with trigger_dag will still run.
use_job_schedule = True
# Allow externally triggered DagRuns for Execution Dates in the future
# Only has effect if schedule_interval is set to None in DAG
allow_trigger_in_future = False
[ldap]
# set this to ldaps://<your.ldap.server>:<port>
uri =
user_filter = objectClass=*
user_name_attr = uid
group_member_attr = memberOf
superuser_filter =
data_profiler_filter =
bind_user = cn=Manager,dc=example,dc=com
bind_password = insecure
basedn = dc=example,dc=com
cacert = /etc/ca/ldap_ca.crt
search_scope = LEVEL
# This setting allows the use of LDAP servers that either return a
# broken schema, or do not return a schema.
ignore_malformed_schema = False
[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.
# Example: failover_timeout = 604800
# failover_timeout =
# Enable framework authentication for mesos
# See http://mesos.apache.org/documentation/latest/configuration/
authenticate = False
# Mesos credentials, if authentication is enabled
# Example: default_principal = admin
# default_principal =
# Example: default_secret = admin
# default_secret =
# Optional Docker Image to run on slave before running the command
# This image should be accessible from mesos slave i.e mesos slave
# should be able to pull this docker image before executing the command.
# Example: docker_image_slave = puckel/docker-airflow
# docker_image_slave =
[kerberos]
ccache = /tmp/airflow_krb5_ccache
# gets augmented with fqdn
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab
[github_enterprise]
api_rev = v3
[admin]
# UI to hide sensitive variable fields when set to True
hide_sensitive_variable_fields = True
[elasticsearch]
# Elasticsearch host
host =
# Format of the log_id, which is used to query for a given tasks logs
log_id_template = {dag_id}-{task_id}-{execution_date}-{try_number}
# Used to mark the end of a log stream for a task
end_of_log_mark = end_of_log
# Qualified URL for an elasticsearch frontend (like Kibana) with a template argument for log_id
# Code will construct log_id using the log_id template from the argument above.
# NOTE: The code will prefix the https:// automatically, don't include that here.
frontend =
# Write the task logs to the stdout of the worker, rather than the default files
write_stdout = False
# Instead of the default log formatter, write the log lines as JSON
json_format = False
# Log fields to also attach to the json output, if enabled
json_fields = asctime, filename, lineno, levelname, message
[elasticsearch_configs]
use_ssl = False
verify_certs = True
Below are the Worker logs:
Worker logs File
Airflow 2.0 has massive scheduler improvements. I you should upgrade to 2.0 and enjoy the new scheduler.
If the last scheduler heartbeat happened more than scheduler_health_check_threshold ago (in seconds), scheduler is considered unhealthy. This is used by the health check in the “/health” endpoint
Reference: Airflow Documentation
Make sure scheduler health check thresh hold is higher than scheduler heartbeat sec.
scheduler_health_check_threshold = 300
scheduler_heartbeat_sec = 60
I'm working on a little proof of concept about Airflow on Google Cloud.
Essentially, I want to create a workflow that download data from an REST API (https), transform this data into JSON format and upload it on a Google Cloud storage unit.
I've already done this with pure Python code and it works. Pretty straightforward! But because I want to schedule this and there is some dependencies, Airflow should be the ideal tool for this.
After careful reading of the Airflow documentation, I've seen the HttpOperator and/or HttpHook can do the trick for the download part.
I've created my Http connection into the WebUI with my email/password for the authorization as the following:
{Conn Id: "atlassian_marketplace", Conn Type: "HTTP", Host: "https://marketplace.atlassian.com/rest/2", Schema: None/Blank, Login: "my username", Password: "my password", Port: None/Blank, Extra: None/Blank}
First question:
-When to use the SimpleHttpOperator versus the HttpHook?
Second question:
-How do we use SimpleHttpOperator or HttpHook with HTTPs calls?
Third question:
-How do we access the data returned by the API call?
In my case, the XCOM feature will not do the trick because these API calls can return a lot of data (100-300mb)!
I've look on Google to find an example code on how to use the operaor/hook for my use case but i didn't find anything useful, yet.
Any ideas?
I put here the skeleton of my code so far.
# Usual Airflow import
# Dag creation
dag = DAG(
'get_reporting_links',
default_args=default_args,
description='Get reporting links',
schedule_interval=timedelta(days=1))
# Task 1: Dummy start
start = DummyOperator(task_id="Start", retries=2, dag=dag)
# Task 2: Connect to Atlassian Marketplace
get_data = SimpleHttpOperator(
http_conn_id="atlassian_marketplace",
endpoint="/vendors/{vendorId}/reporting".format({vendorId: "some number"}),
method="GET")
# Task 3: Save JSON data locally
# TODO: transform_json: transform to JSON get_data.json()?
# Task 4: Upload data to GCP
# TODO: upload_gcs: use Airflow GCS connection
# Task 5: Stop
stop = DummyOperator(task_id="Stop", retries=2, dag=dag)
# Dependencies
start >> get_data >> transform_json >> upload_gcs >> stop
Look at the following example:
# Usual Airflow import
# Dag creation
dag = DAG(
'get_reporting_links',
default_args=default_args,
description='Get reporting links',
schedule_interval=timedelta(days=1))
# Task 1: Dummy start
start = DummyOperator(task_id="Start", retries=2, dag=dag)
# Task 2: Connect to Atlassian Marketplace
get_data = SimpleHttpOperator(
task_id="get_data",
http_conn_id="atlassian_marketplace",
endpoint="/vendors/{vendorId}/reporting".format({vendorId: "some number"}),
method="GET",
xcom_push=True,
)
def transform_json(**kwargs):
ti = kwargs['ti']
pulled_value_1 = ti.xcom_pull(key=None, task_ids='get_data')
...
# transform the json here and save the content to a file
# Task 3: Save JSON data locally
save_and_transform = PythonOperator(
task_id="save_and_transform",
python_callable=transform_json,
provide_context=True,
)
# Task 4: Upload data to GCP
upload_to_gcs = FileToGoogleCloudStorageOperator(...)
# Task 5: Stop
stop = DummyOperator(task_id="Stop", retries=2, dag=dag)
# Dependencies
start >> get_data >> save_and_transform >> upload_to_gcs >> stop
Let's say I'm using Terraform to provision two machines inside AWS:
An EC2 Machine running NodeJS
An RDS instance
How does the NodeJS code obtain the address of the RDS instance?
You've got a couple of options here. The simplest one is to create a CNAME record in Route53 for the database and then always point to that CNAME in your application.
A basic example would look something like this:
resource "aws_db_instance" "mydb" {
allocated_storage = 10
engine = "mysql"
engine_version = "5.6.17"
instance_class = "db.t2.micro"
name = "mydb"
username = "foo"
password = "bar"
db_subnet_group_name = "my_database_subnet_group"
parameter_group_name = "default.mysql5.6"
}
resource "aws_route53_record" "database" {
zone_id = "${aws_route53_zone.primary.zone_id}"
name = "database.example.com"
type = "CNAME"
ttl = "300"
records = ["${aws_db_instance.default.endpoint}"]
}
Alternative options include taking the endpoint output from the aws_db_instance and passing that into a user data script when creating the instance or passing it to Consul and using Consul Template to control the config that your application uses.
You may try Sparrowform - a lightweight provision tool for Terraform based instances, it's capable to make an inventory of Terraform resources and provision related hosts, passing all the necessary data:
$ terrafrom apply # bootstrap infrastructure
$ cat sparrowfile # this scenario
# fetches DB address from terraform cache
# and populate configuration file
# at server with node js code:
#!/usr/bin/env perl6
use Sparrowform;
$ sparrowfrom --ssh_private_key=~/.ssh/aws.pem --ssh_user=ec2 # run provision tool
my $rdb-adress;
for tf-resources() -> $r {
my $r-id = $r[0]; # resource id
if ( $r-id 'aws_db_instance.mydb') {
my $r-data = $r[1];
$rdb-address = $r-data<address>;
last;
}
}
# For instance, we can
# Install configuration file
# Next chunk of code will be applied to
# The server with node-js code:
template-create '/path/to/config/app.conf', %(
source => ( slurp 'app.conf.tmpl' ),
variables => %(
rdb-address => $rdb-address
),
);
# sparrowform --ssh_private_key=~/.ssh/aws.pem --ssh_user=ec2 # run provisioning
PS. disclosure - I am the tool author
I have read revel app.conf manual for custom configuration and environment variables. however I couldn't find way to use additional external configuration along with app.conf.
My goal is to achieve external configuration file in addition to internal app.conf. Let's say creating a product called example and example product maintains it's sensible defaults with app.conf (not exposing to end user) instead product exposes config attributes via example.conf (default location could be /etc/example/example.conf) for product users.
For example: http config field from app.conf
http.addr =
http.port = 9000
extend it to example.conf
http.addr =
http.port = 9000
[database]
host = "localhost"
port = 8080
user = "username"
password = "password"
# etc...
Then I read example.conf during an application start use values also apply values on top of app.conf (overriding). Finally revel server starts!
How to achieve this goal with revel go framework?
It appears you are working against the design of the app.conf. It is already setup to be sectioned, for example all this is in a single app.conf file
[dev]
results.pretty = true
watch = true
http.addr = 192.168.1.2
[test]
results.pretty = true
watch = true
http.addr = 192.168.1.22
[prod]
results.pretty = false
watch = false
http.addr = 192.168.1.100
you can launch 3 different scenarios by using three different command line options
revel run bitbucket.org/mycorp/my-app dev
revel run bitbucket.org/mycorp/my-app test
revel run bitbucket.org/mycorp/my-app prod
I know this is not exactly what your goal is but you can acheive a similar result.
In github.com/revel/revel/revel.go around line 152 you have something like
Config, err = LoadConfig("app.conf").
Maybe you can try and modify that with this
if len(os.Getenv("SOME ENV VAR")) > 0 {
Config, err = LoadConfig("path/to/your/example.conf")
} else {
Config, err = LoadConfig("app.conf")
}
You just need to set env var on your prod server.
That way you will not be using app.conf but your example.conf.