i'm running a pyramid webapp, using velruse to make OAuth. if running the app alone, it succeeded.
but if running with uwsgi multiple and set session.type = memory.
request.session will not contain necessary token info when callback from oauth.
production.ini:
session.type = memory
session.data_dir = %(here)s/data/sessions/data
session.lock_dir = %(here)s/data/sessions/lock
session.key = mykey
session.secret = mysecret
[uwsgi]
socket = 127.0.0.1:6543
master = true
workers = 8
max-requests = 65536
debug = false
autoload = true
virtualenv = /home/myname/my_env
pidfile = ./uwsgi.pid
daemonize = ./mypyramid-uwsgi.log
If you use memory as session store only the worker in which the session data has been written will be able to use that infos. You should use another sessione store (that can be shared by all of the workers/processes)
your uWSGI config is not clear (it looks like it only contains the socket option). Can you re-paste it ?
Related
I run a model in triton serving with shared memory and it works correctly.
In order to simulate backend structure I wrote a Fast API for my model and run it with gunicorn with 6 workers. Then I wrote anthor Fast API to route locust requests to my first Fast Fast API as below image(pseudo code). my second Fast API runs with uvicorn. but the problem is when I used multiple workers for my uvicorn, triton serving failed to shared memory.
Note: without shared memory every thing works but my response time is much longer than the shared memory option. so I need to use shared memory option.
here is my triton client code:
I have a functions in my client code named predict function which used the requestGenerator to shared input_simple and output_simple spaces.
this is my requestGenerator generator:
def requestGenerator(self, triton_client, batched_img_data, input_name, output_name, dtype, batch_data):
triton_client.unregister_system_shared_memory()
triton_client.unregister_cuda_shared_memory()
output_simple = "output_simple"
input_simple = "input_simple"
input_data = np.ones(
shape=(batch_data, 3, self.width, self.height), dtype=np.float32)
input_byte_size = input_data.size * input_data.itemsize
output_byte_size = input_byte_size * 2
shm_op0_handle = shm.create_shared_memory_region(
output_name, output_simple, output_byte_size)
triton_client.register_system_shared_memory(
output_name, output_simple, output_byte_size)
shm_ip0_handle = shm.create_shared_memory_region(
input_name, input_simple, input_byte_size)
triton_client.register_system_shared_memory(
input_name, input_simple, input_byte_size)
inputs = []
inputs.append(
httpclient.InferInput(input_name, batched_img_data.shape, dtype))
inputs[0].set_data_from_numpy(batched_img_data, binary_data=True)
outputs = []
outputs.append(
httpclient.InferRequestedOutput(output_name,
binary_data=True))
inputs[-1].set_shared_memory(input_name, input_byte_size)
outputs[-1].set_shared_memory(output_name, output_byte_size)
yield inputs, outputs, shm_ip0_handle, shm_op0_handle
this is my predict function:
def predict(self, triton_client, batched_data, input_layer, output_layer, dtype):
responses = []
results = None
for inputs, outputs, shm_ip_handle, shm_op_handle in self.requestGenerator(
triton_client, batched_data, input_layer, output_layer, type,
len(batched_data)):
self.sent_count += 1
shm.set_shared_memory_region(shm_ip_handle, [batched_data])
responses.append(
triton_client.infer(model_name=self.model_name,
inputs=inputs,
request_id=str(self.sent_count),
model_version="",
outputs=outputs))
output_buffer = responses[0].get_output(output_layer)
if output_buffer is not None:
results = shm.get_contents_as_numpy(
shm_op_handle, triton_to_np_dtype(output_buffer['datatype']),
output_buffer['shape'])
triton_client.unregister_system_shared_memory()
triton_client.unregister_cuda_shared_memory()
shm.destroy_shared_memory_region(shm_ip_handle)
shm.destroy_shared_memory_region(shm_op_handle)
return results
Any help would be appreciated to help me how to use multiple uvicorn workers to send multiple requests concurrently to my triton code without failing.
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 am trying to setup my EMS in FT Mode, I have configured all the parameters in the 2 EMS config files.
But Im getting the warning:
Unable to initialize fault tolerant connection, remote server returned 'invalid user name'
Servername and password are exactly the same in both config files,so I don't know where the error is.
I am attaching the EMS config files that i am using for the EMS servers:
tibemsd.conf:
authorization = enabled
password =
server=EMS-HakanLAL
listen=tcp://7222
Ft_active=tcp://8222
users = users.conf
groups = groups.conf
topics = topics.conf
queues = queues.conf
acl_list = acl.conf
factories = factories.conf
routes = routes.conf
bridges = bridges.conf
transports = transports.conf
tibrvcm = tibrvcm.conf
durables = durables.conf
channels = channels.conf
stores = stores.conf
store = "C:/temp"
tibemsdft.conf:
authorization = enabled
password =
server=EMS-HakanLAL
listen=tcp://8222
Ft_active=tcp://7222
users = C:\Tibco\ems\8.1\BackUp\users.conf
groups = C:\Tibco\ems\8.1\BackUp\groups.conf
topics = C:\Tibco\ems\8.1\BackUp\topics.conf
queues = C:\Tibco\ems\8.1\BackUp\queues.conf
acl_list = C:\Tibco\ems\8.1\BackUp\acl.conf
factories = C:\Tibco\ems\8.1\BackUp\factories.conf
routes = C:\Tibco\ems\8.1\BackUp\routes.conf
bridges = C:\Tibco\ems\8.1\BackUp\bridges.conf
transports = C:\Tibco\ems\8.1\BackUp\transports.conf
tibrvcm = C:\Tibco\ems\8.1\BackUp\tibrvcm.conf
durables = C:\Tibco\ems\8.1\BackUp\durables.conf
channels = C:\Tibco\ems\8.1\BackUp\channels.conf
stores = C:\Tibco\ems\8.1\BackUp\stores.conf
store = "C:\ProgramData\TIBCO3\tibco\cfgmgmt\ems\data"
your tibemsd.conf and tibemsdft.conf looks fine. What you are probably missing is registering the server-name as a user within the users.conf.
If you make that entry, both servers should be able to connect to each other.
http://hadoop.apache.org/docs/r2.1.0-beta/hadoop-yarn/hadoop-yarn-site/WritingYarnApplications.html
I am try to make the example work well from the above link.but I can't compile the code below
Resource capability = Records.newRecord(Resource.class);
capability.setMemory(512);
amContainer.setResource(capability);
// Set the container launch content into the
// ApplicationSubmissionContext
appContext.setAMContainerSpec(amContainer);
amContainer is ContainerLaunchContext and my hadoop version is 2.1.0-beta.
I did some investigation. I found there's no method "setResource" in ContainerLaunchContext
I have 3 question about this
1) the method has been removed or something?
2) if the method has been removed, how can I do now?
3) is there any doc about yarn, because I found the doc in website is very easy, I hope I can get a manual or something. for example,
capability.setMemory(512);
I don't know it's 512k or 512M according comments in code.
This is actually proper solution to the question. Previous answer might cause incorrect execution !!!
#Dyin I couldn't fit it in the comment ;) Validated for 2.2.0 and 2.3.0
Driver setting up resources for AppMaster:
ApplicationSubmissionContext appContext = app.getApplicationSubmissionContext();
ApplicationId appId = appContext.getApplicationId();
appContext.setApplicationName(this.appName);
// Set up the container launch context for the application master
ContainerLaunchContext amContainer = Records.newRecord(ContainerLaunchContext.class);
Resource capability = Records.newRecord(Resource.class);
capability.setMemory(amMemory);
appContext.setResource(capability);
appContext.setAMContainerSpec(amContainer);
Priority pri = Records.newRecord(Priority.class);
pri.setPriority(amPriority);
appContext.setPriority(pri);
appContext.setQueue(amQueue);
// Submit the application to the applications manager
yarnClient.submitApplication(appContext); // this.yarnClient = YarnClient.createYarnClient();
In ApplicationMaster this is how you should specify resources for containers (workers).
private AMRMClient.ContainerRequest setupContainerAskForRM() {
// setup requirements for hosts
// using * as any host will do for the distributed shell app
// set the priority for the request
Priority pri = Records.newRecord(Priority.class);
pri.setPriority(requestPriority);
// Set up resource type requirements
// For now, only memory is supported so we set memory requirements
Resource capability = Records.newRecord(Resource.class);
capability.setMemory(containerMemory);
AMRMClient.ContainerRequest request = new AMRMClient.ContainerRequest(capability, null, null,
pri);
return request;
}
Some run() or main() method in your AppMaster
AMRMClientAsync.CallbackHandler allocListener = new RMCallbackHandler();
resourceManager = AMRMClientAsync.createAMRMClientAsync(1000, allocListener);
resourceManager.init(conf);
resourceManager.start();
for (int i = 0; i < numTotalContainers; ++i) {
AMRMClient.ContainerRequest containerAsk = setupContainerAskForRM();
resourceManager.addContainerRequest(containerAsk); //
}
Launching containers
You can use the original answer solution (java cmd), but it's just a cherry on top. It should work anyway.
You can set memory available to ApplicationMaster via commend. As such:
// Set the necessary command to execute the application master
Vector<CharSequence> vargs = new Vector<CharSequence>(30);
...
vargs.add("-Xmx" + amMemory + "m"); // notice "m" indicating megabytes, you can use also -Xms combined with -Xmx
... // transform vargs to String commands
amContainer.setCommands(commands);
This should solve your problem. As for the 3 questions. Yarn is rapidly evolving software. My advice forget documentation, get source code and read it. This will answer a lot of your questions.
I'm testing RIAK-CS as an S3 alternative.
I currently have 3 nodes with 4GB RAM and 2GB SWAP each. I pushed 10GB of data to RIAK-CS. Except the high IO, everything was fine.
Then I tried to pull that data with the following python script.
conn = boto.connect_s3(
aws_access_key_id = access_key,
aws_secret_access_key = secret_key,
host = riak_host,
proxy = riak_host,
proxy_port = 8080,
calling_format = boto.s3.connection.OrdinaryCallingFormat(),
debug = 2,
is_secure = False,
)
bucket = conn.create_bucket('bucket_name')
for row in rows:
key=bucket.new_key(valid_key_name)
if key.exists():
key.open_read()
Things started to be pulled from RIAK. After couple of minutes, except the huge IO again, I noticed that the riak-cs process is "eating" more and more memory and eventually crashed as it took all 6GB of RAM+SWAP.
If I changed the python script as the following, riak-cs remained at ~2-300 MB. Notice the connect in FOR loop.
for row in rows:
conn = boto.connect_s3(
aws_access_key_id = access_key,
aws_secret_access_key = secret_key,
calling_format = boto.s3.connection.OrdinaryCallingFormat(),
debug = 2,
is_secure = False,
)
bucket = conn.create_bucket('bucket_name')
key=bucket.new_key(valid_key_name)
if key.exists():
key.open_read()
Any explanation why this would happen?
Thank you!
This is possibly related to the issue which is fixed at version 1.4.
https://github.com/basho/riak_cs/pull/570
https://github.com/basho/riak_cs/blob/release/1.4/RELEASE-NOTES.org#bugs-fixed-1