I followed the BlazeMeter article to monitor Gatling tests with Grafana and InfluxDB but no data is sent to InfluxDB and not any database created with the name "gatlingdb". InfluxDB is up and listen to port :2003. This is the log from InfluxDB:
2022-01-07T13:57:53.019217Z info Starting graphite service {"log_id": "0YuD8znW000", "service": "graphite", "addr": ":2003", "batch_size": 5000, "batch_timeout": "1s"}
And I set gatling.conf fields to these:
data {
writers = [console,file,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 = "localhost" # 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 influxdb.conf contains below parameters
[[graphite]]
# Determines whether the graphite endpoint is enabled.
enabled = true
database = "gatlingdb"
# retention-policy = ""
bind-address = ":2003"
protocol = "tcp"
consistency-level = "one"
# These next lines control how batching works. You should have this enabled
# otherwise you could get dropped metrics or poor performance. Batching
# will buffer points in memory if you have many coming in.
# Flush if this many points get buffered
batch-size = 5000
# number of batches that may be pending in memory
# batch-pending = 10
# Flush at least this often even if we haven't hit buffer limit
# batch-timeout = "1s"
# UDP Read buffer size, 0 means OS default. UDP listener will fail if set above OS max.
# udp-read-buffer = 0
### This string joins multiple matching 'measurement' values providing more control over the final measurement name.
separator = "."
### Default tags that will be added to all metrics. These can be overridden at the template level
### or by tags extracted from metric
# tags = ["region=us-east", "zone=1c"]
### Each template line requires a template pattern. It can have an optional
### filter before the template and separated by spaces. It can also have optional extra
### tags following the template. Multiple tags should be separated by commas and no spaces
### similar to the line protocol format. There can be only one default template.
templates = [
"gatling.*.*.*.count measurement.simulation.request.status.field",
"gatling.*.*.*.min measurement.simulation.request.status.field",
"gatling.*.*.*.max measurement.simulation.request.status.field",
"gatling.*.*.*.percentiles95 measurement.simulation.request.status.field",
"gatling.*.*.*.percentiles99 measurement.simulation.request.status.field"
]
Now I am running test through gatling but after successful completion of test no database with name galingdb is getting created on influxdb.
I am not sure what else I need to add.
You need create database manually:
> influx
> CREATE DATABASE gatlingdb
I am trying to import some readymade dashboard from grafana for my setup( gatling/influxdb) but those are not working somehow specially simulation parameter in grafana dashboard.if someone using same setup , can please share their json file.
below is my config for gatling and influxdb
gatling.conf
data {
writers = [console,file,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 = "localhost" # 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 influxdb.conf contains below parameters
[graphite]]
# Determines whether the graphite endpoint is enabled.
enabled = true
database = "gatling"
# retention-policy = ""
bind-address = ":2003"
protocol = "tcp"
consistency-level = "one"
# These next lines control how batching works. You should have this enabled
# otherwise you could get dropped metrics or poor performance. Batching
# will buffer points in memory if you have many coming in.
# Flush if this many points get buffered
batch-size = 5000
# number of batches that may be pending in memory
# batch-pending = 10
# Flush at least this often even if we haven't hit buffer limit
# batch-timeout = "1s"
# UDP Read buffer size, 0 means OS default. UDP listener will fail if set above OS max.
# udp-read-buffer = 0
### This string joins multiple matching 'measurement' values providing more control over the final measurement name.
separator = "."
You can find free dashboards via Grafana search: https://grafana.com/grafana/dashboards/?search=gatling
In my system (with raspberry) I have some sensors that publish data to Mosquitto, I'm using Telegraf to transfer the data do an influxDB database and I'm using Grafana to show the data.
During the test without TLS connection (in mosquittos) everything works correctly but when I activated the TLS I start to have a problem with Telegraf.
The sensor are sending the data to the broker using the client.key, client.crt and ca.crt.
In the broker I can see the data from the sensor. So I think the problem in not in this.
In telegraf (I suppose it works as client) I tried to configure the TLS connection.
Looking at the telegraf.service status , it is active and running. Looking at the journal I don't see errors in the connection but I can't see any data from the broker.
In Telegraf.conf I set the certificate as you can see here below. Instead using pem file I used the file that I use for the sensor or other client connected to the system: the extension is different and I don't know if the problem is here.
Here the configuration of Telegraf (mqtt_consumer)
# # Read metrics from MQTT topic(s)
[[inputs.mqtt_consumer]]
# ## Broker URLs for the MQTT server or cluster. To connect to multiple
# ## clusters or standalone servers, use a seperate plugin instance.
# ## example: servers = ["tcp://localhost:1883"]
# ## servers = ["ssl://localhost:1883"]
# ## servers = ["ws://localhost:1883"]
servers = ["tcp://192.168.1.58:8883"]
#
# ## Topics that will be subscribed to.
topics = [
"sensors/#"
]
#
# ## The message topic will be stored in a tag specified by this value. If set
# ## to the empty string no topic tag will be created.
# # topic_tag = "topic"
#
# ## QoS policy for messages
# ## 0 = at most once
# ## 1 = at least once
# ## 2 = exactly once
# ##
# ## When using a QoS of 1 or 2, you should enable persistent_session to allow
# ## resuming unacknowledged messages.
# # qos = 0
#
# ## Connection timeout for initial connection in seconds
# # connection_timeout = "30s"
#
# ## Maximum messages to read from the broker that have not been written by an
# ## output. For best throughput set based on the number of metrics within
# ## each message and the size of the output's metric_batch_size.
# ##
# ## For example, if each message from the queue contains 10 metrics and the
# ## output metric_batch_size is 1000, setting this to 100 will ensure that a
# ## full batch is collected and the write is triggered immediately without
# ## waiting until the next flush_interval.
# # max_undelivered_messages = 1000
#
# ## Persistent session disables clearing of the client session on connection.
# ## In order for this option to work you must also set client_id to identify
# ## the client. To receive messages that arrived while the client is offline,
# ## also set the qos option to 1 or 2 and don't forget to also set the QoS when
# ## publishing.
# # persistent_session = false
#
# ## If unset, a random client ID will be generated.
client_id = ""
#
# ## Username and password to connect MQTT server.
#username = ""
#password = ""
#
# ## Optional TLS Config
tls_ca = "/etc/telegraf/ca.crt"
tls_cert = "/etc/telegraf/client.crt"
tls_key = "/etc/telegraf/client.key"
# ## Use TLS but skip chain & host verification
# insecure_skip_verify = false
#
# ## Data format to consume.
# ## Each data format has its own unique set of configuration options, read
# ## more about them here:
# ## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
data_format = "influx"
How can I check the connection to the broker in Telegraf? Is it correct the configuration or I should use only .pem file?
Your MQTT URL starts with tcp:// but it should start with ssl:// for a MQTT over SSL connection.
I'm trying to cluster my Airflow setup and I'm using this article to do so. I just configured my airflow.cfg file to use the CeleryExecutor, I pointed my sql_alchemy_conn to my postgresql database that's running on the same master node, I've set the broker_url to use SQS (I didn't set the access_key_id or secret_key since it's running on an EC2-Instance it doesn't need those), and I've set the celery_result_backend to my postgresql server too. I saved my new airflow.cfg changes, I ran airflow initdb, and then I ran airflow scheduler and I'm getting this error from the scheduler,
[2018-06-07 21:07:33,420] {celery_executor.py:101} ERROR - Error syncing the celery executor, ignoring it:
[2018-06-07 21:07:33,421] {celery_executor.py:102} ERROR - Can't load plugin: sqlalchemy.dialects:psycopg2
Traceback (most recent call last):
File "/usr/local/lib/python3.6/site-packages/airflow/executors/celery_executor.py", line 83, in sync
state = async.state
File "/usr/local/lib/python3.6/site-packages/celery/result.py", line 433, in state
return self._get_task_meta()['status']
File "/usr/local/lib/python3.6/site-packages/celery/result.py", line 372, in _get_task_meta
return self._maybe_set_cache(self.backend.get_task_meta(self.id))
File "/usr/local/lib/python3.6/site-packages/celery/backends/base.py", line 344, in get_task_meta
meta = self._get_task_meta_for(task_id)
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/__init__.py", line 53, in _inner
return fun(*args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/__init__.py", line 122, in _get_task_meta_for
session = self.ResultSession()
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/__init__.py", line 99, in ResultSession
**self.engine_options)
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/session.py", line 59, in session_factory
engine, session = self.create_session(dburi, **kwargs)
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/session.py", line 45, in create_session
engine = self.get_engine(dburi, **kwargs)
File "/usr/local/lib/python3.6/site-packages/celery/backends/database/session.py", line 42, in get_engine
return create_engine(dburi, poolclass=NullPool)
File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/__init__.py", line 424, in create_engine
return strategy.create(*args, **kwargs)
File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/strategies.py", line 57, in create
entrypoint = u._get_entrypoint()
File "/usr/local/lib/python3.6/site-packages/sqlalchemy/engine/url.py", line 156, in _get_entrypoint
cls = registry.load(name)
File "/usr/local/lib/python3.6/site-packages/sqlalchemy/util/langhelpers.py", line 221, in load
(self.group, name))
sqlalchemy.exc.NoSuchModuleError: Can't load plugin: sqlalchemy.dialects:psycopg2
Here is my airflow.cfg file,
[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /home/ec2-user/airflow
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = /home/ec2-user/airflow/dags
# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = /home/ec2-user/airflow/logs
# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
# must supply an Airflow connection id that provides access to the storage
# location.
remote_log_conn_id =
encrypt_s3_logs = False
# Logging level
logging_level = INFO
# Logging class
# Specify the class that will specify the logging configuration
# This class has to be on the python classpath
# logging_config_class = my.path.default_local_settings.LOGGING_CONFIG
logging_config_class =
# Log format
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor
#executor = SequentialExecutor
executor = CeleryExecutor
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
#sql_alchemy_conn = sqlite:////home/ec2-user/airflow/airflow.db
sql_alchemy_conn = postgresql+psycopg2://postgres:$password#localhost/datalake_airflow_cluster_v1_master1_database_1
# 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 = True
# Where your Airflow plugins are stored
plugins_folder = /home/ec2-user/airflow/plugins
# Secret key to save connection passwords in the db
fernet_key = ibwZ5uSASmZGphBmwdJ4BIhd1-5WZXMTTgMF9u1_dGM=
# 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
# The class to use for running task instances in a subprocess
task_runner = BashTaskRunner
# If set, tasks without a `run_as_user` argument will be run with this user
# Can be used to de-elevate a sudo user running Airflow when executing tasks
default_impersonation =
# What security module to use (for example kerberos):
security =
# Turn unit test mode on (overwrites many configuration options with test
# values at runtime)
unit_test_mode = False
# Name of handler to read task instance logs.
# Default to use file task handler.
task_log_reader = file.task
# Whether to enable pickling for xcom (note that this is insecure and allows for
# RCE exploits). This will be deprecated in Airflow 2.0 (be forced to False).
enable_xcom_pickling = True
# When a task is killed forcefully, this is the amount of time in seconds that
# it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED
killed_task_cleanup_time = 60
[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
endpoint_url = http://localhost:8080
[api]
# How to authenticate users of the API
auth_backend = airflow.api.auth.backend.default
[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 = 512
default_disk = 512
default_gpus = 0
[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
# 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 =
web_server_ssl_key =
# Number of seconds the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 120
# 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
# Secret key used to run your flask app
secret_key = temporary_key
# Number of workers to run the Gunicorn web server
workers = 4
# The worker class gunicorn should use. Choices include
# sync (default), eventlet, gevent
worker_class = sync
# Log files for the gunicorn webserver. '-' means log to stderr.
access_logfile = -
error_logfile = -
# Expose the configuration file in the web server
expose_config = False
# Set to true to turn on authentication:
# http://pythonhosted.org/airflow/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
# 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
[email]
email_backend = airflow.utils.email.send_email_smtp
[smtp]
# If you want airflow to send emails on retries, failure, and you want to use
# the airflow.utils.email.send_email_smtp function, you have to configure an
# smtp server here
smtp_host = localhost
smtp_starttls = True
smtp_ssl = False
# Uncomment and set the user/pass settings if you want to use SMTP AUTH
# smtp_user = airflow
# smtp_password = airflow
smtp_port = 25
smtp_mail_from = airflow#example.com
[celery]
# This section only applies if you are using the CeleryExecutor in
# [core] section above
# The app name that will be used by celery
celery_app_name = airflow.executors.celery_executor
# The concurrency that will be used when starting workers with the
# "airflow worker" command. This defines the number of task instances that
# a worker will take, so size up your workers based on the resources on
# your worker box and the nature of your tasks
celeryd_concurrency = 16
# When you start an airflow worker, airflow starts a tiny web server
# subprocess to serve the workers local log files to the airflow main
# web server, who then builds pages and sends them to users. This defines
# the port on which the logs are served. It needs to be unused, and open
# visible from the main web server to connect into the workers.
worker_log_server_port = 8793
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
# a sqlalchemy database. Refer to the Celery documentation for more
# information.
#broker_url = sqla+mysql://airflow:airflow#localhost:3306/airflow
broker_url = sqs://
# Another key Celery setting
#celery_result_backend = db+mysql://airflow:airflow#localhost:3306/airflow
celery_result_backend = db+psycopg2://postgres:$password#localhost/datalake_airflow_cluster_v1_master1_database_1
# 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
# 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
# Import path for celery configuration options
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
[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
[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
# after how much time a new DAGs should be picked up from the filesystem
min_file_process_interval = 0
dag_dir_list_interval = 300
# How often should stats be printed to the logs
print_stats_interval = 30
child_process_log_directory = /home/ec2-user/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 = 300
# 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.
# This depends on query length limits and how long you are willing to hold locks.
# 0 for no limit
max_tis_per_query = 0
# 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.
max_threads = 2
authenticate = 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
[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
[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
I'm not too sure what's going on here. Is there additional setup I need to do on Celery or anything else? I'm also confused as to how it knows which SQS queue to use on AWS? Does it create a new queue itself or do I need to create the queue on AWS and put that url somewhere?
See this question here: https://stackoverflow.com/a/39967889/5191221
Taken from there:
So replace:
celery_result_backend = postgresql+psycopg2://username:password#192.168.1.2:5432/airflow
with something like:
celery_result_backend = db+postgresql://username:password#192.168.1.2:5432/airflow
I have two containers inside the same pod. One is an haproxy container and I'm pushing the haproxy statistics to a socket inside the container. I want to access the socket inside the haproxy container from the other container. I tried to use volume type mkdir but an error occurred mentioning that there is no unix sockets under the directory which I'm trying to access.
I'm new to these technologies and please help me to solve this problem.
The yaml file is as follows.
yaml file
In reference to kubernetes documentation :
Every container in a Pod shares the network namespace, including the IP address and network ports.
You don't need to use a volume to access to haproxy statistics, just use 127.0.0.1 and the port where the process for haproxy statistics is bound.
Here is an example of a telegraph configuration container deployed in the same pod of an haproxy :
# Telegraf Configuration
[global_tags]
env = "$ENV"
tenant = "$TENANT"
[agent]
round_interval = true
metric_batch_size = 1000
metric_buffer_limit = 10000
collection_jitter = "0s"
flush_jitter = "5s"
precision = ""
debug = false
quiet = false
logfile = ""
hostname = ""
omit_hostname = false
[[outputs.influxdb]]
urls = ["http://influxdb.host:2001"]
database = "db_name"
retention_policy = ""
write_consistency = "any"
timeout = "5s"
[[inputs.haproxy]]
servers = [ "http://$STATS_USERNAME:$STATS_PASSWORD#127.0.0.1:$STATS_PORT/haproxy?stats" ]
Input use haproxy plugin, output use influxdb. $STATS_USERNAME $STATS_PASSWORDand $STATS_PORTare environment variable shared between 2 containers.