Merging upstream version 1.45.3+dfsg.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 0 insertions, 108 deletions

diff --git a/collectors/python.d.plugin/zscores/zscores.conf b/collectors/python.d.plugin/zscores/zscores.conf
deleted file mode 100644
index 07d62ebe6..000000000
--- a/collectors/python.d.plugin/zscores/zscores.conf
+++ /dev/null
@@ -1,108 +0,0 @@
-# netdata python.d.plugin configuration for example
-#
-# This file is in YaML format. Generally the format is:
-#
-# name: value
-#
-# There are 2 sections:
-#  - global variables
-#  - one or more JOBS
-#
-# JOBS allow you to collect values from multiple sources.
-# Each source will have its own set of charts.
-#
-# JOB parameters have to be indented (using spaces only, example below).
-
-# ----------------------------------------------------------------------
-# Global Variables
-# These variables set the defaults for all JOBs, however each JOB
-# may define its own, overriding the defaults.
-
-# update_every sets the default data collection frequency.
-# If unset, the python.d.plugin default is used.
-update_every: 5
-
-# priority controls the order of charts at the netdata dashboard.
-# Lower numbers move the charts towards the top of the page.
-# If unset, the default for python.d.plugin is used.
-# priority: 60000
-
-# penalty indicates whether to apply penalty to update_every in case of failures.
-# Penalty will increase every 5 failed updates in a row. Maximum penalty is 10 minutes.
-# penalty: yes
-
-# autodetection_retry sets the job re-check interval in seconds.
-# The job is not deleted if check fails.
-# Attempts to start the job are made once every autodetection_retry.
-# This feature is disabled by default.
-# autodetection_retry: 0
-
-# ----------------------------------------------------------------------
-# JOBS (data collection sources)
-#
-# The default JOBS share the same *name*. JOBS with the same name
-# are mutually exclusive. Only one of them will be allowed running at
-# any time. This allows autodetection to try several alternatives and
-# pick the one that works.
-#
-# Any number of jobs is supported.
-#
-# All python.d.plugin JOBS (for all its modules) support a set of
-# predefined parameters. These are:
-#
-# job_name:
-#     name: myname            # the JOB's name as it will appear at the
-#                             # dashboard (by default is the job_name)
-#                             # JOBs sharing a name are mutually exclusive
-#     update_every: 1         # the JOB's data collection frequency
-#     priority: 60000         # the JOB's order on the dashboard
-#     penalty: yes            # the JOB's penalty
-#     autodetection_retry: 0  # the JOB's re-check interval in seconds
-#
-# Additionally to the above, example also supports the following:
-#
-# - none
-#
-# ----------------------------------------------------------------------
-# AUTO-DETECTION JOBS
-# only one of them will run (they have the same name)
-
-local:
-    name: 'local'
-
-    # what host to pull data from
-    host: '127.0.0.1:19999'
-
-    # what charts to pull data for - A regex like 'system\..*|' or 'system\..*|apps.cpu|apps.mem' etc.
-    charts_regex: 'system\..*'
-
-    # Charts to exclude, useful if you would like to exclude some specific charts. 
-    # Note: should be a ',' separated string like 'chart.name,chart.name'.
-    charts_to_exclude: 'system.uptime'
-
-    # length of time to base calculations off for mean and stddev
-    train_secs: 14400 # use last 4 hours to work out the mean and stddev for the zscore
-
-    # offset preceding latest data to ignore when calculating mean and stddev
-    offset_secs: 300 # ignore last 5 minutes of data when calculating the mean and stddev
-
-    # recalculate the mean and stddev every n steps of the collector
-    train_every_n: 900 # recalculate mean and stddev every 15 minutes
-
-    # smooth the z score by averaging it over last n values
-    z_smooth_n: 15 # take a rolling average of the last 15 zscore values to reduce sensitivity to temporary 'spikes'
-
-    # cap absolute value of zscore (before smoothing) for better stability
-    z_clip: 10 # cap each zscore at 10 so as to avoid really large individual zscores swamping any rolling average
-
-    # set z_abs: 'true' to make all zscores be absolute values only.
-    z_abs: 'true'
-
-    # burn in period in which to initially calculate mean and stddev on every step
-    burn_in: 2 # on startup of the collector continually update the mean and stddev in case any gaps or initial calculations fail to return
-
-    # mode can be to get a zscore 'per_dim' or 'per_chart'
-    mode: 'per_chart' # 'per_chart' means individual dimension level smoothed zscores will be aggregated to one zscore per chart per time step
-
-    # per_chart_agg is how you aggregate from dimension to chart when mode='per_chart'
-    per_chart_agg: 'mean' # 'absmax' will take the max absolute value across all dimensions but will maintain the sign. 'mean' will just average.