/m (Metrics)

# Pepperdata REST API Python example for retrieving metrics.
# Sections are denoted by '#' characters.

########################### IMPORTS ###########################

# Not required, but recommended for date and time manipulation.
# Required in this example.
import datetime

# To get this use the following: `pip install requests`
# If you do not have the pip Python package management system,
# install it from the python.org website:
# https://packaging.python.org/tutorials/installing-packages/
import requests
import os

# The authorization process (and therefore the required code imports) is
# different for recently-generated keys and legacy keys
# (keys generated before late August, 2017). Recently-generated
# keys are listed in the Active Keys section of the API Keys page
# (https://dashboard.pepperdata.com/account/apikeys); legacy keys are
# listed in the Legacy Keys section.
#
# **** IMPORTANT ****
#
# Be sure to use the correct commands (comment/uncomment the applicable
# lines) wherever you see "Active Key" or "Legacy Key" in the comments in
# this script.
#
# Legacy keys are DEPRECATED as of 2022 Oct 19. If you have one, generate a new 
# key at https://dashboard.pepperdata.com/account/apikeys and follow the 
# Active Key instructions. 
#

# *******************

# (Legacy Key) Required
#from requests.auth import HTTPBasicAuth


####################### DEFINE GLOBALS ########################

# Name of the your cluster/realm configured on Pepperdata
# **** IMPORTANT ****
#
# Before you run this Python example script, be sure to assign the correct
# realm name to the REALM_NAME environment variable by exporting an
# appropriate value for 'your-realm-name':
#
# export REALM_NAME='your-realm-name'
#
# *******************
REALM_NAME = os.environ['REALM_NAME']

# Base url where the Pepperdata dashboard REST API metrics
# resource for your realm is hosted
base_url = 'https://dashboard.pepperdata.com/{0}/api/m'.format(REALM_NAME)

# For Pepperdata-hosted dashboards (the default implementation),
# API access requires authentication via an API key.
# To obtain an API key, go to the following web page:
# https://dashboard.pepperdata.com/account/apikeys
#
# The "ID" is the portion of the API key before the ":"
# The password (secret) token is the portion of the API key after the ":"
# **** IMPORTANT ****
#
# Before you run this Python example script, be sure to assign the correct
# values to the API_KEY_ID and API_KEY_SECRET environment variables by
# exporting the correct values for 'your-api-key-id' and 'your-api-key-secret':
#
# export API_KEY_ID='your-api-key-id'
# export API_KEY_SECRET='your-api-key-secret'
#
# *******************
API_KEY_ID = os.environ['API_KEY_ID']
API_KEY_SECRET = os.environ['API_KEY_SECRET']

# The metrics API uses this format for time OR default Unix time.
pepperdata_time_format = "%Y/%m/%d-%H:%M"

# Although there is no maximum for the time range that you can specify,
# we recommend setting it to a maximum of 14 days.
# This example uses 1 day for the time range.
end_time = datetime.datetime.now()
start_time = end_time - datetime.timedelta(days=1)

# The distance between two data points in the returned time series in number of milliseconds.
sample_millis = 60000

# To determine the name of a metric on a chart, hover your mouse over the JSON
# export option link. Use the link's m={metric_url_name} parameter.
# The full list of metrics is on the Metrics Definitions page on the dashboard,
# http://dashboard.pepperdata.com/your-realm-name/metricdefinitions.
# This example use the "Active Tasks" metric, with m='tasks'.
metric_url_name = 'tasks'

# Time Zone, offset from UTC
time_zone_hour_offset = -7

# Output format: JSON or CSV. For fastest API performance, use JSON.
output_format = 'JSON'

# Maximum number of series (results) to return for the query
max_series = 20

# Assemble all the parameters to easily pass to the HTTP request portion.
# This example changes from the datetime format to the Pepperdata format
# by using strftime(). You could use Unix time instead.
pepperdata_params = {
  's': start_time.strftime(pepperdata_time_format),
  'e': end_time.strftime(pepperdata_time_format),
  'sample': sample_millis,
  'm': metric_url_name,
  'tzo': time_zone_hour_offset,
  'format': output_format,
  'ms': max_series
}

# To list the series by host/user/job/etc., add breakdowns to the parameters.
# This example breaks down the results by user ('u') and job ('j').
# For the complete list of parameters, refer to the following help page
# on your dashboard:
# https://dashboard.pepperdata.com/your-realm-name/metricdefinitions?print=1
pepperdata_params['u'] = '*'
pepperdata_params['j'] = '*'

# (Active Key) Required header
headers = {
  'Accept': 'application/json',
  'Authorization': 'PDAPI ' + API_KEY_ID + ":" + API_KEY_SECRET
}
# (Legacy Key) Required header
#headers = {'Accept': 'application/json'}


########### MAKE THE REQUEST AND PARSE THE RESPONSE ###########

# Send the request to the Pepperdata REST API metrics resource,
# passing the base url, all the parameters, the username and password,
# and the required header, which you defined previously.

# (Active Key) request:
try:
  response = requests.get(base_url, pepperdata_params,
                          headers=headers)
# (Legacy Key) request:
try:
#  response = requests.get(base_url, pepperdata_params, auth=HTTPBasicAuth(API_KEY_ID, API_KEY_SECRET),
#                          headers=headers)
except:
  print ("Error receiving response from server.")

# Parse the response as JSON, as specified by the output_format, above.
json_response = response.json()

# Store the data portion of the response. Everything else in the response
# is the progress updates as the database loaded the response.
json_data = json_response['data']

# Parse the data portion of the response, and store all the returned series
# in the all_series object.
all_series = json_data["allSeries"]

# Get the first point in the series, from the all_series object that
# stores the point elements as arrays of format [timestamp, data]. In this case,
# the point is [timestamp, active_tasks_at_timestamp]. The entirety of
# dataPoints is a list of these two element lists.
first_series_points = all_series[0]["dataPoints"]

# Get the object that contains the summary statistics of the series, including
# the following statistics:
# Area, Average, Minimum, Percentile 05, Maximum, and Percentile 95 over
# the entire series.
first_series_summary_stats = all_series[0]["summaryStats"]

# Get the unique series ID for this particular query.
first_series_id = all_series[0]["seriesId"]

# Get the map of name components for this series. If there are filters on
# this query, it will let you know that this series is a breakdown by a
# particular host and job, whose names are included here.
first_series_name_components = all_series[0]["name"]["seriesNameMap"]

# Print the output nicely.
for series in all_series:
    print "{0} {1}".format(series["summaryStats"]["avg"], series["seriesId"])