Overview

Introduction

Pymetrix can export two types of data:

1. Time Series
2. Aggregate

in two types of modes:

1. Snapshot
2. Live Stream

Data Types

1. Time Series

Pymetrix can export time series data with the following format:

{
   "message" : {
      "id" : "Test",
      "nodes" : [
         {
            "callers" : [
               {
                  "caller" : null,
                  "time" : "2022-01-09 18:26:51.410001"
               }
            ],
            "hits" : 1,
            "id" : "Home",
            "time" : "2022-01-09 18:26:51.409901"
         },
         {
            "callers" : [
               {
                  "caller" : null,
                  "time" : "2022-01-09 18:27:02.361461"
               }
            ],
            "hits" : 1,
            "id" : "Blog",
            "time" : "2022-01-09 18:27:02.361310"
         },
         ...
      ]
   },
   "response" : 200
}

which can be obtained by:

1. calling the time_series() method
2. iterating over the pipeline(data="time_series", mode="live") generator method, or calling the pipeline(data="time_series", mode="snapshot") method and accessing the values of the node key

in the metricman object.

Note that the pipeline() method is still buggy.

2. Aggregate

Pymetrix can also export the aggregated hits data in the following format:

{
   "message" : [
      {
         "hits" : 1,
         "id" : "Home"
      },
      {
         "hits" : 1,
         "id" : "Blog"
      }
      ...
   ],
   "response" : 200
}

which can be obtained by:

1. calling the aggregate() method
2. iterating over the pipeline(data="aggregate", mode="live") generator method, or calling the pipeline(data="aggregate", mode="snapshot") method

in the metricman object.

Note that the pipeline() method is still buggy.

Modes

1. Live

This mode gives the live data of the targets. The live data can be either time series or aggregate.

2. Snapshot

This mode gives the data of the targets UPTO the time when the method is called. As with the live mode, the data can be either time series or aggregate.