FileStorage🔗
The multimeter.storages.file.FileStorage
exports the measurement Results to the file system. Currently, it supports the different formats
JSON and Line.
Configuration🔗
save_directory (pathlib.Path)🔗
The only required configuration for its constructor
multimeter.storages.file.FileStorage.__init__
is a path to a directory, where the files will be stored. The path should be a
pathlib.Path instance pointing to a directory, that is automatically created if not
existing.
file_format (multimeter.FileFormat)🔗
An instance of
multimeter.storages.file.FileFormat
that should be used for storing the results. Currently, multimeter supports
multimeter.storages.file.JsonFormat and multimeter.storages.file.LineFormat.
Example🔗
from multimeter import Multimeter, ResourceProbe
from multimeter.storages.file import FileStorage, JsonFormat
meter = Multimeter(ResourceProbe(), storage=FileStorage(
save_directory='/path/to/my/results',
file_format=JsonFormat(),
))
Format JSON🔗
The multimeter.JsonFormat stores the result as a single JSON object which contains meta-data about the measurement as well as the measured values. This file format contains all the data created and known to multimeter and is therefore best suited for manual processing.
The json structure is a single object with fixed attributes:
{
"identifier": "measure-id",
"tags": {
"tag": "value"
},
"meta_data": {},
"schema": {
"metrics": [
{
"key": "cpu_rate_user",
"description": "The rate of the time where the CPU is executing user-space code.",
"unit": "",
"value_type": "float",
"min_value": 0.0,
"max_value": 1.0
},
{
"key": "cpu_rate_system",
"description": "The rate of the time where the CPU is executing system code.",
"unit": "",
"value_type": "float",
"min_value": 0.0,
"max_value": 1.0
}
],
"subjects": [
{
"key": "process",
"description": "The current running process."
}
],
"measures": [
{
"key": "process.cpu_rate_user",
"subject": "process",
"metric": "cpu_rate_user"
},
{
"key": "process.cpu_rate_system",
"subject": "process",
"metric": "cpu_rate_system"
}
]
},
"points": [
{
"datetime": "2021-11-27T18:43:51.033+00:00",
"values": {
"process.cpu_rate_user": 0.8287030088703983,
"process.cpu_rate_system": 0.2367722882486841
}
},
{
"datetime": "2021-11-27T18:43:52.039+00:00",
"values": {
"process.cpu_rate_user": 1.0000167626132233,
"process.cpu_rate_system": 5.469854836835e-05
}
}
],
"marks": [
{
"datetime": "2021-11-27T18:43:51.733+00:00",
"label": "Call X"
}
]
}
identifier🔗
The identifier for the measurement, which created this result.
tags🔗
Tags are user-defined when the measurement is created.
meta_data🔗
Contains the meta data that can be added to the result using add_meta_data().
schema🔗
The schema contains a description of the different values that are measured. Their definition originates the metrics, subjects and measures:
metrics🔗
A list of objects describing the metrics that are included in the results. Each object contains the attributes "key", "description", "unit", "value_type", "min_value" and "max_value".
subjects🔗
A list of objects where each object represents a subject from which the metrics were sampled. Each object contains a "key" and a human-readable "description".
measure🔗
A list of objects representing individual measures describing which metric was sampled for which subject. Each object contains a "key" and references to a "subject" and a "metric".
points🔗
The list of individual measuring points. Each point has an attribute "datetime" containing the datetime (UTC) the values were measured and an attribute "values" with a javascript object, where the keys reference the measures and the values contain the measured values at this time.
marks🔗
The list of timestamps with a mark. Each mark has an attribute "datetime" containing the datetime (UTC) and an attribute "label" which contains the label of the mark, that was given when the mark was made.
Format Line🔗
This format implements the line protocol that is mainly used by InfluxDB and uses the '.line' file extension. It contains only the measured values with timestamp, tags and measurement id but lacks all the schema related information as well as the marks. It is recommended to be used for importing the results into InfluxDB.