metrics-gen 0.2.2

Fake deployment and data generator

metrics-gen

dummy metrics generator

Getting Started

Metrics generator is built upon three main components:

• Deployment: The indexes of the table, for example:
  • symbol in stock market.
  • (data_center, device_id) for devices in data centers
• Static Data: Static data regarding the deployment, for example:
  • model_number for a device
  • score for a model
• Metrics: Continuous metrics to generate about the deployment, for example:
  • cpu_utilization of a device
  • price of a stock

The first step in setting up the generator is creating a deployment. Then using the deployment, you can generate static data or continuous stream of metrics.

Create a deployment from configuration

To create a deployment from configuration you need to provide a yaml file containing the following:

deployment:
    <level_name>:
      faker: <faker_type>
      num_items: <num_items in the level>

Where level_name will be the name of the index, faker_type is the name of the faker generator and num_items is how many keys to create for this index.
Each provided level will create another num_items instances for each entry in it's previous levels.

Example: Given the following configuration yaml file:

deployment:
    device:
      faker: msisdn
      num_items: 2
    core:
      faker: msisdn
      num_items: 2

and running the following command:

from metrics_gen.deployment_generator import deployment_generator

dep_gen = deployment_generator()
deployment = dep_gen.generate_deployment(configuration=configuration)

Will generate the following example deployment:

	device	core
0	4120271911677	6950611701382
1	4120271911677	2255426557707
2	4120271911677	7717168891372
3	2260158002886	3213635322383
4	2260158002886	4007792940086
5	2260158002886	3720953132595

Notice that each extra level, multiplies the number of items created by num_item, thus we got 2 * 3 = 6 items created.

Create Static Data

To create a static data generator you need to supply a deployment dataframe and a configuration yaml.

The static data generator knows how to generator from two kinds of feature configurations: range and choice which should be specified in the yaml.

static:
    <feature_name>:
        kind: range
        min_range: <min_feature_range>, defaults to 0
        max_range: <max_feature_range>
        as_integer: <int or float>, defaults to False
    <feature_name>:
        kind: choice
        choices: <list of possible choices>

Each provided feature will generate a new feature column in the generated dataframe.

Example: Given the following yaml:

static:
    models: 
      kind: range
      min_range: 10
      max_range: 15
      as_integer: True
    country: 
      kind: choice
      choices: [A, B, C, D, E, F, G]

And the previous deployment:

from metrics_gen.static_data_generator import Static_data_generator


static_data_generator = Static_data_generator(
    deployment, static_configuration
)

generated_df = static_data_generator.generate_static_data()

Will generate the following dataframe:

	device	core	models	country
0	4120271911677	6950611701382	13	A
1	4120271911677	2255426557707	14	C
2	4120271911677	7717168891372	14	G
3	2260158002886	3213635322383	14	G
4	2260158002886	4007792940086	11	G
5	2260158002886	3720953132595	14	D

Create Continuous Metrics

To create a continuous metrics stream you need to provide a deployment dataframe and metrics creation configuration yaml.

errors:
    rate_in_ticks: < ~ticks between errors>
    length_in_ticks: < ~length of error mode>
timestamps:
    interval: <time between samples in seconds>
    stochastic_interval: <create random intervals (around interval)>
metrics:
  <metric name>:
    accuracy: <decimals to produce>
    distribution: normal
    distribution_params:
        mu: <mean>
        noise: <noise>
        sigma: <std>
    is_threshold_below: <True to produce max when in error mode, False for min>
    past_based_value: <True to add the latest metric to the last result (like in daily stock market), False to replace normally)
    produce_max: <True for candles-like presentation>
    produce_min: <True for candles-like presentation>
    validation:
        distribution: # per-sample validation
            max: <max value for individual sample>
            min: <min value for individual sample>
            validate: <True to activate validation>
      metric: # metric level validations
        max: <max value for overall-metric sample (only applicable to past-based-values)>
        min: <min value for overall-metric sample (only applicable to past-based-values)>
        validate: <True to activate validation>

Each configured feature will generate additional metric for your deployment.

Example: Given the following yaml

errors: {length_in_ticks: 10, rate_in_ticks: 5}
timestamps: {interval: 5s, stochastic_interval: true}
metrics:
  cpu_utilization:
    accuracy: 2
    distribution: normal
    distribution_params: {mu: 70, noise: 0, sigma: 10}
    is_threshold_below: true
    past_based_value: false
    produce_max: false
    produce_min: false
    validation:
      distribution: {max: 1, min: -1, validate: false}
      metric: {max: 100, min: 0, validate: true}
  throughput:
    accuracy: 2
    distribution: normal
    distribution_params: {mu: 250, noise: 0, sigma: 20}
    is_threshold_below: false
    past_based_value: false
    produce_max: false
    produce_min: false
    validation:
      distribution: {max: 1, min: -1, validate: false}
      metric: {max: 300, min: 0, validate: true}

And the previous deployment:

from metrics_gen.metrics_generator import Generator_df

metrics_generator = Generator_df(metrics_configuration, user_hierarchy=deployment)
generator = metrics_generator.generate(as_df=True)

df = next(generator)

Will generate the following dataframe:

timestamp	core	device	cpu_utilization	cpu_utilization_is_error	throughput	throughput_is_error	is_error
2022-01-31 19:20:21.007087	2113309831673	4469221325973	100.0	True	0.0	True	True
2022-01-31 19:20:21.007087	2115933686087	4469221325973	100.0	True	235.0679405785135	False	False
2022-01-31 19:20:21.007087	0175482390171	4469221325973	70.26657388732976	False	208.34378630077305	False	False
2022-01-31 19:20:21.007087	1626403145660	4038890878426	59.932750968399404	False	217.4335871243806	False	False
2022-01-31 19:20:21.007087	7247058922310	4038890878426	83.98361382584898	False	265.3476318369042	False	False
2022-01-31 19:20:21.007087	7030239128061	4038890878426	100.0	False	225.16604191632058	False	False

To generate new samples all we need to do is call next(generator) and a new sample will be generated.