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timeseries-mock

Install

To setup the data generator on OpenShift simply use the s2i by running:

oc new-app centos/python-36-centos7~https://github.com/ruivieira/timeseries-mock \
  -e KAFKA_BROKERS=kafka:9092 \
  -e KAFKA_TOPIC=example \
  -e CONF=examples/mean_continuous.yml \
  --name=emitter

This will deploy the data generator, which will emit data (modelled as defined by the configuration file examples/mean_continuous.yml) into the Kafka topic example.

Data model configuration

To configure a data stream, you must specify both the structure of the time-series as well and the type of observation in a .yml file.

Structure

The structure can be specified by specifiying several fundamental components, which are ultimately composed to create a single structure. Some core components are:

Mean

This specifies an underlying mean. Using a single "mean" component will result in a random-walk type time-series:

structure:
  - type: mean
    start: 0.0
    noise: 1.5

All components need a start and noise value. The start specifies the general probable area for the series start and noise specifies how much the component will vary over time.

Seasonal

This will represent a seasonal component:

structure:
  - type: season
    period: 200
    start: 0.0
    noise: 0.7

The period represents how often will the season repeat. Note that this is relative to your specified rate.

That is, if your rate is 0.1 (the generator will emit new data every 0.1 seconds), then a period of 20 means that the season will last rate * period = 2 seconds. But if your rate is 100 seconds, the season will repeat every 33.33 minutes.

The seasonal component Fourier representation consists of n harmonics, which can be either specified in the configuration as:

structure:
  - type: season
    # ...
    harmonics: 6
    # ...

or just default to n=3 harmonics if not specified.

Composing

Structures can be composed simply by listing them under structure in the .yml file. For instance, composing the above mean and seasonality examples would simply be:

structure:
  - type: mean     # component 1
    start: 0.0
    noise: 1.5
  - type: season   # component 2
    period: 200
    start: 0.0
    noise: 0.7

Observations

The observation type can be configured using the observations key. The main supported observation types are detailed below.

Continuous

Continuous observations allow us to model any floating point type measure. Note that this is not bound by upper or lower limits (range ]-inf, +inf[[1]). If you use continuous to simulate, say, temperature readings from a sensor, keep in mind that the simulated readings might drift to very high or very low values depending on the structure. [2]

observations:
  - type: continuous
    noise: 1.5

Discrete

Discrete observations allow us to model any integer measure in the range [0, +inf[. The notes about drift in the continuous section also apply.

observations:
  - type: discrete

Please note that (at the moment) the discrete case only allows the noise to be specified at the structure level, since the observations are based on a Poisson model.

Categorical

Categorical observations allow to model any set of categories represented by an integer.

observations:
 - type: categorical
   categories: 16

The typical example would be setting categories to 1. This would simulate a stream of "binary" values 0 and 1. In the above example, setting categories to 16 would output a stream taking any value from [0, 1, 2, ..., 16].

A variant of this generator consists in passing a list of values directly. Let's assume we wanted to generate a stream of random DNA nucleotides, that is, C,T,A,G. This corresponds to four categories which we can specify in the values field:

observations:
 - type: categorical
   values: C,T,A,G

Comma-separated values are taken as the categories, without needing to specify anything else. The output is the a random element of values at each timepoint, in this case the time-series would be:

G -> T -> G -> T -> A -> A -> A -> C -> ...

A complete example

A full configuration file would look something like this:

name: "status"
rate: 0.1
structure:
  - type: mean
    start: 0.0
    noise: 0.5
  - type: season
    period: 600
    start: 0.0
    noise: 1.7    
observations:
  - type: categorical
    values: pass,fail

This configuration generate a stream of values pass or fail with a rate one value every 0.1 seconds, with a random-walk-like mean and a cyclic pattern every minute.

Multivariate data

The previous example was for a univariate observation, however in a real-world application it is very likely we might need to use multivariate data.

To compose a multivariate data model we simply use the model specification above and add as many models together as we want.

To define a multivariate model we declare the individual components inside a compose clause. For instance, to declare a bivariate continous stream a minimal example would be:

name: "bivariate"
rate: 0.5
compose:
  - structure:          # component 1
    - type: mean
    start: 0.0
    noise: 0.5
  - observations:
    - type: continuous
    noise: 0.5
  - structure:          # component 2
    - type: mean
    start: 5.0
    noise: 3.7
  - observations:
    - type: continuous
    noise: 1.5

This would output a stream of bivariate observations such as

[-0.6159691811574524, 6.70524660538598]
[0.09028869591370958, 6.519194818247104]
[-0.1980867909796035, 6.503466768530726]
[0.0063771543008148135, 5.2229932206447405]
...

In the specific case where you wish to simulate a multivariate observation with components that follow the same structure, you can use the shorthand replicate. The observation is then replicated n times. For example, to simulate bivariate samples where the components had the same underlying structure, we could write:

name: "bivariate"
rate: 0.5
compose:
  - replicate: 2
    structure:
      - type: mean
        start: 0.0
        noise: 0.5
    observations:
      - type: continuous
        noise: 0.5

A more complex example

In this example we will generate a fake HTTP log stream. The multivariate data will contain a request type (GET, POST or PUT), a URL from a provided list and random IP address. We want the URL to have seasonality, that is, users will tend more to a certain URL than others over time in a cyclic fashion.

We can define this model as:

name: "HTTP log"
period: 0.1
compose:
    - structure:
      - type: mean
        start: 0.0
        noise: 0.01
      observations:
        type: categorical
        values: GET,POST,PUT
    - structure:
      - type: mean
        start: 0.0
        noise: 0.01
      - type: season
        start: 1.0
        period: 15
        noise: 0.2
      observations:
        type: categorical
        values: /site/page.htm,/site/index.htm,/internal/example.htm
    - replicate: 4
      structure:
      - type: mean
        start: 0.0
        noise: 2.1
      observations:
        type: categorical
        categories: 255

An example output would be

["PUT", "/internal/example.htm", 171, 158, 59, 89]
["GET", "/internal/example.htm", 171, 253, 71, 146]
["PUT", "/internal/example.htm", 224, 252, 9, 156]
["POST", "/site/index.htm", 143, 253, 6, 126]
["POST", "/site/page.htm", 238, 254, 2, 48]
["GET", "/site/page.htm", 228, 252, 52, 126]
["POST", "/internal/example.htm", 229, 234, 103, 233]
["GET", "/internal/example.htm", 185, 221, 109, 195]
...

Acknowledgements

This project is based on elmiko's Kafka OpenShift Python Emitter, a part of the Bones Brigade set of OpenShift application skeletons.

[1] - whatever the minimum and maximum double/float values are, of course

[2] - In this case I suggest using some auto-regressive component in the structure.