Skip to main content

series

Synth's series generator creates streams of events based on different 'processes' (a process here can be an auto-correlated process, a poisson process, a cyclical process etc.).

The series generators are used in several different contexts:

  • Creating fake events for event-driven systems
  • Modelling time-independent events like 'orders' on a website or 'requests' made to a web server
  • Modelling seasonal behaviour, like an increase in flight frequency for a given airline over the summer

Date Time#

All series are modelled on so called 'Naive Date Times' - that is 'Date Times' that do not have a timezone. This can be interpreted as Timestamps in UTC. There is future work to improve functionality to add other chrono types.

The format of a series can be set by using the optional format field; if format is omitted, the default format is %Y-%m-%d %H:%M:%S.

Duration#

The series generators will often make use of durations as generation parameters. A duration as a quantity like '1 hour' or '5.7 milliseconds'.

The series generators use humantime to make it easy to specify human readable quantities like 3hr 5m 2s.

incrementing#

The incrementing series simply increments at a fixed duration. This could be for example a stock ticker.

The incrementing series has 2 parameters:

  • start: The time at which the first event occurs
  • increment: The increment between two consecutive events

Example#

Below is an example stock ticker for AAPL sampled at regular intervals every minute. 

{    "type": "array",    "length": {        "type": "number",        "constant": 10    },    "content": {        "type": "object",        "ticker": {            "type": "string",            "pattern": "AAPL"        },        "timestamp": {            "type": "series",            "format" : "%Y-%m-%d %H:%M:%S",            "incrementing": {                "start" : "2021-02-01 09:00:00",                "increment" : "1m"            }        },        "price": {            "type": "number",            "subtype" : "f64",            "range" : {                "high": 105,                 "low": 100,                "step": 0.01            }        }    }}

poisson#

The poisson series models independent events which occur at random, but which tend to occur at an average rate when viewed as a group.

One example of a poisson process could be earthquakes occurring during the course of a year, or customers arriving at a store, or cars crossing a bridge etc.

The poisson series has 2 parameters:

  • start: The time at which the first event occurs
  • rate: The average duration between two consecutive events

Example#

The below is an example HTTP server, which was brought up on a given date and has an average of 1 request every 1 minute.

{    "type": "array",    "length": {        "type": "number",        "constant": 10    },    "content": {        "type": "object",        "ip": {            "type": "string",            "faker": {                "generator": "ipv4"            }        },        "timestamp": {            "type": "series",            "format": "%d/%b/%Y:%H:%M:%S",            "poisson": {                "start": "10/Oct/2000:13:55:36",                "rate": "1m"            }        },        "request": {            "type": "string",            "categorical": {                "GET /index.html HTTP/1.0": 10,                "GET /home.html HTTP/1.0": 5,                "GET /login.html HTTP/1.0": 3            }        },        "response_code": {            "type": "number",            "subtype": "u64",            "categorical": {                "200": 95,                "500": 5            }        },        "response_size": {            "type": "number",            "range": {                "low": 500,                "high": 3000,                "step": 1            }        }    }}

cyclical#

The cyclical series models events which have a 'cyclical' or 'periodic' frequency.

For example, the frequency of orders placed in an online store peaks during the day and is at it's lowest during the night.

The cyclical series has 4 parameters:

  • start: The time at which the first event occurs
  • max_rate: The maximum average duration between two events.
  • min_rate: The minimum average duration between two events
  • period: The period of the cyclical series.

Example#

The below is a minimal example of orders being placed in an online store.

{    "type": "array",    "length": {        "type": "number",        "constant": 10    },    "content": {        "type": "object",        "order_id": {            "type": "number",            "id": {}        },        "item": {            "type": "string",            "categorical": {                "t-shirt": 4,                "jeans": 1,                "jacket": 1,                "belt": 2            }        },        "timestamp": {            "type": "series",            "cyclical": {                "start": "2021-02-01 00:00:00",                "period": "1d",                "min_rate": "10m",                "max_rate": "30s"            }        }    }}

zip#

The zip series combines 2 or more series together by zipping the output together. That is, the two series are super imposed.

The zip series has 1 parameter:

  • series: The child series to be zipped together
{    "type": "array",    "length": {        "type": "number",        "constant": 10    },    "content": {        "type": "object",        "order_id": {            "type": "number",            "id": {}        },        "item": {            "type": "string",            "categorical": {                "t-shirt": 4,                "jeans": 1,                "jacket": 1,                "belt": 2            }        },        "timestamp": {            "type": "series",            "zip": {                "series": [                    {                        "cyclical": {                            "start": "2021-02-01 00:00:00",                            "period": "1w",                            "min_rate": "1m",                            "max_rate": "1s"                        }                    },                    {                        "cyclical": {                            "start": "2021-02-01 00:00:00",                            "period": "1d",                            "min_rate": "10m",                            "max_rate": "30s"                        }                    }                ]            }        }    }}