The basic idea here is to have a random data generator build fairly realistic files for analysis. The primary use of log-synth has been to generate data based on a specified schema, but there is an older system to generate data that looks like a particular kind of web server log. See web-log.md in this directory for more information about the web log generator.
Please note that contributions are very welcome, but you will need to agree to a contributor license agreement similar to the Apache ICLA before any significant changes are accepted. Ping me (ted.dunning@gmail.com) for details. Ping me if you need a different language or output structure as well.
Log-synth allows you to generate data based on a rough schema. Schema-driven generation supports the generation of addresses, dates, foreign key references, unique id numbers, random integers, sort of realistic personal names and fanciful street names.
In addition to these primitive generators of strings and numbers, nested structures of arrays and objects can also be generated. You can also generate files that link together via ID's so that complex star schema structures can be build. In a future release, it is anticipated that the generator will execute arbitrary Javascript in order to allow arbitrary dependencies and transformations of data as it is generated.
To generate data, follow the compilation directions below, that will
create a standalone executable that you can use to generate data. This
generates a million records using the schema in the file schema.synth.
target/log-synth -count 1M -schema schema.synth
The allowable arguments include:
-count n Defines how many lines of data to emit. Default value is 1000. Suffixes including k, M, and G have customary meanings.
-schema file Defines where to get the schema definition from. The schema is in JSON format and consists of a list of field specifications. Each field specification is a JSON object and is required to have the following value
class- Defines the distribution that is used to sample values for this field. Possible values includeaddress,date,foreign-key,id,int, andstreet-name. Additional values that may be allowed or required for specific generators are detailed below.
Commonly, field specifications also need to give a name
name- This is the name of the field. The output will consist of fields ordered as in the schema definition and any header file will contain the names for each field as defined by this value.
See the longer examples below for more information.
-format CSV | TSV | JSON | XML Defines what format the output should
use. Note that XML format assumes that the root element is called
"root" and each record element should be callled "OBJECT_NODE". There
is no way that is what most people really want. If you need a better
kind of XML format, file an issue or send a pull request with what is
really needed.
Note that the JSON output is a list of individual JSON maps, each one to a line, to convert such data to a single JSON array, use the following sed command:
sed -i .bak -e '1 s/^/[\n/' -e '$ s/$/\n]/' -e '$ ! s/$/,/' oldfile.json
Note that this only works with gnu sed and won't work with the version of sed you will find by default on Macs as part of OSX. On OSX, try this instead
sed -i .bak -e '1 s/^/[\'$'\n/' -e '$ s/$/\'$'\n]/' -e '$ ! s/$/,/' oldfile.json
This last form should work on Linux as well, but I haven't tested it.
-output output-directory-name Designates an output
directory. Output files will be created in this directory named
according to the pattern synth-<thread>.<ext> where <thread> part
is replaced by the thread number that created the file and <ext> is
replaced by an appropriate file extension.
-threads n Indicates how many threads to use for generating data. Requires -output. Note that the schema is
shared across all of the threads so a schema with an id sampler will still generate all consecutive values in order, but the values will be distributed pretty much randomly across the output files.
Note also that the number of threads that gives best throughput is somewhat surprisingly larger than you might think. >100 threads can be useful. Having lots of threads can, on the other hand, consume a lot of memory. Trust, but verify.
Check the end of this README for worked examples.
The following classes of values are allowed (in approximately alphabetical order):
This distribution generates fairly plausible, if somewhat fanciful street addresses. There are no additional parameters allowed.
{"name":"address", "class":"address"},
This sampler converts a nested list of lists into a flat list. This can be useful if used in conjunction with the sequence sampler (see the example for sequence).
Samples from browser types with kind of plausible frequency distribution.
{"name":"br", "class":"browser"}Samples simulated automotive data from commuters.
The idea here is that we have some number of commuters who each have a home and work location. These commuters tend to drive to work in the morning rush hour and home in the evening rush hour, although they may do either commute at other times. While at home, these simulated commuters may decide to run some errands.
Underneath this life-style model is a traffic model that has each driver pick a route either on local roads or on a highway. Speeds on local roads are lower and more variable than on highways. Highways also go nearly directly to the destination while local roads are bound to north-south or east-west directions. The choice of which kind of segment to pick depends mostly on the distance to the destination. Note that there is no pre-defined set of roads, the model just makes it up as segments are chosen. This means that there really isn't any sort of congestion modeling happening here, just variable speeds.
Below the route planning model is a physical model that involves cars that respond to the control inputs generated at the higher levels to try to maintain desired speeds. The cars look roughly like they have moderately powerful engines with 7 speed automatic transmissions. The shift points are set to roughly match a diesel engine. All shifting is done based on fixed shift points and all throttle settings are done using a simple closed loop model that tries to match the desired road speed. The performance level of the cars is chosen to be moderately good in that they can do 0-60 MPH in about 7-8 seconds.
The output from the commuter model can be either in nested or flattened form according to whether flat: true is
used in the schema. In nested form there is one record per simulated vehicle. Inside each of these vehicle traces is
a history of what the vehicle did during the test in the form of a list of trips. Each trip has descriptive information
about the trip such as distance, start time, duration and type (errand, to_home, to_work).
In the flattened form, each sample in the nested form is retained, but all nesting is removed with all of the fields from the outer structures being repeated in each sample record.
Here is a sample schema for the commuter model:
[
{
"name": "vehicle",
"class": "vin",
"verbose": "true"
},
{
"name": "trip",
"class": "commuter",
"home": {
"class":"zip" ,
"fields":"latitude, longitude, zip"
},
"work": 20,
"start": "2015-09-03 0:00:00",
"end": "2015-09-04 0:00:00"
}
]The commuter model also allows you to add some extra fields to every sample in the generated trips. The way that this works is that you define a field called extras that contains a complete log-synth schema.
In this next example, our basic commuter schema is augmented so that three extra fields are generated for each sample point.
[
{
"name": "vehicle",
"class": "vin",
"verbose": "true"
},
{
"name": "trip",
"class": "commuter",
"home": {
"class":"zip" ,
"fields":"latitude, longitude, zip"
},
"work": 20,
"start": "2015-09-03 0:00:00",
"end": "2015-09-04 0:00:00",
"extras": [
{
"name": "door-open",
"class": "int",
"min": 0,
"max": 0
},
{
"name": "warning-light",
"class": "string",
"dist": {
"on": 1,
"off": 100
}
},
{
"name": "temperature",
"class": "random-walk",
"mean":100,
"sd":1
}
]
}
]Note that the commuter model produces a lot of data per record due to the frequent sampling of engine data. This means that you won't get very many output records per second of simulator run-time, especially if you ask for long histories. This also means that some tools may choke on the output due to the size of each input records. To deal with this, you can produce flattened data, you can generate just a single day of data at a time or you can request a feature to make the engine sampling frequency to be extended. Your feedback would be helpful here if you need this model.
Samples from ISO country codes.
{"name":"co", "class":"country"}This distribution generates dates which are some time before an epoch date. Dates shortly before the epoch are more common than those long before. On average, the dates generated are 100 days before the epoch. A format field is allowed which takes a format for the data in the style of Java's SimpleDateFormatter. Note that the order of options is significant in that the format will apply to the start and end options if it comes before them. By default, these are formatted like yyyy-MM-dd, but you can specify a different format using the format option. Dates are selected by default to be before July 1, 2013. The amount before is selected exponentially with mean of 100 days. If you specify start or end dates, the dates will be sampled uniformly between your dates. The default start is January 1, 1970. The default end is July 1, 2013.
[
{"name":"first_visit", "class":"date", "format":"MM/dd/yyyy"},
{"name":"second_date", "class":"date", "start":"2014-01-31", "end":"2014-02-07"},
{"name":"third_date", "class":"date", "format":"MM/dd/yyyy", "start":"01/31/1995", "end":"02/07/1999"}]
Samples DNS queries with some details about domain and source of the query. The basic idea is that each sample is a history of a particular source of DNS queries. The domains queried are sampled from a very skewed distribution (technically, a shared Pitman-Yor sampler) that generates very long tails. The most commonly queried domains are taken from the actual domains of Fortune 500 companies, but others are jibberish generated as they are needed.
Each query source is sampled for the time period of interest, but the rates for different sources can be very different. Each source is modeled as being either 'active' or 'inactive'. During active times, DNS queries are generated at a higher rate than during inactive times. Different query sources can have active rates that differ by a factor of 1000 or more to emulate the difference between, say, a lone person using a phone or an office full of people sharing an Internet connection. The transition from inactive to active and back is done by picking an abstract interval until the next transition. This abstract interval represents a warped sort of time that passes more quickly during the local 'daytime' for a source and more slowly during local 'nighttime'. The definition of 'daytime' and 'nighttime' varies by source. The overall model thus has parameters for night time dilation, average active and inactive times and query rates during active and inactive periods. Each of these parameters are sampled for each query source and the distributions for these parameters are controlled by the following hyper-parameters:
Name Controls Default Description
dilation Night-time interval dilation 6 Night dilation is distributed as
`rexp(rgamma(shape=dilation))`
active Average active time (seconds) 10 minutes Activity duration is distributed as
`rexp(rate=1/t_active)` where `t_active ~
rexp(rate=1/active)`
inactive Average inactive time (seconds) 4 hours Duration of inactivity is distributed as
`rexp(rate=1/t_inactive)` where `t_inactive`
is sampled for each source independently
according to `t_inactive ~
rexp(rate=1/inactive)`.
rate Average query rate per second 10 queries/h Queries are Poisson distributed with rate
while active distributed as rexp(rate=1/rate)
idle How much does query rate slow 6 Query rate is decreased by ratio distributed
when inactive as exp(rgamma(shape=idle))
alpha Growth scale of unique domains 10000 Pitman-Yor cale factor for number of unique
domains.
discount Growth rate of unique domains 0.5 Number of unique domains grows with `alpha *
N^discount` if `discount > 0` and `alpha *
log(N)` if `discount == 0`
In addition to these parameters, you can add json 'flat'=true to force the output to consist of individual DNS
queries. If json 'flat'=false, the output consists of one row per source IP with a list of JSON objects containing
all requested fields.
The start, and end required parameters can be used to set the beginning and ending time for the simulation.
The format parameter determines the format for the start and end as well as the format for the time
field in the output. The format parameter can be used after start and end are set to produce output
in a different format than that used to specify the start and end parameters. By default all time expressions
parsed as "yyyy-MM-dd", or, if that fails, as "yyyy-MM-dd HH:mm:ss". By default, output is produced as
"yyyy-MM-dd HH:mm:ss."
You can also set fields to a comma-separated list of fields that should be included in the output of the sampler.
Available fields include domain, source_ip, reverse_domain, time.
The following examples show how this sampler can be used
{"class": "dns", "start": "2014-01-01", "end": "2014-03-01", "format": "MM/dd/yyyy HH:mm:ss",
"fields": "domain, reverse_domain, source_ip"} Samples Poisson distributed event times with specified rates.
{"name":"foo1", "class":"event", "rate": "0.1/d"}
{"name":"foo2", "class":"event", "start": "2014-01-01", "format":"yyyy-MM-dd HH:mm:ss", "rate": "10/s"}
{"name":"foo3", "class":"event", "format": "MM/dd/yyyy HH:mm:ss", "start": "02/01/2014 00:00:00", "rate": "0.5/s"}Turns an object into fields.
Some samplers such as zip or vin return complex objects with many fields. If you want to output each of these fields
as a separate field in CSV format, you could post process a JSON output file or you can use the flatten sampler
to promote these fields to the level above. As an example, the snippet below results in samples with fields like zipType,
zip, latitude, longitude and others.
{
"class": "flatten",
"value": { "class": "zip" },
"prefix": ""
}Notice how there is no name here for the flatten sampler. This is because the resulting values are named using the
prefix (empty in this example) and the names fo the fields from the sub-sampler (the zip sampler in this case).
The prefix used to form the flattened variable names can be specified explicitly, but unless you want it to be empty
it is usually simpler to just name the flatten sampler. This gives you a default prefix that is the name of the
flattener with a dash appended. For instance, this snippet
[
{
"name": "x",
"class": "flatten",
"value": { "class": "zip", "fields": "latitude, longitude" }
},
{
"name": "y",
"class": "flatten",
"value": { "class": "zip", "fields": "latitude, longitude" }
}
]would give samples with fields named x-latitude, x-longitude, y-latitude and y-longitude which makes it
easy to keep track of which fields are associated with each other.
This distribution generates randomized references to an integer key from another table. You must specify the size of the table being referenced using the size parameter. The default value of size is 1000. You may optionally specify a skewness factor in the range [0,3]. A value of 0 gives uniform distribution. A value of 1 gives a classic Zipf distribution. The default skew is 0.5. Values are biased towards smaller values. This sampler uses space proportional to size so be slightly cautious.
This distribution returns consecutive integers starting at the value of the start parameter.
{"name":"id", "class":"id"}Samples values from min (inclusive) to max (exclusive) with an adjustable skew toward small values. If you set skew to a negative number, larger values will be preferred.
[
{"name":"size", "class":"int", "min":10, "max":99},
{"name": "z", "class": "int", "min": 10, "max": 20, "skew": -1},
{"name":"x", "class":"lookup", "resource":"data.json", "skew":1}
]You can also specify an explicit distribution for the values returned from this sampler by using the dist parameter. For instance, this gives you two values, a and b. The a value will have values 1, 2, 3, 4 distributed 50%, 25%, 15%, 10% while b will have values distributed 10%, 15%, 25%, 50%.
[
{"name":"a", "class":"int", "dist":[1, 50, 2, 25, 3, 15, 4, 10]},
{"name":"b", "class":"int", "dist":[4, 50, 3, 25, 2, 15, 1, 10]}
]Glues together an array of strings. You can specify a separator that goes between the joined strings with the separator parameter. The value parameter specifies how to generate the arrays of strings.
This snippet will generate silly file names nested three deep:
{
"name": "filename",
"class": "join",
"separator": "/",
"value": {
"class":"sequence",
"length":3,
"array":[
{"class":"string", "dist":{"top1":10, "top2":5, "top3":2}},
{"class":"string", "dist":{"mid1":10, "mid2":5, "mid3":2}},
{"class":"string", "dist":{"alice":10, "bob":5, "charles":2, "dahlia":1, "ephraim":1}}
]
}
}Samples from ISO language codes according to prevalence on the web.
{"name":"la", "class":"language"}Samples from lines of a file located in the resources folder. The filename must have .csv, .tsv, or .json suffix and the first line is expected to be a header row containing names for each field.
{"name":"bmw-models", "class":"lookup", "resource":"bmw-models.tsv"}
Samples from complex objects, fields of which are sampled according to a recursive schema you specify.
{
"name": "stuff",
"class": "map",
"value": [
{"name": "a", "class": "int", "min": 3, "max": 4},
{"name": "b","class": "int","min": 4,"max": 5}
]
}Samples from (slightly) plausible names. The allowable types are
first, last, first_last and last_first. The default type is first_last.
{"name":"name", "class":"name", "type":"first_last"}Samples from a normal distribution. You can set mean and either sd or precision.
If you want a predictable sequence set the seed. You can also trim the distribution by setting min and/or max.
{"name":"name", "class":"normal", "mean":3, "sd": 2, "min":0}Repeatedly samples from a single distribution and returns an array of the results.
This example produces variable length results with exponentially distributed lengths. Some of the results have length 0.
{"name":"c", "class":"sequence", "base":{"class":"os"}}This example produces values with lengths that are exponentially distributed with mean length of 10.
{"name":"d", "class":"sequence", "base":{"class":"int", "min":3, "max":9}, "length":10}This example produces results that always have three values, each of which has a different distribution.
{
"name": "x",
"class": "sequence",
"array": [
{
"class": "int",
"min": 3,
"max": 4
},
{
"class": "int",
"min": 6,
"max": 7
},
{
"class": "int",
"min": 8,
"max": 9
}
]
}Normally a sequence produces arrays of result whose length is randomly chosen from an exponential distribution.
If you set the lengthDistribution parameter instead of the length parameter, then you can control how the length is
chosen. Somewhat confusingly, if you set lengthDistribution to a constant you get lists with the same length every
time. Here are some examples:
{
// generates lists with exactly 5 samples each
"name": "fixed-length",
"class":"sequence",
"lengthDistribution":5,
"base": {
// insert distribution here
}
} {
// generates lists with 5-10 samples in each
"name": "fixed-length",
"class":"sequence",
"lengthDistribution":{"class":"integer", "min":5, "max":10},
"base": {
// insert sampler definition here
}
}A sequence sampler also normally returns all values as a list that is assigned to a field in the resulting records. You can
opt to flatten the results by adding "flat":true as an option specification of the sequence. Thus, this schema:
[
{"name":"a", "class":"vin"},
{"name":"c", "class":"sequence", "base":{"class":"os"}}
]will generate records with a VIN number in field "a" and lists of variable length in field "c".
This schema, in contrast:
[
{"name":"a", "class":"vin"},
{"name":"c", "class":"sequence", "base":{"class":"os"}, "flat":true}
]will produce records with a field "a" containing a VIN and a field "c" containing a string from the os sampler. Each
list that the sequence sample produces will result in the VIN being repeated once for each of the samples in the list.
Sometimes you want to not only flatten the results of a sequence so that multiple records are produced, but you may want to promote the fields of the samples in the sequence to be top-level fields in the resulting records. This is particularly true if you want to return the data in CSV or other formats that don't handle nested data well. To do this, simply omit the name of the sequence variable. For instance, this schema
[
{
"name":"id", "class":"uuid"
},
{
"class":"sequence",
"length": 3,
"base":{
"class":"map",
"value": [
{
"name": "sample-time",
"class": "event",
"rate": "3/h",
"format":"yyyy-MM-dd HH:mm:ss",
"start": "2019-04-01 17:43:00"
},
{
"name": "temperature",
"class": "random-walk",
"mean":0,
"sd":1,
"start":30
}
]
},
"flat":true
}
]will produce multiple records for each device that look like this:
{"id":"dace817b-f0c6-47a8-90a6-a21bb6d7a1d2","sample-time":"2019-04-01 17:43:00","temperature":30.146672000677313}
{"id":"21925927-55c7-40e9-bec6-3aa14ca4ba80","sample-time":"2019-04-01 17:43:00","temperature":31.337189250191063}
{"id":"21925927-55c7-40e9-bec6-3aa14ca4ba80","sample-time":"2019-04-01 18:15:20","temperature":31.903948039663167}
{"id":"21925927-55c7-40e9-bec6-3aa14ca4ba80","sample-time":"2019-04-01 18:17:51","temperature":32.00336665938387}
{"id":"08c90cac-d841-4f34-9fb3-359a299d027b","sample-time":"2019-04-01 17:43:00","temperature":31.145485476447}
{"id":"08c90cac-d841-4f34-9fb3-359a299d027b","sample-time":"2019-04-01 18:13:39","temperature":34.18391712370743}Samples somewhat realistic SSN's
A social security number (SSN) has fields ssn, state, description and type. The ssn field is what you might expect. The
state field is the two letter abbreviation of the state that the SSN was issued in (assuming that the SSN was issued before the 2011 conversion to
random assignment). The description is the longer form of the state. The type field can have the value normal or extra. The extra type applies to locations
that don't correspond to the 52 values that most people might expect (50 states + DC + PR).
You can limit which fields you get back with default fields of ssn, state``description. You can also limit the types of values you get back.
For example:
{
"name": "z",
"class": "ssn"
}or
{
"name": "zLimited",
"class": "ssn",
"fields": "ssn,state,description,type",
"types": "normal,extra",
"seed": 123
}As is common with many samplers, you can set the seed if you like.
If you only want a string with the SSN in it, you can set the verbose flag to false:
{
"name": "z",
"class": "ssn",
"verbose": false
}Samples from any of the 58 USPS state abbreviations. Yes, there are 58 possible values.
{"name":"st", "class":"state"}This distribution generates fanciful three word street names.
This distribution generates a specified distribution of strings. One parameter called dist is
required. This parameter should be a structure with string keys and numerical values. The probability for each
key is proportional to the value.
{"name":"foo", "class":"string", "dist":{"YES":0.95, "NO":0.05, "NA":1}}Generates random UUIDs.
Samples from operating system codes. My own bias will show here.
{"name":"os", "class":"os"}Allows sampling from a random walk.
The random-walk sample by default samples steps from a normal distribution and accumulates those steps into a current position.
The returned value is the sum of those steps. Optionally, the actual distribution of the steps can be specified explicitly.
The defaults for the random-walk sampler are sensible so that this
{
"name": "v1",
"class": "random-walk"
}samples steps from a unit normal distribution. The scale of the steps can be changed by setting the s (standard deviation),
variance (squared standard deviation) or precision (inverse of variance) parameters. Here is an example of setting the
scale of the step distribution:
{
"name": "v2",
"class": "random-walk",
"s": 2
}If you are setting the scale to
a constant, the s parameter would normally be used. You can also set these parameters to have values that are themselves
random variables that are sampled each step. For example, this sets the precision to be sampled from a gamma distribution.
The result of this second-order sampling will be a t-distribution with dof = 2. Using very small values of dof gives
a very heavy-tailed distribution that occasionally takes enormous steps.
{
"name": "v3",
"class": "random-walk",
"precision": {
"class": "gamma",
"dof": 2
}
}The verbose flag can be set to true. If verbose is not set, or is explicitly set to false, then the value of the
current state will be returned. If verbose is set to true, then the current value and the latest step will both be
returned in a structure.
Setting the scale of the steps to a random variable is usually done by setting the precision parameter to be
sampled from a gamma distribution since the gamma is the conjugate distribution to the normal. The gamma sampler
can be adjusted using alpha (shape), beta (scale), rate (rate or 1/beta) or dof and scale parameters.
When used to set the step size distribution for a random-walk sampler, it is common to use the dof and scale
parameters.
{
"name": "g1",
"class": "gamma",
"alpha": 0.2,
"beta": 0.2
}When setting dof and scale, these are translated as alpha = dof / 2, beta = scale * dof / 2.
If desired, the mean step size can also be set either to a constant or a distribution. This helps model walks that have a consistent drift. As an example, we could model the sampling times for a data acquisition that makes a measurement every 100 microseconds with 2 microseconds of jitter this way:
{
"name": "t",
"class": "random-walk",
"mean":100,
"sd":5
}In special cases, none of the methods above are satisfactory and it is desirable to set the step distribution
explicitly. For instance, it might be desirable to have steps that have a trimmed normal distribution or even
a gamma distribution if you want particularly long-tailed walks. This can be done by setting the step-distribution
parameter as in the following
{
"name": "v3",
"class": "random-walk",
"step-distribution": {
"class": "normal",
"mean": 2,
"sd": 2,
"min": 0
}
}
Samples from sort of realistic VIN numbers.
Here is are three different ways to use the VIN sampler. The first one v1, uses a seed to force the generated sequence to be identical every time.
{
"name": "v1",
"class": "vin",
"seed": 12,
"country": "north_america",
"make": "ford",
"years": "2007-2011"
}The second example uses the verbose setting to generate a JSON structure instead of just a single value containing a VIN. This structure includes additional clear text information about where the vehicle was supposedly made, what kind of engine and so on.
{
"name": "v2",
"class": "vin",
"country": "north_america",
"make": "ford",
"years": "2007-2011",
"verbose": "true"
}With verbose set to true, the output of the sampler looks like this
{
"VIN":"3FAFW33407M000098",
"manufacturer":"Ford",
"model":"Ford F-Series, F-350, Crew Cab, 4WD, Dual Rear Wheels",
"engine":"V6,Essex,3.8 L,EFI,Gasoline,190hp",
"year":2007
}Note that there can be implausible combinations of engine, year and model such as a 2007 DeLorean. Also, the sampler currently only has information about Ford and BMW models. For other makes, the model engine and plant information is just gibberish.
The third example shows how the country and year fields can have more complex constraints.
{
"name": "v3",
"class": "vin",
"countries": "ca, mx",
"make": "ford",
"years": "2002,2007-2011"
}Currently all sampling for constructing a VIN is done by uniformly sampling all of the possible options. This could easily be changed if desired.
Samples words at random. A seed file is given, but if more words are needed than seeded, they will be invented.
Produces a user history that emulates a common point of compromise fraud scenario. Contact tdunning@maprtech.com for more info.
[
{
"name": "id",
"class": "id"
},
{
"name": "history",
"class": "common-point-of-compromise",
"seed": 12,
"exploitStart": "2014-01-20 00:00:00",
"exploitEnd": "2014-02-20 00:00:00",
"end": "2014-03-31 00:00:00",
"compromisedFraudRate": 0.02,
"uncompromisedFraudRate": 0.001
}
]Samples from a table of US Zip Codes. This gives you latitude, longitude and other common parameters for zip codes. This can be used to generate random ish locations for various purposes that have nothing to do with the postal system.
All parameters for this sampler are optional.
[
{
"name": "z",
"class": "zip",
"seed": 12,
"latitudeFuzz": 1,
"longitudeFuzz": 1,
"onlyContinental": "true"
}
]Zip samplers can also limit the points returned by using a latitude/longitude bounding box or by specifying a single point and a distance radius (in miles).
This gives all zips with centers within 200 miles of a point in Los Angeles
{
"name": "zLosAngeles",
"class": "zip",
"near": "33.97,-118.24",
"milesFrom": 200
}Note that having a small radius here will make the sampler very slow because it will have to reject many samples. A radius of 200 miles makes the sampler about 10 times slower.
Likewise, this gives zips that have latitude from 20 to 30 degrees
{
"name": "zLosAngeles",
"class": "zip",
"latitude": "20,30"
}If you only want the zip code as a string without all the supporting information, set the verbose flag to false. For example:
{
"name": "zLosAngeles",
"class": "zip",
"near": "33.97,-118.24",
"milesFrom": 200,
"verbose": false
}The following schema generates a typical fact table from a simulated star schema:
[
{"name":"id", "class":"id"},
{"name":"user_id", "class": "foreign-key", "size": 10000},
{"name":"item_id", "class": "foreign-key", "size": 2000}
]Here we have an id and two foreign key references to dimension tables for user information and item information. This definition assumes that we will generate 10,000 users and 2000 item records.
The users can be generated using this schema.
[
{"name":"id", "class":"id"},
{"name":"name", "class":"name", "type":"first_last"},
{"name":"gender", "class":"string", "dist":{"MALE":0.5, "FEMALE":0.5, "OTHER":0.02}},
{"name":"address", "class":"address"},
{"name":"first_visit", "class":"date", "format":"MM/dd/yyyy"}
]For each user we generate an id, a name, an address and a date the user first visited the site.
Items are simpler and are generated using this schema
[
{"name":"id", "class":"id"},
{"name":"size", "class":"int", "min":10, "max":99}
]Each item has an id and a size which is just a random integer from 10 to 99.
You can use the sequence type to generate variable or fixed-length arrays of values which can themselves be complex. If you use the JSON output format, this structure will be preserved. If you want to flatten an array produced by sequence, you can use the flatten sampler.
For example, this produces users with names and variable length query strings
[
{"name":"user_name", "class":"name", "type": "last_first"},
{"name":"query", "class":"array-flatten", "value": {
"class": "sequence", "length":4, "base": {
"class": "word"
}
}}
]If you use the TSV format with this schema, the queries will be comma delimited unquoted strings. If you omit the array-flatten step, you will get a list of strings surrounded by square brackets and each string will be quoted (i.e. an array in JSON format).
This example shows how you can generate measurement data that appears to come from multiple devices, each with a fixed location.
Here is the schema, followed by some explanations
[
{
"name": "device-id",
"class": "uuid",
"seed": 1
},
{
"class": "flatten",
"prefix": "",
"value": {
"class":"zip" ,
"onlyContinental": true,
"fields":"latitude, longitude"
}
},
{
"class": "sequence",
"lengthDistribution": 100,
"base": {
"class": "map",
"value": [
{
"name": "time",
"class": "event",
"format":"yyyy-MM-dd HH:mm:ss",
"rate": "1/m"
},
{
"name": "temp",
"class": "random-walk",
"start": 30,
"mean": 0,
"sd":1
},
{
"name": "case-open",
"class": "int",
"dist": [0, 999, 1, 1]
}
]
},
"flat": true
}
] This schema consists of three clauses. The first generates device IDs and is pretty simple. Internally, the UUID sampler generates some integers and formats them in very techy looking hexadecimal with dashes. The result looks like real UUIDs.
The second clause generates locations. In fact, however, it really generates zip codes. Lots of other fields are generated in addition to the actual zip code (city name, state and such) but we only retain the longitude and latitude. In order to avoid generating any Zip codes that don't have an actual location (such as military overseas codes), we use the onlyContinental flag. Furthermore, we use a flattener to unnest the location fields so that they appear at the top level of resulting records.
The last clause generates many values for each sample, each of which is a structure with three fields. These fields include time, temp and case-open. The time field in these structures is generated using the event sampler with an average rate of one event per minute. The case-open field is generated by sampling either a 1 or a 0 but with a much higher probability of picking a 0.
The interesting field among these three is the temp field. This is generated by a process that keeps the running total of normally distributed values. This gives reasonably varying real values that look different from device to device.
This last clause generates a variable number of values for each device with 100 samples on average. Overall, this means that each sample from this schema consists of one UUID, one location and one sequence of 100, more or less, time/temp/case-op flags. This single sample is flattened into multiple records because of the flat:true flag on the sequence clause in the schema. Moreover, since there is no name for the sequence clause, the structures in the sequence of samples are each unnested so that the records that are produced have five fields, none of which contains nested data. This makes it possible to generate normally looking CSV formatted data using this schema.
You can also generate arbitrarily nested data by using the map sampler. For example, this schema will produce records with an id and a map named stuff that has two integers ("a" and "b") in it.
[
{"name": "id", "class": "id"},
{
"name": "stuff", "class": "map",
"value": [
{"name": "a", "class": "int", "min": 3, "max": 4},
{"name": "b", "class": "int", "min": 4, "max": 5}
]
}
]Suppose that we want to generate this kind of data:
{"name":{"first":"Marcelene","last":"Gillette"},
"student":{
"details":{"school.id":7608,"school.name":"Lloyd Burton Junior High School"}}}
{"name":{"first":"Samuel","last":"Colston"},
"student":{
"details":{"school.id":3555,"school.name":"Kenneth Smith Junior High School"}}}
{"name":{"first":"Jan","last":"Acton"},
"student":{
"details":{"school.id":4295,"school.name":"Jeanette Harden Elementary School"}}}This is harder than it looks because there are some surprises:
- The name is a structure, not a string
- The school name is all fancy with a name and a kind of a school
The schema to generate this is a little long, but not really all that complicated. There are a few tricks that I used:
-
the map type allows me to generate a structure for the name and the details structure.
-
the join combined with the sequence type allows me to build the school name by combining a name and a randomly selected string.
Here is the schema (it looks fancier than it is):
[{
"name": "name",
"class": "map",
"value": [{
"name": "first",
"class": "name",
"type": "first"
}, {
"name": "last",
"class": "name",
"type": "last"
}]
}, {
"name": "student",
"class": "map",
"value": [{
"name": "details",
"class": "map",
"value": [{
"name": "school.id",
"class": "int",
"min": 1000,
"max": 9999
}, {
"name": "school.name",
"class": "join",
"separator": " ",
"value": {
"class": "sequence",
"length": 2,
"array": [{
"class": "name",
"type": "first_last"
}, {
"class": "string",
"dist": {
"High School": 1,
"Junior High School": 2,
"Elementary School": 5
}
}]
}
}]
}]
}]By default all strings in CSV or TSV formats are fully quoted. This can confuse some software (sadly) so there are additional options to control how quoting is done.
There are three basic strategies supported:
-
DOUBLE_QUOTE This is the previous behavior where all strings are safely quoted according to JSON conventions.
-
BACK_SLASH With this convention, all internal spaces, tabs, back slashes and commas are quoted by preceding them with backslash character. This convention is useful with Hive.
-
OPTIMISTIC With this convention, no quoting of strings is done. This should not normally be used since it is very easy to get unparseable data.
The default convention is DOUBLE_QUOTE.
This approach uses Freemarker template engine to render custom templates. The data variables in the template are fed from a specified schema.
-template file link to a Freemarker template
-schema file to specify the schema (see above)
## Template notation
To print the value of a variable in the template, use ${name.asText()} placeholder.
To generate a sample vCard simply create a file template.txt:
BEGIN:VCARD
VERSION:3.0
N:${last_name.asText()};${first_name.asText()};;${title.asText()}
ORG:Sample Org
TITLE:${title.asText()}
PHOTO;VALUE=URL;TYPE=GIF:http://thumbs.example.com/${filename.asText()}/${first_name.asText()?lower_case }.gif
TEL;TYPE=HOME,VOICE:${phone_number.asText()}
ADR;TYPE=WORK:;;${address.asText()?split(" ")?join(";")}
EMAIL;TYPE=PREF,INTERNET:${first_name.asText()[0]?lower_case}${last_name.asText()?lower_case}@example.com
REV:${first_visit.asText()}
END:VCARD
Then a schema file, let’s call it schema.txt:
[
{"name":"title", "class":"string", "dist":{"Mr":0.5, "Mrs.":0.14, "Miss":0.36}},
{"name":"first_name", "class":"name", "type":"first"},
{"name":"last_name", "class":"name", "type":"last"},
{"name": "filename", "class": "join", "separator": "/", "value": {
"class":"sequence",
"length":2,
"array":[
{"class":"string", "dist":{"small":10, "medium":5, "large":2}},
{"class":"string", "dist":{"high":10, "low":5, "mobile":15}}
]
}},
{"name": "phone_number", "class": "join", "separator": "-", "value": {
"class":"sequence",
"length":3,
"array":[
{ "class": "int", "min": 100, "max": 999},
{ "class": "int", "min": 100, "max": 999},
{ "class": "int", "min": 100, "max": 999}
]
}},
{"name":"address", "class":"address"},
{"name":"first_visit", "class":"date", "format":"yyyy-MM-dd HH:mm:ssZ"}
]
To invoke the log-synth, just do:
./target/log-synth -count 5000 -schema schema.txt -template template.txt -format TEMPLATE -output output/
The output documents will end up in the output/ folder as expected and they will look like:
BEGIN:VCARD
VERSION:3.0
N:Kittle;Gwendolyn;;Mr
ORG:Sample Org
TITLE:Mr
PHOTO;VALUE=URL;TYPE=GIF:http://thumbs.example.com/small/mobile/gwendolyn.gif
TEL;TYPE=HOME,VOICE:774-383-580
ADR;TYPE=WORK:;;18033;Quaking;Brook;Avenue
EMAIL;TYPE=PREF,INTERNET:gkittle@example.com
REV:2013-07-14 01:37:08+0100
END:VCARDBEGIN:VCARD