Event data simulator. Generates a stream of pseudo-random events from a set of users, designed to simulate web traffic.
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README.md

eventsim

Eventsim is a program that generates event data for testing and demos. It's written in Scala, because we are big data hipsters (at least sometimes). It's designed to replicate page requests for a fake music web site (picture something like Spotify); the results look like real use data, but are totally fake. You can configure the program to create as much data as you want: data for just a few users for a few hours, or data for a huge number of users of users over many years. You can write the data to files, or pipe it out to Apache Kafka.

You can use the fake data for product development, correctness testing, demos, performance testing, training, or in any other place where a stream of real looking data is useful. You probably shouldn't use this data to research machine learning algorithms, and definitely shouldn't use it to understand how real people behave.

Statistical Model

I wrote this simulator based on observations about how real users behave. I wanted to make sure that data looked real: users would come and go randomly, some users would stay much longer than others, users would be more likely to use the service in the middle of the day than the middle of the night, and much less likely to use the service on weekends and holidays.

To make this work, I did the following:

  • If you set the "damping" factors to zero, then users randomly arrive at the site according to a Poisson (memoryless) process, but with a minimum gap of 30 minutes between sessions.
  • The time between events is given by a log-normal distribution
  • Once a sessions has started, the user will randomly traverse a set of states until the session ends. The probability of each state transition (including end of session) depends on the current state.
  • On average, users will behave the same way in a session, regardless of the time of day or day of week
  • If you enable damping for weekends and holidays, the probability that a user arrives on weekends and holidays drops. The odds are scaled linearly over a course of a few hours (by default) around midnight (by default).
  • If you enable damping for nighttime, the probability that a user arrives in the middle of the night is lower than the probability that they arrive in the middle of the day. The odds roughly follow a sine wave.

How the simulation works

When you run the simulator, it starts by generating a set of users with randomly picked properties. This includes attributes like names and location as well as usage characteristics, like user engagement. Eventsim uses a pseudo-random number generator: the generator is deterministic, but looks random.

You need to specify a configuration file (samples are included in examples). This file specifies how sessions are generated and how the fake website works. The simulator will also load in a set of data files that describe distributions for different parameters (like places, song names, and user agents).

The simulator works by creating a priority queue of user sessions, ordered by the timestamp of the next event in each session. The simulator picks each session off the queue, outputs the details of the event, then determines the next event in the session for each user (or creates a new session for the user), and puts the session back in the queue.

When the simulator figures out the next event in the sessions, it looks at all of the possible transitions from the current state to other states. If the total probability of all the transitions is 1.0, then there will always be a next page. However, if the probability is n < 1.0, then with probability 1.0 - n the session will end, and the user will next be seen at a future session.

Most of the time, the next event will occur at a non-deterministic, log-normally distributed time after the current event. But there are two exceptions: "nextSong" events and redirects. The next song events will typically occur after the duration of the current song. Redirects occur at a fixed time afterwards (we did this to model the action of submitting a form then being redirected to a new page).

By default, song titles are picked randomly based on how popular they are. But optionally, the simulator can use data on similar songs to pick sequences of similar songs. (To do this, you need the similar songs data file. That file was too big to include, but we included the utility to generate it. Run eventsim with the generate-similars option to create it.)

By the way: the current version of the simulator is hard-coded for a music web site. You can modify it to work for other types of sites, but doing so will probably require modifications to the code (and not just to the config files).

Config File

Take a look at the sample config file. It's a JSON file, with key-value pairs. Here is an explanation of the values (many of which match command line options):

  • seed For the pseudo-random number generator. Changing this value will change the output (all other parameters being equal).
  • alpha This is the expected number of seconds between events for a user. This is randomly generated from a lognormal distrbution
  • beta This is the expected session interarrival time (in seconds). This is thirty minutes plus a randomly selected value from an exponential distribution
  • damping Controls the depth of daily cycles (larger values yield stronger cycles, smaller yield milder)
  • weekend-damping Controls the difference between weekday and weekend traffic volume
  • weekend-damping-offset Controls when the weekend/holiday starts (relative to midnight), in minutes
  • weeeknd-damping-scale Controls how long traffic tapering lasts, in minutes
  • session-gap Minimum time between sessions, in seconds
  • start-date Start date for data (in ISO8601 format)
  • end-date End date for data (in ISO8601 format)
  • n-users Number of users at start-date
  • first-user-id User id assigned to first user (these are assigned sequentially)
  • growth-rate Annual growth rate for users
  • tag Tag added to each line of the output

You also specify the event state machine. Each state includes a page, an HTTP status code, a user level, and an authentication status. Status should be used to describe a user's status: unregistered, logged in, logged out, cancelled, etc. Pages are used to describe a user's page. Here is how you specify the state machine:

  • Transitions. Describe the pair of page and status before and after each transition, and the probability of the transition.
  • New user. Describes the page and status for each new user (and probability of arriving for the first time with one of those states).
  • New session. Describes that page and status for each new session.
  • Show user details. For each status, states whether or not users are shown in the generated event log.

When you run the simulator, you specify the mean values for alpha and beta and the simulator picks values for specific users.

Usage

To build the executable, run

$ sbt assembly
$ # make sure the script is executable
$ chmod +x bin/eventsim

(By the way, eventsim requires Java 8.)

The program can accept a number of command line options:

$ bin/eventsim --help
    -a, --attrition-rate  <arg>    annual user attrition rate (as a fraction of
                                   current, so 1% => 0.01) (default = 0.0)
    -c, --config  <arg>            config file
        --continuous               continuous output
        --nocontinuous             run all at once
    -e, --end-time  <arg>          end time for data
                                   (default = 2015-08-12T14:56:25.006)
    -f, --from  <arg>              from x days ago (default = 15)
        --generate-counts          generate listen counts file then stop
        --nogenerate-counts        run normally
        --generate-similars        generate similar song file then stop
        --nogenerate-similars      run normally
    -g, --growth-rate  <arg>       annual user growth rate (as a fraction of
                                   current, so 1% => 0.01) (default = 0.0)
        --kafkaBrokerList  <arg>   kafka broker list
    -k, --kafkaTopic  <arg>        kafka topic
    -n, --nusers  <arg>            initial number of users (default = 1)
    -r, --randomseed  <arg>        random seed
    -s, --start-time  <arg>        start time for data
                                   (default = 2015-08-05T14:56:25.040)
        --tag  <arg>               tag applied to each line (for example, A/B test
                                   group)
    -t, --to  <arg>                to y days ago (default = 1)
    -u, --userid  <arg>            first user id (default = 1)
        --help                     Show help message

   trailing arguments:
    output-file (not required)   File name

Only the config file is required.

Parameters can be specified in three ways: you can accept the default value, you can specify them in the config file, or you can specify them on the command line. Config file values override defaults; command line options override everything.

Example for about 2.5 M events (1000 users for a year, growing at 1% annually):

$ bin/eventsim -c "examples/site.json" --from 365 --nusers 1000 --growth-rate 0.01 data/fake.json
Initial number of users: 1000, Final number of users: 1010
Starting to generate events.
Damping=0.0625, Weekend-Damping=0.5
Start: 2013-10-06T06:27:10, End: 2014-10-05T06:27:10, Now: 2014-10-05T06:27:07, Events:2468822

Example for more events (30,000 users for a year, growing at 30% annually):

$ bin/eventsim -c "examples/site.json" --from 365 --nusers 30000 --growth-rate 0.30 data/fake.json

Building huge data sets in parallel

You can run multiple instances of this application simultaneously if you need to generate a lot of data very quickly. To do this, we recommend the following strategy:

  • Use a different random seed for each instance. This assures that each instance will produce different data.
  • Use a different starting user id for each instance (and make sure the ranges don't overlap). This assures that each instance will produce data with different user ids.
  • Create a set of configuration files, one for each instance of eventsim. This will help you re-create your data, and help you remember the details of the data
  • Do not generate data for different time periods. The generator doesn't generate full sessions if they cross the start and end dates; you will find some incomplete data between files.

A Cool Example

To simulate A/B tests, create multiple data sets for the same time period with different sets of member ids, different tags, and different parameters for alpha, beta, transition probabilities, or growth rates. For example, you can geneate two files of about 5000 users with different characteristics with two commands like this:

    $ bin/eventsim --config examples/example-config.json --tag control -n 5000 \
    --start-time "2015-06-01T00:00:00" --end-time "2015-09-01T00:00:00" \
    --growth-rate 0.25 --userid 1 --randomseed 1 control.data.json
    Loading song file...
    385000	...done loading song file. 385252 tracks loaded.
    Loading similar song file...
    Could not load similar song file (don't worry if it's missing)

    Initial number of users: 5000, Final number of users: 5335
    Start: 2015-06-01T00:00, End: 2015-09-01T00:00
    Starting to generate events.
    Damping=0.09375, Weekend-Damping=0.5
    Now: 2015-08-31T15:38:02, Events:1430000, Rate: 147058 eps

    $bin/eventsim --config examples/alt-example-config.json --tag test -n 5000 \
    > --start-time "2015-06-01T00:00:00" --end-time "2015-09-01T00:00:00" \
    > --growth-rate 0.25 --userid 5336 --randomseed 2 test.data.json
    Loading song file...
    385000	...done loading song file. 385252 tracks loaded.
    Loading similar song file...
    Could not load similar song file (don't worry if it's missing)

    Initial number of users: 5000, Final number of users: 5352
    Start: 2015-06-01T00:00, End: 2015-09-01T00:00
    Starting to generate events.
    Damping=0.09425, Weekend-Damping=0.53
    Now: 2015-08-31T20:25:04, Events:1870000, Rate: 114942 eps

Issues and Future Work

Want to pitch in and help? Here are some ideas on ways to make this better?

  • We haven't made the generator multi-threaded yet, but there isn't a good reason that we can't do that. (The only state that needs to be shared between threads is the priority queue, and access to that can be easily controlled).
  • The models to generate data could also use more work. We made some rough guesses based on experience, but aren't sure how well they reflect reality. (In particular, the sine curve for cycles bugs me.)
  • The simulator is tied closely to simulating a web site, specifically a music web site. It would be great to make the simulation more abstract, so that different configuration files could be used for dramatically different use cases.
  • This is written by a novice Scala programmer. An expert could improve the code quality.

License

We have adopted the MIT license (see the file LICENSE.txt) for this project.

About the source data

The results of this simulation are fake... but they are based on real data. (We thought that using real data on songs would make the simulation more colorful and interesting.)

The song data is from the Million Song Dataset, official website by Thierry Bertin-Mahieux, available at: http://labrosa.ee.columbia.edu/millionsong/. For more information, see this paper:

Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2011.

The last names come from the US Census Bureau (see http://www.census.gov/genealogy/www/data/2000surnames/index.html).

The first names come from the Social Security Administration (see http://www.ssa.gov/oact/babynames/#&ht=1); we took the top 1000 names for each sex from this site.

(Note that the first and last names are chosen independently. This leads to some unexpected, but awesome results.)

Location names are from the Census Bureau (see https://www.census.gov/popest/data/datasets.html).

User agents are from http://techblog.willshouse.com/2012/01/03/most-common-user-agents/

For the real novice

If you aren't familiar with the Java toolkit (and Scala), here's a few commands to get your started.

On a Mac, you'll need to install Java 8 and Scala. I use Homebrew for package management, so I can just install it with this command:

$ brew install scala

On Linux (specifically Ubuntu), it's a little more complicated. Here's what works for me:

$ echo "deb http://dl.bintray.com/sbt/debian /" | sudo tee -a /etc/apt/sources.list.d/sbt.list
$ sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 642AC823
$ sudo apt-get update
$ sudo apt-get install openjdk-8-jdk scala sbt

To build the executable, run

$ sbt assembly
$ # make sure the script is executable
$ chmod +x bin/eventsim