voluble

⚠️ Voluble is now deprecated, see http://shadowtraffic.io

I've unfortunately run out of time to maintain this open-source connector. In its place, I've launched an improved commercial offering so that I can sustainably provide a similar service. Thanks for understanding!

When working with Apache Kafka, you often find yourself wanting to continuously populate your topics with something that approximates the shape of your production data. Voluble solves that problem. The primary things it supports are:

Creating realistic data by integrating with Java Faker.
- To get an idea of the entities that JavaFaker can create, see this example site.
Cross-topic relationships
Populating both keys and values of records
Making both primitive and complex/nested values
Bounded or unbounded streams of data
Tombstoning

Voluble ships as a Kafka connector to make it easy to scale and change serialization formats. You can use Kafka Connect through its REST API or integrated with ksqlDB. In this guide, I demonstrate using the latter, but the configuration is the same for both. I leave out Connect specific configuration like serializers and tasks that need to be configured for any connector.

Installation

Install the Connector with confluent-hub:

confluent-hub install mdrogalis/voluble:0.3.1

If you want to use it as a library:

[io.mdrogalis/voluble "0.3.1"]

Quick example

Let's dive right in and look at an example. This exercises a bunch of features to give you an idea of what it's like to work with Voluble:

CREATE SOURCE CONNECTOR s WITH (
  'connector.class' = 'io.mdrogalis.voluble.VolubleSourceConnector',

  'genkp.owners.with' = '#{Internet.uuid}',
  'genv.owners.name->full.with' = '#{Name.full_name}',
  'genv.owners.creditCardNumber.with' = '#{Finance.credit_card}',

  'genk.cats.name.with' = '#{FunnyName.name}',
  'genv.cats.owner.matching' = 'owners.key',

  'genk.diets.catName.matching' = 'cats.key.name',
  'genv.diets.dish.with' = '#{Food.vegetables}',
  'genv.diets.measurement.with' = '#{Food.measurements}',
  'genv.diets.size.with' = '#{Food.measurement_sizes}',

  'genk.adopters.name.sometimes.with' = '#{Name.full_name}',
  'genk.adopters.name.sometimes.matching' = 'adopters.key.name',
  'genv.adopters.jobTitle.with' = '#{Job.title}',
  'attrk.adopters.name.matching.rate' = '0.05',
  'topic.adopters.tombstone.rate' = '0.10',

  'global.throttle.ms' = '500',
  'global.history.records.max' = '100000'
);

This example generates data for 4 topics: owners, cats, diets, and adopters.

The owners topic consists of records with primitive UUID keys and complex values (name and creditCardNumber). These values are generated through Java Faker expressions.
The value of the events for cats has a field named owner which has to match one of the UUID generated in the owner topic.
In the diets topic, you can see a similar property set for the key, except this one grabs a value from a complex key (name in the key of the cats topic).
The adopters topic has keys that are sometimes new, but sometimes repeated (sometimes.matching is running against the same topic as its specified for). This is basically a nice easy to represent mutation. A tombstone record is generated for this topic 10% of the time.

Lastly, Voluble will only generate a new record per topic every ~500 ms and keep at most 100000 records of history in memory per topic to perform all this matching against.

When you run this connector, you'll get data looking roughly like the following:

[
    {
        "topic": "owners",
        "event": {
            "key": "57da6bd0-dc33-4c2d-9a3f-046d2a008085",
            "value": {
                "name": {
                    "full": "Rene Bashirian"
                },
                "creditCardNumber": "3680-695522-0973"
            }
        }
    },
    {
        "topic": "cats",
        "event": {
            "key": {
                "name": "Kenny Dewitt"
            },
            "value": {
                "owner": "57da6bd0-dc33-4c2d-9a3f-046d2a008085"
            }
        }
    },
    {
        "topic": "diets",
        "event": {
            "key": {
                "name": "Kenny Dewitt"
            },
            "value": {
                "dish": "Celery",
                "measurement": "teaspoon",
                "size": "1/3"
            }
        }
    },
    {
        "topic": "adopters",
        "event": {
            "key": {
                "name": "Sheldon Feeney"
            },
            "value": {
                "jobTitle": "IT Producer"
            }
        }
    }
]

Usage

All configuration for Voluble is done through properties that are passed to the connector.

Generating data

Generating events with Voluble basically take the form of:

'<directive>.<topic>.[attribute?].[qualifier?].<generator>' = 'expression'

Let's break down what exactly that means.

Directives

There are 4 top-level directives for generating a single Kafka record: genk, genkp, genv, and genvp. Those stand for generate key, generate key primitive, generate value, and generate value primitive. Non-primitive, or complex, generators are for generating maps of values where the keys are named. Key and value generators can be used together in any combination, but primitive and complex generators for the same part of a record, like the key, are mutually exclusive.

Topic and attribute

Topic is the topic that Voluble will generate data to for this expression. If you're generating a complex value, you'll also need to specify an attribute, which is just the key that the expression will be generated for. For example, you might specify that an attribute is "name". When you generate events, you'll get maps with a "name" key in them.

Generator

There are two types of generators: with and matching. with takes a Java Faker expression as its value and generates data irrespective of any other topics. matching allows you to generate data that has already been generated in another topic. This is useful when your real data has relationships and might be joined downstream. The value for a matching generator takes the form of: <topic>.[key|value].[attribute?]. This syntax let's you target data in another topic in either the key or value of a record. If it's a complex data structure, you can get a single key out of it.

matching is "stable", in that for every iteration, Voluble selects a single event from another topic and runs all matches against that.

Qualifier

Qualifiers let you control how generators work. Right now there is only one qualifier: sometimes. Sometimes you want to generate data that matches another topic, but not always. This is useful if you're modeling a single topic who's key's represent mutability. Or maybe you want to model a stream/stream join. sometimes allows you to control the probability that Voluble will generate a matching value versus a brand new one. You'd use it roughly like the following: genv.users.team.sometimes.matching = team.key.name, genv.users.team.sometimes.with = '#{Team.name}'. When you use sometimes, you need to specify both matching and with. By default there is now a 0.1 probability rate of matching, instead of 1. You can control the probability to suit your circumstances (see the configuration section).

Expressions

When a with generator is used, the value is passed verbatim to Java Faker to create a value. Java Faker has a huge number of categories that it can generate data for. Just check out the project to get a sense for what you can do. Under the covers, the expression method of Faker is being invoked to dynamically create data without going through its Java classes.

Some Faker categories take parameters as arguments. The parser for arguments is rather strict: all values surrounded with apostrophes, and no spaces in between each value. Some arguments (like TimeUnit) appear to be case-sensitive. Here are some examples:

#{number.number_between '-9','9'}
#{date.birthday}
#{date.birthday '10','20'}
#{date.past '10','DAYS'}
#{date.between 'Sun Mar 22 01:59:02 PDT 2020','Sun Mar 24 01:59:02 PDT 2020'}

If you get stuck generating something that you want, just instantiate Faker directly in a Java program and call faker.expression() until you get the thing you're looking for.

Nesting

You can nest data and access nested data in other events with arrow syntax (->). You can use this to generate data, as in genv.owners.name->full.with = #{Name.full_name}, which will create maps like {"name": {"full": "Rene Bashirian"}}. You can also reference nested values in a matching statement, as in genv.cats.owner.matching = owners.value.name->full. Arrow syntax can be used anywhere that an attribute is allowed.

More examples

For concision, I just list out the relevant configuration.

A primitive key and primitive value

'genkp.people.with' = '#{Internet.uuid}'
'genvp.people.with' = '#{Name.full_name}'

A primitive key and complex value

'genkp.people.with' = '#{Internet.uuid}'
'genv.people.name.with' = '#{Name.full_name}'
'genv.people.bloodType.with' = '#{Name.blood_group}'

A complex key and complex value

'genk.people.id.with' = '#{Internet.uuid}'
'genk.people.avatar.with' = '#{Internet.avatar}'
'genv.people.name.with' = '#{Name.full_name}'
'genv.people.bloodType.with' = '#{Name.blood_group}'

A topic that gets part of its value from another topic's primitive key

key can instead be value to reference that part of the record.

'genkp.users.with' = '#{Name.full_name}'
'genvp.users.with' = '#{Name.blood_group}'

'genkp.publications.matching' = 'users.key'
'genv.publications.title.with' = '#{Book.title}'

A topic that gets part of its value from another topic's complex key

'genk.users.name.with' = '#{Name.full_name}'
'genvp.users.with' = '#{Name.blood_group}'

'genkp.publications.matching' = 'users.key.name'
'genv.publications.title.with' = '#{Book.title}'

A topic with keys that update once in a while

Notice that a record can match against itself. Useful for representing updates in a topic.

'genkp.users.sometimes.with' = '#{Name.full_name}'
'genkp.users.sometimes.matching' = 'users.key'
'genv.users.bloodType.with' = '#{Name.blood_group}'

Two topics that sometimes share a key in common

Useful for modeling stream/stream joins.

'genkp.teamA.sometimes.with' = '#{Team.name}'
'genkp.teamA.sometimes.matching' = 'teamB.key'

'genkp.teamB.sometimes.with' = '#{Team.name}'
'genkp.teamB.sometimes.matching' = 'teamA.key'

Creating nested data and accessing it from a match

'genv.teamA.stadium->location.with' = '#{Address.state}'
'genv.teamB.backupLocation.matching' = 'teamA.value.stadium->location'

Configuration

Voluble has a few other knobs for controlling useful properties. Some properties can be defined at the attribute, topic, and global level. The most granular scope takes precedence (topic over global, etc).

Throttling

By default, Voluble will try to generate data to Kafka as fast as possible. But sometimes this is simply too much. You can throttle how fast data is generated with global.throttle.ms, or per topic with topic.<topic>.throttle.ms.

Matching probability

When you use the sometimes qualifier, the rate of matching is reduced from a certainty (1) to 0.1. You can control this probability at both the global and attribute level. To alter the matching rate global, configure global.matching.rate to be a value between 0 and 1. To configure it at the attribute level, configure it roughly like attrk.<topic>.<attribute>.matching.rate. Attribute configuration mirrors generator configuration: attrk, attrkp, attrv, and attrvp the same as generators.

Tombstoning

In Kafka, a key with a null value is called a tombstone. It conventionally represents the deletion of a key in a topic. Sometimes it's useful to generate tombstones. By default Voluble won't do this, but you can turn it on per topic with topic.<topic>.tombstone.rate = p. p is a value between 0 and 1, and it represents the probability that a tombstone will be generated instead of a value.

Null value rates

Sometimes when you're generating complex values, you might want the value for a key to be null. By default Voluble doesn't do this, but you can turn configure it at the attribute level like so: attrv.<topic>.<attribute>.null.rate = p.

Bounded event streams

There are some cases where it's realistic to produce exactly n records to a topic, no more and no less. This might be the case if you're loading a table of static reference data into Kafka. You can instruct Voluble to do this with the form topic.<topic>.records.exactly = n, where n is an integer > 0.

History capacity

To perform matching expressions, Voluble needs to keep the history of previously generated records for each topic. By default, only the most recent 1,000,000 records are kept per topic. You can override this per topic with topic.<topic>.history.records.max = n, or globally with global.history.records.max = n.

Reference

Key form	Value form	Default value	Meaning
`(genkp\|genvp).<topic>.with`	`#{expr}`	unset	Makes a new record and sends it to `<topic>`. Evaluates the Java Faker expression and supplies it as a primitive type for the key or value.
`(genk\|genv).<topic>.<attr>.with`	`#{expr}`	unset	Makes a new record and sends it to `<topic>`. Evaluates the Java Faker expression and supplies it as a map for the key or value. The map has form: `<attr>` -> Java Faker expr.
`(genkp\|genvp).<topic>.matching`	`<src>.(key\|value).[attr?]`	unset	Makes a new record and sends it to `<topic>`. The key or value of the record is derived from a previous record produced to topic `<src>`'s key or value. Can optionally drill into an attribute of the resulting value. The value will be supplied as a primitive value to the new record.
`(genk\|genv).<topic>.<attr>.matching`	`<src>.(key\|value).[attr?]`	unset	Makes a new record and sends it to `<topic>`. The key or value of the record is derived from a previous record produced to topic `<src>`'s key or value. Can optionally drill into an attribute of the resulting value. The value will be a map with form: `<attr>` -> derived value.
`(genkp\|genvp).<topic>.sometimes.with`	`#{expr}`	unset	Makes a new record and sends it to `<topic>`. Same semantics as `with` without `sometimes`. Will be chosen according to the specified matching rate. When present, `sometimes.matching` must also be included.
`(genk\|genv).<topic>.<attr>.sometimes.with`	`#{expr}`	unset	Makes a new record and sends it to `<topic>`. Same semantics as `with` without `sometimes`. Will be chosen according to the specified matching rate. When present, `sometimes.matching` must also be included.
`(genkp\|genvp).<topic>.sometimes.matching`	`<src>.(key\|value).[attr?]`	unset	Makes a new record and sends it to `<topic>`. Same semantics as `matching` without `sometimes`. Will be chosen according to the specified matching rate. When present, `sometimes.with` must also be included.
`(genk\|genv).<topic>.<attr>.sometimes.matching`	`<src>.(key\|value).[attr?]`	unset	Makes a new record and sends it to `<topic>`. Same semantics as `matching` without `sometimes`. Will be chosen according to the specified matching rate. When present, `sometimes.with` must also be included.
`global.throttle.ms`	`<long>`	`0`	Wait at least this number of milliseconds before producing a new record for each topic.
`topic.<topic>.throttle.ms`	`<long>`	unset	Wait at least this number of milliseconds before producing a new record for `<topic>`. Overrides its global sibling.
`topic.<topic>.tombstone.rate`	`<double>`	`0`	Probability of producing a record to `<topic>` with a tombstone (null) value. Must be a value between `0` and `1`.
`(attrkp\|attrvp).<topic>.null.rate`	`<double>`	`0`	Probability of producing a record to `<topic>` with key or value as null. Must be a value between `0` and `1`.
`(attrk\|attrv).<topic>.<attr>.null.rate`	`<double>`	`0`	Probability of producing a record to `<topic>` the key or value as null. Must be a value between `0` and `1`.
`global.history.records.max`	`<long>`	`1000000`	The maximum number of records to remember for matching for each topic. The oldest records will be deleted first.
`topic.<topic>.history.records.max`	`<long>`	unset	The maximum number of records to remember for matching for `<topic>`. The oldest records will be deleted first. Overrides its global sibling.
`global.matching.rate`	`<double>`	`0.1`	Probability of matching when `sometimes` is used anywhere. Must be a value between `0` and `1`.
`(attrkp\|attrvp).<topic>.matching.rate`	`<double>`	unset	Probability of matching when `sometimes` is used for `<topic>`. Must be a value between `0` and `1`. Overrides its global sibling.
`(attrk\|attrv).<topic>.<attr>.matching.rate`	`<double>`	unset	Probability of matching when `sometimes` is used for `<topic>` with `<attr>`. Must be a value between `0` and `1`. Overrides its global sibling.
`topic.<topic>.records.exactly`	`<long>`	unset	Generates exactly this many records for `<topic>`. `<topic>` will be retired from generating new records after this count is reached.

Limitations

There is no built-in support for multi-schema topics. If you want to do this, just create multiple instances of Voluble with different generator configurations.
Voluble doesn't yet validate that you're not asking for something impossible, like a circular dependency. If it's internal state machine can't advance and generate a new event after 100 iterations, the connector will permanently abort.

If Voluble doesn't do something you want it to, feel free to open and issue or submit a patch.

Things to add in the future

Maximal unique values: within generates at most N unique values.
Distributions: matching selects with a uniform, normal, etc distribution

License

Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.

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README.md

README.md

voluble

Installation

Quick example

Usage

Generating data

Directives

Topic and attribute

Generator

Qualifier

Expressions

Nesting

More examples

Configuration

Throttling

Matching probability

Tombstoning

Null value rates

Bounded event streams

History capacity

Reference

Limitations

Things to add in the future

License

Files

README.md

Latest commit

History

README.md

File metadata and controls

voluble

Installation

Quick example

Usage

Generating data

Directives

Topic and attribute

Generator

Qualifier

Expressions

Nesting

More examples

Configuration

Throttling

Matching probability

Tombstoning

Null value rates

Bounded event streams

History capacity

Reference

Limitations

Things to add in the future

License