Datagen CLI

This command line interface application allows you to take schemas defined in JSON (.json), Avro (.avsc), or SQL (.sql) and produce believable fake data to Kafka in JSON or Avro format.

The benefits of using this datagen tool are:

• You can specify what values are generated using the expansive FakerJS API to craft data that more faithfully imitates your use case. This allows you to more easily apply business logic downstream.
• This is a relatively simple CLI tool compared to other Kafka data generators that require Kafka Connect.
• When using the avro output format, datagen connects to Schema Registry. This allows you to take advantage of the benefits of using Schema Registry.
• Often when you generate random data, your downstream join results won't make sense because it's unlikely a randomly generated field in one dataset will match a randomly generated field in another. With this datagen tool, you can specify relationships between your datasets so that related columns will match up, resulting in meaningful joins downstream. Jump to the end-to-end ecommerce tutorial for a full example.

🚧 Specifying relationships between datasets currently requires using JSON for the input schema.

Installation

npm

npm install -g @materializeinc/datagen

Docker

docker pull materialize/datagen

From Source

git clone https://github.com/MaterializeInc/datagen.git
cd datagen
npm install
npm run build
npm link

Setup

Create a file called .env with the following environment variables

# Kafka Brokers
KAFKA_BROKERS=

# For Kafka SASL Authentication:
SASL_USERNAME=
SASL_PASSWORD=
SASL_MECHANISM=

# For Kafka SSL Authentication:
SSL_CA_LOCATION=
SSL_CERT_LOCATION=
SSL_KEY_LOCATION=

# Connect to Schema Registry if using '--format avro'
SCHEMA_REGISTRY_URL=
SCHEMA_REGISTRY_USERNAME=
SCHEMA_REGISTRY_PASSWORD=

The datagen program will read the environment variables from .env in the current working directory.

Usage

datagen -h

Usage: datagen [options]

Fake Data Generator

Options:
  -V, --version             output the version number
  -s, --schema <char>       Schema file to use
  -f, --format <char>       The format of the produced data (choices: "json", "avro", default: "json")
  -n, --number <char>       Number of records to generate. For infinite records, use -1 (default: "10")
  -c, --clean               Clean (delete) Kafka topics and schema subjects previously created
  -dr, --dry-run            Dry run (no data will be produced to Kafka)
  -d, --debug               Output extra debugging information
  -w, --wait <int>          Wait time in ms between record production
  -rs, --record-size <int>  Record size in bytes, eg. 1048576 for 1MB
  -p, --prefix <char>       Kafka topic and schema registry prefix
  -h, --help                display help for command

Quick Examples

See example input schema files in examples and tests folders.

Quickstart

1. Iterate through a schema defined in SQL 10 times, but don't actually interact with Kafka or Schema Registry ("dry run"). Also, see extra output with debug mode.

   datagen \
     --schema tests/products.sql \
     --format avro \
     --dry-run \
     --debug

2. Same as above, but actually create the schema subjects and Kafka topics, and actually produce the data. There is less output because debug mode is off.

   datagen \
       --schema tests/products.sql \
       --format avro

3. Same as above, but produce to Kafka continuously. Press Ctrl+C to quit.

   datagen \
       -s tests/products.sql \
       -f avro \
       -n -1

4. If you want to generate a larger payload, you can use the --record-size option to specify number of bytes of junk data to add to each record. Here, we generate a 1MB record. So if you have to generate 1GB of data, you run the command with the following options:

   datagen \
       -s tests/products.sql \
       -f avro \
       -n 1000 \
       --record-size 1048576

   This will add a recordSizePayload field to the record with the specified size and will send the record to Kafka.

   📓 The 'Max Message Size' of your Kafka cluster needs to be set to a higher value than 1MB for this to work.

5. Clean (delete) the topics and schema subjects created above

   datagen \
       --schema tests/products.sql \
       --format avro \
       --clean

Generate records with sequence numbers

To simulate auto incrementing primary keys, you can use the iteration.index variable in the schema.

This is particularly useful when you want to generate a small set of records with sequence of IDs, for example 1000 records with IDs from 1 to 1000:

[
    {
        "_meta": {
            "topic": "mz_datagen_users"
        },
        "id": "iteration.index",
        "name": "faker.internet.userName()",
    }
]

Example:

datagen \
    -s tests/iterationIndex.json \
    -f json \
    -n 1000 \
    --dry-run

Docker

Call the docker container like you would call the CLI locally, except:

• include --rm to remove the container when it exits
• include -it (interactive teletype) to see the output as you would locally (e.g. colors)
• mount .env and schema files into the container
• note that the working directory in the container is /app

docker run \
  --rm -it \
  -v ${PWD}/.env:/app/.env \
  -v ${PWD}/tests/schema.json:/app/blah.json \
      materialize/datagen -s blah.json -n 1 --dry-run

Input Schemas

You can define input schemas using JSON (.json), Avro (.avsc), or SQL (.sql). Within those schemas, you use the FakerJS API to define the data that is generated for each field.

You can pass arguments to faker methods by escaping quotes. For example, here is faker.datatype.number with min and max arguments:

"faker.datatype.number({min: 100, max: 1000})"

🚧 Right now, JSON is the only kind of input schema that supports generating relational data.

⚠️ Please inspect your input schema file since faker methods can contain arbitrary Javascript functions that datagen will execute.

JSON Schema

Here is the general syntax for a JSON input schema:

[
  {
    "_meta": {
      "topic": "<my kafka topic>",
      "key": "<field to be used for kafka record key>" ,
      "relationships": [
        {
          "topic": "<topic for dependent dataset>",
          "parent_field": "<field in this dataset>",
          "child_field": "<matching field in dependent dataset>",
          "records_per": <number of records in dependent dataset per record in this dataset>
        },
        ...
      ]
    },
    "<my first field>": "<method from the faker API>",
    "<my second field>": "<another method from the faker API>",
    ...
  },
  {
    ...
  },
  ...
]

Go to the end-to-end ecommerce tutorial to walk through an example that uses a JSON input schema with relational data.

SQL Schema

The SQL schema option allows you to use a CREATE TABLE statement to define what data is generated. You specify the FakerJS API method using a COMMENT on the column. Here is an example:

CREATE TABLE "ecommerce"."products" (
  "id" int PRIMARY KEY,
  "name" varchar COMMENT 'faker.internet.userName()',
  "merchant_id" int NOT NULL COMMENT 'faker.datatype.number()',
  "price" int COMMENT 'faker.datatype.number()',
  "status" int COMMENT 'faker.datatype.boolean()',
  "created_at" datetime DEFAULT (now())
);

This will produce the desired mock data to the topic ecommerce.products.

Avro Schema

🚧 Avro input schema currently does not support arbitrary FakerJS methods. Instead, data is randomly generated based on the type.

Here is an example Avro input schema from tests/schema.avsc that will produce data to a topic called products:

{
  "type": "record",
  "name": "products",
  "namespace": "exp.products.v1",
  "fields": [
    { "name": "id", "type": "string" },
    { "name": "productId", "type": ["null", "string"] },
    { "name": "title", "type": "string" },
    { "name": "price", "type": "int" },
    { "name": "isLimited", "type": "boolean" },
    { "name": "sizes", "type": ["null", "string"], "default": null },
    { "name": "ownerIds", "type": { "type": "array", "items": "string" } }
  ]
}