A small CLI tool to quickly generate millions of pseudo-random BSON documents and insert them into a MongoDB instance. Quickly test new data structure or how your application responds when your database grows!
Try it online: mongoplayground.net
- Support all bson types listed in MongoDB bson types
- Generate real data using gofakeit
- Create referenced fields across collections
- Aggregate data across collections
- Create sharded collection
- Create collections in multiple databases
- Output result as JSON
Download the binary from the release page
or
Build from source:
First, make sure that go is installed on your machine (see install go for details ). Then clone the repo and build it:
git clone https://github.com/feliixx/mgodatagen.git
cd mgodatagen
go build
If you are using go 1.17 or higher, you can simply use go install:
go install github.com/feliixx/mgodatagen@latest
Several options are available (use mgodatagen --help to print this):
Usage:
mgodatagen -f config_file.json
template:
--new=<filename> create an empty configuration file
configuration:
-f, --file=<configfile> JSON config file. This field is required
-a, --append if present, append documents to the collection without
removing older documents or deleting the collection
-i, --indexonly if present, mgodatagen will just try to rebuild index
-x, --indexfirst if present, mgodatagen will create index before
inserting documents
-n, --numWorker=<nb> number of concurrent workers inserting documents
in database. Default is number of CPU
-b, --batchsize=<size> bulk insert batch size (default: 1000)
-s, --seed=<seed> specific seed to use. Passing the same seed garentees
the same output for evey run with the same config.
Has to be in [1, 18446744073709551615]
-o, --output=<output> where documents should be written. Options are:
- mongodb (default)
- stdout
- filename
--prettyprint if present, indent the output. Only for stdout or file
output
connection infos:
--uri=<uri> connection string URI. If present, takes precedence over all
other options. For detail on URI format, see
https://docs.mongodb.com/manual/reference/connection-string/
-h, --host=<hostname> mongodb host to connect to (default: 127.0.0.1)
--port=<port> server port (default: 27017)
-u, --username=<username> username for authentification
-p, --password=<password> password for authentification
--authenticationMechanism=<mechanism> authentication mechanism
for now only PLAIN and MONGODB-X509 are supported
--tlsCertificateKeyFile=<path> PEM certificate/key file for TLS
--tlsCAFile=<path> Certificate Authority file for TLS
general:
--help show this help message
-v, --version print the tool version and exit
-q, --quiet quieter output
Only the configuration file is required ( -f | --file flag). A basic usage of mgodatagen would be
./mgodatagen -f config.json
If no host/port is specified, mgodatagen tries to connect to mongodb://127.0.0.1:27017.
The config file is an array of JSON documents, where each documents holds the configuration for a collection to create
See MongoDB documentation for details on parameters:
- shardConfig: shardCollection
- indexes: indexes
- collation: collation
[
// first collection to create
{
// REQUIRED FIELDS
//
"database": <string>, // required, database name
"collection": <string>, // required, collection name
"count": <int>, // required, number of document to insert in the collection
"content": { // required, the actual schema to generate documents
"fieldName1": <generator>, // optional, see Generator below
"fieldName2": <generator>,
...
},
// OPTIONAL FIELDS
//
// compression level (for WiredTiger engine only)
// possible values:
// - none
// - snappy
// - zlib
"compressionLevel": <string>, // optional, default: snappy
// configuration for sharded collection
"shardConfig": { // optional
"shardCollection": <string>.<string>, // required. <database>.<collection>
"key": <object>, // required, shard key, eg: {"_id": "hashed"}
"unique": <boolean>, // optional, default: false
"numInitialChunks": <int>, // optional
"collation": { // optional
"locale": <string>, // required
"caseLevel": <boolean>, // optional
"caseFirst": <string>, // optional
"strength": <int>, // optional
"numericOrdering": <boolean>, // optional
"alternate": <string>, // optional
"maxVariable": <string>, // optional
"backwards": <boolean>, // optional
"normalization": <string> // optional
}
},
// list of index to build
"indexes": [ // optional
{
"name": <string>, // required, index name
"key": <object>, // required, index key, eg: {"name": 1}
"sparse": <boolean>, // optional, default: false
"unique": <boolean>, // optional, default: false
"bits": <int>, // optional, for 2d indexes only, default: 26
"min": <double>, // optional, for 2d indexes only, default: -180.0
"max": <double>, // optional, for 2d index only, default: 180.0
"bucketSize": <double>, // optional, for geoHaystack indexes only
"expireAfterSeconds": <int>, // optional, for TTL indexes only
"weights": <string>, // optional, for text indexes only
"defaultLanguage": <string>, // optional, for text index only
"languageOverride": <string>, // optional, for text index only
"textIndexVersion": <int>, // optional, for text index only
"partialFilterExpression": <object>, // optional
"hidden": <boolean>, // optional
"storageEngine": <object>, // optional
"wildcardProjection": <object>, // optional, for wildcard index only
"2dsphereIndexVersion": <int>, // optional, for 2dsphere index only
"collation": { // optional
"locale": <string>, // required
"caseLevel": <boolean>, // optional
"caseFirst": <string>, // optional
"strength": <int>, // optional
"numericOrdering": <boolean>, // optional
"alternate": <string>, // optional
"maxVariable": <string>, // optional
"backwards": <boolean>, // optional
"normalization": <string> // optional
}
]
},
// second collection to create
{
...
}
]A set of sample config files can be found in datagen/generators/testdata/. To use it, make sure that you have a mongodb instance running (on 127.0.0.1:27017 for example) and run
./mgodatagen -f datagen/generators/testdata/ref.json
This will insert 1000 random documents in collections test and link of database
mgodatagen_test with the structure defined in the config file.
Generators have a common structure:
"fieldName": { // required, field name in generated document
"type": <string>, // required, type of the field
"typeParam": ..., // specific parameters for this type
"maxDistinctValue": <int>, // optional, maximum number of distinct values for this field
"nullPercentage": <int> // optional, int between 0 and 100. Percentage of documents
// that will have this field
}List of <generator> types:
- string
- stringFromParts
- int
- long
- double
- decimal
- autoincrement
- boolean
- objectId
- UUID
- binary
- date
- coordinates (formerly position)
- constant
- enum (formerly fromArray)
- reference
- faker
- array
- object
- countAggregator
- valueAggregator
- boundAggregator
Generates a random string of a certain length. String is composed of char within this list:
abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-_
"fieldName": {
"type": "string", // required
"minLength": <int>, // optional, must be >= 0
"maxLength": <int>, // optional, must be >= minLength
"unique": <bool>, // optional, see details below
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}If unique is set to true, the field will only contains unique strings. Unique strings
have a fixed length, minLength is taken as length for the string.
There is 64^x possible unique string for strings of length x. This number has to
be inferior or equal to the number of documents you want to generate.
For example, if you want unique strings of length 3, there is 64 * 64 * 64 = 262144 possible
strings
They will look like
"aaa",
"aab",
"aac",
"aad",
...
Generates a random string from several generators. parts generators can't have unique or maxDistinctValue attributes set.
"fieldName": {
"type": "stringFromParts", // required
"parts": [ // required. Can't be empty. An array
<generator>, // of generators of any basic type
<generator>
...
],
"nullPercentage": <int> // optional
}Example:
To generate phone number like '(555) 565-2431', you can combine several generators
like this:
"phone": {
"type": "stringFromParts",
"parts": [
{
"type": "constant",
"constVal": "(555) "
},
{
"type": "int",
"min": 100,
"max": 999
},
{
"type": "constant",
"constVal": "-"
},
{
"type": "int",
"min": 1000,
"max": 9999
},
]
}Generates a random int within bounds.
"fieldName": {
"type": "int", // required
"min": <int>, // optional
"max": <int>, // optional, must be >= min
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Generates a random long within bounds.
"fieldName": {
"type": "long", // required
"min": <long>, // optional
"max": <long>, // optional, must be >= min
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Generates a random double within bounds.
"fieldName": {
"type": "double", // required
"min": <double>, // optional
"max": <double>, // optional, must be >= min
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Generates a random decimal128.
"fieldName": {
"type": "decimal", // required
"nullPercentage": <int>, // optional
"maxDistinctValue": <int>, // optional
}Generates an autoincremented value (type <long> or <int>).
"fieldName": {
"type": "autoincrement", // required
"autoType": <string>, // required, either "int" or "long"
"start": <int|long>, // optional, start value
"nullPercentage": <int> // optional
}Generates a random boolean.
"fieldName": {
"type": "boolean", // required
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Generates a random objectId.
"fieldName": {
"type": "objectId", // required
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Generates a random UUID
"fieldName": {
"type": "uuid", // required
"format": <string> // optional, either "string" or "binary".
// default is "string"
"nullPercentage": <int> // optional
}If format is "string", the field will be a simple string like "f1b9b567-9b34-45af-9d9c-35f565d57716".
If format is "binary", the field will be stored as a bson UUID like UUID("f1b9b567-9b34-45af-9d9c-35f565d57716")
Generates random binary data of length within bounds.
"fieldName": {
"type": "binary", // required
"minLength": <int>, // optional, must be >= 0
"maxLength": <int>, // optional, must be >= minLength
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Generates a random date (stored as ISODate ).
startDate and endDate are string representation of a Date following RFC3339:
format: "yyyy-MM-ddThh:mm:ss+00:00"
"fieldName": {
"type": "date", // required
"startDate": <string>, // required
"endDate": <string>, // required, must be >= startDate
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Generates random GeoJSON coordinates (a GPS position in WGS84 Decimal Degrees with folowing format: [ longitude, latitude ] )
eg : [40.741895, -73.989308]
"fieldName": {
"type": "coordinates", // required
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Adds the same value to each document.
"fieldName": {
"type": "constant", // required
"constVal": <object>, // required, can be of any type including object and array
// eg: {"k": 1, "v": "val"}
// to set constant ObjectId eg: { "$oid": "5a934e000102030405000001" }
"nullPercentage": <int> // optional
}Picks an object from an array as value for the field. Currently, objects in the array have to be of the same type. By default, items are picked from the array in the order where they appear.
"fieldName": {
"type": "enum", // required
"values": [ // required. Can't be empty. An array of object of
<object>, // any type, including object and array.
<object>
...
],
"randomOrder": <bool>, // optional. If set to true, objects will be picked
// from the array in random order.
"nullPercentage": <int> // optional
}Use the same list of values for fields in different collection.
"fieldName":{
"type": "reference", // required
"id": <int>, // required, generator id used to link
// field between collections
"refContent": <generator>, // required, generator to use to create the
// list of values
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}generator in other collections:
"fieldName": {
"type": "reference", // required
"id": <int>, // required, same id as previous generator
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}It can also be used to duplicate a field in a single collection ( see reference_same_collection.json )
Generates a random array of bson object.
"fieldName": {
"type": "array", // required
"arrayContent": <generator>, // required, generator use to create element
// to fill the array. Can be of any type
"minLength": <int>, // optional, must be >= 0
"maxLength": <int>, // optional, must be >= minLength
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Generates random nested object.
"fieldName": {
"type": "object", // required
"objectContent": { // required, list of generator used to
"nestedFieldName1": <generator>, // generate the nested document
"nestedFieldName2": <generator>,
...
},
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}Count documents from <database>.<collection> matching a specific query. To use a
variable of the document in the query, prefix it with "$$".
The query can't be empty or null.
"fieldName": {
"type": "countAggregator", // required
"database": <string>, // required, db to use to perform aggregation
"collection": <string>, // required, collection to use to perform aggregation
"query": <object> // required, query that selects which documents to count in
// the collection
}Example:
Assuming that the collection first contains:
{"_id": 1, "field1": 1, "field2": "a" }
{"_id": 2, "field1": 1, "field2": "b" }
{"_id": 3, "field1": 2, "field2": "c" }and that the generator for collection second is:
{
"database": "test",
"collection": "second",
"count": 2,
"content": {
"_id": {
"type": "autoincrement",
"autoType": "int"
"start": 0
},
"count": {
"type": "countAggregator",
"database": "test",
"collection": "first",
"query": {
"field1": "$$_id"
}
}
}
}The collection second will contain:
{"_id": 1, "count": 2}
{"_id": 2, "count": 1}Get distinct values for a specific field for documents from
<database>.<collection> matching a specific query. To use a variable of
the document in the query, prefix it with "$$".
The query can't be empty or null.
"fieldName": {
"type": "valueAggregator", // required
"database": <string>, // required, db to use to perform aggregation
"collection": <string>, // required, collection to use to perform aggregation
"key": <string>, // required, the field for which to return distinct values.
"query": <object> // required, query that specifies the documents from which
// to retrieve the distinct values
}Example:
Assuming that the collection first contains:
{"_id": 1, "field1": 1, "field2": "a" }
{"_id": 2, "field1": 1, "field2": "b" }
{"_id": 3, "field1": 2, "field2": "c" }and that the generator for collection second is:
{
"database": "test",
"collection": "second",
"count": 2,
"content": {
"_id": {
"type": "autoincrement",
"autoType": "int"
"start": 0
},
"count": {
"type": "valueAggregator",
"database": "test",
"collection": "first",
"key": "field2",
"values": {
"field1": "$$_id"
}
}
}
}The collection second will contain:
{"_id": 1, "values": ["a", "b"]}
{"_id": 2, "values": ["c"]}Get the lowest and highest value for a specific field of documents in
<database>.<collection> matching a specific query. To use a variable of
the document in the query, prefix it with "$$"
The query can't be empty or null
"fieldName": {
"type": "boundAggregator", // required
"database": <string>, // required, db to use to perform aggregation
"collection": <string>, // required, collection to use to perform aggregation
"key": <string>, // required, the field for which to return distinct values.
"query": <object> // required, query that specifies the documents from which
// to retrieve lower/higher value
}Example:
Assuming that the collection first contains:
{"_id": 1, "field1": 1, "field2": "0" }
{"_id": 2, "field1": 1, "field2": "10" }
{"_id": 3, "field1": 2, "field2": "20" }
{"_id": 4, "field1": 2, "field2": "30" }
{"_id": 5, "field1": 2, "field2": "15" }
{"_id": 6, "field1": 2, "field2": "200" }and that the generator for collection second is:
{
"database": "test",
"collection": "second",
"count": 2,
"content": {
"_id": {
"type": "autoincrement",
"autoType": "int"
"start": 0
},
"count": {
"type": "valueAggregator",
"database": "test",
"collection": "first",
"key": "field2",
"values": {
"field1": "$$_id"
}
}
}
}The collection second will contain:
{"_id": 1, "values": {"m": 0, "M": 10}}
{"_id": 2, "values": {"m": 15, "M": 200}}where m is the min value, and M the max value.
Generate 'real' data using gofakeit library.
"fieldName": {
"type": "faker", // required
"method": <string>, // required, faker method to use, for example: Name
"nullPercentage": <int>, // optional
"maxDistinctValue": <int> // optional
}List of faker methods:
"FirstName"
"LastName"
"Name"
"NamePrefix"
"NameSuffix"
"Gender"
"Phone"
"PhoneFormatted"
"Username"
"Email"
"BS"
"BuzzWord"
"Company"
"CompanySuffix"
"JobDescriptor"
"JobLevel"
"JobTitle"
"Language"
"LanguageAbbreviation"
"CreditCardCvv"
"CreditCardExp"
"CreditCardType"
"CurrencyLong"
"CurrencyShort"
"DomainName"
"DomainSuffix"
"HTTPMethod"
"IPv4Address"
"IPv6Address"
"MacAddress"
"FileMimeType"
"SSN"
"URL"
"UserAgent"
"SafariUserAgent"
"OperaUserAgent"
"ChromeUserAgent"
"FileExtension"
"FirefoxUserAgent"
"TimeZone"
"TimeZoneAbv"
"TimeZoneFull"
"Month"
"WeekDay"
"Word"
"Question"
"Quote"
"Letter"
"ProgrammingLanguage"
"ProgrammingLanguageBest"
"HexColor"
"Color"
"HipsterWord"
"SafeColor"
"Street"
"StreetName"
"StreetNumber"
"StreetPrefix"
"StreetSuffix"
"City"
"State"
"StateAbr"
"Zip"
"Country"
"CountryAbr"
"Emoji"
"EmojiAlias"
"EmojiCategory"
"EmojiDescription"
"EmojiTag"
"HackerAbbreviation"
"HackerAdjective"
"HackeringVerb"
"HackerNoun"
"HackerPhrase"
"HackerVerb"
"CarMaker"
"CarModel"
"CarTransmissionType"
"CarFuelType"
"CarType"
"Animal"
"AnimalType"
"Cat"
"Dog"
"FarmAnimal"
"PetName"
"BeerAlcohol"
"BeerBlg"
"BeerHop"
"BeerIbu"
"BeerMalt"
"BeerName"
"BeerStyle"
"BeerYeast"
If you're building large datasets (10M+ documents) you should avoid faker generators and use main or custom generators instead, as faker may be way slower.