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Version: 2.3.8

FakeSource

FakeSource connector

Support Those Engines

Spark
Flink
SeaTunnel Zeta

Description

The FakeSource is a virtual data source, which randomly generates the number of rows according to the data structure of the user-defined schema, just for some test cases such as type conversion or connector new feature testing

Key Features

Source Options

NameTypeRequiredDefaultDescription
tables_configslistno-Define Multiple FakeSource, each item can contains the whole fake source config description below
schemaconfigyes-Define Schema information
rowsconfigno-The row list of fake data output per degree of parallelism see title Options rows Case.
row.numintno5The total number of data generated per degree of parallelism
split.numintno1the number of splits generated by the enumerator for each degree of parallelism
split.read-intervallongno1The interval(mills) between two split reads in a reader
map.sizeintno5The size of map type that connector generated
array.sizeintno5The size of array type that connector generated
bytes.lengthintno5The length of bytes type that connector generated
string.lengthintno5The length of string type that connector generated
string.fake.modestringnorangeThe fake mode of generating string data, support range and template, default range,if use configured it to template, user should also configured string.template option
string.templatelistno-The template list of string type that connector generated, if user configured it, connector will randomly select an item from the template list
tinyint.fake.modestringnorangeThe fake mode of generating tinyint data, support range and template, default range,if use configured it to template, user should also configured tinyint.template option
tinyint.mintinyintno0The min value of tinyint data that connector generated
tinyint.maxtinyintno127The max value of tinyint data that connector generated
tinyint.templatelistno-The template list of tinyint type that connector generated, if user configured it, connector will randomly select an item from the template list
smallint.fake.modestringnorangeThe fake mode of generating smallint data, support range and template, default range,if use configured it to template, user should also configured smallint.template option
smallint.minsmallintno0The min value of smallint data that connector generated
smallint.maxsmallintno32767The max value of smallint data that connector generated
smallint.templatelistno-The template list of smallint type that connector generated, if user configured it, connector will randomly select an item from the template list
int.fake.templatestringnorangeThe fake mode of generating int data, support range and template, default range,if use configured it to template, user should also configured int.template option
int.minintno0The min value of int data that connector generated
int.maxintno0x7fffffffThe max value of int data that connector generated
int.templatelistno-The template list of int type that connector generated, if user configured it, connector will randomly select an item from the template list
bigint.fake.modestringnorangeThe fake mode of generating bigint data, support range and template, default range,if use configured it to template, user should also configured bigint.template option
bigint.minbigintno0The min value of bigint data that connector generated
bigint.maxbigintno0x7fffffffffffffffThe max value of bigint data that connector generated
bigint.templatelistno-The template list of bigint type that connector generated, if user configured it, connector will randomly select an item from the template list
float.fake.modestringnorangeThe fake mode of generating float data, support range and template, default range,if use configured it to template, user should also configured float.template option
float.minfloatno0The min value of float data that connector generated
float.maxfloatno0x1.fffffeP+127The max value of float data that connector generated
float.templatelistno-The template list of float type that connector generated, if user configured it, connector will randomly select an item from the template list
double.fake.modestringnorangeThe fake mode of generating float data, support range and template, default range,if use configured it to template, user should also configured double.template option
double.mindoubleno0The min value of double data that connector generated
double.maxdoubleno0x1.fffffffffffffP+1023The max value of double data that connector generated
double.templatelistno-The template list of double type that connector generated, if user configured it, connector will randomly select an item from the template list
vector.dimensionintno4Dimension of the generated vector, excluding binary vectors
binary.vector.dimensionintno8Dimension of the generated binary vector
vector.float.minfloatno0The min value of float data in vector that connector generated
vector.float.maxfloatno0x1.fffffeP+127The max value of float data in vector that connector generated
common-optionsno-Source plugin common parameters, please refer to Source Common Options for details

Task Example

Simple:

This example Randomly generates data of a specified type. If you want to learn how to declare field types, click here.

schema = {
fields {
c_map = "map<string, array<int>>"
c_map_nest = "map<string, {c_int = int, c_string = string}>"
c_array = "array<int>"
c_string = string
c_boolean = boolean
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
c_decimal = "decimal(30, 8)"
c_null = "null"
c_bytes = bytes
c_date = date
c_timestamp = timestamp
c_row = {
c_map = "map<string, map<string, string>>"
c_array = "array<int>"
c_string = string
c_boolean = boolean
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
c_decimal = "decimal(30, 8)"
c_null = "null"
c_bytes = bytes
c_date = date
c_timestamp = timestamp
}
}
}

Random Generation

16 data matching the type are randomly generated

source {
# This is a example input plugin **only for test and demonstrate the feature input plugin**
FakeSource {
row.num = 16
schema = {
fields {
c_map = "map<string, string>"
c_array = "array<int>"
c_string = string
c_boolean = boolean
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
c_decimal = "decimal(30, 8)"
c_null = "null"
c_bytes = bytes
c_date = date
c_timestamp = timestamp
}
}
result_table_name = "fake"
}
}

Customize the data content Simple:

This is a self-defining data source information, defining whether each piece of data is an add or delete modification operation, and defining what each field stores

source {
FakeSource {
schema = {
fields {
c_map = "map<string, string>"
c_array = "array<int>"
c_string = string
c_boolean = boolean
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
c_decimal = "decimal(30, 8)"
c_null = "null"
c_bytes = bytes
c_date = date
c_timestamp = timestamp
}
}
rows = [
{
kind = INSERT
fields = [{"a": "b"}, [101], "c_string", true, 117, 15987, 56387395, 7084913402530365000, 1.23, 1.23, "2924137191386439303744.39292216", null, "bWlJWmo=", "2023-04-22", "2023-04-22T23:20:58"]
}
{
kind = UPDATE_BEFORE
fields = [{"a": "c"}, [102], "c_string", true, 117, 15987, 56387395, 7084913402530365000, 1.23, 1.23, "2924137191386439303744.39292216", null, "bWlJWmo=", "2023-04-22", "2023-04-22T23:20:58"]
}
{
kind = UPDATE_AFTER
fields = [{"a": "e"}, [103], "c_string", true, 117, 15987, 56387395, 7084913402530365000, 1.23, 1.23, "2924137191386439303744.39292216", null, "bWlJWmo=", "2023-04-22", "2023-04-22T23:20:58"]
}
{
kind = DELETE
fields = [{"a": "f"}, [104], "c_string", true, 117, 15987, 56387395, 7084913402530365000, 1.23, 1.23, "2924137191386439303744.39292216", null, "bWlJWmo=", "2023-04-22", "2023-04-22T23:20:58"]
}
]
}
}

Due to the constraints of the HOCON specification, users cannot directly create byte sequence objects. FakeSource uses strings to assign bytes type values. In the example above, the bytes type field is assigned "bWlJWmo=", which is encoded from "miIZj" with base64. Hence, when assigning values to bytes type fields, please use strings encoded with base64.

Specified Data number Simple:

This case specifies the number of data generated and the length of the generated value

FakeSource {
row.num = 10
map.size = 10
array.size = 10
bytes.length = 10
string.length = 10
schema = {
fields {
c_map = "map<string, array<int>>"
c_array = "array<int>"
c_string = string
c_boolean = boolean
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
c_decimal = "decimal(30, 8)"
c_null = "null"
c_bytes = bytes
c_date = date
c_timestamp = timestamp
c_row = {
c_map = "map<string, map<string, string>>"
c_array = "array<int>"
c_string = string
c_boolean = boolean
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
c_decimal = "decimal(30, 8)"
c_null = "null"
c_bytes = bytes
c_date = date
c_timestamp = timestamp
}
}
}
}

Template data Simple:

Randomly generated according to the specified template

Using template

FakeSource {
row.num = 5
string.fake.mode = "template"
string.template = ["tyrantlucifer", "hailin", "kris", "fanjia", "zongwen", "gaojun"]
tinyint.fake.mode = "template"
tinyint.template = [1, 2, 3, 4, 5, 6, 7, 8, 9]
smalling.fake.mode = "template"
smallint.template = [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
int.fake.mode = "template"
int.template = [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]
bigint.fake.mode = "template"
bigint.template = [30, 31, 32, 33, 34, 35, 36, 37, 38, 39]
float.fake.mode = "template"
float.template = [40.0, 41.0, 42.0, 43.0]
double.fake.mode = "template"
double.template = [44.0, 45.0, 46.0, 47.0]
schema {
fields {
c_string = string
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
}
}
}

Range data Simple:

The specified data generation range is randomly generated

FakeSource {
row.num = 5
string.template = ["tyrantlucifer", "hailin", "kris", "fanjia", "zongwen", "gaojun"]
tinyint.min = 1
tinyint.max = 9
smallint.min = 10
smallint.max = 19
int.min = 20
int.max = 29
bigint.min = 30
bigint.max = 39
float.min = 40.0
float.max = 43.0
double.min = 44.0
double.max = 47.0
schema {
fields {
c_string = string
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
}
}
}

Generate Multiple tables

This is a case of generating a multi-data source test.table1 and test.table2

FakeSource {
tables_configs = [
{
row.num = 16
schema {
table = "test.table1"
fields {
c_string = string
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
}
}
},
{
row.num = 17
schema {
table = "test.table2"
fields {
c_string = string
c_tinyint = tinyint
c_smallint = smallint
c_int = int
c_bigint = bigint
c_float = float
c_double = double
}
}
}
]
}

Options rows Case

rows = [
{
kind = INSERT
fields = [1, "A", 100]
},
{
kind = UPDATE_BEFORE
fields = [1, "A", 100]
},
{
kind = UPDATE_AFTER
fields = [1, "A_1", 100]
},
{
kind = DELETE
fields = [1, "A_1", 100]
}
]

Options table-names Case


source {
# This is a example source plugin **only for test and demonstrate the feature source plugin**
FakeSource {
table-names = ["test.table1", "test.table2", "test.table3"]
parallelism = 1
schema = {
fields {
name = "string"
age = "int"
}
}
}
}

Options defaultValue Case

Custom data can be generated by row and columns. For the time type, obtain the current time by CURRENT_TIMESTAMPCURRENT_TIMECURRENT_DATE

    schema = {
fields {
pk_id = bigint
name = string
score = int
time1 = timestamp
time2 = time
time3 = date
}
}
# use rows
rows = [
{
kind = INSERT
fields = [1, "A", 100, CURRENT_TIMESTAMP, CURRENT_TIME, CURRENT_DATE]
}
]
      schema = {
# use columns
columns = [
{
name = book_publication_time
type = timestamp
defaultValue = "2024-09-12 15:45:30"
comment = "book publication time"
},
{
name = book_publication_time2
type = timestamp
defaultValue = CURRENT_TIMESTAMP
comment = "book publication time2"
},
{
name = book_publication_time3
type = time
defaultValue = "15:45:30"
comment = "book publication time3"
},
{
name = book_publication_time4
type = time
defaultValue = CURRENT_TIME
comment = "book publication time4"
},
{
name = book_publication_time5
type = date
defaultValue = "2024-09-12"
comment = "book publication time5"
},
{
name = book_publication_time6
type = date
defaultValue = CURRENT_DATE
comment = "book publication time6"
}
]
}

Use Vector Example


source {
FakeSource {
row.num = 10
# Low priority
vector.dimension= 4
binary.vector.dimension = 8
# Low priority
schema = {
table = "simple_example"
columns = [
{
name = book_id
type = bigint
nullable = false
defaultValue = 0
comment = "primary key id"
},
{
name = book_intro_1
type = binary_vector
columnScale =8
comment = "vector"
},
{
name = book_intro_2
type = float16_vector
columnScale =4
comment = "vector"
},
{
name = book_intro_3
type = bfloat16_vector
columnScale =4
comment = "vector"
},
{
name = book_intro_4
type = sparse_float_vector
columnScale =4
comment = "vector"
}
]
}
}
}


Changelog

2.2.0-beta 2022-09-26

  • Add FakeSource Source Connector

2.3.0-beta 2022-10-20

  • [Improve] Supports direct definition of data values(row) (2839)
  • [Improve] Improve fake source connector: (2944)
    • Support user-defined map size
    • Support user-defined array size
    • Support user-defined string length
    • Support user-defined bytes length
  • [Improve] Support multiple splits for fake source connector (2974)
  • [Improve] Supports setting the number of splits per parallelism and the reading interval between two splits (3098)

next version

  • [Feature] Support config fake data rows 3865
  • [Feature] Support config template or range for fake data 3932