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

• batch
• stream
• exactly-once
• column projection
• parallelism
• support user-defined split

Source Options

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"
      }
    }
  }
}

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