Generate test data

The built-in load generator can generate mock data, which can be used in demos and tests. It provides an easy way to simulate the data stream without connecting to an actual external data source.

Use the SQL statement below to connect RisingWave to the built-in load generator.

Syntax

Diagram

Code

CREATE TABLE source_name ( column_name data_type, ... ) 
WITH (
   connector = ' datagen ',
   fields.column_name.column_parameter = ' value ', ...  -- Configure the generator for each column. See detailed information below.
   datagen.rows.per.second = ' rows_integer '  -- Specify how many rows of records to generate every second. For example, '20'.
) FORMAT PLAIN ENCODE JSON;

WITH options - column_parameter

The following table shows the data types that can be generated for each load generator type.

Generator \ Data	Number	Timestamp	Timestamptz	Varchar	Struct	Array
Sequence	✓	✕	✕	✕	✓	✓
Random	✓	✓	✓	✓	✓	✓

Select the type of data to be generated.

Number

Sequence

The sequence load generator can generate numbers, incremented by 1, from the starting number to the ending number. For example, 1, 2, 3, ... and 1.56, 2.56, 3.56, ...

Specify the following fields for every column.

column_parameter	Description	Value	Required?
kind	Generator type	Set to sequence.	False Default: random
start	Starting number Must be smaller than the ending number.	Any number of the column data type Example: 50	False Default: 0
end	Ending number Must be larger than the starting number.	Any number of the column data type Example: 100	False Default: 32767

Random

The random number generator produces random numbers within a certain range.

Specify the following fields for every column in the source you are creating.

column_parameter	Description	Value	Required?
kind	Generator type	Set to random.	False Default: random
min	The minimum number can be generated. Must be smaller than the maximum number.	Any number of the column data type Example: 50	False Default: 0
max	The maximum number can be generated. Must be larger than the minimum number.	Any number of the column data type Example: 100	False Default: 32767
seed	A seed number that initializes the random load generator. The sequence of the generated numbers is determined by the seed value. If given the same seed number, the generator will produce the same sequence of numbers.	A positive integer Example: 3	False If not specified, a fixed sequence of numbers will be generated.

Timestamp and Timestamptz

The random timestamp and timestamptz generator produces random timestamps and timestamps with time zone, respectively, earlier than the current date and time or the source creation time.

Specify the following fields for every column in the source you are creating.

column_parameter	Description	Value	Required?
kind	Generator type	Set to random.	False Default: random
max_past	Specify the maximum deviation from the baseline timestamp or timestamptz to determine the earliest possible timestamp or timestamptz that can be generated.	An interval Example: 2h 37min	False Default: 1 day
max_past_mode	Specify the baseline timestamp or timestamptz. The range for generated timestamps or timestamptzs is [base time - max_past , base time]	absolute — The base time is set to the execution time of the generator. The base time is fixed for each generation. relative — The base time is the system time obtained each time a new record is generated.	False Default: absolute
basetime	If set, the generator will ignore max_past_mode and use the specified time as the base time.	A date and time string Example: 2023-04-01T16:39:57-08:00	False Default: generator execution time
seed	A seed number that initializes the random load generator. The sequence of the generated timestamps or timestamptzs is determined by the seed value. If given the same seed number, the generator will produce the same sequence of timestamps or timestamptzs.	A positive integer Example: 3	False If not specified, a fixed sequence of timestamps or timestamptzs will be generated (if the system time is constant).

Varchar

The random varchar generator produces random combination of uppercase and lowercase letters and numbers.

Specify the following fields for every column in the source you are creating.

column_parameter	Description	Value	Required?
kind	Generator type	Set to random.	False Default: random
length	The length of the varchar to be generated.	A positive integer Example: 16	False Default: 10
seed	A seed number that initializes the random load generator. The sequence of the generated characters is determined by the seed value. If given the same seed number, the generator will produce the same sequence of characters.	A positive integer Example: 3	False If not specified, a fixed sequence of characters will be generated.

Struct

The generator supports generating data in a struct. A column of struct type can contain multiple nested columns of different types.

The following statement creates a load generator source which contains one column, v1. v1 consists of two nested columns v2 and v3.

CREATE TABLE s1 (v1 struct<v2 int, v3 double>)
WITH (
     connector = 'datagen',
     fields.v1.v2.kind = 'sequence',
     fields.v1.v2.start = '-10',
     fields.v1.v3.kind = 'sequence',
     fields.v1.v3.start = '1.5',
     datagen.rows.per.second = '5'
 ) FORMAT PLAIN ENCODE JSON;

info

• You need to configure each nested column in the struct. Select other tabs according to the data type of the nested columns for information on column parameters.
• When you configure a nested column, use column.nested_column to specify it. For example, v1.v2 and v1.v3 in the WITH clause above.

Array

The generator supports generating data in an array. An array is a list of elements of the same type. Append [] to the data type of the column when creating the source.

The following statement creates a load generator source which contains one column, c1. c1 is an array of varchar.

CREATE TABLE s1 (c1 varchar [])
WITH (
     connector = 'datagen',
     fields.c1.length = '3',
     fields.c1._.kind = 'random',
     fields.c1._.length = '1',
     fields.c1._.seed = '3',
     datagen.rows.per.second = '10'
 ) FORMAT PLAIN ENCODE JSON;

info

• You need to specify the number of elements in the array in the WITH clause. fields.c1.length = '3' in the example above means that c1 is an array of three elements.
• When you configure the elements in an array, use column._ to specify them. For example, c1._ in the WITH clause above.
  Select other tabs according to the data type of the array for information on column parameters.

If you want to generate an array of struct, your statement should look like the following.

CREATE TABLE s1 (v1 struct<v2 int> []) 
WITH (
    connector = 'datagen',
    fields.v1.length = '2',
    fields.v1._.v2.kind = 'random',
    fields.v1._.v2.min = '1',
    fields.v1._.v2.max = '2',
    fields.v1._.v2.seed = '1',
    datagen.rows.per.second = '10'
) FORMAT PLAIN ENCODE JSON;

Example

Here is an example of connecting RisingWave to the built-in load generator.

The following statement creates a source s1 with five columns:

• i1 — An array of three integers starting from 1 and incrementing by 1
• v1 — Structs that contain random integers v2 ranging from -10 to 10 and random floating-point numbers v3 ranging from 15 to 55
• t1 — Random timestamps from as early as 2 hours as 37 minutes prior to the generator execution time
• z1 - Random timestamps with timezones from as early as 2 hours as 37 minutes prior to the generator execution time
• c1 — Random strings with each consists of 16 characters

CREATE TABLE s1 (i1 int [], v1 struct<v2 int, v3 double>, t1 timestamp, z1 timestamptz, c1 varchar) 
WITH (
     connector = 'datagen',
  
     fields.i1.length = '3',
     fields.i1._.kind = 'sequence',
     fields.i1._.start = '1',
  
     fields.v1.v2.kind = 'random',
     fields.v1.v2.min = '-10',
     fields.v1.v2.max = '10',
     fields.v1.v2.seed = '1',
  
     fields.v1.v3.kind = 'random',
     fields.v1.v3.min = '15',
     fields.v1.v3.max = '55',
     fields.v1.v3.seed = '1',
  
     fields.t1.kind = 'random',
     fields.t1.max_past = '2h 37min',
     fields.t1.max_past_mode = 'relative',
     fields.t1.seed = '3',

     fields.z1.kind = 'random',
     fields.z1.max_past = '2h 37min',
     fields.z1.max_past_mode = 'relative',
     fields.z1.seed = '3',

     fields.c1.kind = 'random',
     fields.c1.length = '16',
     fields.c1.seed = '3',
  
     datagen.rows.per.second = '10'
 ) FORMAT PLAIN ENCODE JSON;

Let's query s1 after a few seconds.

SELECT * FROM s1 ORDER BY i1 LIMIT 20;

     i1     |            v1            |             t1             |                z1                |        c1        
------------+--------------------------+----------------------------+----------------------------------+------------------
 {1,2,3}    | (7,53.96978949033611)    | 2023-11-28 13:35:04.967040 | 2023-11-28 21:35:04.967330+00:00 | pGWJLsbmPJZZWpBe
 {4,5,6}    | (5,44.24453663454818)    | 2023-11-28 14:13:15.264457 | 2023-11-28 22:13:15.264481+00:00 | FT7BRdifYMrRgIyI
 {7,8,9}    | (3,18.808367835800485)   | 2023-11-28 15:12:41.918935 | 2023-11-28 23:12:41.919590+00:00 | 0zsMbNLxQh9yYtHh
 {10,11,12} | (-4,26.893033246334525)  | 2023-11-28 14:55:43.193883 | 2023-11-28 22:55:43.193917+00:00 | zujxzBql3QHxENyy
 {13,14,15} | (-3,28.68505963291612)   | 2023-11-28 13:35:05.967253 | 2023-11-28 21:35:05.967520+00:00 | aBJTDJpinRv8mLvQ
 {16,17,18} | (4,36.32012760913261)    | 2023-11-28 14:13:16.264624 | 2023-11-28 22:13:16.264646+00:00 | HVur4zU3hQFgVh74
 {19,20,21} | (-10,16.212694434604053) | 2023-11-28 15:12:42.919339 | 2023-11-28 23:12:42.919465+00:00 | LVLAhd1pQvhXVL8p
 {22,23,24} | (-8,28.388082274426225)  | 2023-11-28 14:55:44.193726 | 2023-11-28 22:55:44.193787+00:00 | siFqrkdlCnNZqAUT
 {25,26,27} | (2,40.86763449564268)    | 2023-11-28 15:19:51.600898 | 2023-11-28 23:19:51.600977+00:00 | ORjwy3oMNbl1Yi6X
 {28,29,30} | (3,29.179236922708526)   | 2023-11-28 15:27:49.755084 | 2023-11-28 23:27:49.755105+00:00 | YIVLnWxHyfsiPHQo
 {31,32,33} | (6,26.03842827701958)    | 2023-11-28 16:07:02.012019 | 2023-11-29 00:07:02.012133+00:00 | lpzCxwpoJp9njIAa
 {34,35,36} | (-2,20.2351457847852)    | 2023-11-28 14:23:37.167393 | 2023-11-28 22:23:37.167453+00:00 | oW8xmndvmXMRp1Rc
 {37,38,39} | (2,36.51138960926262)    | 2023-11-28 15:19:52.600699 | 2023-11-28 23:19:52.600741+00:00 | 0m1Qxn96Xeq42H3Z
 {40,41,42} | (0,34.2997487580596)     | 2023-11-28 15:27:50.754878 | 2023-11-28 23:27:50.754899+00:00 | 1jT3TnEEj56YNa7w
 {43,44,45} | (7,39.13577932700749)    | 2023-11-28 16:07:03.011837 | 2023-11-29 00:07:03.011905+00:00 | linRToOjph0WlJrd
 {46,47,48} | (7,37.43674199879566)    | 2023-11-28 14:23:38.167161 | 2023-11-28 22:23:38.167271+00:00 | beql98l3IIkjomTl
 {49,50,51} | (1,41.62099792202798)    | 2023-11-28 15:24:46.803776 | 2023-11-28 23:24:46.803844+00:00 | xHbIYlJismRlIKFw
 {52,53,54} | (9,49.240259695092895)   | 2023-11-28 15:39:22.752067 | 2023-11-28 23:39:22.752115+00:00 | TDYNZsSNYMpOpZgC
 {55,56,57} | (6,54.64984398127376)    | 2023-11-28 13:32:15.049957 | 2023-11-28 21:32:15.050089+00:00 | jqPQM3oyA2lOXLcn
 {58,59,60} | (-4,54.197350082045176)  | 2023-11-28 14:07:53.278392 | 2023-11-28 22:07:53.278457+00:00 | 72cIOHPb7DE8FTme
(20 rows)
...