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

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Syntax

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;

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WITH options - columnparameter[]

The following table shows the data types that can be generated for each load generator type.
Generator \ DataNumberTimestampTimestamptzVarcharStructArray
Sequence
Random
Select the type of data to be generated.
  • Number
  • Timestamp and Timestamptz
  • Varchar
  • Struct
  • Array
  • Sequence
  • Random
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_parameterDescriptionValueRequired?
kindGenerator type.Set to sequence.False. Default: random
startStarting number must be smaller than the ending number.Any number of the column data type. Example: 50False. Default: 0
endEnding number must be larger than the starting number.Any number of the column data type. Example: 100False. Default: 32767

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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)
...