> ## Documentation Index
> Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-8a08bda2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Generating random test data in ClickHouse
> Learn about Generating Random Test Data in ClickHouse
Generating random data is useful when testing new use cases or benchmarking your implementation.
ClickHouse has a [wide range of functions for generating random data](/reference/functions/regular-functions/random-functions) that, in many cases, avoid the need for an external data generator.
This guide provides several examples of how to generate random datasets in ClickHouse with different randomness requirements.
**Use-case**: Generate a quick dataset of user events with random timestamps and event types.
```sql theme={null}
CREATE TABLE user_events (
event_id UUID,
user_id UInt32,
event_type LowCardinality(String),
event_time DateTime
) ENGINE = MergeTree
ORDER BY event_time;
INSERT INTO user_events
SELECT
generateUUIDv4() AS event_id,
rand() % 10000 AS user_id,
arrayElement(['click','view','purchase'], toUInt32(rand()) % 3 + 1) AS event_type,
now() - INTERVAL rand() % 3600*24 SECOND AS event_time
FROM numbers(1000000);
```
* `rand() % 10000`: uniform distribution of 10k users
* `arrayElement(...)`: randomly selects one of three event types
* Timestamps spread over the previous 24 hours
***
Exponential distribution
**Use-case**: Simulate purchase amounts where most values are low, but a few are high.
```sql theme={null}
CREATE TABLE purchases (
dt DateTime,
customer_id UInt32,
total_spent Float32
) ENGINE = MergeTree
ORDER BY dt;
INSERT INTO purchases
SELECT
now() - INTERVAL randUniform(1,1_000_000) SECOND AS dt,
number AS customer_id,
15 + round(randExponential(1/10), 2) AS total_spent
FROM numbers(500000);
```
* Uniform timestamps over recent period
* `randExponential(1/10)` — most totals near 0, offset by 15 as a minimum (\[ClickHouse]\[1], \[ClickHouse]\[2], \[Atlantic.Net]\[3], \[GitHub]\[4])
***
Time-distributed events (Poisson)
**Use-case**: Simulate event arrivals that cluster around a specific period (e.g., peak hour).
```sql theme={null}
CREATE TABLE events (
dt DateTime,
event_type String
) ENGINE = MergeTree
ORDER BY dt;
INSERT INTO events
SELECT
toDateTime('2022-12-12 12:00:00')
- ((12 + randPoisson(12)) * 3600) AS dt,
'click' AS event_type
FROM numbers(200000);
```
* Events peak around noon, with Poisson-distributed deviation
***
Time-varying normal distribution
**Use-case**: Emulate system metrics (e.g., CPU usage) that vary over time.
```sql theme={null}
CREATE TABLE IF NOT EXISTS cpu_metrics (
host String,
ts DateTime,
usage Float32
) ENGINE = MergeTree
ORDER BY (host, ts);
INSERT INTO cpu_metrics
SELECT
arrayJoin(['host1','host2','host3']) AS host,
now() - INTERVAL number SECOND AS ts,
greatest(0.0, least(100.0,
(50 + 30 * sin(toUInt32(number) % 86400 / 86400.0 * 2 * pi()))
+ randNormal(0, 10)
)) AS usage
FROM numbers(10000);
```
* `usage` follows a diurnal sine wave + randomness
* Values bounded to \[0,100]
***
Categorical and nested data
**Use-case**: Create user profiles with multi-valued interests.
```sql theme={null}
CREATE TABLE user_profiles (
user_id UInt32,
interests Array(String),
scores Array(UInt8)
) ENGINE = MergeTree
ORDER BY user_id;
INSERT INTO user_profiles
SELECT
number AS user_id,
arrayShuffle(['sports','music','tech'])[1 + rand() % 3 : 1 + rand() % 3] AS interests,
[rand() % 100, rand() % 100, rand() % 100] AS scores
FROM numbers(20000);
```
* Random array length between 1–3
* Three per-user scores for each interest
Read the [Generating Random Data in ClickHouse](https://clickhouse.com/blog/generating-random-test-distribution-data-for-clickhouse) blog for even more examples.
Generating random tables
The [`generateRandomStructure`](/reference/functions/regular-functions/other-functions#generateRandomStructure) function is particularly useful when combined with the [`generateRandom`](/reference/functions/table-functions/generate) table engine for testing, benchmarking, or creating mock data with arbitrary schemas.
Let's start by just seeing what a random structure looks like using the `generateRandomStructure` function:
```sql theme={null}
SELECT generateRandomStructure(5);
```
You might see something like:
```response theme={null}
c1 UInt32, c2 Array(String), c3 DateTime, c4 Nullable(Float64), c5 Map(String, Int16)
```
You can also use a seed to get the same structure every time:
```sql theme={null}
SELECT generateRandomStructure(3, 42);
```
```response theme={null}
c1 String, c2 Array(Nullable(Int32)), c3 Tuple(UInt8, Date)
```
Now let's create an actual table and fill it with random data:
```sql theme={null}
CREATE TABLE my_test_table
ENGINE = MergeTree
ORDER BY tuple()
AS SELECT *
FROM generateRandom(
'col1 UInt32, col2 String, col3 Float64, col4 DateTime',
1, -- seed for data generation
10 -- number of different random values
)
LIMIT 100; -- 100 rows
-- Step 2: Query your new table
SELECT * FROM my_test_table LIMIT 5;
```
```response theme={null}
┌───────col1─┬─col2──────┬─────────────────────col3─┬────────────────col4─┐
│ 4107652264 │ &b!M-e;7 │ 1.0013455832230728e-158 │ 2059-08-14 19:03:26 │
│ 652895061 │ Dj7peUH{T │ -1.032074207667996e112 │ 2079-10-06 04:18:16 │
│ 2319105779 │ =D[ │ -2.066555415720528e88 │ 2015-04-26 11:44:13 │
│ 1835960063 │ _@}a │ -1.4998020545039013e110 │ 2063-03-03 20:36:55 │
│ 730412674 │ _}! │ -1.3578492992094465e-275 │ 2098-08-23 18:23:37 │
└────────────┴───────────┴──────────────────────────┴─────────────────────┘
```
Let's combine both functions for a completely random table.
First, see what structure we'll get:
```sql theme={null}
SELECT generateRandomStructure(7, 123) AS structure FORMAT vertical;
```
```response theme={null}
┌─structure──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ c1 Decimal64(7), c2 Enum16('c2V0' = -21744, 'c2V1' = 5380), c3 Int8, c4 UUID, c5 UUID, c6 FixedString(190), c7 Map(Enum16('c7V0' = -19581, 'c7V1' = -10024, 'c7V2' = 27615, 'c7V3' = -10177, 'c7V4' = -19644, 'c7V5' = 3554, 'c7V6' = 29073, 'c7V7' = 28800, 'c7V8' = -11512), Float64) │
└────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
```
Now create the table with that structure and use the `DESCRIBE` statement to see what we created:
```sql theme={null}
CREATE TABLE fully_random_table
ENGINE = MergeTree
ORDER BY tuple()
AS SELECT *
FROM generateRandom(generateRandomStructure(7, 123), 1, 10)
LIMIT 1000;
DESCRIBE TABLE fully_random_table;
```
```response theme={null}
┌─name─┬─type─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┬─default_type─┬─default_expression─┬─comment─┬─codec_expression─┬─ttl_expression─┐
1. │ c1 │ Decimal(18, 7) │ │ │ │ │ │
2. │ c2 │ Enum16('c2V0' = -21744, 'c2V1' = 5380) │ │ │ │ │ │
3. │ c3 │ Int8 │ │ │ │ │ │
4. │ c4 │ UUID │ │ │ │ │ │
5. │ c5 │ UUID │ │ │ │ │ │
6. │ c6 │ FixedString(190) │ │ │ │ │ │
7. │ c7 │ Map(Enum16('c7V4' = -19644, 'c7V0' = -19581, 'c7V8' = -11512, 'c7V3' = -10177, 'c7V1' = -10024, 'c7V5' = 3554, 'c7V2' = 27615, 'c7V7' = 28800, 'c7V6' = 29073), Float64) │ │ │ │ │ │
└──────┴──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┴──────────────┴────────────────────┴─────────┴──────────────────┴────────────────┘
```
Inspect the first row for a sample of the generated data:
```sql theme={null}
SELECT * FROM fully_random_table LIMIT 1 FORMAT vertical;
```
```response theme={null}
Row 1:
──────
c1: 80416293882.257732 -- 80.42 billion
c2: c2V1
c3: -84
c4: 1a9429b3-fd8b-1d72-502f-c051aeb7018e
c5: 7407421a-031f-eb3b-8571-44ff279ddd36
c6: g̅b�&��rҵ���5C�\�|��H�>���l'V3��R�[��=3�G�LwVMR*s緾/2�J.���6#��(�h>�lە��L^�M�:�R�9%d�ž�zv��W����Y�S��_no��BP+��u��.0��UZ!x�@7:�nj%3�Λd�S�k>���w��|�&��~
c7: {'c7V8':-1.160941256852442}
```