StarRocks Sample Data

This page describes a ready-to-run SQL script that creates a complete set of sample StarRocks tables, fills them with test data, and registers the myOLAPcube OLAP cube from the Unified example.

Use this script to explore XLTable features without setting up your own data.

The script file: starrocks_sample.sql

---

What the script creates

Table	Rows	Description
db.Times	1096	Calendar: every day from 2023-01-01 to 2025-12-31
db.Regions	4	Sales regions: North, South, East, West
db.Managers	5	Sales managers linked to regions (many-to-many)
db.Stores	8	Retail stores, each assigned to a region
db.Models	8	Product models (Alpha … Theta)
db.Sales	3 000	Sales transactions: store, model, date, quantity, amount
db.Stock	500	Inventory snapshots: store, model, quantity on hand
db.olap_definition	1	OLAP cube definition read by XLTable

The cube myOLAPcube exposes:

• Measures: Sales Quantity, Sales Amount, Sales last year (Qty & Amount), Average Stock Quantity, calculated Turnover ratio

• Dimensions: Store ID, Store, Region, Manager, Model, Date hierarchy (Year → Quarter → Month → Day)

---

Data model

             ┌─────────────┐
             │  db.Times   │
             │  (calendar) │
             └──────┬──────┘
                    │ day_str
       ┌────────────┴────────────┐
       │                         │
┌──────┴──────┐           ┌──────┴──────┐
│  db.Sales   │           │  db.Stock   │
└──────┬──────┘           └──────┬──────┘
       │ store / model           │ store / model
┌──────┴──────┐           ┌──────┴──────┐
│  db.Stores  ├───────────┤  db.Models  │
└──────┬──────┘           └─────────────┘
       │ region
┌──────┴──────┐
│ db.Regions  │
└──────┬──────┘
       │ id  (many-to-many)
┌──────┴──────┐
│db.Managers  │
└─────────────┘

---

Prerequisites

• StarRocks 3.1+ cluster (local or remote) reachable from your workstation (the script uses generate_series(), available since 3.1)

• mysql command-line client installed (StarRocks speaks the MySQL protocol)

• A StarRocks user with CREATE DATABASE, CREATE TABLE, INSERT privileges

• XLTable server already installed and running (see Installation)

Note

The script sets PROPERTIES ("replication_num" = "1") on every table so it runs on single-node test clusters. On a production cluster remove this property (or set it to "3").

---

Step 1: Run the SQL script

Download starrocks_sample.sql and run it against your StarRocks cluster through the FE query port (9030 by default):

mysql -h <your_starrocks_host> -P 9030 -u <user> -p < starrocks_sample.sql

After a successful run the output should contain no errors. Verify that all tables were created and populated:

SELECT 'Times'            AS `table`, COUNT(*) AS rows FROM db.Times
UNION ALL SELECT 'Regions',           COUNT(*) FROM db.Regions
UNION ALL SELECT 'Managers',          COUNT(*) FROM db.Managers
UNION ALL SELECT 'Stores',            COUNT(*) FROM db.Stores
UNION ALL SELECT 'Models',            COUNT(*) FROM db.Models
UNION ALL SELECT 'Sales',             COUNT(*) FROM db.Sales
UNION ALL SELECT 'Stock',             COUNT(*) FROM db.Stock
UNION ALL SELECT 'olap_definition',   COUNT(*) FROM db.olap_definition
ORDER BY `table`;

Expected output:

table            | rows
-----------------+------
Managers         |    5
Models           |    8
Regions          |    4
Sales            | 3000
Stock            |  500
Stores           |    8
Times            | 1096
olap_definition  |    1

---

Step 2: Configure XLTable

Open /usr/olap/xltable/setting/settings.json and update the database connection block:

{
    "SERVER_DB": "StarRocks",
    "CREDENTIAL_DB": {
        "host": "<your_starrocks_host>",
        "port": 9030,
        "user": "<user>",
        "password": "<password>",
        "ssl_disabled": true
    },
    "WRITE_LOG": false,
    "DUMP_XMLA": false,
    "LOG_RETENTION_DAYS": 14,
    "MAX_CELLS": 1000000,
    "OVERLOAD_GUARD": {
        "MAX_MEMORY_PERCENT": 90,
        "MAX_CPU_PERCENT": 95,
        "MIN_FREE_DISK_MB": 512
    },
    "CONVERT_FIELDS_TO_STRING": true,
    "USERS": {"user1": "pass1", "user2": "pass2"},
    "USER_GROUPS": {"user1": ["olap_users", "olap_admins"], "user2": ["olap_users"]},
    "ADMIN_GROUPS": ["olap_admins"],
    "LDAP_CACHE_TIMEOUT": 300
}

If your cluster requires TLS, set "ssl_disabled": false and provide the certificate path in "ssl_ca" (see Database connections).

XLTable automatically discovers all cubes stored in the olap_definition table, so no additional cube configuration is needed.

---

Step 3: Apply the settings

XLTable re-reads settings.json automatically within a few seconds of saving — no restart is needed. If the service is not running yet, start it:

sudo supervisorctl start olap

---

Step 4: Connect Excel

1. Open Excel and go to Data → Get Data → From Database → From Analysis Services.

2. Enter the server URL: http://your_server_ip

3. Log in with user1 / pass1.

4. Select myOLAPcube.

5. Drag any measures and dimensions onto the Pivot Table — done.

Available fields in the Pivot Table:

Field name (Excel)	Type	Notes
Sales Quantity	Measure	sum(sales.qty)
Sales Amount	Measure	sum(sales.amount)
Sales last year Quantity	Measure	Same query, dates shifted +1 year via Jinja
Sales last year Amount	Measure	Same query, dates shifted +1 year via Jinja
Average Stock Quantity	Measure	avg(stock.qty)
Turnover	Calculated	Sales Quantity ÷ Average Stock Quantity
Store ID / Store	Dimension
Region	Dimension	North · South · East · West
Manager	Dimension	Many-to-many with Region
Model	Dimension	Alpha … Theta
Year / Quarter / Month / Day	Dimension	Dates hierarchy, drill-down supported

---

Customising the script

Change the date range

The calendar is generated for 2023–2025. To extend it to 2026, adjust the generate_series upper bound:

-- In db.Times INSERT — add 365 days for 2026 (1095 + 365 = 1460)
FROM (SELECT generate_series AS n FROM TABLE(generate_series(0, 1460))) t;

Then update the cube definition inside db.olap_definition:

WHERE year_str IN ('2023', '2024', '2025', '2026')

Add more stores or models

Extend the INSERT INTO db.Stores / db.Models sections and update the CASE blocks in the db.Sales and db.Stock inserts accordingly.

Use a different database name

Replace every occurrence of db. with your own prefix, e.g. mydb., including inside the OLAP cube definition string stored in db.olap_definition.

---

Troubleshooting

Unknown table function generate_series

generate_series() requires StarRocks 3.1 or newer. On older versions, generate the number sequence with a helper table or upgrade the cluster.

Table creation failed: replication num should be less than or equal to the number of backends

A different error direction: your cluster has fewer backends than the requested replication. The script already uses "replication_num" = "1"; make sure the property was not removed.

ERROR 1064 near 'PROPERTIES'

The statement was cut mid-way — make sure the whole script file is piped into mysql and no statement separator was lost.

No cubes visible in Excel

Verify the definition row exists:

SELECT id FROM db.olap_definition;

Also confirm that USER_GROUPS in settings.json contains "olap_users" for the connecting user.

Access denied when running the script

The StarRocks user needs at minimum: CREATE DATABASE, CREATE TABLE, INSERT, DROP TABLE on db.*.

---

Full script

-- =============================================================================
-- XLTable OLAP – StarRocks sample data script
-- =============================================================================
-- Creates the `db` database, all required dimension and fact tables,
-- fills them with ~3 500 rows of deterministic test data, and registers
-- the `myOLAPcube` OLAP cube definition (see reference.html#unified-example).
--
-- Prerequisites:
--   - StarRocks 3.1+ cluster reachable from your workstation
--     (generate_series() requires 3.1; for older versions build the
--      number sequence with a helper table)
--   - A user with CREATE DATABASE, CREATE TABLE, INSERT privileges
--
-- Usage (mysql client, StarRocks FE query port 9030):
--   mysql -h <your_starrocks_host> -P 9030 -u <user> -p < starrocks_sample.sql
--
-- Note: PROPERTIES ("replication_num" = "1") is set for single-node test
--       clusters. Remove it (or set to "3") on production clusters.
-- =============================================================================


-- ─── 1. Database ─────────────────────────────────────────────────────────────

CREATE DATABASE IF NOT EXISTS db;


-- ─── 2. Drop existing tables (safe re-run) ───────────────────────────────────

DROP TABLE IF EXISTS db.olap_definition;
DROP TABLE IF EXISTS db.Sales;
DROP TABLE IF EXISTS db.Stock;
DROP TABLE IF EXISTS db.Managers;
DROP TABLE IF EXISTS db.Stores;
DROP TABLE IF EXISTS db.Regions;
DROP TABLE IF EXISTS db.Models;
DROP TABLE IF EXISTS db.Times;


-- ─── 3. Dimension tables ─────────────────────────────────────────────────────

-- Calendar: every day of 2023, 2024 and 2025 (365 + 366 + 365 = 1096 rows)
CREATE TABLE db.Times (
    day_str   VARCHAR(10),
    month_str VARCHAR(7),
    year_str  VARCHAR(4)
)
DUPLICATE KEY(day_str)
DISTRIBUTED BY HASH(day_str) BUCKETS 1
PROPERTIES ("replication_num" = "1");

INSERT INTO db.Times
SELECT
    date_format(date_add('2023-01-01', INTERVAL n DAY), '%Y-%m-%d') AS day_str,
    date_format(date_add('2023-01-01', INTERVAL n DAY), '%Y-%m')    AS month_str,
    date_format(date_add('2023-01-01', INTERVAL n DAY), '%Y')       AS year_str
FROM (SELECT generate_series AS n FROM TABLE(generate_series(0, 1095))) t;


-- Sales regions (4 rows)
CREATE TABLE db.Regions (
    id   VARCHAR(10),
    name VARCHAR(50)
)
DUPLICATE KEY(id)
DISTRIBUTED BY HASH(id) BUCKETS 1
PROPERTIES ("replication_num" = "1");

INSERT INTO db.Regions VALUES
    ('R1', 'North'),
    ('R2', 'South'),
    ('R3', 'East'),
    ('R4', 'West');


-- Sales managers – many-to-many with Regions (5 rows)
CREATE TABLE db.Managers (
    name   VARCHAR(50),
    region VARCHAR(10)
)
DUPLICATE KEY(name)
DISTRIBUTED BY HASH(name) BUCKETS 1
PROPERTIES ("replication_num" = "1");

INSERT INTO db.Managers VALUES
    ('Alice Johnson', 'R1'),
    ('Bob Smith',     'R2'),
    ('Carol White',   'R3'),
    ('David Brown',   'R4'),
    ('Emma Davis',    'R1');


-- Retail stores, each in one region (8 rows)
CREATE TABLE db.Stores (
    id     VARCHAR(10),
    name   VARCHAR(50),
    region VARCHAR(10)
)
DUPLICATE KEY(id)
DISTRIBUTED BY HASH(id) BUCKETS 1
PROPERTIES ("replication_num" = "1");

INSERT INTO db.Stores VALUES
    ('S01', 'Downtown North', 'R1'),
    ('S02', 'Uptown North',   'R1'),
    ('S03', 'South Market',   'R2'),
    ('S04', 'South Center',   'R2'),
    ('S05', 'East Plaza',     'R3'),
    ('S06', 'East Mall',      'R3'),
    ('S07', 'West Gate',      'R4'),
    ('S08', 'West Park',      'R4');


-- Product catalogue (8 rows)
CREATE TABLE db.Models (
    id   VARCHAR(10),
    name VARCHAR(50)
)
DUPLICATE KEY(id)
DISTRIBUTED BY HASH(id) BUCKETS 1
PROPERTIES ("replication_num" = "1");

INSERT INTO db.Models VALUES
    ('M01', 'Product Alpha'),
    ('M02', 'Product Beta'),
    ('M03', 'Product Gamma'),
    ('M04', 'Product Delta'),
    ('M05', 'Product Epsilon'),
    ('M06', 'Product Zeta'),
    ('M07', 'Product Eta'),
    ('M08', 'Product Theta');


-- ─── 4. Fact tables ──────────────────────────────────────────────────────────

-- Sales transactions: 3 000 rows spread across 2023–2024
-- murmur_hash3_32 provides deterministic pseudo-random distribution.
CREATE TABLE db.Sales (
    store     VARCHAR(10),
    model     VARCHAR(10),
    date_sale VARCHAR(10),
    qty       INT,
    amount    DOUBLE
)
DUPLICATE KEY(store)
DISTRIBUTED BY HASH(store) BUCKETS 1
PROPERTIES ("replication_num" = "1");

INSERT INTO db.Sales
SELECT
    CASE MOD(n, 8)
        WHEN 0 THEN 'S01' WHEN 1 THEN 'S02' WHEN 2 THEN 'S03' WHEN 3 THEN 'S04'
        WHEN 4 THEN 'S05' WHEN 5 THEN 'S06' WHEN 6 THEN 'S07' ELSE      'S08'
    END                                                                      AS store,
    CASE MOD(ABS(murmur_hash3_32(CAST(n * 7 AS VARCHAR))), 8)
        WHEN 0 THEN 'M01' WHEN 1 THEN 'M02' WHEN 2 THEN 'M03' WHEN 3 THEN 'M04'
        WHEN 4 THEN 'M05' WHEN 5 THEN 'M06' WHEN 6 THEN 'M07' ELSE      'M08'
    END                                                                      AS model,
    date_format(
        date_add('2023-01-01',
            INTERVAL MOD(ABS(murmur_hash3_32(CAST(n * 3 AS VARCHAR))), 731) DAY),
        '%Y-%m-%d')                                                          AS date_sale,
    CAST(1 + MOD(ABS(murmur_hash3_32(CAST(n * 11 AS VARCHAR))), 100) AS INT) AS qty,
    ROUND((50 + MOD(ABS(murmur_hash3_32(CAST(n * 13 AS VARCHAR))), 950)) * 1.5, 2) AS amount
FROM (SELECT generate_series AS n FROM TABLE(generate_series(0, 2999))) t;


-- Stock inventory snapshots: 500 rows
CREATE TABLE db.Stock (
    store VARCHAR(10),
    model VARCHAR(10),
    qty   INT
)
DUPLICATE KEY(store)
DISTRIBUTED BY HASH(store) BUCKETS 1
PROPERTIES ("replication_num" = "1");

INSERT INTO db.Stock
SELECT
    CASE MOD(n, 8)
        WHEN 0 THEN 'S01' WHEN 1 THEN 'S02' WHEN 2 THEN 'S03' WHEN 3 THEN 'S04'
        WHEN 4 THEN 'S05' WHEN 5 THEN 'S06' WHEN 6 THEN 'S07' ELSE      'S08'
    END                                                                      AS store,
    CASE MOD(ABS(murmur_hash3_32(CAST(n * 5 AS VARCHAR))), 8)
        WHEN 0 THEN 'M01' WHEN 1 THEN 'M02' WHEN 2 THEN 'M03' WHEN 3 THEN 'M04'
        WHEN 4 THEN 'M05' WHEN 5 THEN 'M06' WHEN 6 THEN 'M07' ELSE      'M08'
    END                                                                      AS model,
    CAST(10 + MOD(ABS(murmur_hash3_32(CAST(n * 17 AS VARCHAR))), 500) AS INT) AS qty
FROM (SELECT generate_series AS n FROM TABLE(generate_series(0, 499))) t;


-- ─── 5. OLAP cube definition ─────────────────────────────────────────────────
-- XLTable reads cube definitions from the `olap_definition` table.
-- Single quotes inside the definition string are escaped by doubling them ('').

CREATE TABLE db.olap_definition (
    id         VARCHAR(100),
    definition STRING
)
DUPLICATE KEY(id)
DISTRIBUTED BY HASH(id) BUCKETS 1
PROPERTIES ("replication_num" = "1");

INSERT INTO db.olap_definition VALUES (
'myOLAPcube',
'
with calendar as (
    SELECT * FROM db.Times WHERE year_str IN (''2023'', ''2024'', ''2025'')
)

--olap_cube
--olap_calculated_fields Calculated fields
(sales_sum_qty / stock_avg_qty) as calc_turnover --translation=`Turnover` --format=`#,##0.00;-#,##0.00`
--olap_jinja
{{ sql_text | replace("salesly.date_sale", "date_format(years_add(CAST(salesly.date_sale AS DATE), 1), ''%Y-%m-%d'')") }}

--olap_source Sales
SELECT
--olap_measures
 sum(sales.qty)    as sales_sum_qty --translation=`Sales Quantity` --format=`#,##0;-#,##0`
,sum(sales.amount) as sales_sum_sum --translation=`Sales Amount`   --format=`#,##0.00;-#,##0.00`
FROM db.Sales sales
LEFT JOIN db.Stores stores ON sales.store = stores.id
LEFT JOIN db.Models models ON sales.model = models.id
LEFT JOIN calendar times ON sales.date_sale = times.day_str
--olap_drillthrough
stores_name, regions_name, models_name, times_day_str, sales_sum_qty, sales_sum_sum

--olap_source Sales last year
SELECT
--olap_measures
 sum(salesly.qty)    as salesly_sum_qty --translation=`Sales last year Quantity` --format=`#,##0;-#,##0`
,sum(salesly.amount) as salesly_sum_sum --translation=`Sales last year Amount`   --format=`#,##0.00;-#,##0.00`
FROM db.Sales salesly
LEFT JOIN db.Stores stores ON salesly.store = stores.id
LEFT JOIN db.Models models ON salesly.model = models.id
LEFT JOIN calendar times ON salesly.date_sale = times.day_str

--olap_source Stock
SELECT
--olap_measures
 avg(stock.qty) as stock_avg_qty --translation=`Average Stock Quantity`
FROM db.Stock stock
LEFT JOIN db.Stores stores ON stock.store = stores.id
LEFT JOIN db.Models models ON stock.model = models.id

--olap_source Stores
SELECT
--olap_dimensions
 stores.id   as store_id    --translation=`Store ID`
,stores.name as stores_name --translation=`Store`
FROM db.Stores stores
LEFT JOIN db.Regions regions ON stores.region = regions.id

--olap_source Regions
SELECT
--olap_dimensions
 regions.name as regions_name --translation=`Region`
FROM db.Regions regions
LEFT JOIN db.Managers managers ON regions.id = managers.region --relationship=`many-to-many`

--olap_source Managers
SELECT
--olap_dimensions
 managers.name as managers_name --translation=`Manager`
FROM db.Managers managers

--olap_source Models
SELECT
--olap_dimensions
 models.name as models_name --translation=`Model`
FROM db.Models models

--olap_source Dates
SELECT
--olap_dimensions
 times.year_str as times_year_str --hierarchy=`Dates` --translation=`Year`
,date_format(date_trunc(''quarter'', CAST(times.day_str AS DATE)), ''%Y-%m'') as times_quarter_str --hierarchy=`Dates` --translation=`Quarter`
,times.month_str as times_month_str --hierarchy=`Dates` --translation=`Month`
,times.day_str   as times_day_str   --hierarchy=`Dates` --translation=`Day`
FROM calendar times

--olap_user_role
--olap_user_groups
olap_users
--olap_calculated_fields_visible
all
--olap_measures_visible
all
--olap_dimensions_visible
all
--olap_access_filters
');