context stringlengths 11 9.12k | question stringlengths 0 1.06k | SQL stringlengths 2 4.44k | source stringclasses 28
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CREATE TABLE restaurants (id INT, dish VARCHAR(255), category VARCHAR(255), calories INT); | What is the average calorie count for vegetarian dishes in the restaurants table? | SELECT AVG(calories) FROM restaurants WHERE category = 'vegetarian'; | gretelai_synthetic_text_to_sql |
CREATE TABLE DroughtImpact (Id INT, Location VARCHAR(100), Impact INT, Year INT); INSERT INTO DroughtImpact (Id, Location, Impact, Year) VALUES (1, 'Region1', 3, 2018); INSERT INTO DroughtImpact (Id, Location, Impact, Year) VALUES (2, 'Region1', 5, 2019); INSERT INTO DroughtImpact (Id, Location, Impact, Year) VALUES (3... | Which location had the highest drought impact in 2018? | SELECT Location, MAX(Impact) FROM DroughtImpact WHERE Year = 2018 GROUP BY Location; | gretelai_synthetic_text_to_sql |
CREATE TABLE sales_data (id INT, user_id INT, city VARCHAR(50), amount DECIMAL(10,2)); INSERT INTO sales_data (id, user_id, city, amount) VALUES (1, 1, 'London', 600), (2, 2, 'Paris', 700), (3, 3, 'London', 300), (4, 4, 'Paris', 400); | What is the total revenue generated from users in London and Paris, for users who have spent more than $500? | SELECT city, SUM(amount) as total_revenue FROM sales_data WHERE city IN ('London', 'Paris') AND amount > 500 GROUP BY city; | gretelai_synthetic_text_to_sql |
CREATE TABLE Sustainable_Materials (Type VARCHAR(255), Price FLOAT); INSERT INTO Sustainable_Materials (Type, Price) VALUES ('Organic Cotton', 3.5), ('Recycled Polyester', 4.2), ('Hemp', 2.8); | Find the top 3 most expensive sustainable material types and their average prices. | SELECT Type, AVG(Price) as Average_Price FROM (SELECT Type, Price, ROW_NUMBER() OVER (ORDER BY Price DESC) as Rank FROM Sustainable_Materials) WHERE Rank <= 3 GROUP BY Type; | gretelai_synthetic_text_to_sql |
CREATE TABLE SongStreams (id INT, song VARCHAR(50), country VARCHAR(20), streams INT); INSERT INTO SongStreams (id, song, country, streams) VALUES (1, 'Bohemian Rhapsody', 'USA', 1000000), (2, 'Heat Waves', 'Canada', 800000); | How many streams did song 'Heat Waves' by Glass Animals get in Canada? | SELECT streams FROM SongStreams WHERE song = 'Heat Waves' AND country = 'Canada'; | gretelai_synthetic_text_to_sql |
CREATE TABLE cities (id INT, name VARCHAR(50)); INSERT INTO cities (id, name) VALUES (1, 'CityA'), (2, 'CityB'); CREATE TABLE projects (id INT, city_id INT, type VARCHAR(50), capacity INT); INSERT INTO projects (id, city_id, type, capacity) VALUES (1, 1, 'Solar', 1000), (2, 2, 'Solar', 2000), (3, 1, 'Wind', 1500); | What is the total installed solar capacity for each city? | SELECT c.name, SUM(p.capacity) as total_solar_capacity FROM cities c INNER JOIN projects p ON c.id = p.city_id WHERE p.type = 'Solar' GROUP BY c.name; | gretelai_synthetic_text_to_sql |
CREATE TABLE cities (id INT, name TEXT, country TEXT); CREATE TABLE recycling_centers (id INT, city_id INT, type TEXT); INSERT INTO cities VALUES (1, 'City A', 'Country A'), (2, 'City B', 'Country A'), (3, 'City C', 'Country B'); INSERT INTO recycling_centers VALUES (1, 1, 'Glass'), (2, 1, 'Paper'), (3, 2, 'Plastic'), ... | List the top 5 cities with the highest number of recycling centers. | SELECT cities.name, COUNT(recycling_centers.id) AS center_count FROM cities INNER JOIN recycling_centers ON cities.id = recycling_centers.city_id GROUP BY cities.name ORDER BY center_count DESC LIMIT 5; | gretelai_synthetic_text_to_sql |
CREATE TABLE mine (id INT, name TEXT, location TEXT); CREATE TABLE resource_extraction (id INT, mine_id INT, date DATE, quantity INT); | What is the total amount of resources extracted from each mine, in the past quarter? | SELECT mine.name, SUM(resource_extraction.quantity) as total_quantity FROM mine INNER JOIN resource_extraction ON mine.id = resource_extraction.mine_id WHERE resource_extraction.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH) AND CURRENT_DATE GROUP BY mine.name; | gretelai_synthetic_text_to_sql |
CREATE TABLE mining_sites (site_id INT, site_name VARCHAR(50), state VARCHAR(20));CREATE VIEW environmental_impact AS SELECT site_id, SUM(pollution_level) AS total_impact FROM pollution_data GROUP BY site_id; | List all the mining sites located in 'California' with their respective environmental impact scores. | SELECT s.site_name, e.total_impact FROM mining_sites s INNER JOIN environmental_impact e ON s.site_id = e.site_id WHERE state = 'California'; | gretelai_synthetic_text_to_sql |
CREATE TABLE exploitation_attempts (id INT, vulnerability_id INT, attempts INT, success BOOLEAN); INSERT INTO exploitation_attempts (id, vulnerability_id, attempts, success) VALUES (1, 1, 5, true), (2, 1, 3, false), (3, 2, 10, true); | What is the maximum number of attempts for unsuccessful exploitation of a specific vulnerability? | SELECT MAX(attempts) FROM exploitation_attempts WHERE success = false; | gretelai_synthetic_text_to_sql |
CREATE TABLE volunteers (id INT, program_id INT, is_active BOOLEAN); | Get the total number of volunteers for each program | SELECT p.name, COUNT(v.program_id) as total_volunteers FROM programs p JOIN volunteers v ON p.id = v.program_id GROUP BY p.id; | gretelai_synthetic_text_to_sql |
CREATE TABLE policy (policy_id INT, underwriter_id INT, issue_date DATE, zip_code INT, risk_score INT); CREATE TABLE claim (claim_id INT, policy_id INT, claim_amount INT); | What is the total number of policies and total claim amount for policies issued in the last month, grouped by underwriter? | SELECT underwriter_id, COUNT(policy_id) as policy_count, SUM(claim_amount) as total_claim_amount FROM claim JOIN policy ON claim.policy_id = policy.policy_id WHERE policy.issue_date >= DATEADD(MONTH, -1, GETDATE()) GROUP BY underwriter_id; | gretelai_synthetic_text_to_sql |
CREATE TABLE landfill (id INT, name VARCHAR(20), location VARCHAR(20), capacity INT, start_date DATE); INSERT INTO landfill (id, name, location, capacity, start_date) VALUES (1, 'Mumbai Landfill', 'Mumbai', 120000, '2018-01-01'); | Update the capacity of the landfill in Mumbai to 150000 units and update its start date to 2015-01-01. | UPDATE landfill SET capacity = 150000, start_date = '2015-01-01' WHERE name = 'Mumbai Landfill'; | gretelai_synthetic_text_to_sql |
CREATE TABLE wells (well_id INT, well_name VARCHAR(50), region VARCHAR(50), production_rate FLOAT); INSERT INTO wells (well_id, well_name, region, production_rate) VALUES (16, 'Well P', 'Caspian Sea', 7000), (17, 'Well Q', 'Caspian Sea', 8000), (18, 'Well R', 'Caspian Sea', 9000); | What is the maximum production rate of wells in the 'Caspian Sea'? | SELECT MAX(production_rate) FROM wells WHERE region = 'Caspian Sea'; | gretelai_synthetic_text_to_sql |
CREATE TABLE military_tech (id INT, tech_name VARCHAR(255), country VARCHAR(255), tech_date DATE); | What are the details of the military technologies that were developed by a specific country, say 'USA', from the 'military_tech' table? | SELECT * FROM military_tech WHERE country = 'USA'; | gretelai_synthetic_text_to_sql |
CREATE TABLE community_health_workers (id INT, name VARCHAR, location VARCHAR, patients_served INT); INSERT INTO community_health_workers (id, name, location, patients_served) VALUES (1, 'John Doe', 'Rural', 50); INSERT INTO community_health_workers (id, name, location, patients_served) VALUES (2, 'Jane Smith', 'Urban'... | How many community health workers serve patients in rural areas? | SELECT location, SUM(patients_served) as total_patients FROM community_health_workers WHERE location = 'Rural' GROUP BY location; | gretelai_synthetic_text_to_sql |
CREATE TABLE temperature_readings (location TEXT, temperature FLOAT); INSERT INTO temperature_readings (location, temperature) VALUES ('Arctic Ocean', -2.34), ('North Atlantic', 12.56), ('North Pacific', 15.43); | Which ocean has the minimum temperature? | SELECT location FROM temperature_readings WHERE temperature = (SELECT MIN(temperature) FROM temperature_readings); | gretelai_synthetic_text_to_sql |
CREATE TABLE Accommodations (id INT, type VARCHAR(255), cost FLOAT, student VARCHAR(255)); CREATE TABLE Students (id INT, name VARCHAR(255), age INT, disability VARCHAR(255)); | What is the average cost of accommodations per student for each accommodation type? | SELECT type, AVG(cost) FROM Accommodations GROUP BY type; | gretelai_synthetic_text_to_sql |
CREATE TABLE drug_approval (drug_name VARCHAR(255), country VARCHAR(255), approval_date DATE); | What was the approval date of a specific drug in a certain country? | SELECT approval_date FROM drug_approval WHERE drug_name = 'DrugB' AND country = 'CountryX'; | gretelai_synthetic_text_to_sql |
CREATE TABLE countries (country_name VARCHAR(50), continent VARCHAR(50)); INSERT INTO countries VALUES ('France', 'Europe'); INSERT INTO countries VALUES ('Brazil', 'South America'); CREATE TABLE world_heritage_sites (site_name VARCHAR(50), country VARCHAR(50)); INSERT INTO world_heritage_sites VALUES ('Eiffel Tower', ... | What is the average number of UNESCO World Heritage Sites per country in Europe? | SELECT C.continent, AVG(CASE WHEN C.continent = 'Europe' THEN COUNT(WHS.country) END) as avg_world_heritage_sites FROM countries C JOIN world_heritage_sites WHS ON C.country_name = WHS.country GROUP BY C.continent; | gretelai_synthetic_text_to_sql |
CREATE TABLE swimming (athlete VARCHAR(50), event VARCHAR(50), time TIME); | What is the average time taken for each athlete to complete the swimming events, in the swimming table? | SELECT athlete, AVG(EXTRACT(EPOCH FROM time)/60) AS avg_time FROM swimming GROUP BY athlete; | gretelai_synthetic_text_to_sql |
CREATE TABLE wastewater_facilities ( id INT PRIMARY KEY, name VARCHAR(50), facility_type VARCHAR(50), region VARCHAR(20), capacity_bod INT, operational_status VARCHAR(20) ); INSERT INTO wastewater_facilities (id, name, facility_type, region, capacity_bod, operational_status) VALUES (1, 'Facility A', 'Sewage Treatment P... | Increase the capacity of the 'Screening Facility' in the 'Southeast' region in the wastewater_facilities table by 50000 BOD | UPDATE wastewater_facilities SET capacity_bod = capacity_bod + 50000 WHERE name = 'Facility B' AND region = 'Southeast'; | gretelai_synthetic_text_to_sql |
CREATE TABLE WasteReduction (reduction_date DATE, waste_reduction INT, biodegradable_materials BOOLEAN); | What was the total waste reduction in the USA in Q1 2022 from using biodegradable materials? | SELECT SUM(waste_reduction) FROM WasteReduction WHERE reduction_date BETWEEN '2022-01-01' AND '2022-03-31' AND biodegradable_materials = TRUE AND country = 'USA'; | gretelai_synthetic_text_to_sql |
CREATE TABLE investment (id INT, company_id INT, investor TEXT, year INT, amount FLOAT); INSERT INTO investment (id, company_id, investor, year, amount) VALUES (1, 1, 'Kleiner Perkins', 2022, 12000000.0); CREATE TABLE company (id INT, name TEXT, industry TEXT, founder TEXT, PRIMARY KEY (id)); INSERT INTO company (id, n... | How many investments have been made in women-founded startups in the healthcare sector in the last 2 years? | SELECT COUNT(*) FROM investment i JOIN company c ON i.company_id = c.id WHERE c.founder = 'Female' AND c.industry = 'Healthcare' AND i.year >= (SELECT YEAR(CURRENT_DATE()) - 2); | gretelai_synthetic_text_to_sql |
CREATE TABLE ticket_sales (sale_date DATE, team VARCHAR(50), tickets_sold INT); INSERT INTO ticket_sales (sale_date, team, tickets_sold) VALUES ('2022-01-01', 'Team A', 1000), ('2022-01-02', 'Team B', 1200), ('2022-02-01', 'Team A', 1500); | Display total ticket sales by month | SELECT DATE_FORMAT(sale_date, '%Y-%m') AS month, SUM(tickets_sold) AS total_sales FROM ticket_sales GROUP BY month; | gretelai_synthetic_text_to_sql |
CREATE TABLE military_threats (threat_id INT, country VARCHAR(255), level VARCHAR(255), threat_date DATE); | List the top 5 military threats in the last month | SELECT country, level, threat_date FROM military_threats WHERE threat_date >= DATE(NOW()) - INTERVAL 1 MONTH ORDER BY threat_date DESC LIMIT 5; | gretelai_synthetic_text_to_sql |
CREATE TABLE events (id INT, title VARCHAR(50), event_type VARCHAR(50), city VARCHAR(50), tickets_sold INT); INSERT INTO events (id, title, event_type, city, tickets_sold) VALUES (1, 'The Nutcracker', 'theater', 'Chicago', 1800); INSERT INTO events (id, title, event_type, city, tickets_sold) VALUES (2, 'Swan Lake', 'da... | How many tickets were sold for each event type (theater, dance, music) at cultural centers in Chicago? | SELECT event_type, SUM(tickets_sold) FROM events WHERE city = 'Chicago' GROUP BY event_type; | gretelai_synthetic_text_to_sql |
CREATE TABLE AssetTransactions (AssetID int, TransactionDate date, Value float); INSERT INTO AssetTransactions (AssetID, TransactionDate, Value) VALUES (1, '2021-01-02', 100.5), (2, '2021-02-15', 250.7), (3, '2021-05-03', 75.3), (1, '2021-12-30', 1500.0); | What is the average transaction value per digital asset? | SELECT AssetID, AVG(Value) as AvgTransactionValue FROM AssetTransactions GROUP BY AssetID; | gretelai_synthetic_text_to_sql |
CREATE TABLE space_exploration (id INT, mission_name VARCHAR(255), mission_status VARCHAR(255), agency VARCHAR(255), launch_date DATE); | Show all records in the space_exploration table where the mission_status is 'Active' and agency is 'NASA' | SELECT * FROM space_exploration WHERE mission_status = 'Active' AND agency = 'NASA'; | gretelai_synthetic_text_to_sql |
CREATE TABLE financial_wellbeing_gender (person_id INT, gender VARCHAR(6), score INT); INSERT INTO financial_wellbeing_gender (person_id, gender, score) VALUES (1, 'Male', 7), (2, 'Female', 8), (3, 'Male', 9), (4, 'Female', 6), (5, 'Male', 8); | What is the average financial wellbeing score for each gender? | SELECT gender, AVG(score) FROM financial_wellbeing_gender GROUP BY gender; | gretelai_synthetic_text_to_sql |
CREATE TABLE products (id INT, name TEXT, material TEXT, sustainable BOOLEAN); INSERT INTO products (id, name, material, sustainable) VALUES (1, 'Shirt', 'Organic Cotton', 1), (2, 'Pants', 'Conventional Cotton', 0); | How many items are made of materials that are not sustainably sourced? | SELECT COUNT(*) FROM products WHERE sustainable = 0; | gretelai_synthetic_text_to_sql |
CREATE TABLE innovations (id INT PRIMARY KEY, innovation_name VARCHAR(100), description TEXT, category VARCHAR(50), funding FLOAT); | Update the funding for military innovations in the 'innovations' table | UPDATE innovations SET funding = 12000000.00 WHERE innovation_name = 'Hypersonic Missile'; | gretelai_synthetic_text_to_sql |
CREATE TABLE BuildingPermits (PermitID INT, PermitType TEXT, DateIssued DATE, City TEXT); | What is the total number of building permits issued in each city, for the past year? | SELECT City, Count(PermitID) AS Count FROM BuildingPermits WHERE DateIssued >= DATEADD(year, -1, GETDATE()) GROUP BY City; | gretelai_synthetic_text_to_sql |
CREATE TABLE Textile_Suppliers (supplier_id INT, supplier_name TEXT, country TEXT, is_sustainable BOOLEAN); CREATE TABLE Brands_Textile_Suppliers (brand_id INT, supplier_id INT); CREATE TABLE Brands (brand_id INT, brand_name TEXT, country TEXT, is_sustainable BOOLEAN); | What are the top 5 textile suppliers for sustainable brands in Germany? | SELECT s.supplier_name, COUNT(DISTINCT bts.brand_id) as sustainable_brand_count FROM Textile_Suppliers s JOIN Brands_Textile_Suppliers bts ON s.supplier_id = bts.supplier_id JOIN Brands b ON bts.brand_id = b.brand_id WHERE s.is_sustainable = TRUE AND b.country = 'Germany' GROUP BY s.supplier_name ORDER BY sustainable_b... | gretelai_synthetic_text_to_sql |
CREATE TABLE users (id INT PRIMARY KEY, name VARCHAR(50), email VARCHAR(50), signup_date DATE, signup_source VARCHAR(20)); | Delete all users who signed up using a social media account | DELETE FROM users WHERE signup_source IN ('facebook', 'twitter', 'google'); | gretelai_synthetic_text_to_sql |
CREATE TABLE SatelliteOrbits (SatelliteID INT, OrbitType VARCHAR(50), OrbitHeight INT); INSERT INTO SatelliteOrbits (SatelliteID, OrbitType, OrbitHeight) VALUES (101, 'LEO', 500), (201, 'MEO', 8000), (301, 'GEO', 36000), (401, 'LEO', 600), (501, 'MEO', 10000); | Which satellites are in a specific orbit type, based on the SatelliteOrbits table? | SELECT SatelliteID, OrbitType FROM SatelliteOrbits WHERE OrbitType = 'LEO'; | gretelai_synthetic_text_to_sql |
CREATE TABLE ResearchProjects (Id INT, Name TEXT, Location TEXT); INSERT INTO ResearchProjects (Id, Name, Location) VALUES (1, 'Project A', 'Japan'); INSERT INTO ResearchProjects (Id, Name, Location) VALUES (2, 'Project B', 'South Korea'); | What is the total number of autonomous driving research projects in Japan and South Korea? | SELECT COUNT(*) FROM ResearchProjects WHERE Location IN ('Japan', 'South Korea'); | gretelai_synthetic_text_to_sql |
CREATE TABLE company (id INT, name VARCHAR(255), country VARCHAR(255), num_employees INT, avg_salary DECIMAL(10,2));CREATE VIEW mining_companies AS SELECT * FROM company WHERE industry = 'Mining'; | What is the average salary of employees in mining companies in the top 3 countries with the highest total salary costs? | SELECT AVG(c.avg_salary) as avg_salary FROM company c JOIN (SELECT country, SUM(num_employees * avg_salary) as total_salary_costs FROM mining_companies GROUP BY country ORDER BY total_salary_costs DESC LIMIT 3) mc ON c.country = mc.country WHERE c.industry = 'Mining'; | gretelai_synthetic_text_to_sql |
CREATE TABLE ArtHeritage (id INT, name VARCHAR(50), type VARCHAR(50), year INT, country VARCHAR(50)); INSERT INTO ArtHeritage (id, name, type, year, country) VALUES (1, 'Pottery', 'Art', 2005, 'Mexico'); INSERT INTO ArtHeritage (id, name, type, year, country) VALUES (2, 'Woven Baskets', 'Art', 1950, 'USA'); | Which art pieces in the 'ArtHeritage' table have been preserved for more than 50 years? | SELECT name, type, year, country FROM ArtHeritage WHERE year <= (EXTRACT(YEAR FROM CURRENT_DATE) - 50); | gretelai_synthetic_text_to_sql |
CREATE TABLE financial_capability (individual_id TEXT, training_date DATE, country TEXT); INSERT INTO financial_capability (individual_id, training_date, country) VALUES ('11111', '2022-01-01', 'Germany'); INSERT INTO financial_capability (individual_id, training_date, country) VALUES ('22222', '2022-02-01', 'France'); | What is the number of individuals in Europe who have received financial capability training in the last 12 months? | SELECT COUNT(individual_id) FROM financial_capability WHERE training_date >= DATEADD(year, -1, CURRENT_DATE) AND country = 'Europe'; | gretelai_synthetic_text_to_sql |
CREATE TABLE properties (id INT, city VARCHAR(50), state VARCHAR(2), build_date DATE, co_owners INT); INSERT INTO properties (id, city, state, build_date, co_owners) VALUES (1, 'Austin', 'TX', '2015-01-01', 2), (2, 'Dallas', 'TX', '2005-01-01', 1); | Find the number of co-owned properties in Austin, TX that were built after 2010. | SELECT COUNT(*) FROM properties WHERE city = 'Austin' AND state = 'TX' AND build_date > '2010-01-01' AND co_owners > 1; | gretelai_synthetic_text_to_sql |
CREATE TABLE CustomerSizesUS (CustomerID INT, Country TEXT, AvgSize DECIMAL(5,2)); INSERT INTO CustomerSizesUS (CustomerID, Country, AvgSize) VALUES (1, 'US', 8.5), (2, 'US', 7.5), (3, 'US', 9.5), (4, 'US', 6.5); CREATE TABLE CustomerSizesCA (CustomerID INT, Country TEXT, AvgSize DECIMAL(5,2)); INSERT INTO CustomerSize... | What is the difference in average customer size between the US and Canada? | SELECT AVG(CSUS.AvgSize) - AVG(CSCA.AvgSize) FROM CustomerSizesUS CSUS, CustomerSizesCA CSCA WHERE CSUS.Country = 'US' AND CSCA.Country = 'Canada'; | gretelai_synthetic_text_to_sql |
CREATE TABLE if not exists habitat_monitoring (id INT, habitat VARCHAR(255), animal VARCHAR(255), PRIMARY KEY(id, habitat, animal)); INSERT INTO habitat_monitoring (id, habitat, animal) VALUES (1, 'Forest', 'Gorilla'), (2, 'Grassland', 'Lion'), (3, 'Wetlands', 'Crocodile'), (4, 'Forest', 'Elephant'), (5, 'Forest', 'Gor... | Count of monitored habitats with gorillas | SELECT habitat, COUNT(*) FROM habitat_monitoring WHERE animal = 'Gorilla' GROUP BY habitat; | gretelai_synthetic_text_to_sql |
CREATE TABLE graduates (id INT, name VARCHAR(50), department VARCHAR(50), gpa DECIMAL(3,2)); INSERT INTO graduates (id, name, department, gpa) VALUES (1, 'James Smith', 'Mathematics', 3.3), (2, 'Emily Johnson', 'Physics', 2.9); | Delete graduate student records with GPA below 3.0. | DELETE FROM graduates WHERE gpa < 3.0; | gretelai_synthetic_text_to_sql |
CREATE TABLE nba_games (game_id INT, home_team_id INT, away_team_id INT); CREATE TABLE nba_game_scores (game_id INT, team_id INT, player_name VARCHAR(255), points INT); | Identify the players who scored more than 30 points in a game, for each game in the 'nba_games' table. | SELECT game_id, home_team_id AS team_id, player_name, points FROM nba_game_scores WHERE points > 30 UNION ALL SELECT game_id, away_team_id, player_name, points FROM nba_game_scores WHERE points > 30; | gretelai_synthetic_text_to_sql |
CREATE TABLE facility_data (facility_id INT, facility_location VARCHAR(255), CO2_emission INT, year INT); | What is the CO2 emission of each production facility in the Asia-Pacific region for the year 2021? | SELECT facility_location, SUM(CO2_emission) AS total_CO2_emission FROM facility_data WHERE facility_location LIKE 'Asia-Pacific%' AND year = 2021 GROUP BY facility_location; | gretelai_synthetic_text_to_sql |
CREATE TABLE DancePrograms (programID INT, communityType VARCHAR(20), fundingAmount DECIMAL(10,2)); INSERT INTO DancePrograms (programID, communityType, fundingAmount) VALUES (1, 'Underrepresented', 25000.00), (2, 'General', 15000.00), (3, 'Underrepresented', 30000.00); | What is the total funding received by dance programs targeting underrepresented communities? | SELECT SUM(fundingAmount) FROM DancePrograms WHERE communityType = 'Underrepresented'; | gretelai_synthetic_text_to_sql |
CREATE TABLE hotel_revenue (hotel_id INT, country VARCHAR(20), daily_revenue FLOAT); INSERT INTO hotel_revenue (hotel_id, country, daily_revenue) VALUES (1, 'France', 100), (2, 'France', 120), (3, 'Italy', 150), (4, 'Italy', 140); CREATE TABLE museum_visitors (visit_id INT, country VARCHAR(20), daily_visitors INT); INS... | Find the average daily revenue of eco-friendly hotels in France and Italy, and the average daily visitor count to museums in these two countries. | SELECT AVG(daily_revenue) FROM hotel_revenue WHERE country = 'France' UNION ALL SELECT AVG(daily_visitors) FROM museum_visitors WHERE country = 'France' UNION ALL SELECT AVG(daily_revenue) FROM hotel_revenue WHERE country = 'Italy' UNION ALL SELECT AVG(daily_visitors) FROM museum_visitors WHERE country = 'Italy'; | gretelai_synthetic_text_to_sql |
CREATE TABLE city_electric_vehicles (city_name VARCHAR(255), country VARCHAR(255), num_electric_vehicles INT); INSERT INTO city_electric_vehicles (city_name, country, num_electric_vehicles) VALUES ('San Francisco', 'USA', 15000), ('Los Angeles', 'USA', 20000), ('Toronto', 'Canada', 10000), ('Montreal', 'Canada', 8000),... | What is the average number of electric vehicles per city in the 'transportation' schema, grouped by country? | SELECT country, AVG(num_electric_vehicles) FROM city_electric_vehicles GROUP BY country; | gretelai_synthetic_text_to_sql |
CREATE TABLE production (id INT, country VARCHAR(255), element VARCHAR(255), quantity INT); INSERT INTO production (id, country, element, quantity) VALUES (1, 'China', 'Terbium', 900), (2, 'China', 'Lanthanum', 8000), (3, 'USA', 'Terbium', 700), (4, 'USA', 'Lanthanum', 5000), (5, 'Australia', 'Terbium', 800), (6, 'Aust... | Identify the countries that have higher production of Terbium than Lanthanum. | SELECT country FROM production WHERE element = 'Terbium' AND quantity > (SELECT quantity FROM production WHERE element = 'Lanthanum' AND country = production.country) GROUP BY country; | gretelai_synthetic_text_to_sql |
CREATE TABLE malware_data (id INT, name VARCHAR(255), region VARCHAR(255), last_seen DATETIME); INSERT INTO malware_data (id, name, region, last_seen) VALUES (1, 'WannaCry', 'Asia-Pacific', '2022-01-01 12:00:00'), (2, 'Emotet', 'North America', '2022-01-02 13:00:00'); | Which malware has been detected in the 'Asia-Pacific' region in the last week? | SELECT name FROM malware_data WHERE region = 'Asia-Pacific' AND last_seen >= DATE_SUB(NOW(), INTERVAL 1 WEEK); | gretelai_synthetic_text_to_sql |
CREATE TABLE water_conservation_initiatives (id INT, name VARCHAR(50), description TEXT, start_date DATE, end_date DATE); | Insert a new water conservation initiative | INSERT INTO water_conservation_initiatives (id, name, description, start_date, end_date) VALUES (1, 'Watering Restrictions', 'Restrictions on watering lawns and gardens', '2023-01-01', '2023-12-31'); | gretelai_synthetic_text_to_sql |
CREATE TABLE hospitals_indonesia (id INT, name TEXT, personnel INT); INSERT INTO hospitals_indonesia (id, name, personnel) VALUES (1, 'Hospital Z', 250); | What is the average number of medical personnel per hospital in Indonesia? | SELECT AVG(personnel) FROM hospitals_indonesia; | gretelai_synthetic_text_to_sql |
CREATE TABLE region (id INT, name VARCHAR(255), rainfall FLOAT, rainfall_timestamp DATETIME); INSERT INTO region (id, name, rainfall, rainfall_timestamp) VALUES (1, 'MX-SON', 15.5, '2022-02-25 14:30:00'), (2, 'MX-SIN', 13.8, '2022-02-27 09:15:00'), (3, 'MX-CHI', 17.9, '2022-03-01 12:00:00'); | What is the total rainfall in the last week for region 'MX-SON'? | SELECT SUM(rainfall) FROM region WHERE name = 'MX-SON' AND rainfall_timestamp >= DATEADD(week, -1, CURRENT_TIMESTAMP); | gretelai_synthetic_text_to_sql |
CREATE TABLE timber_production_2 (id INT, name VARCHAR(50), area FLOAT); INSERT INTO timber_production_2 (id, name, area) VALUES (1, 'Timber Inc.', 1000.0), (2, 'WoodCo', 600.0), (3, 'Forest Ltd.', 1200.0); | Which timber production sites have an area larger than 800? | SELECT name FROM timber_production_2 WHERE area > 800; | gretelai_synthetic_text_to_sql |
CREATE TABLE event_attendance_2 (event_name VARCHAR(50), city VARCHAR(50), attendees INT); INSERT INTO event_attendance_2 (event_name, city, attendees) VALUES ('Film Appreciation', 'Seattle', 25); | Which 'Film Appreciation' events in Seattle had less than 30 attendees? | SELECT event_name, city FROM event_attendance_2 WHERE event_name = 'Film Appreciation' AND city = 'Seattle' AND attendees < 30; | gretelai_synthetic_text_to_sql |
CREATE TABLE military_spending (country VARCHAR(50), region VARCHAR(50), spending NUMERIC(10,2)); INSERT INTO military_spending (country, region, spending) VALUES ('USA', 'North America', 7319340000), ('Canada', 'North America', 22597000000), ('Mexico', 'North America', 640000000); | What is the average military spending by countries in the North American region? | SELECT AVG(spending) FROM military_spending WHERE region = 'North America'; | gretelai_synthetic_text_to_sql |
CREATE TABLE student_mental_health (student_id INT, mental_health_score INT); INSERT INTO student_mental_health (student_id, mental_health_score) VALUES (1, 80), (2, 85), (3, 70), (4, 82), (5, 78), (6, 75); | How many students have a mental health score greater than 80? | SELECT COUNT(*) FROM student_mental_health WHERE mental_health_score > 80; | gretelai_synthetic_text_to_sql |
CREATE TABLE Events (id INT, state VARCHAR(50), date DATE, event_type VARCHAR(50)); INSERT INTO Events (id, state, date, event_type) VALUES (1, 'California', '2021-01-01', 'Comedy'), (2, 'New York', '2021-02-01', 'Comedy'); CREATE TABLE Attendance (id INT, event_id INT, is_new_attendee BOOLEAN, gender VARCHAR(10)); INS... | How many unique first-time attendees were there at comedy events, in the past six months, for each state and gender? | SELECT e.state, a.gender, COUNT(DISTINCT a.id) AS count FROM Events e INNER JOIN Attendance a ON e.id = a.event_id AND a.is_new_attendee = TRUE WHERE e.date >= DATE_SUB(CURRENT_DATE, INTERVAL 6 MONTH) AND e.event_type = 'Comedy' GROUP BY e.state, a.gender; | gretelai_synthetic_text_to_sql |
CREATE TABLE adaptation_measures (region TEXT, year INT, measure TEXT); INSERT INTO adaptation_measures (region, year, measure) VALUES ('Asia', 2015, 'Building sea walls'), ('Asia', 2015, 'Planting mangroves'), ('Asia', 2018, 'Improving irrigation systems'), ('Asia', 2018, 'Constructing early warning systems'), ('South... | Which adaptation measures were implemented in South America in 2018? | SELECT measure FROM adaptation_measures WHERE region = 'South America' AND year = 2018; | gretelai_synthetic_text_to_sql |
CREATE TABLE buildings (id INT, name TEXT, state TEXT, co2_emissions FLOAT); INSERT INTO buildings (id, name, state, co2_emissions) VALUES (1, 'Building A', 'Texas', 120.5), (2, 'Building B', 'California', 150.3), (3, 'Building C', 'California', 100.2); | Update the CO2 emissions of Building A in Texas to 110.5. | UPDATE buildings SET co2_emissions = 110.5 WHERE name = 'Building A' AND state = 'Texas'; | gretelai_synthetic_text_to_sql |
CREATE TABLE co2_emission (garment_type VARCHAR(20), country VARCHAR(20), year INT, co2_emission FLOAT); INSERT INTO co2_emission (garment_type, country, year, co2_emission) VALUES ('tops', 'Brazil', 2020, 5.5), ('bottoms', 'Brazil', 2020, 6.2), ('dresses', 'Brazil', 2020, 4.8); | What was the minimum CO2 emission for any garment production in Brazil in 2020? | SELECT MIN(co2_emission) FROM co2_emission WHERE country = 'Brazil' AND year = 2020; | gretelai_synthetic_text_to_sql |
CREATE TABLE social_impact_bonds (bond_id INT, bond_name TEXT, issuer_country TEXT); CREATE TABLE esg_scores (bond_id INT, esg_score INT); INSERT INTO social_impact_bonds (bond_id, bond_name, issuer_country) VALUES (1, 'SIB A', 'USA'), (2, 'SIB B', 'Germany'); INSERT INTO esg_scores (bond_id, esg_score) VALUES (1, 80),... | List all social impact bonds along with their issuer countries and the corresponding ESG scores. | SELECT s.bond_name, s.issuer_country, e.esg_score FROM social_impact_bonds s JOIN esg_scores e ON s.bond_id = e.bond_id; | gretelai_synthetic_text_to_sql |
CREATE TABLE ElectricVehicles (id INT, name VARCHAR(50), horsepower INT, release_year INT); INSERT INTO ElectricVehicles (id, name, horsepower, release_year) VALUES (1, 'Tesla Model 3', 258, 2020); INSERT INTO ElectricVehicles (id, name, horsepower, release_year) VALUES (2, 'Nissan Leaf', 147, 2020); | What is the average horsepower of electric vehicles released in 2020? | SELECT AVG(horsepower) FROM ElectricVehicles WHERE release_year = 2020 AND horsepower IS NOT NULL; | gretelai_synthetic_text_to_sql |
CREATE TABLE ports (id INT, name VARCHAR(50)); CREATE TABLE cargo_handling (id INT, port_id INT, date DATE, cargo_weight INT); INSERT INTO ports (id, name) VALUES (1, 'PortA'), (2, 'PortB'); INSERT INTO cargo_handling (id, port_id, date, cargo_weight) VALUES (1, 1, '2021-01-01', 5000), (2, 2, '2021-02-01', 6000); | List all ports with their corresponding cargo handling records, sorted by the date of cargo handling in descending order. | SELECT ports.name, cargo_handling.date, cargo_handling.cargo_weight FROM ports INNER JOIN cargo_handling ON ports.id = cargo_handling.port_id ORDER BY cargo_handling.date DESC; | gretelai_synthetic_text_to_sql |
CREATE TABLE PolicyEvents (city VARCHAR(50), event_category VARCHAR(50), participation INT); INSERT INTO PolicyEvents (city, event_category, participation) VALUES ('CityA', 'Workshop', 50), ('CityA', 'Meeting', 30), ('CityB', 'Workshop', 40), ('CityB', 'Conference', 60); | What is the total number of policy making events in each city, partitioned by event category? | SELECT city, event_category, SUM(participation) AS total_participation FROM PolicyEvents GROUP BY city, event_category; | gretelai_synthetic_text_to_sql |
CREATE TABLE chemical_substances (substance_id INT, substance_name VARCHAR(255)); INSERT INTO chemical_substances (substance_id, substance_name) VALUES (1, 'SubstanceA'), (2, 'SubstanceB'), (3, 'SubstanceC'), (4, 'SubstanceA'); | How many unique chemical substances are there in the chemical_substances table? | SELECT COUNT(DISTINCT substance_name) AS unique_substances FROM chemical_substances; | gretelai_synthetic_text_to_sql |
CREATE TABLE excavation_sites (site_id INT, site_name VARCHAR(50), country VARCHAR(50)); INSERT INTO excavation_sites (site_id, site_name, country) VALUES (1, 'Site A', 'USA'); CREATE TABLE artifacts (artifact_id INT, site_id INT, excavation_date DATE); | What was the earliest excavation date for each site? | SELECT e.site_name, MIN(a.excavation_date) as earliest_date FROM excavation_sites e JOIN artifacts a ON e.site_id = a.site_id GROUP BY e.site_id, e.site_name ORDER BY earliest_date ASC; | gretelai_synthetic_text_to_sql |
CREATE TABLE space_debris (debris_id INT, name VARCHAR(100), origin VARCHAR(100), mass FLOAT, launch_date DATE); | What is the total mass of space debris in the space_debris table, in kilograms, for debris with a known origin? | SELECT SUM(mass) FROM space_debris WHERE origin IS NOT NULL; | gretelai_synthetic_text_to_sql |
CREATE TABLE protected_zone (tree_id INT, species VARCHAR(50), age INT, height INT, location VARCHAR(50));CREATE TABLE unprotected_zone (tree_id INT, species VARCHAR(50), age INT, height INT, location VARCHAR(50)); | What is the average height of trees in the protected_zone table, and how does it compare to the average height of trees in the unprotected_zone table? | SELECT AVG(height) FROM protected_zone;SELECT AVG(height) FROM unprotected_zone; | gretelai_synthetic_text_to_sql |
CREATE TABLE drug_approval (drug_name VARCHAR(255), approval_date DATE); INSERT INTO drug_approval (drug_name, approval_date) VALUES ('Drug A', '2018-01-01'), ('Drug B', '2018-06-15'), ('Drug C', '2018-12-25'); | What was the average drug approval time for drugs approved in 2018? | SELECT AVG(DATEDIFF('2018-12-31', approval_date)) FROM drug_approval; | gretelai_synthetic_text_to_sql |
CREATE SCHEMA municipality; CREATE SCHEMA city; CREATE SCHEMA county; CREATE TABLE municipality.policy_data (id INT, name VARCHAR(255), is_evidence_based BOOLEAN); CREATE TABLE city.policy_data (id INT, name VARCHAR(255), is_evidence_based BOOLEAN); CREATE TABLE county.policy_data (id INT, name VARCHAR(255), is_evidenc... | Find the total number of evidence-based policy making data sets in 'municipality', 'city', and 'county' schemas. | SELECT COUNT(*) FROM ( (SELECT * FROM municipality.policy_data WHERE is_evidence_based = true) UNION (SELECT * FROM city.policy_data WHERE is_evidence_based = true) UNION (SELECT * FROM county.policy_data WHERE is_evidence_based = true) ) AS combined_policy_data; | gretelai_synthetic_text_to_sql |
CREATE TABLE marine_sites (site_id INT, site_name VARCHAR(255), longitude DECIMAL(9,6), latitude DECIMAL(9,6), depth DECIMAL(5,2)); | Find the average depth of all marine life research sites | SELECT AVG(depth) FROM marine_sites; | gretelai_synthetic_text_to_sql |
CREATE TABLE eSports_games_3 (id INT, team1 TEXT, team2 TEXT, winner TEXT); INSERT INTO eSports_games_3 (id, team1, team2, winner) VALUES (1, 'Green', 'Blue', 'Green'), (2, 'Yellow', 'Green', 'Yellow'), (3, 'Green', 'Purple', 'Green'); | What is the percentage of games won by team 'Green' in the eSports tournament? | SELECT (COUNT(*) FILTER (WHERE winner = 'Green')) * 100.0 / COUNT(*) FROM eSports_games_3 WHERE team1 = 'Green' OR team2 = 'Green'; | gretelai_synthetic_text_to_sql |
CREATE TABLE wells (well_id INT, well_type VARCHAR(10), location VARCHAR(20), production_rate FLOAT); INSERT INTO wells (well_id, well_type, location, production_rate) VALUES (1, 'offshore', 'Gulf of Mexico', 1000), (2, 'onshore', 'Texas', 800), (3, 'offshore', 'North Sea', 1200), (4, 'onshore', 'Alberta', 900); | Find the wells with production rates in the top 10 percentile. | SELECT * FROM (SELECT well_id, well_type, location, production_rate, PERCENT_RANK() OVER (ORDER BY production_rate DESC) pr FROM wells) t WHERE pr >= 0.9; | gretelai_synthetic_text_to_sql |
CREATE TABLE startup (id INT, industry TEXT, founder_demographics TEXT); INSERT INTO startup (id, industry, founder_demographics) VALUES (1, 'Software', 'Latinx Female'), (2, 'Hardware', 'Asian Male'), (3, 'Healthcare', 'Latinx Non-binary'), (4, 'AI', 'Black Female'); | List the unique industries for startups founded by Latinx individuals that have received Series A funding or higher. | SELECT DISTINCT industry FROM startup WHERE founder_demographics LIKE '%Latinx%' AND industry IN ('Series A', 'Series B', 'Series C', 'Series D', 'Series E'); | gretelai_synthetic_text_to_sql |
CREATE TABLE assists (assist_id INT, player_id INT, match_id INT, team_id INT, assists INT); INSERT INTO assists (assist_id, player_id, match_id, team_id, assists) VALUES (1, 12, 13, 107, 6); | What is the average number of assists per basketball player in the 'assists' table? | SELECT AVG(assists) FROM assists; | gretelai_synthetic_text_to_sql |
CREATE TABLE invoices (invoice_id INT, invoice_date DATE); | Delete all records from the 'invoices' table where invoice_date is older than 6 months | DELETE FROM invoices WHERE invoice_date < (CURRENT_DATE - INTERVAL '6 months'); | gretelai_synthetic_text_to_sql |
CREATE TABLE sales (sale_id INT, dish_id INT, sale_price DECIMAL(5,2), country VARCHAR(255)); INSERT INTO sales (sale_id, dish_id, sale_price, country) VALUES (1, 1, 9.99, 'USA'), (2, 3, 7.99, 'Mexico'), (3, 2, 12.99, 'USA'); CREATE TABLE dishes (dish_id INT, dish_name VARCHAR(255), cuisine VARCHAR(255)); INSERT INTO d... | Identify dishes that contribute to the least revenue | SELECT d.dish_id, d.dish_name, SUM(s.sale_price) as revenue FROM dishes d LEFT JOIN sales s ON d.dish_id = s.dish_id GROUP BY d.dish_id, d.dish_name ORDER BY revenue ASC LIMIT 1; | gretelai_synthetic_text_to_sql |
CREATE TABLE patients (id INT, country VARCHAR(255)); CREATE TABLE treatments (id INT, patient_id INT, treatment_date DATE); CREATE TABLE conditions (id INT, patient_id INT, condition VARCHAR(255)); INSERT INTO patients (id, country) VALUES (1, 'Germany'), (2, 'Germany'), (3, 'Germany'), (4, 'Germany'); INSERT INTO tre... | Which mental health conditions were treated most frequently in Germany during 2020? | SELECT conditions.condition, COUNT(conditions.condition) AS count FROM conditions JOIN patients ON conditions.patient_id = patients.id JOIN treatments ON patients.id = treatments.patient_id WHERE patients.country = 'Germany' AND treatments.treatment_date >= '2020-01-01' AND treatments.treatment_date < '2021-01-01' GROU... | gretelai_synthetic_text_to_sql |
CREATE TABLE sa_models (model_name TEXT, region TEXT, explainability_score INTEGER); INSERT INTO sa_models (model_name, region, explainability_score) VALUES ('Model1', 'South America', 90), ('Model2', 'South America', 80), ('Model3', 'South America', 88); | What is the total number of AI models developed in South America with an explainability score above 85? | SELECT SUM(incident_count) FROM sa_models WHERE region = 'South America' AND explainability_score > 85; | gretelai_synthetic_text_to_sql |
CREATE TABLE rental_cars (id INT, country VARCHAR(255), co2_emission INT); INSERT INTO rental_cars (id, country, co2_emission) VALUES (1, 'USA', 150), (2, 'USA', 180), (3, 'Germany', 120), (4, 'Germany', 130), (5, 'Brazil', 200), (6, 'Brazil', 220), (7, 'India', 100), (8, 'India', 110); | What is the average CO2 emission of rental cars in each country, ranked by the highest emission? | SELECT country, AVG(co2_emission) AS avg_co2_emission, RANK() OVER (ORDER BY AVG(co2_emission) DESC) AS rank FROM rental_cars GROUP BY country ORDER BY rank; | gretelai_synthetic_text_to_sql |
CREATE TABLE Training_Programs (Program_Name VARCHAR(50), Trainer VARCHAR(20), Location VARCHAR(20), Start_Date DATE, End_Date DATE); CREATE TABLE Trainers (Trainer_ID INT, Trainer VARCHAR(20), Specialization VARCHAR(20)); | List all trainers who have conducted diversity and inclusion training in the USA or Canada. | SELECT Trainer FROM Training_Programs WHERE Program_Name LIKE '%diversity%' AND (Location = 'USA' OR Location = 'Canada') INTERSECT SELECT Trainer FROM Trainers; | gretelai_synthetic_text_to_sql |
CREATE TABLE Digital_Interactions (id INT, location VARCHAR(50), quarter INT, year INT, interaction_count INT); | Find the total number of digital museum interactions in New York and Chicago in Q2 of 2020. | SELECT SUM(interaction_count) FROM Digital_Interactions WHERE location IN ('New York', 'Chicago') AND quarter = 2 AND year = 2020; | gretelai_synthetic_text_to_sql |
CREATE TABLE transactions (transaction_id INT, transaction_type VARCHAR(20), transaction_fee DECIMAL(10,2)); INSERT INTO transactions (transaction_id, transaction_type, transaction_fee) VALUES (1, 'Gold', 50.00), (2, 'Silver', 25.00); | What is the total transaction fee for all gold transactions? | SELECT SUM(transaction_fee) FROM transactions WHERE transaction_type = 'Gold'; | gretelai_synthetic_text_to_sql |
CREATE TABLE network_investments (investment_id INT, investment_type VARCHAR(50), investment_date DATE, investment_amount DECIMAL(10,2)); | Delete all records from the network_investments table where the investment_date is older than 3 years | DELETE FROM network_investments WHERE investment_date < (CURRENT_DATE - INTERVAL '3' YEAR); | gretelai_synthetic_text_to_sql |
CREATE TABLE EsportsEvents (PlayerID INT, Game VARCHAR(20), Event VARCHAR(20)); INSERT INTO EsportsEvents (PlayerID, Game, Event) VALUES (1, 'Counter-Strike: Global Offensive', 'ESL One Cologne'), (2, 'StarCraft II', 'WCS Global Finals'), (3, 'Fortnite', 'World Cup'); | Show the total number of esports events for 'Counter-Strike: Global Offensive' and 'StarCraft II' | SELECT COUNT(DISTINCT Event) FROM EsportsEvents WHERE Game IN ('Counter-Strike: Global Offensive', 'StarCraft II') | gretelai_synthetic_text_to_sql |
CREATE TABLE RecyclingRates (WasteType VARCHAR(50), Region VARCHAR(50), RecyclingRate DECIMAL(5,2)); INSERT INTO RecyclingRates (WasteType, Region, RecyclingRate) VALUES ('Municipal Solid Waste', 'European Union', 0.35), ('Industrial Waste', 'European Union', 0.70), ('Municipal Solid Waste', 'United States', 0.30), ('I... | Which recycling rates are higher, for municipal solid waste or for industrial waste, in the European Union? | SELECT WasteType, RecyclingRate FROM RecyclingRates WHERE Region = 'European Union' AND WasteType IN ('Municipal Solid Waste', 'Industrial Waste') ORDER BY RecyclingRate DESC LIMIT 1; | gretelai_synthetic_text_to_sql |
CREATE TABLE Space_Missions (Mission VARCHAR(50), Duration INT, Launch_Date DATE); INSERT INTO Space_Missions (Mission, Duration, Launch_Date) VALUES ('Mission1', 123, '2021-01-01'), ('Mission2', 456, '2021-02-01'), ('Mission3', 789, '2021-03-01'); | List space missions with duration more than the average, along with their mission names and launch dates. | SELECT Mission, Duration, Launch_Date FROM Space_Missions WHERE Duration > (SELECT AVG(Duration) FROM Space_Missions); | gretelai_synthetic_text_to_sql |
CREATE TABLE clients (client_id INT, name TEXT, region TEXT, transaction_amount DECIMAL); INSERT INTO clients (client_id, name, region, transaction_amount) VALUES (1, 'John Doe', 'Asia', 500.00); INSERT INTO clients (client_id, name, region, transaction_amount) VALUES (2, 'Jane Smith', 'Europe', 600.00); INSERT INTO cl... | Increase the transaction amount for the client with the highest transaction by 10%. | UPDATE clients SET transaction_amount = transaction_amount * 1.10 WHERE client_id = (SELECT client_id FROM clients WHERE transaction_amount = (SELECT MAX(transaction_amount) FROM clients)); | gretelai_synthetic_text_to_sql |
CREATE TABLE malware (type VARCHAR(50), affected_software TEXT); INSERT INTO malware (type, affected_software) VALUES ('Ransomware', 'Windows 7, Windows 10'); | List the unique malware types and their affected software for the healthcare sector, sorted by malware type. | SELECT DISTINCT type, affected_software FROM malware WHERE type IN (SELECT type FROM malware_sectors WHERE sector = 'Healthcare') ORDER BY type; | gretelai_synthetic_text_to_sql |
CREATE TABLE temperature (temp_id INT, location TEXT, temperature FLOAT); INSERT INTO temperature (temp_id, location, temperature) VALUES (1, 'Atlantic', 20.5), (2, 'Indian', 25.7); | What is the average temperature in the Atlantic and Indian oceans? | SELECT AVG(temperature) FROM temperature WHERE location IN ('Atlantic', 'Indian') | gretelai_synthetic_text_to_sql |
CREATE TABLE malicious_activity (id INT, type VARCHAR(50), timestamp DATETIME); | What are the top 5 most common types of malicious activity in the last week? | SELECT type, COUNT(*) as num_occurrences FROM malicious_activity WHERE timestamp > DATE_SUB(CURRENT_DATE, INTERVAL 1 WEEK) GROUP BY type ORDER BY num_occurrences DESC LIMIT 5; | gretelai_synthetic_text_to_sql |
CREATE TABLE Expenses (expense_id INT, category VARCHAR(50), amount DECIMAL(10,2)); INSERT INTO Expenses (expense_id, category, amount) VALUES (1, 'Office Supplies', 100.00); | Insert a new record into the 'Expenses' table for 'Travel Expenses' | INSERT INTO Expenses (expense_id, category, amount) VALUES (2, 'Travel Expenses', 0); | gretelai_synthetic_text_to_sql |
CREATE TABLE VeganSkincareSales (sale_id INT, product_name TEXT, is_vegan BOOLEAN, sale_amount FLOAT, sale_date DATE); INSERT INTO VeganSkincareSales (sale_id, product_name, is_vegan, sale_amount, sale_date) VALUES (1, 'Vegan Cleanser', TRUE, 60.00, '2019-12-25'); INSERT INTO VeganSkincareSales (sale_id, product_name, ... | find the number of vegan skincare products that were sold between 2019 and 2020 | SELECT COUNT(*) FROM VeganSkincareSales WHERE is_vegan = TRUE AND YEAR(sale_date) BETWEEN 2019 AND 2020; | gretelai_synthetic_text_to_sql |
CREATE TABLE hotel_revenue_data_2 (hotel_id INT, country TEXT, pms_type TEXT, daily_revenue FLOAT); INSERT INTO hotel_revenue_data_2 (hotel_id, country, pms_type, daily_revenue) VALUES (1, 'UAE', 'cloud-based', 5000), (2, 'UAE', 'cloud-based', 6000), (3, 'UAE', 'legacy', 4000), (4, 'Qatar', 'cloud-based', 7000); | What is the minimum revenue per day for hotels in the UAE that have adopted cloud-based PMS? | SELECT MIN(daily_revenue) FROM hotel_revenue_data_2 WHERE country = 'UAE' AND pms_type = 'cloud-based'; | gretelai_synthetic_text_to_sql |
CREATE TABLE hospital_beds (country VARCHAR(20), beds_per_1000 INT); INSERT INTO hospital_beds (country, beds_per_1000) VALUES ('High-Income', 50); INSERT INTO hospital_beds (country, beds_per_1000) VALUES ('Low-Income', 10); | What is the minimum number of hospital beds per 1000 people in low-income countries? | SELECT MIN(beds_per_1000) FROM hospital_beds WHERE country = 'Low-Income'; | gretelai_synthetic_text_to_sql |
CREATE TABLE EmployeeDemographics (EmployeeID int, Gender varchar(10), Department varchar(20)); INSERT INTO EmployeeDemographics (EmployeeID, Gender, Department) VALUES (1, 'Female', 'Engineering'), (2, 'Male', 'IT'), (3, 'Non-binary', 'Engineering'), (4, 'Female', 'Sales'), (5, 'Male', 'Sales'), (6, 'Female', 'Sales')... | What is the percentage of female, male, and non-binary employees in the Sales department? | SELECT Department, ROUND(COUNT(CASE WHEN Gender = 'Female' THEN 1 END) * 100.0 / COUNT(*), 1) AS FemalePercentage, ROUND(COUNT(CASE WHEN Gender = 'Male' THEN 1 END) * 100.0 / COUNT(*), 1) AS MalePercentage, ROUND(COUNT(CASE WHEN Gender = 'Non-binary' THEN 1 END) * 100.0 / COUNT(*), 1) AS NonBinaryPercentage FROM Employ... | gretelai_synthetic_text_to_sql |
CREATE TABLE containers (container_id INT, container_size INT, ship_id INT); INSERT INTO containers (container_id, container_size, ship_id) VALUES (1, 10, 1), (2, 15, 1), (3, 12, 2); CREATE TABLE ships (ship_id INT, ship_name VARCHAR(100), country VARCHAR(100)); INSERT INTO ships (ship_id, ship_name, country) VALUES (1... | What is the total number of containers that were transported by cargo ships from Asian countries to the Port of Singapore? | SELECT COUNT(*) FROM containers JOIN ships ON containers.ship_id = ships.ship_id WHERE ships.country = 'Asia' AND ports.port_name = 'Port of Singapore'; | gretelai_synthetic_text_to_sql |
CREATE TABLE network_investments (id INT, country VARCHAR(50), region VARCHAR(20), investment FLOAT); INSERT INTO network_investments (id, country, region, investment) VALUES (1, 'South Africa', 'Africa', 2000000); | List all mobile subscribers with their monthly usage and network investment in the 'Africa' region. | SELECT mobile_subscribers.name, customer_usage.usage, network_investments.investment FROM mobile_subscribers INNER JOIN customer_usage ON mobile_subscribers.id = customer_usage.subscriber_id INNER JOIN network_investments ON mobile_subscribers.country = network_investments.country WHERE network_investments.region = 'Af... | gretelai_synthetic_text_to_sql |
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