release: v0.10.0

Author: hello-world-bfree

README.md

@@ -1,16 +1,16 @@
 -- Example DuckDB queries for the sample data files
--- Open the data files in Neovim and run these queries using :DuckDB
+-- Select a query and run it using visual selection + <leader>dq
 
 -- ============================================================================
 -- BASIC QUERIES (employees.csv)
 -- ============================================================================
 
 -- Get all employees
-SELECT * FROM buffer;
+SELECT * FROM read_csv('./examples/employees.csv');
 
 -- Count employees by department
 SELECT department, COUNT(*) as count
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 GROUP BY department
 ORDER BY count DESC;
 
@@ -20,18 +20,18 @@ SELECT department,
        AVG(salary) as avg_salary,
        MIN(salary) as min_salary,
        MAX(salary) as max_salary
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 GROUP BY department;
 
 -- Top 5 highest paid employees
 SELECT name, department, salary
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 ORDER BY salary DESC
 LIMIT 5;
 
 -- Employees hired in 2020
 SELECT name, department, hire_date
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 WHERE hire_date >= '2020-01-01' AND hire_date < '2021-01-01'
 ORDER BY hire_date;
 
@@ -41,20 +41,20 @@ ORDER BY hire_date;
 
 -- Products by category
 SELECT category, COUNT(*) as count, AVG(price) as avg_price
-FROM buffer
+FROM read_json('./examples/products.json')
 GROUP BY category;
 
 -- High-rated products in stock
 SELECT name, price, stock, rating
-FROM buffer
+FROM read_json('./examples/products.json')
 WHERE rating >= 4.5 AND stock > 0
 ORDER BY rating DESC, price ASC;
 
 -- Total inventory value
 SELECT category,
        SUM(price * stock) as total_value,
        SUM(stock) as total_units
-FROM buffer
+FROM read_json('./examples/products.json')
 GROUP BY category
 ORDER BY total_value DESC;
 
@@ -67,33 +67,36 @@ SELECT name,
        department,
        salary,
        RANK() OVER (PARTITION BY department ORDER BY salary DESC) as dept_rank
-FROM buffer;
+FROM read_csv('./examples/employees.csv');
 
 -- Running total of salaries
 SELECT name,
        salary,
        SUM(salary) OVER (ORDER BY hire_date) as running_total
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 ORDER BY hire_date;
 
 -- Department salary percentiles
 SELECT department,
        name,
        salary,
        PERCENT_RANK() OVER (PARTITION BY department ORDER BY salary) as percentile
-FROM buffer;
+FROM read_csv('./examples/employees.csv');
 
 -- ============================================================================
 -- ADVANCED QUERIES
 -- ============================================================================
 
 -- CTEs: High earners analysis
-WITH high_earners AS (
-  SELECT * FROM buffer WHERE salary > 80000
+WITH employees AS (
+  SELECT * FROM read_csv('./examples/employees.csv')
+),
+high_earners AS (
+  SELECT * FROM employees WHERE salary > 80000
 ),
 dept_stats AS (
   SELECT department, AVG(salary) as avg_salary
-  FROM buffer
+  FROM employees
   GROUP BY department
 )
 SELECT he.name, he.salary, ds.avg_salary,
@@ -103,9 +106,10 @@ JOIN dept_stats ds ON he.department = ds.department
 ORDER BY difference DESC;
 
 -- Subqueries: Above average salaries
+WITH employees AS (SELECT * FROM read_csv('./examples/employees.csv'))
 SELECT name, department, salary
-FROM buffer
-WHERE salary > (SELECT AVG(salary) FROM buffer)
+FROM employees
+WHERE salary > (SELECT AVG(salary) FROM employees)
 ORDER BY salary DESC;
 
 -- CASE expressions: Salary categories
@@ -117,7 +121,7 @@ SELECT name,
          WHEN salary < 100000 THEN 'Senior'
          ELSE 'Principal'
        END as level
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 ORDER BY salary;
 
 -- ============================================================================
@@ -128,14 +132,14 @@ ORDER BY salary;
 SELECT name,
        hire_date,
        DATEDIFF('year', CAST(hire_date AS DATE), CURRENT_DATE) as years_of_service
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 ORDER BY years_of_service DESC;
 
 -- Employees by hire quarter
 SELECT EXTRACT(YEAR FROM CAST(hire_date AS DATE)) as year,
        EXTRACT(QUARTER FROM CAST(hire_date AS DATE)) as quarter,
        COUNT(*) as hires
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 GROUP BY year, quarter
 ORDER BY year, quarter;
 
@@ -147,11 +151,11 @@ ORDER BY year, quarter;
 SELECT SPLIT_PART(name, ' ', 1) as first_name,
        department,
        salary
-FROM buffer;
+FROM read_csv('./examples/employees.csv');
 
 -- Search for names containing 'son'
 SELECT name, department
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 WHERE name LIKE '%son%';
 
 -- ============================================================================
@@ -166,30 +170,39 @@ SELECT department,
        STDDEV(salary) as std_dev,
        MIN(salary) as min,
        MAX(salary) as max
-FROM buffer
+FROM read_csv('./examples/employees.csv')
 GROUP BY department;
 
 -- ============================================================================
--- JSON QUERIES (products.json)
+-- JSONL QUERIES (orders.jsonl)
 -- ============================================================================
 
--- Filter by nested properties (if JSON has nested structure)
--- Example: SELECT * FROM buffer WHERE price > 100;
+-- Order totals by customer
+SELECT customer_id, COUNT(*) as orders, SUM(total) as total_spent
+FROM read_json('./examples/orders.jsonl', format='newline_delimited')
+GROUP BY customer_id
+ORDER BY total_spent DESC;
+
+-- Orders by status
+SELECT status, COUNT(*) as count
+FROM read_json('./examples/orders.jsonl', format='newline_delimited')
+GROUP BY status;
 
 -- ============================================================================
--- MULTI-BUFFER QUERIES
--- (Open both employees.csv and products.json, then run these)
+-- MULTI-TABLE QUERIES
 -- ============================================================================
 
--- Cross join (Cartesian product) - be careful with large datasets!
--- SELECT e.name, p.name as product
--- FROM buffer('employees.csv') e, buffer('products.json') p
--- LIMIT 10;
+-- Join employees and products (cross join example)
+SELECT e.name as employee, p.name as product, p.price
+FROM read_csv('./examples/employees.csv') e,
+     read_json('./examples/products.json') p
+WHERE e.department = 'Engineering'
+LIMIT 10;
 
 -- ============================================================================
 -- EXPORT EXAMPLES (via Lua)
 -- ============================================================================
 
--- Run these from Neovim using Lua:
--- :lua require('duckdb').query('SELECT * FROM buffer WHERE salary > 90000', {export = '/tmp/high_earners.csv', format = 'csv'})
--- :lua require('duckdb').query('SELECT * FROM buffer', {export = '/tmp/all_employees.json', format = 'json'})
+-- Run these from Neovim command line:
+-- :lua require('duckdb').query("SELECT * FROM read_csv('./examples/employees.csv') WHERE salary > 90000", {export = '/tmp/high_earners.csv', format = 'csv'})
+-- :lua require('duckdb').query("SELECT * FROM read_csv('./examples/employees.csv')", {export = '/tmp/all_employees.json', format = 'json'})

examples/example_queries.sql

@@ -0,0 +1,9 @@
+id,name,category,price,stock,rating
+1,Laptop Pro 15,Electronics,1299.99,45,4.5
+2,Wireless Mouse,Electronics,24.99,230,4.2
+3,Office Desk,Furniture,299.99,15,4.7
+4,Ergonomic Chair,Furniture,399.99,8,4.8
+5,"Monitor 27""",Electronics,349.99,62,4.6
+6,Keyboard Mechanical,Electronics,89.99,120,4.4
+7,Desk Lamp,Furniture,45.99,75,4.3
+8,USB-C Hub,Electronics,49.99,95,4.1