Inspecting your Lance Datasets

SELECT

-- Select all data from a table
SELECT * FROM users;

-- Select specific columns
SELECT id, name, email FROM users;

-- Query with WHERE clause
SELECT * FROM users WHERE age > 25;

-- Aggregate queries
SELECT department, COUNT(*) as employee_count 
FROM users 
GROUP BY department;

-- Join queries
SELECT u.name, p.title
FROM users u
JOIN projects p ON u.id = p.user_id;

SHOW TABLES

-- Show all tables in the default namespace
SHOW TABLES;

-- Show all tables in a specific namespace ns2
 SNOW TABLES IN ns2;

DESCRIBE TABLE

-- Describe table structure
DESCRIBE TABLE users;

-- Show detailed table information
DESCRIBE EXTENDED users;

DataFrame Read

PythonScalaJava

# Load table as DataFrame
users_df = spark.table("lance.default.users")

# Use DataFrame operations
filtered_users = users_df.filter("age > 25").select("name", "email")
filtered_users.show()

// Load table as DataFrame
val usersDF = spark.table("lance.default.users")

// Use DataFrame operations
val filteredUsers = usersDF.filter("age > 25").select("name", "email")
filteredUsers.show()

// Load table as DataFrame
Dataset<Row> usersDF = spark.table("lance.default.users");

// Use DataFrame operations
Dataset<Row> filteredUsers = usersDF.filter("age > 25").select("name", "email");
filteredUsers.show();