HeliosDB SQL API Reference

Version: 6.0 (PostgreSQL 17 Compatible) Last Updated: November 2, 2025

Introduction

HeliosDB provides a PostgreSQL-compatible SQL interface with extensions for multi-model data operations. This reference documents all supported SQL statements, functions, operators, and multi-model extensions.

Connection

Connection String

psql "postgresql://username:password@localhost:5432/heliosdb"

Connection Parameters

host=localhost
port=5432
dbname=heliosdb
user=admin
password=secret
tenant=acme-corp         # Multi-tenancy
application_name=myapp

Using pg Client Libraries

import psycopg2

conn = psycopg2.connect(
    host="localhost",
    port=5432,
    dbname="heliosdb",
    user="admin",
    password="secret"
)

Data Definition Language (DDL)

CREATE TABLE

Basic Table:

CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    username VARCHAR(255) NOT NULL UNIQUE,
    email VARCHAR(255) NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

With Constraints:

CREATE TABLE orders (
    id SERIAL PRIMARY KEY,
    user_id INTEGER NOT NULL,
    status VARCHAR(50) CHECK (status IN ('pending', 'completed', 'cancelled')),
    total DECIMAL(10, 2) CHECK (total >= 0),
    created_at TIMESTAMP NOT NULL DEFAULT NOW(),

    CONSTRAINT fk_user FOREIGN KEY (user_id)
        REFERENCES users(id) ON DELETE CASCADE
);

Partitioned Table:

CREATE TABLE events (
    id BIGSERIAL,
    event_type VARCHAR(100),
    payload JSONB,
    created_at TIMESTAMP NOT NULL
) PARTITION BY RANGE (created_at);

CREATE TABLE events_2025_01 PARTITION OF events
    FOR VALUES FROM ('2025-01-01') TO ('2025-02-01');

Vector Table:

CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    title TEXT,
    content TEXT,
    embedding VECTOR(1536)  -- Multi-model extension
);

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

ALTER TABLE

Add Column:

ALTER TABLE users ADD COLUMN phone VARCHAR(20);
ALTER TABLE users ADD COLUMN age INTEGER DEFAULT 0;

Drop Column:

ALTER TABLE users DROP COLUMN phone;

Modify Column:

ALTER TABLE users ALTER COLUMN email TYPE TEXT;
ALTER TABLE users ALTER COLUMN username SET NOT NULL;
ALTER TABLE users ALTER COLUMN age DROP DEFAULT;

Add Constraint:

ALTER TABLE users ADD CONSTRAINT unique_email UNIQUE (email);
ALTER TABLE orders ADD CONSTRAINT check_total CHECK (total >= 0);

Rename:

ALTER TABLE users RENAME TO customers;
ALTER TABLE customers RENAME COLUMN username TO login;

DROP TABLE

DROP TABLE users;
DROP TABLE IF EXISTS users;
DROP TABLE users CASCADE;  -- Also drop dependent objects

CREATE INDEX

B-Tree Index (Default):

CREATE INDEX idx_users_email ON users(email);
CREATE UNIQUE INDEX idx_users_username ON users(username);

Partial Index:

CREATE INDEX idx_active_users ON users(created_at)
WHERE status = 'active';

Composite Index:

CREATE INDEX idx_orders_user_status ON orders(user_id, status);

Expression Index:

CREATE INDEX idx_users_lower_email ON users(LOWER(email));

Full-Text Search Index:

CREATE INDEX idx_documents_content ON documents
USING GIN (to_tsvector('english', content));

Vector Index (HNSW):

CREATE INDEX idx_documents_embedding ON documents
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);

Spatial Index:

CREATE INDEX idx_locations_geom ON locations
USING GIST (geom);

CREATE VIEW

Simple View:

CREATE VIEW active_users AS
SELECT id, username, email
FROM users
WHERE status = 'active';

Materialized View:

CREATE MATERIALIZED VIEW user_stats AS
SELECT
    DATE_TRUNC('day', created_at) as day,
    COUNT(*) as user_count,
    COUNT(DISTINCT email) as unique_emails
FROM users
GROUP BY DATE_TRUNC('day', created_at);

-- Refresh materialized view
REFRESH MATERIALIZED VIEW user_stats;
REFRESH MATERIALIZED VIEW CONCURRENTLY user_stats;

CREATE SCHEMA

CREATE SCHEMA analytics;
CREATE SCHEMA IF NOT EXISTS reporting;

-- Create table in schema
CREATE TABLE analytics.metrics (
    id SERIAL PRIMARY KEY,
    metric_name VARCHAR(255),
    value DOUBLE PRECISION
);

CREATE TYPE

Enum Type:

CREATE TYPE user_role AS ENUM ('admin', 'user', 'guest');

CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    role user_role DEFAULT 'user'
);

Composite Type:

CREATE TYPE address AS (
    street VARCHAR(255),
    city VARCHAR(100),
    country VARCHAR(100),
    postal_code VARCHAR(20)
);

CREATE TABLE customers (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255),
    shipping_address address
);

Data Manipulation Language (DML)

SELECT

Basic Query:

SELECT * FROM users;
SELECT id, username, email FROM users;
SELECT username AS login, email FROM users;

WHERE Clause:

SELECT * FROM users WHERE age >= 18;
SELECT * FROM users WHERE status = 'active' AND age > 21;
SELECT * FROM users WHERE email LIKE '%@gmail.com';
SELECT * FROM users WHERE created_at BETWEEN '2025-01-01' AND '2025-12-31';
SELECT * FROM users WHERE id IN (1, 2, 3, 4, 5);

ORDER BY:

SELECT * FROM users ORDER BY created_at DESC;
SELECT * FROM users ORDER BY age ASC, username DESC;
SELECT * FROM users ORDER BY RANDOM() LIMIT 10;  -- Random sample

LIMIT and OFFSET:

SELECT * FROM users LIMIT 10;
SELECT * FROM users LIMIT 10 OFFSET 20;
SELECT * FROM users ORDER BY id LIMIT 10;  -- Top 10

GROUP BY and HAVING:

SELECT status, COUNT(*) as count
FROM users
GROUP BY status;

SELECT DATE_TRUNC('month', created_at) as month, COUNT(*) as signups
FROM users
GROUP BY DATE_TRUNC('month', created_at)
HAVING COUNT(*) > 100
ORDER BY month DESC;

DISTINCT:

SELECT DISTINCT status FROM users;
SELECT DISTINCT ON (email) * FROM users ORDER BY email, created_at DESC;

JOINS:

-- INNER JOIN
SELECT u.username, o.total
FROM users u
INNER JOIN orders o ON u.id = o.user_id;

-- LEFT JOIN
SELECT u.username, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
GROUP BY u.id, u.username;

-- RIGHT JOIN
SELECT u.username, o.id as order_id
FROM users u
RIGHT JOIN orders o ON u.id = o.user_id;

-- FULL OUTER JOIN
SELECT u.username, o.id
FROM users u
FULL OUTER JOIN orders o ON u.id = o.user_id;

-- CROSS JOIN
SELECT a.name, b.color
FROM products a
CROSS JOIN colors b;

Subqueries:

-- Scalar subquery
SELECT username,
       (SELECT COUNT(*) FROM orders WHERE user_id = users.id) as order_count
FROM users;

-- IN subquery
SELECT * FROM users
WHERE id IN (SELECT DISTINCT user_id FROM orders WHERE total > 100);

-- EXISTS subquery
SELECT * FROM users u
WHERE EXISTS (SELECT 1 FROM orders o WHERE o.user_id = u.id);

-- WITH (CTE)
WITH active_users AS (
    SELECT * FROM users WHERE status = 'active'
),
user_orders AS (
    SELECT user_id, COUNT(*) as order_count
    FROM orders
    GROUP BY user_id
)
SELECT au.username, COALESCE(uo.order_count, 0) as orders
FROM active_users au
LEFT JOIN user_orders uo ON au.id = uo.user_id;

Window Functions:

-- ROW_NUMBER
SELECT username,
       ROW_NUMBER() OVER (ORDER BY created_at) as row_num
FROM users;

-- RANK and DENSE_RANK
SELECT username, score,
       RANK() OVER (ORDER BY score DESC) as rank,
       DENSE_RANK() OVER (ORDER BY score DESC) as dense_rank
FROM leaderboard;

-- PARTITION BY
SELECT username, category, sales,
       RANK() OVER (PARTITION BY category ORDER BY sales DESC) as rank_in_category
FROM sales_data;

-- LAG and LEAD
SELECT date, revenue,
       LAG(revenue) OVER (ORDER BY date) as prev_revenue,
       LEAD(revenue) OVER (ORDER BY date) as next_revenue
FROM daily_revenue;

-- Running totals
SELECT date, amount,
       SUM(amount) OVER (ORDER BY date) as running_total
FROM transactions;

INSERT

Single Row:

INSERT INTO users (username, email) VALUES ('john', 'john@example.com');

Multiple Rows:

INSERT INTO users (username, email) VALUES
    ('alice', 'alice@example.com'),
    ('bob', 'bob@example.com'),
    ('charlie', 'charlie@example.com');

Returning:

INSERT INTO users (username, email)
VALUES ('dave', 'dave@example.com')
RETURNING id, created_at;

From SELECT:

INSERT INTO archived_users
SELECT * FROM users WHERE created_at < '2020-01-01';

ON CONFLICT (Upsert):

INSERT INTO users (id, username, email)
VALUES (1, 'john', 'john@example.com')
ON CONFLICT (id) DO UPDATE SET
    username = EXCLUDED.username,
    email = EXCLUDED.email,
    updated_at = NOW();

-- Do nothing on conflict
INSERT INTO users (username, email)
VALUES ('john', 'john@example.com')
ON CONFLICT (username) DO NOTHING;

UPDATE

Basic Update:

UPDATE users SET status = 'active' WHERE id = 1;
UPDATE users SET age = age + 1;

Multiple Columns:

UPDATE users SET
    email = 'newemail@example.com',
    updated_at = NOW()
WHERE id = 1;

From Another Table:

UPDATE orders o
SET status = 'completed'
FROM shipments s
WHERE o.id = s.order_id AND s.delivered_at IS NOT NULL;

Returning:

UPDATE users
SET last_login = NOW()
WHERE id = 1
RETURNING id, username, last_login;

DELETE

Basic Delete:

DELETE FROM users WHERE id = 1;
DELETE FROM users WHERE status = 'inactive';

Delete All:

DELETE FROM users;  -- Deletes all rows (use with caution!)
TRUNCATE TABLE users;  -- Faster for deleting all rows

Returning:

DELETE FROM users WHERE status = 'banned'
RETURNING id, username;

Using Another Table:

DELETE FROM users u
USING banned_emails b
WHERE u.email = b.email;

Transaction Control Language (TCL)

Transactions

Basic Transaction:

BEGIN;
INSERT INTO users (username, email) VALUES ('test', 'test@example.com');
UPDATE accounts SET balance = balance - 100 WHERE id = 1;
UPDATE accounts SET balance = balance + 100 WHERE id = 2;
COMMIT;

Rollback:

BEGIN;
DELETE FROM users WHERE status = 'inactive';
-- Oops, made a mistake!
ROLLBACK;

Savepoints:

BEGIN;
INSERT INTO users (username) VALUES ('user1');
SAVEPOINT sp1;

INSERT INTO users (username) VALUES ('user2');
SAVEPOINT sp2;

INSERT INTO users (username) VALUES ('user3');
-- Rollback to savepoint
ROLLBACK TO sp2;

COMMIT;  -- Only user1 and user2 are inserted

Isolation Levels:

BEGIN TRANSACTION ISOLATION LEVEL READ COMMITTED;
BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ;
BEGIN TRANSACTION ISOLATION LEVEL SERIALIZABLE;

Multi-Model Extensions

Vector Operations

Vector Similarity Search:

-- Cosine similarity
SELECT id, title,
       embedding <=> '[0.1, 0.2, 0.3, ...]'::VECTOR as distance
FROM documents
ORDER BY embedding <=> '[0.1, 0.2, 0.3, ...]'::VECTOR
LIMIT 10;

-- Euclidean distance (L2)
SELECT id, title,
       embedding <-> '[0.1, 0.2, 0.3, ...]'::VECTOR as distance
FROM documents
ORDER BY embedding <-> '[0.1, 0.2, 0.3, ...]'::VECTOR
LIMIT 10;

-- Inner product
SELECT id, title,
       embedding <#> '[0.1, 0.2, 0.3, ...]'::VECTOR as distance
FROM documents
ORDER BY embedding <#> '[0.1, 0.2, 0.3, ...]'::VECTOR DESC
LIMIT 10;

Vector Functions:

-- Vector dimensions
SELECT vector_dims(embedding) FROM documents LIMIT 1;

-- Vector norm
SELECT vector_norm(embedding) FROM documents;

-- Normalize vector
UPDATE documents SET embedding = vector_normalize(embedding);

-- Average vector
SELECT AVG(embedding) FROM documents;

Graph Operations

Recursive CTE for Graph Traversal:

WITH RECURSIVE connected AS (
    -- Base case: start node
    SELECT id, name, parent_id, 1 as depth
    FROM categories
    WHERE id = 1

    UNION ALL

    -- Recursive case: connected nodes
    SELECT c.id, c.name, c.parent_id, co.depth + 1
    FROM categories c
    INNER JOIN connected co ON c.parent_id = co.id
    WHERE co.depth < 10  -- Max depth
)
SELECT * FROM connected;

Cypher-Style Graph Query:

-- Find friends of friends
SELECT DISTINCT u3.username
FROM users u1
JOIN friendships f1 ON u1.id = f1.user_id
JOIN users u2 ON f1.friend_id = u2.id
JOIN friendships f2 ON u2.id = f2.user_id
JOIN users u3 ON f2.friend_id = u3.id
WHERE u1.username = 'alice' AND u3.id != u1.id;

Shortest Path:

WITH RECURSIVE paths AS (
    SELECT
        id as node,
        ARRAY[id] as path,
        0 as length
    FROM nodes
    WHERE id = 1  -- Start node

    UNION ALL

    SELECT
        e.target_node,
        p.path || e.target_node,
        p.length + e.weight
    FROM paths p
    JOIN edges e ON p.node = e.source_node
    WHERE NOT e.target_node = ANY(p.path)  -- Avoid cycles
)
SELECT path, length
FROM paths
WHERE node = 10  -- End node
ORDER BY length
LIMIT 1;

Document Operations

JSON Queries:

-- Extract JSON field
SELECT id, metadata->>'name' as name
FROM documents
WHERE metadata->>'status' = 'active';

-- JSON array contains
SELECT * FROM documents
WHERE metadata @> '{"tags": ["important"]}';

-- JSON path query
SELECT id, jsonb_path_query(metadata, '$.user.email') as email
FROM documents;

-- Aggregate JSON
SELECT jsonb_agg(metadata) FROM documents;

MongoDB-Compatible Syntax:

-- Find documents
SELECT * FROM collection
WHERE doc @> '{"status": "active", "age": {"$gte": 18}}';

-- Update document
UPDATE collection
SET doc = jsonb_set(doc, '{status}', '"inactive"')
WHERE doc->>'_id' = '123';

Time-Series Operations

Time-Bucket Aggregation:

SELECT
    time_bucket('1 hour', timestamp) as hour,
    AVG(value) as avg_value,
    MAX(value) as max_value,
    MIN(value) as min_value
FROM metrics
WHERE timestamp > NOW() - INTERVAL '24 hours'
GROUP BY time_bucket('1 hour', timestamp)
ORDER BY hour;

Gap Filling:

SELECT
    time_bucket_gapfill('5 minutes', timestamp) as time,
    interpolate(AVG(temperature)) as temperature
FROM sensor_data
WHERE timestamp BETWEEN '2025-11-01' AND '2025-11-02'
GROUP BY time_bucket_gapfill('5 minutes', timestamp);

Continuous Aggregates:

CREATE MATERIALIZED VIEW hourly_metrics
WITH (timescaledb.continuous) AS
SELECT
    time_bucket('1 hour', timestamp) as hour,
    sensor_id,
    AVG(value) as avg_value
FROM metrics
GROUP BY hour, sensor_id;

Spatial Operations

Point in Polygon:

SELECT * FROM locations
WHERE ST_Contains(
    ST_GeomFromText('POLYGON((0 0, 0 10, 10 10, 10 0, 0 0))'),
    geom
);

Distance Query:

SELECT id, name,
       ST_Distance(geom, ST_MakePoint(-122.4194, 37.7749)) as distance
FROM locations
WHERE ST_DWithin(geom, ST_MakePoint(-122.4194, 37.7749), 10000)  -- 10km
ORDER BY distance
LIMIT 10;

Nearest Neighbor:

SELECT id, name
FROM locations
ORDER BY geom <-> ST_MakePoint(-122.4194, 37.7749)
LIMIT 5;

Built-in Functions

String Functions

SELECT CONCAT('Hello', ' ', 'World');  -- 'Hello World'
SELECT SUBSTRING('Hello World' FROM 1 FOR 5);  -- 'Hello'
SELECT UPPER('hello');  -- 'HELLO'
SELECT LOWER('WORLD');  -- 'world'
SELECT LENGTH('Hello');  -- 5
SELECT TRIM('  hello  ');  -- 'hello'
SELECT REPLACE('Hello World', 'World', 'Universe');  -- 'Hello Universe'
SELECT SPLIT_PART('a,b,c', ',', 2);  -- 'b'
SELECT REGEXP_REPLACE('Hello123', '[0-9]', '', 'g');  -- 'Hello'

Numeric Functions

SELECT ABS(-5);  -- 5
SELECT CEIL(4.3);  -- 5
SELECT FLOOR(4.8);  -- 4
SELECT ROUND(4.567, 2);  -- 4.57
SELECT POWER(2, 3);  -- 8
SELECT SQRT(16);  -- 4
SELECT MOD(10, 3);  -- 1
SELECT RANDOM();  -- Random number between 0 and 1

Date/Time Functions

SELECT NOW();  -- Current timestamp
SELECT CURRENT_DATE;  -- Current date
SELECT CURRENT_TIME;  -- Current time
SELECT EXTRACT(YEAR FROM NOW());  -- 2025
SELECT DATE_TRUNC('hour', NOW());  -- Truncate to hour
SELECT AGE('2025-01-01', '2020-01-01');  -- Interval '5 years'
SELECT NOW() + INTERVAL '1 day';  -- Tomorrow
SELECT TO_CHAR(NOW(), 'YYYY-MM-DD HH24:MI:SS');  -- Format timestamp

Aggregate Functions

SELECT COUNT(*) FROM users;
SELECT COUNT(DISTINCT email) FROM users;
SELECT SUM(total) FROM orders;
SELECT AVG(age) FROM users;
SELECT MIN(created_at), MAX(created_at) FROM users;
SELECT STRING_AGG(username, ', ') FROM users;  -- 'alice, bob, charlie'
SELECT ARRAY_AGG(id) FROM users;  -- [1, 2, 3, ...]
SELECT JSON_AGG(username) FROM users;  -- ["alice", "bob", ...]

JSON Functions

SELECT json_build_object('name', 'John', 'age', 30);
SELECT json_agg(username) FROM users;
SELECT jsonb_pretty('{"a":1}'::jsonb);
SELECT jsonb_set('{"a":1}'::jsonb, '{b}', '2'::jsonb);
SELECT jsonb_strip_nulls('{"a":1,"b":null}'::jsonb);

Array Functions

SELECT ARRAY[1, 2, 3, 4, 5];
SELECT ARRAY_LENGTH(ARRAY[1, 2, 3], 1);  -- 3
SELECT 2 = ANY(ARRAY[1, 2, 3]);  -- true
SELECT ARRAY_AGG(id) FROM users;
SELECT UNNEST(ARRAY[1, 2, 3]);  -- Expands array to rows

Conditional Functions

SELECT COALESCE(NULL, NULL, 'default');  -- 'default'
SELECT NULLIF(value, 0);  -- Returns NULL if value is 0
SELECT GREATEST(1, 5, 3, 9, 2);  -- 9
SELECT LEAST(1, 5, 3, 9, 2);  -- 1
SELECT CASE
    WHEN age < 18 THEN 'minor'
    WHEN age < 65 THEN 'adult'
    ELSE 'senior'
END as category FROM users;

Full-Text Search

Basic Search

-- Create text search vector
SELECT to_tsvector('english', 'The quick brown fox jumps over the lazy dog');

-- Search
SELECT * FROM documents
WHERE to_tsvector('english', content) @@ to_tsquery('english', 'fox & dog');

With Ranking

SELECT id, title,
       ts_rank(to_tsvector('english', content), query) as rank
FROM documents, to_tsquery('english', 'database & search') query
WHERE to_tsvector('english', content) @@ query
ORDER BY rank DESC;

With Index

-- Add generated column
ALTER TABLE documents
ADD COLUMN content_tsv tsvector
GENERATED ALWAYS AS (to_tsvector('english', content)) STORED;

-- Create GIN index
CREATE INDEX idx_documents_content_tsv ON documents USING GIN(content_tsv);

-- Query
SELECT * FROM documents
WHERE content_tsv @@ to_tsquery('english', 'database & search');

Examples

-- Register user
INSERT INTO users (username, email, password_hash)
VALUES ('john', 'john@example.com', crypt('password123', gen_salt('bf')))
RETURNING id;

-- Login verification
SELECT id, username FROM users
WHERE username = 'john'
  AND password_hash = crypt('password123', password_hash);

E-Commerce Order Processing

BEGIN;

-- Create order
INSERT INTO orders (user_id, status, total)
VALUES (123, 'pending', 0)
RETURNING id INTO @order_id;

-- Add order items
INSERT INTO order_items (order_id, product_id, quantity, price)
SELECT @order_id, product_id, quantity, price
FROM cart_items
WHERE user_id = 123;

-- Update order total
UPDATE orders
SET total = (
    SELECT SUM(quantity * price)
    FROM order_items
    WHERE order_id = @order_id
)
WHERE id = @order_id;

-- Clear cart
DELETE FROM cart_items WHERE user_id = 123;

COMMIT;

Analytics Query

WITH daily_metrics AS (
    SELECT
        DATE_TRUNC('day', created_at) as day,
        COUNT(*) as signups,
        COUNT(*) FILTER (WHERE referrer IS NOT NULL) as referred_signups
    FROM users
    WHERE created_at >= NOW() - INTERVAL '30 days'
    GROUP BY DATE_TRUNC('day', created_at)
),
retention AS (
    SELECT
        DATE_TRUNC('day', u.created_at) as cohort_day,
        COUNT(DISTINCT CASE WHEN a.created_at >= u.created_at + INTERVAL '7 days' THEN u.id END) as week_1_retained
    FROM users u
    LEFT JOIN activities a ON u.id = a.user_id
    GROUP BY DATE_TRUNC('day', u.created_at)
)
SELECT
    dm.day,
    dm.signups,
    dm.referred_signups,
    ROUND(100.0 * dm.referred_signups / dm.signups, 2) as referral_rate,
    COALESCE(r.week_1_retained, 0) as week_1_retained
FROM daily_metrics dm
LEFT JOIN retention r ON dm.day = r.cohort_day
ORDER BY dm.day DESC;

Hybrid Search (Vector + Full-Text)

WITH vector_results AS (
    SELECT id, title,
           embedding <=> $1::vector as vector_distance
    FROM documents
    ORDER BY embedding <=> $1::vector
    LIMIT 100
),
text_results AS (
    SELECT id, title,
           ts_rank(to_tsvector('english', content), query) as text_rank
    FROM documents, to_tsquery('english', $2) query
    WHERE to_tsvector('english', content) @@ query
    ORDER BY text_rank DESC
    LIMIT 100
)
SELECT DISTINCT
    COALESCE(vr.id, tr.id) as id,
    COALESCE(vr.title, tr.title) as title,
    (0.7 * (1 - COALESCE(vr.vector_distance, 1)) +
     0.3 * COALESCE(tr.text_rank, 0)) as combined_score
FROM vector_results vr
FULL OUTER JOIN text_results tr ON vr.id = tr.id
ORDER BY combined_score DESC
LIMIT 20;

Performance Tips

Use indexes for frequently queried columns
EXPLAIN ANALYZE to understand query plans
Batch inserts instead of many single inserts
Use CTEs for complex queries
Partition large tables by date or range
Materialized views for expensive aggregations
Connection pooling for better concurrency
Prepared statements to avoid SQL injection and improve performance

HeliosDB SQL API Reference

HeliosDB SQL API Reference

Introduction

Connection

Connection String

Connection Parameters

Using pg Client Libraries

Data Definition Language (DDL)

CREATE TABLE

ALTER TABLE

DROP TABLE

CREATE INDEX

CREATE VIEW

CREATE SCHEMA

CREATE TYPE

Data Manipulation Language (DML)

SELECT

INSERT

UPDATE

DELETE

Transaction Control Language (TCL)

Transactions

Multi-Model Extensions

Vector Operations

Graph Operations

Document Operations

Time-Series Operations

Spatial Operations

Built-in Functions

String Functions

Numeric Functions

Date/Time Functions

Aggregate Functions

JSON Functions

Array Functions

Conditional Functions

Full-Text Search

Basic Search

With Ranking

With Index

Examples

User Registration and Login

E-Commerce Order Processing

Analytics Query

Hybrid Search (Vector + Full-Text)

Performance Tips

See Also