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Quickstart Guide

Get up and running with AVA Database in under 5 minutes.

Installation

Prerequisites

  • Linux (Ubuntu 20.04+ or CentOS 7+) or macOS
  • Python 3.7+ or R 3.6+ (for API bindings)
  • 4GB RAM minimum (8GB+ recommended)
  • Valid AVA license (trial or commercial)

Step 1: Download AVA

Download the latest release from our website or use the installer script: 

# Download installer
curl -O https://download.avainformatics.com/ava-install.sh

# Make executable
chmod +x ava-install.sh

# Run installer
./ava-install.sh

Step 2: Install Python API (Optional) 

# Install via pip
pip install avapy

# Or build from source
cd api/python
python setup.py install

Step 3: Set Up License

Place your license file in one of these locations: 

# Option 1: Current directory
cp ava.license ./

# Option 2: Home directory
cp ava.license ~/ava.license

# Option 3: System-wide
sudo cp ava.license /etc/ava/ava.license

# Option 4: Environment variable
export AVA_LICENSE_FILE=/path/to/ava.license

Download Sample Data

Get started quickly with our sample stock prices dataset. This CSV file contains historical stock price data with TICKER, DATE, and PRICE columns.

prices.csv

Sample stock price data for testing and learning

Download Sample Data

Dataset Information

  • Format: CSV (comma-separated values)
  • Columns: TICKER (string), DATE (date), PRICE (double)
  • Size: ~12 MB
  • Use case: Perfect for following along with all SQL examples in this documentation

Your First Query

Download the sample data above, then load it and run a query using the Python API: 

import avapy

# Load the sample CSV file
avapy.LoadTable("prices.csv", "prices")

# Run a SQL query - get top 10 stocks by average price
result = avapy.asql("""
    SELECT TICKER,
           COUNT(*) as num_records,
           AVG(PRICE) as avg_price,
           MIN(PRICE) as min_price,
           MAX(PRICE) as max_price
    FROM prices
    GROUP BY TICKER
    ORDER BY avg_price DESC
    LIMIT 10
""")

print("Top 10 Stocks by Average Price")
print(result)

Success!

You've just run your first AVA query! The results show stock tickers with their record counts, average prices, and price ranges - all computed in milliseconds.

Common Operations

Loading Data 

# From CSV
avapy.LoadTable("data.csv", "my_table")

# From multiple files
for file in ["jan.csv", "feb.csv", "mar.csv"]:
    avapy.LoadTable(file, "sales")

# View table structure
avapy.asql("DESCRIBE my_table")

Querying Data 

# Basic SELECT
avapy.asql("SELECT * FROM sales WHERE revenue > 1000")

# Aggregations
avapy.asql("""
    SELECT region,
           COUNT(*) as num_sales,
           SUM(revenue) as total_revenue,
           AVG(revenue) as avg_revenue
    FROM sales
    GROUP BY region
""")

# Joins
avapy.asql("""
    SELECT s.*, c.customer_name, c.segment
    FROM sales s
    JOIN customers c ON s.customer_id = c.id
""")

Running Regression 

# Create regression model
avapy.asql("""
    CREATE REGRESSION MODEL price_model
    AS SELECT sqft, bedrooms, bathrooms, age
    FROM housing_data
    PREDICT price
""")

# Make predictions
predictions = avapy.asql("""
    SELECT *,
           PREDICT(price_model) as predicted_price
    FROM new_listings
""")

Next Steps