Overview

SQL Explorer gives you direct SQL access to billions of rows of indexed blockchain data, without managing infrastructure. Query trades, orders, positions, liquidations, and more using standard SQL, all within the same platform you already use for RPC and Streams.

SQL Explorer is currently in Beta. We are actively expanding dataset coverage and adding new features based on user feedback.

Using AI Agents?

Install the Quicknode Skill to equip your agent with full knowledge of SQL Explorer and the broader Quicknode platform:

npx skills add quiknode-labs/blockchain-skills

For SQL query generation, download the machine-readable schema and include in your agent's context. See the Schema Reference for full details.

Why SQL Explorer

SQL Explorer enables flexible querying of indexed blockchain data using standard SQL. Instead of relying on predefined API endpoints, you can write custom queries directly against the underlying datasets.

Key characteristics:

• No infrastructure to manage: Query blockchain data through SQL without running nodes, databases, or indexers
• Full query flexibility: Write SQL queries with standard syntax including functions, subqueries, window functions, and joins
• Built for scale: Queries execute against billions of rows using columnar storage with partition-aware optimization
• Fully Integrated: Available alongside RPC, Streams, and other Quicknode products in the same platform

Features of SQL Explorer

SQL Explorer includes tools for querying, analyzing, and integrating blockchain data:

• SQL Editor: In-browser editor with syntax highlighting and support for standard SQL, including subqueries, window functions, GROUP BY, and JOIN
• Schema Browser: View table metadata including row counts, column names, data types, sort keys (⚡), and partition strategies. See the Schema Reference for full documentation
• Pre-built Queries: Ready-to-use queries for common use cases across supported chains
• Visualization: Generate charts with configurable axes, multi-series overlays, and hover tooltips
• REST API Access: Execute queries programmatically via POST to https://api.quicknode.com/sql/rest/v1/query with API key authentication
• Query Builder: Generate REST API calls (cURL) from the UI for any query
• Saved Query Library: Save your frequently used queries for quick access
• Execution Metrics:: View query time, rows returned, rows scanned, and bytes processed for each query

How to Access SQL Explorer

SQL Explorer is available through the Quicknode Dashboard for interactive queries and the REST API for programmatic access.

Using the Dashboard

The Quicknode Dashboard provides an interactive environment for building, testing, and visualizing SQL queries:

1. Open SQL Explorer in your Quicknode dashboard
2. Browse the Tables tab to explore available datasets, view schema details, or select a pre-built query from the Queries tab
3. Write a new query or modify a pre-built query in the editor
4. Click Run to execute your query
5. View results in the Results tab or switch to Charts for visualization
6. Click Save to store the query, or API to generate the corresponding REST request

Using the REST API

The REST API allows you to execute SQL queries programmatically.

SQL queries are executed via HTTP POST request to https://api.quicknode.com/sql/rest/v1/query using your Quicknode API key. Results are returned as JSON with query execution statistics.

Get the API Call from Dashboard:

After running a query in the SQL Explorer Dashboard, click API in the top to generate a ready-to-use cURL request with your query included.

Example Request:

curl -X POST 'https://api.quicknode.com/sql/rest/v1/query' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -H 'x-api-key: YOUR_API_KEY' \
  -d '{
    "query": "SELECT time, validator, reward, block_number FROM hyperliquid_validator_rewards ORDER BY block_number DESC LIMIT 100",
    "clusterId": "hyperliquid-core-mainnet"
  }'

Example Response:

{
    "meta": [
        {
            "name": "time",
            "type": "DateTime64(6, 'UTC')"
        }
    ],
    "data": [
        {
            "time": "2026-03-30 16:24:00.058516",
            "validator": "0xf8efb4cb844a8458114994203d7b0bfe2422a288",
            "reward": 0.18548686,
            "block_number": 940269912
        },
        {
            "time": "2026-03-30 16:24:00.058516",
            "validator": "0xeeee86f718f9da3e7250624a460f6ea710e9c006",
            "reward": 0.50774076,
            "block_number": 940269912
        }
    ],
    "rows": 100,
    "rows_before_limit_at_least": 120,
    "statistics": {
        "elapsed": 0.073805665,
        "rows_read": 71918,
        "bytes_read": 5322148
    }
}

Understanding the Response:

The API response contains the following fields:

• meta: Array of column metadata with name and type for each column in the result set
• data: Array of objects containing the actual query results, with each object representing one row
• rows: Number of rows returned in this response
• rows_before_limit_at_least: Total number of rows that matched the query before applying LIMIT (useful for pagination)
• statistics: Performance metrics for query optimization:
  • elapsed: Total query execution time in seconds
  • rows_read: Total number of rows scanned during execution (may be more than returned rows due to filtering)
  • bytes_read: Total data scanned in bytes (helpful for understanding query cost and optimization opportunities)

Use the statistics fields to understand query performance and optimize scans. Lower rows_read and bytes_read generally indicate more efficient queries.

See the REST API Overview guide for detailed instructions, authentication setup, and examples.

Get Started with Queries

Explore the SQL Explorer Cookbook for 40+ pre-built queries covering wallet activity, validator rewards, liquidations, and more. Each example includes customizable parameters and ready-to-use code in SQL, cURL, TypeScript, and Python.

Billing

SQL Explorer uses API credits from your existing Quicknode account. Credit usage is based on query execution time and data scanned:

credits = max(10, [elapsed×1000×0.05 + bytes_read/1048576×2])

Where:

• Minimum charge: 10 credits per query
• elapsed: Query execution time in seconds (shown in response statistics)
• bytes_read: Total data scanned in bytes (shown in response statistics)

Each API response includes these values in the statistics object, so you can monitor and optimize query costs. You can view your credit usage in the Quicknode Dashboard.

Supported Networks

SQL Explorer currently supports the following chains with additional chains coming soon:

Chain	Network	Tables	Total Rows	Coverage
Hyperliquid	Mainnet	View Schema	Billions+	Trades, orders, fills, funding, order book diffs, perpetual markets, spot markets, blocks, transactions, system actions, builder activity, staking, ledger, clearinghouse states, oracle prices, sub-accounts, vault equities, agents, bridge events, display names, asset transfers, TWAP statuses, referrals, validator rewards, delegator rewards, summaries, metrics, and more

Available Schema

SQL Explorer provides comprehensive coverage of Hyperliquid HyperCore with billions of rows of indexed data across multiple tables including trades, orders, fills, funding, market data, and more.

View Complete Schema

Browse the complete schema reference for column definitions, data types, sort keys, and partitioning details. The schema is continuously updated and optimized for querying.

Frequently Asked Questions

What is Quicknode SQL Explorer?▼

SQL Explorer provides direct SQL access to indexed blockchain data without requiring any infrastructure. You can query billions of rows of on-chain data using standard SQL syntax and receive results in seconds.

How do I access SQL Explorer?▼

You can access SQL Explorer through the Quicknode Dashboard for interactive queries or via the SQL Explorer REST API for programmatic access.

Which chains are supported?▼

SQL Explorer currently supports Hyperliquid Mainnet with billions of rows of indexed data. Additional chains are coming soon.

What data tables are available?▼

For Hyperliquid, SQL Explorer provides 37 tables covering trading data (trades, orders, fills, order book diffs), market data (perpetual/spot markets, market contexts), funding, infrastructure (blocks, transactions, system actions), builder activity, staking, ledger updates, clearinghouse states, oracle prices, referrals, sub-accounts, vault equities, agents, bridge events, display names, hourly metrics, daily metrics, and more.

Can I create my own queries?▼

Yes. SQL Explorer supports standard SQL syntax including functions, subqueries, window functions, GROUP BY, and JOINs. You can write any SQL query to access the data you need.

How does the REST API work?▼

Any SQL query can be executed programmatically via a standard HTTP POST request to https://api.quicknode.com/sql/rest/v1/query using your Quicknode API key. Results are returned as structured JSON. See the REST API Overview guide for detailed instructions.

How does billing work?▼

SQL Explorer uses API credits from your existing Quicknode account. Credits are calculated using this formula: credits = max(10, [elapsed×1000×0.05 + bytes_read/1048576×2]). This means each query costs a minimum of 10 credits, with additional credits based on query execution time (elapsed in seconds) and data scanned (bytes_read). The statistics object in each API response shows these values so you can estimate costs and optimize queries.

Is SQL Explorer production ready?▼

SQL Explorer is currently in Beta and available to all Quicknode users. We are actively expanding dataset coverage and adding new features based on user feedback.

How do I optimize my queries?▼

Use the schema browser to identify sort keys (marked ⚡) and partition strategies. Write partition-aware queries that filter on time ranges to skip irrelevant partitions. Hyperliquid tables partition monthly by toYYYYMM(block_time). Every query returns execution stats (query time, result rows, rows read, bytes read) so you can optimize iteratively.

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If you have any feedback or questions about this documentation, let us know. We'd love to hear from you!