Database Analytics (ClickHouse)

Demonstrates how a product manager can directly ask natural language questions to a ClickHouse database to get LLM usage insights. This use case showcases LangDB’s ability to translate plain queries into structured SQL and return visual or tabular outputs in real-time.

Objective

Ask natural language questions about flight operations, delays, and patterns from the ontime dataset, such as:

Example Queries:

• "What is the number of flights per day from the year 2000 to 2008"

• "How many flights delayed by more than 10 minutes, grouped by the day of the week, for 2000-2008"

• "What are the total number of delays"

Behind the Scenes

An AI agent interprets your question, generates optimized ClickHouse SQL, and returns a clean, structured result.

MCP Server Setup: ClickHouse

To connect an AI agent to your ClickHouse database, the following environment variables must be configured:

Variable	Description
clickhouse_host	ClickHouse server hostname
clickhouse_port	ClickHouse server port
clickhouse_user	ClickHouse username
clickhouse_password	ClickHouse password
clickhouse_secure	Whether to use TLS
clickhouse_verify	Whether to verify TLS certificates
clickhouse_connect_timeout	Connection timeout (seconds, optional)
clickhouse_send_receive_timeout	Send/receive timeout (seconds, optional)

Credentials are securely stored and encrypted. Never share them externally.

Checkout Clickhouse MCP Server: https://app.langdb.ai/mcp-servers/clickhouse

Interaction Flow

How many flights delayed by more than 10 minutes

Output

{
  "delayed_flights_over_10_min": 264924
}

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Key Takeaways

• Product teams can ask questions directly to the database.

• No SQL writing required — just ask and analyze.

• Enables faster decision-making around usage trends and cost controls.