Impala SQL Agent

The Impala SQL Agent provides an intelligent interface for Apache Impala databases. It translates natural language questions into optimized Impala SQL queries, handles distributed query execution, and processes large-scale data analytics while maintaining Impala-specific optimizations.

Impala SQL Agent workflow and architecture

Configuration Parameters

Required Input Parameters

dsn: Data Source Name for Impala connection
username: Authentication username
password: Authentication password
question: Natural language query to be processed

Optional Configuration

llm: Language model configuration
- model_name: Name of the language model
- temperature: Response creativity (0-1)
- max_tokens: Maximum response length
databases: List of accessible databases
connection_params: Additional connection parameters
- auth_mechanism: Authentication type
- use_ssl: Enable SSL connection
- timeout: Query timeout in seconds

Output Format

{
  "query_results": {
    "sql": string,
    "results": array,
    "columns": [
      {
        "name": string,
        "type": string,
        "nullable": boolean
      }
    ],
    "metadata": {
      "row_count": number,
      "bytes_scanned": number,
      "execution_time": number
    }
  },
  "performance_metrics": {
    "cpu_time": number,
    "memory_usage": number,
    "io_stats": {
      "hdfs_bytes_read": number,
      "local_bytes_read": number,
      "cache_hit_ratio": number
    },
    "resource_pools": {
      "name": string,
      "memory_limit": number,
      "cpu_cores": number
    }
  },
  "execution_profile": {
    "query_plan": string,
    "bottlenecks": array,
    "optimization_suggestions": array
  }
}

Features

Natural language to Impala SQL translation
Distributed query optimization
HDFS integration
Resource pool management
Query performance monitoring
Schema inference
Error handling and recovery
Query plan optimization

Note: Ensure proper resource pool configuration for optimal query performance. Consider using partitioned tables and statistics for better query optimization.

Tip: Utilize Impala's caching mechanisms and metadata caching for frequently accessed tables. Monitor resource usage and adjust pool configurations accordingly.

Example Usage

const impalaAgent = new ImpalaSQLAgent({
  dsn: "impala://cluster.example.com:21050",
  username: "analyst",
  password: "****",
  llm: {
    model_name: "gpt-4",
    temperature: 0.3
  },
  databases: ["sales", "marketing", "operations"],
  connection_params: {
    auth_mechanism: "LDAP",
    use_ssl: true,
    timeout: 300
  }
});

const results = await impalaAgent.query({
  question: "What were the top 10 selling products last quarter by revenue?"
});