Hive SQL Agent

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

Hive SQL Agent workflow and architecture

Configuration Parameters

Required Input Parameters

jdbc_url: Hive JDBC connection URL
http_path: HTTP path for Hive server
username: Authentication username
password: Authentication password
jdbc_driver_path: Path to Hive JDBC driver
database: Target Hive database
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
connection_params: Additional connection parameters
- fetch_size: Number of rows to fetch per batch
- timeout: Query timeout in seconds
- ssl_enabled: Enable SSL connection

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": {
    "map_reduce_stats": {
      "map_tasks": number,
      "reduce_tasks": number,
      "bytes_read": number,
      "bytes_written": number
    },
    "resource_usage": {
      "cpu_time": number,
      "memory_used": number,
      "hdfs_io": number
    },
    "execution_stats": {
      "compile_time": number,
      "execution_time": number,
      "total_time": number
    }
  },
  "optimization_info": {
    "partitions_scanned": number,
    "partition_pruning": boolean,
    "vectorization": boolean,
    "storage_format": string
  }
}

Features

Natural language to HiveQL translation
Query optimization for Hive
Partition pruning
Vectorized query execution
Performance monitoring
Resource usage tracking
Error handling and recovery
Result set formatting

Note: Consider using ORC or Parquet file formats for better query performance. Enable partition pruning and vectorization when possible.

Tip: Use appropriate fetch sizes for large result sets. Monitor resource usage and adjust configurations based on workload patterns.

Example Usage

const hiveAgent = new HiveSQLAgent({
  jdbc_url: "jdbc:hive2://hiveserver:10000",
  http_path: "cliservice",
  username: "hive_user",
  password: "****",
  jdbc_driver_path: "/path/to/hive-jdbc.jar",
  database: "sales_db",
  llm: {
    model_name: "gpt-4",
    temperature: 0.3
  }
});

const results = await hiveAgent.query({
  question: "What were the total sales by region for last quarter?",
  connection_params: {
    fetch_size: 1000,
    timeout: 300,
    ssl_enabled: true
  }
});