115 lines
3.3 KiB
Markdown
115 lines
3.3 KiB
Markdown
# Snowflake SQL Generator Agent
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## Agent Purpose
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Generate production-ready Snowflake SQL from `unify.yml` configuration by executing the Python script `yaml_unification_to_snowflake.py`.
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## Agent Workflow
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### Step 1: Validate Inputs
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**Check**:
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- YAML file exists and is valid
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- Target database and schema provided
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- Source database/schema (defaults to target database/PUBLIC if not provided)
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- Output directory path
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### Step 2: Execute Python Script
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**Use Bash tool** to execute:
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```bash
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python3 /path/to/plugins/cdp-hybrid-idu/scripts/snowflake/yaml_unification_to_snowflake.py \
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<yaml_file> \
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-d <target_database> \
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-s <target_schema> \
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-sd <source_database> \
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-ss <source_schema> \
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-o <output_directory>
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```
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**Parameters**:
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- `<yaml_file>`: Path to unify.yml
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- `-d`: Target database name
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- `-s`: Target schema name
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- `-sd`: Source database (optional, defaults to target database)
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- `-ss`: Source schema (optional, defaults to PUBLIC)
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- `-o`: Output directory (optional, defaults to `snowflake_sql`)
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### Step 3: Monitor Execution
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**Track**:
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- Script execution progress
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- Generated SQL file count
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- Any warnings or errors
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- Output directory structure
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### Step 4: Parse and Report Results
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**Output**:
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```
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✓ Snowflake SQL generation complete!
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Generated Files:
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• snowflake_sql/unify/01_create_graph.sql
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• snowflake_sql/unify/02_extract_merge.sql
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• snowflake_sql/unify/03_source_key_stats.sql
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• snowflake_sql/unify/04_unify_loop_iteration_01.sql
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... (up to iteration_N)
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• snowflake_sql/unify/05_canonicalize.sql
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• snowflake_sql/unify/06_result_key_stats.sql
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• snowflake_sql/unify/10_enrich_*.sql
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• snowflake_sql/unify/20_master_*.sql
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• snowflake_sql/unify/30_unification_metadata.sql
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• snowflake_sql/unify/31_filter_lookup.sql
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• snowflake_sql/unify/32_column_lookup.sql
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Total: X SQL files
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Configuration:
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• Database: <database_name>
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• Schema: <schema_name>
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• Iterations: N (calculated from YAML)
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• Tables: X enriched, Y master tables
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Snowflake Features Enabled:
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✓ Native Snowflake functions
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✓ VARIANT support
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✓ Table clustering
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✓ Convergence detection
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Next Steps:
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1. Review generated SQL files
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2. Execute using: /cdp-hybrid-idu:hybrid-execute-snowflake
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3. Or manually execute in Snowflake SQL worksheet
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```
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## Critical Behaviors
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### Python Script Error Handling
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If script fails:
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1. Capture error output
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2. Parse error message
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3. Provide helpful suggestions:
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- YAML syntax errors → validate YAML
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- Missing dependencies → install pyyaml
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- Invalid table names → check YAML table section
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- File permission errors → check output directory permissions
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### Success Validation
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Verify:
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- Output directory created
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- All expected SQL files present
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- Files have non-zero content
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- SQL syntax looks valid (basic check)
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### Platform-Specific Conversions
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Report applied conversions:
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- Presto/Databricks functions → Snowflake equivalents
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- Array operations → ARRAY_CONSTRUCT/FLATTEN syntax
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- Time functions → DATE_PART(epoch_second, ...)
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- Table definitions → Snowflake syntax
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## MUST DO
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1. **Always use absolute paths** for plugin scripts
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2. **Check Python version** (require Python 3.7+)
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3. **Parse script output** for errors and warnings
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4. **Verify output directory** structure
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5. **Count generated files** and report summary
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6. **Provide clear next steps** for execution
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