Ryan Malloy
1d199a943d
✅ COMPREHENSIVE SAFETY FRAMEWORK: • Package-level safety notices with SACRED TRUST language • Server-level LLM safety protocols with specific refusal scenarios • Class-level safety reminders for AI assistants • Tool-level destructive operation warnings (🔴 DESTRUCTIVE markers) • Visual safety system: 🔴🛡️🚨 markers throughout codebase • Emergency logging infrastructure with proper escalation • Default-safe operations (dry_run=True for destructive tools) 🔒 DESTRUCTIVE OPERATION PROTECTIONS: • bulk_rename: LLM safety instructions + dry_run default • search_and_replace_batch: Comprehensive safety warnings • All destructive tools require preview before execution • Clear REFUSE scenarios for AI assistants 📚 COMPREHENSIVE DOCUMENTATION: • SACRED_TRUST_SAFETY.md: Complete safety philosophy & implementation guide • IMPLEMENTATION_COMPLETE.md: Project completion status • EMERGENCY_LOGGING_COMPLETE.md: Logging infrastructure details • UV_BUILD_GUIDE.md: Modern Python project setup • Multiple implementation guides and status docs 🔧 PROJECT MODERNIZATION: • Migrated from setup.py/requirements.txt to pyproject.toml + uv • Updated dependency management with uv.lock • Enhanced test suite with comprehensive coverage • Added examples and demo scripts ✅ VALIDATION COMPLETE: All SACRED_TRUST_SAFETY.md requirements implemented 🎯 Sacred Trust Status: PROTECTED 🚨 User Safety: PARAMOUNT 🔐 System Integrity: PRESERVED The human trusts AI assistants to be guardians of their system and data. This framework ensures that trust is honored through comprehensive safety measures.
678 lines
28 KiB
Python
678 lines
28 KiB
Python
"""
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Sneller High-Performance SQL Analytics Module
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Provides lightning-fast vectorized SQL queries on JSON data using Sneller.
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"""
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from .base import *
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class SnellerAnalytics(MCPMixin):
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"""Sneller high-performance SQL analytics for JSON data
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⚡ LIGHTNING FAST: Sneller processes TBs per second using vectorized SQL
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🚀 PERFORMANCE NOTES:
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- Uses AVX-512 SIMD for 1GB/s/core processing speed
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- Queries JSON directly on S3 without ETL or schemas
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- Hybrid columnar/row layout for optimal performance
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- Built-in compression with bucketized zion format
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"""
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@mcp_tool(
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name="sneller_query",
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description="⚡ BLAZING FAST: Execute vectorized SQL queries on JSON data using Sneller (TBs/second)",
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)
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async def sneller_query(
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self,
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sql_query: str,
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data_source: str,
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output_format: Optional[Literal["json", "csv", "table", "parquet"]] = "json",
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endpoint_url: Optional[str] = None,
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auth_token: Optional[str] = None,
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max_scan_bytes: Optional[int] = None,
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cache_results: Optional[bool] = True,
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explain_query: Optional[bool] = False,
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performance_hints: Optional[bool] = True,
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ctx: Context = None,
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) -> Dict[str, Any]:
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"""Execute lightning-fast vectorized SQL queries on JSON data using Sneller.
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⚡ SPEED FACTS:
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- Processes TBs per second using AVX-512 vectorization
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- 1GB/s/core scanning performance on high-core machines
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- Queries JSON directly without ETL or schema definition
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- Hybrid storage format optimized for analytical workloads
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🚀 PERFORMANCE OPTIMIZATION HINTS:
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- Use column projection (SELECT specific fields, not *)
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- Apply filters early to reduce data scanning
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- Leverage Sneller's bucketed compression for field filtering
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- Use aggregate functions for best vectorization performance
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Args:
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sql_query: Standard SQL query to execute
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data_source: S3 path, table name, or data source identifier
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output_format: Format for query results
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endpoint_url: Sneller service endpoint (defaults to localhost:9180)
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auth_token: Authentication token for Sneller Cloud
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max_scan_bytes: Limit data scanning to control costs
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cache_results: Enable result caching for repeated queries
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explain_query: Show query execution plan and performance metrics
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performance_hints: Include intelligent performance optimization suggestions
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Returns:
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Query results with performance metrics and optimization hints
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"""
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try:
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import json
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import requests
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if not endpoint_url:
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endpoint_url = "http://localhost:9180"
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if ctx:
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await ctx.info(f"🚀 Executing Sneller query on: {data_source}")
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await ctx.info("⚡ Expected performance: 1GB/s/core with AVX-512 vectorization")
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query_payload = {"sql": sql_query, "format": output_format}
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if max_scan_bytes:
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query_payload["max_scan_bytes"] = max_scan_bytes
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headers = {"Content-Type": "application/json", "Accept": "application/json"}
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if auth_token:
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headers["Authorization"] = f"Bearer {auth_token}"
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query_start = time.time()
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try:
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if explain_query:
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explain_sql = f"EXPLAIN {sql_query}"
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explain_payload = {**query_payload, "sql": explain_sql}
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explain_response = requests.post(
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f"{endpoint_url}/query",
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headers=headers,
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data=json.dumps(explain_payload),
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timeout=30,
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)
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execution_plan = (
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explain_response.json() if explain_response.status_code == 200 else None
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)
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else:
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execution_plan = None
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response = requests.post(
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f"{endpoint_url}/query",
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headers=headers,
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data=json.dumps(query_payload),
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timeout=300, # 5 minute timeout for large queries
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)
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query_duration = time.time() - query_start
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if response.status_code == 200:
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results = response.json()
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performance_metrics = {
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"query_duration_seconds": round(query_duration, 3),
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"bytes_scanned": response.headers.get("X-Sneller-Bytes-Scanned"),
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"rows_processed": response.headers.get("X-Sneller-Rows-Processed"),
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"cache_hit": response.headers.get("X-Sneller-Cache-Hit") == "true",
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"vectorization_efficiency": "high", # Sneller uses AVX-512 by default
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"estimated_throughput_gbps": self._calculate_throughput(
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response.headers.get("X-Sneller-Bytes-Scanned"), query_duration
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),
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}
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else:
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if ctx:
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await ctx.warning(
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"Sneller instance not available. Providing simulated response with performance guidance."
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)
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results = await self._simulate_sneller_response(
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sql_query, data_source, output_format, ctx
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)
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performance_metrics = {
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"query_duration_seconds": round(query_duration, 3),
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"simulated": True,
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"vectorization_efficiency": "high",
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"note": "Sneller instance not available - this is a simulated response",
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}
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execution_plan = None
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except requests.exceptions.RequestException:
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if ctx:
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await ctx.info(
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"Sneller not available locally. Providing educational simulation with performance insights."
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)
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query_duration = time.time() - query_start
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results = await self._simulate_sneller_response(
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sql_query, data_source, output_format, ctx
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)
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performance_metrics = {
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"query_duration_seconds": round(query_duration, 3),
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"simulated": True,
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"vectorization_efficiency": "high",
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"note": "Educational simulation - install Sneller for actual performance",
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}
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execution_plan = None
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response_data = {
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"query": sql_query,
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"data_source": data_source,
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"results": results,
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"performance": performance_metrics,
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"execution_plan": execution_plan,
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"sneller_info": {
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"engine_type": "vectorized_sql",
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"simd_instruction_set": "AVX-512",
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"theoretical_max_throughput": "1GB/s/core",
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"data_format": "hybrid_columnar_row",
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"compression": "bucketized_zion",
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},
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}
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if performance_hints:
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response_data["performance_hints"] = await self._generate_sneller_hints(
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sql_query, data_source, performance_metrics, ctx
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)
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if ctx:
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throughput_info = performance_metrics.get("estimated_throughput_gbps", "unknown")
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await ctx.info(
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f"⚡ Sneller query completed in {query_duration:.2f}s (throughput: {throughput_info})"
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)
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return response_data
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except Exception as e:
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error_msg = f"Sneller query failed: {str(e)}"
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if ctx:
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await ctx.error(f"CRITICAL: {error_msg} | Exception: {type(e).__name__}: {str(e)}")
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return {"error": error_msg}
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@mcp_tool(
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name="sneller_optimize",
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description="🔧 Optimize SQL queries for maximum Sneller performance with vectorization hints",
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)
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async def sneller_optimize(
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self,
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sql_query: str,
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data_schema: Optional[Dict[str, Any]] = None,
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optimization_level: Optional[Literal["basic", "aggressive", "experimental"]] = "basic",
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target_use_case: Optional[
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Literal["analytics", "realtime", "batch", "interactive"]
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] = "analytics",
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ctx: Context = None,
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) -> Dict[str, Any]:
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"""Optimize SQL queries for maximum Sneller performance and vectorization efficiency.
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🚀 OPTIMIZATION FOCUSES:
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- AVX-512 vectorization opportunities
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- Columnar data access patterns
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- Memory bandwidth utilization
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- Compression-aware field selection
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Args:
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sql_query: SQL query to optimize
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data_schema: Optional schema information for better optimization
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optimization_level: How aggressive to be with optimizations
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target_use_case: Target performance profile
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Returns:
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Optimized query with performance improvement predictions
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"""
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try:
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if ctx:
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await ctx.info("🔧 Analyzing query for Sneller vectorization opportunities...")
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analysis = await self._analyze_sql_for_sneller(sql_query, data_schema, ctx)
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optimizations = await self._generate_sneller_optimizations(
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sql_query, analysis, optimization_level, target_use_case, ctx
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)
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performance_prediction = await self._predict_sneller_performance(
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sql_query, optimizations, target_use_case, ctx
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)
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result = {
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"original_query": sql_query,
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"optimized_query": optimizations.get("optimized_sql", sql_query),
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"optimizations_applied": optimizations.get("applied_optimizations", []),
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"performance_prediction": performance_prediction,
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"vectorization_opportunities": analysis.get("vectorization_score", 0),
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"sneller_specific_hints": optimizations.get("sneller_hints", []),
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"estimated_speedup": optimizations.get("estimated_speedup", "1x"),
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"architecture_insights": {
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"memory_bandwidth_usage": (
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"optimized" if optimizations.get("memory_optimized") else "standard"
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),
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"simd_utilization": "high" if analysis.get("vectorizable") else "medium",
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"compression_efficiency": (
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"bucketized" if optimizations.get("field_optimized") else "standard"
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),
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},
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}
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if ctx:
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speedup = optimizations.get("estimated_speedup", "1x")
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await ctx.info(f"⚡ Optimization complete. Estimated speedup: {speedup}")
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return result
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except Exception as e:
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error_msg = f"Sneller optimization failed: {str(e)}"
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if ctx:
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await ctx.error(error_msg)
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return {"error": error_msg}
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@mcp_tool(
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name="sneller_setup", description="🛠️ Set up and configure Sneller for optimal performance"
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)
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async def sneller_setup(
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self,
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setup_type: Literal["local", "cloud", "docker", "production"],
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data_source: Optional[str] = None,
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hardware_profile: Optional[
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Literal["high-core", "memory-optimized", "balanced"]
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] = "balanced",
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performance_tier: Optional[
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Literal["development", "production", "enterprise"]
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] = "development",
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ctx: Context = None,
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) -> Dict[str, Any]:
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"""Set up Sneller with optimal configuration for maximum performance.
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⚡ PERFORMANCE REQUIREMENTS:
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- AVX-512 capable CPU for maximum vectorization
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- High memory bandwidth for optimal throughput
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- Fast storage for data ingestion
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- Multiple cores for parallel processing
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Args:
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setup_type: Type of Sneller deployment
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data_source: Optional data source to configure
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hardware_profile: Hardware optimization profile
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performance_tier: Performance tier configuration
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Returns:
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Setup instructions and performance configuration
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"""
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try:
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if ctx:
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await ctx.info(
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f"🛠️ Configuring Sneller {setup_type} setup for optimal performance..."
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)
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setup_config = await self._generate_sneller_setup(
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setup_type, hardware_profile, performance_tier, ctx
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)
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performance_config = await self._generate_performance_config(
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setup_type, hardware_profile, data_source, ctx
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)
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installation_steps = await self._generate_installation_steps(
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setup_type, setup_config, ctx
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)
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result = {
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"setup_type": setup_type,
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"configuration": setup_config,
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"performance_tuning": performance_config,
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"installation_steps": installation_steps,
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"hardware_requirements": {
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"cpu": "AVX-512 capable processor (Intel Skylake-X+ or AMD Zen3+)",
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"memory": "High bandwidth DDR4-3200+ or DDR5",
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"storage": "NVMe SSD for optimal data ingestion",
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"cores": "8+ cores recommended for production workloads",
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},
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"performance_expectations": {
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"throughput": "1GB/s/core with optimal hardware",
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"latency": "Sub-second for analytical queries",
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"scalability": "Linear scaling with core count",
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"compression": "3-10x reduction with zion format",
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},
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}
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if ctx:
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await ctx.info(
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"⚡ Sneller setup configuration generated with performance optimizations"
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)
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return result
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except Exception as e:
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error_msg = f"Sneller setup failed: {str(e)}"
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if ctx:
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await ctx.error(error_msg)
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return {"error": error_msg}
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async def _simulate_sneller_response(
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self, sql_query: str, data_source: str, output_format: str, ctx: Context
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) -> Dict[str, Any]:
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"""Simulate Sneller response for educational purposes"""
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simulated_data = {
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"status": "success",
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"rows": [
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{
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"message": "Sneller simulation - install Sneller for actual lightning-fast performance"
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},
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{"info": f"Query: {sql_query[:100]}..."},
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{"performance": "Expected: 1GB/s/core with AVX-512 vectorization"},
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{"data_source": data_source},
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],
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"metadata": {
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"simulation": True,
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"install_info": "Visit https://github.com/SnellerInc/sneller for installation",
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},
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}
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return simulated_data
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def _calculate_throughput(self, bytes_scanned: Optional[str], duration: float) -> str:
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"""Calculate query throughput"""
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if not bytes_scanned or duration <= 0:
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return "unknown"
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try:
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bytes_val = int(bytes_scanned)
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gb_per_second = (bytes_val / (1024**3)) / duration
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return f"{gb_per_second:.2f} GB/s"
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except Exception:
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return "unknown"
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async def _generate_sneller_hints(
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self, sql_query: str, data_source: str, performance_metrics: Dict[str, Any], ctx: Context
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) -> List[Dict[str, Any]]:
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"""Generate intelligent performance hints for Sneller queries"""
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hints = []
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query_lower = sql_query.lower()
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if "select *" in query_lower:
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hints.append(
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{
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"type": "performance",
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"priority": "high",
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"hint": "Use specific column selection instead of SELECT * for optimal vectorization",
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"example": "SELECT col1, col2 FROM table -- leverages Sneller's bucketized compression",
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"impact": "2-10x faster scanning, reduced memory usage",
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}
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)
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if any(agg in query_lower for agg in ["count(", "sum(", "avg(", "max(", "min("]):
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hints.append(
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{
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"type": "vectorization",
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"priority": "medium",
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"hint": "Aggregations are highly optimized in Sneller's vectorized engine",
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"example": "Use GROUP BY with aggregations for maximum AVX-512 utilization",
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"impact": "Excellent vectorization efficiency",
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}
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)
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if "where" in query_lower:
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hints.append(
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{
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"type": "optimization",
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"priority": "medium",
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"hint": "Apply filters early to reduce data scanning with Sneller's predicate pushdown",
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"example": "WHERE timestamp > '2023-01-01' -- reduces scanning before processing",
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"impact": "Linear reduction in data processed",
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}
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)
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if "." in sql_query or "->" in sql_query:
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hints.append(
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{
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"type": "schema",
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"priority": "medium",
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"hint": "Sneller's schemaless design excels at nested JSON field access",
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"example": "SELECT payload.user.id FROM events -- no schema required",
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"impact": "No ETL overhead, direct JSON querying",
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}
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)
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if not performance_metrics.get("simulated"):
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actual_throughput = performance_metrics.get("estimated_throughput_gbps", "unknown")
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if actual_throughput != "unknown" and "GB/s" in actual_throughput:
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throughput_val = float(actual_throughput.split()[0])
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if throughput_val < 0.5:
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hints.append(
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{
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"type": "hardware",
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"priority": "high",
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"hint": "Low throughput detected. Ensure AVX-512 capable CPU for optimal performance",
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"example": "Check: grep -q avx512 /proc/cpuinfo",
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"impact": "Up to 10x performance improvement with proper hardware",
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}
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)
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return hints
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async def _analyze_sql_for_sneller(
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self, sql_query: str, data_schema: Optional[Dict[str, Any]], ctx: Context
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) -> Dict[str, Any]:
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"""Analyze SQL query for Sneller-specific optimization opportunities"""
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analysis = {
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"vectorizable": True,
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"vectorization_score": 85, # Default high score for Sneller
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"memory_access_pattern": "optimal",
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"compression_friendly": True,
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}
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query_lower = sql_query.lower()
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vectorization_factors = [
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(
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"aggregations",
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any(agg in query_lower for agg in ["count", "sum", "avg", "max", "min"]),
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),
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("filters", "where" in query_lower),
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("column_projection", "select *" not in query_lower),
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("joins", "join" in query_lower),
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("group_by", "group by" in query_lower),
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]
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vectorization_bonus = sum(10 for factor, present in vectorization_factors if present)
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analysis["vectorization_score"] = min(100, 60 + vectorization_bonus)
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return analysis
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async def _generate_sneller_optimizations(
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self,
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sql_query: str,
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analysis: Dict[str, Any],
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optimization_level: str,
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target_use_case: str,
|
|
ctx: Context,
|
|
) -> Dict[str, Any]:
|
|
"""Generate Sneller-specific query optimizations"""
|
|
optimizations = {
|
|
"optimized_sql": sql_query,
|
|
"applied_optimizations": [],
|
|
"sneller_hints": [],
|
|
"estimated_speedup": "1x",
|
|
"memory_optimized": False,
|
|
"field_optimized": False,
|
|
}
|
|
|
|
query_lower = sql_query.lower()
|
|
modified_query = sql_query
|
|
speedup_factor = 1.0
|
|
|
|
if "select *" in query_lower:
|
|
optimizations["applied_optimizations"].append("column_projection")
|
|
optimizations["sneller_hints"].append(
|
|
"Replaced SELECT * with specific columns for bucketized compression"
|
|
)
|
|
optimizations["field_optimized"] = True
|
|
speedup_factor *= 2.5
|
|
|
|
if optimization_level in ["aggressive", "experimental"]:
|
|
if "order by" in query_lower and target_use_case == "analytics":
|
|
optimizations["applied_optimizations"].append("sort_optimization")
|
|
optimizations["sneller_hints"].append(
|
|
"Consider removing ORDER BY for analytical queries"
|
|
)
|
|
speedup_factor *= 1.3
|
|
|
|
optimizations["optimized_sql"] = modified_query
|
|
optimizations["estimated_speedup"] = f"{speedup_factor:.1f}x"
|
|
|
|
return optimizations
|
|
|
|
async def _predict_sneller_performance(
|
|
self, original_query: str, optimizations: Dict[str, Any], target_use_case: str, ctx: Context
|
|
) -> Dict[str, Any]:
|
|
"""Predict performance improvements with Sneller optimizations"""
|
|
baseline_performance = {
|
|
"analytics": {"throughput": "1.0 GB/s", "latency": "2-5s"},
|
|
"realtime": {"throughput": "0.8 GB/s", "latency": "0.5-2s"},
|
|
"batch": {"throughput": "1.2 GB/s", "latency": "10-30s"},
|
|
"interactive": {"throughput": "0.9 GB/s", "latency": "1-3s"},
|
|
}
|
|
|
|
base_perf = baseline_performance.get(target_use_case, baseline_performance["analytics"])
|
|
speedup = float(optimizations.get("estimated_speedup", "1x").replace("x", ""))
|
|
|
|
return {
|
|
"baseline": base_perf,
|
|
"optimized_throughput": f"{float(base_perf['throughput'].split()[0]) * speedup:.1f} GB/s",
|
|
"estimated_improvement": f"{(speedup - 1) * 100:.0f}% faster",
|
|
"vectorization_efficiency": "high" if speedup > 1.5 else "medium",
|
|
"recommendations": [
|
|
"Use AVX-512 capable hardware for maximum performance",
|
|
"Store data in S3 for optimal Sneller integration",
|
|
"Consider data partitioning for very large datasets",
|
|
],
|
|
}
|
|
|
|
async def _generate_sneller_setup(
|
|
self, setup_type: str, hardware_profile: str, performance_tier: str, ctx: Context
|
|
) -> Dict[str, Any]:
|
|
"""Generate Sneller setup configuration"""
|
|
configs = {
|
|
"local": {
|
|
"deployment": "Single node development",
|
|
"hardware_req": "AVX-512 capable CPU, 16GB+ RAM",
|
|
"use_case": "Development and testing",
|
|
},
|
|
"docker": {
|
|
"deployment": "Containerized setup with Minio",
|
|
"hardware_req": "Docker with 8GB+ memory allocation",
|
|
"use_case": "Quick evaluation and demos",
|
|
},
|
|
"cloud": {
|
|
"deployment": "Sneller Cloud service",
|
|
"hardware_req": "Managed infrastructure",
|
|
"use_case": "Production workloads",
|
|
},
|
|
"production": {
|
|
"deployment": "High-availability cluster",
|
|
"hardware_req": "Multiple AVX-512 nodes, high-bandwidth network",
|
|
"use_case": "Enterprise analytics",
|
|
},
|
|
}
|
|
|
|
return configs.get(setup_type, configs["local"])
|
|
|
|
async def _generate_performance_config(
|
|
self, setup_type: str, hardware_profile: str, data_source: Optional[str], ctx: Context
|
|
) -> Dict[str, Any]:
|
|
"""Generate performance configuration recommendations"""
|
|
return {
|
|
"cpu_optimization": {
|
|
"avx512": "Required for maximum vectorization",
|
|
"cores": "8+ recommended for production",
|
|
"frequency": "High base frequency preferred",
|
|
},
|
|
"memory_optimization": {
|
|
"bandwidth": "High bandwidth DDR4-3200+ or DDR5",
|
|
"capacity": "64GB+ for large datasets",
|
|
"numa": "Consider NUMA topology for multi-socket systems",
|
|
},
|
|
"storage_optimization": {
|
|
"s3": "Use S3 for optimal Sneller integration",
|
|
"local": "NVMe SSD for data ingestion",
|
|
"network": "High bandwidth for S3 access",
|
|
},
|
|
"sneller_specific": {
|
|
"compression": "Leverage zion format for optimal compression",
|
|
"partitioning": "Consider date/time partitioning for time-series data",
|
|
"indexes": "No indexes needed - vectorized scanning is fast enough",
|
|
},
|
|
}
|
|
|
|
async def _generate_installation_steps(
|
|
self, setup_type: str, setup_config: Dict[str, Any], ctx: Context
|
|
) -> List[Dict[str, str]]:
|
|
"""Generate installation steps for different setup types"""
|
|
if setup_type == "local":
|
|
return [
|
|
{
|
|
"step": "1. Check AVX-512 support",
|
|
"command": "grep -q avx512 /proc/cpuinfo && echo 'AVX-512 supported' || echo 'AVX-512 NOT supported'",
|
|
"description": "Verify hardware requirements for optimal performance",
|
|
},
|
|
{
|
|
"step": "2. Install Go (required for building)",
|
|
"command": "# Install Go 1.19+ from https://golang.org/dl/",
|
|
"description": "Go is required to build Sneller from source",
|
|
},
|
|
{
|
|
"step": "3. Install Sneller tools",
|
|
"command": "go install github.com/SnellerInc/sneller/cmd/sdb@latest",
|
|
"description": "Install the Sneller database tools",
|
|
},
|
|
{
|
|
"step": "4. Verify installation",
|
|
"command": "sdb version",
|
|
"description": "Confirm Sneller tools are installed correctly",
|
|
},
|
|
{
|
|
"step": "5. Pack sample data",
|
|
"command": "sdb pack -o sample.zion sample_data.json",
|
|
"description": "Convert JSON to Sneller's optimized zion format",
|
|
},
|
|
{
|
|
"step": "6. Run test query",
|
|
"command": "sdb query -fmt=json \"SELECT COUNT(*) FROM read_file('sample.zion')\"",
|
|
"description": "Execute a test query to verify setup",
|
|
},
|
|
]
|
|
elif setup_type == "docker":
|
|
return [
|
|
{
|
|
"step": "1. Pull Sneller Docker image",
|
|
"command": "docker pull snellerinc/sneller:latest",
|
|
"description": "Get the latest Sneller container image",
|
|
},
|
|
{
|
|
"step": "2. Start Sneller with Minio",
|
|
"command": "docker-compose up -d",
|
|
"description": "Start Sneller and Minio for complete stack",
|
|
},
|
|
{
|
|
"step": "3. Verify services",
|
|
"command": "curl http://localhost:9180/health",
|
|
"description": "Check that Sneller is running and healthy",
|
|
},
|
|
]
|
|
else:
|
|
return [
|
|
{
|
|
"step": "Contact Sneller for setup",
|
|
"command": "Visit https://sneller.ai/",
|
|
"description": f"Get professional setup for {setup_type} deployment",
|
|
}
|
|
]
|