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seaweedfs/weed/query/engine/select_test.go

124 lines
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feat: Phase 2 - Add DDL operations and real MQ broker integration Implements comprehensive DDL support for MQ topic management: New Components: - Real MQ broker connectivity via BrokerClient - CREATE TABLE → ConfigureTopic gRPC calls - DROP TABLE → DeleteTopic operations - DESCRIBE table → Schema introspection - SQL type mapping (SQL ↔ MQ schema types) Enhanced Features: - Live topic discovery from MQ broker - Fallback to cached/sample data when broker unavailable - MySQL-compatible DESCRIBE output - Schema validation and error handling - CREATE TABLE with column definitions Key Infrastructure: - broker_client.go: gRPC communication with MQ broker - sql_types.go: Bidirectional SQL/MQ type conversion - describe.go: Table schema introspection - Enhanced engine.go: Full DDL routing and execution Supported SQL Operations: ✅ SHOW DATABASES, SHOW TABLES (live + fallback) ✅ CREATE TABLE table_name (col1 INT, col2 VARCHAR(50), ...) ✅ DROP TABLE table_name ✅ DESCRIBE table_name / SHOW COLUMNS FROM table_name Known Limitations: - SQL parser issues with reserved keywords (e.g., 'timestamp') - Requires running MQ broker for full functionality - ALTER TABLE not yet implemented - DeleteTopic method needs broker-side implementation Architecture Decisions: - Broker discovery via filer lock mechanism (same as shell commands) - Graceful fallback when broker unavailable - ConfigureTopic for CREATE TABLE with 6 default partitions - Schema versioning ready for ALTER TABLE support Testing: - Unit tests updated with filer address parameter - Integration tests for DDL operations - Error handling for connection failures Next Phase: SELECT query execution with Parquet scanning
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package engine
import (
"context"
"fmt"
"strings"
"testing"
)
func TestSQLEngine_SelectBasic(t *testing.T) {
engine := NewSQLEngine("localhost:8888")
// Test SELECT * FROM table
result, err := engine.ExecuteSQL(context.Background(), "SELECT * FROM user_events")
if err != nil {
t.Fatalf("Expected no error, got %v", err)
}
if result.Error != nil {
t.Fatalf("Expected no query error, got %v", result.Error)
}
if len(result.Columns) == 0 {
t.Error("Expected columns in result")
}
if len(result.Rows) == 0 {
t.Error("Expected rows in result")
}
feat: Time Filter Extraction - Complete Performance Optimization ✅ FOURTH HIGH PRIORITY TODO COMPLETED! ⏰ **Time Filter Extraction & Push-Down Optimization** (engine.go:198-199) - Replaced hardcoded StartTimeNs=0, StopTimeNs=0 with intelligent extraction - Added extractTimeFilters() with recursive WHERE clause analysis - Smart time column detection (\_timestamp_ns, created_at, timestamp, etc.) - Comprehensive time value parsing (nanoseconds, ISO dates, datetime formats) - Operator reversal handling (column op value vs value op column) 🧠 **Intelligent WHERE Clause Processing:** - AND expressions: Combine time bounds (intersection) ✅ - OR expressions: Skip extraction (safety) ✅ - Parentheses: Recursive unwrapping ✅ - Comparison operators: >, >=, <, <=, = ✅ - Multiple time formats: nanoseconds, RFC3339, date-only, datetime ✅ 🚀 **Performance Impact:** - Push-down filtering to hybrid scanner level - Reduced data scanning at source (live logs + Parquet files) - Time-based partition pruning potential - Significant performance gains for time-series queries 📊 **Comprehensive Testing (21 tests passing):** - ✅ Time filter extraction (6 test scenarios) - ✅ Time column recognition (case-insensitive) - ✅ Time value parsing (5 formats) - ✅ Full integration with SELECT queries - ✅ Backward compatibility maintained 💡 **Real-World Query Examples:** Before: Scans ALL data, filters in memory SELECT * FROM events WHERE \_timestamp_ns > 1672531200000000000; After: Scans ONLY relevant time range at source level → StartTimeNs=1672531200000000000, StopTimeNs=0 → Massive performance improvement for large datasets! 🎯 **Production Ready Features:** - Multiple time column formats supported - Graceful fallbacks for invalid dates - OR clause safety (avoids incorrect optimization) - Comprehensive error handling **ALL MEDIUM PRIORITY TODOs NOW READY FOR NEXT PHASEtest ./weed/query/engine/ -v* 🎉
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// Should have sample data with 4 columns (includes _source from hybrid scanner)
expectedColumns := []string{"user_id", "event_type", "data", "_source"}
feat: Phase 2 - Add DDL operations and real MQ broker integration Implements comprehensive DDL support for MQ topic management: New Components: - Real MQ broker connectivity via BrokerClient - CREATE TABLE → ConfigureTopic gRPC calls - DROP TABLE → DeleteTopic operations - DESCRIBE table → Schema introspection - SQL type mapping (SQL ↔ MQ schema types) Enhanced Features: - Live topic discovery from MQ broker - Fallback to cached/sample data when broker unavailable - MySQL-compatible DESCRIBE output - Schema validation and error handling - CREATE TABLE with column definitions Key Infrastructure: - broker_client.go: gRPC communication with MQ broker - sql_types.go: Bidirectional SQL/MQ type conversion - describe.go: Table schema introspection - Enhanced engine.go: Full DDL routing and execution Supported SQL Operations: ✅ SHOW DATABASES, SHOW TABLES (live + fallback) ✅ CREATE TABLE table_name (col1 INT, col2 VARCHAR(50), ...) ✅ DROP TABLE table_name ✅ DESCRIBE table_name / SHOW COLUMNS FROM table_name Known Limitations: - SQL parser issues with reserved keywords (e.g., 'timestamp') - Requires running MQ broker for full functionality - ALTER TABLE not yet implemented - DeleteTopic method needs broker-side implementation Architecture Decisions: - Broker discovery via filer lock mechanism (same as shell commands) - Graceful fallback when broker unavailable - ConfigureTopic for CREATE TABLE with 6 default partitions - Schema versioning ready for ALTER TABLE support Testing: - Unit tests updated with filer address parameter - Integration tests for DDL operations - Error handling for connection failures Next Phase: SELECT query execution with Parquet scanning
2025-08-31 21:01:23 -07:00
if len(result.Columns) != len(expectedColumns) {
t.Errorf("Expected %d columns, got %d", len(expectedColumns), len(result.Columns))
}
feat: Time Filter Extraction - Complete Performance Optimization ✅ FOURTH HIGH PRIORITY TODO COMPLETED! ⏰ **Time Filter Extraction & Push-Down Optimization** (engine.go:198-199) - Replaced hardcoded StartTimeNs=0, StopTimeNs=0 with intelligent extraction - Added extractTimeFilters() with recursive WHERE clause analysis - Smart time column detection (\_timestamp_ns, created_at, timestamp, etc.) - Comprehensive time value parsing (nanoseconds, ISO dates, datetime formats) - Operator reversal handling (column op value vs value op column) 🧠 **Intelligent WHERE Clause Processing:** - AND expressions: Combine time bounds (intersection) ✅ - OR expressions: Skip extraction (safety) ✅ - Parentheses: Recursive unwrapping ✅ - Comparison operators: >, >=, <, <=, = ✅ - Multiple time formats: nanoseconds, RFC3339, date-only, datetime ✅ 🚀 **Performance Impact:** - Push-down filtering to hybrid scanner level - Reduced data scanning at source (live logs + Parquet files) - Time-based partition pruning potential - Significant performance gains for time-series queries 📊 **Comprehensive Testing (21 tests passing):** - ✅ Time filter extraction (6 test scenarios) - ✅ Time column recognition (case-insensitive) - ✅ Time value parsing (5 formats) - ✅ Full integration with SELECT queries - ✅ Backward compatibility maintained 💡 **Real-World Query Examples:** Before: Scans ALL data, filters in memory SELECT * FROM events WHERE \_timestamp_ns > 1672531200000000000; After: Scans ONLY relevant time range at source level → StartTimeNs=1672531200000000000, StopTimeNs=0 → Massive performance improvement for large datasets! 🎯 **Production Ready Features:** - Multiple time column formats supported - Graceful fallbacks for invalid dates - OR clause safety (avoids incorrect optimization) - Comprehensive error handling **ALL MEDIUM PRIORITY TODOs NOW READY FOR NEXT PHASEtest ./weed/query/engine/ -v* 🎉
2025-08-31 22:03:04 -07:00
// Should have 4 sample rows (hybrid data includes both live_log and parquet_archive)
if len(result.Rows) != 4 {
t.Errorf("Expected 4 rows, got %d", len(result.Rows))
feat: Phase 2 - Add DDL operations and real MQ broker integration Implements comprehensive DDL support for MQ topic management: New Components: - Real MQ broker connectivity via BrokerClient - CREATE TABLE → ConfigureTopic gRPC calls - DROP TABLE → DeleteTopic operations - DESCRIBE table → Schema introspection - SQL type mapping (SQL ↔ MQ schema types) Enhanced Features: - Live topic discovery from MQ broker - Fallback to cached/sample data when broker unavailable - MySQL-compatible DESCRIBE output - Schema validation and error handling - CREATE TABLE with column definitions Key Infrastructure: - broker_client.go: gRPC communication with MQ broker - sql_types.go: Bidirectional SQL/MQ type conversion - describe.go: Table schema introspection - Enhanced engine.go: Full DDL routing and execution Supported SQL Operations: ✅ SHOW DATABASES, SHOW TABLES (live + fallback) ✅ CREATE TABLE table_name (col1 INT, col2 VARCHAR(50), ...) ✅ DROP TABLE table_name ✅ DESCRIBE table_name / SHOW COLUMNS FROM table_name Known Limitations: - SQL parser issues with reserved keywords (e.g., 'timestamp') - Requires running MQ broker for full functionality - ALTER TABLE not yet implemented - DeleteTopic method needs broker-side implementation Architecture Decisions: - Broker discovery via filer lock mechanism (same as shell commands) - Graceful fallback when broker unavailable - ConfigureTopic for CREATE TABLE with 6 default partitions - Schema versioning ready for ALTER TABLE support Testing: - Unit tests updated with filer address parameter - Integration tests for DDL operations - Error handling for connection failures Next Phase: SELECT query execution with Parquet scanning
2025-08-31 21:01:23 -07:00
}
}
func TestSQLEngine_SelectWithLimit(t *testing.T) {
engine := NewSQLEngine("localhost:8888")
// Test SELECT with LIMIT
result, err := engine.ExecuteSQL(context.Background(), "SELECT * FROM user_events LIMIT 2")
if err != nil {
t.Fatalf("Expected no error, got %v", err)
}
if result.Error != nil {
t.Fatalf("Expected no query error, got %v", result.Error)
}
// Should have exactly 2 rows due to LIMIT
if len(result.Rows) != 2 {
t.Errorf("Expected 2 rows with LIMIT 2, got %d", len(result.Rows))
}
}
func TestSQLEngine_SelectSpecificColumns(t *testing.T) {
engine := NewSQLEngine("localhost:8888")
// Test SELECT specific columns (this will fall back to sample data)
result, err := engine.ExecuteSQL(context.Background(), "SELECT user_id, event_type FROM user_events")
if err != nil {
t.Fatalf("Expected no error, got %v", err)
}
if result.Error != nil {
t.Fatalf("Expected no query error, got %v", result.Error)
}
// Should have all columns for now (sample data doesn't implement projection yet)
if len(result.Columns) == 0 {
t.Error("Expected columns in result")
}
}
func TestSQLEngine_SelectFromNonExistentTable(t *testing.T) {
engine := NewSQLEngine("localhost:8888")
// Test SELECT from non-existent table
result, _ := engine.ExecuteSQL(context.Background(), "SELECT * FROM nonexistent_table")
if result.Error == nil {
t.Error("Expected error for non-existent table")
}
if !strings.Contains(result.Error.Error(), "not found") {
t.Errorf("Expected 'not found' error, got: %v", result.Error)
}
}
func TestSQLEngine_SelectDifferentTables(t *testing.T) {
engine := NewSQLEngine("localhost:8888")
// Test different sample tables
tables := []string{"user_events", "system_logs"}
for _, tableName := range tables {
result, err := engine.ExecuteSQL(context.Background(), fmt.Sprintf("SELECT * FROM %s", tableName))
if err != nil {
t.Errorf("Error querying table %s: %v", tableName, err)
continue
}
if result.Error != nil {
t.Errorf("Query error for table %s: %v", tableName, result.Error)
continue
}
if len(result.Columns) == 0 {
t.Errorf("No columns returned for table %s", tableName)
}
if len(result.Rows) == 0 {
t.Errorf("No rows returned for table %s", tableName)
}
t.Logf("Table %s: %d columns, %d rows", tableName, len(result.Columns), len(result.Rows))
}
}