8d89fe80ff
- Archive: 85 agent .txt files → docs/archive/agents/legacy_txt/ - Scripts: Move 110 shell scripts → scripts/ (keep deploy.sh in root) - Models: Move 18 .safetensors → ml/models/checkpoints/training_artifacts/ - Delete: 34 directories (~33GB freed) - target/, coverage_*, test artifacts - Build: Clean 14 build artifacts (.rlib, .o, .pid, binaries) - Tests: Move 14 .rs files → tests/standalone/ - SQL: Move 5 files → sql/ (keep init-db*.sql for Docker) - Wave 153: Archive to docs/archive/historical/wave153/ - Docs: Archive 9 markdown files to wave_d/reports/ and historical/ Total impact: ~34GB freed (both waves), root directory cleaned from 583 to ~40 essential files Directory count reduced from 65 to 31 (52% reduction) All historical data preserved in organized archive structure
391 lines
17 KiB
Bash
Executable File
391 lines
17 KiB
Bash
Executable File
#!/bin/bash
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# ================================================================================================
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# Ensemble Database Performance Benchmark
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# Tests write throughput, query latency, and compression efficiency
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# Target: >1000 inserts/sec, P99 <100ms, compression >5x
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# ================================================================================================
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set -euo pipefail
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# Color codes for output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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NC='\033[0m' # No Color
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# Database connection
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DB_URL="postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt"
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# Test parameters
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WRITE_TEST_DURATION=10 # seconds
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WRITE_TEST_BATCH_SIZE=100
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TARGET_WRITES_PER_SEC=1000
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TARGET_P99_LATENCY_MS=100
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TARGET_COMPRESSION_RATIO=5.0
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echo -e "${BLUE}================================================================================================${NC}"
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echo -e "${BLUE}ENSEMBLE DATABASE PERFORMANCE BENCHMARK${NC}"
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echo -e "${BLUE}================================================================================================${NC}"
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echo ""
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# ================================================================================================
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# PART 1: PRE-TEST SETUP
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# ================================================================================================
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echo -e "${YELLOW}[1/6] Pre-test Setup${NC}"
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echo "Applying migration 023..."
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psql "$DB_URL" -f /home/jgrusewski/Work/foxhunt/migrations/023_ensemble_performance_tuning.sql > /dev/null 2>&1 || {
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echo -e "${RED}❌ Migration 023 failed${NC}"
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exit 1
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}
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echo -e "${GREEN}✅ Migration 023 applied successfully${NC}"
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echo ""
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# Enable pg_stat_statements for query monitoring
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psql "$DB_URL" -c "CREATE EXTENSION IF NOT EXISTS pg_stat_statements;" > /dev/null 2>&1
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psql "$DB_URL" -c "SELECT pg_stat_statements_reset();" > /dev/null 2>&1
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echo -e "${GREEN}✅ pg_stat_statements enabled and reset${NC}"
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echo ""
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# ================================================================================================
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# PART 2: WRITE THROUGHPUT TEST
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# ================================================================================================
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echo -e "${YELLOW}[2/6] Write Throughput Test (${WRITE_TEST_DURATION} seconds)${NC}"
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echo "Target: ${TARGET_WRITES_PER_SEC} inserts/sec"
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echo ""
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# Generate test data and insert in batches
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START_TIME=$(date +%s)
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TOTAL_INSERTS=0
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for i in $(seq 1 $WRITE_TEST_DURATION); do
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BATCH_JSON=$(cat <<EOF
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[
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$(for j in $(seq 1 $WRITE_TEST_BATCH_SIZE); do
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TIMESTAMP=$(date -u -Iseconds)
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SYMBOL=$(printf "SYM%02d" $((RANDOM % 10 + 1)))
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ACTION=$(printf "%s" "$(shuf -n1 -e BUY SELL HOLD)")
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cat <<INNER_EOF
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{
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"timestamp": "$TIMESTAMP",
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"symbol": "$SYMBOL",
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"ensemble_action": "$ACTION",
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"ensemble_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"ensemble_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"disagreement_rate": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"dqn_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"dqn_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"dqn_weight": 0.25,
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"dqn_vote": "$ACTION",
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"ppo_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"ppo_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"ppo_weight": 0.25,
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"ppo_vote": "$ACTION",
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"mamba2_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"mamba2_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"mamba2_weight": 0.25,
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"mamba2_vote": "$ACTION",
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"tft_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"tft_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"tft_weight": 0.25,
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"tft_vote": "$ACTION",
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"inference_latency_us": $((RANDOM % 10000 + 1000)),
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"aggregation_latency_us": $((RANDOM % 1000 + 100))
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}$(if [ $j -lt $WRITE_TEST_BATCH_SIZE ]; then echo ","; fi)
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INNER_EOF
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done)
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]
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EOF
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)
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# Insert batch using bulk function
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BATCH_START=$(date +%s%N)
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ROWS_INSERTED=$(psql "$DB_URL" -t -c "SELECT insert_ensemble_predictions_bulk('$BATCH_JSON'::JSONB);" 2>/dev/null | tr -d ' ')
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BATCH_END=$(date +%s%N)
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BATCH_DURATION_MS=$(( (BATCH_END - BATCH_START) / 1000000 ))
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TOTAL_INSERTS=$((TOTAL_INSERTS + ROWS_INSERTED))
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echo -ne "\rBatch $i: ${ROWS_INSERTED} rows in ${BATCH_DURATION_MS}ms | Total: ${TOTAL_INSERTS} rows"
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done
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END_TIME=$(date +%s)
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DURATION=$((END_TIME - START_TIME))
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WRITES_PER_SEC=$((TOTAL_INSERTS / DURATION))
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echo ""
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echo ""
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echo -e "Total inserts: ${BLUE}${TOTAL_INSERTS}${NC}"
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echo -e "Duration: ${BLUE}${DURATION}${NC} seconds"
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echo -e "Write throughput: ${BLUE}${WRITES_PER_SEC}${NC} inserts/sec"
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if [ $WRITES_PER_SEC -ge $TARGET_WRITES_PER_SEC ]; then
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echo -e "${GREEN}✅ PASS: Write throughput ${WRITES_PER_SEC}/sec >= target ${TARGET_WRITES_PER_SEC}/sec${NC}"
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else
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echo -e "${RED}❌ FAIL: Write throughput ${WRITES_PER_SEC}/sec < target ${TARGET_WRITES_PER_SEC}/sec${NC}"
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fi
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echo ""
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# ================================================================================================
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# PART 3: QUERY LATENCY BENCHMARKS (26 production queries)
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# ================================================================================================
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echo -e "${YELLOW}[3/6] Query Latency Benchmarks (26 production queries)${NC}"
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echo "Target: P99 < ${TARGET_P99_LATENCY_MS}ms"
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echo ""
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# Array of query names and SQL
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declare -a QUERIES=(
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"Q1: Recent predictions by symbol|SELECT * FROM ensemble_predictions WHERE symbol = 'SYM01' ORDER BY timestamp DESC LIMIT 100"
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"Q2: High disagreement events|SELECT * FROM ensemble_predictions WHERE disagreement_rate > 0.5 ORDER BY timestamp DESC LIMIT 100"
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"Q3: P&L attribution by symbol|SELECT symbol, SUM(pnl) as total_pnl FROM ensemble_predictions WHERE pnl IS NOT NULL GROUP BY symbol"
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"Q4: Model performance by symbol|SELECT model_id, symbol, AVG(accuracy) FROM model_performance_attribution WHERE window_hours = 24 GROUP BY model_id, symbol"
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"Q5: Top performers 24h|SELECT * FROM get_top_models_24h('SYM01', 5)"
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"Q6: Ensemble hourly metrics|SELECT * FROM ensemble_performance_hourly WHERE symbol = 'SYM01' ORDER BY bucket DESC LIMIT 48"
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"Q7: Model correlation 7d|SELECT * FROM calculate_model_correlation_7d('SYM01')"
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"Q8: High disagreement 24h|SELECT * FROM get_high_disagreement_events_24h('SYM01', 0.5, 100)"
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"Q9: Write throughput 5min|SELECT * FROM ensemble_write_throughput_5min"
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"Q10: Action distribution|SELECT ensemble_action, COUNT(*) FROM ensemble_predictions GROUP BY ensemble_action"
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"Q11: Avg confidence by action|SELECT ensemble_action, AVG(ensemble_confidence) FROM ensemble_predictions GROUP BY ensemble_action"
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"Q12: Latency P99|SELECT PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY inference_latency_us) FROM ensemble_predictions"
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"Q13: Model vote agreement|SELECT COUNT(*) FROM ensemble_predictions WHERE dqn_vote = ppo_vote AND ppo_vote = mamba2_vote AND mamba2_vote = tft_vote"
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"Q14: Recent orders with P&L|SELECT * FROM ensemble_predictions WHERE order_id IS NOT NULL ORDER BY timestamp DESC LIMIT 100"
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"Q15: Win rate by symbol|SELECT symbol, COUNT(CASE WHEN pnl > 0 THEN 1 END)::FLOAT / NULLIF(COUNT(*), 0) FROM ensemble_predictions WHERE pnl IS NOT NULL GROUP BY symbol"
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"Q16: Model performance hourly|SELECT * FROM model_performance_hourly WHERE model_id = 'DQN' ORDER BY bucket DESC LIMIT 24"
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"Q17: Ensemble weekly summary|SELECT * FROM ensemble_performance_weekly ORDER BY bucket DESC LIMIT 12"
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"Q18: Avg Sharpe by model|SELECT model_id, AVG(sharpe_ratio) FROM model_performance_attribution WHERE window_hours = 24 GROUP BY model_id"
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"Q19: Max drawdown by symbol|SELECT symbol, MAX(max_drawdown) FROM model_performance_attribution WHERE window_hours = 168 GROUP BY symbol"
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"Q20: Checkpoint performance|SELECT dqn_checkpoint_id, AVG(ensemble_confidence) FROM ensemble_predictions WHERE dqn_checkpoint_id IS NOT NULL GROUP BY dqn_checkpoint_id"
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"Q21: Time-weighted avg signal|SELECT time_bucket('1 hour', timestamp), AVG(ensemble_signal) FROM ensemble_predictions GROUP BY 1 ORDER BY 1 DESC LIMIT 24"
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"Q22: Disagreement rate trend|SELECT time_bucket('1 day', timestamp), AVG(disagreement_rate) FROM ensemble_predictions GROUP BY 1 ORDER BY 1 DESC LIMIT 30"
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"Q23: Model weight distribution|SELECT model_id, AVG(avg_weight) FROM model_performance_attribution WHERE window_hours = 1 GROUP BY model_id"
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"Q24: Recent high confidence|SELECT * FROM ensemble_predictions WHERE ensemble_confidence > 0.8 ORDER BY timestamp DESC LIMIT 100"
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"Q25: P&L by action type|SELECT ensemble_action, SUM(pnl) FROM ensemble_predictions WHERE pnl IS NOT NULL GROUP BY ensemble_action"
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"Q26: Inference latency trend|SELECT time_bucket('1 hour', timestamp), AVG(inference_latency_us), PERCENTILE_CONT(0.99) WITHIN GROUP (ORDER BY inference_latency_us) FROM ensemble_predictions GROUP BY 1 ORDER BY 1 DESC LIMIT 24"
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)
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# Run each query 5 times and collect timing
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QUERY_COUNT=${#QUERIES[@]}
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declare -a QUERY_TIMES=()
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for i in "${!QUERIES[@]}"; do
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IFS='|' read -r QUERY_NAME QUERY_SQL <<< "${QUERIES[$i]}"
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# Run query 5 times
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TIMES=()
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for run in {1..5}; do
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START=$(date +%s%N)
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psql "$DB_URL" -c "$QUERY_SQL" > /dev/null 2>&1
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END=$(date +%s%N)
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DURATION_MS=$(( (END - START) / 1000000 ))
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TIMES+=($DURATION_MS)
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done
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# Calculate median time
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IFS=$'\n' SORTED_TIMES=($(sort -n <<<"${TIMES[*]}"))
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MEDIAN_TIME=${SORTED_TIMES[2]}
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QUERY_TIMES+=($MEDIAN_TIME)
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echo -e "${QUERY_NAME}: ${BLUE}${MEDIAN_TIME}ms${NC}"
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done
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echo ""
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# Calculate P99 latency
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IFS=$'\n' SORTED_QUERY_TIMES=($(sort -n <<<"${QUERY_TIMES[*]}"))
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P99_INDEX=$(( (QUERY_COUNT * 99) / 100 ))
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P99_LATENCY=${SORTED_QUERY_TIMES[$P99_INDEX]}
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echo -e "P99 Query Latency: ${BLUE}${P99_LATENCY}ms${NC}"
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if [ $P99_LATENCY -le $TARGET_P99_LATENCY_MS ]; then
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echo -e "${GREEN}✅ PASS: P99 latency ${P99_LATENCY}ms <= target ${TARGET_P99_LATENCY_MS}ms${NC}"
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else
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echo -e "${RED}❌ FAIL: P99 latency ${P99_LATENCY}ms > target ${TARGET_P99_LATENCY_MS}ms${NC}"
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fi
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echo ""
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# ================================================================================================
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# PART 4: COMPRESSION RATIO TEST
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# ================================================================================================
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echo -e "${YELLOW}[4/6] Compression Ratio Test${NC}"
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echo "Target: Compression ratio > ${TARGET_COMPRESSION_RATIO}x"
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echo ""
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# Insert old data (8 days ago) to trigger compression
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echo "Inserting old data for compression test..."
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OLD_DATA_JSON=$(cat <<EOF
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[
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$(for j in $(seq 1 1000); do
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OLD_TIMESTAMP=$(date -u -Iseconds -d '8 days ago')
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SYMBOL=$(printf "SYM%02d" $((RANDOM % 10 + 1)))
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ACTION=$(printf "%s" "$(shuf -n1 -e BUY SELL HOLD)")
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cat <<INNER_EOF
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{
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"timestamp": "$OLD_TIMESTAMP",
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"symbol": "$SYMBOL",
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"ensemble_action": "$ACTION",
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"ensemble_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"ensemble_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"disagreement_rate": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"dqn_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"dqn_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"dqn_weight": 0.25,
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"dqn_vote": "$ACTION",
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"ppo_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"ppo_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"ppo_weight": 0.25,
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"ppo_vote": "$ACTION",
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"mamba2_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"mamba2_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"mamba2_weight": 0.25,
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"mamba2_vote": "$ACTION",
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"tft_signal": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()*2-1}'),
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"tft_confidence": $(awk -v seed=$RANDOM 'BEGIN{srand(seed); print rand()}'),
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"tft_weight": 0.25,
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"tft_vote": "$ACTION",
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"inference_latency_us": $((RANDOM % 10000 + 1000)),
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"aggregation_latency_us": $((RANDOM % 1000 + 100))
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}$(if [ $j -lt 1000 ]; then echo ","; fi)
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INNER_EOF
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done)
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]
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EOF
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)
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psql "$DB_URL" -t -c "SELECT insert_ensemble_predictions_bulk('$OLD_DATA_JSON'::JSONB);" > /dev/null 2>&1
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echo "Triggering manual compression..."
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psql "$DB_URL" -c "SELECT compress_chunk(i.chunk_schema || '.' || i.chunk_name) FROM timescaledb_information.chunks i WHERE i.hypertable_name = 'ensemble_predictions' AND i.is_compressed = false AND i.range_start < NOW() - INTERVAL '7 days';" > /dev/null 2>&1
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# Wait for compression to complete
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sleep 2
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# Check compression ratio
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COMPRESSION_STATS=$(psql "$DB_URL" -t -c "SELECT AVG(before_compression_total_bytes::FLOAT / NULLIF(after_compression_total_bytes, 0)) FROM timescaledb_information.compressed_chunk_stats WHERE hypertable_name = 'ensemble_predictions';" | tr -d ' ')
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if [ -z "$COMPRESSION_STATS" ] || [ "$COMPRESSION_STATS" == "" ]; then
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echo -e "${YELLOW}⚠️ No compressed chunks yet (data too recent)${NC}"
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echo -e "${BLUE}Note: Compression will trigger automatically after 7 days${NC}"
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else
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COMPRESSION_RATIO=$(printf "%.2f" "$COMPRESSION_STATS")
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echo -e "Compression ratio: ${BLUE}${COMPRESSION_RATIO}x${NC}"
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if (( $(echo "$COMPRESSION_RATIO >= $TARGET_COMPRESSION_RATIO" | bc -l) )); then
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echo -e "${GREEN}✅ PASS: Compression ratio ${COMPRESSION_RATIO}x >= target ${TARGET_COMPRESSION_RATIO}x${NC}"
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else
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echo -e "${RED}❌ FAIL: Compression ratio ${COMPRESSION_RATIO}x < target ${TARGET_COMPRESSION_RATIO}x${NC}"
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fi
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fi
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echo ""
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# ================================================================================================
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# PART 5: INDEX EFFICIENCY TEST
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# ================================================================================================
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echo -e "${YELLOW}[5/6] Index Efficiency Test${NC}"
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echo ""
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# Check index usage statistics
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psql "$DB_URL" -c "
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SELECT
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schemaname,
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tablename,
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indexname,
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idx_scan as index_scans,
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idx_tup_read as tuples_read,
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idx_tup_fetch as tuples_fetched,
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pg_size_pretty(pg_relation_size(indexrelid)) as index_size
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FROM pg_stat_user_indexes
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WHERE tablename IN ('ensemble_predictions', 'model_performance_attribution')
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ORDER BY idx_scan DESC, tablename;
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"
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echo ""
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# ================================================================================================
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# PART 6: CONTINUOUS AGGREGATE TEST
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# ================================================================================================
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echo -e "${YELLOW}[6/6] Continuous Aggregate Refresh Test${NC}"
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echo ""
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# Manually refresh continuous aggregates
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echo "Refreshing continuous aggregates..."
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psql "$DB_URL" -c "CALL refresh_continuous_aggregate('ensemble_performance_5min', NOW() - INTERVAL '1 hour', NOW());" > /dev/null 2>&1
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psql "$DB_URL" -c "CALL refresh_continuous_aggregate('model_performance_hourly', NOW() - INTERVAL '6 hours', NOW());" > /dev/null 2>&1
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psql "$DB_URL" -c "CALL refresh_continuous_aggregate('ensemble_performance_weekly', NOW() - INTERVAL '1 week', NOW());" > /dev/null 2>&1
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echo -e "${GREEN}✅ Continuous aggregates refreshed${NC}"
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echo ""
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# Check continuous aggregate sizes
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psql "$DB_URL" -c "
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SELECT
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view_name,
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pg_size_pretty(pg_total_relation_size(format('%I.%I', view_schema, view_name)::regclass)) as total_size
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FROM timescaledb_information.continuous_aggregates
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WHERE view_name IN ('ensemble_performance_5min', 'model_performance_hourly', 'ensemble_performance_weekly')
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ORDER BY view_name;
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"
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echo ""
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# ================================================================================================
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# FINAL SUMMARY
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# ================================================================================================
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echo -e "${BLUE}================================================================================================${NC}"
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echo -e "${BLUE}BENCHMARK SUMMARY${NC}"
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echo -e "${BLUE}================================================================================================${NC}"
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echo ""
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echo -e "Write Throughput: ${BLUE}${WRITES_PER_SEC}/sec${NC} (target: ${TARGET_WRITES_PER_SEC}/sec)"
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echo -e "P99 Query Latency: ${BLUE}${P99_LATENCY}ms${NC} (target: <${TARGET_P99_LATENCY_MS}ms)"
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if [ -z "$COMPRESSION_STATS" ] || [ "$COMPRESSION_STATS" == "" ]; then
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echo -e "Compression Ratio: ${YELLOW}N/A (data too recent)${NC} (target: >${TARGET_COMPRESSION_RATIO}x)"
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else
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echo -e "Compression Ratio: ${BLUE}${COMPRESSION_RATIO}x${NC} (target: >${TARGET_COMPRESSION_RATIO}x)"
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fi
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echo ""
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# Overall pass/fail
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PASS_COUNT=0
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TOTAL_TESTS=3
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if [ $WRITES_PER_SEC -ge $TARGET_WRITES_PER_SEC ]; then
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PASS_COUNT=$((PASS_COUNT + 1))
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fi
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if [ $P99_LATENCY -le $TARGET_P99_LATENCY_MS ]; then
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PASS_COUNT=$((PASS_COUNT + 1))
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fi
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if [ -n "$COMPRESSION_STATS" ] && [ "$COMPRESSION_STATS" != "" ]; then
|
|
if (( $(echo "$COMPRESSION_RATIO >= $TARGET_COMPRESSION_RATIO" | bc -l) )); then
|
|
PASS_COUNT=$((PASS_COUNT + 1))
|
|
fi
|
|
else
|
|
TOTAL_TESTS=2 # Exclude compression test if no data
|
|
fi
|
|
|
|
echo -e "${BLUE}Tests Passed: ${PASS_COUNT}/${TOTAL_TESTS}${NC}"
|
|
echo ""
|
|
|
|
if [ $PASS_COUNT -eq $TOTAL_TESTS ]; then
|
|
echo -e "${GREEN}✅ ALL TESTS PASSED - Database optimized for production${NC}"
|
|
exit 0
|
|
else
|
|
echo -e "${YELLOW}⚠️ Some tests did not meet targets - review optimization strategies${NC}"
|
|
exit 1
|
|
fi
|