backpressure-analyzer

Detect and resolve backpressure issues in data pipelines, message queues, and streaming systems. Identify bottleneck stages, measure queue depths and process...

Find where your pipeline is backing up. Measure processing rates at each stage, identify the bottleneck, detect growing queues, and recommend flow control strategies — bounded buffers, rate limiting, load shedding, or autoscaling.

Use when: "pipeline is slow", "queue keeps growing", "messages backing up", "consumer can't keep up", "producer faster than consumer", "backpressure", "flow control", "bottleneck analysis", or when processing delays increase over time.

Commands

1. detect — Find Backpressure Points

Step 1: Measure Queue Depths

# Kafka consumer lag
kafka-consumer-groups --bootstrap-server $KAFKA_BROKER --describe --all-groups 2>/dev/null | \
  awk 'NR>1 && $6>0 {printf "%-30s %-20s lag=%s\n", $1, $4, $6}' | sort -t= -k2 -rn | head -20

# RabbitMQ queue depths
rabbitmqctl list_queues name messages consumers 2>/dev/null | \
  awk '$2>0 {print $2 "\t" $1 "\tconsumers=" $3}' | sort -rn | head -20

# AWS SQS
for queue_url in $(aws sqs list-queues --query 'QueueUrls[]' --output text); do
  attrs=$(aws sqs get-queue-attributes --queue-url "$queue_url" \
    --attribute-names ApproximateNumberOfMessages ApproximateNumberOfMessagesNotVisible \
    --output json 2>/dev/null)
  visible=$(echo "$attrs" | python3 -c "import json,sys;print(json.load(sys.stdin)['Attributes'].get('ApproximateNumberOfMessages','0'))")
  inflight=$(echo "$attrs" | python3 -c "import json,sys;print(json.load(sys.stdin)['Attributes'].get('ApproximateNumberOfMessagesNotVisible','0'))")
  if [ "$visible" -gt 0 ] 2>/dev/null; then
    echo "Queue: $(basename $queue_url) — pending=$visible, in-flight=$inflight"
  fi
done

# Redis Streams
redis-cli XINFO STREAM mystream 2>/dev/null | grep -E "length|groups"
redis-cli XINFO GROUPS mystream 2>/dev/null

Step 2: Measure Processing Rates

# Measure throughput at each pipeline stage
# Take two snapshots 60s apart and calculate rate

# Kafka: messages produced vs consumed per second
kafka-consumer-groups --bootstrap-server $KAFKA_BROKER --describe --group mygroup 2>/dev/null | \
  awk 'NR>1 {lag+=$6; offset+=$4} END {print "Total lag:", lag, "Current offset:", offset}'

# Process-level: messages processed per second
# Check application metrics endpoint
curl -s http://localhost:9090/metrics | grep -E "messages_processed_total|items_processed_total"

Step 3: Identify Bottleneck

Map the pipeline stages and their rates:

Producer (1000 msg/s) → Queue A (depth: 5) → Stage 1 (800 msg/s) → Queue B (depth: 50000) → Stage 2 (200 msg/s) → Queue C (depth: 2) → Stage 3 (500 msg/s)
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                                                                        BOTTLENECK: Stage 2 can't keep up

The bottleneck is the stage with:

• Growing queue depth (input queue getting deeper over time)
• Lowest throughput relative to its input rate
• Highest resource utilization (CPU, memory, I/O at capacity)

Step 4: Generate Report

# Backpressure Analysis Report

## Pipeline: Order Processing

## Flow Map

API (1000 req/s) → order-events (Kafka, lag: 45,000 ⚠️, growing +200/min) → order-validator (3 pods, 350 msg/s each = 1050 total) → validated-orders (Kafka, lag: 200, stable ✅) → payment-processor (2 pods, 150 msg/s each = 300 total) → payment-results (Kafka, lag: 85,000 🔴, growing +700/min) → notification-sender (1 pod, 500 msg/s)

## Bottleneck: payment-processor
- **Input rate:** 1050 msg/s (from validator)
- **Processing rate:** 300 msg/s (2 pods × 150 msg/s)
- **Deficit:** 750 msg/s accumulating in queue
- **Current backlog:** 85,000 messages (~4.7 hours to drain at current rate)
- **Resource utilization:** CPU 95%, memory 60%, network 20%
- **Root cause:** CPU-bound — payment validation is computationally expensive

## Recommendations (in order)
1. **Scale out:** Increase payment-processor to 7 pods (7 × 150 = 1050 msg/s)
   - Cost: +5 pods × $X/month
   - Time to drain backlog: ~2.5 hours after scaling

2. **Optimize processing:** Profile payment validation for optimization
   - Current: 6.7ms per message
   - Target: 1ms per message (would need only 2 pods)

3. **Add backpressure signal:** Have payment-processor signal order-validator to slow down
   - Reactive Streams-style demand signaling
   - Or: consumer pause when lag > threshold

4. **Load shedding (last resort):** Drop low-priority messages when queue > 100K
   - Only for non-critical notifications, never for payments

2. strategies — Recommend Flow Control

Based on the pipeline characteristics, recommend:

• Bounded buffers: Set max queue size, block producer when full
• Rate limiting: Limit producer rate to match slowest consumer
• Autoscaling: Scale consumers based on queue depth
• Load shedding: Drop low-priority messages under pressure
• Batch processing: Accumulate and process in batches for efficiency
• Circuit breaker: Stop sending to overwhelmed downstream
• Priority queues: Process critical messages first when backed up

3. monitor — Set Up Backpressure Alerts

Generate alerting rules:

# Prometheus alert rules
groups:
  - name: backpressure
    rules:
      - alert: KafkaConsumerLagHigh
        expr: kafka_consumergroup_lag_sum > 10000
        for: 5m
        labels:
          severity: warning
      - alert: KafkaConsumerLagCritical
        expr: kafka_consumergroup_lag_sum > 100000
        for: 5m
        labels:
          severity: critical
      - alert: QueueDepthGrowing
        expr: rate(kafka_consumergroup_lag_sum[5m]) > 0
        for: 15m
        labels:
          severity: warning