---
title: "Distributed Query Optimization Guide"
---

# Distributed Query Optimization Guide

## Overview

The HeliosDB Distributed Query Optimizer provides ML-powered, cost-based query optimization for distributed queries across multi-node clusters. It automatically selects optimal execution strategies, minimizes network traffic, and learns from query execution history to improve performance over time.

## Key Features

### 1. **Cost-Based Optimization**
- Accurate cost estimation for distributed operations
- Multi-dimensional cost model (CPU, network, I/O, memory)
- Adaptive cost adjustments based on actual execution

### 2. **Intelligent Query Planning**
- Automatic partition pruning
- Predicate pushdown optimization
- Join reordering for optimal execution
- Data locality-aware routing

### 3. **Adaptive Learning**
- ML-based join strategy selection
- Cost model self-tuning
- Historical query analysis
- Continuous performance improvement

### 4. **Distributed Execution Strategies**
- **MapReduce**: Parallel map and reduce phases
- **Scatter-Gather**: Distribute and collect results
- **Pipeline**: Streaming execution across nodes

## Architecture

```
┌─────────────────────────────────────────────────────┐
│         Distributed Query Optimizer                 │
├─────────────────────────────────────────────────────┤
│                                                      │
│  ┌──────────────┐  ┌──────────────┐  ┌───────────┐ │
│  │   Planner    │  │  Optimizer   │  │  Executor │ │
│  └──────────────┘  └──────────────┘  └───────────┘ │
│         │                  │                  │     │
│         ▼                  ▼                  ▼     │
│  ┌──────────────┐  ┌──────────────┐  ┌───────────┐ │
│  │   Router     │  │  Cost Model  │  │   Cache   │ │
│  └──────────────┘  └──────────────┘  └───────────┘ │
│         │                  │                        │
│         ▼                  ▼                        │
│  ┌──────────────┐  ┌──────────────┐                │
│  │  Statistics  │  │   Adaptive   │                │
│  │  Collector   │  │   Planner    │                │
│  └──────────────┘  └──────────────┘                │
└─────────────────────────────────────────────────────┘
```

## Optimization Techniques

### 1. Partition Pruning

Automatically eliminates irrelevant partitions based on filter predicates (for example, a date filter can reduce a 16-partition scan to 2 partitions).

**Performance Impact**: 70-90% reduction in I/O and network traffic

### 2. Join Optimization

Selects optimal join strategy based on table sizes and data distribution:

- **Broadcast Join** (small table × large table): the small table is broadcast to all nodes
- **Shuffle Join** (large table × large table): both tables are hash-partitioned

**Performance**: 5-50x speedup vs. single-node execution

### 3. Adaptive Learning

The optimizer learns from query execution history.

### 4. Query Routing

Routes queries to nodes with best data locality and available resources.

## Performance Benchmarks

Indicative figures, measured on representative fixtures; reproduce on your own hardware.

### Planning Performance

| Cluster Size | Partitions | Planning Time | Target |
|--------------|------------|---------------|--------|
| 4 nodes | 4 | 15μs | <100ms |
| 16 nodes | 64 | 42μs | <100ms |
| 64 nodes | 256 | 89μs | <100ms |

### Query Speedup

| Query Type | Single-Node | Distributed | Speedup |
|----------------------|-------------|-------------|---------|
| Table scan (1B rows) | 120s | 8s | 15x |
| Hash join (100M×10M) | 45s | 2.1s | 21x |
| Group-by aggregate | 30s | 1.8s | 17x |
| Complex multi-join | 180s | 4.2s | 43x |

### Network Efficiency

| Optimization | Without | With | Reduction |
|----------------------|------------|------------|-----------|
| Partition pruning | 10 GB | 1.2 GB | 88% |
| Predicate pushdown | 8 GB | 1.5 GB | 81% |
| Broadcast join | 15 GB | 2.8 GB | 81% |
| Combined | 20 GB | 2.1 GB | 89% |

## Best Practices

### 1. Statistics Collection

Keep statistics up-to-date for accurate optimization, and update table statistics after bulk loads.

### 2. Cost Model Tuning

Adjust cost weights for your workload: weight CPU more heavily for CPU-bound workloads and network more heavily for network-constrained environments.

### 3. Cache Management

Configure caching for query plan reuse: larger plan caches with longer TTLs (for example 10,000 plans and 10 minutes) for high-throughput environments, smaller caches with shorter TTLs (for example 500 plans and 3 minutes) for memory-constrained environments.

### 4. Monitoring

Track optimizer performance, such as plan cache hit rate and active queries.

## Troubleshooting

### High Planning Time

**Symptom**: Planning takes >100ms
**Solution**:
- Reduce `max_join_reorder_size` (default: 8)
- Enable plan caching
- Simplify query structure

### Suboptimal Plan Selection

**Symptom**: Queries slower than expected
**Solution**:
- Update table statistics
- Tune cost model weights
- Enable adaptive learning
- Check data skew

### High Network Traffic

**Symptom**: Excessive data transfer
**Solution**:
- Enable partition pruning
- Use broadcast joins for small tables
- Add more selective filters
- Check partition key design

## Support

For questions or issues:
- **Documentation**: [HeliosDB Full documentation](/docs/full/)
- Support: [support@heliosdb.com](mailto:support@heliosdb.com)
- **Community**: https://discord.gg/yTykuUrFXc