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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 SizePartitionsPlanning TimeTarget
4 nodes415μs<100ms
16 nodes6442μs<100ms
64 nodes25689μs<100ms

Query Speedup

Query TypeSingle-NodeDistributedSpeedup
Table scan (1B rows)120s8s15x
Hash join (100M×10M)45s2.1s21x
Group-by aggregate30s1.8s17x
Complex multi-join180s4.2s43x

Network Efficiency

OptimizationWithoutWithReduction
Partition pruning10 GB1.2 GB88%
Predicate pushdown8 GB1.5 GB81%
Broadcast join15 GB2.8 GB81%
Combined20 GB2.1 GB89%

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: