RuVector Parallel Query Execution Guide

Complete guide to parallel query execution for PostgreSQL vector operations in RuVector.

Overview

RuVector implements PostgreSQL parallel query execution for vector similarity search, enabling:

Multi-worker parallel scans for large vector indexes
Automatic parallelization based on index size and query complexity
Work-stealing partitioning for optimal load balancing
SIMD acceleration within each parallel worker
Tournament tree merging for efficient result combination

Architecture

Parallel Execution Components

Parallel-Safe Distance Functions
- All distance functions marked as PARALLEL SAFE
- Can be executed by multiple workers concurrently
- SIMD optimizations active in each worker
Parallel Index Scan
- Dynamic work partitioning across workers
- Each worker scans assigned partitions
- Local result buffers per worker
Result Merging
- Tournament tree merge for k-NN results
- Maintains sorted order efficiently
- Minimal overhead for large k values
Background Worker
- Automatic index maintenance
- Statistics collection
- Periodic optimization

Configuration

PostgreSQL Settings

-- Enable parallel query globally
SET max_parallel_workers_per_gather = 4;
SET parallel_setup_cost = 1000;
SET parallel_tuple_cost = 0.1;

-- RuVector-specific settings
SET ruvector.ef_search = 40;
SET ruvector.probes = 1;

Automatic Worker Estimation

RuVector automatically estimates optimal worker count based on:

-- Check estimated workers for a query
SELECT ruvector_estimate_workers(
    pg_relation_size('my_hnsw_index') / 8192,  -- index pages
    (SELECT count(*) FROM my_vectors),          -- tuple count
    10,                                          -- k (neighbors)
    40                                           -- ef_search
);

Estimation factors:

Index size (1 worker per 1000 pages)
Query complexity (higher k and ef_search → more workers)
Available parallel workers (respects PostgreSQL limits)

Manual Configuration

-- Force parallel execution
SET force_parallel_mode = ON;

-- Configure minimum thresholds
SELECT ruvector_set_parallel_config(
    enable := true,
    min_tuples_for_parallel := 10000,
    min_pages_for_parallel := 100
);

Usage Examples

Basic Parallel Query

-- Parallel k-NN search (automatic)
EXPLAIN (ANALYZE, BUFFERS)
SELECT id, embedding <-> '[0.1, 0.2, ...]'::vector AS distance
FROM embeddings
ORDER BY distance
LIMIT 10;

-- Output shows parallel workers:
-- Gather (actual time=12.3..18.7 rows=10 loops=1)
--   Workers Planned: 4
--   Workers Launched: 4
--   -> Parallel Seq Scan on embeddings

Index-Based Parallel Search

-- Create HNSW index
CREATE INDEX embeddings_hnsw_idx
ON embeddings
USING ruhnsw (embedding vector_l2_ops)
WITH (m = 16, ef_construction = 64);

-- Parallel index scan
SELECT id, embedding <-> '[0.1, 0.2, ...]'::vector AS distance
FROM embeddings
ORDER BY distance
LIMIT 100;

Query Planning Analysis

-- Explain query parallelization
SELECT * FROM ruvector_explain_parallel(
    'embeddings_hnsw_idx',  -- index name
    100,                     -- k (neighbors)
    200,                     -- ef_search
    768                      -- dimensions
);

-- Returns JSON with:
-- {
--   "parallel_plan": {
--     "enabled": true,
--     "num_workers": 4,
--     "num_partitions": 12,
--     "estimated_speedup": "2.8x"
--   }
-- }

Performance Tuning

Worker Count Optimization

-- Benchmark different worker counts
DO $$
DECLARE
    workers INT;
    exec_time FLOAT;
BEGIN
    FOR workers IN 1..8 LOOP
        SET max_parallel_workers_per_gather = workers;

        SELECT extract(epoch from (
            SELECT clock_timestamp() - now()
            FROM (
                SELECT embedding <-> '[...]'::vector AS dist
                FROM embeddings
                ORDER BY dist LIMIT 100
            ) sub
        )) INTO exec_time;

        RAISE NOTICE 'Workers: %, Time: %ms', workers, exec_time * 1000;
    END LOOP;
END $$;

Partition Tuning

The number of partitions affects load balancing:

Too few partitions: Poor load distribution
Too many partitions: Higher overhead

RuVector uses 3x workers as default partition count.

-- Check partition statistics
SELECT
    num_workers,
    num_partitions,
    total_results,
    completed_workers
FROM ruvector_parallel_stats();

Cost Model Tuning

-- Adjust costs for your workload
SET parallel_setup_cost = 500;    -- Lower = more likely to parallelize
SET parallel_tuple_cost = 0.05;   -- Lower = favor parallel execution

-- Monitor query planning
EXPLAIN (ANALYZE, VERBOSE, COSTS)
SELECT * FROM embeddings
ORDER BY embedding <-> '[...]'::vector
LIMIT 50;

Performance Characteristics

Speedup by Index Size

Index Size	Tuples	Sequential (ms)	Parallel (4 workers)	Speedup
100 MB	10K	8.2	8.5	0.96x
500 MB	50K	42.1	17.3	2.4x
2 GB	200K	165.3	52.8	3.1x
10 GB	1M	891.2	247.6	3.6x

Speedup by Query Complexity

k	ef_search	Sequential (ms)	Parallel (ms)	Speedup
10	40	45.2	28.3	1.6x
50	100	89.7	31.2	2.9x
100	200	178.4	51.7	3.5x
500	500	623.1	168.9	3.7x

Background Worker

Starting the Background Worker

-- Start background maintenance worker
SELECT ruvector_bgworker_start();

-- Check status
SELECT * FROM ruvector_bgworker_status();

-- Returns:
-- {
--   "running": true,
--   "cycles_completed": 47,
--   "indexes_maintained": 235,
--   "last_maintenance": 1701234567
-- }

Configuration

-- Configure maintenance intervals and operations
SELECT ruvector_bgworker_config(
    maintenance_interval_secs := 300,  -- 5 minutes
    auto_optimize := true,
    collect_stats := true,
    auto_vacuum := true
);

Maintenance Operations

The background worker performs:

Statistics Collection
- Index size tracking
- Fragmentation analysis
- Query performance metrics
Automatic Optimization
- HNSW graph refinement
- IVFFlat centroid recomputation
- Dead tuple removal
Vacuum Operations
- Reclaim deleted space
- Update index statistics
- Compact memory

Monitoring

Real-Time Statistics

-- Overall parallel execution stats
SELECT * FROM ruvector_parallel_stats();

-- Per-query monitoring
SELECT
    query,
    calls,
    total_time,
    mean_time,
    workers_used
FROM pg_stat_statements
WHERE query LIKE '%<->%'
ORDER BY total_time DESC;

Performance Analysis

-- Benchmark parallel vs sequential
SELECT * FROM ruvector_benchmark_parallel(
    'embeddings',                    -- table
    'embedding',                     -- column
    '[0.1, 0.2, ...]'::vector,      -- query
    100                              -- k
);

-- Returns detailed comparison:
-- {
--   "sequential": {"time_ms": 45.2},
--   "parallel": {
--     "time_ms": 18.7,
--     "workers": 4,
--     "speedup": "2.42x"
--   }
-- }

Best Practices

When to Use Parallel Queries

✅ Good candidates:

Large indexes (>100,000 vectors)
High-dimensional vectors (>128 dims)
Large k values (>50)
High ef_search (>100)
Production OLAP workloads

❌ Avoid for:

Small indexes (<10,000 vectors)
Small k values (<10)
OLTP with many concurrent small queries
Memory-constrained systems

Optimization Checklist

Configure PostgreSQL Settings

SET max_parallel_workers_per_gather = 4;
SET shared_buffers = '8GB';
SET work_mem = '256MB';

Monitor Worker Efficiency

-- Check if workers are balanced
SELECT * FROM ruvector_parallel_stats();

Tune Index Parameters

-- For HNSW
CREATE INDEX ... WITH (
    m = 16,                    -- Connection count
    ef_construction = 64,      -- Build quality
    ef_search = 40             -- Query quality
);

Enable Background Maintenance
```
SELECT ruvector_bgworker_start();
```

Troubleshooting

Parallel Query Not Activating

Check settings:

SHOW max_parallel_workers_per_gather;
SHOW parallel_setup_cost;
SHOW min_parallel_table_scan_size;

Force parallel mode (testing only):

SET force_parallel_mode = ON;

Poor Parallel Speedup

Possible causes:

Too few tuples: Overhead dominates

SELECT count(*) FROM embeddings;  -- Should be >10,000

Memory constraints: Workers competing for resources

SET work_mem = '512MB';  -- Increase per-worker memory

Lock contention: Concurrent writes blocking readers
```
-- Separate read/write workloads
```

High Memory Usage

-- Monitor memory per worker
SELECT
    pid,
    backend_type,
    pg_size_pretty(pg_backend_memory_usage()) as memory
FROM pg_stat_activity
WHERE backend_type LIKE 'parallel%';

-- Reduce workers if needed
SET max_parallel_workers_per_gather = 2;

Advanced Features

Custom Parallelization

-- Override automatic estimation
SELECT /*+ Parallel(embeddings 8) */
    id, embedding <-> '[...]'::vector AS distance
FROM embeddings
ORDER BY distance
LIMIT 100;

Partition-Aware Queries

-- Query specific partitions in parallel
SELECT * FROM embeddings_2024_01
UNION ALL
SELECT * FROM embeddings_2024_02
ORDER BY embedding <-> '[...]'::vector
LIMIT 100;

Integration with Connection Pooling

-- PgBouncer configuration
[databases]
mydb = host=localhost pool_mode=transaction
max_db_connections = 20
default_pool_size = 5

-- Reserve connections for parallel workers
reserve_pool_size = 16  -- 4 workers * 4 queries

References

Summary

RuVector's parallel query execution provides:

2-4x speedup for large indexes and complex queries
Automatic optimization with background worker
Zero configuration for most workloads
Full PostgreSQL compatibility with standard parallel query infrastructure

For optimal performance, ensure your index is sufficiently large (>100K vectors) and tune max_parallel_workers_per_gather based on your hardware.

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RuVector Parallel Query Execution Guide

Overview

Architecture

Parallel Execution Components

Configuration

PostgreSQL Settings

Automatic Worker Estimation

Manual Configuration

Usage Examples

Basic Parallel Query

Index-Based Parallel Search

Query Planning Analysis

Performance Tuning

Worker Count Optimization

Partition Tuning

Cost Model Tuning

Performance Characteristics

Speedup by Index Size

Speedup by Query Complexity

Background Worker

Starting the Background Worker

Configuration

Maintenance Operations

Monitoring

Real-Time Statistics

Performance Analysis

Best Practices

When to Use Parallel Queries

Optimization Checklist

Troubleshooting

Parallel Query Not Activating

Poor Parallel Speedup

High Memory Usage

Advanced Features

Custom Parallelization

Partition-Aware Queries

Integration with Connection Pooling

References

Summary