GeoSpatial Performance & Optimization

Optimize geospatial query performance with Apache Pinot. This guide covers everything from basic performance principles to tuning techniques.

Query Plan Analysis

Use EXPLAIN PLAN to understand and verify index usage

Index Optimization

Configure H3 resolutions for optimal performance

Query Patterns

Structure queries to use geospatial indexes effectively

Performance Monitoring

Monitor and measure geospatial query performance

Performance Fundamentals

How H3 Index Acceleration Works

Understanding the two-phase optimization approach is key to writing efficient queries:

Coarse Filtering (H3 Index)

The H3 index quickly identifies candidate records within nearby hexagons, eliminating the vast majority of data from consideration.

Precise Filtering (Geospatial Functions)

Only records in candidate hexagons undergo expensive precise geospatial calculations (ST_Distance, ST_Within, etc.).

Example: Finding locations within 5km

Without Index: Calculate exact distance for ALL records (expensive)
With H3 Index:
1. Find hexagon containing given point
2. Find hexagons fully contained within the searched area
3. Include all points associated with those hexagons
4. Find hexagons overlapping searched area
5. Calculate exact distance for records in those hexagons

Important: If query distance is >100x the hexagon size, the index is automatically bypassed and falls back to full scan for performance reasons.

Query Plan Analysis

Using EXPLAIN PLAN FOR

The most important tool for geospatial performance optimization: Single-stage query engine (SSQE):

EXPLAIN PLAN FOR
SELECT store_name, address, 
       ST_Distance(location, ST_Point(-122.4194, 37.7749)) as distance
FROM stores 
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000
ORDER BY distance;

Multi-stage query engine MSQE:

set explainAskingServers=true;
EXPLAIN PLAN FOR
SELECT store_name, address,
       ST_Distance(location, ST_Point(-122.4194, 37.7749)) as distance
FROM stores
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000
ORDER BY distance;

Setting explainAskingServers query option is necessary for MSQE to show usage of segment-level indexes.

Index Usage Operators

Look for these operators in your query plans:

Shows as FilterH3Index in MSQE. When: ST_Distance with range conditions

WHERE ST_Distance(location_column, literal_point) < distance
WHERE ST_Distance(location_column, literal_point) > distance  
WHERE ST_Distance(location_column, literal_point) BETWEEN min_dist AND max_dist

Query Plan Example: SSQE:

BROKER_REDUCE(limit:1000)
└── COMBINE_SELECT
    └── SELECT(selectList:[store_name, address, distance])
        └── TRANSFORM_PASSTHROUGH(distance:ST_DISTANCE(...))
            └── FILTER_H3_INDEX(predicate:ST_DISTANCE(...) < 5000)
                └── SEGMENT_SCAN(table:stores)

MSQE:

PinotLogicalExchange(distribution=[broadcast])
  LeafStageCombineOperator(table=[stores])
    StreamingInstanceResponse
      StreamingCombineSelect
        SelectStreaming(segment=[stores_OFFLINE_0], table=[starbucksStores])
          Project(columns=[[store_name, address, distance]])
            DocIdSet(maxDocs=[10000])
              FilterH3Index(predicate=[stdistance(...) < '5000.0'], indexLookUp=[h3_index], operator=[RANGE])

EXPLAIN PLAN Examples

Here are real examples of what you should see when H3 indexes are working: FILTER_H3_INDEX Example (ST_Distance):

EXPLAIN PLAN FOR
SELECT * FROM starbucksStores
WHERE ST_Distance(location_st_point, ST_Point(-122.4194, 37.7749)) < 5000;

Output:

BROKER_REDUCE(limit:10)
COMBINE_SELECT
PLAN_START(numSegmentsForThisPlan:1)
SELECT(selectList:address, lat, location_st_point, lon, name)
PROJECT(name, lon, address, location_st_point, lat)
DOC_ID_SET
FILTER_H3_INDEX

INCLUSION_FILTER_H3_INDEX Example (ST_Contains):

EXPLAIN PLAN FOR
SELECT * FROM starbucksStores
WHERE ST_Contains(ST_GeomFromText('POLYGON((0 0, 4 0, 4 4, 0 4, 0 0))'), location_st_point);

Output:

BROKER_REDUCE(limit:10)
COMBINE_SELECT
PLAN_START(numSegmentsForThisPlan:1)
SELECT(selectList:address, lat, location_st_point, lon, name)
PROJECT(name, lon, address, location_st_point, lat)
DOC_ID_SET
INCLUSION_FILTER_H3_INDEX

Key Indicators:

FILTER_H3_INDEX for distance-based queries
INCLUSION_FILTER_H3_INDEX for containment queries
DOC_ID_SET showing efficient document filtering
No FILTER operator (which would indicate full scan)

Index Optimization

Resolution Selection Strategy

Choose H3 resolutions based on your query patterns and performance requirements:

Single Resolution Approach

When to use: Uniform query patterns with consistent search radius

{
  "indexes": {
    "h3": {
      "resolution": [8]  // ~500m precision, good for city-wide queries
    }
  }
}

Query Performance:

Excellent for queries around the target resolution
Suboptimal for very different search radii

Multi-Resolution Approach (Recommended)

When to use: Varied query patterns with different search radii

{
  "indexes": {
    "h3": {
      "resolution": [5, 8, 11]  // Coarse, medium, fine
    }
  }
}

Resolution Selection Algorithm:

Resolution 5 (8km): For queries >20km radius
Resolution 8 (500m): For queries 1-20km radius
Resolution 11 (25m): For queries <1km radius

Performance vs Storage Analysis

Resolutions	Query Performance	Storage Overhead	Memory Usage	Use Case
[8]	⭐⭐⭐	⭐⭐⭐⭐⭐	Low	Uniform patterns
[5, 8]	⭐⭐⭐⭐	⭐⭐⭐⭐	Medium	Most applications
[5, 8, 11]	⭐⭐⭐⭐⭐	⭐⭐⭐	Medium-High	Varied patterns
[3, 6, 9, 12]	⭐⭐⭐⭐⭐	⭐⭐	High	Complex analytics
[1, 4, 7, 10, 13]	⭐⭐⭐⭐	⭐	Very High	Over-optimization

Resolution Tuning Examples

Retail Store Locator:

{
  "resolution": [7, 10]  // 1.2km and 65m
}

Resolution 7: “Stores within 5km” queries
Resolution 10: “Nearest store” precision queries

Logistics & Delivery:

{
  "resolution": [4, 7, 10]  // 22km, 1.2km, 65m
}

Resolution 4: Regional route planning
Resolution 7: City-wide delivery zones
Resolution 10: Precise delivery locations

Global IoT Network:

{
  "resolution": [2, 5, 8, 11]  // 158km, 8km, 500m, 25m
}

Resolution 2: Country/region analysis
Resolution 5: City-level aggregation
Resolution 8: Local area monitoring
Resolution 11: Device-level precision

Optimized Query Patterns

Distance-Based Queries

Index-Optimized Patterns:

-- Correct argument order (column first, literal second)
WHERE ST_Distance(location_column, ST_Point(-122.4194, 37.7749)) < 5000

-- Range queries work well
WHERE ST_Distance(location_column, ST_Point(-122.4194, 37.7749)) BETWEEN 1000 AND 5000

-- Use with ORDER BY for nearest neighbor
SELECT * FROM stores 
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 10000
ORDER BY ST_Distance(location, ST_Point(-122.4194, 37.7749))
LIMIT 10;

Non-Optimized Patterns:

-- Wrong argument order (literal first)
WHERE ST_Distance(ST_Point(-122.4194, 37.7749), location_column) < 5000

-- Both arguments are columns (can't use index)
WHERE ST_Distance(pickup_location, dropoff_location) < 5000

-- Complex expressions in spatial function
WHERE ST_Distance(location, ST_Point(lon_column, lat_column)) < 5000

Containment Queries

ST_Within Optimization:

-- Optimized: column first, literal second  
SELECT * FROM events
WHERE ST_Within(
  event_location,
  ST_GeomFromText('POLYGON((-122.5 37.7, -122.4 37.7, -122.4 37.8, -122.5 37.8, -122.5 37.7))')
);

-- Not optimized: literal first
WHERE ST_Within(
  ST_GeomFromText('POLYGON(...)'),
  event_location
);

ST_Contains Optimization:

-- Optimized: literal first, column second
SELECT * FROM delivery_addresses
WHERE ST_Contains(
  ST_GeomFromText('POLYGON((-122.5 37.7, -122.4 37.7, -122.4 37.8, -122.5 37.8, -122.5 37.7))'),
  address_location
);

-- Not optimized: column first  
WHERE ST_Contains(service_area, fixed_point);

Advanced Query Optimization

Multiple Spatial Conditions:

-- Combine spatial and non-spatial filters efficiently
SELECT store_name, category
FROM stores 
WHERE category = 'restaurant'  -- Fast filter first
  AND ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 2000  -- Spatial filter
  AND rating > 4.0;  -- Additional filters after spatial

Subquery Optimization:

-- Use spatial filtering in subquery to reduce dataset
SELECT s.store_name, s.address, r.avg_rating
FROM (
  SELECT store_id, store_name, address 
  FROM stores 
  WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000
) s
JOIN ratings r ON s.store_id = r.store_id;

Spatial Aggregations:

-- Efficient spatial grouping
SELECT 
  region,
  COUNT(*) as store_count,
  AVG(ST_Distance(location, ST_Point(-122.4194, 37.7749))) as avg_distance
FROM stores 
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 20000
GROUP BY region
ORDER BY avg_distance;

Performance Monitoring

Key Performance Metrics

Monitor:

Overall query response time
Spatial filter execution time
Index effectiveness ratio

Tools:

-- Enable query execution metrics
SET enableQueryExecutionMetrics = true;

-- Run your spatial query and check execution time
SELECT store_name FROM stores 
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000;

Performance Benchmarking

Create Test Scenarios:

-- Benchmark different query patterns
-- Small radius (high precision)
SELECT 'Small Radius' as test_type, COUNT(*) as result_count
FROM stores 
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 1000;

-- Medium radius (typical use case)
SELECT 'Medium Radius' as test_type, COUNT(*) as result_count  
FROM stores
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000;

-- Large radius (coarse filtering)
SELECT 'Large Radius' as test_type, COUNT(*) as result_count
FROM stores 
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 25000;

Performance Comparison:

-- Compare indexed vs non-indexed performance
-- (Temporarily disable index for comparison)

-- Time indexed query
SELECT store_name FROM stores_indexed
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000;

-- Time non-indexed query  
SELECT store_name FROM stores_no_index
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000;

Advanced Optimization Techniques

Query Rewriting for Performance

Distance Approximation:

-- For very large datasets, use approximate filtering first
WITH nearby_candidates AS (
  SELECT store_id, location
  FROM stores 
  WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 6000  -- Slightly larger radius
)
SELECT s.store_name, s.address
FROM nearby_candidates nc
JOIN stores s ON nc.store_id = s.store_id
WHERE ST_Distance(nc.location, ST_Point(-122.4194, 37.7749)) < 5000;  -- Exact filter

Batch Spatial Operations:

-- Process multiple locations efficiently
WITH locations_of_interest(location_name, lon, lat) AS (
  VALUES 
    ('SF', -122.4194, 37.7749),
    ('Oakland', -122.2711, 37.8044),
    ('San Jose', -121.8863, 37.3382)
)
SELECT 
  loi.location_name,
  COUNT(*) as nearby_stores
FROM locations_of_interest loi
CROSS JOIN stores s
WHERE ST_Distance(s.location, ST_Point(loi.lon, loi.lat)) < 5000
GROUP BY loi.location_name;

Memory Optimization

Column Selection:

-- Select only needed columns
SELECT store_name, address 
FROM stores 
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000;

-- Avoid SELECT * with spatial data
SELECT * FROM stores  -- Includes large spatial columns
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 5000;

Result Set Limiting:

-- Use LIMIT for interactive queries
SELECT store_name, 
       ST_Distance(location, ST_Point(-122.4194, 37.7749)) as distance
FROM stores 
WHERE ST_Distance(location, ST_Point(-122.4194, 37.7749)) < 10000
ORDER BY distance
LIMIT 20;  -- Only get top 20 results

Troubleshooting Performance Issues

Common Performance Problems

Slow Query Performance

Symptoms: Queries taking >1 second, high CPU usageDiagnosis:

EXPLAIN PLAN FOR your_slow_query;

Solutions:

Verify H3 index operators are present
Check argument order in spatial functions
Ensure resolutions match query patterns
Add additional non-spatial filters

Index Not Being Used

Symptoms: EXPLAIN PLAN shows FILTER instead of FILTER_H3_INDEXDiagnosis Checklist:

Column configured with encodingType: "RAW"
Column in noDictionaryColumns array
H3 index configured with appropriate resolution
Query uses correct argument order
Spatial function is supported (ST_Distance, ST_Within, ST_Contains)
Query distance is not >100x the hexagon size (auto-reverts to full scan)

Distance vs Resolution Check: If your query distance is much larger than hexagon size, the H3IndexFilterOperator automatically reverts to non-indexed expression for performance reasons.Fix Example:

{
  "fieldConfigList": [{
    "name": "location",
    "encodingType": "RAW",  // Must be RAW
    "indexes": {
      "h3": {
        "resolution": [8]  // Single resolution only
      }
    }
  }]
}

High Memory Usage

Symptoms: Out of memory errors, slow ingestionSolutions:

Reduce number of H3 resolutions (keep 2-3 max)
Remove very high resolutions (13-15) if not needed
Optimize query to select fewer columns
Use LIMIT in interactive queries

Memory-Optimized Config:

{
  "indexes": {
    "h3": {
      "resolution": [6, 9]  // Reduced from [3, 6, 9, 12, 15]
    }
  }
}

Poor Index Effectiveness

Symptoms: High filteredDocs/totalDocs ratio in query plansAnalysis:

-- Check filtering effectiveness
EXPLAIN PLAN FOR 
SELECT COUNT(*) FROM your_table 
WHERE ST_Distance(location, ST_Point(-122.4, 37.7)) < your_radius;

Solutions:

Adjust H3 resolutions to better match query radius
Add complementary non-spatial filters
Consider table partitioning strategies

Performance Tuning Workflow

Baseline Measurement

Measure current performance and identify bottlenecks using EXPLAIN PLAN and query timing.

Index Configuration

Optimize H3 resolutions based on query patterns and test different configurations.

Query Optimization

Rewrite queries to use indexes effectively and add non-spatial filters where possible.

Validation

Verify improvements using query plans and performance benchmarks.

Monitoring

Set up ongoing monitoring of query performance and index effectiveness.

Best Practices Summary

Configuration Best Practices

Use 2-3 H3 resolutions for most applications
Choose resolutions based on typical query radius
Always disable dictionary encoding (encodingType: "RAW")
Test resolution configurations with real query patterns

Query Best Practices

Use correct argument order in spatial functions
Combine spatial and non-spatial filters efficiently
Use EXPLAIN PLAN to verify index usage
Limit result sets in interactive applications

Performance Monitoring

Monitor query execution times regularly
Check index effectiveness ratios
Benchmark different query patterns
Set up alerts for performance degradation

Get Started

Ingestion

Query Data

Manage Data

Visualize Data

Manage Security

Release Notes

Reference

Query Plan Analysis

Index Optimization

Query Patterns

Performance Monitoring

​Performance Fundamentals

​How H3 Index Acceleration Works

​Query Plan Analysis

​Using EXPLAIN PLAN FOR

​Index Usage Operators

​EXPLAIN PLAN Examples

​Index Optimization

​Resolution Selection Strategy

​Single Resolution Approach

​Multi-Resolution Approach (Recommended)

​Performance vs Storage Analysis

​Resolution Tuning Examples

​Optimized Query Patterns

​Distance-Based Queries

​Containment Queries

​Advanced Query Optimization

​Performance Monitoring

​Key Performance Metrics

​Performance Benchmarking

​Advanced Optimization Techniques

​Query Rewriting for Performance

​Memory Optimization

​Troubleshooting Performance Issues

​Common Performance Problems

​Performance Tuning Workflow

​Best Practices Summary

​Configuration Best Practices

​Query Best Practices

​Performance Monitoring

Performance Fundamentals

How H3 Index Acceleration Works

Query Plan Analysis

Using EXPLAIN PLAN FOR

Index Usage Operators

EXPLAIN PLAN Examples

Index Optimization

Resolution Selection Strategy

Single Resolution Approach

Multi-Resolution Approach (Recommended)

Performance vs Storage Analysis

Resolution Tuning Examples

Optimized Query Patterns

Distance-Based Queries

Containment Queries

Advanced Query Optimization

Performance Monitoring

Key Performance Metrics

Performance Benchmarking

Advanced Optimization Techniques

Query Rewriting for Performance

Memory Optimization

Troubleshooting Performance Issues

Common Performance Problems

Performance Tuning Workflow

Best Practices Summary

Configuration Best Practices

Query Best Practices

Performance Monitoring