-- Conceptual example of geospatial partitioning
CREATE TABLE sensor_locations (
    sensor_id STRING,
    timestamp TIMESTAMP,
    location GEOMETRY,
    temperature DOUBLE
) 
PARTITIONED BY (geo_hash(location, 8))

SELECT * FROM sensor_locations 
WHERE ST_Contains(
    ST_GeomFromText('POLYGON((...))')
    location
)

# Conceptual example of optimization
query = """
SELECT COUNT(*) FROM traffic_data 
WHERE ST_Within(location, study_area_polygon)
"""
# Iceberg automatically optimizes:
# 1. Evaluates bounding boxes in metadata
# 2. Eliminates files that don't intersect
# 3. Applies spatial filters only where necessary

-- Traffic pattern analysis by zone
WITH traffic_zones AS (
  SELECT 
    zone_geometry,
    COUNT(*) as vehicle_count,
    AVG(speed) as avg_speed
  FROM traffic_sensors 
  WHERE ST_Within(sensor_location, city_boundary)
    AND timestamp >= '2025-01-01'
  GROUP BY ST_GeohashGrid(sensor_location, 7)
)
SELECT * FROM traffic_zones 
WHERE avg_speed < 30;

-- Delivery route optimization
SELECT 
  warehouse_location,
  ST_Distance(warehouse_location, customer_location) as distance,
  delivery_time
FROM shipments s
JOIN warehouses w ON ST_DWithin(s.destination, w.location, 50000)
WHERE delivery_date = CURRENT_DATE
ORDER BY distance;

-- Air quality analysis by region
SELECT 
  region_name,
  ST_Centroid(region_geometry) as center_point,
  AVG(air_quality_index) as avg_aqi,
  COUNT(sensor_readings) as reading_count
FROM environmental_data
WHERE ST_Intersects(sensor_location, protected_areas)
GROUP BY region_geometry
HAVING avg_aqi > 100;

import org.apache.sedona.spark.SedonaContext

val sedona = SedonaContext.create(spark)
sedona.sql("""
  SELECT ST_Area(geometry_column) 
  FROM iceberg_geospatial_table
""")

// Real-time processing of geospatial streams
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.fromSource(icebergSource)
   .filter(record -> isWithinBoundary(record.getGeometry()))
   .process(new SpatialAggregationFunction());

-- Federated queries with geospatial data
SELECT 
  iceberg_table.location,
  postgres_table.population
FROM iceberg.geospatial.cities iceberg_table
JOIN postgresql.public.demographics postgres_table
  ON ST_Within(iceberg_table.location, postgres_table.admin_boundary);

-- Native geospatial queries in Snowflake with Iceberg v3 tables
SELECT 
  store_location,
  ST_Distance(store_location, customer_location) as distance_km,
  sales_amount
FROM iceberg_stores s
JOIN iceberg_customers c ON ST_DWithin(s.store_location, c.customer_location, 5000)
WHERE sales_date >= '2025-01-01'
ORDER BY distance_km;

-- Geographic coverage analysis
WITH coverage_analysis AS (
  SELECT 
    region_id,
    ST_Union(ST_Buffer(location, coverage_radius)) as total_coverage,
    COUNT(*) as tower_count
  FROM iceberg_cell_towers
  GROUP BY region_id
)
SELECT 
  region_id,
  ST_Area(total_coverage) / 1000000 as coverage_km2,
  tower_count,
  (ST_Area(total_coverage) / tower_count) as efficiency_ratio
FROM coverage_analysis
ORDER BY efficiency_ratio DESC;

-- Optimized configuration for geospatial tables in Snowflake
ALTER TABLE iceberg_locations 
CLUSTER BY (ST_GeohashGrid(location, 7), event_date);

-- Automatic statistics for optimization
ALTER TABLE iceberg_locations 
SET AUTO_CLUSTERING = TRUE;

-- Storage as strings, slow processing
SELECT * FROM locations 
WHERE ST_GeomFromText(location_wkt) && study_area
-- Requires parsing in every query

-- Native types, optimized processing
SELECT * FROM locations 
WHERE location && study_area
-- Direct operations on native geometries

# Same code works with multiple engines
query = """
SELECT 
  ST_Area(polygon_geom) as area,
  ST_Perimeter(polygon_geom) as perimeter
FROM land_parcels
WHERE ST_Intersects(polygon_geom, :region_of_interest)
"""

# Works in Spark, Flink, Trino, etc.

-- Schema evolution with geospatial types
ALTER TABLE sensor_data 
ADD COLUMN coverage_area GEOMETRY;

-- Migrating existing data
UPDATE sensor_data 
SET coverage_area = ST_Buffer(sensor_location, detection_radius);

# Analysis of existing geospatial data
def analyze_spatial_data(dataframe):
    geom_types = dataframe.geometry.geom_type.value_counts()
    coord_systems = dataframe.crs.unique()
    return {
        'geometry_types': geom_types,
        'coordinate_systems': coord_systems,
        'recommended_iceberg_type': recommend_type(geom_types)
    }

-- Gradual migration by partitions
CREATE TABLE locations_v3 (
  id BIGINT,
  name STRING,
  location GEOMETRY,  -- New native type
  created_date DATE
) USING ICEBERG
PARTITIONED BY (created_date);

-- Incremental copy of historical data
INSERT INTO locations_v3
SELECT 
  id,
  name, 
  ST_GeomFromText(location_wkt) as location,
  created_date
FROM locations_legacy
WHERE created_date BETWEEN '2024-01-01' AND '2024-12-31';

# Optimizations for geospatial data
write.target-file-size-bytes=512MB
write.parquet.bloom-filter-enabled.geometry_column=true
write.parquet.compression-codec=zstd
write.metadata.delete-after-commit.enabled=true

-- Hybrid partitioning: temporal + spatial
CREATE TABLE traffic_events (
  event_id BIGINT,
  location GEOMETRY,
  event_type STRING,
  timestamp TIMESTAMP,
  severity INTEGER
) USING ICEBERG
PARTITIONED BY (
  date(timestamp),
  ST_GeohashGrid(location, 6)  -- Approx. 1km x 1km
);

-- Recommended configuration for geospatial workloads
ALTER WAREHOUSE analytics_geo SET 
  WAREHOUSE_SIZE = 'LARGE'
  AUTO_SUSPEND = 300
  AUTO_RESUME = TRUE
  INITIALLY_SUSPENDED = FALSE;

-- For intensive geospatial analysis
ALTER WAREHOUSE geo_processing SET
  WAREHOUSE_SIZE = 'X-LARGE'
  MAX_CLUSTER_COUNT = 4
  SCALING_POLICY = 'STANDARD';

-- Optimized geospatial table creation
CREATE ICEBERG TABLE location_analytics (
  event_id NUMBER,
  location GEOMETRY,
  attributes VARIANT,
  timestamp TIMESTAMP_NTZ,
  region STRING
)
CLUSTER BY (ST_GeohashGrid(location, 8), DATE(timestamp))
CATALOG = 'SNOWFLAKE'
EXTERNAL_VOLUME = 'iceberg_volume'
BASE_LOCATION = 's3://your-bucket/iceberg-tables/';

-- Retention and compaction configuration
ALTER ICEBERG TABLE location_analytics SET
  DATA_RETENTION_TIME_IN_DAYS = 90
  MAX_DATA_EXTENSION_TIME_IN_DAYS = 14;

-- Snowflake-specific monitoring queries
SELECT 
  table_name,
  clustering_key,
  total_micro_partitions,
  clustered_micro_partitions,
  (clustered_micro_partitions / total_micro_partitions) * 100 as clustering_efficiency
FROM INFORMATION_SCHEMA.AUTOMATIC_CLUSTERING_HISTORY 
WHERE table_name LIKE '%geospatial%'
  AND start_time >= DATEADD(hour, -24, CURRENT_TIMESTAMP());

-- Optimal design for different use cases
CREATE TABLE location_events (
  -- Identifiers
  event_id BIGINT NOT NULL,
  
  -- Geospatial data - choose appropriate type
  point_location GEOMETRY,      -- For local analysis
  global_location GEOGRAPHY,    -- For global analysis
  
  -- Spatial metadata
  coordinate_system STRING,
  spatial_accuracy DOUBLE,
  
  -- Temporal data
  event_timestamp TIMESTAMP,
  
  -- Business data
  event_type STRING,
  attributes MAP<STRING, STRING>
) 
USING ICEBERG
PARTITIONED BY (
  date(event_timestamp),
  ST_GeohashGrid(point_location, 7)
);

-- Use spatial indexes effectively
WITH spatial_filter AS (
  SELECT * FROM locations
  WHERE ST_DWithin(location, ST_Point(-74.0, 40.7), 1000)  -- 1km radius
),
temporal_filter AS (
  SELECT * FROM spatial_filter
  WHERE event_time >= '2025-01-01'
)
SELECT COUNT(*) FROM temporal_filter;

# Geospatial performance monitoring
def monitor_spatial_queries():
    metrics = {
        'avg_query_time': get_avg_spatial_query_time(),
        'spatial_index_hit_rate': get_spatial_index_metrics(),
        'partition_pruning_effectiveness': get_pruning_stats()
    }
    return metrics