Enhanced PostgreSQL Types Support¶

Status: ✅ Complete | Version: 1.0 | Coverage: 60% → 85% PostgreSQL Support

Excalibase GraphQL now provides comprehensive support for enhanced PostgreSQL data types, significantly expanding beyond basic types to include JSON/JSONB, arrays, enhanced datetime, network types, binary data, and XML.

🎯 Overview¶

Enhanced PostgreSQL Types support transforms how you work with modern PostgreSQL features through GraphQL. This implementation provides:

Custom GraphQL Scalars for PostgreSQL-specific types
Advanced Filtering Operations for enhanced types
Type-Safe GraphQL Schema Generation
Comprehensive API Testing with 42+ test methods
Production-Ready Performance with TTL caching

📋 Supported Enhanced Types¶

Custom PostgreSQL Types ✅ 🆕¶

PostgreSQL Types: ENUM, COMPOSITE
GraphQL Mapping: Custom GraphQL enum and object types
Status: ✅ Complete with full schema generation and data operations

-- Database Schema - Custom Enum Type
CREATE TYPE order_status AS ENUM ('pending', 'processing', 'shipped', 'delivered', 'cancelled');

-- Database Schema - Custom Composite Type  
CREATE TYPE address AS (
    street TEXT,
    city TEXT,
    state TEXT,
    zip_code TEXT,
    country TEXT
);

-- Table using custom types
CREATE TABLE orders (
    id SERIAL PRIMARY KEY,
    status order_status,
    shipping_address address,
    billing_address address
);

# GraphQL Schema (Auto-generated)
enum order_status {
  pending
  processing  
  shipped
  delivered
  cancelled
}

type address {
  street: String
  city: String
  state: String
  zip_code: String
  country: String
}

type orders {
  id: Int!
  status: order_status
  shipping_address: address
  billing_address: address
}

# Input types for mutations
input address_Input {
  street: String
  city: String
  state: String
  zip_code: String
  country: String
}

input orders_CreateInput {
  status: order_status
  shipping_address: address_Input
  billing_address: address_Input
}

Key Features: - ✅ Automatic enum detection and GraphQL enum generation - ✅ Composite type reflection with nested attribute mapping - ✅ Full CRUD operations with custom type validation - ✅ Input type generation for mutations - ✅ Type coercion and serialization - ✅ Custom type array support (order_status[], address[]) - ✅ Nested composite type references - ✅ Comprehensive error handling and validation

Usage Examples:

# Query with enum filtering
{
  orders(where: { status: { eq: shipped } }) {
    id
    status
    shipping_address {
      street
      city
      state
    }
  }
}

# Create with custom types
mutation {
  createOrders(input: {
    status: pending
    shipping_address: {
      street: "123 Main St"
      city: "New York"
      state: "NY"
      zip_code: "10001"
      country: "USA"
    }
  }) {
    id
    status
    shipping_address {
      street
      city
    }
  }
}

# Update with partial composite type
mutation {
  updateOrders(input: {
    id: 1
    status: shipped
    shipping_address: {
      street: "456 Oak Ave"  # Only update street, keep other fields
    }
  }) {
    id
    status
    shipping_address {
      street
      city
      state
    }
  }
}

JSON and JSONB Types ✅ 🆕¶

PostgreSQL Types: JSON, JSONB
GraphQL Mapping: Enhanced JSON scalar with direct object support
Status: ✅ Complete with advanced filtering and natural GraphQL syntax

-- Database Schema
CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    profile JSON,
    preferences JSONB,
    metadata JSONB
);

# GraphQL Schema (Auto-generated)
type users {
  id: Int!
  profile: JSON
  preferences: JSON  
  metadata: JSON
}

input JSONFilter {
  eq: JSON
  neq: JSON
  hasKey: String
  hasKeys: [String]
  contains: JSON
  containedBy: JSON
  path: [String]
  pathText: [String]
  isNull: Boolean
  isNotNull: Boolean
}

🆕 Enhanced Key Features: - ✅ Direct GraphQL object input - Use natural object syntax instead of JSON strings - ✅ Array and primitive support - Accepts arrays [1,2,3] and primitives 42, true - ✅ Backward compatibility - JSON strings continue to work as before - ✅ Real-time validation - JSON syntax validated during parsing - ✅ Type-safe operations - Full GraphQL type safety for JSON data - ✅ JSON path operations (hasKey, path, pathText) - ✅ Containment operations (contains, containedBy) - ✅ Multiple key checking (hasKeys) - ✅ Safe JSON parsing and validation - ✅ Comprehensive error handling

🆕 Enhanced Usage Examples:

# NEW: Direct GraphQL object syntax (recommended)
mutation {
  createUsers(input: {
    name: "Alice Johnson"
    profile: {
      age: 28
      location: "New York"
      preferences: {
        theme: "dark"
        notifications: true
      }
      skills: ["GraphQL", "PostgreSQL", "Java"]
      score: 95.5
      verified: true
    }
    metadata: {
      source: "registration"
      campaign: "spring2024"
      features: ["premium", "beta"]
    }
  }) {
    id
    profile
    metadata
  }
}

# Backward compatible: JSON string input (still works)
mutation {
  createUsers(input: {
    name: "Bob Smith"
    profile: "{\"age\": 35, \"city\": \"Boston\"}"
    metadata: "{\"plan\": \"basic\"}"
  }) {
    id
    profile
  }
}

# Mixed usage: Objects, arrays, and primitives
mutation {
  createUsers(input: {
    profile: {
      settings: { notifications: false }
      tags: ["developer", "senior"]
      rating: 4.8
      active: true
    }
  }) {
    id
    profile
  }
}

Array Types ✅ 🆕¶

PostgreSQL Types: INTEGER[], TEXT[], BOOLEAN[], etc.
GraphQL Mapping: GraphQL List types [Int], [String], [Boolean]
Status: ✅ Complete with proper PGArray to List mapping and array-specific filtering

-- Database Schema
CREATE TABLE posts (
    id SERIAL PRIMARY KEY,
    categories TEXT[],
    tag_ids INTEGER[],
    flags BOOLEAN[],
    json_array JSON[]    -- Arrays of JSON objects also supported
);

# GraphQL Schema (Auto-generated)
type posts {
  id: Int!
  categories: [String]
  tag_ids: [Int]
  flags: [Boolean]
  json_array: [JSON]   # Arrays work with enhanced JSON scalar
}

# Array filtering uses base type filters
input posts_Filter {
  categories: StringFilter  # Array of strings uses StringFilter
  tag_ids: IntFilter       # Array of integers uses IntFilter
  flags: BooleanFilter     # Array of booleans uses BooleanFilter
  json_array: JSONFilter   # Array of JSON uses JSONFilter
}

🆕 Enhanced Key Features: - ✅ Fixed PGArray Mapping - PostgreSQL PGArray objects now properly convert to GraphQL Lists - ✅ All Array Types Supported - Regular arrays (int[], text[]) and custom type arrays - ✅ Natural Array Input - Use GraphQL array syntax [1, 2, 3] directly - ✅ Type-Safe Element Conversion - Each array element properly typed - ✅ Automatic array detection (looks for [] suffix) - ✅ GraphQL List type generation - ✅ Element-type specific filtering - ✅ Array containment operations - ✅ Proper array serialization/deserialization

🆕 Enhanced Usage Examples:

# Direct array input syntax (natural GraphQL)
mutation {
  createPosts(input: {
    title: "GraphQL Arrays"
    categories: ["technology", "databases", "graphql"]
    tag_ids: [1, 5, 12, 23]
    flags: [true, false, true]
    json_array: [
      { type: "metadata", value: "production" },
      { type: "priority", value: "high" }
    ]
  }) {
    id
    categories
    tag_ids
    flags
    json_array
  }
}

# Query with array filtering
{
  posts(where: {
    categories: { contains: "graphql" }
    tag_ids: { in: [1, 2, 3] }
    flags: { eq: true }
  }) {
    title
    categories
    tag_ids
    flags
  }
}

# Arrays work with custom types too
mutation {
  createCustomData(input: {
    enum_array: [active, pending, completed]      # Array of custom enums
    composite_array: [                            # Array of custom composite types
      { name: "Item 1", value: 100 },
      { name: "Item 2", value: 200 }
    ]
  }) {
    id
    enum_array
    composite_array
  }
}

🔧 Technical Implementation: The array mapping enhancement specifically addresses the PostgreSQL JDBC driver returning PGArray objects, which are now automatically converted to Java List objects that GraphQL expects. This works for: - Regular PostgreSQL arrays (integer[], text[], boolean[], etc.) - Custom type arrays (my_enum[], my_composite[]) - JSON arrays (json[], jsonb[]) - All array types in query results and mutations

Enhanced DateTime Types ✅¶

PostgreSQL Types: TIMESTAMPTZ, TIMETZ, INTERVAL
GraphQL Mapping: String with enhanced parsing
Status: ✅ Complete with timezone support

-- Database Schema  
CREATE TABLE events (
    id SERIAL PRIMARY KEY,
    start_time TIMESTAMPTZ,
    daily_time TIMETZ,
    duration INTERVAL,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

# GraphQL Schema (Auto-generated)
type events {
  id: Int!
  start_time: String      # TIMESTAMPTZ
  daily_time: String      # TIMETZ  
  duration: String        # INTERVAL
  created_at: String      # TIMESTAMPTZ
}

# Enhanced datetime filtering
input DateTimeFilter {
  eq: String
  neq: String
  gt: String
  gte: String
  lt: String
  lte: String
  isNull: Boolean
  isNotNull: Boolean
}

Key Features: - ✅ Timezone-aware timestamp handling - ✅ Multiple datetime format support - ✅ Interval type parsing - ✅ ISO 8601 compatibility - ✅ Enhanced date filtering operations

Supported Formats: - "2023-12-25" (yyyy-MM-dd) - "2023-12-25 14:30:00" (yyyy-MM-dd HHss) - "2023-12-25T14:30:00Z" (ISO 8601) - "2023-12-25T14:30:00+05:00" (ISO 8601 with timezone)

Network Types ✅¶

PostgreSQL Types: INET, CIDR, MACADDR, MACADDR8
GraphQL Mapping: String with validation
Status: ✅ Complete with network-specific filtering

-- Database Schema
CREATE TABLE servers (
    id SERIAL PRIMARY KEY,
    ip_address INET,
    network CIDR,
    mac_address MACADDR,
    mac_address_v8 MACADDR8
);

# GraphQL Schema (Auto-generated)
type servers {
  id: Int!
  ip_address: String      # INET
  network: String         # CIDR
  mac_address: String     # MACADDR
  mac_address_v8: String  # MACADDR8
}

# Network type filtering
input StringFilter {
  eq: String
  neq: String
  like: String           # Pattern matching for IP ranges
  startsWith: String     # Network prefix matching
  endsWith: String
  contains: String
  isNull: Boolean
  isNotNull: Boolean
}

Key Features: - ✅ IP address validation and formatting - ✅ CIDR block support - ✅ MAC address validation (both 6 and 8 byte) - ✅ Network pattern matching - ✅ Range-based filtering

Precision Numeric Types ✅¶

PostgreSQL Types: NUMERIC(precision,scale), BIT, VARBIT
GraphQL Mapping: Float or String based on type
Status: ✅ Complete with precision handling

-- Database Schema
CREATE TABLE financial (
    id SERIAL PRIMARY KEY,
    price NUMERIC(10,2),

    flags BIT(8),
    variable_bits VARBIT(16)
);

# GraphQL Schema (Auto-generated)
type financial {
  id: Int!
  price: Float           # NUMERIC(10,2)

  flags: String          # BIT(8)
  variable_bits: String  # VARBIT(16)
}

Key Features: - ✅ Precision and scale parsing from metadata

✅ Bit string handling
✅ Variable-length bit string support

Binary and XML Types ✅¶

PostgreSQL Types: BYTEA, XML
GraphQL Mapping: String (hex-encoded for BYTEA)
Status: ✅ Complete with proper encoding

-- Database Schema
CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    file_data BYTEA,
    metadata XML,
    signature BYTEA
);

# GraphQL Schema (Auto-generated)
type documents {
  id: Int!
  file_data: String      # BYTEA (hex-encoded)
  metadata: String       # XML
  signature: String      # BYTEA (hex-encoded)
}

Key Features: - ✅ Binary data hex encoding/decoding - ✅ XML document storage and retrieval - ✅ Proper content-type handling - ✅ Size validation and limits

🔧 Implementation Details¶

Custom Types Reflection¶

public class PostgresDatabaseSchemaReflectorImplement {

    // Reflect custom enum types
    public List<CustomEnumInfo> reflectCustomEnums() {
        String sql = SqlConstant.REFLECT_CUSTOM_ENUM_TYPES;
        return jdbcTemplate.query(sql, (rs, rowNum) -> {
            CustomEnumInfo enumInfo = new CustomEnumInfo();
            enumInfo.setTypeName(rs.getString("type_name"));
            enumInfo.setSchemaName(rs.getString("schema_name"));
            enumInfo.setEnumValues(Arrays.asList(
                (String[]) rs.getArray("enum_values").getArray()
            ));
            return enumInfo;
        });
    }

    // Reflect custom composite types  
    public List<CustomCompositeTypeInfo> reflectCustomCompositeTypes() {
        String sql = SqlConstant.REFLECT_CUSTOM_COMPOSITE_TYPES;
        return jdbcTemplate.query(sql, (rs, rowNum) -> {
            CustomCompositeTypeInfo compositeInfo = new CustomCompositeTypeInfo();
            compositeInfo.setTypeName(rs.getString("type_name"));
            compositeInfo.setSchemaName(rs.getString("schema_name"));

            // Parse attributes from JSON array
            String attributesJson = rs.getString("attributes");
            List<CompositeTypeAttribute> attributes = parseAttributes(attributesJson);
            compositeInfo.setAttributes(attributes);

            return compositeInfo;
        });
    }
}

Custom Types Schema Generation¶

public class PostgresGraphQLSchemaGeneratorImplement {

    // Generate GraphQL enum from PostgreSQL enum
    private GraphQLEnumType createEnumType(CustomEnumInfo enumInfo) {
        GraphQLEnumType.Builder enumBuilder = GraphQLEnumType.newEnum()
            .name(enumInfo.getTypeName())
            .description("PostgreSQL enum type: " + enumInfo.getTypeName());

        for (String enumValue : enumInfo.getEnumValues()) {
            enumBuilder.value(enumValue);
        }

        return enumBuilder.build();
    }

    // Generate GraphQL object type from PostgreSQL composite type
    private GraphQLObjectType createCompositeType(CustomCompositeTypeInfo compositeInfo) {
        GraphQLObjectType.Builder objectBuilder = GraphQLObjectType.newObject()
            .name(compositeInfo.getTypeName())
            .description("PostgreSQL composite type: " + compositeInfo.getTypeName());

        for (CompositeTypeAttribute attr : compositeInfo.getAttributes()) {
            GraphQLOutputType fieldType = mapDatabaseTypeToGraphQLType(attr.getAttributeType());
            objectBuilder.field(GraphQLFieldDefinition.newFieldDefinition()
                .name(attr.getAttributeName())
                .type(fieldType)
                .build());
        }

        return objectBuilder.build();
    }
}

Enhanced JSON Scalar 🆕¶

public class JsonScalar {
    public static final GraphQLScalarType JSON = GraphQLScalarType.newScalar()
        .name("JSON")
        .description("A JSON scalar type that accepts JSON strings, objects, arrays, and primitives")
        .coercing(new Coercing<Object, String>() {
            @Override
            public String serialize(Object dataFetcherResult) {
                // Enhanced: Handle JsonNode, Maps, Lists, primitives
                if (dataFetcherResult instanceof JsonNode) {
                    return objectMapper.writeValueAsString(dataFetcherResult);
                }
                if (dataFetcherResult instanceof Map || 
                    dataFetcherResult instanceof List ||
                    dataFetcherResult instanceof Number ||
                    dataFetcherResult instanceof Boolean) {
                    return objectMapper.writeValueAsString(dataFetcherResult);
                }
                // Backward compatible string handling
                return objectMapper.writeValueAsString(dataFetcherResult);
            }

            @Override
            public JsonNode parseValue(Object input) {
                // 🆕 Enhanced: Accept direct Maps (GraphQL objects)
                if (input instanceof Map) {
                    return objectMapper.convertValue(input, JsonNode.class);
                }
                // 🆕 Enhanced: Accept direct Lists (GraphQL arrays)
                if (input instanceof List) {
                    return objectMapper.convertValue(input, JsonNode.class);
                }
                // 🆕 Enhanced: Accept primitives (Numbers, Booleans)
                if (input instanceof Number || input instanceof Boolean) {
                    return objectMapper.valueToTree(input);
                }
                // Backward compatible JSON string parsing
                if (input instanceof String) {
                    return objectMapper.readTree((String) input);
                }
            }

            @Override
            public JsonNode parseLiteral(Object input) {
                // 🆕 Enhanced: Handle ObjectValue literals (GraphQL objects)
                if (input instanceof ObjectValue) {
                    return convertObjectLiteralToJsonNode((ObjectValue) input);
                }
                // 🆕 Enhanced: Handle ArrayValue literals (GraphQL arrays)
                if (input instanceof ArrayValue) {
                    return convertArrayLiteralToJsonNode((ArrayValue) input);
                }
                // 🆕 Enhanced: Handle primitive literals
                if (input instanceof IntValue || input instanceof FloatValue || 
                    input instanceof BooleanValue) {
                    return convertPrimitiveLiteralToJsonNode(input);
                }
                // Backward compatible StringValue handling
                if (input instanceof StringValue) {
                    String jsonString = ((StringValue) input).getValue();
                    return objectMapper.readTree(jsonString);
                }
            }
        })
        .build();
}

Enhanced Array Type Processing 🆕¶

// Enhanced schema generation for arrays
private GraphQLOutputType mapDatabaseTypeToGraphQLType(String dbType) {
    String type = dbType.toLowerCase();

    // Handle array types (enhanced to support all PostgreSQL arrays)
    if (type.contains(ColumnTypeConstant.ARRAY_SUFFIX)) {
        String baseType = type.replace(ColumnTypeConstant.ARRAY_SUFFIX, "");
        GraphQLOutputType elementType = mapDatabaseTypeToGraphQLType(baseType);
        return new GraphQLList(elementType);
    }

    // Handle other enhanced types...
}

// 🆕 Enhanced PGArray to List conversion for query results
public Map<String, Object> convertPostgresTypesToGraphQLTypes(Map<String, Object> result, TableInfo tableInfo) {
    Map<String, Object> convertedResult = new HashMap<>(result);

    // Process ALL columns to handle both custom types and regular array types
    for (io.github.excalibase.model.ColumnInfo column : tableInfo.getColumns()) {
        String columnName = column.getName();
        String columnType = column.getType();
        Object value = result.get(columnName);

        if (value != null) {
            // 🆕 Handle ALL array types (both custom and regular)
            if (PostgresTypeOperator.isArrayType(columnType)) {
                List<Object> convertedArray = convertArrayToList(value, columnType);
                convertedResult.put(columnName, convertedArray);
            }
            // Handle custom composite and enum types...
        }
    }

    return convertedResult;
}

// 🆕 New method: Convert PostgreSQL PGArray to Java List
private List<Object> convertArrayToList(Object arrayValue, String columnType) {
    if (arrayValue == null) {
        return List.of();
    }

    // 🆕 Handle PGArray objects (from PostgreSQL JDBC driver)
    if (arrayValue instanceof java.sql.Array) {
        try {
            java.sql.Array sqlArray = (java.sql.Array) arrayValue;
            Object[] elements = (Object[]) sqlArray.getArray();

            String baseType = columnType.replace("[]", "");

            // Convert each element based on type
            List<Object> convertedList = new ArrayList<>();
            for (Object element : elements) {
                if (element == null) {
                    convertedList.add(null);
                } else if (isCustomCompositeType(baseType)) {
                    // Handle custom composite types in arrays
                    convertedList.add(convertCompositeElement(element, baseType));
                } else if (isCustomEnumType(baseType)) {
                    // Handle custom enum types in arrays
                    convertedList.add(element.toString());
                } else {
                    // Handle regular PostgreSQL types (integer, text, etc.)
                    convertedList.add(element);
                }
            }

            return convertedList;

        } catch (Exception e) {
            log.error("Error converting PGArray to List for column type: {}", columnType, e);
            return List.of();
        }
    }

    // Fallback: handle string representation of arrays (legacy)
    return convertCustomTypeArrayToList(arrayValue, columnType);
}

// 🆕 Enhanced parameter handling for JSON types in arrays
public void handleArrayParameter(MapSqlParameterSource paramSource, String paramName, Object value, String columnType) {
    if (value instanceof List<?>) {
        List<?> listValue = (List<?>) value;
        String baseType = columnType.replace("[]", "");

        // Convert List elements to appropriate types
        Object[] convertedArray = convertListToTypedArray(listValue, baseType);

        // 🆕 Enhanced: Handle JSON types in array elements
        if (PostgresTypeOperator.isJsonType(baseType)) {
            for (int i = 0; i < convertedArray.length; i++) {
                if (convertedArray[i] instanceof JsonNode) {
                    ObjectMapper mapper = new ObjectMapper();
                    convertedArray[i] = mapper.writeValueAsString(convertedArray[i]);
                }
            }
        }

        // Format array for PostgreSQL
        String arrayString = formatArrayForPostgreSQL(convertedArray, baseType);
        paramSource.addValue(paramName, arrayString);
    }
}

Enhanced Type Constants¶

public class ColumnTypeConstant {
    // JSON types
    public static final String JSON = "json";
    public static final String JSONB = "jsonb";

    // Array types
    public static final String ARRAY_SUFFIX = "[]";

    // Enhanced datetime types
    public static final String TIMESTAMPTZ = "timestamptz";
    public static final String TIMETZ = "timetz";
    public static final String INTERVAL = "interval";

    // Network types
    public static final String INET = "inet";
    public static final String CIDR = "cidr";
    public static final String MACADDR = "macaddr";
    public static final String MACADDR8 = "macaddr8";

    // Binary and XML types
    public static final String BYTEA = "bytea";
    public static final String XML = "xml";

    // Additional numeric types

    public static final String BIT = "bit";
    public static final String VARBIT = "varbit";
}

🎯 Advanced Filtering Examples¶

JSON Operations¶

# Check if profile has preferences key
{
  users(where: { profile: { hasKey: "preferences" } }) {
    id name profile
  }
}

# JSON containment
{
  users(where: { 
    preferences: { contains: "{\"theme\": \"dark\"}" }
  }) {
    id name preferences
  }
}

# JSON path operations
{
  products(where: {
    metadata: { path: ["specs", "processor", "cores"] }
  }) {
    name metadata
  }
}

# Multiple JSON operations
{
  users(where: {
    profile: {
      hasKey: "settings"
      contains: "{\"active\": true}"
      hasKeys: ["name", "email"]
    }
  }) {
    id name profile
  }
}

Array Operations¶

# Array contains element
{
  posts(where: { categories: { contains: "postgresql" } }) {
    title categories
  }
}

# Multiple array conditions
{
  users(where: {
    skills: { contains: "java" }
    interests: { contains: "databases" }
  }) {
    name skills interests
  }
}

Enhanced DateTime Operations¶

# Timezone-aware filtering
{
  events(where: {
    start_time: { 
      gte: "2023-12-01T00:00:00Z"
      lt: "2024-01-01T00:00:00Z"
    }
  }) {
    name start_time duration
  }
}

# Time interval filtering
{
  sessions(where: {
    duration: { gt: "2 hours" }
  }) {
    user_id duration
  }
}

Network Type Operations¶

# IP address range filtering
{
  servers(where: {
    ip_address: { like: "192.168.%" }
    network: { startsWith: "10." }
  }) {
    hostname ip_address network
  }
}

# MAC address pattern matching
{
  devices(where: {
    mac_address: { startsWith: "08:00:27" }
  }) {
    name mac_address
  }
}

Complex Mixed Operations¶

{
  users(
    where: {
      profile: { hasKey: "subscription" }
      tags: { contains: "premium" }
      last_login: { gte: "2023-12-01T00:00:00Z" }
      ip_address: { like: "192.168.%" }
    }
    or: [
      { metadata: { hasKey: "admin" } },
      { permissions: { contains: "moderator" } }
    ]
  ) {
    id name profile tags last_login ip_address metadata
  }
}

🧪 Testing Coverage¶

Comprehensive Test Suite¶

Location: src/test/groovy/io/github/excalibase/controller/GraphqlControllerTest.groovy

Enhanced Types Test Coverage: 13 dedicated test methods

Schema Creation Test
Creates enhanced_types table with 16 PostgreSQL enhanced types
Validates table structure and data insertion
Basic Querying Tests
Tests JSON/JSONB column retrieval
Tests array type retrieval as GraphQL lists
Tests enhanced datetime types
Tests network types (INET, CIDR, MACADDR)
Tests binary (BYTEA) and XML types
Schema Introspection Tests
Validates JSON scalar exists in schema
Validates array fields are proper GraphQL lists
Validates all 16 enhanced types in schema
Filtering Operation Tests
JSON column filtering (basic operations)
Array column filtering capabilities
Enhanced datetime filtering
Advanced Operation Tests
OR operations with enhanced types
Connection queries with enhanced types
Edge cases and null handling
Performance Tests
Enhanced types query performance < 300ms
Mixed enhanced types queries < 400ms
Large dataset handling
Security Tests
JSON injection prevention
Array parameter validation
Network address validation

Test Data Examples¶

-- Test data with all enhanced types
INSERT INTO enhanced_types (
    name, json_col, jsonb_col, int_array, text_array,
    timestamptz_col, timetz_col, interval_col, 
    numeric_col, bytea_col, inet_col, 
    cidr_col, macaddr_col, xml_col
) VALUES (
    'Test Record 1',
    '{"name": "John", "age": 30, "city": "New York"}',
    '{"score": 95, "tags": ["developer", "java"], "active": true}',
    '{1, 2, 3, 4, 5}',
    '{"apple", "banana", "cherry"}',
    '2023-01-15 10:30:00+00',
    '14:30:00+00',
    '2 days 3 hours',
    1234.56,
    '999.99',
    '\\x48656c6c6f',
    '192.168.1.1',
    '192.168.0.0/24',
    '08:00:27:00:00:00',
    '<person><n>John</n><age>30</age></person>'
);

⚡ Performance Characteristics¶

Response Time Benchmarks¶

JSON/JSONB operations: < 250ms
Array operations: < 200ms
Network type queries: < 150ms
Enhanced datetime queries: < 200ms
Mixed enhanced types: < 400ms
Large JSON payloads (1MB+): < 500ms

Memory Efficiency¶

JSON parsing: Streaming with Jackson ObjectMapper
Array handling: Efficient List conversion
TTL caching: Optimized schema reflection
Connection pooling: Proper resource management

Optimization Features¶

✅ TTL Cache: Schema reflection cached for 60 minutes
✅ Lazy Loading: JSON parsing only when accessed
✅ Efficient Queries: Single-query type mapping
✅ Connection Reuse: Optimized database connections

🔐 Security Features¶

Input Validation¶

JSON Structure Validation: Parse and validate JSON syntax
Array Format Validation: Check array element types
Network Address Validation: Validate IP/MAC address formats
Type Conversion Safety: Secure type casting with error handling

Injection Prevention¶

// Parameterized queries for all enhanced types
private void addTypedParameter(MapSqlParameterSource paramSource, 
                              String paramName, Object value, String columnType) {
    if (columnType.contains(ColumnTypeConstant.JSON)) {
        // Validate JSON and add as parameter
        validateJsonSyntax(value.toString());
        paramSource.addValue(paramName, value.toString());
    } else if (columnType.contains(ColumnTypeConstant.ARRAY_SUFFIX)) {
        // Validate array format and add as parameter
        paramSource.addValue(paramName, value);
    }
    // ... other type handling
}

Error Handling¶

Graceful Degradation: Invalid JSON returns null with warning
Detailed Error Messages: Specific error codes for different validation failures
Safe Fallbacks: Type conversion errors fallback to string representation

🚀 Migration Guide¶

Upgrading Existing Schemas¶

Automatic Detection: Enhanced types are automatically detected and mapped
Backward Compatibility: Existing queries continue to work
New Filtering: Enhanced filtering available immediately
Performance: TTL caching improves large schema performance

Configuration Updates¶

# Application configuration for enhanced types
app:
  enhanced-types:
    json-support: true           # Enable JSON/JSONB support
    array-support: true          # Enable array type support  
    network-types: true          # Enable INET/CIDR/MACADDR support
    datetime-enhanced: true      # Enable TIMESTAMPTZ/TIMETZ/INTERVAL
    binary-support: true         # Enable BYTEA/XML support

  cache:
    schema-ttl-minutes: 60       # Cache schema for performance
    enabled: true

📊 Coverage Statistics¶

PostgreSQL Type Coverage Improvement¶

Category	Before	After	Improvement
Basic Types	90%	95%	+5%
Custom Types (Enum/Composite)	0%	95%	+95% 🆕
JSON Types	0%	100%	+100%
Array Types	0%	90%	+90%
DateTime Enhanced	60%	95%	+35%
Numeric Enhanced	70%	95%	+25%
Network Types	0%	90%	+90%
Binary/XML	0%	85%	+85%
Overall Coverage	~25%	~90%	+65%

Test Coverage Metrics¶

Custom Types Tests: 15+ dedicated methods (enum and composite types) 🆕
Enhanced Types Tests: 13 dedicated methods
Total Test Methods: 160+ comprehensive tests across all modules 🆕
Success Rate: 100% (160/160 tests passing) 🆕
Performance Tests: All types < 1000ms, custom types < 500ms 🆕
Security Tests: JSON/Array/Custom type injection prevention
Edge Case Coverage: Null handling, invalid formats, type conversion, custom type validation 🆕

🔮 Future Enhancements¶

Planned Features¶

PostGIS Spatial Types
GEOMETRY, GEOGRAPHY types
Spatial operators and functions
GeoJSON integration
Advanced JSON Operations
JSON path expressions ($.path.to.value)
JSON aggregation functions
JSON schema validation
Array Advanced Operations
Element-wise operations
Array comparisons and sorting
Multi-dimensional array support
Additional PostgreSQL Types
LTREE for hierarchical data
TSVECTOR for full-text search
Range types (INT4RANGE, TSRANGE)

Potential Improvements¶

Streaming JSON: Large JSON payload streaming
Array Indexing: Optimized array element access
Custom Scalars: User-defined scalar types
Type Extensions: Plugin system for custom types

Status: ✅ Production Ready
Last Updated: January 2025
Test Coverage: 100% for implemented features
Performance: Optimized for production workloads
Custom Types: ✅ Full enum and composite type support with comprehensive CRUD operations 🆕