Type System Integration

public class AvroTypeInfo<T extends SpecificRecordBase> extends PojoTypeInfo<T> {
    // Constructor
    public AvroTypeInfo(Class<T> typeClass);
    
    // Serializer creation
    public TypeSerializer<T> createSerializer(SerializerConfig config);
}

import org.apache.flink.formats.avro.typeutils.AvroTypeInfo;

// Create type information for specific record
AvroTypeInfo<User> userTypeInfo = new AvroTypeInfo<>(User.class);

// Use in DataStream operations
DataStream<User> userStream = env.fromCollection(users, userTypeInfo);

// Explicit type hints
DataStream<User> transformedStream = rawStream
    .map(data -> parseUser(data))
    .returns(userTypeInfo);

// Create DataSet with explicit type information
DataSet<User> userDataSet = env.fromCollection(userList, userTypeInfo);

// Type-safe operations
DataSet<String> names = userDataSet
    .map(user -> user.getName().toString())
    .returns(Types.STRING);

public class GenericRecordAvroTypeInfo extends TypeInformation<GenericRecord> {
    // Constructor
    public GenericRecordAvroTypeInfo(Schema schema);
    
    // Type system integration
    public TypeSerializer<GenericRecord> createSerializer(SerializerConfig config);
    public boolean isBasicType();
    public boolean isTupleType();
    public int getArity();
    public int getTotalFields();
}

import org.apache.flink.formats.avro.typeutils.GenericRecordAvroTypeInfo;
import org.apache.avro.Schema;
import org.apache.avro.generic.GenericRecord;

// Parse schema
Schema schema = new Schema.Parser().parse(schemaString);

// Create type information
GenericRecordAvroTypeInfo typeInfo = new GenericRecordAvroTypeInfo(schema);

// Use in streaming
DataStream<GenericRecord> recordStream = env
    .fromCollection(genericRecords, typeInfo);

// Type-safe transformations
DataStream<String> nameStream = recordStream
    .map(record -> record.get("name").toString())
    .name("extract-names");

// Handle multiple schema versions
Schema readerSchema = getReaderSchema();
Schema writerSchema = getWriterSchema();

GenericRecordAvroTypeInfo readerTypeInfo = new GenericRecordAvroTypeInfo(readerSchema);

// Process with schema evolution
DataStream<GenericRecord> evolvedStream = rawStream
    .map(new SchemaEvolutionMapper(writerSchema, readerSchema))
    .returns(readerTypeInfo);

public class AvroSerializer<T> extends TypeSerializer<T> {
    // Constructor
    public AvroSerializer(Class<T> type);
    
    // Serialization operations
    public boolean isImmutableType();
    public TypeSerializer<T> duplicate();
    public T createInstance();
    public T copy(T from);
    public T copy(T from, T reuse);
    public int getLength();
    public void serialize(T record, DataOutputView target) throws IOException;
    public T deserialize(DataInputView source) throws IOException;
    public T deserialize(T reuse, DataInputView source) throws IOException;
    public void copy(DataInputView source, DataOutputView target) throws IOException;
}

// Serializer supports object reuse for better performance
AvroSerializer<User> serializer = new AvroSerializer<>(User.class);

// Reuse instance across deserialization calls
User reusableUser = serializer.createInstance();
User deserializedUser = serializer.deserialize(reusableUser, dataInput);

// Efficient copying without full deserialization
serializer.copy(inputView, outputView); // Direct byte copying when possible

public class AvroSchemaConverter {
    // Schema conversion methods
    public static Schema convertToSchema(RowType rowType);
    public static Schema convertToSchema(RowType rowType, boolean legacyTimestampMapping);
    public static LogicalType convertToLogicalType(Schema schema);
    public static RowType convertToRowType(Schema schema);
    public static RowType convertToRowType(Schema schema, boolean legacyTimestampMapping);
}

import org.apache.flink.table.types.logical.*;
import org.apache.flink.formats.avro.typeutils.AvroSchemaConverter;

// Define Flink row type
RowType rowType = RowType.of(
    new LogicalType[] {
        new VarCharType(50),        // name
        new IntType(),              // age  
        new BooleanType(),          // active
        new TimestampType(3),       // created_at
        new DecimalType(10, 2),     // salary
        new ArrayType(new VarCharType(20)) // tags
    },
    new String[] {"name", "age", "active", "created_at", "salary", "tags"}
);

// Convert to Avro schema
Schema avroSchema = AvroSchemaConverter.convertToSchema(rowType);

// With legacy timestamp mapping
Schema legacySchema = AvroSchemaConverter.convertToSchema(rowType, true);

// Parse Avro schema
String schemaJson = "{ \"type\": \"record\", \"name\": \"User\", ... }";
Schema schema = new Schema.Parser().parse(schemaJson);

// Convert to Flink row type
RowType flinkRowType = AvroSchemaConverter.convertToRowType(schema);

// Use in Table API
Table table = tableEnv.fromDataStream(stream, Schema.of(flinkRowType));

public class SerializableAvroSchema implements Serializable {
    // Constructor
    public SerializableAvroSchema(Schema schema);
    
    // Schema access
    public Schema getAvroSchema();
    
    // Serialization support
    private void writeObject(ObjectOutputStream out) throws IOException;
    private void readObject(ObjectInputStream in) throws IOException, ClassNotFoundException;
}

// Wrap schema for serialization
Schema schema = new Schema.Parser().parse(schemaString);
SerializableAvroSchema serializableSchema = new SerializableAvroSchema(schema);

// Use in map function
DataStream<GenericRecord> processed = recordStream.map(new RichMapFunction<GenericRecord, GenericRecord>() {
    private transient Schema schema;
    
    @Override
    public void open(Configuration parameters) {
        this.schema = serializableSchema.getAvroSchema();
    }
    
    @Override
    public GenericRecord map(GenericRecord record) throws Exception {
        // Use schema in processing
        return processRecord(record, schema);
    }
});

public class AvroFactory {
    // Schema extraction
    public static Schema extractAvroSpecificSchema(Class<?> specificRecordClass, SpecificData specificData);
    
    // SpecificData creation
    public static SpecificData getSpecificDataForClass(Class<? extends SpecificData> specificDataClass, ClassLoader classLoader);
}

// Extract schema from specific record class
Class<User> userClass = User.class;
SpecificData specificData = SpecificData.get();
Schema userSchema = AvroFactory.extractAvroSpecificSchema(userClass, specificData);

// Custom SpecificData with class loader
ClassLoader customClassLoader = Thread.currentThread().getContextClassLoader();
SpecificData customSpecificData = AvroFactory.getSpecificDataForClass(
    SpecificData.class, 
    customClassLoader
);

// Configure execution environment for object reuse
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.getConfig().enableObjectReuse(); // Better performance with Avro serializers

// Provide explicit type information to avoid reflection
DataStream<User> typedStream = rawStream
    .map(data -> parseUser(data))
    .returns(new AvroTypeInfo<>(User.class)); // Avoid type erasure

// Cache converted schemas to avoid repeated conversion
private static final ConcurrentHashMap<String, Schema> SCHEMA_CACHE = new ConcurrentHashMap<>();

public Schema getCachedSchema(RowType rowType) {
    String key = rowType.toString();
    return SCHEMA_CACHE.computeIfAbsent(key, 
        k -> AvroSchemaConverter.convertToSchema(rowType));
}

try {
    Schema convertedSchema = AvroSchemaConverter.convertToSchema(rowType);
} catch (IllegalArgumentException e) {
    logger.error("Unsupported type conversion: " + e.getMessage());
    // Handle unsupported type
}

try {
    serializer.serialize(record, output);
} catch (IOException e) {
    logger.error("Serialization failed", e);
    // Implement error recovery
}

// Handle schema evolution gracefully
try {
    GenericRecord evolved = SchemaEvolution.evolve(record, oldSchema, newSchema);
} catch (AvroRuntimeException e) {
    logger.warn("Schema evolution failed, using default values", e);
    // Apply default values or skip record
}