Response Handling

class StreamingBody:
    def __init__(
        self,
        raw_stream,
        content_length: int
    ):
        """
        Initialize streaming body wrapper.
        
        Args:
            raw_stream: Underlying HTTP response stream
            content_length: Expected content length in bytes
        """

    def read(self, amt: int = None) -> bytes:
        """
        Read at most amt bytes from the stream.
        
        Args:
            amt: Maximum number of bytes to read. If None, read all data.
            
        Returns:
            bytes: Data read from stream
            
        Raises:
            ReadTimeoutError: If read operation times out
            ResponseStreamingError: If streaming protocol error occurs
            IncompleteReadError: If content length validation fails
        """

    def readinto(self, b) -> int:
        """
        Read bytes into a pre-allocated, writable bytes-like object.
        
        Args:
            b: Pre-allocated buffer to read data into
            
        Returns:
            int: Number of bytes read
            
        Raises:
            ReadTimeoutError: If read operation times out
            ResponseStreamingError: If streaming protocol error occurs
            IncompleteReadError: If content length validation fails
        """

    def iter_lines(
        self,
        chunk_size: int = 1024,
        keepends: bool = False
    ) -> Iterator[bytes]:
        """
        Return an iterator to yield lines from the raw stream.
        
        Args:
            chunk_size: Size of chunks to read while iterating
            keepends: Whether to preserve line ending characters
            
        Yields:
            bytes: Individual lines from the stream
        """

    def iter_chunks(self, chunk_size: int = 1024) -> Iterator[bytes]:
        """
        Return an iterator to yield chunks from the raw stream.
        
        Args:
            chunk_size: Size of each chunk in bytes
            
        Yields:
            bytes: Data chunks from the stream
        """

    def close(self) -> None:
        """Close the underlying HTTP response stream."""

    def readable(self) -> bool:
        """Check if the stream is readable."""

    def tell(self) -> int:
        """Return current position in the stream."""

    def set_socket_timeout(self, timeout: float) -> None:
        """
        Set timeout on the underlying socket.
        
        Args:
            timeout: Timeout value in seconds
        """

def get_response(
    operation_model: OperationModel,
    http_response
) -> Tuple[HTTPResponse, dict]:
    """
    Process HTTP response into parsed response data.
    
    Args:
        operation_model: Service operation model
        http_response: Raw HTTP response object
        
    Returns:
        tuple: (http_response, parsed_response_data)
    """

from botocore.session import get_session

# Create session and S3 client
session = get_session()
s3_client = session.create_client('s3', region_name='us-east-1')

# Get streaming object
response = s3_client.get_object(Bucket='mybucket', Key='large-file.txt')
streaming_body = response['Body']

# Read entire content
content = streaming_body.read()
print(f"Read {len(content)} bytes")

# Always close when done
streaming_body.close()

# Read file in chunks to manage memory usage
response = s3_client.get_object(Bucket='mybucket', Key='large-dataset.csv')
streaming_body = response['Body']

try:
    total_size = 0
    for chunk in streaming_body.iter_chunks(chunk_size=8192):
        # Process chunk (e.g., write to file, analyze data)
        total_size += len(chunk)
        print(f"Processed chunk of {len(chunk)} bytes")
    
    print(f"Total size: {total_size} bytes")
finally:
    streaming_body.close()

# Process text files line by line
response = s3_client.get_object(Bucket='mybucket', Key='log-file.txt')
streaming_body = response['Body']

try:
    line_count = 0
    for line in streaming_body.iter_lines(chunk_size=4096):
        # Process each line
        decoded_line = line.decode('utf-8')
        if 'ERROR' in decoded_line:
            print(f"Error found: {decoded_line.strip()}")
        line_count += 1
    
    print(f"Processed {line_count} lines")
finally:
    streaming_body.close()

# Use context manager for automatic cleanup
response = s3_client.get_object(Bucket='mybucket', Key='data.json')

with response['Body'] as streaming_body:
    # Stream will be automatically closed when exiting context
    data = streaming_body.read()
    parsed_data = json.loads(data.decode('utf-8'))

# Read into pre-allocated buffer for memory efficiency
response = s3_client.get_object(Bucket='mybucket', Key='binary-data.bin')
streaming_body = response['Body']

try:
    buffer = bytearray(8192)  # 8KB buffer
    total_read = 0
    
    while True:
        bytes_read = streaming_body.readinto(buffer)
        if bytes_read == 0:
            break
        
        # Process buffer contents
        total_read += bytes_read
        print(f"Read {bytes_read} bytes, total: {total_read}")
        
        # Process the data in buffer[:bytes_read]
finally:
    streaming_body.close()

# Handle streaming responses from Lambda invocations
lambda_client = session.create_client('lambda', region_name='us-east-1')

response = lambda_client.invoke(
    FunctionName='my-function',
    InvocationType='RequestResponse',
    Payload=json.dumps({'key': 'value'})
)

if 'Payload' in response:
    streaming_body = response['Payload']
    
    try:
        # Read Lambda response payload
        payload_data = streaming_body.read()
        result = json.loads(payload_data.decode('utf-8'))
        print(f"Lambda result: {result}")
    finally:
        streaming_body.close()

# Process Kinesis data streams
kinesis_client = session.create_client('kinesis', region_name='us-east-1')

response = kinesis_client.get_records(ShardIterator='shard-iterator-value')

for record in response['Records']:
    # Kinesis record data is typically base64 encoded
    data = record['Data']
    
    # If data is a StreamingBody (for large records)
    if hasattr(data, 'read'):
        try:
            content = data.read()
            decoded_data = base64.b64decode(content)
            print(f"Record data: {decoded_data}")
        finally:
            data.close()
    else:
        # Direct bytes data
        decoded_data = base64.b64decode(data)
        print(f"Record data: {decoded_data}")

# Get response with metadata
response = s3_client.get_object(Bucket='mybucket', Key='file.txt')

# Access response metadata
metadata = response['ResponseMetadata']
print(f"HTTP Status: {metadata['HTTPStatusCode']}")
print(f"Request ID: {metadata['RequestId']}")

# Access HTTP headers
http_headers = metadata['HTTPHeaders']
print(f"Content-Type: {http_headers.get('content-type')}")
print(f"Content-Length: {http_headers.get('content-length')}")
print(f"ETag: {http_headers.get('etag')}")

# Object-specific metadata
print(f"Last Modified: {response.get('LastModified')}")
print(f"ETag: {response.get('ETag')}")
print(f"Content Type: {response.get('ContentType')}")

# Process custom metadata for S3 objects
response = s3_client.get_object(Bucket='mybucket', Key='file-with-metadata.txt')

# Custom metadata (user-defined)
metadata = response.get('Metadata', {})
for key, value in metadata.items():
    print(f"Custom metadata {key}: {value}")

# Standard S3 metadata
if 'CacheControl' in response:
    print(f"Cache Control: {response['CacheControl']}")
if 'ContentDisposition' in response:
    print(f"Content Disposition: {response['ContentDisposition']}")
if 'ContentEncoding' in response:
    print(f"Content Encoding: {response['ContentEncoding']}")

from botocore.exceptions import (
    ClientError,
    ReadTimeoutError,
    ResponseStreamingError,
    IncompleteReadError,
    NoCredentialsError
)

def safe_stream_processing(bucket, key):
    """Safely process streaming response with comprehensive error handling."""
    
    try:
        # Create client
        s3_client = session.create_client('s3', region_name='us-east-1')
        
        # Get object
        response = s3_client.get_object(Bucket=bucket, Key=key)
        streaming_body = response['Body']
        
        try:
            # Set socket timeout for read operations
            streaming_body.set_socket_timeout(30.0)
            
            # Process data
            processed_bytes = 0
            for chunk in streaming_body.iter_chunks(chunk_size=8192):
                # Process chunk
                processed_bytes += len(chunk)
                
            return processed_bytes
            
        except ReadTimeoutError as e:
            print(f"Read timeout error: {e}")
            return None
            
        except ResponseStreamingError as e:
            print(f"Streaming protocol error: {e}")
            return None
            
        except IncompleteReadError as e:
            print(f"Incomplete read - expected {e.expected_bytes}, got {e.actual_bytes}")
            return None
            
        finally:
            # Always close the stream
            streaming_body.close()
            
    except ClientError as e:
        error_code = e.response['Error']['Code']
        if error_code == 'NoSuchBucket':
            print(f"Bucket {bucket} does not exist")
        elif error_code == 'NoSuchKey':
            print(f"Key {key} does not exist in bucket {bucket}")
        elif error_code == 'AccessDenied':
            print(f"Access denied to {bucket}/{key}")
        else:
            print(f"Client error: {error_code} - {e.response['Error']['Message']}")
        return None
        
    except NoCredentialsError:
        print("AWS credentials not found or invalid")
        return None
        
    except Exception as e:
        print(f"Unexpected error: {e}")
        return None

# Usage
result = safe_stream_processing('mybucket', 'large-file.txt')
if result is not None:
    print(f"Successfully processed {result} bytes")

# Configure timeouts for streaming operations
from botocore.config import Config

# Create config with custom timeouts
config = Config(
    connect_timeout=30,    # Connection timeout
    read_timeout=60,       # Read timeout
    retries={
        'max_attempts': 3,
        'mode': 'adaptive'
    }
)

# Create client with timeout configuration
s3_client = session.create_client('s3', region_name='us-east-1', config=config)

# Use client for streaming operations
response = s3_client.get_object(Bucket='mybucket', Key='large-file.bin')
streaming_body = response['Body']

try:
    # Additional socket-level timeout
    streaming_body.set_socket_timeout(45.0)
    
    # Process with configured timeouts
    data = streaming_body.read()
    
finally:
    streaming_body.close()

def process_large_file_optimally(bucket, key):
    """Process large files with memory-efficient streaming."""
    
    response = s3_client.get_object(Bucket=bucket, Key=key)
    streaming_body = response['Body']
    
    # Determine optimal chunk size based on content length
    content_length = int(response.get('ContentLength', 0))
    
    if content_length < 1024 * 1024:  # < 1MB
        chunk_size = 8192  # 8KB chunks
    elif content_length < 100 * 1024 * 1024:  # < 100MB
        chunk_size = 64 * 1024  # 64KB chunks
    else:  # > 100MB
        chunk_size = 1024 * 1024  # 1MB chunks
    
    try:
        processed_chunks = 0
        for chunk in streaming_body.iter_chunks(chunk_size=chunk_size):
            # Process chunk without storing entire file in memory
            process_chunk(chunk)
            processed_chunks += 1
            
            # Optional: Progress reporting
            if processed_chunks % 100 == 0:
                print(f"Processed {processed_chunks} chunks")
                
    finally:
        streaming_body.close()

def process_chunk(chunk):
    """Process individual data chunk."""
    # Implement chunk processing logic
    pass

def streaming_pipeline(bucket, key, processors):
    """Create a processing pipeline for streaming data."""
    
    response = s3_client.get_object(Bucket=bucket, Key=key)
    streaming_body = response['Body']
    
    try:
        for line in streaming_body.iter_lines(chunk_size=16384):
            data = line
            
            # Apply processing pipeline
            for processor in processors:
                data = processor(data)
                if data is None:  # Processor filtered out data
                    break
            
            if data is not None:
                yield data
                
    finally:
        streaming_body.close()

# Example processors
def decode_processor(data):
    """Decode bytes to string."""
    try:
        return data.decode('utf-8')
    except UnicodeDecodeError:
        return None  # Skip invalid lines

def json_processor(data):
    """Parse JSON from string."""
    try:
        return json.loads(data)
    except json.JSONDecodeError:
        return None  # Skip invalid JSON

def filter_processor(data):
    """Filter data based on criteria."""
    if isinstance(data, dict) and data.get('status') == 'active':
        return data
    return None

# Usage
processors = [decode_processor, json_processor, filter_processor]
for processed_item in streaming_pipeline('mybucket', 'data.jsonl', processors):
    print(f"Processed item: {processed_item}")

class StreamProcessor:
    """Context manager for safe stream processing."""
    
    def __init__(self, client, bucket, key):
        self.client = client
        self.bucket = bucket
        self.key = key
        self.streaming_body = None
        
    def __enter__(self):
        response = self.client.get_object(Bucket=self.bucket, Key=self.key)
        self.streaming_body = response['Body']
        return self.streaming_body
        
    def __exit__(self, exc_type, exc_val, exc_tb):
        if self.streaming_body:
            self.streaming_body.close()
        # Return False to propagate exceptions

# Usage with automatic cleanup
with StreamProcessor(s3_client, 'mybucket', 'large-file.csv') as stream:
    for line in stream.iter_lines():
        # Process line
        decoded_line = line.decode('utf-8')
        # Stream is automatically closed when exiting context

# Use S3 Select with streaming responses
def query_s3_with_select(bucket, key, query):
    """Query S3 object content using S3 Select."""
    
    response = s3_client.select_object_content(
        Bucket=bucket,
        Key=key,
        Expression=query,
        ExpressionType='SQL',
        InputSerialization={
            'CSV': {'FileHeaderInfo': 'USE'},
            'CompressionType': 'NONE'
        },
        OutputSerialization={'CSV': {}}
    )
    
    # Process streaming results
    for event in response['Payload']:
        if 'Records' in event:
            # Records event contains streaming data
            records_data = event['Records']['Payload']
            if hasattr(records_data, 'read'):
                # Handle as streaming body
                try:
                    chunk = records_data.read()
                    yield chunk.decode('utf-8')
                finally:
                    records_data.close()
            else:
                yield records_data.decode('utf-8')

# Usage
query = "SELECT * FROM s3object[*] WHERE age > 25"
for result_chunk in query_s3_with_select('mybucket', 'data.csv', query):
    print(f"Query result: {result_chunk}")

# Stream CloudWatch Logs
logs_client = session.create_client('logs', region_name='us-east-1')

def stream_log_events(log_group, log_stream):
    """Stream log events from CloudWatch Logs."""
    
    response = logs_client.get_log_events(
        logGroupName=log_group,
        logStreamName=log_stream,
        startFromHead=True
    )
    
    for event in response['events']:
        # Process log event
        timestamp = event['timestamp']
        message = event['message']
        
        # Convert timestamp to readable format
        readable_time = datetime.fromtimestamp(timestamp / 1000)
        print(f"[{readable_time}] {message}")

# Usage
stream_log_events('/aws/lambda/my-function', '2024/01/01/stream-name')