Asynchronous Operations

def async_arctic_submit(store, fun, is_modifier, *args, **kwargs):
    """
    Submit Arctic operation for asynchronous execution.
    
    Parameters:
    - store: Arctic store instance (VersionStore, TickStore, etc.)
    - fun: Function/method to execute asynchronously
    - is_modifier: Whether the operation modifies data (affects request tracking)
    - *args: Positional arguments for the function
    - **kwargs: Keyword arguments for the function
    
    Returns:
    AsyncRequest: Request object for tracking execution status
    
    Example:
    request = async_arctic_submit(version_store, 'read', False, 'AAPL')
    """

def async_wait_request(request, timeout=None):
    """
    Wait for single asynchronous request to complete.
    
    Parameters:
    - request: AsyncRequest object from async_arctic_submit
    - timeout: Maximum time to wait in seconds (None = no timeout)
    
    Returns:
    Result of the asynchronous operation
    
    Raises:
    - RequestDurationException: If operation times out
    - Original exception: If the async operation failed
    """

def async_wait_requests(requests, timeout=None):
    """
    Wait for multiple asynchronous requests to complete.
    
    Parameters:
    - requests: List of AsyncRequest objects
    - timeout: Maximum time to wait for all requests in seconds
    
    Returns:
    List of results in the same order as input requests
    
    Raises:
    - RequestDurationException: If any operation times out
    - Original exceptions: If any async operations failed
    """

def async_shutdown(timeout=None):
    """
    Shutdown the asynchronous thread pool gracefully.
    
    Parameters:
    - timeout: Maximum time to wait for shutdown in seconds
    
    Stops accepting new requests and waits for current operations
    to complete before shutting down the thread pool.
    """

def async_await_termination(timeout=None):
    """
    Wait for all asynchronous operations to complete and terminate.
    
    Parameters:
    - timeout: Maximum time to wait for termination in seconds
    
    Blocks until all submitted requests have completed execution
    and the thread pool has been fully terminated.
    """

def async_reset_pool(pool_size=None, timeout=None):
    """
    Reset the asynchronous thread pool with new configuration.
    
    Parameters:
    - pool_size: New thread pool size (None = use default)
    - timeout: Timeout for shutting down existing pool
    
    Shuts down the current thread pool and creates a new one
    with the specified size for handling async operations.
    """

def async_total_requests():
    """
    Get total number of asynchronous requests processed.
    
    Returns:
    int: Total count of requests submitted since startup
    
    Useful for monitoring throughput and system usage patterns.
    """

ASYNC_ARCTIC = AsyncArctic()
"""
Global singleton instance for asynchronous Arctic operations.

Provides centralized management of the thread pool and request
tracking for all async operations across Arctic stores.
"""

class AsyncArcticException(ArcticException):
    """
    Base exception for asynchronous Arctic operations.
    
    Raised when async-specific errors occur during request
    submission, execution, or result retrieval.
    """

class RequestDurationException(AsyncArcticException):
    """
    Exception raised when asynchronous requests exceed timeout limits.
    
    Raised by async_wait_request and async_wait_requests when
    operations take longer than the specified timeout period.
    """

from arctic import Arctic, VERSION_STORE
from arctic.asynchronous import (
    async_arctic_submit, async_wait_request, async_wait_requests
)
import time

# Setup
arctic_conn = Arctic('mongodb://localhost:27017')
lib = arctic_conn['market_data']

# Submit multiple read requests asynchronously
symbols = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA']
requests = []

start_time = time.time()

# Submit all requests for concurrent execution
for symbol in symbols:
    request = async_arctic_submit(lib, 'read', symbol)
    requests.append(request)

print(f"Submitted {len(requests)} requests in {time.time() - start_time:.3f}s")

# Wait for all requests to complete
results = async_wait_requests(requests, timeout=60)
print(f"Completed all requests in {time.time() - start_time:.3f}s")

# Process results
for symbol, result in zip(symbols, results):
    print(f"{symbol}: {result.data.shape[0]} data points")

# Submit single request
request = async_arctic_submit(lib, 'read', 'AAPL')

# Do other work while request executes
print("Request submitted, doing other work...")
time.sleep(1)

# Wait for specific request
try:
    result = async_wait_request(request, timeout=30)
    print(f"AAPL data: {result.data.shape}")
except RequestDurationException:
    print("Request timed out")
except Exception as e:
    print(f"Request failed: {e}")

import pandas as pd
import numpy as np

# Generate sample data for multiple symbols
symbols_data = {}
for symbol in ['AAPL', 'GOOGL', 'MSFT']:
    dates = pd.date_range('2020-01-01', periods=1000, freq='min')
    data = pd.DataFrame({
        'price': np.random.randn(1000).cumsum() + 100,
        'volume': np.random.randint(100, 1000, 1000)
    }, index=dates)
    symbols_data[symbol] = data

# Submit write operations asynchronously
write_requests = []
for symbol, data in symbols_data.items():
    request = async_arctic_submit(lib, 'write', symbol, data)
    write_requests.append((symbol, request))

# Wait for all writes to complete
for symbol, request in write_requests:
    try:
        result = async_wait_request(request, timeout=60)
        print(f"Successfully wrote {symbol}")
    except Exception as e:
        print(f"Failed to write {symbol}: {e}")

from arctic.date import DateRange
from datetime import datetime, timedelta

# Submit mixed operations
operations = []

# Read operations
for symbol in ['AAPL', 'GOOGL']:
    date_range = DateRange(
        datetime(2020, 1, 1),
        datetime(2020, 1, 31)
    )
    request = async_arctic_submit(lib, 'read', symbol, date_range=date_range)
    operations.append(('read', symbol, request))

# Metadata operations
for symbol in ['MSFT', 'AMZN']:
    request = async_arctic_submit(lib, 'read_metadata', symbol)
    operations.append(('metadata', symbol, request))

# List operations
list_request = async_arctic_submit(lib, 'list_symbols')
operations.append(('list', 'all', list_request))

# Process all operations
results = []
for op_type, symbol, request in operations:
    try:
        result = async_wait_request(request, timeout=30)
        results.append((op_type, symbol, result))
        print(f"Completed {op_type} for {symbol}")
    except Exception as e:
        print(f"Failed {op_type} for {symbol}: {e}")

from arctic.asynchronous import (
    async_reset_pool, async_shutdown, async_total_requests,
    async_await_termination
)

# Check current usage
total_requests = async_total_requests()
print(f"Total requests processed: {total_requests}")

# Reset thread pool with custom size
async_reset_pool(pool_size=8, timeout=10)
print("Thread pool reset with 8 threads")

# Submit high-volume operations
batch_requests = []
for i in range(50):
    symbol = f'SYM{i:03d}'
    request = async_arctic_submit(lib, 'has_symbol', symbol)
    batch_requests.append(request)

# Wait for batch completion
batch_results = async_wait_requests(batch_requests, timeout=120)
print(f"Processed {len(batch_results)} operations")

# Graceful shutdown
print("Shutting down async operations...")
async_shutdown(timeout=30)
async_await_termination(timeout=60)
print("Async system shutdown complete")

# Handle timeouts and errors gracefully
def safe_async_operation(store, operation, *args, **kwargs):
    """Safely execute async operation with error handling."""
    try:
        request = async_arctic_submit(store, operation, *args, **kwargs)
        result = async_wait_request(request, timeout=30)
        return result, None
    except RequestDurationException:
        return None, "Operation timed out"
    except Exception as e:
        return None, f"Operation failed: {str(e)}"

# Use safe wrapper for critical operations
symbols = ['AAPL', 'INVALID_SYMBOL', 'GOOGL']
for symbol in symbols:
    result, error = safe_async_operation(lib, 'read', symbol)
    if error:
        print(f"{symbol}: {error}")
    else:
        print(f"{symbol}: Success - {result.data.shape[0]} rows")

# Batch operations with error handling
success_count = 0
error_count = 0

requests = []
for symbol in symbols:
    request = async_arctic_submit(lib, 'read', symbol)
    requests.append((symbol, request))

for symbol, request in requests:
    try:
        result = async_wait_request(request, timeout=10)
        success_count += 1
        print(f"{symbol}: Success")
    except RequestDurationException:
        error_count += 1
        print(f"{symbol}: Timeout")
    except Exception as e:
        error_count += 1
        print(f"{symbol}: Error - {e}")

print(f"Results: {success_count} success, {error_count} errors")

# Optimize for different workload patterns

# High-throughput metadata checks
metadata_requests = []
symbols_to_check = [f'SYMBOL_{i:04d}' for i in range(100)]

start_time = time.time()
for symbol in symbols_to_check:
    request = async_arctic_submit(lib, 'has_symbol', symbol)
    metadata_requests.append(request)

# Process in batches to avoid overwhelming the system
batch_size = 20
results = []

for i in range(0, len(metadata_requests), batch_size):
    batch = metadata_requests[i:i + batch_size]
    batch_results = async_wait_requests(batch, timeout=30)
    results.extend(batch_results)
    print(f"Processed batch {i//batch_size + 1}")

total_time = time.time() - start_time
print(f"Checked {len(symbols_to_check)} symbols in {total_time:.2f}s")
print(f"Rate: {len(symbols_to_check)/total_time:.1f} operations/second")

# Memory-efficient large data reads
large_symbols = ['LARGE_DATASET_1', 'LARGE_DATASET_2', 'LARGE_DATASET_3']

# Process one at a time to manage memory usage
for symbol in large_symbols:
    request = async_arctic_submit(lib, 'read', symbol)
    try:
        result = async_wait_request(request, timeout=300)  # Longer timeout
        # Process result immediately to free memory
        data_size = result.data.memory_usage().sum()
        print(f"{symbol}: {data_size / 1024**2:.1f} MB")
        del result  # Explicit cleanup
    except Exception as e:
        print(f"Failed to process {symbol}: {e}")