args

args = getResolvedOptions(sys.argv, ['JOB_NAME', 'input', 'output']) sc = SparkContext() glueContext = GlueContext(sc) spark = glueContext.spark_session job = Job(glueContext) job.init(args['JOB_NAME'], args) # Read data from S3 datasourc…

# Return total costs with self.lock: return self.costs def calculate_cost(query): # Calculate cost for a given query cost = 0 # Add costs based on query parameters return cost monitor = CostMoni…

Istio is a robust service mesh that provides comprehensive tools for managing latency and improving the overall performance of your microservices architecture. Its advanced traffic management, circuit breaking, and observability features ma…

public static void main(String[] args) throws Exception { // Initialize JMeter JMeterUtils.loadJMeterProperties("/path/to/jmeter.properties"); JMeterUtils.setLocale(java.util.Locale.US); // Create the te…

System.out.println(serviceName + ": Building..."); try { Thread.sleep(500); // Simulate shorter build time for each service } catch (InterruptedException e) { Thread.curren…

start_time = time.perf_counter() result = func(*args, **kwargs) end_time = time.perf_counter() latency = end_time - start_time logging.info(f"Function {func.__name__} took {latency:.6f} seconds") …

# Check if the result is already in the cache cached_result = r.get(cache_key) if cached_result: return SearchResponse.parse_raw(cached_result) # Call the original…

To implement a multi-stage caching system, you need to ensure that the decorators are applied in the correct order and that the caching logic is properly nested. Here's a revised version of your implementation: ### Revised Implementation …

from functools import wraps def timer_decorator(func): @wraps(func) def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() print(f"Function {func…

2. **Concurrency**: Use threading or multiprocessing to handle multiple queries concurrently. 3. **Caching**: Cache frequent queries to avoid redundant processing. 4. **Model Optimization**: If you are using a machine learning model, consid…

def profile_function(func, *args, **kwargs): pr = cProfile.Profile() pr.enable() result = func(*args, **kwargs) pr.disable() s = io.StringIO() ps = Stats(pr, stream=s).sort_stats('cumtime') ps.print_stats() p…