### Smooth Weighted Round-Robin Algorithm in Python (Nginx) Source: https://context7.com/linnik/roundrobin/llms.txt Implements the smooth weighted round-robin balancing algorithm similar to Nginx. This algorithm distributes selections more evenly across iterations, preventing bursts to a single item. The dataset should be a list of (item, weight) tuples. ```python import roundrobin # Smooth weighted round-robin for better distribution servers = [ ("primary-server.example.com", 5), ("backup-server1.example.com", 1), ("backup-server2.example.com", 1) ] get_server = roundrobin.smooth(servers) # Distribute 7 requests - note the interleaved pattern results = [get_server() for _ in range(7)] print(results) # Compare weighted vs smooth distribution patterns data = [("A", 5), ("B", 1), ("C", 1)] weighted_rr = roundrobin.weighted(data) smooth_rr = roundrobin.smooth(data) weighted_result = ''.join([weighted_rr() for _ in range(7)]) smooth_result = ''.join([smooth_rr() for _ in range(7)]) print(f"Weighted: {weighted_result}") print(f"Smooth: {smooth_result}") ``` -------------------------------- ### Python Round Robin Utilities Source: https://github.com/linnik/roundrobin/blob/master/README.md Demonstrates the usage of basic, weighted, and smooth weighted round robin algorithms from the 'roundrobin' library. These functions are useful for load balancing and distributing tasks evenly. The basic version cycles through items, while weighted versions consider assigned weights for distribution. ```python import roundrobin # Basic round robin get_roundrobin = roundrobin.basic(["A", "B", "C"]) print(''.join([get_roundrobin() for _ in range(7)])) # Weighted round-robin balancing algorithm (LVS style) get_weighted = roundrobin.weighted([("A", 5), ("B", 1), ("C", 1)]) print(''.join([get_weighted() for _ in range(7)])) # Smooth weighted round-robin balancing algorithm (Nginx style) get_weighted_smooth = roundrobin.smooth([("A", 5), ("B", 1), ("C", 1)]) print(''.join([get_weighted_smooth() for _ in range(7)])) ``` -------------------------------- ### Smooth Weighted Round Robin Source: https://context7.com/linnik/roundrobin/llms.txt The `roundrobin.smooth(dataset)` function implements the smooth weighted round-robin algorithm used in Nginx. It distributes selections more evenly across iterations, preventing bursts of requests to a single server. ```APIDOC ## Smooth Weighted Round Robin ### Description Implements the smooth weighted round-robin balancing algorithm as used in Nginx. Unlike the standard weighted algorithm, this distributes selections more evenly across iterations, preventing bursts of requests to a single server. The dataset should be a list of tuples containing (item, weight) pairs. ### Method `roundrobin.smooth(dataset)` ### Parameters #### Path Parameters None #### Query Parameters None #### Request Body None ### Request Example ```python import roundrobin servers = [ ("primary-server.example.com", 5), ("backup-server1.example.com", 1), ("backup-server2.example.com", 1) ] get_server = roundrobin.smooth(servers) results = [get_server() for _ in range(7)] print(results) ``` ### Response #### Success Response (200) Returns a callable function that yields the next item with smoother distribution. #### Response Example ``` ['primary-server.example.com', 'primary-server.example.com', 'backup-server1.example.com', 'primary-server.example.com', 'backup-server2.example.com', 'primary-server.example.com', 'primary-server.example.com'] ``` ``` -------------------------------- ### Weighted Round-Robin Algorithm in Python (LVS) Source: https://context7.com/linnik/roundrobin/llms.txt Implements the weighted round-robin balancing algorithm similar to Linux Virtual Server (LVS). Items with higher weights are selected more frequently. The dataset must be a list of (item, weight) tuples. ```python import roundrobin # Weighted round-robin with servers of different capacities servers = [ ("primary-server.example.com", 5), ("backup-server1.example.com", 1), ("backup-server2.example.com", 1) ] get_server = roundrobin.weighted(servers) # Distribute 7 requests - primary gets 5, backups get 1 each results = [get_server() for _ in range(7)] print(results) # Verify proportional distribution over 100 requests data = [("A", 50), ("B", 35), ("C", 15)] get_next = roundrobin.weighted(data) counts = {} for _ in range(100): key = get_next() counts[key] = counts.get(key, 0) + 1 print(counts) ``` -------------------------------- ### Basic Round Robin Source: https://context7.com/linnik/roundrobin/llms.txt The `roundrobin.basic(dataset)` function creates a simple round-robin iterator that cycles through elements in order. It's suitable for scenarios requiring equal distribution. ```APIDOC ## Basic Round Robin ### Description Creates a simple round-robin iterator that cycles through elements in order, returning each item in sequence before starting over. This is ideal for equal distribution scenarios where all items should receive the same amount of traffic or workload. ### Method `roundrobin.basic(dataset)` ### Parameters #### Path Parameters None #### Query Parameters None #### Request Body None ### Request Example ```python import roundrobin servers = ["server1.example.com", "server2.example.com", "server3.example.com"] get_server = roundrobin.basic(servers) for request_id in range(10): server = get_server() print(f"Request {request_id} -> {server}") ``` ### Response #### Success Response (200) Returns a callable function that yields the next item in the sequence. #### Response Example ``` Request 0 -> server1.example.com Request 1 -> server2.example.com Request 2 -> server3.example.com Request 3 -> server1.example.com ... ``` ``` -------------------------------- ### Basic Round-Robin Iterator in Python Source: https://context7.com/linnik/roundrobin/llms.txt Creates a basic round-robin iterator that cycles through elements in order. Ideal for equal distribution scenarios. It takes a dataset as input and returns a callable function that yields the next item in sequence. ```python import roundrobin # Basic round-robin with servers servers = ["server1.example.com", "server2.example.com", "server3.example.com"] get_server = roundrobin.basic(servers) # Distribute 10 requests across servers for request_id in range(10): server = get_server() print(f"Request {request_id} -> {server}") # Verify equal distribution data = ["A", "B", "C"] get_next = roundrobin.basic(data) result = ''.join([get_next() for _ in range(7)]) print(result) ``` -------------------------------- ### Weighted Round Robin Source: https://context7.com/linnik/roundrobin/llms.txt The `roundrobin.weighted(dataset)` function implements the weighted round-robin algorithm used in LVS. Items with higher weights are selected more frequently, with selections for one weight level completing before moving to the next. ```APIDOC ## Weighted Round Robin ### Description Implements the weighted round-robin balancing algorithm as used in Linux Virtual Server (LVS). Items with higher weights are selected more frequently, with all selections for one weight level completing before moving to the next. The dataset should be a list of tuples containing (item, weight) pairs. ### Method `roundrobin.weighted(dataset)` ### Parameters #### Path Parameters None #### Query Parameters None #### Request Body None ### Request Example ```python import roundrobin servers = [ ("primary-server.example.com", 5), ("backup-server1.example.com", 1), ("backup-server2.example.com", 1) ] get_server = roundrobin.weighted(servers) results = [get_server() for _ in range(7)] print(results) ``` ### Response #### Success Response (200) Returns a callable function that yields the next item based on its weight. #### Response Example ``` ['primary-server.example.com', 'primary-server.example.com', 'primary-server.example.com', 'primary-server.example.com', 'primary-server.example.com', 'backup-server1.example.com', 'backup-server2.example.com'] ``` ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.