cn_go_patterns

README

cn_go_patterns

This repository contains cloud native patterns written in Go

Stability Patterns

Circuit breaker

Circuit Breaker automatically degrades service functions in response to a likely fault, preventing larger or cascading failures by eliminating recurring errors and providing reasonable error responses.

Participants

This pattern includes the following participants:

• Circuit The function that interacts with the service.

• Breaker A closure with the same function signature as Circuit.

Debounce

Debounce limits the frequency of a function invocation so that only the first or last in a cluster of calls is actually performed.

Participants

This pattern includes the following participants:

• Circuit The function to regulate.

• Debounce A closure with the same function signature as Circuit.

Retry

Retry accounts for a possible transient fault in a distributed system by transparently retrying a failed operation.

Participants

This pattern includes the following participants:

• Effector The function that interacts with the service.

• Retry A function that accepts Effector and returns a closure with the same function signature as effector.

Throttle

Throttle limits the frequency of a function call to some maximum number of invocations per unit of time.

Participants

This pattern includes the following participants:

• Effector The function to regulate

• Throttle A function that accepts Effector and returns a closure with the same function signature as Effector.

Timout

Timeout allows a process to stop waiting for an answer once it's clear that an answer may not be coming.

Participants

This pattern includes the following participants:

• Client The client who wants to execute SlowFunction.

• SlowFunction The long-running function that implements the functionality desired by Client.

• Timeout A wrapper function around SlowFunction that implements the timeout logic.

Concurrency Patterns

Fan-In

Fan-in multiplexes multiple input channels onto one output channel. Services that have some number of workers that all generate output may find it useful to combine all of the workers outputs to be prociessed as a single unified stream.

Participants

This pattern includes the following participants:

• Sources A set of one or more input channels with the same type. Accepted by Funnel

• Destination An output channel of the same type as Sources. Created and provided by Funnel.

• Funnel Accepts Sources and immediately returns Destination. Any input from any Sources will be output by Destination.

Fan-Out

Fan-out evenly distributes messages from an input channel to multiple output channels. Fan-out receives messages from an input channel, distributing them evenly among output channels, and is a useful pattern for parallelizing CPU and I/O utilization.

Participants

This pattern includes the following participants:

• Source An input channel. Accepted by Split.

• Destinations An output channel of the same type as Source. Created and provided by Split.

• Split A function that accepts Source and immediately returns Destinations. Any input from Source will be output to a Destination.

Future

Future provides a placeholder for a value that's not yet known. Futures (also known as Promises or Delays) are a synchronization construct that provide a placeholder for a value that's still being generated by an asynchronous process.

Participants

This pattern includes the following participants:

• Future The interface that is received by the consumer to retrieve the eventual result.

• SlowFunction A wrapper function around some function to be asynchronously executed; provides Future.

• InnerFuture Satisfies the Future interface; includes an attached method that contains the result access logic.

Sharding

Sharding splits a large data structure into multiple partitions to localize the effects of read/write locks. The term sharding is typically used in the context of distributed state to describe data that is partitioned between server instances. This kind of horizontal sharding is commonly used by databases and other data stores to distribute load and provide redundancy.

Participants

This pattern includes the following participants:

• ShardedMap An abstraction around one or more Shards providing read and write access as if the Shards were a single map.

• Shard An individually lockable collection representing a single data partition.

Other notes

Throttle and the Token Bucket algorithm

The Token Bucket algorithm uses the analogy of a bucket that can hold some maximum number of tokens. When a function is called, a token is taken from the bucket, which then refills at some fixed rate.

Common strategies for handling requests using Throttle pattern:

• Return an error

  • Useful to restrict unreasonable or potentially abusive numbers of client requests. A RESTful service example might return a status code of 429 (Too Many Requests).

• Replay the response of the last successful function call

  • Useful when a service or expensive fucntion call is likely to provide an identical result if called to soon.

• Enqueue the request for execution when sufficient tokens are available

  • Useful when you eventualy want to handle all requests. More complex and may require care to be taken to ensure that memory isn't exhausted.

Difference between Throttle and Debounce

Throttle limits the event rate; Debounce allows only one event in a cluster. example for 20 input requests within a given time frame say once per second, throttle might limit the event rate to 1 request every other second effectively handling 10 out of 20 requests. Debounce on the other hand will handle the first out of 20 requests if debounce rate is set to 20sec and might cache the response to be returned immediately.

Multiplexer (mux)

In electronics a mutliplexer or mux, also known as a data selector, is a device that selects between multiple digital or analog input signals and forwards the selected input to a single output line. In CS the same aspects can be applied to provide a single channel for multiple user or process requests.

Variadic function

A variadic function is a function of indefinite arity i.e., one which accepts a variable number of arguments.

Fan-Out and 2 forwarding techniques

• Using a single goroutine that reads the values from Source and forwards them to the Destinations in a round-robin fashion. This has the virtue of requiring only one master goroutine, but if the next channel isn't ready to read yet, it'll slow the entire process.

• Using separate goroutines for each Destination that competes to read the next value from Source and forward it to their respective Destination. This requires slightly more resources, but is less likely to get bogged down by a single slow-running worker.

Horizontal vs. Vertical Sharding

Large data structures can be sharded, or partitioned, in two different ways:

• Horizontal Sharding is the partitioning of data across service instances. This can provide data redundancy and allow load to be balanced betweeen instances, but also adds the latency and complexity that comes with distributed data.

• Vertical Sharding is the partitioning of data within a single instance. This can reduce read/write contention between concurrent processes, but also doesn't scale or provide any redundancy.