307 lines
14 KiB
Markdown
307 lines
14 KiB
Markdown
# Design a key-value cache to save the results of the most recent web server queries
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*Note: This document links directly to relevant areas found in the [system design topics](https://github.com/donnemartin/system-design-primer-interview#index-of-system-design-topics-1) to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.*
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## Step 1: Outline use cases and constraints
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> Gather requirements and scope the problem.
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> Ask questions to clarify use cases and constraints.
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> Discuss assumptions.
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Without an interviewer to address clarifying questions, we'll define some use cases and constraints.
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### Use cases
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#### We'll scope the problem to handle only the following use cases
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* **User** sends a search request resulting in a cache hit
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* **User** sends a search request resulting in a cache miss
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* **Service** has high availability
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### Constraints and assumptions
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#### State assumptions
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* Traffic is not evenly distributed
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* Popular queries should almost always be in the cache
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* Need to determine how to expire/refresh
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* Serving from cache requires fast lookups
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* Low latency between machines
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* Limited memory in cache
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* Need to determine what to keep/remove
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* Need to cache millions of queries
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* 10 million users
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* 10 billion queries per month
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#### Calculate usage
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**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.**
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* Cache stores ordered list of key: query, value: results
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* `query` - 50 bytes
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* `title` - 20 bytes
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* `snippet` - 200 bytes
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* Total: 270 bytes
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* 2.7 TB of cache data per month if all 10 billion queries are unique and all are stored
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* 270 bytes per search * 10 billion searches per month
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* Assumptions state limited memory, need to determine how to expire contents
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* 4,000 requests per second
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Handy conversion guide:
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* 2.5 million seconds per month
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* 1 request per second = 2.5 million requests per month
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* 40 requests per second = 100 million requests per month
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* 400 requests per second = 1 billion requests per month
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## Step 2: Create a high level design
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> Outline a high level design with all important components.
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## Step 3: Design core components
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> Dive into details for each core component.
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### Use case: User sends a request resulting in a cache hit
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Popular queries can be served from a **Memory Cache** such as Redis or Memcached to reduce read latency and to avoid overloading the **Reverse Index Service** and **Document Service**. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.<sup><a href=https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know>1</a></sup>
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Since the cache has limited capacity, we'll use a least recently used (LRU) approach to expire older entries.
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* The **Client** sends a request to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy-web-server)
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* The **Web Server** forwards the request to the **Query API** server
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* The **Query API** server does the following:
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* Parses the query
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* Removes markup
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* Breaks up the text into terms
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* Fixes typos
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* Normalizes capitalization
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* Converts the query to use boolean operations
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* Checks the **Memory Cache** for the content matching the query
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* If there's a hit in the **Memory Cache**, the **Memory Cache** does the following:
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* Updates the cached entry's position to the front of the LRU list
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* Returns the cached contents
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* Else, the **Query API** does the following:
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* Uses the **Reverse Index Service** to find documents matching the query
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* The **Reverse Index Service** ranks the matching results and returns the top ones
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* Uses the **Document Service** to return titles and snippets
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* Updates the **Memory Cache** with the contents, placing the entry at the front of the LRU list
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#### Cache implementation
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The cache can use a doubly-linked list: new items will be added to the head while items to expire will be removed from the tail. We'll use a hash table for fast lookups to each linked list node.
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**Clarify with your interviewer how much code you are expected to write**.
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**Query API Server** implementation:
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```
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class QueryApi(object):
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def __init__(self, memory_cache, reverse_index_service):
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self.memory_cache = memory_cache
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self.reverse_index_service = reverse_index_service
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def parse_query(self, query):
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"""Remove markup, break text into terms, deal with typos,
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normalize capitalization, convert to use boolean operations.
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"""
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...
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def process_query(self, query):
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query = self.parse_query(query)
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results = self.memory_cache.get(query)
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if results is None:
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results = self.reverse_index_service.process_search(query)
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self.memory_cache.set(query, results)
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return results
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```
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**Node** implementation:
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```
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class Node(object):
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def __init__(self, query, results):
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self.query = query
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self.results = results
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```
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**LinkedList** implementation:
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```
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class LinkedList(object):
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def __init__(self):
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self.head = None
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self.tail = None
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def move_to_front(self, node):
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...
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def append_to_front(self, node):
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...
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def remove_from_tail(self):
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...
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```
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**Cache** implementation:
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```
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class Cache(object):
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def __init__(self, MAX_SIZE):
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self.MAX_SIZE = MAX_SIZE
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self.size = 0
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self.lookup = {} # key: query, value: node
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self.linked_list = LinkedList()
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def get(self, query)
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"""Get the stored query result from the cache.
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Accessing a node updates its position to the front of the LRU list.
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"""
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node = self.lookup[query]
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if node is None:
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return None
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self.linked_list.move_to_front(node)
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return node.results
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def set(self, results, query):
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"""Set the result for the given query key in the cache.
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When updating an entry, updates its position to the front of the LRU list.
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If the entry is new and the cache is at capacity, removes the oldest entry
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before the new entry is added.
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"""
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node = self.lookup[query]
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if node is not None:
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# Key exists in cache, update the value
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node.results = results
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self.linked_list.move_to_front(node)
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else:
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# Key does not exist in cache
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if self.size == self.MAX_SIZE:
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# Remove the oldest entry from the linked list and lookup
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self.lookup.pop(self.linked_list.tail.query, None)
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self.linked_list.remove_from_tail()
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else:
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self.size += 1
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# Add the new key and value
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new_node = Node(query, results)
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self.linked_list.append_to_front(new_node)
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self.lookup[query] = new_node
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```
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#### When to update the cache
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The cache should be updated when:
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* The page contents change
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* The page is removed or a new page is added
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* The page rank changes
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The most straightforward way to handle these cases is to simply set a max time that a cached entry can stay in the cache before it is updated, usually referred to as time to live (TTL).
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Refer to [When to update the cache](https://github.com/donnemartin/system-design-primer-interview#when-to-update-the-cache) for tradeoffs and alternatives. The approach above describes [cache-aside](https://github.com/donnemartin/system-design-primer-interview#cache-aside).
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## Step 4: Scale the design
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> Identify and address bottlenecks, given the constraints.
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**Important: Do not simply jump right into the final design from the initial design!**
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State you would 1) **Benchmark/Load Test**, 2) **Profile** for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See [Design a system that scales to millions of users on AWS]() as a sample on how to iteratively scale the initial design.
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It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a **Load Balancer** with multiple **Web Servers**? **CDN**? **Master-Slave Replicas**? What are the alternatives and **Trade-Offs** for each?
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We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.
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*To avoid repeating discussions*, refer to the following [system design topics](https://github.com/donnemartin/system-design-primer-interview#) for main talking points, tradeoffs, and alternatives:
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* [DNS](https://github.com/donnemartin/system-design-primer-interview#domain-name-system)
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* [Load balancer](https://github.com/donnemartin/system-design-primer-interview#load-balancer)
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* [Horizontal scaling](https://github.com/donnemartin/system-design-primer-interview#horizontal-scaling)
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* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy-web-server)
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* [API server (application layer)](https://github.com/donnemartin/system-design-primer-interview#application-layer)
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* [Cache](https://github.com/donnemartin/system-design-primer-interview#cache)
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* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns)
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* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns)
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### Expanding the Memory Cache to many machines
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To handle the heavy request load and the large amount of memory needed, we'll scale horizontally. We have three main options on how to store the data on our **Memory Cache** cluster:
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* **Each machine in the cache cluster has its own cache** - Simple, although it will likely result in a low cache hit rate.
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* **Each machine in the cache cluster has a copy of the cache** - Simple, although it is an inefficient use of memory.
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* **The cache is [sharded](https://github.com/donnemartin/system-design-primer-interview#sharding) across all machines in the cache cluster** - More complex, although it is likely the best option. We could use hashing to determine which machine could have the cached results of a query using `machine = hash(query)`. We'll likely want to use [consistent hashing](https://github.com/donnemartin/system-design-primer-interview#consistent-hashing).
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## Additional talking points
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> Additional topics to dive into, depending on the problem scope and time remaining.
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### SQL scaling patterns
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* [Read replicas](https://github.com/donnemartin/system-design-primer-interview#master-slave)
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* [Federation](https://github.com/donnemartin/system-design-primer-interview#federation)
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* [Sharding](https://github.com/donnemartin/system-design-primer-interview#sharding)
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* [Denormalization](https://github.com/donnemartin/system-design-primer-interview#denormalization)
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* [SQL Tuning](https://github.com/donnemartin/system-design-primer-interview#sql-tuning)
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#### NoSQL
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* [Key-value store](https://github.com/donnemartin/system-design-primer-interview#)
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* [Document store](https://github.com/donnemartin/system-design-primer-interview#)
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* [Wide column store](https://github.com/donnemartin/system-design-primer-interview#)
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* [Graph database](https://github.com/donnemartin/system-design-primer-interview#)
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* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer-interview#)
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### Caching
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* Where to cache
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* [Client caching](https://github.com/donnemartin/system-design-primer-interview#client-caching)
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* [CDN caching](https://github.com/donnemartin/system-design-primer-interview#cdn-caching)
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* [Web server caching](https://github.com/donnemartin/system-design-primer-interview#web-server-caching)
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* [Database caching](https://github.com/donnemartin/system-design-primer-interview#database-caching)
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* [Application caching](https://github.com/donnemartin/system-design-primer-interview#application-caching)
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* What to cache
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* [Caching at the database query level](https://github.com/donnemartin/system-design-primer-interview#caching-at-the-database-query-level)
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* [Caching at the object level](https://github.com/donnemartin/system-design-primer-interview#caching-at-the-object-level)
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* When to update the cache
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* [Cache-aside](https://github.com/donnemartin/system-design-primer-interview#cache-aside)
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* [Write-through](https://github.com/donnemartin/system-design-primer-interview#write-through)
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* [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer-interview#write-behind-write-back)
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* [Refresh ahead](https://github.com/donnemartin/system-design-primer-interview#refresh-ahead)
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### Asynchronism and microservices
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* [Message queues](https://github.com/donnemartin/system-design-primer-interview#)
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* [Task queues](https://github.com/donnemartin/system-design-primer-interview#)
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* [Back pressure](https://github.com/donnemartin/system-design-primer-interview#)
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* [Microservices](https://github.com/donnemartin/system-design-primer-interview#)
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### Communications
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* Discuss tradeoffs:
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* External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer-interview#representational-state-transfer-rest)
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* Internal communications - [RPC](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc)
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* [Service discovery](https://github.com/donnemartin/system-design-primer-interview#service-discovery)
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### Security
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Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
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### Latency numbers
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See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
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### Ongoing
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* Continue benchmarking and monitoring your system to address bottlenecks as they come up
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* Scaling is an iterative process
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