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+# Design a key-value cache to save the results of the most recent web server queries
+
+*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.*
+
+## Step 1: Outline use cases and constraints
+
+> Gather requirements and scope the problem.
+> Ask questions to clarify use cases and constraints.
+> Discuss assumptions.
+
+Without an interviewer to address clarifying questions, we'll define some use cases and constraints.
+
+### Use cases
+
+#### We'll scope the problem to handle only the following use cases
+
+* **User** sends a search request resulting in a cache hit
+* **User** sends a search request resulting in a cache miss
+* **Service** has high availability
+
+### Constraints and assumptions
+
+#### State assumptions
+
+* Traffic is not evenly distributed
+    * Popular queries should almost always be in the cache
+    * Need to determine how to expire/refresh
+* Serving from cache requires fast lookups
+* Low latency between machines
+* Limited memory in cache
+    * Need to determine what to keep/remove
+    * Need to cache millions of queries
+* 10 million users
+* 10 billion queries per month
+
+#### Calculate usage
+
+**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.**
+
+* Cache stores ordered list of key: query, value: results
+    * `query` - 50 bytes
+    * `title` - 20 bytes
+    * `snippet` - 200 bytes
+    * Total: 270 bytes
+* 2.7 TB of cache data per month if all 10 billion queries are unique and all are stored
+    * 270 bytes per search * 10 billion searches per month
+    * Assumptions state limited memory, need to determine how to expire contents
+* 4,000 requests per second
+
+Handy conversion guide:
+
+* 2.5 million seconds per month
+* 1 request per second = 2.5 million requests per month
+* 40 requests per second = 100 million requests per month
+* 400 requests per second = 1 billion requests per month
+
+## Step 2: Create a high level design
+
+> Outline a high level design with all important components.
+
+![Imgur](http://i.imgur.com/KqZ3dSx.png)
+
+## Step 3: Design core components
+
+> Dive into details for each core component.
+
+### Use case: User sends a request resulting in a cache hit
+
+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>
+
+Since the cache has limited capacity, we'll use a least recently used (LRU) approach to expire older entries.
+
+* 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)
+* The **Web Server** forwards the request to the **Query API** server
+* The **Query API** server does the following:
+    * Parses the query
+        * Removes markup
+        * Breaks up the text into terms
+        * Fixes typos
+        * Normalizes capitalization
+        * Converts the query to use boolean operations
+    * Checks the **Memory Cache** for the content matching the query
+        * If there's a hit in the **Memory Cache**, the **Memory Cache** does the following:
+            * Updates the cached entry's position to the front of the LRU list
+            * Returns the cached contents
+        * Else, the **Query API** does the following:
+            * Uses the **Reverse Index Service** to find documents matching the query
+                * The **Reverse Index Service** ranks the matching results and returns the top ones
+            * Uses the **Document Service** to return titles and snippets
+            * Updates the **Memory Cache** with the contents, placing the entry at the front of the LRU list
+
+#### Cache implementation
+
+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.
+
+**Clarify with your interviewer how much code you are expected to write**.
+
+**Query API Server** implementation:
+
+```
+class QueryApi(object):
+
+    def __init__(self, memory_cache, reverse_index_service):
+        self.memory_cache = memory_cache
+        self.reverse_index_service = reverse_index_service
+
+    def parse_query(self, query):
+        """Remove markup, break text into terms, deal with typos,
+        normalize capitalization, convert to use boolean operations.
+        """
+        ...
+
+    def process_query(self, query):
+        query = self.parse_query(query)
+        results = self.memory_cache.get(query)
+        if results is None:
+            results = self.reverse_index_service.process_search(query)
+            self.memory_cache.set(query, results)
+        return results
+```
+
+**Node** implementation:
+
+```
+class Node(object):
+
+    def __init__(self, query, results):
+        self.query = query
+        self.results = results
+```
+
+**LinkedList** implementation:
+
+```
+class LinkedList(object):
+
+    def __init__(self):
+        self.head = None
+        self.tail = None
+
+    def move_to_front(self, node):
+        ...
+
+    def append_to_front(self, node):
+        ...
+
+    def remove_from_tail(self):
+        ...
+```
+
+**Cache** implementation:
+
+```
+class Cache(object):
+
+    def __init__(self, MAX_SIZE):
+        self.MAX_SIZE = MAX_SIZE
+        self.size = 0
+        self.lookup = {}  # key: query, value: node
+        self.linked_list = LinkedList()
+
+    def get(self, query)
+        """Get the stored query result from the cache.
+
+        Accessing a node updates its position to the front of the LRU list.
+        """
+        node = self.lookup[query]
+        if node is None:
+            return None
+        self.linked_list.move_to_front(node)
+        return node.results
+
+    def set(self, results, query):
+        """Set the result for the given query key in the cache.
+
+        When updating an entry, updates its position to the front of the LRU list.
+        If the entry is new and the cache is at capacity, removes the oldest entry
+        before the new entry is added.
+        """
+        node = self.lookup[query]
+        if node is not None:
+            # Key exists in cache, update the value
+            node.results = results
+            self.linked_list.move_to_front(node)
+        else:
+            # Key does not exist in cache
+            if self.size == self.MAX_SIZE:
+                # Remove the oldest entry from the linked list and lookup
+                self.lookup.pop(self.linked_list.tail.query, None)
+                self.linked_list.remove_from_tail()
+            else:
+                self.size += 1
+            # Add the new key and value
+            new_node = Node(query, results)
+            self.linked_list.append_to_front(new_node)
+            self.lookup[query] = new_node
+```
+
+#### When to update the cache
+
+The cache should be updated when:
+
+* The page contents change
+* The page is removed or a new page is added
+* The page rank changes
+
+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).
+
+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).
+
+## Step 4: Scale the design
+
+> Identify and address bottlenecks, given the constraints.
+
+![Imgur](http://i.imgur.com/4j99mhe.png)
+
+**Important: Do not simply jump right into the final design from the initial design!**
+
+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.
+
+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?
+
+We'll introduce some components to complete the design and to address scalability issues.  Internal load balancers are not shown to reduce clutter.
+
+*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:
+
+* [DNS](https://github.com/donnemartin/system-design-primer-interview#domain-name-system)
+* [Load balancer](https://github.com/donnemartin/system-design-primer-interview#load-balancer)
+* [Horizontal scaling](https://github.com/donnemartin/system-design-primer-interview#horizontal-scaling)
+* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy-web-server)
+* [API server (application layer)](https://github.com/donnemartin/system-design-primer-interview#application-layer)
+* [Cache](https://github.com/donnemartin/system-design-primer-interview#cache)
+* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns)
+* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns)
+
+### Expanding the Memory Cache to many machines
+
+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:
+
+* **Each machine in the cache cluster has its own cache** - Simple, although it will likely result in a low cache hit rate.
+* **Each machine in the cache cluster has a copy of the cache** - Simple, although it is an inefficient use of memory.
+* **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).
+
+## Additional talking points
+
+> Additional topics to dive into, depending on the problem scope and time remaining.
+
+### SQL scaling patterns
+
+* [Read replicas](https://github.com/donnemartin/system-design-primer-interview#master-slave)
+* [Federation](https://github.com/donnemartin/system-design-primer-interview#federation)
+* [Sharding](https://github.com/donnemartin/system-design-primer-interview#sharding)
+* [Denormalization](https://github.com/donnemartin/system-design-primer-interview#denormalization)
+* [SQL Tuning](https://github.com/donnemartin/system-design-primer-interview#sql-tuning)
+
+#### NoSQL
+
+* [Key-value store](https://github.com/donnemartin/system-design-primer-interview#)
+* [Document store](https://github.com/donnemartin/system-design-primer-interview#)
+* [Wide column store](https://github.com/donnemartin/system-design-primer-interview#)
+* [Graph database](https://github.com/donnemartin/system-design-primer-interview#)
+* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer-interview#)
+
+### Caching
+
+* Where to cache
+    * [Client caching](https://github.com/donnemartin/system-design-primer-interview#client-caching)
+    * [CDN caching](https://github.com/donnemartin/system-design-primer-interview#cdn-caching)
+    * [Web server caching](https://github.com/donnemartin/system-design-primer-interview#web-server-caching)
+    * [Database caching](https://github.com/donnemartin/system-design-primer-interview#database-caching)
+    * [Application caching](https://github.com/donnemartin/system-design-primer-interview#application-caching)
+* What to cache
+    * [Caching at the database query level](https://github.com/donnemartin/system-design-primer-interview#caching-at-the-database-query-level)
+    * [Caching at the object level](https://github.com/donnemartin/system-design-primer-interview#caching-at-the-object-level)
+* When to update the cache
+    * [Cache-aside](https://github.com/donnemartin/system-design-primer-interview#cache-aside)
+    * [Write-through](https://github.com/donnemartin/system-design-primer-interview#write-through)
+    * [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer-interview#write-behind-write-back)
+    * [Refresh ahead](https://github.com/donnemartin/system-design-primer-interview#refresh-ahead)
+
+### Asynchronism and microservices
+
+* [Message queues](https://github.com/donnemartin/system-design-primer-interview#)
+* [Task queues](https://github.com/donnemartin/system-design-primer-interview#)
+* [Back pressure](https://github.com/donnemartin/system-design-primer-interview#)
+* [Microservices](https://github.com/donnemartin/system-design-primer-interview#)
+
+### Communications
+
+* Discuss tradeoffs:
+    * External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer-interview#representational-state-transfer-rest)
+    * Internal communications - [RPC](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc)
+* [Service discovery](https://github.com/donnemartin/system-design-primer-interview#service-discovery)
+
+### Security
+
+Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
+
+### Latency numbers
+
+See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
+
+### Ongoing
+
+* Continue benchmarking and monitoring your system to address bottlenecks as they come up
+* Scaling is an iterative process
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+# -*- coding: utf-8 -*-
+
+class QueryApi(object):
+
+    def __init__(self, memory_cache, reverse_index_cluster):
+        self.memory_cache = memory_cache
+        self.reverse_index_cluster = reverse_index_cluster
+
+    def parse_query(self, query):
+        """Remove markup, break text into terms, deal with typos,
+        normalize capitalization, convert to use boolean operations.
+        """
+        ...
+
+    def process_query(self, query):
+        query = self.parse_query(query)
+        results = self.memory_cache.get(query)
+        if results is None:
+            results = self.reverse_index_cluster.process_search(query)
+            self.memory_cache.set(query, results)
+        return results
+
+
+class Node(object):
+
+    def __init__(self, query, results):
+        self.query = query
+        self.results = results
+
+
+class LinkedList(object):
+
+    def __init__(self):
+        self.head = None
+        self.tail = None
+
+    def move_to_front(self, node):
+        ...
+
+    def append_to_front(self, node):
+        ...
+
+    def remove_from_tail(self):
+        ...
+
+
+class Cache(object):
+
+    def __init__(self, MAX_SIZE):
+        self.MAX_SIZE = MAX_SIZE
+        self.size = 0
+        self.lookup = {}
+        self.linked_list = LinkedList()
+
+    def get(self, query)
+        """Get the stored query result from the cache.
+
+        Accessing a node updates its position to the front of the LRU list.
+        """
+        node = self.lookup[query]
+        if node is None:
+            return None
+        self.linked_list.move_to_front(node)
+        return node.results
+
+    def set(self, results, query):
+        """Set the result for the given query key in the cache.
+
+        When updating an entry, updates its position to the front of the LRU list.
+        If the entry is new and the cache is at capacity, removes the oldest entry
+        before the new entry is added.
+        """
+        node = self.map[query]
+        if node is not None:
+            # Key exists in cache, update the value
+            node.results = results
+            self.linked_list.move_to_front(node)
+        else:
+            # Key does not exist in cache
+            if self.size == self.MAX_SIZE:
+                # Remove the oldest entry from the linked list and lookup
+                self.lookup.pop(self.linked_list.tail.query, None)
+                self.linked_list.remove_from_tail()
+            else:
+                self.size += 1
+            # Add the new key and value
+            new_node = Node(query, results)
+            self.linked_list.append_to_front(new_node)
+            self.lookup[query] = new_node