System Design Concepts

Whether building a website, mobile app, or other software product, understanding system design is essential to architecting complex systems that scale. Here are some key system design concepts newcomers should know:Requirements Gathering Understand the full business requirements, user stories, use cases, and constraints that will guide the design. Develop comprehensive functional and nonfunctional requirements.

Architectural Patterns Study common software architectures like monolithic vs microservices, event-driven, pipeline, etc. Choose patterns that fit your needs.Modularity and Separation of Concerns Decompose the system into self-contained, loosely coupled components with clear responsibilities. Enforce separation of concerns.

Data Modeling Plan logical and physical data models. Choose appropriate storage systems, schemas, caching, and data processing.API Design Define APIs between components using REST, RPC, asynchronous messaging, and other paradigms. Enforce consistency and versioning.

Scaling and Performance Plan for scaling users, traffic, data volume, and other dimensions. Set performance metrics. Choose horizontally scaling architectures.Availability and Reliability Build in redundancy to meet uptime needs. Handle failures gracefully. Use health checks, monitoring, and resiliency patterns.

Security Incorporate encryption, access controls, and other security principles throughout the layers, based on the sensitivity of data.There are many more considerations like UI/UX design, DevOps, analytics, and others. But mastering these foundational concepts will provide strong footing to tackle complex systems and make wise design decisions.

The Need for Speed: A Guide to Caching

Latency is the bane of any software system, causing frustration for users and lost revenue. Caching is a crucial technique for optimizing performance by avoiding expensive computations or database queries and returning fast responses. Let’s explore common caching strategies.

Cache Application Data Store computed results or rendered templates to avoid recalculating for each request. Cache at the application layer using a local in-memory store like Memcached.

Implement a CDN A content delivery network (CDN) caches static assets like images, CSS, and JS files in distributed edge servers. Users fetch from nearby edge servers.

Cache Database Queries Query results can be cached to prevent hitting the database for each request. The cache is invalidated when data changes. This reduces load on the database.

Leverage Client-Side Caching Set cache headers so browsers cache static resources locally. Configured properly, browsers won’t re-download unchanged assets on repeat visits.

Distributed Cache A distributed cache like Redis provides a shared cache layer accessible by multiple application servers. Useful for shared query results or application data.

Smart Cache Invalidation Carefully invalidate cache entries when source data is modified. Granularly clear only affected cache portions vs the entire cache.

Caching Layers Implement multiple caching layers for faster misses – a local in-memory cache, then distributed cache, finally the data store.There are many other caching strategies like browser localStorage or service worker caching for offline use. Caching is essential for delivering low-latency experiences, but beware of stale, inconsistent data. Balance freshness with speed.

The Case of LRU Cache

The LRU cache is implemented using an OrderedDict which maintains insertion order. When capacity is reached, the least recently inserted item is popped off.get() and put() move accessed items to the end to reflect most recent usage.This allows basic get and put operations in O(1) time. The tradeoff is cache misses are slower as the entire cache must be searched.

from collections import OrderedDict

class LRUCache:

    def __init__(self, capacity):
        self.cache = OrderedDict()
        self.capacity = capacity

    def get(self, key):
        if key not in self.cache:
            return -1
        else:
            self.cache.move_to_end(key)
            return self.cache[key]

    def put(self, key, value):
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.capacity:
            self.cache.popitem(last=False)

cache = LRUCache(3)
cache.put(1, 1)
cache.put(2, 2)
cache.put(3, 3)
print(cache.get(2)) # 2
cache.put(4, 4)
print(cache.get(1)) # -1

Building Google Search

Building a Search Engine like Google

Google processes over 40,000 search queries every second! Their search serves as an iconic example of a scalable search engine. Here is a simplified overview of how you might implement a basic Google-style search:

Crawling the Web The first step is crawling the internet to index pages. Start with a seed list of URLs then follow links recursively. Store pages in a document store like Elasticsearch.

PageRank Analysis
Analyze link structures to calculate a PageRank for each page based on number and quality of inbound links. This approximates importance.

Indexing Clean pages and tokenize into words. Create an inverted index mapping words to documents they appear in for fast lookups.

Serving Queries For user search queries, look up relevant pages from inverted index. Rank by PageRank, word proximity, and other relevance signals.

Results Page Return ranked results along with titles, snippets, links. Allow pagination for more results.

Query Understanding Use natural language processing to interpret query intent and expand or rewrite queries to improve results.

Index Updates
Periodically recrawl and update the index to keep it fresh. Use differential updates to avoid reindexing everything.

There are many complex subsystems required like distributed crawling, real-time indexing, spell correction, auto-complete, etc. But this is the high-level architecture powering Google’s famously fast and relevant search capabilities.