“𝐇𝐨𝐰 𝐆𝐨𝐨𝐠𝐥𝐞 𝐬𝐡𝐨𝐰𝐬 𝐲𝐨𝐮 𝐬𝐞𝐚𝐫𝐜𝐡 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐢𝐧 𝟎.𝟑 𝐬𝐞𝐜𝐨𝐧𝐝𝐬 (𝐭𝐡𝐞𝐲’𝐫𝐞 𝐜𝐡𝐞𝐚𝐭𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐭𝐨𝐨)”

𝐇𝐦𝐦 𝐈 𝐭𝐡𝐢𝐧𝐤 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭 𝐜𝐚𝐧𝐧𝐨𝐭 𝐛𝐞 𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐝, 𝐲𝐨𝐮 𝐨𝐧𝐥𝐲 𝐥𝐨𝐨𝐤 𝐮𝐩 𝐨𝐧 𝐠𝐨𝐨𝐠𝐥𝐞 𝐬𝐞𝐞 𝐰𝐡𝐲?

You type “𝒃𝒆𝒔𝒕 𝒋𝒐𝒍𝒍𝒐𝒇 𝒓𝒊𝒄𝒆 𝒊𝒏 𝑳𝒂𝒈𝒐𝒔” into Google. Three hundred milliseconds later, you have ten perfect results. In that time, Google supposedly searched through hundreds of billions of web pages, ranked them by relevance, personalized results based on your location and history, and sent everything back to your phone.

Except they didn’t. That’s physically impossible. Here’s what actually happened, and why it changes everything about how you should build fast applications.

𝐓𝐡𝐞 𝐢𝐥𝐥𝐮𝐬𝐢𝐨𝐧 𝐨𝐟 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐬𝐞𝐚𝐫𝐜𝐡

When you press search, you’re not actually searching the internet. You’re searching Google’s pre-computed index of the internet, which is a completely different thing.

Imagine you have a library with one million books and someone asks you to find every book that mentions “jollof rice.” If you have to open every single book and scan every page, you’d need days or weeks. But what if you spent months beforehand creating an index, a massive book that lists every word that appears in your library and exactly which books and pages contain that word?

Now when someone asks for “jollof rice,” you just open your index book to the “jollof” section, see the list of books and page numbers, do the same for “rice,” find the books that appear in both lists, and hand them the answer in seconds. You did the hard work before anyone asked the question.

That’s exactly what Google does, just at planetary scale.

𝐖𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐡𝐚𝐩𝐩𝐞𝐧𝐬 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮 𝐬𝐞𝐚𝐫𝐜𝐡

Let’s break down what happens in those three hundred milliseconds:

𝐒𝐭𝐞𝐩 𝟏: Your search query hits Google’s Front End servers (GFE). These are globally distributed servers that sit close to users. If you’re in Lagos, you’re hitting a GFE somewhere in West Africa, not one in California.

𝐒𝐭𝐞𝐩 2: The query goes through spelling correction. Google’s algorithms check if you meant something else and might auto-correct obvious typos. This runs on Google’s internal infrastructure called Borg (which later inspired Kubernetes).

𝐒𝐭𝐞𝐩 3: Your corrected query gets sent to Google’s index shards. The index isn’t one huge database. It’s split into thousands of shards (pieces) distributed across data centers worldwide. Each shard holds a portion of the internet’s inverted index plus the actual document data.

𝐒𝐭𝐞𝐩 4: Each relevant shard quickly fetches the top one thousand possible documents that match your query. This is fast because the index is already sorted and optimized for exactly this type of lookup.

𝐒𝐭𝐞𝐩 5: All those results get sent to Google’s ranking system, which uses over two hundred signals to sort them. Your location, past search history, how recently the page was updated, how many other sites link to it, whether it’s mobile-friendly, page load speed, and dozens of other factors all get calculated in milliseconds.

𝐒𝐭𝐞𝐩 6: The top ten results get formatted and sent back to your browser. The whole process from start to finish takes under half a second.

The critical insight: Google isn’t searching billions of pages in real-time. They’re looking up pre-computed results in an index that was built ahead of time and is constantly being updated in the background.

𝐓𝐡𝐞 𝐢𝐧𝐯𝐞𝐫𝐭𝐞𝐝 𝐢𝐧𝐝𝐞𝐱: 𝐆𝐨𝐨𝐠𝐥𝐞’𝐬 𝐬𝐞𝐜𝐫𝐞𝐭 𝐰𝐞𝐚𝐩𝐨𝐧

An inverted index is one of the most powerful data structures in computer science. Instead of storing documents and their contents, you store words and lists of which documents contain them.

Normal storage: Document 1 contains “I love jollof rice”
Normal storage: Document 2 contains “Best jollof in Lagos”

Inverted index:

• “𝒋𝒐𝒍𝒍𝒐𝒇” 𝒂𝒑𝒑𝒆𝒂𝒓𝒔 𝒊𝒏: 𝑫𝒐𝒄𝒖𝒎𝒆𝒏𝒕 1, 𝑫𝒐𝒄𝒖𝒎𝒆𝒏𝒕 2
• “𝒓𝒊𝒄𝒆” 𝒂𝒑𝒑𝒆𝒂𝒓𝒔 𝒊𝒏: 𝑫𝒐𝒄𝒖𝒎𝒆𝒏𝒕 1
• “𝑳𝒂𝒈𝒐𝒔” 𝒂𝒑𝒑𝒆𝒂𝒓𝒔 𝒊𝒏: 𝑫𝒐𝒄𝒖𝒎𝒆𝒏𝒕 2

Now when someone searches for “jollof Lagos,” you instantly know Document 2 is the only one that contains both words. You didn’t scan any documents. You just looked up two words in your index.

Google’s inverted index is incomprehensibly massive. It tracks hundreds of billions of web pages and trillions of words. But because it’s pre-computed and sharded across thousands of servers, lookups are extremely fast.

𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐢𝐧𝐝𝐞𝐱𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐆𝐨𝐨𝐠𝐥𝐞 𝐬𝐭𝐚𝐲𝐬 𝐜𝐮𝐫𝐫𝐞𝐧𝐭

The internet changes constantly. New pages appear, old pages update, sites go offline. If Google had to rebuild their entire index from scratch every time something changed, they’d never keep up.

Instead, they use incremental indexing. Google’s crawlers constantly browse the web, looking for new or changed content. When they find something new, they update just the relevant portions of the index. Popular news sites get crawled every few minutes. Smaller sites might get crawled every few days or weeks.

This means Google’s index is never perfectly up-to-date, but it’s current enough that users don’t notice. A breaking news article might appear in search results within minutes of being published. A small blog post might take hours or days.

𝐖𝐡𝐲 𝟗𝟗% 𝐨𝐟 𝐚𝐩𝐩𝐬 𝐚𝐫𝐞 𝐬𝐥𝐨𝐰

Here’s the brutal truth: most applications are slow because they do full database scans or complex calculations every single time a user makes a request. Google’s approach is the opposite: pre-compute everything you possibly can.

Let’s say you built a food delivery app. A user opens the app and wants to see restaurants near them. The slow approach is:

1. User opens app
2. App gets user’s current location
3. App queries database: “Find all restaurants, calculate distance from user, filter by those within five kilometers, sort by rating”
4. Database scans through thousands of restaurants doing complex distance calculations
5. User waits three seconds staring at a loading spinner

The Google approach would be:

1. Every few minutes, pre-compute which restaurants are near every major neighborhood in your city
2. Store these pre-computed lists in fast memory (like Redis)
3. User opens app
4. App gets user’s location
5. App looks up the pre-computed list for that neighborhood
6. Results appear in two hundred milliseconds

You moved the expensive work (calculating distances, filtering, sorting) from request time to background time. The user doesn’t wait for computation to happen. They just see the pre-computed result.

𝐑𝐞𝐚𝐥 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐰𝐡𝐞𝐫𝐞 𝐩𝐫𝐞-𝐜𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬

𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬: YouTube doesn’t calculate “what should we recommend to this user” every time you open the app. Their algorithms run constantly in the background, updating recommendation lists for hundreds of millions of users. When you open YouTube, you’re seeing a list that was pre-computed minutes or hours ago.

𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 𝐟𝐞𝐞𝐝: Instagram doesn’t generate your feed in real-time when you pull to refresh. They pre-assemble your feed in the background based on who you follow and what you engage with. Refreshing just fetches the pre-built feed.

𝐄-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 𝐬𝐞𝐚𝐫𝐜𝐡: Amazon doesn’t scan their entire product database every time you search. They maintain inverted indexes of products by keywords, categories, and attributes. Your search looks up the index, not the raw product data.

𝐁𝐚𝐧𝐤𝐢𝐧𝐠 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬: When you check your account balance and transaction history, the bank isn’t querying their main transaction database in real-time. That data was already aggregated and cached specifically for dashboard views. The main database handles new transactions, but reporting views are pre-computed.

𝐓𝐡𝐞 𝐭𝐫𝐚𝐝𝐞𝐨𝐟𝐟: 𝐟𝐫𝐞𝐬𝐡𝐧𝐞𝐬𝐬 𝐯𝐬 𝐬𝐩𝐞𝐞𝐝

Pre-computation has one major tradeoff: the data might be slightly stale. If Google’s index was updated ten minutes ago and a website changed five minutes ago, Google’s search results won’t reflect that change yet.

For most use cases, this is acceptable. Users don’t expect real-time perfection. They expect fast results that are reasonably current. The trick is deciding what level of staleness your application can tolerate.

For a social media feed, showing posts from five minutes ago is fine. For a stock trading app, showing prices from five minutes ago would be disastrous. You need real-time data there, which means you can’t fully pre-compute.

But even in scenarios requiring real-time data, you can pre-compute parts of the work. A stock trading app might pre-compute which stocks a user is watching and just update the prices in real-time, rather than recalculating the entire watchlist for every price update.

𝐇𝐨𝐰 𝐭𝐨 𝐭𝐡𝐢𝐧𝐤 𝐥𝐢𝐤𝐞 𝐆𝐨𝐨𝐠𝐥𝐞 𝐰𝐡𝐞𝐧 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐚𝐩𝐩

Ask yourself these questions:

𝐖𝐡𝐚𝐭 𝐚𝐦 𝐈 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐧𝐠 𝐫𝐞𝐩𝐞𝐚𝐭𝐞𝐝𝐥𝐲 𝐭𝐡𝐚𝐭 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐜𝐡𝐚𝐧𝐠𝐞 𝐨𝐟𝐭𝐞𝐧? If you’re showing the same product catalog to every user, calculate it once and cache it. Don’t rebuild it for every request.

𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐈 𝐩𝐫𝐞𝐝𝐢𝐜𝐭 𝐮𝐬𝐞𝐫𝐬 𝐰𝐢𝐥𝐥 𝐚𝐬𝐤 𝐟𝐨𝐫? If most users search for similar things, pre-compute those popular queries. Google doesn’t pre-compute every possible search, but they definitely have fast paths for common ones.

𝐖𝐡𝐚𝐭 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐜𝐚𝐧 𝐡𝐚𝐩𝐩𝐞𝐧 𝐢𝐧 𝐭𝐡𝐞 𝐛𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝?Analytics, reports, recommendations, leaderboards, these don’t need to be real-time. Calculate them every few minutes or hours and show the pre-computed result.

𝐂𝐚𝐧 𝐈 𝐛𝐫𝐞𝐚𝐤 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧𝐭𝐨 𝐬𝐦𝐚𝐥𝐥𝐞𝐫 𝐩𝐢𝐞𝐜𝐞𝐬?Instead of one massive calculation at request time, can you do small incremental updates constantly in the background?

𝐓𝐡𝐞 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐰𝐞’𝐯𝐞 𝐥𝐞𝐚𝐫𝐧𝐞𝐝

Day 1 taught us to move data closer to users (Netflix’s Open Connect). Day 2 showed us efficiency through simplicity (WhatsApp’s Erlang). Day 3 explained caching frequently accessed data. Day 4 covered distributing work across servers. Now Day 5 reveals the ultimate optimization: don’t do the work when users ask, do it before they ask.

All these principles work together. Google’s search results are cached, served from nearby data centers, distributed across thousands of servers, and most importantly, pre-computed. That’s why it feels instant.

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The practical takeaway

Stop making users wait for expensive operations. Move that work to background jobs. Pre-compute what you can predict. Cache what you can’t predict but gets requested frequently. Update in the background at regular intervals.

Your app doesn’t need to be as complex as Google to benefit from this thinking. Even simple pre-computation can transform a three-second load time into a three-hundred-millisecond load time.

Tomorrow (Day 6): We’re diving into why your messages on WhatsApp get delivered even when the recipient’s phone is off. The secret is message queues, the invisible infrastructure that makes asynchronous communication possible.

JOIN THE CLASS AND SEE HOW GOOGLE INVERTED INDEX APPROACH COULD MAKE YOUR APP FASTER AND MORE RELIABLE? {https://ssic.ng}

Type “Keep going” if you’re ready to think like Google and stop making users wait for calculations that could’ve been done hours ago.

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