Social Media to Market Makers

A Hidden System Powering Our Economy

Franz Kafka based image generated by AI

Ever pause to consider the overwhelming challenge social media platforms face? Every second, millions of users create content while billions simultaneously consume it. The fundamental problem is deceptively complex: how do you deliver, prioritize, and organize the vast ocean of information that each user might be interested in? How does your Instagram feed know to show you your best friend's engagement announcement before a casual acquaintance's lunch photo?

This seemingly impossible task is elegantly solved through a pattern known as a Publisher/Subscriber (pub/sub) system. In this architecture, content creators (publishers) send messages to a central broker that organizes them into topics. Users (subscribers) receive only the content from topics they've expressed interest in, with sophisticated algorithms determining delivery order based on relevance, recency, and relationship factors. Systems like Apache Kafka have refined this model to handle millions of messages per second, ensuring everyone sees what matters most to them without being overwhelmed.

What is particularly fascinating is this architectural concept powering your social media experience also operates in an entirely unrelated domain: financial markets. Market makers are business entities providing liquidity on stock exchanges. They operate using systems structured remarkably like the pub/sub model handling your Instagram feed, revealing how fundamental design patterns repeat across our digital economy.

The Social Media Feed

Behind the smooth scrolling experience of your social media feed is a sophisticated message queue system. Every post, image, or video is essentially a message from a publisher (the content creator) waiting to be delivered to appropriate subscribers (followers or interested users).

These systems organize messages through topics—sometimes explicitly through hashtags, but increasingly through AI-powered content analysis that automatically categorizes content based on sentiment, subject matter, and context. When you follow an account or express interest in a topic, you're effectively subscribing to a particular message stream.

This is where technologies like Apache Kafka come in. Originally developed at LinkedIn by Jay Kreps—who named it after the writer Franz Kafka (though the connection ends there)—these pub/sub architectures process massive event streams by prioritizing, weighting, and routing messages. Your social feed isn't chronological because these systems apply complex algorithms that determine content priority based on numerous factors such as:

• Your past engagement patterns (what you've liked, commented on, or shared)

• Content performance across the network (how others are responding)

• Recency and relevance signals (timeliness, personal connections)

• Paid promotion (advertisers bidding for attention)

When Instagram decides to show you a particular friend's vacation photo first, it's because a pub/sub system calculated this message has higher priority for you than the hundreds of other potential messages waiting in the queue.

The Metadata Advantage

What makes these systems truly powerful isn't just the messages themselves, but the rich metadata attached to them. Every social media post carries additional attributes beyond the visible content: timestamp, creator identity, engagement metrics, device information, and more.

Consider an advertisement in your feed. When advertisers submit promotional content, they include extensive metadata that influences display frequency and targeting. Fields like audience demographics, bid amounts, campaign objectives, and performance thresholds all affect how the message is weighted in the system.

This creates fascinating engineering challenges. Systems optimized for one metric (such as user engagement) can produce unintended consequences in others (like filter bubbles or misinformation spread). The perfect pub/sub system would deliver exactly the right message to each user at the right time—but perfection remains elusive as engineers balance competing priorities like relevance, diversity, timeliness, and system performance.

Market Makers as the Wall Street Equivalent

Now let's cross domains to financial markets, where market makers perform a critical function. These entities (typically large financial institutions) provide liquidity by standing ready to buy and sell securities at publicly quoted prices. Their fundamental job is to maintain orderly markets by narrowing the bid-ask spread—the difference between the highest price a buyer is willing to pay and the lowest price a seller is willing to accept.

Market makers manage an order book of pending buy and sell orders, much like a message queue. Their systems must efficiently match buyers with sellers, ensuring timely execution while maintaining their own risk parameters.

The parallels to pub/sub architecture are striking:

• Buy/sell orders function as messages

• Different order types (e.g., market, limit, stop) operate like topics

• Traders act as both publishers (when submitting orders) and subscribers (when receiving fills)

• The market maker itself serves as the central broker (like Kafka), routing and matching messages

Just as social platforms determine which content appears first in your feed, market maker systems determine which orders get matched and executed, with sophisticated algorithms weighing factors like price, time priority, and order type.

Parallel Behaviors

Both domains face remarkably similar technical challenges. Latency is measured in milliseconds—whether delivering breaking news or events to your phone or executing a time-sensitive trade. Message volume can spike dramatically during viral events or market volatility, requiring systems that scale elastically. Reliability is non-negotiable, as downtime means either frustrated users or missed trading opportunities.

Perhaps most interesting is how information asymmetry manifests in both systems. In pub/sub architectures, not all subscribers have equal access to message streams. Similarly, in markets, participants have different levels of information access:

• Message priority differentiation parallels how certain order types receive execution preference

• Subscription scope differences mirror how institutional investors access premium market data feeds

• Historical log analysis capabilities compare to how sophisticated traders analyze market patterns

• Processing speed advantages reflect how co-location services provide lower-latency access (Social media advertisements can and will have similar latency thresholds)

The Inescapable Process

The invisible infrastructure shaping both your digital social experience and global financial markets shares remarkable design patterns. By recognizing these parallels between pub/sub systems like Kafka and market making operations, we gain insight into the fundamental architectures powering our networked world.

These systems will continue to evolve as engineers refine their approaches to message delivery, processing, and prioritization. But the core pattern—publishers, subscribers, and the brokers connecting them—remains a powerful lens for understanding how information flows through our increasingly complex digital economy.