How LinkedIn Accidentally Created the Perfect Architecture for AI Agents (The Apache Kafka and Pinot Story)

TL;DR: LinkedIn created “Who viewed your profile” → Spawned Apache Kafka and Pinot → Accidentally solved AI agent problems 10 years before they existed. Key insight: Your database isn’t ready for an AI agent running 100 parallel queries instead of one.

Introduction: From Simple Counter to Data Revolution

Imagine: You log into LinkedIn and see “23 people viewed your profile.” Simple number, right?

Wrong.

Behind that number lies an architecture that:

Processes 10,000 requests per second
Responds in 100 milliseconds
Spawned two open-source giants (Apache Kafka and Pinot)
Accidentally solved AI agent problems 10 years before they appeared

This feature’s story isn’t just another Silicon Valley tech tale. It’s a blueprint for a future where your primary users aren’t humans, but autonomous AI agents.

Let’s break down 4 key lessons.

Lesson 1: One Feature → Two Open-Source Giants

LinkedIn’s Problem (2010)

In 2010, LinkedIn was a “resume graveyard.” People uploaded profiles and… that was it. No activity, no engagement.

The team launched an experiment: “Who Viewed Your Profile?”

Result? 💥 Explosive growth in activity.

But a technical problem emerged:

1 billion users want to know who viewed them
10,000 requests per second at peak hours
Latency must be < 100ms (or the app “lags”)
Data must be fresh (not yesterday’s)

Birth of Apache Kafka (2010)

Existing message queue systems couldn’t handle it. LinkedIn created Kafka — a distributed streaming platform.

Kafka’s Key Principles:

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1. Events as first-class citizens
2. Horizontal scaling
3. Built-in fault-tolerance
4. Throughput > 1M events/sec

Every click, view, like — it’s an event in Kafka. The system became LinkedIn’s “central nervous system.”

Birth of Apache Pinot (2013)

Kafka collected events, but how to instantly analyze them?

Traditional OLAP systems:

❌ Too slow (seconds, not milliseconds)
❌ Can’t handle concurrency (1000+ parallel queries)
❌ Batch-oriented (data gets stale)

LinkedIn created Pinot — a real-time OLAP database.

Pinot Architecture:

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Ingestion:
  - Real-time: directly from Kafka
  - Batch: historical data

Indexing:
  - Inverted Index: text search
  - Range Index: numeric ranges
  - JSON Index: nested structures
  - Vector Index: semantic search (new!)

Query:
  - Latency: < 100ms at 99th percentile
  - Concurrency: 10K+ QPS
  - Freshness: seconds from event to query

Result: One user-facing feature spawned two technologies now used by Uber, Stripe, Walmart, and dozens of other companies.

Lesson 2: Best Analytics Are Customer-Facing

Two Worlds of Analytics

World 1: Internal Analytics (for managers)

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# Typical scenario
analyst.run_query("SELECT revenue FROM sales WHERE date = yesterday")
analyst.go_get_coffee()  # Query runs for 30 seconds
analyst.view_dashboard()  # T+1 data (yesterday's)

World 2: User-Facing Analytics (for customers)

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# LinkedIn scenario
user.clicks("Who viewed my profile")
# Expectation: < 100ms
# Freshness: real-time
# Concurrency: 1M+ users simultaneously

The Trinity Challenge of User-Facing Analytics

1. Data Freshness

Internal: “Yesterday’s data? Fine.”
User-facing: “Someone viewed my profile? I want to know NOW.”

2. Query Latency

Internal: “30 seconds? I’ll get coffee.”
User-facing: “100ms or the user leaves.”

3. Concurrency

Internal: 10-100 analysts
User-facing: 1M+ users simultaneously

Why This Is Revolutionary

“Data’s true value is revealed when you return it to customers, not lock it in manager dashboards.”

Examples of user-facing analytics today:

Spotify Wrapped: Your music statistics
Strava: Real-time workout analysis
Uber: “Your driver arrives in 3 minutes”
Trading apps: Real-time quotes and portfolio

Lesson 3: AI Agents Are Machines, Not Humans (And Your DB Isn’t Ready)

Human vs AI Agent: Query Patterns

Human:

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-- One query, wait for response
SELECT * FROM users WHERE country = 'USA'
-- Think...
-- Another query
SELECT avg(age) FROM users WHERE country = 'USA'

AI Agent:

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# Task: "Find anomalies in data"
# Agent generates 20 queries IN PARALLEL:

queries = [
    "SELECT * FROM posts WHERE likes > 10000 AND comments < 10",
    "SELECT user_id, follower_growth FROM users WHERE growth_rate > 1000%",
    "SELECT content FROM posts WHERE created_at - user_created_at < 1_day",
    # ... 17 more queries
]

# Execute all simultaneously
results = parallel_execute(queries)
# Analyze correlations
find_patterns(results)

Real Example from Demo

In one demonstration, an AI agent was given the task: “Find suspicious accounts in social network”

The agent independently:

Generated 15-20 SQL queries
Looked for patterns:
- “Posts with 10K+ likes but < 10 comments”
- “Follower growth > 1000% per day”
- “Accounts created yesterday with 100K followers”
Executed all queries in parallel
Found correlations between results

What This Means for Your Architecture

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Traditional DB:
  Optimized for: Sequential queries from humans
  Concurrency: 10-100 users
  Pattern: Query → Wait → Query

AI Agent Requirements:
  Optimized for: Parallel burst queries
  Concurrency: 1000+ queries from ONE agent
  Pattern: 100 queries simultaneously → Analyze

Solution: Apache Pinot
  - Designed for 10K+ QPS
  - Sub-100ms latency at 99%
  - Real-time freshness

Key insight: If your DB struggles with 10 analysts, imagine when 100 AI agents start firing 100 queries each.

Lesson 4: Future = Vector Search + Real-time Filters

Why Pure Vector Search Isn’t Enough

Typical vector search:

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# Find documents similar in meaning to "authentication failure"
vector_db.search("authentication failure", k=10)

Real business query:

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# Find similar to "authentication failure", BUT:
# - Only from last hour
# - Only for Enterprise customers
# - Only in EU region
# - Sort by severity

vector_db.search(
    query="authentication failure",
    filters={
        "timestamp": "> now() - 1h",
        "plan": "Enterprise",
        "region": "EU"
    },
    order_by="severity DESC"
)

Problem with Vector DBs

Pure vector DBs (Pinecone, Weaviate) struggle with hybrid queries:

First vector search (slow on large volume)
Then filtering (inefficient)
Performance degrades with more filters

Pinot’s Solution: Hybrid Architecture

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Apache Pinot Indexes:
  Inverted Index: Text search
  Range Index: Numeric ranges
  JSON Index: Nested structures
  Timestamp Index: Time-series data
  Vector Index: Semantic search (NEW!)

Magic: ALL indexes work TOGETHER in one query

Example hybrid query in Pinot:

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SELECT
  log_message,
  COSINE_DISTANCE(embedding, [0.1, 0.2, ...]) as similarity
FROM logs
WHERE
  timestamp > NOW() - INTERVAL '1' HOUR
  AND customer_tier = 'Enterprise'
  AND region = 'EU'
  AND TEXT_MATCH(log_message, 'error OR failure')
ORDER BY similarity ASC
LIMIT 100

Result:

⚡ 10-100x faster than pure vector DBs on hybrid queries
✅ One engine for all search types
🔄 Real-time ingestion from Kafka

Practical Takeaways: What to Do Right Now

If You’re Building a User-Facing Product:

Rethink analytics
- Not “dashboards for managers”
- But “insights for users”

Check real-time readiness

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# Your checklist:
□ Data freshness < 1 second?
□ Query latency < 100ms?
□ Concurrency > 1000 QPS?

If any "no" → explore Kafka + Pinot

If You’re Implementing AI Agents:

Test the load

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# Simulate AI agent
for _ in range(100):
    thread.start(run_random_query)
# Did your DB survive?

Prepare architecture
- Event streaming (Kafka) for data collection
- Real-time OLAP (Pinot) for analytics
- Hybrid indexes for vector + structured search

If You’re a Data Engineer:

Study LinkedIn’s stack
- Apache Kafka — already industry standard
- Apache Pinot — gaining momentum

Try in sandbox

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# Quick start with Docker
docker run -p 9000:9000 apachepinot/pinot:latest \
  QuickStart -type hybrid

Conclusion: History Repeats Itself

2010: LinkedIn created user-facing analytics → Spawned Kafka and Pinot

2025: AI agents become new “users” → Require the same architecture

LinkedIn’s story teaches us the main thing:

Technologies created to solve real user problems outlive their creators and find applications in areas no one could dream of.

The “Who viewed your profile” feature was created to increase engagement.

But it accidentally solved problems that would appear 15 years later — when AI agents start “viewing” our data at 10,000 requests per second.

The question isn’t whether your infrastructure is ready for AI agents.

The question is whether you’ll be ready before your competitors launch theirs.

Useful Links

Technologies

Use Cases

For Developers

Have experience with Kafka or Pinot? Implementing AI agents? Share in comments or reach me on Telegram!

How LinkedIn Accidentally Created the Perfect Architecture for AI Agents (The Apache Kafka and Pinot Story)#

Introduction: From Simple Counter to Data Revolution#

Lesson 1: One Feature → Two Open-Source Giants#

LinkedIn’s Problem (2010)#

Birth of Apache Kafka (2010)#

Birth of Apache Pinot (2013)#

Lesson 2: Best Analytics Are Customer-Facing#

Two Worlds of Analytics#

The Trinity Challenge of User-Facing Analytics#

Why This Is Revolutionary#

Lesson 3: AI Agents Are Machines, Not Humans (And Your DB Isn’t Ready)#

Human vs AI Agent: Query Patterns#

Real Example from Demo#

What This Means for Your Architecture#

Lesson 4: Future = Vector Search + Real-time Filters#

Why Pure Vector Search Isn’t Enough#

Problem with Vector DBs#

Pinot’s Solution: Hybrid Architecture#

Practical Takeaways: What to Do Right Now#

If You’re Building a User-Facing Product:#

If You’re Implementing AI Agents:#

If You’re a Data Engineer:#

Conclusion: History Repeats Itself#

Useful Links#

Technologies#

Use Cases#

For Developers#