Intro to Redis: Architecture, Patterns, and Scaling in Production

If you are building modern, high-performance distributed systems, caching isn’t just an optimization—it is a foundational requirement. At the center of this ecosystem sits Redis. Often misunderstood as “just a cache,” Redis is actually a highly versatile, in-memory data structure server.

Whether you are designing a real-time gaming leaderboard, protecting your APIs with rate limiters, or scaling a high-throughput microservice architecture, mastering Redis is a superpower. Let’s dive deep into how it works, the patterns that define it, and how to scale it in production.

1. What is Redis and Why Redis?
#

Redis (Remote Dictionary Server) is an open-source, in-memory, key-value data store. Unlike traditional relational databases (like PostgreSQL or MySQL) that write data to spinning disks or SSDs, Redis holds all of its data in RAM.

Why choose Redis?

Blistering Speed: Because accessing RAM is orders of magnitude faster than accessing a disk, Redis routinely delivers sub-millisecond response times, handling millions of operations per second.
Rich Data Structures: It doesn’t just store dumb strings. It natively understands complex data structures like Lists, Sets, Hashes, and Sorted Sets, allowing you to offload computational complexity from your application servers to the database.
Versatility: It operates effectively as a database, a cache, a message broker, and a streaming engine.

2. Redis Architecture: The Secret to Speed
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Understanding Redis requires understanding its somewhat counterintuitive architectural choices.

The Single-Threaded Event Loop
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The most surprising fact about Redis is that it uses a single thread to process commands. In an era of multi-core processors, why limit a database to one thread? Because memory access is so fast, the CPU is almost never the bottleneck—network I/O and memory bandwidth are. By using a single thread, Redis completely eliminates the overhead of context switching, thread locks, and race conditions. It handles massive concurrency using an I/O multiplexing model (like epoll), queuing up thousands of requests and executing them sequentially with ruthless efficiency.

Persistence Mechanisms
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Even though Redis is “in-memory,” it doesn’t mean your data vanishes when the server reboots. Redis offers robust persistence:

RDB (Redis Database): Takes point-in-time snapshots of your data and saves them to disk at specified intervals.
AOF (Append Only File): Logs every single write operation received by the server. If the server crashes, Redis replays this log to reconstruct the exact state.

3. Data Structures in Action
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The brilliance of Redis lies in its data structures. Instead of querying a relational database and sorting data in your application code, you push data into a structure that is already optimized for what you need to do.

A. Strings
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The most basic Redis type. A string can contain text, serialized objects (like JSON), or even binary data (up to 512MB).

Application Scenario: Session caching, HTML page caching, and rate-limiting/counters.
Example (Counters): Because Redis is single-threaded, commands like INCR (increment) are atomic. You can use it to count page views or API requests safely without race conditions.
```
SET api_requests:user1001 0
INCR api_requests:user1001  // Returns 1
```

B. Hashes
#

Hashes are maps between string fields and string values. Think of them as flat JSON objects or a row in a SQL database.

Application Scenario: Storing object data, like user profiles, where you frequently need to access or update individual fields rather than the whole object.

Example (User Profile):

HSET user:205 name "Alice" role "Admin" status "Active"
HGET user:205 role  // Returns "Admin"

C. Lists
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Lists are linked lists of strings. Because they are linked lists, inserting at the head or tail is incredibly fast — $O(1)$ complexity — but accessing elements by index in the middle is slower — $O(N)$.

Application Scenario: Message queues, activity streams, or “latest N items” (like a Twitter timeline).

Example (Activity Stream): Pushing a new activity to the front of a list and keeping only the latest 100.

LPUSH timeline:alice "tweet_id_99"
LTRIM timeline:alice 0 99 // Trims the list to only keep the newest 100

D. Sets
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Sets are unordered collections of unique strings. You can easily add, remove, and test for the existence of members in $O(1)$ time.

Application Scenario: Tracking unique items (e.g., unique IP addresses visiting a site), tagging systems, or finding relationships (intersections/unions).

Example (Mutual Friends):

SADD friends:alice "bob" "charlie" "david"
SADD friends:eve "charlie" "david" "frank"
SINTER friends:alice friends:eve // Returns "charlie", "david" (mutual friends)

E. Sorted Sets (ZSets)
#

Similar to Sets, but every element is associated with a floating-point number called a “score.” Elements are always kept ordered by this score.

Application Scenario: Leaderboards, priority queues, time-series data (using timestamps as the score).

Example (Gaming Leaderboard):

ZADD global_leaderboard 1500 "PlayerA"
ZADD global_leaderboard 2100 "PlayerB"
ZREVRANGE global_leaderboard 0 9 WITHSCORES // Gets top 10 players sorted highest to lowest

Examples
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1. The Rate Limiter (API Protection)
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When you have a popular API, you must protect your backend databases from being overwhelmed by too many requests from a single user or IP address.

The Data Structure: Strings (for Fixed Window) or Sorted Sets (for Sliding Window). Let’s look at the most robust method for exact accuracy: the Sliding Window Log using Sorted Sets (ZSET).

How it works: We use the timestamp of the request as both the score and the value in a Sorted Set. When a request comes in, we remove all timestamps older than our window, count what is left, and decide if the request is allowed.

Redis Commands (Simulating a limit of 3 requests per 60 seconds for User 123):

// 1. A request comes in at timestamp 1700000000. Add it to the set.
ZADD rate_limit:user123 1700000000 "1700000000"

// 2. Remove any requests that happened more than 60 seconds ago (score < 1699999940)
ZREMRANGEBYSCORE rate_limit:user123 -inf 1699999940

// 3. Count how many requests are in the current window
ZCARD rate_limit:user123

// 4. Set an expiry on the whole key so we don't leak memory for inactive users
EXPIRE rate_limit:user123 60

Architectural Note: In a real application, you would execute these four commands together inside a Redis Lua script so they run atomically, preventing race conditions between concurrent requests.

2. The Distributed Lock (Resource Synchronization)
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Imagine you have three instances of a worker service, and they all wake up at midnight to process the exact same daily billing report. If they all run it, you charge customers three times. You need a lock that spans across your distributed system.

The Data Structure: Strings.

How it works: You attempt to set a key. If the key already exists, someone else has the lock. If it doesn’t, you get the lock. Crucially, you must set an expiration time (Time-To-Live or TTL) so that if your worker crashes while holding the lock, the lock eventually releases itself.

Redis Commands:

// Attempt to acquire the lock. 
// NX = Only set if it does NOT exist. 
// PX 30000 = Expire in 30,000 milliseconds (30 seconds).
SET lock:billing_report "worker_node_1_random_uuid" NX PX 30000

If Redis returns OK, this worker has the lock. If it returns (nil), another worker has it, and this worker should back off.

Architectural Note: Why the random UUID? When a worker finishes, it needs to delete the lock. However, if the worker took too long and the lock auto-expired, another worker might have grabbed it. The worker must check if the value matches its own UUID before deleting it, ensuring it doesn’t accidentally delete someone else’s lock.

3. The Real-Time Leaderboard (Gaming or Analytics)
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Relational databases struggle with massive, real-time leaderboards. Running ORDER BY score DESC on a million rows every time a user checks their rank is a recipe for database failure.

The Data Structure: Sorted Sets (ZSET).

How it works: Sorted sets maintain data in memory already perfectly ordered by the score. Fetching the top 10, or finding a specific user’s rank, is an $O(\log(N))$ operation.

Redis Commands:

// Add players and their scores
ZADD global_leaderboard 1500 "PlayerA"
ZADD global_leaderboard 2100 "PlayerB"
ZADD global_leaderboard 800 "PlayerC"

// Player A wins a match and gains 50 points
ZINCRBY global_leaderboard 50 "PlayerA"

// Get the top 2 players (highest score first)
ZREVRANGE global_leaderboard 0 1 WITHSCORES

// Find Player C's exact rank (0-indexed, starting from highest score)
ZREVRANK global_leaderboard "PlayerC"

4. Geo-Spatial Queries (Ride-sharing or Proximity Search)
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If you are building an app that needs to find “drivers within a 5km radius of the user,” calculating the Haversine formula across a SQL database of coordinates is intensely slow.

The Data Structure: Geo Maps (which are actually Sorted Sets under the hood, utilizing a geohash algorithm to map 2D coordinates into a 1D score).

How it works: You add members with their longitude and latitude. Redis handles the complex math to index them and allows you to search by radius or bounding box.

Redis Commands:

// Add drivers to the map (Longitude, Latitude, Name)
GEOADD drivers -122.0289 37.3323 "Driver_Alice"
GEOADD drivers -121.8863 37.3382 "Driver_Bob" // Located in San Jose

// Find all drivers within 10 kilometers of a user's coordinate
GEOSEARCH drivers FROMLONLAT -121.8900 37.3300 BYRADIUS 10 km WITHDIST

4. Redis Atomicity, Lua Scripts, and Python
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Because Redis is single-threaded, individual commands are atomic. However, what if you need to run multiple commands together safely, ensuring no other client interrupts them?

You could use Redis Transactions (MULTI/EXEC), but Lua Scripts are the modern industry standard. When you send a Lua script to Redis, the entire script is executed atomically. This prevents race conditions and drastically reduces network latency by combining multiple operations into a single round-trip.

Python `redis-py` Lua Script Example: A Flawless Rate Limiter
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Here is how you execute an atomic rate limiter in Python using a Lua script. We use register_script so the script is compiled and cached on the Redis server, saving bandwidth.

import redis

# Connect to Redis
r = redis.Redis(host='localhost', port=6379, decode_responses=True)

# 1. Define the Lua Script
# Increments a key, sets a TTL if it's new, and returns 1 if allowed, 0 if blocked.
lua_script = """
local current = redis.call("INCR", KEYS[1])
if current == 1 then
    redis.call("EXPIRE", KEYS[1], ARGV[1])
end
if current > tonumber(ARGV[2]) then
    return 0
end
return 1
"""

# 2. Register the script with the Redis server
rate_limit_script = r.register_script(lua_script)

def is_allowed(user_id, window_seconds=60, max_requests=10):
    key = f"rate:{user_id}"
    # 3. Execute the script atomically
    return bool(rate_limit_script(keys=[key], args=[window_seconds, max_requests]))

# Usage
if is_allowed("user_999"):
    print("Request processed!")
else:
    print("HTTP 429: Too Many Requests")

5. Caching Patterns: When and Why
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Moving from how to write Redis commands to where to place Redis in your overall architecture is what separates good developers from great system designers.

When we talk about caching patterns, we are defining the relationship and the flow of data between three core components: your Application, the Cache (Redis), and the Primary Database (like PostgreSQL or MongoDB).

1. Cache-Aside (Lazy Loading)
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How it works (Read): App asks Cache. If miss, App asks Database. App saves to Cache. App returns data.
How it works (Write): App writes directly to Database. App then deletes (invalidates) the specific key in the Cache.
Reasoning: It is highly resilient. If Redis goes down, your application still works (it just hits the database directly and gets slower). It’s perfect for read-heavy workloads. By only caching what is actually requested (lazy loading), you don’t waste memory on unused data.
Example: Loading a user’s profile. You don’t need to load every user into Redis on startup; you only cache the profiles of users who are currently active.

2. Read-Through
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In this pattern, the application treats the cache as the main data store. The application code never talks to the database directly for reads.

How it works (Read): App asks Cache. If miss, the Cache itself (or a smart caching library bridging them) fetches from the Database, updates itself, and returns the data to the App.
Reasoning: It dramatically simplifies application code. Your app just says get(user_id) and doesn’t care where it comes from. It ensures the cache and database schemas are aligned.
Example: A Content Management System (CMS) like WordPress. The application asks for the article content. A data access layer handles checking Redis, fetching from MySQL if needed, and returning the result.

3. Write-Through
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This pattern prioritizes absolute data consistency over write speed.

How it works (Write): App writes to the Cache. The Cache immediately and synchronously writes to the Database. Only when both are successful does the operation return to the App.
Reasoning: You use this when you cannot tolerate stale data under any circumstances. Every read against the cache will always reflect the absolute latest state of the database. The tradeoff is that writes are slower because they incur the latency of two network hops (App -> Cache -> DB).
Example: Banking transactions or real-time inventory systems where selling an item that isn’t actually in stock is catastrophic.

4. Write-Behind (Write-Back)
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This is the most aggressive pattern for write performance, but it comes with the highest risk.

How it works (Write): App writes data to the Cache. The Cache immediately returns “Success” to the App. Asynchronously, in the background, the Cache batches those updates and flushes them to the Database later.
Reasoning: Extreme performance. Your application’s write speed is only limited by Redis’s RAM speed. It completely shields your slow database from massive spikes in write traffic. However, if the Redis server crashes before the background flush occurs, that data is permanently lost.
Example: Counting “Likes” on a viral social media post, capturing high-velocity IoT sensor data, or tracking video view progress. If you lose a few milliseconds of “Likes” during a crash, it’s not the end of the world.

5. Write-Around
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This pattern is used to optimize the cache’s memory space and prevent “cache pollution.”

How it works (Write): App writes directly to the Database, completely bypassing the Cache.
Reasoning: You use this when data is written once but rarely read back immediately. If you write massive log files or upload large images using Write-Through, you fill up your expensive Redis RAM with data nobody is looking at, kicking out important data. Write-Around ensures only data that is explicitly requested gets cached.
Example: Uploading an archival document, chat history backups, or generating monthly PDF reports.

Example scenarios in a CRM product
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1. Categorizing CRM Caching Requirements
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To design the right strategy, we must first segment the data based on its Read/Write ratio and Consistency requirements:

Static Metadata & Config: (e.g., Custom field definitions, dropdown values, UI layouts).
- Requirement: High Read, Very Low Write. Must be available globally.
- Pattern: Read-Through.
User Sessions & Permissions: (e.g., Auth tokens, RBAC roles).
- Requirement: Extremely High Read (checked on every API call). Needs fast expiration.
- Pattern: Distributed Session Store (Redis Hash).
Customer Entities: (e.g., Contact details, Lead info).
- Requirement: Read-heavy but requires “Read-Your-Own-Writes” consistency.
- Pattern: Cache-Aside with explicit invalidation.
Activity Streams & Interaction Logs: (e.g., “User A called Lead B”).
- Requirement: Write-heavy. Absolute real-time consistency is often less critical than ingestion speed.
- Pattern: Write-Behind (Write-Back).
Analytics & Pipeline Reports: (e.g., “Total sales forecast for Q3”).
- Requirement: Computationally expensive queries. Data changes frequently, but users tolerate “stale” data for a few minutes.
- Pattern: Timed Invalidation (TTL-based).

2. Solving Requirements with Specific Patterns
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Scenario A: High-Velocity Activity Logs (The Throughput Problem)
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In a large CRM, thousands of automated syncs (from email, Slack, or VoIP) hit the system simultaneously. Writing every single log to a relational database (SQL) immediately will cause lock contention.

Solution: Use Write-Behind. The application writes the activity to a Redis List or Stream. A background worker batches these and writes them to the database every 5 seconds. This flattens the spike and protects the DB.

Scenario B: Field Permissions & Metadata (The Consistency Problem)
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If an admin changes a user’s permission from “Editor” to “Viewer,” that change must be reflected immediately across all app instances to prevent unauthorized edits.

Solution: Use Cache-Aside with Invalidation. When the admin saves the change (Write-Around to DB), the application explicitly issues a DEL command to the Redis key. The next request will result in a cache miss, forcing a fresh pull of the new permissions.

Scenario C: Sales Dashboards (The Latency Problem)
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Calculating a “Sales Pipeline” involves joining multiple tables (Leads, Deals, Quotes, Users). Running this on every page load is too slow.

Solution: Pre-computation. Instead of calculating on demand, a scheduled job calculates the dashboard data every 10 minutes and stores the final JSON in Redis.

3. Advanced Considerations: The “Thundering Herd” in CRM
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In a CRM, when a major sales report expires in the cache, multiple managers might refresh their browser at the exact same second. If the cache is empty, all those requests hit the database simultaneously to re-calculate the same report.

Solution: Locking/Single-Flighting. When the first request sees a cache miss, it acquires a Redis Distributed Lock. Subsequent requests see the lock is held and simply wait for the first process to finish updating the cache, or they serve the “stale” version temporarily. This ensures the expensive database query only runs once.

6. Cache Eviction Policies
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If you buy a 2GB Redis server, what happens when you try to write 2.1GB of data? By default, Redis throws an Out of Memory (OOM) error and halts all writes.

If you are using Redis purely as a cache, you want it to delete old data to make room for new data. You configure this using Eviction Policies:

allkeys-lru (Least Recently Used): The most popular setting. Evicts the keys that haven’t been accessed in the longest amount of time.
allkeys-lfu (Least Frequently Used): Evicts keys that are rarely accessed overall, protecting viral content that might have a brief pause in traffic.
volatile-ttl: Only evicts keys that have an expiration set (TTL), prioritizing the deletion of keys that are closest to expiring anyway.

7. Scenario-Based Cost and Memory Calculations
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Because RAM is significantly more expensive than disk storage (averaging $15-$20 per GB on managed cloud providers), memory planning is critical. You must account for Redis Overhead—the memory Redis uses to store the key name, expiration timers, and internal pointers.

Scenario A: High-Volume Session Store (Hashes)

Data: 1,000,000 active user sessions.
Payload: 500 bytes of data per session.
Math: Raw Data (500 MB) + Hash Overhead (~90 MB) = 590 MB.
Cost: Easily fits on a 1GB instance. Cost: ~$15/month. Highly cost-effective.

Scenario B: Global Gaming Leaderboard (Sorted Sets)

Data: 10,000,000 players.
Payload: Short Player ID (20 bytes) + Score (8 bytes).
Math: Raw Data (280 MB) + Skip-List Overhead (~1.2 GB) = ~1.48 GB.
Takeaway: Sorted sets require massive pointer overhead to maintain their $O(\log(N))$ sorting speed. You would need a 2GB or 4GB server (~$35-$70/month).

Pro Tip: Always leave a 25% memory buffer on your server for background operations (like BGSAVE for disk snapshots) to prevent OOM crashes during peak loads.

8. Scaling Redis Memory
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Eventually, you will hit the limits of a single machine. You have two paths:

Vertical Scaling (Scaling Up without Downtime)
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If you need to move from a 4GB server to an 8GB server, you don’t have to take your system offline.

Provision the new 8GB server.
Configure it as a Replica of your existing 4GB Master server.
Wait for the data to seamlessly sync in the background.
Execute a rapid failover: Break the replication bond (REPLICAOF NO ONE) to make the 8GB server the new Master, and update your application’s DNS/Connection string to point to the new box. (Managed services like AWS ElastiCache do this for you with a click of a button).

Horizontal Scaling (Redis Cluster)
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When a single machine simply isn’t big enough (e.g., you need 500GB of RAM), you transition to Redis Cluster. Redis Cluster automatically shards (partitions) your data across multiple Redis nodes. It uses a concept called “Hash Slots” (there are exactly 16,384 of them). When you save a key, Redis hashes the key name to assign it to a slot, and distributes those slots evenly across your cluster of machines. This allows you to scale reads, writes, and memory capacity infinitely.

Author

Suriya Shanmugam

1. What is Redis and Why Redis?#

2. Redis Architecture: The Secret to Speed#

The Single-Threaded Event Loop#

Persistence Mechanisms#

3. Data Structures in Action#

A. Strings#

B. Hashes#

C. Lists#

D. Sets#

E. Sorted Sets (ZSets)#

Examples#

1. The Rate Limiter (API Protection)#

2. The Distributed Lock (Resource Synchronization)#

3. The Real-Time Leaderboard (Gaming or Analytics)#

4. Geo-Spatial Queries (Ride-sharing or Proximity Search)#

4. Redis Atomicity, Lua Scripts, and Python#

Python redis-py Lua Script Example: A Flawless Rate Limiter#

5. Caching Patterns: When and Why#

1. Cache-Aside (Lazy Loading)#

2. Read-Through#

3. Write-Through#

4. Write-Behind (Write-Back)#

5. Write-Around#

Example scenarios in a CRM product#

1. Categorizing CRM Caching Requirements#

2. Solving Requirements with Specific Patterns#

Scenario A: High-Velocity Activity Logs (The Throughput Problem)#

Scenario B: Field Permissions & Metadata (The Consistency Problem)#

Scenario C: Sales Dashboards (The Latency Problem)#

3. Advanced Considerations: The “Thundering Herd” in CRM#

6. Cache Eviction Policies#

7. Scenario-Based Cost and Memory Calculations#

8. Scaling Redis Memory#

Vertical Scaling (Scaling Up without Downtime)#

Horizontal Scaling (Redis Cluster)#

1. What is Redis and Why Redis?
#

2. Redis Architecture: The Secret to Speed
#

The Single-Threaded Event Loop
#

Persistence Mechanisms
#

3. Data Structures in Action
#

A. Strings
#

B. Hashes
#

C. Lists
#

D. Sets
#

E. Sorted Sets (ZSets)
#

Examples
#

1. The Rate Limiter (API Protection)
#

2. The Distributed Lock (Resource Synchronization)
#

3. The Real-Time Leaderboard (Gaming or Analytics)
#

4. Geo-Spatial Queries (Ride-sharing or Proximity Search)
#

4. Redis Atomicity, Lua Scripts, and Python
#

Python `redis-py` Lua Script Example: A Flawless Rate Limiter
#

5. Caching Patterns: When and Why
#

1. Cache-Aside (Lazy Loading)
#

2. Read-Through
#

3. Write-Through
#

4. Write-Behind (Write-Back)
#

5. Write-Around
#

Example scenarios in a CRM product
#

1. Categorizing CRM Caching Requirements
#

2. Solving Requirements with Specific Patterns
#

Scenario A: High-Velocity Activity Logs (The Throughput Problem)
#

Scenario B: Field Permissions & Metadata (The Consistency Problem)
#

Scenario C: Sales Dashboards (The Latency Problem)
#

3. Advanced Considerations: The “Thundering Herd” in CRM
#

6. Cache Eviction Policies
#

7. Scenario-Based Cost and Memory Calculations
#

8. Scaling Redis Memory
#

Vertical Scaling (Scaling Up without Downtime)
#

Horizontal Scaling (Redis Cluster)
#