Java Spring AWS

REDIS is a single threaded and it uses io multiplexing internally to achive the speed, pls eloborate this concept ?

Redis is a single-threaded in-memory data store, meaning it uses one thread to execute commands. Despite being single-threaded, Redis achieves high performance due to its efficient architecture, particularly its use of I/O multiplexing. Let’s break this down:

1. Redis as Single-Threaded

Redis uses a single main thread for executing commands. All read/write operations are handled sequentially in this thread.
Being single-threaded eliminates the need for locks or context switching between threads, which simplifies its design and avoids overhead caused by thread management.

2. Challenges in Handling Concurrent Connections

Although Redis is single-threaded, it can handle thousands of concurrent connections efficiently. This is achieved through I/O multiplexing.

3. I/O Multiplexing in Redis

I/O multiplexing allows a single thread to manage multiple I/O streams (sockets) simultaneously. Redis uses this mechanism to handle multiple client connections while remaining single-threaded.

How it works:

The operating system provides mechanisms (like select, poll, or epoll on Linux) to monitor multiple file descriptors (like network sockets).
Redis uses these mechanisms to check which sockets are ready for reading or writing without blocking the thread.
When a socket is ready:
- If there is incoming data, Redis reads and processes the request.
- If the socket is ready for writing, Redis sends the pending response back to the client.

Key Steps:

Monitoring: Redis continuously monitors multiple client connections using I/O multiplexing.
Non-blocking: Instead of waiting for one connection to complete, Redis processes connections as they become ready.
Efficient Processing: The ready connections are processed sequentially in the main thread.

Underlying Library:

Redis uses libevent or libuv, which are libraries implementing I/O multiplexing, depending on the system configuration.

4. Why is Redis Fast with This Approach?

In-memory Operations: Redis stores all its data in memory, making data access very fast.
Efficient Event Loop: The single-threaded event loop avoids context switching and thread synchronization overhead.
Optimized Execution: Redis commands are simple and executed quickly. The cost of multiplexing is minimal compared to the speed of command execution.
No Disk I/O in Main Path: Redis defers disk I/O (for persistence) to a separate thread or process, ensuring the main thread remains unblocked.

5. Use Case for Single Threaded Design

Redis’s single-threaded model is well-suited for most use cases where network latency and CPU overhead are the bottlenecks, not the processing power of multiple cores.

Limitations

Since Redis runs on a single core, its performance might degrade under CPU-intensive workloads. This can be mitigated using techniques like sharding, where multiple Redis instances distribute the workload across different cores.

Summary

Redis uses I/O multiplexing to handle thousands of concurrent client connections efficiently with a single thread. This mechanism allows Redis to monitor multiple connections and process requests as they become ready without blocking, achieving high performance and scalability while maintaining a simple design.

assuming i am a new bie to this concept, your explaination is at very high leve. Sorry i didn't understand the internalls. Lets take an example, if 2 requests and coming into Redis, which will edit a key, how can we avoid dirty reads if there is no locking mechanism ?

Got it! Let’s break it down step by step with a simple example to explain how Redis avoids dirty reads even though it is single-threaded and doesn’t use explicit locking mechanisms.

Key Idea: Single-Threaded Execution Ensures Atomicity

In Redis, all commands are executed sequentially by its single thread. This means that only one command can be executed at a time. Because commands don’t overlap, there is no risk of two commands trying to read or write to the same key simultaneously.

Example Scenario: Two Requests to Modify the Same Key

Let’s say two clients send requests to modify the same key, myKey, in Redis:

Client A sends a command to increment the value of myKey by 5 (INCRBY myKey 5).
Client B sends a command to increment the value of myKey by 10 (INCRBY myKey 10).

What Happens Internally in Redis:

Step 1: Requests Arrive
- Both requests (from Client A and Client B) arrive at the Redis server, which manages them in its event loop.
Step 2: Redis Handles Requests Sequentially
- Redis processes the commands one by one. The first command (e.g., INCRBY myKey 5) is executed completely before the second command (INCRBY myKey 10) starts.
- Redis ensures atomicity because commands are executed sequentially in the single thread. There is no "partial execution" of commands that might allow another command to read or modify the key simultaneously.
Step 3: Updates to myKey
- When INCRBY myKey 5 is executed, Redis retrieves the current value of myKey, adds 5, and updates the value in memory. This operation is complete before Redis starts processing the next command.
- Redis then processes INCRBY myKey 10 in the same way, ensuring no overlap.

Avoiding Dirty Reads

A dirty read occurs when one client reads data that another client is in the process of modifying but hasn’t finished yet. Here’s why this doesn’t happen in Redis:

No Parallel Execution:
- Since Redis processes commands one at a time, no client can read data that is "partially updated." Each command completes fully before the next one starts.
Atomic Operations:
- Redis commands like INCRBY are atomic by design. They combine the read-modify-write cycle into a single, indivisible operation.

Visualizing the Process

Imagine Redis as a single cashier at a store:

Each customer (client) lines up in a queue.
The cashier serves one customer at a time, completing their transaction (command) before moving to the next customer.
This ensures no two customers can interfere with each other’s transactions.

Real-Life Example in Code

Here’s how this would look in practice:

Initial State:

myKey = 0

Clients’ Requests:

Client A: INCRBY myKey 5
Client B: INCRBY myKey 10

Redis Execution (Sequential):

Process Client A’s command:
- Read myKey = 0
- Add 5
- Update myKey = 5
Process Client B’s command:
- Read myKey = 5
- Add 10
- Update myKey = 15

Key Takeaways

Sequential Execution: Redis processes commands one at a time, ensuring there’s no overlap between commands.
Atomic Operations: Each Redis command is atomic, so no client can see a "half-completed" result.
No Dirty Reads: Since Redis completes one command fully before starting the next, the data remains consistent and reliable.

If you have further questions or want more clarification, let me know!

thanks, this is the exactly explaination what i am looking for and it's an excellent way of explaination, if Redis is process the requests sequentially, doesn't all other requests queue up for their turn ? If it's sequential exeuction how it's achiving so quick turnaround time for all the requests ?

Great question! Yes, Redis processes requests sequentially, which means other requests do queue up while one is being executed. However, Redis achieves very quick turnaround times for all requests due to several key reasons. Let’s explore them step by step.

1. Redis Commands Are Extremely Fast

Redis is designed to execute commands quickly. It achieves this by:

In-Memory Operations: All data is stored in memory (RAM), so reading and writing data is extremely fast compared to disk-based systems.
Minimal Processing: Redis commands are simple and optimized. For example, operations like GET, SET, or INCRBY are lightweight and take only a few microseconds to execute.

2. Efficient I/O Multiplexing

Redis uses I/O multiplexing to handle thousands of client connections efficiently:

The single thread monitors all client connections and determines which ones are ready to send or receive data.
While the thread processes commands sequentially, it never "waits" for any network operation because the operating system tells Redis when data is ready to be processed.
This ensures the thread spends nearly all of its time executing commands, not waiting for I/O.

3. Simple and Lock-Free Design

Since Redis is single-threaded, there is:

No Locking Overhead: Multi-threaded systems often require locks to ensure data consistency, which can lead to contention and slow performance. Redis avoids this entirely.
Predictable Behavior: Sequential execution ensures that there are no race conditions or thread contention issues.

4. Low Latency and High Throughput

Redis’s architecture ensures:

Low Latency: Each command takes only a few microseconds to execute, so even if requests are queued, they are processed very quickly.
High Throughput: Redis can process millions of requests per second because the time to execute most commands is negligible.

5. Batching and Pipelining

Clients can send multiple commands to Redis in a single network call using pipelining:

Redis processes all commands in the pipeline sequentially, but since the commands arrive together, network overhead is minimized.
This further improves throughput and reduces the time clients spend waiting.

6. Handling Many Concurrent Connections

Redis can handle thousands of connections because:

It uses non-blocking I/O to serve multiple clients without dedicating a thread to each connection.
While a command is being processed, the thread doesn’t need to actively monitor inactive clients.

7. Typical Latency Breakdown

Let’s see how Redis minimizes latency at each step:

Network Latency: Minimal because Redis uses a simple, efficient protocol (RESP).
Queue Waiting Time: Minimal because commands are processed in microseconds.
Command Execution Time: Minimal because operations are in-memory and optimized.
Response Transmission: Minimal because Redis sends small, efficient responses.

For most workloads, the bottleneck is often the network, not Redis itself.

8. Example: Queue Behavior in Redis

Let’s say there are three clients sending commands:

Client A: SET myKey 42
Client B: INCRBY myKey 10
Client C: GET myKey

How Redis Handles These:

SET myKey 42 is executed. Time taken: ~1 microsecond.
INCRBY myKey 10 is executed next. Time taken: ~1-2 microseconds.
GET myKey is executed last. Time taken: ~1 microsecond.

Even if 10,000 commands arrive, Redis processes them so quickly (in microseconds) that the queue is constantly cleared.

9. When to Scale Redis

For most use cases, Redis’s performance is more than enough. However, in extremely high workloads, you can:

Use Sharding: Split the data across multiple Redis instances, each handling part of the data.
Leverage Replication: Use read replicas to distribute read-heavy workloads.

Key Takeaways

Requests do queue up in Redis, but the execution time is so short that most clients experience negligible waiting time.
The combination of in-memory storage, single-threaded design, and I/O multiplexing ensures Redis delivers low-latency, high-throughput performance.
For the majority of workloads, Redis’s sequential execution is not a bottleneck because of its speed.

If you’d like to discuss scaling strategies or specific Redis configurations, let me know!

let's assume Redis received 1 million requests of each of the below operations: 1 million requests : SET myKey 42 (1 million different SETs) 1 million requests : INCRBY myKey 10 (1 million different INCRBY) 1 million requests : GET myKey 1 million different GET) can you eloborate how 'I/O Multiplexing' works here to efficiently handles such a huge volume with single thread?

To understand how I/O multiplexing enables Redis to efficiently handle a large number of requests with a single thread, let’s break it down step by step in the context of your example where Redis receives 1 million requests each of SET, INCRBY, and GET.

Redis Workflow: Handling 3 Million Requests

Step 1: Requests Arrive

Redis is listening on a TCP port for client connections. Clients send their commands (SET, INCRBY, GET) over these connections.

Step 2: Redis Uses I/O Multiplexing

I/O multiplexing allows Redis to handle many active client connections simultaneously by monitoring multiple sockets (one for each client) in a non-blocking way. Let’s break this down:

What Happens Without Multiplexing?
- Without I/O multiplexing, Redis would need to block while waiting for one socket (or client connection) to send a command or receive a response.
- This would require one thread per client, which is resource-intensive and doesn’t scale well.
With I/O Multiplexing:
- Redis uses operating system APIs like epoll (Linux), kqueue (macOS/BSD), or select/poll to monitor all client connections.
- These APIs inform Redis when a socket (client connection) is ready for:
  - Reading: New data (a command) is available from the client.
  - Writing: The client is ready to receive a response.

Step 3: Redis Event Loop

Redis processes client requests using a highly efficient event loop:

Monitor Sockets: Redis continuously listens to all open sockets using the multiplexing mechanism.
Identify Ready Sockets: The operating system notifies Redis when a socket is ready (has data to read or can accept a response).
Handle Events: Redis processes the events (e.g., reading a command, executing it, sending a response) sequentially in the main thread.

Efficiently Processing 3 Million Requests

Let’s break down the handling of your 3 million requests step by step.

1. Reading Commands

Redis reads data from the sockets that are flagged as ready by the OS.
Each command (SET, INCRBY, or GET) is read from its respective client’s socket.
Redis’s protocol (RESP) is lightweight, so parsing the commands is very fast.

2. Executing Commands

Each command is executed in the Redis main thread. This happens sequentially:
1. SET myKey 42 commands are processed one by one, updating keys in memory.
2. INCRBY myKey 10 commands are processed, reading and updating keys in memory.
3. GET myKey commands are processed, retrieving keys from memory.
Each operation is highly optimized and typically takes microseconds.

3. Sending Responses

Once a command is executed, Redis sends the response back to the client.
Redis uses non-blocking I/O to ensure it doesn’t get stuck waiting if a client is slow to receive the response.

How Redis Keeps Up with 3 Million Requests

1. Fast Execution Times

Each Redis command (e.g., SET, INCRBY, GET) is optimized to execute in a few microseconds because:

Operations are in-memory, avoiding disk latency.
Redis uses simple data structures (strings, lists, hashes, etc.) that are quick to access and modify.

2. High Throughput with I/O Multiplexing

Redis doesn’t wait for clients to send data or confirm receipt of responses.
While one command is being executed, Redis can read new commands from other ready sockets.
The event loop ensures no time is wasted, keeping the system highly efficient.

3. Avoiding Blocking

Redis’s single-threaded design avoids:

Threading Overhead: No need for locks, thread synchronization, or context switching.
Network Blocking: Redis doesn’t block on slow clients because it processes only ready sockets.

4. Pipelining Optimization

If clients use pipelining (sending multiple commands in one request), Redis processes multiple commands from the same client without waiting for network delays between commands.

Real-World Example

Incoming Commands (Queue):
- Redis receives commands from 1 million clients via their respective sockets.
- Commands are queued internally for processing.
Processing Workflow:
- Redis reads data from ready sockets (e.g., 100,000 sockets ready at a given time).
- Executes commands from these sockets sequentially.
- Sends responses back to the clients.
Throughput Example:
- If each command takes 1 microsecond, Redis can process 1 million commands in about 1 second.
- With 3 million requests, Redis would take about 3 seconds to process them all.

Key Factors Behind Redis’s Speed

In-Memory Storage: Redis keeps all data in memory, so all operations are extremely fast.
Efficient Protocol: RESP minimizes overhead for parsing commands and responses.
Event-Driven Architecture: The I/O multiplexing mechanism ensures Redis spends time only on active clients.
Simple Single-Threaded Model: Redis avoids complexities like locking or context switching, focusing entirely on command execution.

Conclusion

Redis’s use of I/O multiplexing allows it to handle thousands of concurrent connections without the overhead of managing multiple threads. Despite processing requests sequentially, Redis’s speed comes from its:

In-memory nature
Non-blocking I/O
Optimized command execution

As a result, Redis can process millions of requests per second even with a single thread! If you'd like further clarification or a deep dive into specific aspects, feel free to ask!

i am bit confused, if REDIS receives 3 milliion requests as per my example, then how come REDIS operates all 3M sockets in a single thread ? does REDIS leverages the OS calls for each 3M sockets?

That's a fantastic question, and it gets to the heart of how I/O multiplexing works in Redis and why it's so efficient. Let’s break this down further to clarify how Redis manages 3 million sockets with a single thread using operating system (OS) support.

How Redis Handles Millions of Sockets in a Single Thread

1. I/O Multiplexing: The Core Concept

I/O multiplexing is a technique where Redis doesn’t deal with each socket individually all the time. Instead, Redis uses the OS’s I/O readiness notification mechanisms (like epoll on Linux) to efficiently manage multiple sockets. Here's how it works:

Redis registers all the sockets (connections) with the OS.
The OS monitors these sockets and notifies Redis when a specific socket is ready to perform I/O (e.g., there is data to read or the client is ready to receive a response).
Redis only interacts with the sockets that are ready, instead of polling or blocking on all 3 million sockets.

2. Leveraging OS Mechanisms

Redis uses high-performance OS-level APIs to manage sockets:

On Linux: epoll
On macOS/BSD: kqueue
On Windows: IOCP

These APIs are designed to handle massive numbers of file descriptors (sockets) efficiently. Here’s how they work:

Single System Call for All Sockets:
- Redis makes a single system call (epoll_wait or equivalent) to check all 3 million sockets.
- The OS maintains a list of "events" (e.g., sockets ready to read or write).
Notification of Readiness:
- The OS tells Redis only about the sockets that are ready. For example, if 10,000 out of 3 million sockets have data to read, Redis only deals with those 10,000 sockets.
- This avoids wasting CPU cycles on inactive sockets.

3. Redis’s Event Loop Workflow

Here’s how Redis processes the 3 million sockets:

Initialization:
- Redis registers all 3 million sockets with epoll (or equivalent) during their creation.
- The OS handles the complexity of monitoring these sockets.
Event Loop Iteration:
- Redis calls epoll_wait (or similar) to get a list of ready sockets.
- If 10,000 sockets are ready, the OS returns this list in one call.
Processing Ready Sockets:
- Redis processes these ready sockets one by one, sequentially:
  - Reads the command from each socket (e.g., SET, INCRBY, GET).
  - Executes the command.
  - Sends the response back to the client.
Back to Idle Sockets:
- Once Redis finishes processing the ready sockets, it goes back to waiting for the next batch of events.

Why This Works for 3 Million Sockets

1. Minimal Overhead

The OS’s readiness notification mechanisms like epoll are extremely efficient:

They work with O(1) time complexity, meaning the time taken to check readiness doesn’t depend on the total number of sockets (3 million in this case).
Redis only processes active sockets, ignoring idle ones.

2. High Scalability

Modern OS APIs like epoll and kqueue are designed to handle millions of file descriptors efficiently, making Redis capable of scaling to high numbers of connections.

3. Fast Event Processing

Once a socket is flagged as ready, Redis handles it very quickly:

Commands like SET, INCRBY, and GET take only a few microseconds to execute.
Redis’s single-threaded execution ensures no context switching, locks, or thread overhead, keeping processing fast.

Example: Redis with 3 Million Requests

Let’s put this into perspective with numbers:

Total Sockets: 3 million (each client sends 1 request).
Ready Sockets per Batch: Let’s say 10,000 sockets are ready at a time.
Execution Time per Command: ~1 microsecond (for simple commands like GET).
Processing Batch of 10,000:
- Time taken: $10,000 \times 1 \, \mu s = 10 \, ms$ .
Processing All 3 Million Requests:
- Redis processes 10,000 sockets in each loop.
- Number of loops: $3,000,000 / 10,000 = 300$ .
- Total time: $300 \times 10 \, ms = 3 \, s$ .

Redis can handle the entire workload in about 3 seconds for 3 million requests, assuming ideal conditions.

Why Redis Scales with Single Thread

Efficient Use of OS APIs:
- epoll ensures Redis doesn’t waste time on inactive sockets.
In-Memory Execution:
- All data operations are extremely fast since they occur in memory.
Optimized Event Loop:
- Redis’s event loop ensures processing is streamlined and sequential.
Low Command Overhead:
- Redis commands are designed to be lightweight and execute quickly.

Key Takeaways

Redis doesn’t interact with all 3 million sockets simultaneously; it only works on sockets flagged as ready by the OS.
I/O multiplexing ensures Redis makes a single system call to monitor all sockets efficiently.
With its optimized event loop and lightweight commands, Redis can handle millions of requests quickly, even on a single thread.

If you want to dive deeper into how epoll or other OS mechanisms work under the hood, let me know!

3 questions: 1) just to understand in a broder manner, how many connection sockets an OS can open ? because just for REDIS if operating system opens 10k sockets, what about other processes running on the same OS? 2) how REDIS knows that what all the sockets should it register 'epoll' for ? I believe sockets are not fixed in nature? or does REDIS asks OS that it has to reserve 10K sockets to it? 3) Does 'epoll' command sends a 'ready' message or does it reads the value from the socket and sends it to the Redis thread ?

Let’s tackle your questions one by one for a clear understanding.

1. How Many Connection Sockets Can an OS Open?

The number of sockets an OS can handle depends on several factors, including hardware resources (CPU, memory) and system configurations.

Key Factors Affecting Socket Limit

File Descriptor Limits:
- Each socket is represented by a file descriptor.
- The maximum number of file descriptors an OS can handle is set by system limits:
  - Soft Limit: Can be changed temporarily by a process using commands like ulimit.
  - Hard Limit: A stricter upper bound that only the system administrator (root) can change.
- Example: Linux defaults to ~1024 file descriptors per process but can be increased to millions using ulimit -n.
Kernel Resources:
- The OS kernel must allocate memory and other resources for each socket.
- The more connections (sockets) you open, the more kernel memory is used.
System Configuration:
- Modern OSes like Linux can handle millions of sockets with proper tuning of parameters like:
  - fs.file-max: The maximum number of file descriptors.
  - net.core.somaxconn: The maximum number of queued connections.
  - net.ipv4.ip_local_port_range: The range of ports available for sockets.

Handling Other Processes

Redis’s sockets are just one part of the system. The OS ensures fair allocation of resources for other processes by:

Allowing multiple processes to use file descriptors within their own limits.
Ensuring the total number of sockets across processes does not exceed the system-wide limit.

2. How Does Redis Know Which Sockets to Register with `epoll`?

Redis doesn't directly "know" which sockets to register—it relies on the OS and its own connection handling logic.

How Sockets Are Registered:

Client Connection:
- When a client connects to Redis, the OS creates a new socket for that connection and assigns a file descriptor.
- Redis accepts this connection via the accept() system call and gets the socket's file descriptor.
Registering with epoll:
- Once Redis has a valid socket, it registers this socket with epoll using the epoll_ctl system call.
- Redis tells epoll to monitor the socket for specific events (e.g., "ready to read" or "ready to write").
Dynamic Registration:
- Sockets are not fixed. Redis dynamically registers new sockets as clients connect and deregisters them when clients disconnect.
- There’s no pre-reservation of sockets. Instead, Redis handles sockets on demand.

3. Does `epoll` Send a 'Ready' Message or Read the Socket for Redis?

How `epoll` Works:

epoll does not read the socket's data. It only notifies Redis when a socket is ready for a specific operation (e.g., reading or writing).

Detailed Flow:

Registration:
- Redis registers sockets with epoll and specifies which events it wants to monitor (e.g., EPOLLIN for readable data, EPOLLOUT for writable state).
Monitoring:
- The OS monitors the registered sockets. When a socket becomes ready (e.g., data arrives), it adds this socket to a "ready list."
Notification:
- Redis calls epoll_wait() to fetch the list of ready sockets.
- The OS provides the list of ready sockets, but it does not read or process the socket's data.
Processing:
- Redis processes each socket in the ready list:
  - Reads the incoming data (e.g., the client's command).
  - Executes the command.
  - Writes the response back to the socket if needed.

This separation of "readiness notification" (handled by epoll) and "data handling" (done by Redis) allows Redis to process many connections efficiently without blocking.

Summary of Answers

How Many Sockets Can an OS Open?
- Modern OSes can handle millions of sockets, but this depends on file descriptor limits and system tuning.
- Other processes can coexist because sockets are dynamically allocated per process.
How Does Redis Know Which Sockets to Register?
- Redis registers sockets dynamically as clients connect, using the file descriptors provided by the OS.
- Redis doesn’t reserve sockets in advance.
Does epoll Read Socket Data?
- No, epoll only notifies Redis when a socket is ready for I/O (e.g., has data to read or is ready to write).
- Redis reads the data itself once notified.

Let me know if you’d like to dive deeper into any part of this!

when epoll is already notifying REDS that it's 'ready', then why REDIS uses epoll_wait() to fetch the list of ready sockets?

Great question! Let’s clarify why Redis uses epoll_wait() even though epoll is already notifying readiness.

How `epoll` Works:

The epoll system in Linux operates in two parts:
1. Registration Phase (epoll_ctl):
  - Redis tells epoll what sockets (file descriptors) to monitor and what events to watch for (e.g., readable or writable).
  - The OS starts tracking these sockets internally.
2. Notification Phase (epoll_wait):
  - Redis calls epoll_wait() to get the list of sockets that are currently ready for the requested events.

Why Does Redis Use `epoll_wait()`?

1. `epoll` is Event-Driven, Not Interrupt-Driven

The OS doesn’t actively “interrupt” Redis when a socket becomes ready. Instead, it keeps track of readiness events internally.
Redis must explicitly ask for these events by calling epoll_wait().

2. Efficient Readiness Polling

epoll_wait() is the mechanism through which Redis:
- Blocks until at least one socket is ready (or a timeout occurs).
- Retrieves a batch of ready sockets in one go.
This is efficient because:
- Redis avoids busy-waiting or repeatedly checking sockets.
- It processes multiple ready sockets in a single call.

3. Non-Blocking Alternative

If Redis is configured to operate in non-blocking mode, epoll_wait() can return immediately if no sockets are ready, allowing Redis to continue other work.

Why Doesn’t `epoll` Push Notifications Directly?

The OS doesn’t push notifications to the user process because:
1. Simplified Design: Pushing notifications would require a complex interrupt-based mechanism or callback architecture at the kernel level.
2. Batch Processing: By letting Redis pull events using epoll_wait(), the OS can provide readiness events for multiple sockets in a single call, improving efficiency.
3. Control over Blocking: Redis decides whether to block (wait for events) or check immediately, depending on how it calls epoll_wait().

How `epoll_wait()` Fits in Redis

Redis registers all sockets with epoll using epoll_ctl.
When Redis is ready to process I/O:
- It calls epoll_wait() to fetch the list of ready sockets.
- epoll_wait() blocks until at least one socket is ready or a timeout occurs.
Redis processes all sockets returned by epoll_wait() sequentially in its event loop.

Benefits of This Approach

Efficiency: Redis only works with sockets that are ready, minimizing CPU usage.
Scalability: A single epoll_wait() call can handle thousands or millions of sockets, avoiding per-socket polling overhead.
Flexibility: Redis controls when and how to handle readiness events, keeping its single-threaded event loop simple.

In summary, epoll_wait() is essential because the OS doesn’t proactively notify Redis. Instead, Redis explicitly fetches the list of ready sockets in a controlled and efficient manner. This design keeps Redis’s event loop lightweight and scalable.

so does Redis keeps polling epoll_wait() ?

Yes, Redis keeps polling epoll_wait(), but this isn't "polling" in the traditional sense of repeatedly checking something in a tight loop. Redis uses epoll_wait() as part of its event-driven, non-blocking architecture. Here’s how and why it works effectively:

Redis and `epoll_wait()`

Continuous Event Loop:
- Redis operates in a single-threaded event loop. This loop is responsible for:
  - Checking for I/O readiness (via epoll_wait()).
  - Processing client commands.
  - Writing responses back to clients.
  - Running background tasks (e.g., expiration of keys).
How Redis Calls epoll_wait():
- Redis enters epoll_wait() as part of its event loop and stays there until:
  - One or more sockets are ready (e.g., data available for reading).
  - A timeout occurs (if Redis sets a timeout for epoll_wait()).
- If sockets are ready, epoll_wait() returns a list of those sockets, and Redis processes them sequentially.
Blocking vs. Non-Blocking Behavior:
- Redis uses blocking behavior for epoll_wait():
  - If no sockets are ready, Redis will "wait" at this system call until at least one socket is ready (or until a timeout is reached, if configured).
- This isn’t wasteful because the OS efficiently puts the Redis process to sleep while waiting, consuming minimal CPU.

Why Redis Isn't "Busy Polling"

Redis isn’t “busy polling” in the sense of wasting CPU cycles by continuously checking for events in a loop. Instead:

Efficient Waiting:
- When Redis calls epoll_wait() and no sockets are ready, the OS suspends the Redis process (puts it to sleep).
- Redis is woken up only when the OS detects that one or more sockets are ready.
Batch Processing:
- When epoll_wait() returns, it provides a batch of all sockets that are ready. Redis processes these in one go, avoiding repeated calls to check each socket.
Minimal Overhead:
- Redis does not check the readiness of sockets manually or continuously. The OS handles this internally using the efficient epoll mechanism.

How Redis Avoids Starvation or Blocking

Even though epoll_wait() blocks until sockets are ready, Redis ensures that it remains responsive by:

Timeouts:
- Redis can configure a timeout for epoll_wait() (though it typically uses an indefinite wait to maximize efficiency). A timeout ensures Redis periodically returns to check other tasks, such as:
  - Key expiration.
  - Replication.
  - Pub/Sub message propagation.
Prioritizing Work:
- When Redis processes a batch of ready sockets, it limits how much work it does per loop iteration to avoid neglecting other tasks.

Flow of `epoll_wait()` in Redis

Setup:
- Redis registers sockets with epoll (via epoll_ctl) as clients connect.
- Redis specifies the events it wants to monitor (e.g., readable or writable).
Main Event Loop:
- Redis calls epoll_wait() and waits for the OS to return ready sockets.
Processing:
- When epoll_wait() returns:
  - Redis fetches the list of ready sockets.
  - Reads incoming commands or writes responses for each socket.
Repeat:
- Redis goes back to epoll_wait() for the next batch of events.

Summary

Redis "polls" epoll_wait() as part of its event loop, but this is efficient and not wasteful.
While waiting at epoll_wait(), Redis consumes almost no CPU because the OS suspends it until there’s work to do.
This approach allows Redis to handle thousands or millions of connections in a single thread while remaining fast and resource-efficient.

Let me know if you'd like to dive into how Redis balances I/O with other tasks or if you have further questions!

you are saying Redis polls epoll_wait(), but inside event loop, what does it mean? loop is a loop, it will consume CPU cycles, my simple question, how Redis knows that there is some epoll event waiting in epoll_wait() list ?

Great question! Let’s address this step by step to clarify how Redis operates its event loop without wasting CPU cycles, and how it "knows" there is an event ready in epoll_wait().

Redis’s Event Loop and `epoll_wait()`

Redis’s event loop is designed to be event-driven, not CPU-intensive. The key lies in how epoll_wait() interacts with the OS.

epoll_wait() Is a Blocking Call:
- When Redis calls epoll_wait() in its event loop, it blocks (i.e., the thread stops executing) until:
  - At least one socket is ready for I/O (e.g., a client sent a request or is ready to receive data).
  - A timeout occurs (if Redis has set a timeout value for epoll_wait()).
- While epoll_wait() is blocking, the Redis process is put into a sleeping state by the OS, consuming zero CPU cycles.
The OS Signals Readiness:
- The OS monitors all registered sockets (file descriptors) using efficient mechanisms like epoll internally.
- When a socket becomes ready (e.g., data arrives), the OS "wakes up" the Redis process and makes epoll_wait() return with the list of ready sockets.
- This wake-up mechanism ensures Redis doesn’t waste CPU cycles continuously "polling" or checking for readiness.

How Redis Avoids Wasting CPU in a Loop

The event loop doesn’t waste CPU because:

Blocking on epoll_wait():
- When Redis enters epoll_wait(), it hands control to the OS and stops executing until the OS signals readiness.
- During this time, Redis is not actively consuming CPU.
Returning Only When Necessary:
- When the OS detects readiness on a socket, it adds that event to the "ready list" and wakes up Redis.
- Redis is then notified (via epoll_wait() returning) that there’s work to do.

Flow of Redis’s Event Loop with `epoll_wait()`

Here’s how Redis handles events step by step:

Register Sockets with epoll:
- Redis registers client connections (sockets) with the epoll instance via epoll_ctl.
Call epoll_wait():
- Redis enters its event loop and calls epoll_wait(), blocking until there are ready events.
- If no sockets are ready, the OS puts Redis into a sleeping state, consuming no CPU.
OS Detects Readiness:
- The OS detects when a socket becomes ready for I/O (e.g., data arrives or the socket is writable).
- The OS wakes up Redis by returning the list of ready sockets from epoll_wait().
Process Events:
- Redis processes the list of ready sockets, handling client commands or sending responses.
Re-Enter epoll_wait():
- After processing the ready sockets, Redis goes back to epoll_wait() to wait for the next set of events.

Why Redis Doesn’t Need to "Know" About Events in Advance

Redis doesn’t "poll" in the traditional sense or proactively check for events. Instead:

The OS handles the readiness of sockets internally.
Redis trusts epoll_wait() to block and only return when there’s something to process.
Redis doesn’t need to "know" about events ahead of time because epoll_wait() handles this for it.

Addressing the Concept of a Loop

Yes, Redis runs in a loop, but:

The "loop" primarily consists of waiting on epoll_wait() most of the time.
While waiting, the Redis process is asleep and doesn’t consume CPU.
When woken up, Redis quickly processes the ready sockets and re-enters the loop.

This design ensures Redis is efficient and scalable, handling many connections without wasting resources.

Key Takeaways

Redis doesn’t "poll" epoll_wait() in the traditional sense; it blocks on the call and is woken up only when there’s work to do.
The OS notifies Redis of events by returning ready sockets through epoll_wait().
The event loop doesn’t continuously consume CPU—it’s mostly idle (asleep) until woken up by the OS.

Let me know if you’d like further clarification! 😊

which command does OS uses to notify the Redis to let it know that there is soemthign waiting in epoll_wait() ?

The OS doesn’t explicitly send a "command" to Redis to notify it that there’s something waiting in epoll_wait(). Instead, the mechanism involves signals and waking up the process. Here's how it works in detail:

How the OS Notifies Redis:

1. Blocking in `epoll_wait()`:

When Redis calls epoll_wait(), the OS puts the Redis process into a sleeping state (if there are no ready events at the time).
This sleeping state ensures that Redis does not consume CPU cycles while waiting.

2. Event Detection by the OS:

The OS continuously monitors all sockets registered with epoll (using kernel-level mechanisms like interrupt-driven I/O).
When an event (e.g., new data on a socket) occurs:
- The OS marks the socket as "ready" in its internal data structures.
- It places the corresponding event into the epoll ready list.

3. Waking Up Redis:

Once the ready list is populated (e.g., one or more sockets are ready), the OS wakes up the Redis process.
- This is typically done by the kernel using a lightweight mechanism called a "wake-up signal."
- Redis’s thread, which is blocked in epoll_wait(), resumes execution immediately.

4. Returning from `epoll_wait()`:

The epoll_wait() system call then returns with the list of ready sockets.
Redis processes these sockets and re-enters the event loop.

What Mechanism Wakes Redis?

The OS uses a combination of interrupts and process scheduling to wake up Redis:

Hardware Interrupts:
- When data arrives on a socket, the network interface generates a hardware interrupt.
- The interrupt notifies the kernel that data is ready on a particular socket.
Software Scheduling:
- The kernel marks the Redis process (which was sleeping) as "runnable."
- The CPU scheduler ensures Redis is scheduled for execution as soon as possible.

Why No Explicit Command Is Needed?

Redis is already "waiting" inside the epoll_wait() system call.
When the OS detects an event, it simply wakes up the Redis process by returning control to it via the epoll_wait() function.
The OS doesn’t need to "send" Redis a separate command or signal—it just ensures that epoll_wait() returns the ready events.

Summary

Redis blocks in epoll_wait() until the OS detects a readiness event.
The OS wakes up Redis by signaling the process (via kernel scheduling) when there’s work to do.
Redis resumes execution from where it left off in the epoll_wait() call, now with a list of ready sockets to process.

Let me know if you'd like to explore any of these concepts further! 😊

does Redis tells OS that it means 1000 sockets reserve for it, how many sockets does it open by default for Redis ?

Redis does not reserve a specific number of sockets in the way you might think. Instead, it works dynamically with the OS to open and manage sockets as clients connect. Here’s a deeper look into how Redis manages socket connections and how it interacts with the OS:

1. Redis and Sockets:

Dynamic Socket Opening:
- When a new client connects to Redis, the OS automatically creates a new socket (file descriptor) for the connection.
- Redis uses the accept() system call to accept new connections, and it doesn’t "reserve" a fixed number of sockets in advance. Instead, Redis handles new sockets dynamically as clients connect.
No Pre-Reservation:
- Redis doesn’t tell the OS to "reserve" 1000 sockets or any specific number.
- It simply registers each socket (file descriptor) with epoll to monitor it for I/O events (e.g., data to read, space to write).
- The OS handles socket management and determines how many connections are possible based on system limits.

2. OS and Socket Limits:

The OS imposes limits on the maximum number of sockets that can be opened by any process (like Redis). This is controlled by:

File Descriptor Limits:

Each open socket consumes a file descriptor, and the OS limits the maximum number of file descriptors a process can open.

Soft Limit:
- This is the number of file descriptors a process can open, and it can be modified by the process (or user) using commands like ulimit -n.
Hard Limit:
- This is a higher, system-wide limit set by the OS, usually requiring administrative privileges to change.

The default soft limit in many Linux systems is 1024 file descriptors per process, but this can be increased to millions depending on system configuration.

Redis and System Limits:

Redis can open as many sockets as the OS allows, within the file descriptor limit for the process.
Redis itself doesn't explicitly specify "I want 1000 sockets"—the OS will allow Redis to open sockets as needed, up to the system limit.
If the OS is configured to allow a large number of file descriptors, Redis can handle that many connections.

3. How Many Sockets Does Redis Open by Default?

The actual number of sockets Redis opens depends on the number of client connections it receives. Redis doesn’t open any sockets for itself except those needed to listen for client connections.

By default, Redis listens on one socket for incoming client connections:

Port 6379 (unless configured otherwise).
This socket is used to accept new connections from clients.

Connection-Related Sockets:

When a client connects to Redis, the OS creates a new socket to handle that client. Redis dynamically opens a new file descriptor for each client.
There’s no fixed number of sockets Redis "reserves" for itself. It opens one for each client connection that comes in.

Example:

If 10 clients connect to Redis, Redis will have 1 listening socket (for accepting connections) and 10 client connection sockets.
If 1000 clients connect, Redis will have 1 listening socket and 1000 client connection sockets.

4. Configuring Socket Limits in Redis:

ulimit -n (Soft Limit): You can configure Redis to handle more than the default limit of 1024 sockets by adjusting the soft limit on file descriptors using ulimit -n <number>.
sysctl Settings: For even higher limits, you can modify OS-level settings for file descriptors:
- fs.file-max: The maximum number of open file descriptors for the entire system.
- net.core.somaxconn: The maximum number of connections Redis can accept (not the number of sockets, but the connection backlog).

Summary:

Redis doesn’t reserve a fixed number of sockets.
- It dynamically opens sockets as client connections arrive.
- It registers these sockets with epoll to monitor I/O events.
OS Limits: Redis is subject to OS-imposed limits on the number of file descriptors (sockets) it can open, but these limits can be increased based on system configuration.

Let me know if you'd like to dive deeper into system limits or Redis configuration! 😊

When a client connects to Redis through a port, the OS creates a new socket (file descriptor) for that connection. And then Redis registers this socket with epoll, how Redis came to know that OS created a new socket and it has to register epoll to it ?

Great question! Let’s break down the sequence of events to understand how Redis knows that a new socket (file descriptor) has been created when a client connects, and how it registers that socket with epoll:

Steps Involved in Client Connection to Redis:

Redis Listening for Connections:
- Redis listens for incoming client connections on a listening socket. This is the socket created by Redis when it starts, typically bound to a port (e.g., 6379).
- Redis creates this listening socket using the socket() and bind() system calls. It then tells the OS to listen for incoming connections using the listen() system call.
OS Accepts the Incoming Connection:
- When a client tries to connect to Redis, the OS handles the low-level networking for the connection.
- The OS creates a new socket (file descriptor) specifically for this client connection.
- This socket is separate from the listening socket and is used exclusively for communication with that particular client. It represents the open communication channel for data exchange.
Redis Accepts the Connection:
- Redis uses the accept() system call to accept the incoming client connection.
  - The accept() call tells the OS, "I want to handle this incoming connection."
  - When accept() is called, the OS hands over the new client socket (file descriptor) to Redis, which represents the communication channel with the newly connected client.
Redis Registers the Client Socket with epoll:
- After accepting the client connection, Redis now knows about the new socket (file descriptor) created by the OS.
- Redis needs to monitor this client socket for I/O events like reading data from the client (e.g., commands) or writing data to the client (e.g., responses).
- To do this, Redis registers the new socket with the epoll instance using epoll_ctl(). The registration process tells Redis to start monitoring the socket for read (EPOLLIN) and write (EPOLLOUT) events.
Redis Monitors the Sockets with epoll_wait():
- Once the socket is registered, Redis enters the event loop and calls epoll_wait() to block and wait for any I/O events (e.g., data to read from the client).
- When the OS detects that there’s data to be read from the socket, it notifies Redis, which then processes the request.

How Redis Knows About the New Socket:

Redis initiates the process by calling accept(). When accept() is called, it gets the new socket (file descriptor) from the OS.
The OS creates a new socket (file descriptor) for the client connection and returns that socket to Redis via accept().
Once Redis has the socket (file descriptor), it registers it with epoll to monitor I/O events.

Detailed Explanation of `accept()` and Epoll Registration:

Listening Socket (listen() and accept()):
- Redis creates a listening socket using socket() and bind().
- It then calls listen() to instruct the OS to listen for incoming connections.
- When a client tries to connect, the OS detects the incoming connection and creates a new socket (a new file descriptor).
- Redis calls accept() to accept the connection, and the OS returns the new socket (file descriptor) for the client connection.
accept() Details:
- The accept() system call blocks (waits) until a client connects.
- When a client connects, accept() returns a new socket (file descriptor) that Redis can use to communicate with the client.
- This socket is different from the listening socket and represents the unique communication channel for this specific client.
Register with epoll:
- After receiving the client socket from accept(), Redis registers it with epoll using epoll_ctl(). This tells Redis to monitor this new socket for I/O events.
- Redis can now read from and write to this socket in response to client requests.

Summary of the Process:

OS Creates a New Socket for the Client: When a client connects to Redis, the OS creates a new socket for that specific connection.
Redis Accepts the Connection: Redis calls accept(), and the OS hands the new socket to Redis.
Redis Registers the Socket with epoll: Redis then registers the new client socket with epoll to monitor it for I/O events like reading or writing data.

What Happens If Multiple Clients Connect?

Each new client connection results in the creation of a new socket (file descriptor).
Redis accepts each connection and registers each socket with epoll, enabling efficient monitoring and handling of multiple client connections.

Let me know if you'd like to dive deeper into any of these steps! 😊

till now you discussed about different blocking calls, accept() and epoll_wait() how does Redis handles them internally, definitely in some async calls ? does it manages these 2 calls in different sub-threads ?

Redis is designed to be single-threaded, which means it does not rely on multiple threads or sub-threads to handle concurrent operations like accepting connections or waiting for I/O events. Instead, Redis achieves concurrency and high performance by using non-blocking I/O and an event-driven, asynchronous model. Let's break down how Redis handles blocking system calls like accept() and epoll_wait() in this single-threaded, asynchronous context:

Redis’ Single-Threaded Event Loop

Redis is Single-Threaded:
- Redis operates on a single thread by default, meaning all tasks—such as accepting new connections, reading data, processing commands, and writing responses—are executed in the same thread.
- This design avoids the complexity of multi-threading (e.g., context switching, race conditions) while allowing Redis to achieve high throughput by efficiently handling many client connections concurrently.
Event-Driven Asynchronous Model:
- Redis employs an event-driven architecture, where it listens for events like incoming connections and data availability in a non-blocking manner.
- Instead of waiting for events to happen in a blocking way, Redis uses a mechanism like epoll (on Linux) to monitor multiple file descriptors (sockets) asynchronously.
- Redis does not block on system calls (like accept() or epoll_wait()); instead, it can handle multiple I/O operations without waiting on each one to finish.

Handling Blocking Calls in Redis:

Now, let’s dive into how Redis handles blocking system calls (accept() and epoll_wait()) while still maintaining its single-threaded, event-driven model.

Handling accept():
- When a client tries to connect to Redis, the accept() call is used to accept the incoming client connection.
- Non-blocking Socket: By default, Redis opens the listening socket in non-blocking mode. This means that when Redis calls accept(), it will not block indefinitely, even if no clients are connecting. Instead, Redis will simply return -1 and EAGAIN (or EWOULDBLOCK) if there are no pending connections.
- accept() in Event Loop: When Redis’s event loop calls accept(), it checks if there is a client attempting to connect. If there is, it processes the connection and registers the client’s socket with epoll. If not, it moves to the next event (like checking for data to read or sending responses).
Handling epoll_wait():
- Redis uses epoll_wait() (or equivalent mechanisms) to monitor multiple file descriptors (sockets) for I/O events like read and write.
- Non-blocking Event Loop: The epoll_wait() system call is designed to be non-blocking. Redis typically calls epoll_wait() to check which sockets are ready for I/O events. If no events are ready, epoll_wait() will block until at least one event occurs (e.g., data becomes available to read, or a client can be written to).
- If epoll_wait() detects an event (like data being ready to read on a socket), it returns and Redis processes that event in its event loop.

The Key to Efficiency:

Single Thread, Multiple Connections: Redis does not need to use multiple threads to handle multiple connections. It uses the event loop to handle many connections concurrently in a non-blocking fashion. While Redis is processing one client’s request, it can still monitor other sockets for new events without being blocked by slow or idle clients.
I/O Multiplexing: The core of Redis's performance comes from I/O multiplexing. By using epoll (or similar mechanisms), Redis efficiently handles many I/O operations without waiting on any single operation to complete, making it highly responsive even under heavy load.

No Need for Sub-Threads for Blocking Calls

In Redis, you do not need sub-threads to handle accept() and epoll_wait() concurrently. Here’s why:

Asynchronous I/O: When Redis calls accept() or epoll_wait(), it doesn’t block the entire thread. It only blocks for as long as necessary (i.e., until a new client connection arrives or I/O data is ready to be processed).
Event Loop: The event loop in Redis continuously checks for events (new connections, ready sockets) and handles them one by one. This means that Redis can handle thousands or even millions of simultaneous connections without waiting for each individual connection to complete its task.

Visualizing the Flow:

Redis listens for client connections (accept()), but if no connection is ready, it doesn’t block the entire thread. It just keeps monitoring sockets using epoll_wait().
When a connection is accepted (accept()), Redis registers the socket with epoll to monitor it for I/O events.
Redis processes each event (e.g., reading data, writing responses) in a sequential, non-blocking manner, allowing it to handle many clients simultaneously without using sub-threads.

Does Redis Use Multiple Threads for Blocking Calls?

No, Redis does not use multiple threads to handle accept() or epoll_wait(). Redis runs on a single thread.
It leverages non-blocking I/O and an event-driven model (using epoll or similar mechanisms) to achieve concurrency and handle thousands of connections in a single thread efficiently.

Summary:

Redis is single-threaded: It handles I/O multiplexing and client connections in a single thread.
Non-blocking I/O: Redis makes use of non-blocking system calls like accept() and epoll_wait(). It doesn't block on each individual operation.
Event-driven model: Redis uses an event loop to handle multiple I/O events concurrently without needing multiple threads. It processes each event one at a time in an asynchronous manner.

This design allows Redis to handle large numbers of client connections without the complexity of multi-threading, making it both fast and memory-efficient.

Let me know if you need further details or clarifications on any point! 😊

ok, assume if accept() or epoll_wait() happens sequentially, and accept() returned nothing, so nothing to connect, now epoll_wait() returned 3 million requests, which would take 10 seconds to complete all the requests. Now while this is happening, at 2nd second, a new connection came in and it's waiting for Redis to invoke accept(), now new connection has to wait for 9 seconds to open it's connection and register epoll, becuase Redis is busy in serving epoll_wait() 3M requests.

You’ve raised a very important point about how Redis handles multiple requests, especially when the event loop is busy processing a large number of requests. Let’s walk through your scenario to clarify how Redis can manage new connections, even when it’s processing a heavy load of existing requests.

Scenario Breakdown:

Initial State:
- Redis is in the event loop and has just completed an accept() operation. No new connections were accepted (because no client tried to connect), so Redis moves to epoll_wait() to monitor sockets for I/O events (read/write operations).
- 3 million requests are pending for Redis to process, which take about 10 seconds to complete (this could be due to multiple commands or data to read/write).
During the Event Loop:
- Epoll waits for I/O events: While Redis is busy processing these 3 million requests, it’s still in the middle of handling client requests, reading data, and writing responses. The epoll_wait() function returns the list of ready sockets (3 million in this case), and Redis will sequentially process them one by one.
New Connection During Processing:
- In the second second, a new client attempts to connect to Redis.
- Since Redis is already processing the 3 million requests from epoll_wait(), it doesn't immediately call accept() to handle the new client connection.
- The new client’s connection request will not be handled immediately because Redis is busy processing the existing 3 million requests.
New Connection Waits:
- If Redis is actively processing the 3 million requests and has not yet reached the point where accept() can be called again, the new connection will have to wait until Redis has finished processing the current batch of events. This means the connection might need to wait for 9 seconds (in your example) until the event loop completes the current batch of requests and accept() is called to handle new connections.

Why Does This Happen?

This happens because Redis uses a single-threaded, blocking event loop to handle all connections. While it’s processing existing requests, it can’t handle new connections immediately unless it’s explicitly checking for them.

Here’s why this issue can arise:

Redis is Single-Threaded: Redis processes requests one at a time in a single thread, so it can only process one operation at a time.
Epoll and Accept are Sequential: epoll_wait() will block until at least one event is ready to be processed. While it’s processing events, it doesn’t check for new connections unless it’s explicitly instructed to do so.
No Parallelism for I/O: Redis doesn’t spawn separate threads to handle I/O events concurrently. If it’s busy with a large set of events, like 3 million requests, new connections will have to wait until the current operations are completed.

How Does Redis Handle This in Practice?

While it’s true that Redis can encounter this situation where new connections are delayed during heavy load, Redis has several mechanisms to mitigate such issues:

Efficient Event Loop and epoll:
- Redis is designed to handle I/O events efficiently using epoll, so even if there are 3 million events, the event loop is optimized for performance.
- The time taken to process events is proportional to the operations (e.g., reading, writing, command processing). Redis can process millions of requests per second depending on the workload.
Handling New Connections Quickly:
- Redis does not ignore new connections forever. The event loop checks for new events frequently. Even if epoll_wait() is handling 3 million requests, Redis can still check for new connections in between events or at the end of the event loop cycle.
- Redis polls for events continuously, so it can handle new connections relatively quickly once it finishes with its current batch of events.
Non-blocking accept():
- The accept() call itself is non-blocking. If there are no pending connections, it will immediately return -1 and Redis will continue with the rest of the events.
- When Redis checks for new events with epoll_wait(), it still keeps an eye out for new client connections, so new connections don’t need to wait forever. They just have to wait for the event loop to reach the point where Redis can call accept() again.
Redis’ Event Loop is Fast:
- Redis is optimized to handle a very high volume of requests in a short time. Processing 3 million requests may seem like a long time, but Redis can handle many operations very quickly because of its in-memory architecture and efficient event handling.
- Redis’s event loop is extremely fast at processing events, so even with a high volume of requests, it can usually handle new connections fairly quickly.

Key Points to Address the Concern:

New Connections Wait Temporarily:
- Yes, new connections can wait for the event loop to catch up, but Redis is very responsive in handling new connections as soon as it’s done processing the current batch.
- In high-traffic environments, a few milliseconds of delay in accepting a new connection (while the event loop processes existing requests) might occur, but Redis is optimized to process a huge number of requests in a very short time.
Scaling Redis with Clustering:
- To handle extremely high loads, Redis Clustering can be used. Redis Cluster allows horizontal scaling by splitting the data across multiple Redis nodes, which helps distribute the load and reduces the likelihood of a single instance being overwhelmed.
- If Redis is unable to handle all incoming requests on a single instance, sharding the workload across multiple Redis nodes helps mitigate such bottlenecks.
Latency of New Connections:
- Redis does not block forever for new connections. If Redis is busy, the new connection may have to wait only for the event loop to check again for new connections. This time is usually very short, but in extreme traffic conditions, there could be brief delays.

Conclusion:

Redis, being a single-threaded, event-driven server, handles thousands or even millions of requests by processing them in an efficient event loop. While new connections can temporarily wait if Redis is busy handling a large number of events (like 3 million requests), Redis’s non-blocking I/O model ensures that connections are eventually accepted and processed in a timely manner.

To further optimize Redis for heavy workloads, clustering can be used to distribute the load, and Redis’s performance optimizations (like using epoll) allow it to handle vast numbers of connections efficiently.

Let me know if you'd like further clarification or details!

ok, assume i have written a java code which opens 10 threads, and all these 10 threads hits Redis to read the data, does Redis opens 10 sockets in total, 1 for each thread or does it open only 1 socket as my java process is one ?

In the scenario you described, where you have written a Java program with 10 threads, and each thread hits Redis to read data, the way sockets are used depends on how your Java application is structured and how it interacts with Redis. Let’s break it down:

Socket Behavior in Your Java Application

Java Process with Multiple Threads:
- You mentioned that your Java code opens 10 threads. In Java, threads are lightweight and share the same memory space (the same Java process).
- All 10 threads in your Java application will share one single network connection when they are interacting with Redis via the same Redis client.
Redis Connection in Java:
- When your Java application sends requests to Redis, it does so via a Redis client library (e.g., Jedis, Lettuce, etc.).
- Regardless of the number of threads, the Redis client library typically opens only one connection (socket) to Redis by default. This is because all threads in your Java process use the same connection object to interact with Redis.
- If your Redis client is single-threaded, only one socket is used to send commands to Redis, and the threads will share this connection.
So, in your case, 10 threads will share a single socket for communication with Redis, and Redis will see one connection from your Java process.

How Does This Work Internally?

Multiple Threads, Single Socket:
- When each thread in your Java application makes a request to Redis, it does so by writing to the same socket.
- The Redis client library internally handles the synchronization of these requests, ensuring that only one thread writes to the socket at a time. It effectively queues up the requests from different threads and sends them to Redis in the order they were received, respecting thread safety.
- Redis, as a single-threaded server, handles the commands sequentially, processing one request after the other, and sending the responses back through the same socket.
Connection Pooling:
- In many cases, especially when working with Redis in a multi-threaded environment, connection pooling is used.
- With connection pooling, multiple connections (sockets) to Redis can be opened, allowing each thread to use a separate connection if needed.
- For example, if you're using a connection pool (with libraries like Jedis), you can configure the pool to provide one connection per thread, which would result in multiple sockets being used. In this case, each thread might have its own connection (socket) to Redis.
In this scenario:
- The connection pool can open multiple sockets (e.g., one per thread) to Redis. If you configure the pool to have 10 connections, each thread could use its own socket.
- Redis will then receive 10 different connections from your Java application (each thread has a separate socket).

Summary:

Single Socket: By default, if your Java application uses a single connection to Redis (without connection pooling), all 10 threads will share one socket for communication with Redis.
Multiple Sockets (Connection Pooling): If you configure your Redis client to use connection pooling, then Redis will open one socket per thread. In this case, 10 threads will result in 10 different sockets being opened to Redis.

Example Scenarios:

Single Connection (No Connection Pooling):
- Java process: 1
- Threads: 10
- Redis connections: 1 socket (shared by all 10 threads)
With Connection Pooling (Multiple Connections):
- Java process: 1
- Threads: 10
- Redis connections: 10 sockets (1 per thread, if using connection pooling)

Conclusion:

In your case, if you are not using connection pooling, Redis will see only one socket coming from your Java process, and all 10 threads will share this single socket for communication. If you are using connection pooling, then each thread can open its own socket, and Redis will see 10 connections in total.

Let me know if you have more questions or need further clarification!

I use single redis client object in all the 10 threads, but i have created a Redis connection pool size of 20. how about now ?

If you are using a single Redis client object in all 10 threads, but you have configured a Redis connection pool with a pool size of 20, then the behavior will be different from the default single socket scenario. Let's break this down:

Connection Pooling with 20 Connections

Redis Connection Pool:
- When you create a Redis connection pool with a size of 20, the pool will manage up to 20 connections to the Redis server.
- The connection pool is responsible for creating, distributing, and reusing Redis connections. The Redis client will request connections from the pool when needed, and after a request is completed, the connection will be returned to the pool for reuse.
How It Works with 10 Threads:
- Since you have 10 threads and a connection pool size of 20, the pool can supply up to 20 connections to Redis.
- Each thread in your Java application can acquire a connection from the pool. If all 10 threads need to access Redis, they can use up to 10 connections (since the pool has a maximum size of 20, which is larger than 10 threads).
- If all 10 threads try to access Redis concurrently, they will each get a connection from the pool, and Redis will handle these connections independently.

How Many Sockets Are Open to Redis?

With the connection pool configured to hold 20 connections, Redis will open up to 20 sockets (one socket per connection) to Redis.
If all 10 threads need to access Redis simultaneously, the pool will distribute the connections as follows:
- Each of the 10 threads will borrow one connection from the pool.
- Redis will see up to 10 connections (sockets) from your Java process, but the connection pool allows up to 20 concurrent connections in case additional threads or requests require them.

Thread and Socket Distribution:

Each thread can acquire a separate connection (socket) from the pool if it needs to interact with Redis.
If all 10 threads request a Redis connection at the same time, the pool will supply 10 connections. Redis will now handle 10 connections from your Java process (each thread using one connection).
If there are fewer threads than the pool size (e.g., if only 5 threads are active), only 5 connections will be used, and the remaining connections will stay idle in the pool.

Redis View:

Redis will see each active connection as a separate socket.
If your connection pool is configured with a size of 20, and all 10 threads are actively using Redis, Redis will see 10 active sockets from your Java process.
If you start additional threads or increase the activity of existing threads, Redis can use up to 20 concurrent connections from your application.

Summary:

Single Redis Client, Multiple Threads: The single Redis client object in your Java application can manage multiple threads, but the key to distributing Redis connections is the connection pool.
Connection Pool Size: If your connection pool is set to 20, Redis will see up to 20 concurrent sockets (one socket per connection).
- If all 10 threads are active and requesting Redis at the same time, they will each use a separate connection from the pool, so Redis will see 10 sockets.
- If more threads are added, and if the pool size allows, Redis can handle up to 20 sockets simultaneously.

Final Answer:

With a Redis connection pool size of 20, Redis will open up to 20 sockets. Each thread in your Java application can use a separate connection from the pool. Since you have 10 threads, 10 sockets will be used by your application to communicate with Redis (one per thread). The remaining connections in the pool will be available for future use, up to the maximum pool size of 20.

Let me know if this clears up your question or if you'd like more details!

do you know how a sample Redis socket request would look like which is received from the client ?

Yes, I can explain how a Redis socket request looks like when it is sent from a client to the Redis server. Redis uses a simple protocol called RESP (REdis Serialization Protocol) for communication between the client and the server. RESP is designed to be simple and efficient for the tasks Redis performs.

Overview of Redis Protocol (RESP)

The basic structure of a Redis request is built using RESP, which defines a format for sending commands to the Redis server and receiving responses. RESP has a very simple design, making it lightweight and efficient.

Format of a Redis Command

A Redis command consists of a series of bulk strings or simple strings encoded in a specific format. Below is the structure of a typical Redis request:

Basic Structure:

Length-based encoding: The size of the argument or string is always specified before the actual content.
RESP Encoding: The request consists of a sequence of bulk or simple strings.
Commands: Each command is sent in a simple ASCII format, and the arguments follow the command.

Example: `SET` Command

Let’s look at an example where the Redis client sends a SET command to the server. The command will set a key "myKey" to the value "42".

Redis Client Request for `SET myKey 42`:

plaintext
*3\r\n$3\r\nSET\r\n$5\r\nmyKey\r\n$2\r\n42\r\n

This is the RESP encoded request sent from the client to Redis.

Explanation of the Request:

*3: This means that the request consists of 3 parts (the command SET and two arguments: myKey and 42).
$3: This means the next string is 3 bytes long (i.e., "SET").
\r\n: A carriage return and newline, marking the end of the string or field.
$5: This means the next string is 5 bytes long (i.e., "myKey").
$2: This means the next string is 2 bytes long (i.e., "42").

Thus, this entire request is composed of the following parts:

*3: Indicates 3 parts in the command (1 command + 2 arguments).
$3 SET: The command itself (SET).
$5 myKey: The key (myKey).
$2 42: The value (42).

Another Example: `GET myKey`

Now let’s see an example of a GET command to fetch the value of myKey:

plaintext
*2\r\n$3\r\nGET\r\n$5\r\nmyKey\r\n

Explanation of the Request:

*2: This means the request consists of 2 parts (the command GET and the key myKey).
$3 GET: The command (GET).
$5 myKey: The key (myKey).

Common RESP Data Types

Simple String (+ prefix): A simple string is a single line without line breaks (e.g., +OK).
Errors (- prefix): Errors are returned by Redis when a command is invalid or when there’s an issue (e.g., -ERR wrong number of arguments).
Integers (: prefix): Redis may return integers in response (e.g., :1).
Bulk String ($ prefix): These are used for strings that are larger than simple strings or for returning the result of a command (e.g., $5\r\nvalue\r\n).
Arrays (* prefix): An array is a sequence of bulk strings (e.g., *3\r\n$3\r\nSET\r\n$5\r\nmyKey\r\n$2\r\n42\r\n).

Typical Redis Socket Request Cycle

Client Side:
- The client generates the Redis request (using RESP format).
- The request is sent to Redis through the socket connection.
Server Side (Redis):
- Redis receives the data on the socket.
- It decodes the RESP data to interpret the command and arguments.
- Redis processes the request, and once finished, it sends a response back to the client using RESP format.

Redis Response to Client:

For the SET myKey 42 command, Redis will typically respond with:

plaintext
+OK\r\n

For the GET myKey command, if the key exists, Redis will respond with:

plaintext
$2\r\n42\r\n

If the key doesn’t exist, Redis will respond with:

plaintext
$-1\r\n

Summary of Key Points:

Redis communicates using the RESP (REdis Serialization Protocol), which is simple and efficient.
Each command is encoded with size information (using *, $, +, -, etc.).
A typical request includes a command followed by arguments (e.g., SET myKey 42 or GET myKey).
The format is text-based, with line breaks (\r\n) separating elements, ensuring clear and structured communication.

Conclusion:

So, the Redis socket request sent from the client is essentially a RESP-encoded message containing the command and arguments, followed by a response. The communication between the client and server happens over a socket, with Redis handling multiple such requests efficiently using its single-threaded event loop.

Let me know if you'd like further clarification on any aspect!

Friday, 27 December 2024

Redis internals

ChatGPT said:

1. Redis as Single-Threaded

2. Challenges in Handling Concurrent Connections

3. I/O Multiplexing in Redis

How it works:

Key Steps:

Underlying Library:

4. Why is Redis Fast with This Approach?

5. Use Case for Single Threaded Design

Limitations

Summary

You said:

ChatGPT said:

Key Idea: Single-Threaded Execution Ensures Atomicity

Example Scenario: Two Requests to Modify the Same Key

What Happens Internally in Redis:

Avoiding Dirty Reads

Visualizing the Process

Real-Life Example in Code

Initial State:

Clients’ Requests:

Redis Execution (Sequential):

Key Takeaways

You said:

ChatGPT said:

1. Redis Commands Are Extremely Fast

2. Efficient I/O Multiplexing

3. Simple and Lock-Free Design

4. Low Latency and High Throughput

5. Batching and Pipelining

6. Handling Many Concurrent Connections

7. Typical Latency Breakdown

8. Example: Queue Behavior in Redis

How Redis Handles These:

9. When to Scale Redis

Key Takeaways

You said:

ChatGPT said:

Redis Workflow: Handling 3 Million Requests

Step 1: Requests Arrive

Step 2: Redis Uses I/O Multiplexing

Step 3: Redis Event Loop

Efficiently Processing 3 Million Requests

1. Reading Commands

2. Executing Commands

3. Sending Responses

How Redis Keeps Up with 3 Million Requests

1. Fast Execution Times

2. High Throughput with I/O Multiplexing

3. Avoiding Blocking

4. Pipelining Optimization

Real-World Example

Key Factors Behind Redis’s Speed

Conclusion

You said:

ChatGPT said:

How Redis Handles Millions of Sockets in a Single Thread

1. I/O Multiplexing: The Core Concept

2. Leveraging OS Mechanisms

3. Redis’s Event Loop Workflow

Why This Works for 3 Million Sockets

1. Minimal Overhead

2. High Scalability

3. Fast Event Processing

Example: Redis with 3 Million Requests

Why Redis Scales with Single Thread

Key Takeaways

You said:

ChatGPT said:

1. How Many Connection Sockets Can an OS Open?

Key Factors Affecting Socket Limit

Handling Other Processes

2. How Does Redis Know Which Sockets to Register with epoll?

How Sockets Are Registered:

3. Does epoll Send a 'Ready' Message or Read the Socket for Redis?

How epoll Works:

Detailed Flow:

Summary of Answers

2. How Does Redis Know Which Sockets to Register with `epoll`?

3. Does `epoll` Send a 'Ready' Message or Read the Socket for Redis?

How `epoll` Works:

How `epoll` Works:

Why Does Redis Use `epoll_wait()`?

1. `epoll` is Event-Driven, Not Interrupt-Driven

Why Doesn’t `epoll` Push Notifications Directly?

How `epoll_wait()` Fits in Redis

Redis and `epoll_wait()`

Flow of `epoll_wait()` in Redis

Redis’s Event Loop and `epoll_wait()`

Flow of Redis’s Event Loop with `epoll_wait()`

1. Blocking in `epoll_wait()`:

4. Returning from `epoll_wait()`:

Detailed Explanation of `accept()` and Epoll Registration: