Initial ideas for framework's runtime and integration

[sergefdrv]

This is a discussion for gathering initial ideas concerning the framework's runtime and integration as described in #54.

[anchalshivank]

Here's the combined and updated table with the additional criteria of non-determinism and error handling:

Criteria	Rust	C++	Go	Java	Python
Concurrency	Excellent (async/await, multi-threading without race conditions, highly efficient task scheduling)	Good (std::thread, Boost, manual management required, potential for race conditions)	Excellent (goroutines and channels, lightweight concurrency)	Good (thread pools, ExecutorService, built-in support but JVM limitations)	Poor (due to GIL, but asyncio is useful for I/O-bound concurrency)
Asynchrony	Excellent (native async/await, Tokio for async I/O)	Good (possible with libraries like Boost.Asio, but more complex)	Excellent (native goroutines with asynchronous capabilities)	Good (async features introduced in newer versions, but historically weaker)	Good (asyncio for async I/O, limited for CPU-bound tasks due to GIL)
Memory Safety	Excellent (ownership model, borrow checker prevents common memory issues)	Moderate (manual memory management, potential for memory leaks, unsafe operations)	Good (automatic garbage collection, but no fine-grained memory control)	Good (automatic garbage collection, strong typing helps prevent some memory errors)	Good (automatic garbage collection, but less performance control)
Garbage Collection	No (manual memory management with RAII or smart pointers, predictable memory usage, no GC pauses)	No (manual memory management with RAII, requires careful management to avoid leaks)	Yes (garbage collection, but with occasional pauses and latency spikes)	Yes (sophisticated GC, but can cause long pauses depending on the JVM configuration)	Yes (automatic garbage collection, but performance impacts in memory-intensive apps)
Performance	Excellent (compiles to native code, fine control over performance and memory)	Excellent (compiles to native code, performance tuning possible)	Very good (fast execution, but GC introduces occasional overhead)	Good (JVM optimizations, but generally slower than Rust/C++ due to JVM overhead)	Poor (interpreted, high overhead, especially for CPU-bound tasks)
Cross-Language Integration	Good (excellent FFI support, can interoperate with C, generate bindings for multiple languages)	Good (supports FFI well, easy integration with C, tools for other languages like Python)	Moderate (supports FFI but limited tooling for non-C languages)	Good (JNI provides native integration, but with some performance overhead and complexity)	Excellent (multiple tools for FFI, ctypes, and smooth integration with C)
Non-Determinism	Excellent (manual control, deterministic by default, but can handle non-determinism where needed)	Good (manual control, can handle non-determinism but requires careful management)	Excellent (goroutines and channels handle non-determinism gracefully)	Good (deterministic by default, non-determinism can be managed but not inherently supported)	Moderate (non-determinism managed via asyncio, but GIL limits concurrency)
Error Handling	Excellent (modern error handling with Result and Option types, pattern matching)	Good (exception handling with try/catch, but can be complex and error-prone)	Good (error handling via multiple return values, less sophisticated than exceptions)	Good (exception handling is robust and integrated with language features)	Good (exception handling with try/except, straightforward but not as strong as Rust)

Key Observations:

• Concurrency: Rust and Go offer strong concurrency models, whereas Python’s GIL limits its concurrency capabilities.
• Asynchrony: Rust and Go excel with their native async support, while Java and C++ have less integrated solutions.
• Memory Safety: Rust is outstanding due to its strict ownership model. C++ is less safe due to manual memory management.
• Garbage Collection: Rust and C++ avoid GC, making them suitable for real-time systems. Go, Java, and Python use GC, which adds some overhead.
• Performance: Rust and C++ offer the best performance due to their system-level control and lack of runtime overhead, followed by Go. Java and Python are slower.
• Cross-Language Integration: Rust and C++ have strong FFI support, while Python also offers excellent interop capabilities.
• Non-Determinism: Rust and Go manage non-determinism effectively. C++ and Java handle it with more complexity, while Python is more limited.
• Error Handling: Rust provides modern and safe error handling. C++ and Java also offer robust mechanisms, while Python’s error handling is straightforward but less sophisticated.

Based on these observations, Rust stands out as a robust choice for your framework due to its strong support in concurrency, asynchrony, performance, and error handling, as well as its ability to handle non-determinism effectively.

[sergefdrv]

There's little doubt that Replica_IO should be based on Rust 🙂

[sergefdrv]

There's little doubt that Replica_IO should be based on Rust 🙂

To make it clear, I meant it's almost a sure thing that it'll be based on Rust

[anchalshivank]

Before addressing how the runtime should be designed, let's first define the core terms to establish a clear understanding.

1. Process:

A process is an independent program in execution. It contains its own memory space and system resources. Multiple processes can run in parallel on different CPU cores but do not directly share memory.

Example: Running a web server is an example of a process.

2. Thread:

A thread is the smallest unit of execution within a process. Multiple threads can exist within a single process, sharing the same memory space.

Example: A thread could handle a specific client request on a web server.

3. Task:

A task represents a unit of work that can be executed by a thread. It can be a computational job like processing a network request or performing calculations.

Example: Reading data from a file or processing an incoming HTTP request.

4. CPU Core:

A core is a hardware unit within a CPU capable of executing a single thread at a time. A modern CPU can have multiple cores, allowing for true parallel execution of multiple threads.

Example: A quad-core processor can run four threads in parallel (ignore virtual threads for now).

5. Concurrency:

Concurrency refers to the ability to run multiple tasks at overlapping times. It does not necessarily imply simultaneous execution, as threads may share a single core, with the operating system switching between them.

Example: A web server handling multiple client connections by switching between them.

6. Parallelism:

Parallelism is a specific form of concurrency where tasks actually run simultaneously on multiple CPU cores.

Example: Two cores processing different data at the same time.

7. Asynchrony:

Asynchrony allows a task to be suspended while waiting for external resources (e.g., I/O operations) without blocking the thread, enabling other tasks to proceed.

Example: A web server waiting for a database response but still handling other client requests in the meantime.

8. Non-determinism:

Non-determinism arises when the order of task execution is unpredictable due to factors like thread switching, resource contention, or external events.

Example: Two threads trying to access shared memory may execute in a different order each time the program runs.

9. Cluster:

A cluster is a group of interconnected machines (nodes) that work together to perform distributed tasks. Each node can run its own processes and threads, but the tasks are coordinated to ensure efficient, scalable, and fault-tolerant execution across the entire cluster.

Example: In a distributed system, a cluster can have multiple servers (nodes) handling tasks together. If one node fails, another can take over to maintain system availability.

[anchalshivank]

Let's create an analogy based on a factory environment to explain each of these computing concepts:

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Factory Environment:

• CPU Core: Imagine the factory as having multiple assembly lines. Each assembly line is equivalent to a CPU core. It is capable of working on one task at a time.
• Processes: The factory has various departments, and each department is responsible for building different products (e.g., electronics, furniture, cars). Each department is independent and has its own resources and team. This is like a process that has its own memory and resources.
• Threads: Within a department, you have workers (threads) who share the same tools and space, but each focuses on different aspects of the work. They can work together to build products, and they coordinate with each other to ensure that the overall task is completed.
• Task: A task is a specific piece of work that needs to be completed, like assembling a product. For example, assembling a chair is a task for a furniture department.
• Resources: Resources in this analogy are tools, machines, and the assembly line itself that the workers use to perform their tasks.
• Clusters: A cluster is like having multiple factories spread across different locations, all working together to complete various tasks. These factories can communicate and share work when needed, so if one factory is overloaded or stops working, another can take over.

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Concepts with Analogies:

1. Process (Department)

   • A process is like a department in the factory that is independently responsible for building a specific type of product. Each department has its own dedicated resources (tools, workers) and does not share them with other departments.
   • Example: One department builds cars while another builds furniture. They don’t interfere with each other.

2. Thread (Workers in a department)

   • A thread is like an individual worker inside a department. All the workers share the same tools (memory) and workspace (resources), but each worker can work on a specific step of the product assembly at the same time.
   • Example: One worker handles painting the product while another assembles the parts.

3. Task (Building a product)

   • A task is a specific job that the workers need to perform. For example, assembling a chair is a task in the furniture department.
   • Example: TaskA could mean building a chair, while TaskB means building a table. Both tasks are different, but they may use the same workers and tools.

4. Core (Assembly Line)

   • A core is like an assembly line in the factory. Each line can only work on one product at a time, but if you have multiple assembly lines, different tasks can be worked on simultaneously.
   • Example: A quad-core processor is like a factory with four assembly lines, where four different workers (threads) can build different products at the same time.

5. Concurrency (Overlapping work on a single line)

   • Concurrency is like multiple workers taking turns using the same assembly line. Only one worker can use the line at a time, but the factory switches between them quickly, so it feels like they are all progressing at the same time.
   • Example: Worker A assembles part of a chair, then steps aside, and Worker B starts assembling a table. The factory keeps switching between them, so both tasks seem to be progressing.

6. Parallelism (Multiple assembly lines working at once)

   • Parallelism happens when multiple assembly lines are running simultaneously, each with its own worker, so they don’t need to take turns. Tasks are truly being worked on at the same time.
   • Example: One worker on Assembly Line 1 builds a chair while another worker on Assembly Line 2 builds a table simultaneously.

7. Asynchrony (Non-blocking work)

   • Asynchrony is like a worker in the factory waiting for materials (tools) to arrive, but instead of standing idle, they do something else in the meantime. The worker only resumes the original task once the materials arrive.
   • Example: Worker A starts assembling a chair but pauses because screws aren’t available yet. In the meantime, Worker A starts assembling a table. When the screws arrive, Worker A returns to finish the chair.

8. Non-Determinism (Unpredictable order of execution)

   • Non-determinism happens when the order in which the workers complete their tasks changes every time due to various unpredictable factors like tool availability, which worker gets to use the assembly line first, or external disruptions.
   • Example: Sometimes Worker A finishes building the chair before Worker B finishes the table, but other times Worker B finishes first. The order isn’t predictable because different factors affect how fast each worker can finish their task.

9. Cluster (Multiple Factories Working Together)

   • A cluster is like having multiple factories working together across different locations. Each factory can handle its own production, but they are connected, and if one factory becomes overloaded or shuts down, other factories can step in to share the load or take over. This allows production to continue smoothly without any one factory being overwhelmed.
   • Example: Factory A is working on chairs, but it’s overloaded, so some of its work is sent to Factory B. If Factory A stops working, Factory B can still continue building products, ensuring production doesn’t halt.

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Assumption: Shared Resource

If both Worker A and Worker B need the same tool (Tool A) to complete their tasks, they have to take turns using it. This is like two threads trying to access a shared resource (memory or a file). The factory (operating system) will decide which worker gets to use the tool first. If both workers need the tool at the same time, one has to wait until the other finishes, introducing potential delays or conflicts (like thread contention in programming).

[dev-ng]

@anchalshivank Does it make sense to include Cluster in the core terms?

[anchalshivank]

@anchalshivank Does it make sense to include Cluster in the core terms?

Yes it makes sense to include cluster in the core terms , thanks for pointing it out.

[sergefdrv]

Core (CPU Core)

I would always use "CPU core" rather than just "core" to avoid possible confusion.