DEV Community: NARESH-CN2

🚀 Bypassing the Python GIL: How I Processed 10M Rows in 0.26s with C

NARESH-CN2 — Tue, 28 Apr 2026 09:40:09 +0000

The "Abstraction Tax" is Real

We love Python for its simplicity, but when we hit massive datasets, we pay a price. Standard libraries like Pandas are incredible, but they often struggle with memory overhead and the Global Interpreter Lock (GIL) when pushing the physical limits of hardware.I built HydraCore to prove that you don't always need a bigger AWS instance—sometimes you just need a closer relationship with the metal.🏗️ The Architecture: How it WorksTo achieve these speeds, I moved the ingestion logic out of the Python interpreter and into a native C-extension. The system relies on three architectural pillars:1. Zero-Copy Memory (mmap)Instead of reading a file into a buffer and then copying it into a Python object, I use mmap to map the file directly into the process's address space. This allows the OS to handle paging and gives us direct access to the raw bytes.2. The Hydra (Multi-threading)By using POSIX threads (pthreads) in C, I can bypass the GIL entirely. The engine spawns multiple "heads" to scan the memory-mapped file in parallel, identifying signals and thresholds before Python even knows the data exists.3. Native NumPy HandshakeThe processed data is handed directly to a NumPy array buffer. Because NumPy is built on C-contiguous memory, the "handshake" between the C-engine and Python is nearly instantaneous.📊 The Benchmarks (10M Row CSV)LibraryExecution TimeThroughputStandard Pandas$\approx 2.70$ Seconds~3.7M rows/secHydraCore (C)0.26 Seconds38.4M rows/secPerformance Gain: $10.3\times$ Increase in Throughput.🛠️ Why Build a Custom Extension?You might ask: "Why not just use Polars or DuckDB?" While those tools are fantastic, building a custom C-extension allows for Edge Logic. For example, if you need to perform specific volatility filtering or threshold detection during the ingestion phase to save RAM, a custom engine is the only way to achieve maximum efficiency.📂 Open Source & FeedbackI’ve open-sourced the core logic and the benchmark scripts. I’m looking for feedback from systems engineers on how to further optimize the thread-boundary synchronization.Check the source code here:👉 https://github.com/naresh-cn2/hydra-core

Stop Paying the Abstraction Tax : How I Built a C-Engine 12x Faster than Pandas

NARESH-CN2 — Mon, 27 Apr 2026 06:01:44 +0000

Python is the king of data science, but it charges a heavy price for convenience. When you use pd.read_csv() on a 10GB+ file, Python attempts to load the data into RAM, wrapping every byte in a heavy PyObject.

The result? OOM (Out of Memory) crashes and massive AWS bills. I decided to go to the metal to see if I could bypass this "Abstraction Tax" entirely.

The Problem: The Double-Copy Penalty
Standard data pipelines move data from the SSD ➔ OS Kernel ➔ User Space ➔ Application. This constant copying wastes CPU cycles and explodes the memory footprint.

The Solution: Memory Mapping (mmap)
I built the Axiom Zero-RAM Extractor in pure C. Instead of loading the file, Axiom uses mmap to treat the SSD as a direct array.

Key Architectural Gains:

Zero-Copy: Data is only pulled into the L1/L2 cache in tiny 4KB chunks as the CPU requests them.

Mechanical Sympathy: Sequential access triggers the CPU's Hardware Pre-fetcher, hitting the physical read limit of the NVMe drive.

The 1GB Benchmark (10 Million Rows)
❌ Pandas Baseline: 2.70 seconds (High RAM spike)

✅ Axiom C-Engine: 0.20 seconds (Near-zero RAM used)

The ROI
By dropping the memory footprint to near-zero, this architecture allows you to process 100GB+ files on a $10/month micro-instance instead of a $250/month memory-optimized cluster.

The Source Code
You can find the hardened C-engine, the MIT License, and the benchmark generator here:
https://github.com/naresh-cn2/axiom-zero-ram

Hardening a 1P/3C Broadcast Engine: Achieving 33.92M records/s with C11 Atomics

NARESH-CN2 — Mon, 20 Apr 2026 05:56:41 +0000

Most high-volume data pipelines suffer from a hidden "Abstraction Tax." When you move telemetry through standard Python/Java layers, you aren't just losing speed—you’re risking data integrity due to producer-consumer race conditions.

I’ve just finalized the Axiom Hydra V3.1 architecture to solve this.

The Challenge: Multi-Consumer Integrity
In a 1-Producer / 3-Consumer broadcast model, the primary risk is data being overwritten by the producer before a lagging consumer has finished reading. Standard locking mechanisms kill throughput.

The Solution: Hardware-Aligned Atomics
By implementing a hardened C11 atomic head-tracking array, Axiom Hydra enforces deterministic backpressure. This ensures zero-data-loss integrity while maintaining near-theoretical throughput limits of the NVMe/CPU interface.

📊 V3.1 Hardened Performance
The latest benchmark run on consumer-grade hardware (Ryzen 7 7840HS) confirms:

Throughput: 33.92 Million records/sec

Integrity Model: Atomic Backpressure

Latency: Deterministic sub-millisecond processing

🏢 Business Value
This level of efficiency allows for a 90% Cloud Cost Reduction by processing massive telemetry streams on minimal hardware.

I'm moving the project into the "Maintenance and Audit" phase. The full technical summary and source are live on GitHub for those auditing their own synchronization models.

Full Repository:
https://github.com/naresh-cn2/Axiom-Turbo-IO

7.22M Logs/Sec on a Laptop: Beating the "Abstraction Tax" with C11 Atomics

NARESH-CN2 — Thu, 16 Apr 2026 11:11:25 +0000

I’ve been obsessed with the "Abstraction Tax" lately—the massive performance hit we take when we prioritize developer convenience over hardware reality.

To test this, I built the Axiom Hydra V3.0, a multi-threaded telemetry engine in pure C. I wanted to see how far I could push data ingestion on a consumer-grade Acer Nitro laptop.

The Benchmark (1.74 Billion Logs)
🐍 Python Baseline: 1.26 Million logs/sec (~23 mins compute)

⚡ Axiom Hydra (C): 7.22 Million logs/sec (~2 mins compute)

That is a 91% reduction in compute time. ---

The "S-Rank" Architecture
How do you achieve 11x speedups without a cloud cluster? Mechanical Sympathy.

Cache Alignment (alignas(64))
Most multi-threaded systems suffer from False Sharing. When CPU cores fight over the same 64-byte cache line, the performance collapses. I used explicit hardware alignment for the ring buffer's head and tail pointers to ensure each core has its own dedicated lane.
Lock-Free Synchronization
No mutexes. No semaphores. I utilized stdatomic.h with Acquire/Release memory semantics. This allows the Producer and Consumers to communicate at the hardware bus speed without context-switching to the Kernel.
The Immortal Watchdog
Lock-free structures usually deadlock if a thread hangs. I implemented a heartbeat-based watchdog. If a consumer stalls, the Master Producer detects the "Ghost Head" and skips backpressure, keeping the global stream alive.

The Mission: Titan Aeon
This is Day 18 of my Solo Leveling journey—a 30-month protocol to build institutional-grade infrastructure from a bedroom. Engineering isn't about adding more servers; it’s about removing the friction between your logic and the silicon.

Check out the full source code on GitHub:
https://github.com/naresh-cn2/Axiom-Hydra-Stream

Why I Bypassed Pandas to Process 10M Records in 0.35s Using Raw C and SIMD

NARESH-CN2 — Wed, 15 Apr 2026 12:08:06 +0000

I was recently challenged to build a system that could ingest and analyze 10,000,000 market records (OHLCV) using Smart Money Concepts (SMC) logic in under 0.5 seconds.

Standard wisdom says to use Python/Pandas or Polars. But for specific, high-frequency ingestion, I wanted to see how far I could push the silicon on my Acer Nitro V 16.

The Result: Abolishing the "Abstraction Tax"
By talking directly to the metal, I hit 0.35s for 10M rows. That's a throughput of approximately 28 million records per second.

The Benchmarks:

Python/Pandas Baseline: 3.28s

Axiom Hydra V5 (C): 0.35s

Real BTC History (172k rows): 0.011s

How I Did It (The Tech Stack)
To achieve zero-latency, I focused on four hardware-aligned pillars:

Memory Mapping (mmap): Instead of loading the file into RAM (which causes OOM crashes on large files), I treated the SSD as a direct array. This results in virtually zero RAM usage.

SIMD / AVX2 Vectorization: I packed 8 market records into 256-bit registers, allowing the CPU to process multiple data points in a single clock cycle.

Fixed-Point Arithmetic: Floating-point units have higher latency. I scaled the Bitcoin price data to integers to ensure maximum precision with minimum clock cycles.

POSIX Multithreading: Parallelizing the workload across 8 cores to ensure no CPU cycle is wasted.

The Literal ROI
This isn't just a "speed flex"—it's a financial decision.

Time: Reduced execution from 10 minutes to 1 minute per run.

Compute: Saves ~150 hours of compute monthly for a typical 1,000-run/day pipeline.

Infrastructure: You can downgrade from expensive memory-optimized cloud instances to standard micro-nodes.

The "Solo Leveling" Journey
I am a first-year B.Com student pursuing a 30-month roadmap to master systems engineering and quantitative finance. My goal is to translate machine speed into balance sheet savings.

Check the Source on GitHub:
https://github.com/naresh-cn2/Axiom-Turbo-IO

Entry Offer: If your data pipeline is timing out or bleeding cash, I’ll run a Free Bottleneck Analysis on your first 1GB of logs. I’ll show you exactly where your hardware is being throttled. DM me on LinkedIn or open an issue on the repo.

Bypassing the "Pandas RAM Tax": Building a Zero-Copy CSV Extractor in C

NARESH-CN2 — Tue, 14 Apr 2026 09:19:58 +0000

The Convenience Penalty
Python is a masterpiece of productivity, but for high-volume data ingestion, it charges a massive "Abstraction Tax."

When you run pd.read_csv(), Python isn't just reading data; it’s building a massive object tree in RAM. On a 20GB+ log file, even a simple extraction task can trigger an Out-of-Memory (OOM) crash. The standard "fix" is usually to scale up to an expensive high-memory instance on AWS.

I decided to see how much performance was being left on the table by talking directly to the metal.

The Solution: Axiom Zero-RAM Engine
I built Axiom in pure C to handle raw extraction with near-zero memory overhead.

Instead of loading the file into a buffer, I utilized mmap() (Memory Mapping). This treats the file on the SSD as a direct array in the process's virtual memory space. The OS handles the paging, and my engine uses raw pointers and a custom state machine to scan for delimiters at the hardware limit.

The Benchmarks
I tested a 1GB CSV (10 Million Rows) on my Acer Nitro V 16 (Ryzen 7):

Pandas Baseline: 3.28 seconds (Significant RAM spike/overhead)

Axiom Engine: 1.03 seconds (Zero RAM overhead)

A 3x speedup is great, but the real win is the stability. Axiom allows you to process 100GB+ files on a $10/month micro-instance without ever hitting a memory limit.

The Python Wrapper
I wanted to ensure this was usable for Data Engineers, so I wrote a Python wrapper. You can keep your existing workflow but swap the ingestion layer for a C-binary "scalpel."

Python
import axiom_engine

Extracts specific columns with hardware-level speed

axiom_engine.extract("huge_data.csv", columns=[0, 9], output="optimized.csv")
The Roadmap: Moving to SIMD
A 14-year Lead Engineer recently challenged me to move from Scalar logic (checking characters one-by-one) to SIMD (Single Instruction, Multiple Data).

My next iteration (Day 17) will utilize AVX2 instructions to scan 32 bytes of the CSV at the exact same time.

Check the Source
I’ve open-sourced the v1.0 engine here:
🔗 https://github.com/naresh-cn2/Axiom-Zero-RAM-Extractor

Note: If you’re dealing with a specific data bottleneck that is killing your RAM or cloud budget, I’m currently rewriting slow ingestion scripts in C for a flat fee. DM me or find me on LinkedIn.

Abolishing the "Python Tax": How I hit $3.06 \text{ GB/s}$ CSV Ingestion in C 🧱🔥

NARESH-CN2 — Sat, 11 Apr 2026 08:51:34 +0000

Standard Python data processing (Pandas/CSV) is often plagued by what I call the "Object Tax"—the massive overhead of memory allocation and single-core bottlenecks. This Saturday morning, I decided to see how close I could push my consumer-grade hardware (Acer Nitro 16 / Ryzen 7 7840HS) to its theoretical limits.The result? $3.06 \text{ GB/s}$ throughput. 🚀🏗️ The Technical ArchitectureTo hit these speeds, I had to bypass the high-level abstractions and talk directly to the metal. Here is the strategy:1. SIMD-Accelerated ScanningInstead of a standard character-by-character scan, I utilized memchr (which leverages AVX2/AVX-512 instructions) to process 32-byte chunks per CPU cycle. This identifies newline delimiters at nearly the speed of the memory bus.2. Parallel Memory Mapping (mmap)I moved ingestion to the kernel level. By utilizing a multi-threaded mmap approach, the engine treats the CSV file as a massive array in virtual memory. This eliminates user-space copy overhead and allows the OS to handle paging efficiently.3. Boundary HardeningWhen you process files in parallel chunks, the biggest risk is splitting a row across two workers. I implemented a thread-safe Skip-and-Overlap logic to ensure zero data loss while maintaining absolute concurrency across 16 logical threads.📊 The Benchmark ResultsMetricPython (Standard)Axiom Turbo (C)Performance GainThroughput$\sim 0.16 \text{ GB/s}$$3.06 \text{ GB/s}$$19.1x$Latency (10M Rows)$0.87\text{s}$$0.19\text{s}$$78.1\%$ ReductionRAM Footprint$\sim 1.9 \text{ GB}$$\sim 2 \text{ MB}$$99.9\%$ Reduction💡 Why This Matters (The Business Case)Hardware isn't slow; our abstractions are. If your cloud bill is spiking because your ingestion pipelines are hitting "Out of Memory" walls, you are paying a tax you don't owe. By moving the heavy lifting to the metal, we can process massive logs on low-tier instances that would usually require high-RAM memory-optimized nodes.Full Source & Benchmarks:https://github.com/naresh-cn2/Axiom-Turbo-IO

Stop Paying the "Python Object Tax": 10M Rows in 0.08s with C and Parallel mmap

NARESH-CN2 — Fri, 10 Apr 2026 10:38:10 +0000

I was benchmarking some data ingestion pipelines on my Nitro 16 (Ryzen 7) and honestly got pretty frustrated with how much overhead Python adds to basic I/O. Even with optimized Pandas code, processing 10M rows was hitting a wall because of how Python wraps every single data point in a high-level object.I decided to go "to the metal" to see what the hardware is actually capable of. I built Axiom Turbo-IO, a C-bridge that utilizes two specific systems-level optimizations:1. Memory Mapping (mmap)Instead of standard file I/O (which involves multiple user-space copies), I mapped the entire file directly to the virtual address space. This bypasses the "copying tax" and lets the OS handle paging.2. Parallel PthreadsI split the file into chunks and processed them across all 8 CPU cores simultaneously. By bypassing the Python Global Interpreter Lock (GIL), I’m getting near-instantaneous throughput.The "Grit": Boundary HardeningThe hardest part was ensuring data integrity. When you split a file into 8 chunks, you almost always cut a line in half. I had to write a custom "Skip and Overlap" algorithm to ensure that every thread finds the start of its first full line and finishes its last partial line. No double-counting, no lost data.📊 The Benchmark (10 Million Rows)EngineExecution TimeRAM UsageEfficiencyStandard Python~0.873s~1.5 GBBaselineAxiom Turbo-IO0.083s~8 KB19.08x FasterWhy I’m Open-Sourcing ThisI believe a small C/C++ bridge can save a massive amount of cloud compute cost in a production environment. If you're running massive logs through a high-RAM AWS instance, you might be overpaying for memory you don't actually need.GitHub Repository: https://github.com/naresh-cn2/Axiom-Turbo-IOLet's Talk PerformanceHow are you guys handling 100GB+ datasets? Are you sticking with Polars/DuckDB, or are you writing custom bridges for hyper-specific tasks?P.S. If your pipeline is currently crawling or hitting "Out of Memory" errors, I'm doing 3 free 10-minute performance audits this week. DM me or open an issue on GitHub if you want a second pair of eyes on your ingestion logic.

How to Bypass the Pandas "Object Tax": Building an 8x Faster CSV Engine in C

NARESH-CN2 — Thu, 09 Apr 2026 07:16:35 +0000

The Problem: The "Object Tax"If you’ve ever tried to load a 1GB CSV into a Pandas DataFrame, you’ve seen your RAM usage spike to 3GB or 4GB before the process inevitably crashes with an OutOfMemoryError.This isn't just a "Python is slow" problem. It's an Object Tax problem. Every single value in that CSV is being wrapped in a heavy Python object. When you have 10 million rows, those objects become a massive weight that sinks your performance.The Experiment: Dropping to the MetalI wanted to see exactly how much performance we are leaving on the table. I built a custom C-extension for Python called Axiom-CSV.The ArchitectureTo kill the latency, I used three specific systems-level techniques:Memory Mapping (mmap): Instead of reading the file into RAM, I map the file directly to the process's virtual memory address space.Pointer Arithmetic: I used C pointers to scan the raw bytes for delimiters (, and \n) rather than creating intermediate strings.Zero-Copy Aggregations: Calculations happen on the fly as the pointer moves. No DataFrames, no objects, no bloat.The Benchmarks (10 Million Rows / ~400MB CSV)I ran a simple aggregation (summing a column based on a status filter) against standard Pandas.MetricStandard PandasAxiom-CSV (C-Engine)ImprovementExecution Time10.61 seconds1.33 seconds~8x FasterPeak RAM Usage1,738 MB375 MB78% ReductionNote: The 375MB RAM usage for the C-engine is almost identical to the raw file size on disk. This is "Zero-Bloat" engineering.Why This Matters for Cloud BudgetsBy reducing the memory footprint by 78%, you can move data pipelines from expensive, high-memory AWS instances (like an r5.xlarge) to the cheapest possible instances (like a t3.micro).The result: You save thousands in infrastructure costs while your users get results 8x faster.Check the CodeI've open-sourced the C-bridge and the Python implementation here:👉 https://github.com/naresh-cn2/Axiom-CSVI'm curious—for those of you handling high-throughput data, where are you seeing your biggest bottlenecks? Is it I/O, or is it the Python heap?

How I cut Python JSON memory overhead from 1.9GB to ~0MB (11x Speedup)

NARESH-CN2 — Wed, 08 Apr 2026 09:10:57 +0000

The Problem: The "PyObject" TaxWe all love Python for its developer velocity, but for high-scale data engineering, the interpreter's overhead is a silent killer.I was recently benchmarking standard json.loads() on a 500MB JSON log file.The Result:⏱️ 3.20 seconds of execution time.📈 1,904 MB RAM spike.Why?Python's standard library creates a full-blown PyObject for every single key and value. When you are dealing with millions of log entries, your RAM becomes a graveyard of overhead. For a 500MB file, Python is essentially managing nearly 2GB in memory just to represent the data structures. For cloud infrastructure, this isn't just "slow"—it's an expensive AWS bill and a system crash waiting to happen.The Solution: Axiom-JSON (The C-Bridge)I decided to bypass the Python memory manager entirely for the heavy lifting. I built a bridge using:Memory Mapping ($mmap$): Instead of "loading" the file into a RAM buffer, I mapped the file's address space. The OS handles the paging, keeping the RAM footprint effectively flat regardless of file size.C Pointer Arithmetic: I used memmem to scan raw bytes directly on the disk cache. No dictionaries, no lists, no objects—until the specific data is actually needed by the Python layer.The Benchmarks (500MB JSON)MetricStandard Python (json.loads)Axiom-JSON (C-Bridge)ImprovementExecution Time3.20s0.28s$11.43\times$ FasterRAM Consumption1,904 MB$\approx 0$ MBInfinite ScalabilityThe ROI ArgumentIf you are running data pipelines on AWS or GCP, memory is usually your most expensive constraint. Moving from a 2GB RAM requirement to a few megabytes allows you to:Downgrade instance types (e.g., from memory-optimized r5.large to general-purpose t3.micro).Parallelize workers 10x more efficiently on the same hardware.$$\text{Efficiency Gain} = \frac{\text{Baseline Time}}{\text{Optimized Time}} \approx 11.4\times$$Get the CodeI have open-sourced the C engine and the Python bridge logic for anyone dealing with "Log-Bombing" issues:👉 GitHub: https://github.com/naresh-cn2/Axiom-JSONNeed a Performance Audit?If your Python backend is hitting a RAM wall or your cloud compute bills are ballooning, I’m currently helping teams optimize their data architecture and build custom C-bridges.

Python was too slow for 10M rows—So I built a C-Bridge (and found the hidden data loss)

NARESH-CN2 — Tue, 07 Apr 2026 08:17:40 +0000

The Challenge: The 1-Second Wall

In high-volume data engineering, "fast enough" is a moving target. I was working on a log ingestion problem: 700MB of server logs, roughly 10 million rows.

Standard Python line-by-line iteration (for line in f:) was hitting a consistent wall of 1.01 seconds. For a real-time security auditing pipeline, this latency was unacceptable.

But speed wasn't the only problem. I discovered something worse: Data Loss.

The Silent Killer: Boundary Splits

Most standard parsers read files in chunks (like 8KB). If your target status code (e.g., " 500 ") is physically split between two chunks in memory—say, " 5" at the end of Chunk A and "00 " at the start of Chunk B—the parser misses it entirely.

In my dataset, standard parsing missed 180 critical errors.

The Solution: Axiom-IO (The C-Python Hybrid)

I decided to bypass the Python interpreter's I/O overhead by building a hybrid engine.

1. The Raw C Core

Using C's fread, I pull raw bytes directly into an 8,192-byte buffer. This is hardware-aligned and minimizes system calls.

2. Boundary Overlap Logic

To solve the data loss issue, I implemented a "Slide-and-Prepend" logic. The last few bytes of every buffer read are saved and prepended to the next read. This ensures that no status code is ever sliced in half.

3. The Python Bridge

I used ctypes to create a shared library (.so). This allows Python to handle the high-level orchestration while the heavy lifting happens in memory-safe C.

The Benchmarks (700MB / 10M Rows)

Engine	Execution Time	Data Integrity (Errors Found)
Standard Python	1.01s	1,425,016
Axiom-IO (Hybrid)	0.20s	1,425,196

The result? A 5x speedup and 180 "Ghost" errors caught.

Conclusion

Sometimes, the best way to use Python is to know when to step outside of it. By aligning our software with how hardware actually reads memory, we didn't just gain speed—we gained truth.

Source Code & Benchmarks: https://github.com/naresh-cn2/Axiom-IO-Engine