{"id":4901,"date":"2026-10-08T02:05:05","date_gmt":"2026-10-07T20:35:05","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/"},"modified":"2026-10-08T02:05:05","modified_gmt":"2026-10-07T20:35:05","slug":"mastering-python-code-tips-for-efficient-programming","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/","title":{"rendered":"Mastering Python Code: Tips for Efficient Programming"},"content":{"rendered":"<p>[72439.123456] out_of_memory: Kill process 14201 (python3.11) score 942 or sacrifice child<br \/>\n[72439.123501] Killed process 14201 (python3.11) total-vm:16777216kB, anon-rss:14567424kB, file-rss:0kB, shmem-rss:0kB<br \/>\n[72439.123550] oom_reaper: reaped process 14201 (python3.11), now anon-rss:0kB, file-rss:0kB, shmem-rss:0kB<br \/>\n[72439.123600] traps: python3.11[14202] general protection fault ip:7f8a2c3d4e5f sp:7f8a1b2c3d4e error:0 in libc.so.6[7f8a2c300000+1a0000]<br \/>\nSegmentation fault (core dumped)<\/p>\n<p>$ gdb python3.11 core.14201<br \/>\n(gdb) bt<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6ac870f92d765\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6ac870f92d765\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#0_0x00007f8a2c3d4e5f_in_int_malloc_av0x7f8a2c50a020_bytes1024_at_mallocc_4123\" >0  0x00007f8a2c3d4e5f in _int_malloc (av=0x7f8a2c50a020 , bytes=1024) at malloc.c:4123<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#1_0x00007f8a2c3d6789_in_GI_libc_malloc_bytes1024_at_mallocc_3321\" >1  0x00007f8a2c3d6789 in __GI___libc_malloc (bytes=1024) at malloc.c:3321<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#2_0x00000000005a2b3c_in_PyObject_Malloc\" >2  0x00000000005a2b3c in PyObject_Malloc ()<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#3_0x00000000005b1f2a_in_PyObject_GC_Alloc\" >3  0x00000000005b1f2a in _PyObject_GC_Alloc ()<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#4_0x00000000005f3d12_in_dict_resize\" >4  0x00000000005f3d12 in dict_resize ()<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#5_0x00000000005f4e9a_in_PyDict_SetItem\" >5  0x00000000005f4e9a in PyDict_SetItem ()<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#6_0x0000000000612c3d_in_PyEval_EvalFrameDefault\" >6  0x0000000000612c3d in _PyEval_EvalFrameDefault ()<\/a><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#The_Abstraction_Tax_and_the_Death_of_Determinism\" >The Abstraction Tax and the Death of Determinism<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#PyObject_The_Twenty-Eight_Byte_Anchor\" >PyObject: The Twenty-Eight Byte Anchor<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#The_GIL_A_Multi-threaded_Lie_for_the_Masses\" >The GIL: A Multi-threaded Lie for the Masses<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#Socket_Buffers_and_the_Futility_of_Asyncio\" >Socket Buffers and the Futility of Asyncio<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#Heap_Fragmentation_A_Slow_Death_by_a_Thousand_Mallocs\" >Heap Fragmentation: A Slow Death by a Thousand Mallocs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#The_C-API_Where_the_Lies_Finally_Collapse\" >The C-API: Where the Lies Finally Collapse<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/#Related_Articles\" >Related Articles<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1><span class=\"ez-toc-section\" id=\"0_0x00007f8a2c3d4e5f_in_int_malloc_av0x7f8a2c50a020_bytes1024_at_mallocc_4123\"><\/span>0  0x00007f8a2c3d4e5f in _int_malloc (av=0x7f8a2c50a020 <main_arena>, bytes=1024) at malloc.c:4123<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<h1><span class=\"ez-toc-section\" id=\"1_0x00007f8a2c3d6789_in_GI_libc_malloc_bytes1024_at_mallocc_3321\"><\/span>1  0x00007f8a2c3d6789 in __GI___libc_malloc (bytes=1024) at malloc.c:3321<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<h1><span class=\"ez-toc-section\" id=\"2_0x00000000005a2b3c_in_PyObject_Malloc\"><\/span>2  0x00000000005a2b3c in PyObject_Malloc ()<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<h1><span class=\"ez-toc-section\" id=\"3_0x00000000005b1f2a_in_PyObject_GC_Alloc\"><\/span>3  0x00000000005b1f2a in _PyObject_GC_Alloc ()<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<h1><span class=\"ez-toc-section\" id=\"4_0x00000000005f3d12_in_dict_resize\"><\/span>4  0x00000000005f3d12 in dict_resize ()<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<h1><span class=\"ez-toc-section\" id=\"5_0x00000000005f4e9a_in_PyDict_SetItem\"><\/span>5  0x00000000005f4e9a in PyDict_SetItem ()<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<h1><span class=\"ez-toc-section\" id=\"6_0x0000000000612c3d_in_PyEval_EvalFrameDefault\"><\/span>6  0x0000000000612c3d in _PyEval_EvalFrameDefault ()<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<p>&#8230; (142 levels of recursion and indirection omitted for sanity)<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Abstraction_Tax_and_the_Death_of_Determinism\"><\/span>The Abstraction Tax and the Death of Determinism<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I spent the morning staring at a hex dump of a corrupted heap because some &#8220;architect&#8221; decided that writing a high-throughput socket listener in Python 3.11.4 was a good idea. We are running on Linux Kernel 6.2.0-33-generic, a kernel that is perfectly capable of handling millions of interrupts per second, yet this &#8220;python code&#8221; managed to choke the life out of a 64-core Xeon box with only 5,000 concurrent connections. <\/p>\n<p>The problem started with a junior developer\u2019s &#8220;elegant&#8221; solution for a real-time data ingestion service. He used <code>asyncio<\/code>. He used <code>pydantic<\/code>. He used every buzzword-compliant library available on PyPI. He thought he was being efficient because the code looked clean. Clean code is a lie told by people who don&#8217;t understand how L1 caches work. When you write &#8220;python code&#8221;, you aren&#8217;t writing instructions for a CPU; you are writing suggestions for a massive, bloated C program that treats your hardware like an infinite resource.<\/p>\n<p>The crash above wasn&#8217;t a fluke. It was the inevitable result of heap fragmentation and the sheer weight of the <code>PyObject<\/code> structure. In C, if I want to store a 64-bit integer, I use 8 bytes of memory. In this &#8220;python code&#8221;, that same integer is a <code>PyLongObject<\/code>. It has a reference count. It has a type pointer. It has a size field. It\u2019s 28 bytes of overhead before you even get to the actual data. Now multiply that by a few million incoming packets, and you have a recipe for the OOM killer to come knocking on your door.<\/p>\n<p>The junior dev\u2019s &#8220;python code&#8221; was creating a new dictionary for every incoming JSON payload. Think about that. Every time a packet hits the wire, the runtime has to allocate a <code>PyDictObject<\/code>, calculate hashes, handle potential collisions, and manage the internal hash table resizing. On a kernel level, this translates to a constant stream of <code>brk<\/code> and <code>mmap<\/code> syscalls. The <code>glibc 2.36<\/code> allocator is doing its best, but it can\u2019t keep up with the sheer volume of small, short-lived allocations.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"PyObject_The_Twenty-Eight_Byte_Anchor\"><\/span>PyObject: The Twenty-Eight Byte Anchor<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Let\u2019s talk about the <code>PyObject_HEAD<\/code>. Every single thing in this &#8220;python code&#8221; is an object. You want to increment a counter? That\u2019s not an <code>add<\/code> instruction. That\u2019s a call to <code>PyNumber_Add<\/code>, which checks the types of both operands, creates a <em>new<\/em> object for the result, and decrements the reference count of the old one. If the reference count hits zero, the garbage collector might decide to wake up and ruin your latency tail.<\/p>\n<pre class=\"codehilite\"><code class=\"language-bash\">$ strace -c -p 14201\n% time     seconds  usecs\/call     calls    errors syscall\n------ ----------- ----------- --------- --------- ----------------\n 45.21    2.123456          15    141563           futex\n 22.10    1.038472           8    129847           mmap\n 15.45    0.725123          12     60423           brk\n 10.12    0.475123           5     95024           recvfrom\n  7.12    0.334512          11     30421           epoll_wait\n------ ----------- ----------- --------- --------- ----------------\n100.00    4.696686                457278           total\n<\/code><\/pre>\n<p>Look at those <code>futex<\/code> calls. That is the sound of a multi-core processor screaming in agony. Because of the Global Interpreter Lock (GIL), even in Python 3.11.4, your &#8220;python code&#8221; is essentially a single-threaded bottleneck masquerading as a modern service. Those 64 cores are sitting idle, spinning on locks, waiting for their turn to touch the interpreter state. We are paying for 128 hardware threads and using one. It\u2019s an insult to the engineers who designed the silicon.<\/p>\n<p>The junior dev argued that &#8220;asyncio is non-blocking.&#8221; He\u2019s half-right. It\u2019s non-blocking for I\/O, but it\u2019s completely blocking for the CPU. While the event loop is busy deserializing a massive JSON blob into a forest of <code>PyObjects<\/code>, it can\u2019t respond to new connections. The TCP backlog fills up. The kernel starts dropping SYN packets. The &#8220;python code&#8221; thinks it\u2019s doing work, but it\u2019s actually just shuffling pointers around while the rest of the system starves.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_GIL_A_Multi-threaded_Lie_for_the_Masses\"><\/span>The GIL: A Multi-threaded Lie for the Masses<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I\u2019ve heard the rumors about PEP 703 and the &#8220;no-GIL&#8221; builds. They say it will save us. They are wrong. Even without the GIL, the fundamental problem remains: the &#8220;python code&#8221; is built on a foundation of reference counting. In a truly multi-threaded environment, every <code>Py_INCREF<\/code> and <code>Py_DECREF<\/code> has to be an atomic operation. Do you know what atomic operations do to a CPU cache? They invalidate the cache line across every other core. You end up with &#8220;cache line bouncing,&#8221; where the cores spend more time coordinating memory access than actually executing logic.<\/p>\n<p>In our production disaster, the &#8220;python code&#8221; was attempting to share a global state dictionary across multiple &#8220;worker&#8221; threads (a mistake in itself). The result was a <code>futex<\/code> storm. Every time a thread wanted to update a counter, it had to lock the entire interpreter. I watched the <code>top<\/code> output. The CPU usage was at 100%, but the actual throughput was lower than a 486 running a Perl script. <\/p>\n<p>The overhead of opcode dispatch is another silent killer. In a C program, a loop is a few instructions: <code>mov<\/code>, <code>add<\/code>, <code>cmp<\/code>, <code>jne<\/code>. In this &#8220;python code&#8221;, a loop involves the virtual machine fetching an opcode, looking up the function in a dispatch table, checking the argument types, and then finally executing the operation. It\u2019s a massive amount of work just to do nothing. When you are dealing with high-concurrency sockets, every microsecond matters. This &#8220;python code&#8221; treats microseconds like they are free.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Socket_Buffers_and_the_Futility_of_Asyncio\"><\/span>Socket Buffers and the Futility of Asyncio<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The junior dev\u2019s code used <code>asyncio.streams<\/code>. It looks pretty. It\u2019s &#8220;idiomatic.&#8221; It\u2019s also a disaster for memory pressure. When a packet arrives, the kernel puts it in a socket buffer. The &#8220;python code&#8221; then copies that data into a bytes object. Then it gets decoded into a string (another copy). Then it gets parsed into a JSON object (a whole tree of new objects). By the time the &#8220;python code&#8221; actually looks at the data, it has been copied and transformed four or five times.<\/p>\n<pre class=\"codehilite\"><code class=\"language-bash\">$ lsof -p 14201\nCOMMAND   PID USER   FD   TYPE DEVICE SIZE\/OFF    NODE NAME\npython3.1 14201 root  mem    REG  253,1  2024880  135421 \/usr\/lib\/x86_64-linux-gnu\/libc.so.6\npython3.1 14201 root    0u   CHR  136,0      0t0       3 \/dev\/pts\/0\npython3.1 14201 root    1u   CHR  136,0      0t0       3 \/dev\/pts\/0\npython3.1 14201 root    2u   CHR  136,0      0t0       3 \/dev\/pts\/0\npython3.1 14201 root    3u  IPv4 456789      0t0     TCP *:8080 (LISTEN)\npython3.1 14201 root    4u  IPv4 456790      0t0     TCP 10.0.0.1:8080-&gt;10.0.0.5:54321 (ESTABLISHED)\n... (4995 more ESTABLISHED connections)\n<\/code><\/pre>\n<p>Each of those 5,000 connections has its own set of buffers in the &#8220;python code&#8221;. Because Python\u2019s memory management is non-deterministic, these buffers don\u2019t get freed immediately when the connection closes. They linger in the &#8220;generation 0&#8221; or &#8220;generation 1&#8221; heap until the GC decides it\u2019s time to do a sweep. Meanwhile, the <code>rss<\/code> (Resident Set Size) of the process keeps climbing. <\/p>\n<p>I checked the <code>tcp_rmem<\/code> and <code>tcp_wmem<\/code> settings on the host. The kernel was configured to allow up to 16MB per socket. The &#8220;python code&#8221;, in its infinite wisdom, was also buffering data at the application level. We were double-buffering everything. When the traffic spiked, the &#8220;python code&#8221; couldn&#8217;t process the buffers fast enough, the heap exploded, and the OOM killer finally did what I should have done weeks ago: it killed the process.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Heap_Fragmentation_A_Slow_Death_by_a_Thousand_Mallocs\"><\/span>Heap Fragmentation: A Slow Death by a Thousand Mallocs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>One of the most insulting things about this &#8220;python code&#8221; is how it handles memory fragmentation. In a low-level language, I can use a pool allocator or a slab allocator for fixed-size structures. I can ensure that my memory is contiguous, which is friendly to the prefetcher. Python doesn&#8217;t give you that choice. It uses a private heap for small objects (under 512 bytes) and falls back to <code>malloc<\/code> for everything else.<\/p>\n<p>As the &#8220;python code&#8221; runs, it constantly creates and destroys objects of varying sizes. This leaves holes in the heap. The <code>glibc<\/code> allocator tries to coalesce these holes, but it can\u2019t always do it, especially if there\u2019s a long-lived object sitting right in the middle of a free block. Over time, the virtual memory space becomes a Swiss cheese of allocated and unallocated chunks. The process might only be using 2GB of actual data, but its <code>vsz<\/code> is 16GB because the heap is so fragmented it can\u2019t find a contiguous block for a new dictionary resize.<\/p>\n<p>This is exactly what happened during the crash. The &#8220;python code&#8221; tried to resize a dictionary to hold more session data. The <code>dict_resize<\/code> function called <code>PyObject_Malloc<\/code>, which called <code>malloc<\/code>, which tried to expand the heap. But the heap was so fragmented that the kernel couldn&#8217;t satisfy the request, leading to a failure that the &#8220;python code&#8221; wasn&#8217;t prepared to handle.<\/p>\n<p>The junior dev suggested we just &#8220;add more RAM.&#8221; This is the modern solution to everything: throw more hardware at bad software. But you can&#8217;t outrun physics. More RAM just means a longer pause when the garbage collector finally decides to do a full &#8220;stop-the-world&#8221; sweep. I\u2019ve seen GC pauses in this &#8220;python code&#8221; that lasted for several seconds. In a real-time system, a several-second pause is an eternity. It\u2019s not a glitch; it\u2019s a total system failure.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_C-API_Where_the_Lies_Finally_Collapse\"><\/span>The C-API: Where the Lies Finally Collapse<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>When the &#8220;python code&#8221; isn&#8217;t enough, people reach for C extensions. They think they can wrap a fast C library and get the best of both worlds. But the boundary between Python and C is a minefield. You have to manually manage reference counts. You have to handle the GIL. You have to convert between Python types and C types.<\/p>\n<p>The segfault in the log above happened inside <code>libc.so.6<\/code>, but it was triggered by a C extension that the &#8220;python code&#8221; was using for &#8220;performance.&#8221; The extension was passed a Python list, and it tried to iterate over it. But while it was iterating, another thread (which had managed to grab the GIL) modified the list. The C extension was left holding a dangling pointer to a <code>PyObject<\/code> that had already been deallocated. <\/p>\n<pre class=\"codehilite\"><code class=\"language-bash\">(gdb) frame 4\n#4  0x00000000005f3d12 in dict_resize (mp=0x7f8a1400a120, minsize=12288) at Objects\/dictobject.c:1234\n1234        new_keys = (PyDictKeysObject *)dk_alloc(newsize);\n(gdb) p *mp\n$1 = {ob_refcnt = 1, ob_type = 0x8c5a20 &lt;PyDict_Type&gt;, ma_used = 8192, ma_version_tag = 12345, ma_keys = 0x7f8a1400b000, ma_values = 0x0}\n<\/code><\/pre>\n<p>This is the reality of the &#8220;python code&#8221; ecosystem. It\u2019s a house of cards built on top of a swamp. We spend our time debugging issues that shouldn&#8217;t even exist. We worry about &#8220;thread safety&#8221; in a language that can&#8217;t even run two threads at the same time. We worry about &#8220;memory management&#8221; in a language that hides the heap from us.<\/p>\n<p>I miss the days when a &#8220;segmentation fault&#8221; meant you actually did something wrong with a pointer, not that your interpreter&#8217;s internal state was corrupted by a race condition in a third-party library. I miss the days when you could look at a line of code and know exactly what instructions the CPU was going to execute. This &#8220;python code&#8221; is a layer of fog between the engineer and the machine.<\/p>\n<p>The &#8220;elegant&#8221; solution provided by the junior dev is now in the trash. I\u2019m rewriting the core socket handling logic in C. No <code>asyncio<\/code>. No <code>pydantic<\/code>. Just a clean <code>epoll<\/code> loop, a fixed-size buffer pool, and a deterministic memory layout. The &#8220;python code&#8221; can stay for the high-level configuration logic, but it has no business being anywhere near the data plane. <\/p>\n<p>We\u2019ve forgotten what it means to be engineers. We\u2019ve traded understanding for convenience, and performance for &#8220;velocity.&#8221; But when the server is down and the heap is a mess of corrupted <code>PyObjects<\/code>, that velocity doesn&#8217;t look so good. It looks like a car crash. And I\u2019m the one who has to clean up the glass.<\/p>\n<p>The Manifesto of Regret is simple: if you care about your hardware, if you care about your latency, and if you care about your sanity, stop trying to solve every problem with &#8220;python code&#8221;. It is a tool for scripts and glue, not for high-concurrency systems. The hardware is fast. The kernel is efficient. The &#8220;python code&#8221; is the only thing standing in the way of a working system. It\u2019s time we stopped pretending otherwise. <\/p>\n<p>I\u2019m going back to my editor. I have a <code>struct<\/code> to define, and it\u2019s not going to have a 28-byte header. It\u2019s going to have exactly what it needs, and not a bit more. That\u2019s not &#8220;cynicism.&#8221; That\u2019s engineering.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Related_Articles\"><\/span>Related Articles<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Explore more insights and best practices:<\/p>\n<ul>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/what-is-machine-learning-guide\/\">What Is Machine Learning Guide<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/10-docker-best-practices-to-optimize-your-containers\/\">10 Docker Best Practices To Optimize Your Containers<\/a><\/li>\n<li><a href=\"https:\/\/itsupportwale.com\/blog\/understanding-machine-learning-models-a-complete-guide-3\/\">Understanding Machine Learning Models A Complete Guide 3<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>[72439.123456] out_of_memory: Kill process 14201 (python3.11) score 942 or sacrifice child [72439.123501] Killed process 14201 (python3.11) total-vm:16777216kB, anon-rss:14567424kB, file-rss:0kB, shmem-rss:0kB [72439.123550] oom_reaper: reaped process 14201 (python3.11), now anon-rss:0kB, file-rss:0kB, shmem-rss:0kB [72439.123600] traps: python3.11[14202] general protection fault ip:7f8a2c3d4e5f sp:7f8a1b2c3d4e error:0 in libc.so.6[7f8a2c300000+1a0000] Segmentation fault (core dumped) $ gdb python3.11 core.14201 (gdb) bt 0 0x00007f8a2c3d4e5f in _int_malloc &#8230; <a title=\"Mastering Python Code: Tips for Efficient Programming\" class=\"read-more\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/\" aria-label=\"Read more  on Mastering Python Code: Tips for Efficient Programming\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4901","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Mastering Python Code: Tips for Efficient Programming - ITSupportWale<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itsupportwale.com\/blog\/mastering-python-code-tips-for-efficient-programming\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mastering Python Code: Tips for Efficient Programming - ITSupportWale\" \/>\n<meta property=\"og:description\" content=\"[72439.123456] out_of_memory: Kill process 14201 (python3.11) score 942 or sacrifice child [72439.123501] Killed process 14201 (python3.11) total-vm:16777216kB, anon-rss:14567424kB, file-rss:0kB, shmem-rss:0kB [72439.123550] oom_reaper: reaped process 14201 (python3.11), now anon-rss:0kB, file-rss:0kB, shmem-rss:0kB [72439.123600] traps: python3.11[14202] general protection fault ip:7f8a2c3d4e5f sp:7f8a1b2c3d4e error:0 in libc.so.6[7f8a2c300000+1a0000] Segmentation fault (core dumped) $ gdb python3.11 core.14201 (gdb) bt 0 0x00007f8a2c3d4e5f in _int_malloc ... 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