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Avoiding Memory Leaks By Simple Tricks βœ…

Memory Leaks often cause performance degradation of software. Since, Java manages memory automatically, the developers do not have much control.

But there are still some standard practices which can be used to protect from memory leakages. πŸ™†πŸ»β€β™‚οΈ

1️⃣ - Always release database connections when querying is complete.

2️⃣ - Try to use Finally block as often possible.

3️⃣ - Release instances stored in Static Tables.

#java #memory #performance
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Strings Are Evil πŸ‘Ί

Reducing memory allocations from 7.5GB to 32KB. 😡

In this article we will explore potential optimisations to the import process specifically within the context of reducing memory during the import process. βœ…

This article is awesome❗️


https://t.me/pgimg/138

[ Article ] : bit.do/StraE

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#CSharp #Performance
@ProgrammingTip
.NET 11 performance work is landing across the stack πŸ”₯

Microsoft published the running list of performance improvements headed for .NET 11. This is not one magic runtime switch. The work spans the runtime, JIT, libraries, and tooling paths that show up in normal application code.

Use the post as a migration checklist:

β€’ Find code that is CPU-bound, allocation-heavy, or called per request.
β€’ Run your benchmarks on the .NET 11 SDK, not a synthetic microbenchmark only.
β€’ Check p50 and p99 latency, allocations, startup, and throughput separately.

The best upgrade wins are boring: existing C# gets faster with fewer code changes. But measure your service. A JIT win can disappear behind JSON, EF queries, network calls, or a container CPU limit.

[ Blog ] :
https://devblogs.microsoft.com/dotnet/performance-improvements-in-net-11/

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#dotnet #csharp #Performance #dotnet11
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Cloudflare saved 100 TB of RAM with math and Rust πŸ”₯

Cloudflare reclaimed more than 100 TB of RAM globally in a Pingora-based consistent-hashing service. No new hardware. The win came from changing data representation and the algorithms around it.


βœ… Lessons:

β€’ Measure retained memory, not only allocation rate.
β€’ Check collection shape: duplicated keys, oversized buckets, and pointer-heavy graphs add up fast.
β€’ Fix the model first: a smaller or more compact structure usually beats micro-optimizing a hot loop.

For high-cardinality caches, routing tables, or tenant maps, take a heap dump before reaching for another cache node. A few bytes per entry becomes expensive at fleet scale.

[ Blog ] :
https://blog.cloudflare.com/saving-100-tb-of-ram-with-math

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#Performance #Memory #Rust
@ProgrammingTip
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