This article explains Android's memory allocation and reclamation mechanisms, enumerates common memory problems such as leaks, OOM, and bitmap handling, and demonstrates a comprehensive optimization case study on the JOOX music app that reduced average memory consumption by over 40 MB.
This article explains Redis's five core data types, their internal representations and typical commands, details memory‑saving parameters for each type, compares the four persistence methods with their trade‑offs, and offers practical recommendations for optimal performance and stability.
The article presents a large‑scale DMP caching solution that stores billions of third‑party IDs and demographic tags in memory by compressing keys with MD5‑derived bucket IDs, applying eviction policies, reducing memory fragmentation, and demonstrating up to 78% memory savings.
This article explains Redis’s ziplist (compressed list) memory layout, detailing its overall encoding, node structure—including previous length, encoding, and content fields—illustrates how various Redis data types employ ziplist encoding, and analyzes the benefits and trade‑offs of using this structure.
This article explains how to use Android Studio’s Allocation Tracker, automate its start/stop via code using the ddmlib library, and directly access Dalvik’s internal allocation records to retrieve object size, type, and stack information with minimal performance impact, while discussing limitations and compatibility concerns.
This article explains why PHP arrays consume far more memory than their C equivalents and demonstrates a technique that stores a double array in a string using pack/unpack, which can dramatically lower memory consumption at the cost of some performance.
Redis’s hash‑max‑ziplist‑entries and hash‑max‑ziplist‑value settings control when hash tables switch to compressed ziplist storage, and by segmenting data into 1,000‑item hash tables, Instagram reduced memory usage from ~20 GB to about 5 GB, dramatically improving efficiency.
This article explains Android's DisplayMetrics density values, shows how to calculate a Bitmap's runtime memory footprint using getByteCount, explores the impact of screen density, pixel format, and scaling, and provides practical techniques such as inSampleSize, matrix scaling, and pixel format selection to minimize memory consumption.
This article outlines comprehensive Android memory optimization strategies, covering leak detection with LeakCanary, system‑level hacks, bitmap handling, runtime monitoring, multi‑process usage, and GC tuning, providing practical code snippets and tools to reduce RAM usage, prevent OOM crashes, and improve app performance.
PHP 7.0, released after a decade of development, delivers more than double the speed of PHP 5 and rivals Facebook’s HHVM by cutting memory usage, optimizing CPU cache behavior, and tackling the challenges of adding a JIT compiler to a dynamic language.
This article explains how a table's memory footprint equals row count times row size and shows why careful data‑type selection—using CHAR over VARCHAR for fixed‑length fields and separating article metadata from content—can significantly reduce memory usage.
