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An Android image-loading library is a request-processing system: it fetches image data, decodes it at a useful size, caches reusable results, coordinates work with the target’s lifecycle, and delivers the right result to the UI. Build those responsibilities as an explicit pipeline rather than as a collection of unrelated image utilities.
Why image loading needs a system
A compressed image’s file size does not tell you how much memory its decoded bitmap will use. Android’s illustrative example is a 4048×3036 image in ARGB_8888 format: roughly 48 MB for the bitmap. That is a calculation based on dimensions and pixel format, not a measurement that applies identically to every device. Multiple visible images can make decoding, allocation, and reuse consequential, especially in scrolling lists.
Keep image work off the UI thread so fetching and decoding do not block drawing and interaction. A loader also needs memory and disk caching to avoid repeating expensive work as images are reused. Android’s bitmap guidance recommends Glide for fetching, decoding, and displaying bitmaps in most cases; that recommendation is a useful example of the responsibilities involved, not a requirement to implement Glide’s exact design. Android’s bitmap guidance
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Give every request a clear path through the system. Coil describes its ImageLoader as the component that executes requests and coordinates fetching, decoding, request management, and caches. Glide documents fetching, decoding, displaying, resizing, caching, and resource pooling. These are useful architectural reference points, not a universal API specification.
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- Accept a complete request. The API should take a source or model, target dimensions, transformations, placeholder and error behavior, and relevant cache or network policies. Define a request identity from every option that can affect the output. There is no universal cache-key format: the key is a design decision that must match your request and cache semantics.
- Bind work to a target and its lifecycle. A target may be a view or Compose state. When a recycled view receives a different model, replace or cancel its previous request. When a consumer goes away, decide whether its work should stop. The exact lifecycle contract depends on your integration; document and test it rather than assuming all UI targets behave alike.
- Fetch through a pluggable source layer. Resolve the source types your API promises, such as app resources, files, content URIs, or network data. Keep fetching replaceable when applications need a different network stack. Glide documents flexible network-stack integration, but support for a source type or HTTP behavior is something your implementation must provide.
- Inspect, decode, and transform. Determine the image dimensions and decode near the size the target will actually render. Decode away from the main thread, then apply requested transformations. Downsampling avoids allocating a full-size bitmap when the UI needs only a smaller result.
- Check caches and reuse resources. Use bounded caches with explicit eviction and invalidation behavior. A memory cache can reuse decoded results; a disk cache can reuse fetched data or other persisted results. If transformations or dimensions change the output, represent them in the identity of the cached resource.
- Deliver a state that still belongs to this request. Publish loading, success, and failure as appropriate. Before updating the target, ensure the result belongs to its current request; otherwise an earlier, slower request can replace newer content. Treat this as a concurrency invariant to test, not as a guarantee provided merely by having a request API.
Choose where resizing happens
Resizing on the server and downsampling on the device solve related but different problems. Android’s Compose optimization guidance recommends requesting exact image dimensions from the backend where possible. When a service supports appropriately sized variants, the client can receive fewer bytes and store less data. Android’s Compose image optimization guidance
Client downsampling is still important when the source is fixed, a suitable server variant is unavailable, or the returned image is larger than the target. A robust loader should use the requested display size in decode decisions rather than assuming that a small on-screen image has a small source bitmap.
Separate the two useful kinds of cache
A cache of source data and a cache of decoded resources can prevent different work. Source data—such as fetched bytes or a file—can save another network request. A decoded or transformed resource can save decoding and transformation work. A library may use both, but resource identity must distinguish results that differ by requested size or transformation; otherwise it risks returning the wrong output.
Set a memory bound and an eviction rule rather than allowing decoded results to grow without limit. Android documents LruCache as a least-recently-used cache pattern, which evicts entries that have not been accessed recently when the cache needs room. Android’s bitmap caching guidance
Coil documents memory and disk caches owned by each ImageLoader. That makes loader-instance policy important: creating many loaders can mean separate caches and network clients rather than one shared pool of resources. Coil recommends a shared loader for an application setup; library authors should use coil-core and a privately managed loader rather than replacing the host app’s singleton. Coil ImageLoader documentation Coil FAQ
Make extensibility a deliberate trade-off
Pluggable fetchers, decoders, and transformations make a loader adaptable, but each extension point adds decisions about request identity, cancellation, resource ownership, errors, and cache invalidation. A small first version can support a narrow set of sources and formats well; add formats such as animated GIFs or video frames only when their decode and memory behavior has a defined contract and tests.
Glide’s project documentation lists image and video still loading, animated GIFs, resizing, network-stack integration, and resource pooling. That breadth illustrates possible capabilities, not a checklist every new library must reproduce. Glide project documentation
Use Glide and Coil as reference points, not a speed ranking
Coil is Kotlin-first, coroutine-backed, and integrated with Compose; its ImageLoader executes requests and manages fetching, decoding, and caches. Glide presents itself as an Android image-loading and caching library focused on smooth scrolling, with documented support for multiple formats and network integration. These descriptions help frame feature and integration choices, but they do not establish a universal performance winner.
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Choose or design around the needs of the app: language and UI integration, required formats, source and network flexibility, cache controls, lifecycle expectations, dependency footprint, and measured behavior on the app’s own image sizes and scrolling workload. Android’s Compose guidance likewise frames image-loader choice around project requirements and performance constraints rather than publishing a controlled Glide-versus-Coil benchmark. Android’s Compose image loading guide
When benchmarking your implementation, use representative sources, target dimensions, transformations, cache states, and scrolling patterns. Measure on the devices and configurations the app supports; do not infer performance from a library’s feature list or from a single isolated image.
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