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What is the difference between an ArrayBuffer and a Blob?

javascript
blob
arraybuffer
binary-data
Alex KataevbyAlex Kataev·Dec 22, 2024
TLDR

ArrayBuffer is your low-level superhero for representing a fixed-length raw binary data buffer, ideal for situations that crave byte-level control and performance prowess, such as audio processing or WebGL programming.

Blob, on the other hand, is the unsung file master — perfect for operations that involve handling file-like data and where immutable characteristics, MIME-types, and size are paramount.

// From Blob to ArrayBuffer — kind of like Blob magic! const buffer = await myBlob.arrayBuffer(); // From ArrayBuffer to Blob — let's craft a Blob masterpiece! const blob = new Blob([myArrayBuffer]);

In summary, ArrayBuffer is the go-to for intimate binary data operations, whilst Blob shines in file-like data interactions.

Detailed Exploration

Calculating Performance and Memory

ArrayBuffer, your low-level performance wizard, is designed to provide high-speed computing and data manipulation. It's the secret sauce behind creating multiple views of different data types using typed arrays, such as the Int32Array or Float64Array.

Blob objects, on the flip side, gracefully handle large binary files — think of them as the deliverers of images and sounds, particularly when these need to be stored, transferred, or consumed by another application.

Manipulating and Using Data

For Blob objects, creating Object URLs is as easy as doing window.URL.createObjectURL, making them ideal candidates for tasks like displaying API-fetched images or generating download links for files.

ArrayBuffer, with its typed array views, lays the groundwork for operations like filtering pixels in an image or manipulating binary data received via WebSockets.

Interacting with Network and Storage

Fetching binary data effortlessly from the network using XMLHttpRequest (XHR) or fetch is done by simply setting the xhr.responseType='blob', which allows the data to be processed as—you guessed it!—a Blob.

In WebSockets, the choice between receiving messages as **ArrayBuffer**s or **Blob**s is yours, governed by the binaryType property.

Dealing with File-like properties

One potency of Blob lies in its ability to hold MIME-type information, which is crucial in determining Content-Type headers during HTTP file transfers — ensuring that the file is properly interpreted by browsers and servers.

Practical Applications

Constructing Requests and Downloads

If you're building a file download functionality, Blob is your best friend. It'll hold the data to be downloaded — whether that's text, image, or video — and interact seamlessly with HTML <a> elements to create download links.

// Crafting a download URL — like gift-wrapping a present! const downloadUrl = window.URL.createObjectURL(myBlob); const a = document.createElement('a'); a.href = downloadUrl; a.download = 'filename.ext'; a.click(); // Don't litter! Clean up after your downloads. window.URL.revokeObjectURL(downloadUrl);

Processing Live Streaming Data

When wrangling with live data streams, like a real-time stock ticker or live audio manipulation, **ArrayBuffer**s offer speed and efficiency for quick manipulation of data, thereby supporting low-latency requirements crucial in these applications.

Trouble-busting Tips

Addressing Large Data

Handling enormous binary data can be a challenging task. For ArrayBuffer, memory usage and performance should be on your radar. Break your buffer into smaller, manageable chunks to keep things under control.

With Blob, you can handle large binary files without getting your hands dirty with splitting, since they are largely untouched when compared with ArrayBuffer operations.

Content Type Guidance

One of the common pitfalls when serving Blob data over HTTP is the failure to specify the MIME type. This leads to the browser scratching its head when faced with unknown file content. Always make sure you're using the right MIME type to avoid this dilemma.

Conversion Considerations

While converting between ArrayBuffer and Blob is a piece of cake, frequent conversions or working with large datasets can lead to performance bottlenecks. It's always a good idea to minimize conversions and reuse existing arrays and blobs where possible to optimize your code.