This article addresses the challenge of streaming large JSON responses using Axios without overloading system memory. Traditional methods store entire JSON arrays in memory, which is inefficient for large datasets. Instead, by utilizing Axios's streaming capabilities along with the AsyncParser from the @json2csv/node package, developers can parse JSON data into CSV format directly from the stream. This method not only reduces memory consumption but also allows for concurrent requests, optimizing resource use. The article outlines the steps required to implement this approach effectively, ensuring performance and simplicity in handling large data sets.
Streaming large JSON responses without storing them in memory can be achieved by using a PassThrough stream and the @json2csv/node package for efficient conversion.
Using axios to stream JSON data allows for processing the data in chunks, preventing high memory usage associated with large JSON arrays.
By employing the AsyncParser from @json2csv/node, you can convert the streamed JSON into a CSV format incrementally, enhancing performance for large datasets.
With the described approach, each user's request can consume significantly less memory while seamlessly transforming and uploading large amounts of JSON data.
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