如何在Python中解析大型JSON文件？

如何在Python中高效處理大型JSON文件？ 1. 使用ijson庫流式處理，通過逐項解析避免內存溢出；2. 若為JSON Lines格式，可逐行讀取並用json.loads()處理；3. 或先將大文件拆分為小塊再分別處理。這些方法有效解決內存限制問題，適用於不同場景。

Parsing large JSON files in Python can be tricky if you're used to loading the whole thing into memory with json.load() . But when the file is really big—like hundreds of megabytes or more—that approach just doesn't work anymore. It'll either be super slow or crash your script entirely.

So what do you do? Here are a few practical ways people actually use to handle this kind of situation.

Use ijson for Streaming Large JSON Files

If your JSON file is too big to load all at once, streaming is your best bet. The ijson library lets you read through the JSON file piece by piece, which means it never loads the entire file into memory.

You install it like this:

 pip install ijson

And here's how you might use it to extract something specific, like a list of names from a big array:

 import ijson

with open('big_file.json', 'r') as f:
    parser = ijson.items(f, 'people.item.name')
    for name in parser:
        print(name)

This only keeps one item in memory at a time. It's slower than loading everything up front, but it's the only way to go when dealing with huge files.

A few things to keep in mind:

• ijson uses dot notation to navigate nested structures.
• It works best when you're interested in a specific subset of data rather than the whole structure.
• Performance isn't blazing fast compared to full parsing, but it gets the job done.

Process Line-by-Line If You Have JSON Lines (NDJSON)

Sometimes, large JSON files are stored in a format called JSON Lines , where each line is a separate JSON object. In that case, you can process the file line by line without any special libraries.

Here's how you might do it:

 import json

with open('big_file.jsonl', 'r') as f:
    for line in f:
        data = json.loads(line)
        # Do something with data

This method is straightforward and efficient because:

• Each line is small enough to parse individually.
• You don't need to load the whole file into memory.
• It works great for log files, datasets, or exports from other systems.

Just make sure your file really is in JSON Lines format. If not, this won't work directly.

Split the File First for Easier Handling

If neither of the above methods fits your needs, another option is to split the large JSON file into smaller chunks first.

You can do this manually or write a simple script to break it into manageable pieces. Once it's split, you can load and process each part normally using the standard json module.

Here's a basic idea of how you might split it:

• Read the file line by line.
• Detect when a JSON object starts and ends.
• Write each complete object into its own file or batch them into groups.

This approach is useful if you need to debug or analyze parts of the data separately.

Keep in mind:

• This requires knowing the structure of your JSON.
• It adds an extra step to your workflow.
• It's not ideal if you're working in a memory-constrained environment like a serverless function.

Handling large JSON files in Python isn't hard once you know the right tools and techniques. For most cases, ijson or JSON Lines will get you where you need to go. And if those aren't quite right, splitting the file can sometimes be the easiest workaround.

基本上就這些。

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