Python 将一个大的 json 文件拆分为多个较小的文件
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Split a large json file into multiple smaller files
提问by Tim Halbert
I have a large JSON file, about 5 million records and a file size of about 32GB, that I need to get loaded into our Snowflake Data Warehouse. I need to get this file broken up into chunks of about 200k records (about 1.25GB) per file. I'd like to do this in either Node.JS or Python for deployment to an AWS Lambda function, unfortunately I haven't coded in either, yet. I have C# and a lot of SQL experience, and learning both node and python are on my to do list, so why not dive right in, right!?
我有一个很大的 JSON 文件，大约有 500 万条记录和大约 32GB 的文件大小，我需要将其加载到我们的雪花数据仓库中。我需要将此文件分解为每个文件约 200k 条记录（约 1.25GB）的块。我想在 Node.JS 或 Python 中执行此操作以部署到 AWS Lambda 函数，不幸的是我还没有编码。我有 C# 和很多 SQL 经验，学习 node 和 python 都在我的任务清单上，所以为什么不直接投入进去，对吧！？
My first question is "Which language would better serve this function? Python, or Node.JS?"
我的第一个问题是“哪种语言可以更好地提供此功能？Python 还是 Node.JS？”
I know I don't want to read this entire JSON file into memory (or even the output smallerfile). I need to be able to "stream" it in andout into the new file based on a record count (200k), properly close up the json objects, and continue into a new file for another 200k, and so on. I know Node can do this, but if Python can also do this, I feel like it would be easier to quickly start using for other ETL stuff I'll be doing soon.
我知道我不想将整个 JSON 文件读入内存（甚至输出较小的文件）。我需要能够根据记录计数（200k）将它“流”进和“流出”到新文件中，正确关闭 json 对象，然后继续进入另一个 200k 的新文件，依此类推。我知道 Node 可以做到这一点，但如果 Python 也可以做到这一点，我觉得快速开始使用我很快将要做的其他 ETL 内容会更容易。
My second question is "Based on your recommendation above, can you also recommend what modules I should require/import to help me get started? Primarily as it relates to not pulling the entire json file into memory? Maybe some tips, tricks, or 'How would you do it's? And if you're feeling really generous, some code example to help push me into the deep end on this?
我的第二个问题是“根据您上面的建议，您能否还推荐我应该要求/导入哪些模块来帮助我入门？主要是因为它与不将整个 json 文件拉入内存有关？也许是一些提示、技巧或'你会怎么做？如果你真的很慷慨，一些代码示例可以帮助我深入了解这个问题？
I can't include a sample of the JSON data, as it contains personal information. But I can provide the JSON schema ...
我不能包含 JSON 数据的样本，因为它包含个人信息。但我可以提供 JSON 模式......
{
"$schema": "http://json-schema.org/draft-04/schema#",
"items": {
"properties": {
"activities": {
"properties": {
"activity_id": {
"items": {
"type": "integer"
},
"type": "array"
},
"frontlineorg_id": {
"items": {
"type": "integer"
},
"type": "array"
},
"import_id": {
"items": {
"type": "integer"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"is_source": {
"items": {
"type": "boolean"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"address": {
"properties": {
"city": {
"items": {
"type": "string"
},
"type": "array"
},
"congress_dist_name": {
"items": {
"type": "string"
},
"type": "array"
},
"congress_dist_number": {
"items": {
"type": "integer"
},
"type": "array"
},
"congress_end_yr": {
"items": {
"type": "integer"
},
"type": "array"
},
"congress_number": {
"items": {
"type": "integer"
},
"type": "array"
},
"congress_start_yr": {
"items": {
"type": "integer"
},
"type": "array"
},
"county": {
"items": {
"type": "string"
},
"type": "array"
},
"formatted": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"latitude": {
"items": {
"type": "number"
},
"type": "array"
},
"longitude": {
"items": {
"type": "number"
},
"type": "array"
},
"number": {
"items": {
"type": "string"
},
"type": "array"
},
"observes_dst": {
"items": {
"type": "boolean"
},
"type": "array"
},
"post_directional": {
"items": {
"type": "null"
},
"type": "array"
},
"pre_directional": {
"items": {
"type": "null"
},
"type": "array"
},
"school_district": {
"items": {
"properties": {
"school_dist_name": {
"items": {
"type": "string"
},
"type": "array"
},
"school_dist_type": {
"items": {
"type": "string"
},
"type": "array"
},
"school_grade_high": {
"items": {
"type": "string"
},
"type": "array"
},
"school_grade_low": {
"items": {
"type": "string"
},
"type": "array"
},
"school_lea_code": {
"items": {
"type": "integer"
},
"type": "array"
}
},
"type": "object"
},
"type": "array"
},
"secondary_number": {
"items": {
"type": "null"
},
"type": "array"
},
"secondary_unit": {
"items": {
"type": "null"
},
"type": "array"
},
"state": {
"items": {
"type": "string"
},
"type": "array"
},
"state_house_dist_name": {
"items": {
"type": "string"
},
"type": "array"
},
"state_house_dist_number": {
"items": {
"type": "integer"
},
"type": "array"
},
"state_senate_dist_name": {
"items": {
"type": "string"
},
"type": "array"
},
"state_senate_dist_number": {
"items": {
"type": "integer"
},
"type": "array"
},
"street": {
"items": {
"type": "string"
},
"type": "array"
},
"suffix": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"timezone": {
"items": {
"type": "string"
},
"type": "array"
},
"utc_offset": {
"items": {
"type": "integer"
},
"type": "array"
},
"zip": {
"items": {
"type": "integer"
},
"type": "array"
}
},
"type": "object"
},
"age": {
"type": "integer"
},
"anniversary": {
"properties": {
"date": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"baptism": {
"properties": {
"church_id": {
"type": "null"
},
"date": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"birth_dd": {
"type": "integer"
},
"birth_mm": {
"type": "integer"
},
"birth_yyyy": {
"type": "integer"
},
"church_attendance": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"likelihood": {
"items": {
"type": "integer"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"cohabiting": {
"properties": {
"confidence": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"likelihood": {
"items": {
"type": "null"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"dating": {
"properties": {
"bool": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"divorced": {
"properties": {
"bool": {
"items": {
"type": "null"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"likelihood_considering": {
"items": {
"type": "integer"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"education": {
"properties": {
"est_level": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"email": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"is_work_school": {
"items": {
"type": "boolean"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"engaged": {
"properties": {
"insert_datetime_utc": {
"type": "null"
},
"likelihood": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"est_income": {
"properties": {
"est_level": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"ethnicity": {
"type": "string"
},
"first_name": {
"type": "string"
},
"formatted_birthdate": {
"type": "string"
},
"gender": {
"type": "string"
},
"head_of_household": {
"properties": {
"bool": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"home_church": {
"properties": {
"church_id": {
"type": "null"
},
"group_participant": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"is_coaching": {
"type": "null"
},
"is_giving": {
"type": "null"
},
"is_serving": {
"type": "null"
},
"membership_date": {
"type": "null"
},
"regular_attendee": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"hub_poid": {
"type": "integer"
},
"insert_datetime_utc": {
"type": "string"
},
"ip_address": {
"properties": {
"insert_datetime_utc": {
"type": "null"
},
"string": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"last_name": {
"type": "string"
},
"marriage_segment": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"married": {
"properties": {
"bool": {
"items": {
"type": "boolean"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"middle_name": {
"type": "string"
},
"miscellaneous": {
"properties": {
"attribute": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"value": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"name_suffix": {
"type": "null"
},
"name_title": {
"type": "null"
},
"newlywed": {
"properties": {
"bool": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"parent": {
"properties": {
"bool": {
"items": {
"type": "boolean"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"likelihood_expecting": {
"items": {
"type": "integer"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"person_id": {
"type": "integer"
},
"phone": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"number": {
"items": {
"type": "integer"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"type": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"property_rights": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"psychographic_cluster": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"religion": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"religious_segment": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"separated": {
"properties": {
"bool": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"significant_other": {
"properties": {
"first_name": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"last_name": {
"type": "null"
},
"middle_name": {
"type": "null"
},
"name_suffix": {
"type": "null"
},
"name_title": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"suppressed_datetime_utc": {
"type": "string"
},
"target_group": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
}
},
"type": "object"
},
"type": "array"
}
采纳答案by rsp
Answering the question whether Python or Node will be better for the task would be an opinion and we are not allowed to voice our opinions on Stack Overflow. You have to decide yourself what you have more experience in and what you want to work with - Python or Node.
回答 Python 或 Node 是否更适合该任务的问题将是一种意见，我们不允许对 Stack Overflow 发表意见。你必须自己决定你在什么方面有更多的经验以及你想使用什么——Python 或 Node.js。
If you go with Node, there are some modules that can help you with that task, that do streaming JSON parsing. E.g. those modules:
如果您使用 Node，则有一些模块可以帮助您完成该任务，即流式 JSON 解析。例如那些模块：
- https://www.npmjs.com/package/JSONStream
- https://www.npmjs.com/package/stream-json
- https://www.npmjs.com/package/json-stream
- https://www.npmjs.com/package/JSONStream
- https://www.npmjs.com/package/stream-json
- https://www.npmjs.com/package/json-stream
If you go with Python, there are streaming JSON parsers here as well:
如果你使用 Python，这里也有流式 JSON 解析器：
回答by user3811671
Use this code in linux command prompt
在 linux 命令提示符下使用此代码
split -b 53750k <your-file>
cat xa* > <your-file>
Refer to this link: https://askubuntu.com/questions/28847/text-editor-to-edit-large-4-3-gb-plain-text-file
请参阅此链接：https: //askubuntu.com/questions/28847/text-editor-to-edit-large-4-3-gb-plain-text-file
回答by buncis
consider to use jq to preprocessing your json files
考虑使用 jq 来预处理您的 json 文件
it could split and stream your large json files
它可以拆分和流式传输您的大型 json 文件
jq is like sed for JSON data - you can use it to slice
and filter and map and transform structured data with
the same ease that sed, awk, grep and friends let you play with text.
see the official documentationand this questionsfor more.
extra: for your first questions jq is written by C, it's faster than python/node isn't it ?
额外：对于您的第一个问题，jq 是由 C 编写的，它比 python/node 快，不是吗？
