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JSON vs Protobuf: What are the differences?
Introduction
When choosing a data serialization format for applications, developers often come across JSON and Protobuf. Both JSON (JavaScript Object Notation) and Protobuf (Protocol Buffers) serve the purpose of exchanging data between systems, but they have key differences that distinguish them from each other.
Data Size: One significant difference between JSON and Protobuf is the data size. JSON is text-based and therefore tends to be bulkier compared to Protobuf, which is a binary format. This difference in data size can impact network bandwidth utilization and storage space requirements, especially in situations where large volumes of data need to be transferred quickly and efficiently.
Schema Definition: Another key difference between JSON and Protobuf is the way they handle schema definition. JSON is schema-less, meaning that it does not require a predefined schema to serialize or deserialize data. On the other hand, Protobuf relies on a defined schema (Protocol Buffer Message) to serialize and deserialize data. This schema-based approach in Protobuf offers advantages like improved communication between services and better data consistency.
Parsing Performance: JSON and Protobuf also differ in parsing performance. Due to its lightweight nature and human-readability, JSON parsing can be slower compared to Protobuf, which is designed for efficient serialization and deserialization. This difference in parsing performance can be critical in high-throughput or real-time systems where processing speed is a priority.
Support for Data Types: JSON has limited support for complex data types, primarily dealing with simple data structures like strings, numbers, arrays, and objects. In contrast, Protobuf provides robust support for defining complex data types, including nested structures, enums, and more. This difference in data type support can make Protobuf a better choice for scenarios involving intricate data models.
Compatibility and Versioning: JSON is more forgiving when it comes to changes in data structure or schema, as it allows for flexible evolution of data models. Protobuf, however, enforces stricter schema compatibility rules, making it challenging to evolve schemas without breaking existing implementations. This difference in compatibility and versioning handling can impact the maintainability and extensibility of systems using JSON or Protobuf.
Language Support: JSON has widespread support in various programming languages due to its simplicity and readability. In comparison, while Protobuf has libraries available for multiple languages, it may not be as universally supported as JSON. Developers need to consider the language ecosystem of their project when choosing between JSON and Protobuf to ensure seamless integration and interoperability.
In Summary, JSON and Protobuf differ in data size, schema definition, parsing performance, support for data types, compatibility and versioning, and language support, which can influence the choice of serialization format based on specific project requirements.
Hi. Currently, I have a requirement where I have to create a new JSON file based on the input CSV file, validate the generated JSON file, and upload the JSON file into the application (which runs in AWS) using API. Kindly suggest the best language that can meet the above requirement. I feel Python will be better, but I am not sure with the justification of why python. Can you provide your views on this?
Python is very flexible and definitely up the job (although, in reality, any language will be able to cope with this task!). Python has some good libraries built in, and also some third party libraries that will help here. 1. Convert CSV -> JSON 2. Validate against a schema 3. Deploy to AWS
- The builtins include json and csv libraries, and, depending on the complexity of the csv file, it is fairly simple to convert:
import csv
import json
with open("your_input.csv", "r") as f:
csv_as_dict = list(csv.DictReader(f))[0]
with open("your_output.json", "w") as f:
json.dump(csv_as_dict, f)
The validation part is handled nicely by this library: https://pypi.org/project/jsonschema/ It allows you to create a schema and check whether what you have created works for what you want to do. It is based on the json schema standard, allowing annotation and validation of any json
It as an AWS library to automate the upload - or in fact do pretty much anything with AWS - from within your codebase: https://aws.amazon.com/sdk-for-python/ This will handle authentication to AWS and uploading / deploying the file to wherever it needs to go.
A lot depends on the last two pieces, but the converting itself is really pretty neat.
I would use Go. Since CSV files are flat (no hierarchy), you could use the encoding/csv package to read each row, and write out the values as JSON. See https://medium.com/@ankurraina/reading-a-simple-csv-in-go-36d7a269cecd. You just have to figure out in advance what the key is for each row.
This should be pretty doable in any language. Go with whatever you're most familiar with.
That being said, there's a case to be made for using Node.js since it's trivial to convert an object to JSON and vice versa.
Pros of JSON
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- Widely supported4
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