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Apache Parquet vs JSON: What are the differences?
Introduction
Apache Parquet and JSON are both file formats used for storing and exchanging data. However, there are several key differences between the two that make them suitable for different use cases. In the following paragraphs, we will explore these differences in detail.
Schema-based vs. Schema-less: One of the major differences between Apache Parquet and JSON is their approach to data schema. Parquet is a schema-based file format, which means it requires a predefined schema that specifies the structure of the data. On the other hand, JSON is a schema-less format, allowing for more flexibility as data can be stored without a predefined schema.
Compression: Another key difference between Parquet and JSON is the way they handle data compression. Parquet uses columnar compression, which compresses each column independently. This allows for high compression ratios and efficient query performance, especially in scenarios where only a subset of columns needs to be read. JSON, on the other hand, does not provide built-in compression and the data is usually stored in a verbose manner, leading to larger file sizes.
Data Types: When it comes to data types, Parquet supports a wider range of data types compared to JSON. Parquet includes support for complex data types like arrays, maps, and nested structures, whereas JSON has limited support for these types. JSON primarily relies on string, numeric, boolean, and null types for data representation.
Query Performance: Due to its columnar storage and compression techniques, Parquet generally offers better query performance compared to JSON. Parquet allows for efficient column pruning, where only the required columns are read during query execution, leading to faster data retrieval. JSON, on the other hand, requires parsing the entire document to retrieve specific fields, which can result in slower query performance.
Serialization: Apache Parquet uses a binary format for serialization, which provides a compact representation of data and makes it suitable for use in distributed systems. JSON, being a text-based format, has a larger footprint and may require additional parsing during serialization and deserialization.
Tooling Support: Parquet has extensive tooling support in the Apache Hadoop ecosystem, making it easier to integrate with existing big data processing frameworks like Apache Spark and Apache Hive. JSON, being a widely adopted and simple format, also has good tooling support across various programming languages and platforms.
In summary, Apache Parquet and JSON differ in their approach to data schema, compression techniques, supported data types, query performance, serialization format, and tooling support. Choosing between the two formats depends on the specific requirements of the use case, with Parquet providing better performance and efficiency for structured data, while JSON offers flexibility and simplicity for schema-less data storage.
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.
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.
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.
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