Parquet and CSV are two common file formats used for storing and processing data. Each has its own strengths and weaknesses, and the choice between them depends on your specific use case. Below is a detailed comparison of Parquet and CSV:
1. Overview
|
Feature |
CSV (Comma-Separated Values) |
Parquet (Columnar Storage Format) |
|
Format |
Row-based (stores data row by row) |
Columnar (stores data column by column) |
|
Compression |
Limited or no compression |
Highly compressed (e.g., Snappy, GZIP, ZSTD) |
|
Schema |
Schema-less (no metadata about data types) |
Schema-aware (stores metadata about data types) |
|
Read Performance |
Slower for large datasets (reads entire rows) |
Faster for large datasets (reads only needed columns) |
|
Write Performance |
Faster for small datasets |
Slower for small datasets (due to compression and columnar storage) |
|
Storage Efficiency |
Less efficient (stores data as plain text) |
Highly efficient (compressed and columnar storage) |
|
Use Case |
Simple data exchange, small datasets, human-readable |
Big data processing, analytics, large datasets |
2. Key Differences
|
|
CSV (Comma-Separated Values) |
Parquet (Columnar Storage Format) |
|
Storage Format |
Row-based storage: Each row is stored as a line of text, with values separated by commas (or other delimiters). Human-readable: Easy to view and edit using text editors or spreadsheet software. No metadata: Does not store information about data types or schema. |
Columnar storage: Data is stored column by column, which is more efficient for analytical queries. Binary format: Not human-readable, optimized for machine processing. Schema-aware: Stores metadata about data types, making it self-describing. |
|
Compression |
Typically uncompressed or uses basic compression (e.g., GZIP). Larger file sizes compared to Parquet. |
Highly compressed: Uses advanced compression algorithms (e.g., Snappy, GZIP, ZSTD). Smaller file sizes, reducing storage costs and improving I/O performance. |
|
Performance |
Slower for analytical queries: Reads entire rows, even if only a few columns are needed. Suitable for small datasets or simple data exchange. |
Faster for analytical queries: Reads only the required columns, reducing I/O. Optimized for big data processing and analytics. |
|
Schema Evolution |
Schema-less: Changes in schema (e.g., adding/removing columns) require manual handling. No support for complex data types (e.g., nested structures). |
Schema-aware: Supports schema evolution (e.g., adding/removing columns without rewriting the entire dataset). Supports complex data types (e.g., arrays, maps, nested structures). |
|
Use Cases |
Simple data exchange between systems. Small datasets or prototyping. Human-readable format for manual inspection. |
Big data processing and analytics. Large datasets with complex schemas. Efficient storage and querying in distributed systems (e.g., Hadoop, Spark). |
3. Pros and Cons
CSV
|
Pros |
Cons |
|
Simple and human-readable |
No compression (large file sizes) |
|
Easy to create and edit |
No schema or metadata support |
|
Supported by almost all tools and systems |
Slow for large datasets and complex queries |
|
Suitable for small datasets |
Limited support for complex data types |
Parquet
|
Pros |
Cons |
|
Highly compressed (small file sizes) |
Not human-readable |
|
Columnar storage (fast for analytics) |
Slower to write (due to compression) |
|
Schema-aware (supports complex data types) |
Requires tools/libraries to read/write |
|
Optimized for big data processing |
Overhead for small datasets |
4. When to Use CSV
- Small datasets : When working with small datasets that don't require advanced compression or performance optimizations.
- Human-readable format : When you need to manually inspect or edit the data.
- Simple data exchange : When exchanging data with systems that only support CSV.
- Prototyping : When quickly prototyping or testing data pipelines.
5. When to Use Parquet
- Big data processing : When working with large datasets in distributed systems (e.g., Hadoop, Spark).
- Analytical queries : When performing analytical queries that require reading specific columns.
- Storage efficiency : When you need to reduce storage costs and improve I/O performance.
- Complex data types : When working with nested or complex data structures.
6. Example Use Cases
CSV
- Exporting data from a database for manual analysis.
- Sharing small datasets with non-technical users.
- Loading data into a spreadsheet or simple database.
Parquet
- Storing large datasets in a data lake or data warehouse.
- Running analytical queries on big data platforms (e.g., Spark, Hive).
- Optimizing storage and query performance in distributed systems.
7. Example Code
Reading and Writing CSV in PySpark
|
Spark Sample Code (CSV) |
|
from pyspark.sql import SparkSession
# Initialize Spark session spark = SparkSession.builder.appName("CSV Example").getOrCreate()
# Read CSV df_csv = spark.read.csv("data.csv", header=True, inferSchema=True)
# Write CSV df_csv.write.csv("output.csv", header=True) |
Reading and Writing Parquet in PySpark
|
Spark Sample Code (Parquet) |
|
from pyspark.sql import SparkSession
# Initialize Spark session spark = SparkSession.builder.appName("Parquet Example").getOrCreate()
# Read Parquet df_parquet = spark.read.parquet("data.parquet")
# Write Parquet df_parquet.write.parquet("output.parquet") |
8. Summary
|
Feature |
CSV |
Parquet |
|
Best For |
Small datasets, human-readable format |
Big data, analytics, storage efficiency |
|
Compression |
Limited or none |
Highly compressed |
|
Schema |
Schema-less |
Schema-aware |
|
Performance |
Slower for large datasets |
Faster for analytical queries |
|
Complex Data Types |
Not supported |
Supported |
Conclusion
- Use CSV for small datasets, simple data exchange, or when human readability is important.
- Use Parquet for big data processing, analytical queries, and efficient storage in distributed systems.
