I have learned the format of Parquet files when I was working for Amazon Cloud Services (specifically S3). In short, the Parquet Files are column-based or column-oriented formats.
What is parquet files and why they are useful than the row-based formats?
Parquet is a columnar storage file format optimized for use with big data processing frameworks like Apache Hadoop, Apache Spark, and others. It was developed in collaboration between Twitter and Cloudera. Being a columnar format, it has some distinct advantages over traditional row-based formats, such as CSV or JSON:
Compression: Due to the nature of columnar storage, data is more homogenous in each column, which allows for better compression ratios than row-based storage. For example, a column storing age values will only have integers, allowing for efficient compression.
Read Efficiency: For analytical queries where only a subset of columns is required, Parquet can read only the necessary columns from the disk. This is more efficient than reading entire rows and discarding unwanted data, as would be the case with row-based formats.
Schema Evolution: Parquet supports complex nested data structures, and its schema can evolve over time. This means you can add, remove, or modify columns in your dataset without needing to rewrite the entire dataset.
Performance: The combination of efficient compression and reduced I/O by reading only necessary columns can greatly speed up analytical query performance, especially on large datasets.
Compatibility: Parquet is supported by a wide array of data processing tools, including but not limited to Hadoop, Spark, Presto, Hive, Impala, and many others.
Type Support: Parquet supports rich data structures, including standard primitives (integers, floats, strings) and more complex types (lists, maps, structs).
Compression Algorithms: Parquet supports multiple compression algorithms, allowing users to choose the best trade-off between compression ratio and decompression speed, depending on their use-case.
Predicate Pushdown: Many processing engines can take advantage of Parquet’s columnar nature to push down certain predicates (filters) and only read the necessary data blocks, thus further optimizing query performance.
While Parquet offers these advantages, it’s essential to remember that no single file format is the best choice for every scenario. Depending on the use-case, other formats might be more appropriate. For example, for real-time data streaming or when the schema is not well-defined, formats like Avro or JSON might be more suitable.
An Example of a Parquet File
To understand the Parquet format better, it’s useful to contrast how data might look in a traditional row-based format like CSV versus how it’s stored in a columnar format like Parquet.
Example in CSV (Row-based format):
Let’s say you have a small dataset of people’s names and their ages:
Name, Age John, 25 Jane, 30 Doe, 35
In CSV, the data is stored row by row. So when you read the file, you’d read it line by line.
Parquet (Conceptual Representation):
In Parquet, data would be stored column by column:
Name: John, Jane, Doe Age: 25, 30, 35
So, it is like transposing a Matrix or in this case, transposing a CSV.
This columnar storage means that if you were only interested in querying the “Age” column, you could read just that column’s data without touching the “Name” data. This is a simple and small dataset, but you can imagine the efficiency gains when working with billions of rows and many columns.
Actual Parquet files are binary files, so you wouldn’t be able to open one in a text editor and view it like you would with CSV. Instead, you’d see binary data optimized for the columnar storage, along with metadata about the schema, compression details, and more.
To work with Parquet files, you’d typically use big data processing tools or libraries that can read and write Parquet, such as Apache Spark, Apache Arrow, pandas (with the appropriate plugins), etc.
If you have a Parquet file and would like to see its contents, tools like parquet-tools can be used to inspect and read the data in a human-readable format.
–EOF (The Ultimate Computing & Technology Blog) —
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