An Apache Parquet file is an open source data storage format used for columnar databases in analytical querying. If you have small data sets but millions of rows to search, it might be better to use a columnar format for better performance. Columnar databases store data by grouping columns rather than the standard row-based database which groups by rows. A Parquet file is one of several columnar storage formats.
What Is a Parquet File?
Instead of grouping rows like an Excel spreadsheet or a standard relational database, an Apache Parquet file groups columns together for faster performance. Parquet is a columnar storage format and not a database itself, but the Parquet format is common with data lakes, especially with Hadoop. Since it’s a columnar format, it’s popular with analytic data storage and queries.
Most developers are used to row-based data storage, but imagine rotating an Excel spreadsheet so that the columns are now shown in place of numbered rows. For example, instead of keeping a customer table with a list of first and last name columns where each first and last name is grouped together as a row, a Parquet file stores columns together so that databases can more quickly return information from a specific column instead of searching through each row with numerous columns.
Benefits of Parquet Files
Aside from query performance based on the way Parquet files store data, the other main advantage is cost efficiency. Apache Parquet files have highly efficient compression and decompression, so they don’t take up as much space as a standard database file. By taking less storage space, an enterprise organization could save thousands of dollars in storage costs.
Columnar storage formats are best with big data and analytic queries. Parquet files can store images, videos, objects, files, and standard data, so they can be used in any type of analytic application. Because Parquet file strategies are open source, they’re also good for organizations that want to customize their data storage and query strategies.
How Parquet Files Work
Parquet files contain column-based storage, but they also contain metadata. The columns are grouped together in each row group for query efficiency, and the metadata helps the database engine locate data. The metadata contains information about the columns, row groups containing data, and the schema.
The schema in a Parquet file describes the column-based approach to storage. Schema format is binary, and it can be used in a Hadoop data lake environment. Parquet files can be stored in any file system, so they aren’t limited to Hadoop environments only.
One advantage of the Parquet file storage format is a strategy called predicate pushdown. With predicate pushdown, the database engine filters data early in processing so that more targeted data is transferred down the pipeline. By having less data targeted to a query, it improves query performance. Less data processing also reduces computer resource usage and ultimately lowers costs as well.
Using Parquet Files
Parquet files are Apache files, so you can create them in your own Python scripts provided that you import several libraries. Let’s say that you have a table in Python:
import numpy as np
import pandas as pd
import pyarrow as pa
df = pd.DataFrame({'one': [-1, 4, 1.3],
'two': ['blue', 'green', 'white'],
'three': [False, False, True]},
index=list('abc'))
table = pa.Table.from_pandas(df)
With this table, we can now create a Parquet file:
import pyarrow.parquet as pq
pq.write_table(table, 'mytable.parquet')
The above code creates the file “mytable.parquet” and writes the table to it. You can now read from it from your favorite database and import the data, or you can use the data for your own queries and analysis.
You can also read this table from the file using Python:
pq.read_table('mytable.parquet', columns=['one', 'three'])
The write() function lets you set options when you write the table to a file. You can find a list of options on Apache’s site, but here’s an example of setting the file’s compatibility to Apache Spark:
import numpy as np
import pandas as pd
import pyarrow as pa
df = pd.DataFrame({'one': [-1, 4, 1.3],
'two': ['blue', 'green', 'white'],
'three': [False, False, True]},
flavor=’spark’)
table = pa.Table.from_pandas(df)
Conclusion
If you plan to use Parquet files for Hadoop, Apache Spark, or other compatible databases, you can automate file creation using Python or import files into the database environment for analysis. Parquet files use compression to lower storage space requirements, but you still need excessive storage capacity for large big data silos. Pure Storage can help you with big data storage with our deduplication and compression technology.
