The PySpark read and write Parquet file has posed certain difficulties for a number of users. Our ITtutoria tutorial will alleviate them for you with clear tips and examples.
What Does Parquet Mean?
Apache Parquet is a type of column-oriented and open source of file formats, established to retrieve and store data efficiently. Parquet helps users compress data successfully and encode their schemes with better performance in order to tackle a larger quantity of complex data.
The major goal of Apache Parquet is to interchange formats for interactive and batch workloads, quite the same as other types of column-storage formats that are accessible in Hadoop (ORC and RCFile, specifically).
What Are Some Parquet Characteristics?
Here are things you should keep in mind about Parquet files:
- Open and free source of formats.
- Language agnostic.
- Organized based on columns instead of rows (in short, column-based) to save more storage and speed up the analytic queries.
- Applied in OLAP (analytics) use cases, specifically with conventional OLTP databases.
- Compressing and decompressing data efficiently.
- Supporting types of complicated and advanced-nested types of data structures.
What Are The Purposes of Parquet?
- Great for big data storage (such as structured documents, videos, images, or data tables).
- Saving a great amount of storage by adopting efficient column-based compression, coupled with adjustable encode schemes for any column using different types of data.
- Increasing performance and throughput via methods such as data skipping. Meanwhile, any query that helps fetch particular column values does not have to decode the entire data row.
- Using assembly and record-shredding to accommodate complex structures used to save data.
- Working with a huge quantity of complex data.
- Featuring varied methods of effective encoding and compression types, best for queries that have to analyze specific columns in large tables. Parquet only works with needed columns to minimize unnecessary IO.
Examples of PySpark Read and Write Parquet File
Method 1. Read A Parquet File in PySpark:
df = spark.read.format('parguet').load('filename.parquet') # OR df = spark.read.parquet('filename.parquet')
Method 2. Write A Parquet File in PySpark:
df.write.format('parquet').save('filename.parquet') # OR df.write.parquet('filename.parquet')
1. Do Parquet Files Run Faster Than Other Files?
The trend certainly has ups and downs. Still, we cannot deny that the Parquet/PyArrow combination outperforms, especially for large file sizes. As your file size develops, storing your data in Parquet formats to read via PyArrow will be much more advantageous and faster.
2. Do Parquet Files Store Schema?
Aside from data types, your Parquet specifications also store metadata that records schema in three different levels: page header, chunk/column, and file. Each file’s footer features file metadata.
This article has shed light on fundamental info you need to know about PySpark read and write Parquet file. As you can see, the syntax is far from complicated, only requiring simple commands. Hence, you should have no trouble implementing them! For further instructions on PySpark file reading (for CSV, for instance), feel free to browse our website for more.