The article belows will explain in depth the spectrum of Spark read and write Apache Parquet as well as how to make the best out of it. Jump right in for further details!
What Is Apache Parquet?
As a column-oriented and open-source data file format, Apache Parquet specializes especially for retrieval and storing data efficiently.
With the main mission being to handle complicated data in vast quantities, it offers effective data encoding and compression algorithms performing improved efficiency. There are several languages such as Python, C++, and Java, etc. that support Parquet.
As such, utilizing Apache Parquet will bring forth these advantages to enhance speed and provide access to structure files better as follows:
- Cuts down on IO operations.
- Retrieve the precise columns you want.
- Offer assistance with type-specific encoding.
- Takes up less room.
Example: Apache Parquet Spark
Let’s first utilize the Seq objectcreate a Spark DataFrame before discussing an example of the Apache Parquet Spark.
Keep in mind that you can only use the toDF() function on a sequence object when implicits are imported using spark.sqlContext.implicits.
For reference, this entire Spark parquet instance will be accessible in the Github source.
val data = Seq (("John","","Simon","36536","M",3110), ("Mark","Raph","","40188","M",4310), ("Ruth","","Williams","42014","M",1410), ("Mai","Alex","Jones","39912","F",5510), ("Jenny","Michael","Brown","34461","F",-1)) columns= ["firstname","middlename","lastname","id","location","salary"]
val columns = Seq("firstname","middlename","lastname","dob","gender","salary")
import spark.sqlContext.implicits._ val df = data.toDF(columns:_*)
How To Read And Write Apache Parquet in Spark
Spark Write Apache Parquet
We may write a Spark DataFrame to a Parquet file by employing the DataFrameWriter class’s parquet() feature.
As was already said, Parquet is already included with Spark and doesn’t require any extra libraries or packages to be of use. Isn’t that simple?
Therefore, version and compatibility problems will not be any more a concern of you.
Let’s check out the example below!
All columns will be necessarily changed to be nullable for expedience rations when writing a Spark DataFrame to Parquet format, preserving the data types and column names. Also, don’t forget to take note of the parquet extension on every component file that Spark generates.
Spark Read Apache Parquet
Akin to the above implementation, the parquet() method of the DataFrameReader will help construct a Spark DataFrame by reading parquet files.
In this line of code, an Apache parquet file that we previously created is being read for data.
val parqDF = spark.read.parquet("/tmp/output/people.parquet")
The Bottom Line
Above is neatly all that you need to learn about the Spark Read and Write Apache Parquet. Hopefully, our tutorial can be of great benefit to you. See then!