How do I convert a CSV file to Parquet in PySpark?

Posted on by David Darling

How do I convert a CSV file to Parquet in PySpark?

PySpark

  1. from pyspark.sql import SparkSession.
  2. spark = SparkSession.builder \
  3. . master(“local”) \
  4. . appName(“parquet_example”) \
  5. . getOrCreate()
  6. df = spark.read. csv(‘data/us_presidents.csv’, header = True)
  7. repartition(1).write. mode(‘overwrite’). parquet(‘tmp/pyspark_us_presidents’)

How do I write a Spark DataFrame to Parquet?

Spark Write DataFrame to Parquet file format Using parquet() function of DataFrameWriter class, we can write Spark DataFrame to the Parquet file. As mentioned earlier Spark doesn’t need any additional packages or libraries to use Parquet as it by default provides with Spark.

How do I read a CSV file in Spark data frame?

To read a CSV file you must first create a DataFrameReader and set a number of options.

  1. df=spark.read.format(“csv”).option(“header”,”true”).load(filePath)
  2. csvSchema = StructType([StructField(“id”,IntegerType(),False)])df=spark.read.format(“csv”).schema(csvSchema).load(filePath)

How do I convert CSV to Parquet in AWS?

Walkthrough

  1. Discover the data. Sign in to the AWS Management Console and open the AWS Glue console.
  2. Transform the data from CSV to Parquet format. Now you can configure and run a job to transform the data from CSV to Parquet.
  3. Add the Parquet table and crawler.
  4. Analyze the data with Amazon Athena.

Is Parquet better than CSV?

In a nutshell, Parquet is a more efficient data format for bigger files. You will save both time and money by using Parquet over CSVs.

Why Parquet is best for Spark?

It is well-known that columnar storage saves both time and space when it comes to big data processing. Parquet, for example, is shown to boost Spark SQL performance by 10X on average compared to using text, thanks to low-level reader filters, efficient execution plans, and in Spark 1.6. 0, improved scan throughput!

How do I save a DataFrame as CSV in Spark Scala?

With Spark <2, you can use databricks spark-csv library:

  1. Spark 1.4+: df.write.format(“com.databricks.spark.csv”).save(filepath)
  2. Spark 1.3: df.save(filepath,”com.databricks.spark.csv”)

How do I load a parquet file in Spark?

The following commands are used for reading, registering into table, and applying some queries on it.

  1. Open Spark Shell. Start the Spark shell using following example $ spark-shell.
  2. Create SQLContext Object.
  3. Read Input from Text File.
  4. Store the DataFrame into the Table.
  5. Select Query on DataFrame.

Can AWS Glue convert CSV to Parquet?

Yes, we can convert the CSV/JSON files to Parquet using AWS Glue.

Why Parquet is best for spark?

Are Parquet files faster than CSV?

Parquet files take much less disk space than CSVs (column Size on Amazon S3) and are faster to scan (column Data Scanned).

Is Parquet better than ORC?

PARQUET is more capable of storing nested data. ORC is more capable of Predicate Pushdown. ORC supports ACID properties. ORC is more compression efficient.

How do you save a DataFrame in parquet in PySpark?

  1. Read the CSV file into a dataframe using the function spark. read. load().
  2. Step 4: Call the method dataframe. write. parquet(), and pass the name you wish to store the file as the argument.
  3. Now check the Parquet file created in the HDFS and read the data from the “users_parq. parquet” file.

What is parquet in Spark?

Parquet is a columnar format that is supported by many other data processing systems. Spark SQL provides support for both reading and writing Parquet files that automatically preserves the schema of the original data.

How do I write pandas Dataframe to Parquet?

Pandas DataFrame: to_parquet() function The to_parquet() function is used to write a DataFrame to the binary parquet format. This function writes the dataframe as a parquet file. File path or Root Directory path. Will be used as Root Directory path while writing a partitioned dataset.

Can firehose convert CSV to Parquet?

Amazon Kinesis Data Firehose can convert the format of your input data from JSON to Apache Parquet or Apache ORC before storing the data in Amazon S3.

Why Parquet is smaller than CSV?

CSVs are what you call row storage, while Parquet files organize the data in columns. In a nutshell, column storage files are more lightweight, as adequate compression can be done for each column. That’s not the case with row storage, as one row usually contains multiple data types.

Is parquet better than CSV?