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Reference to pyspark: Difference performance for spark.read.format("csv") vs spark.read.csv
I thought I needed .options("inferSchema" , "true") and .option("header", "true") to print my headers but apparently I could still print my csv with headers.
What is the difference between header and schema? I don't really understand the meaning of "inferSchema: automatically infers column types. It requires one extra pass over the data and is false by default".
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By setting inferSchema=true , Spark will automatically go through the csv file and infer the schema of each column. This requires an extra pass over the file which will result in reading a file with inferSchema set to true being slower. But in return the dataframe will most likely have a correct schema given its input.
If you have a header with column names on file, you need to explicitly specify true for header option using option("header",true) not mentioning this, the API treats the header as a data record. It also reads all columns as a string (StringType) by default.
If you've been working with CSV files in Databricks, you must be familiar with a very useful option called inferSchema while loading CSV files. It is the default option that is widely used by developers to identify the columns, data types, and nullability, automatically while reading the file.
inferSchema option tells the reader to infer data types from the source file. This results in an additional pass over the file resulting in two Spark jobs being triggered. It is an expensive operation because Spark must automatically go through the CSV file and infer the schema for each column. Reading CSV using user-defined Schema
We can pass the file name pattern to spark.read.csv and read all the data in files under hdfs://public/airlines_all/airlines into Data Frame. We can use options such as header and inferSchema to assign names and data types. However inferSchema will end up going through the entire data to assign schema.
We can pass the file name pattern to spark.read.csv and read all the data in files under hdfs://public/airlines_all/airlines into Data Frame. We can use options such as header and inferSchema to assign names and data types.
However inferSchema will end up going through the entire data to assign schema. We can use samplingRatio to process fraction of data and then infer the schema. In case if the data in all the files have similar structure, we should be able to get the schema using one file and then apply it on others.
The header and schema are separate things.
Header:
If the csv file have a header (column names in the first row) then set header=true. This will use the first row in the csv file as the dataframe's column names. Setting header=false (default option) will result in a dataframe with default column names: _c0, _c1, _c2, etc.
Setting this to true or false should be based on your input file.
Schema:
The schema refered to here are the column types. A column can be of type String, Double, Long, etc. Using inferSchema=false (default option) will give a dataframe where all columns are strings (StringType). Depending on what you want to do, strings may not work. For example, if you want to add numbers from different columns, then those columns should be of some numeric type (strings won't work).
By setting inferSchema=true, Spark will automatically go through the csv file and infer the schema of each column. This requires an extra pass over the file which will result in reading a file with inferSchema set to true being slower. But in return the dataframe will most likely have a correct schema given its input.
As an alternative to reading a csv with inferSchema you can provide the schema while reading. This have the advantage of being faster than inferring the schema while giving a dataframe with the correct column types. In addition, for csv files without a header row, column names can be given automatically. To provde schema see e.g.: Provide schema while reading csv file as a dataframe
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