Reading a CSV File in Databricks
Databricks provides several ways to read CSV files, including using Python, Scala, R, and SQL. Here are some examples:
Python Example
To read a CSV file in Python, you can use the following code:
df = spark.read.format("csv")
.option("header", "true")
.option("delimiter", ",")
.option("nullValue", "")
.option("emptyValue", "NULL")
.load(f"{bronze_folder_path}/Test.csv")
Scala Example
In Scala, you can read a CSV file as follows:
val df = spark.read
.option("header", "true")
.option("inferSchema", "true")
.option("delimiter", ",")
.csv(s"${pathVolume}/${fileName}")
R Example
For R, use the following code:
library(SparkR)
df <- read.df(paste(path_volume, "/", file_name, sep=""), source="csv", header = TRUE, inferSchema = TRUE, delimiter = ",")
SQL Example Using `read_files`
SQL users can leverage the `read_files` function:
SELECT * FROM read_files(
's3:////.csv',
format => 'csv',
header => true,
mode => 'FAILFAST')
Handling Malformed Records
Databricks allows you to handle malformed CSV records using modes like `PERMISSIVE`, `DROPMALFORMED`, and `FAILFAST`. For example:
df = spark.read.format("csv")
.option("mode", "PERMISSIVE")
.load("/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv")
Frequently Asked Questions
- Q: What is the default mode for handling malformed records in CSV files?
A: The default mode is `PERMISSIVE`, which inserts nulls for fields that could not be parsed correctly. - Q: How can I specify a custom schema for a CSV file?
A: You can specify a schema using the `schema` option when reading the CSV file. - Q: What is the `badRecordsPath` option used for?
A: The `badRecordsPath` option is used to record corrupt records to a file. - Q: Can I read a subset of columns from a CSV file?
A: Yes, you can specify the columns you want to read by providing a schema with only those columns. - Q: How do I handle CSV files with multiline values?
A: Use the `multiline` option set to `True` to handle multiline values. - Q: What are the limitations of using SQL without `read_files` or temporary views?
A: Without `read_files` or temporary views, you cannot specify data source options or schema for the data. - Q: Is `read_files` available in all Databricks Runtime versions?
A: `read_files` is available in Databricks Runtime 13.3 LTS and above.
Bottom Line
Reading CSV files in Databricks is straightforward and flexible, allowing you to handle various file formats and data quality issues efficiently. Whether you're using Python, Scala, R, or SQL, Databricks provides robust tools to manage and analyze your data.