Reading CSV Files in Databricks
Databricks provides several ways to read CSV files, including using Python, Scala, R, and SQL. Here are some methods to achieve this:
Using Python
To read a CSV file in Python using Databricks, you can use the `spark.read.csv` method. Here’s an example:
df = spark.read.csv("path/to/your/file.csv", header=True, inferSchema=True)
This code loads a CSV file into a DataFrame named `df`, assuming the file has a header row and you want Spark to infer the schema.
Using Scala
In Scala, you can use the following code to read a CSV file:
val df = spark.read
.option("header", "true")
.option("inferSchema", "true")
.option("delimiter", ",")
.csv("path/to/your/file.csv")
Using R
For R users, the `read.df` function from the SparkR package can be used:
library(SparkR)
df <- read.df("path/to/your/file.csv", source="csv", header=TRUE, inferSchema=TRUE, delimiter=",")
Using SQL with `read_files` Function
Databricks recommends using the `read_files` table-valued function for SQL users. This function is available in Databricks Runtime 13.3 LTS and above:
SELECT * FROM read_files(
'path/to/your/file.csv',
format => 'csv',
header => true
)
Handling Malformed Records
When dealing with malformed CSV records, you can specify a mode to handle them:
- PERMISSIVE: Inserts nulls for fields that cannot be parsed.
- DROPMALFORMED: Drops lines containing fields that cannot be parsed.
- FAILFAST: Aborts the reading process if any malformed data is found.
Example using Python:
df = spark.read.format("csv")
.option("mode", "PERMISSIVE")
.load("path/to/your/file.csv")
Frequently Asked Questions
Q1: How do I specify the schema for a CSV file in Databricks?
A1: You can specify the schema using the `schema` option when reading the CSV file. For example, in Python:
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
schema = StructType([
StructField("name", StringType(), True),
StructField("age", IntegerType(), True)
])
df = spark.read.format("csv")
.option("header", "true")
.schema(schema)
.load("path/to/your/file.csv")
Q2: What is the default mode for handling malformed records in CSV files?
A2: The default mode is PERMISSIVE, which inserts nulls for fields that cannot be parsed correctly.
Q3: How can I record corrupt records to a file when reading CSV files?
A3: You can use the `badRecordsPath` option to specify a path where malformed records will be written:
df = spark.read.format("csv")
.option("mode", "PERMISSIVE")
.option("badRecordsPath", "/path/to/bad/records")
.load("path/to/your/file.csv")
Q4: Can I use SQL to read CSV files without using `read_files` or temporary views?
A4: Yes, but you cannot specify data source options or the schema for the data when doing so.
Q5: How do I handle multiline records in CSV files?
A5: You can use the `multiline` option to handle multiline records:
df = spark.read.format("csv")
.option("multiline", True)
.load("path/to/your/file.csv")
Q6: What is the purpose of the `quote` option when reading CSV files?
A6: The `quote` option specifies the character used to enclose values in the CSV file. For example:
df = spark.read.format("csv")
.option("quote", '"')
.load("path/to/your/file.csv")
Q7: Can I read a subset of columns from a CSV file?
A7: Yes, but you need to specify the correct schema to ensure the columns are read correctly. If the schema is incorrect, the results may differ.
Bottom Line
Reading CSV files in Databricks is straightforward and flexible, allowing you to handle various scenarios such as malformed records and custom schema definitions. By choosing the right options and modes, you can efficiently manage your data processing tasks.