> For the complete documentation index, see [llms.txt](https://kks32-courses.gitbook.io/data-analytics/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://kks32-courses.gitbook.io/data-analytics/spark/file-operations.md).

# File operations

## File formats

Spark makes it very simple to load and save data in a large number of file formats. Formats range from unstructured, like text, to semistructured, like JSON, to structured, like SequenceFiles. The input formats that Spark wraps all transparently handle compressed formats based on the file extension.

![File formats](/files/-L9PuEHg_mGbgJwuJwR-)

Text files are very simple to load from and save to with Spark. When we load a single text file as an RDD, each input line becomes an element in the RDD. We can also load multiple whole text files at the same time into a pair RDD, with the key being the name and the value being the contents of each file. Loading a single text file is as simple as calling the `textFile()` function on our SparkContext with the path to the file:

```python
# Loading a text file
input = sc.textFile("file:///user/livy/two-cities.txt")

# Saving as a text file in Python
result.saveAsTextFile(outputFile)
```

JSON is a popular semistructured data format. The simplest way to load JSON data is by loading the data as a text file and then mapping over the values with a JSON parser.

```python
# Loading unstructured JSON
import json
data = input.map(lambda x: json.loads(x))

# Saving JSON
(data.filter(lambda x: x['lovesPandas']).map(lambda x: json.dumps(x))
.saveAsTextFile(outputFile))
```