> For the complete documentation index, see [llms.txt](https://george-jen.gitbook.io/data-science-and-apache-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://george-jen.gitbook.io/data-science-and-apache-spark/bucketing-sorting-and-partitioning.md).

# Bucketing, Sorting and Partitioning

For file-based data source, it is also possible to bucket and sort or partition the output. Bucketing and sorting are applicable only to persistent tables:

```
peopleDF.write.bucketBy(42, "name").sortBy("age").saveAsTable("people_bucketed")
```

while partitioning can be used with both save and saveAsTable when using the Dataset APIs.

```
usersDF.write.partitionBy("favorite_color").format("parquet").save("file:///tmp/namesPartByColor.parquet")
```

It is possible to use both partitioning and bucketing for a single table:

```
usersDF
  .write
  .partitionBy("favorite_color")
  .bucketBy(42, "name")
  .saveAsTable("users_partitioned_bucketed")
```

partitionBy creates a directory structure as described in the Partition Discovery section. Thus, it has limited applicability to columns with high cardinality. 

bucketBy distributes data across a fixed number of buckets and can be used when a number of unique values is unbounded.


---

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