### Path Specs

**Example - Dataset per file**

Bucket structure:

```
test-s3-bucket
├── employees.csv
└── food_items.csv
```

Path specs config
```
path_specs:
    - include: s3://test-s3-bucket/*.csv

```

**Example - Datasets with partitions**

Bucket structure:
```
test-s3-bucket
├── orders
│   └── year=2022
│       └── month=2
│           ├── 1.parquet
│           └── 2.parquet
└── returns
    └── year=2021
        └── month=2
            └── 1.parquet

```

Path specs config:
```
path_specs:
    - include: s3://test-s3-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet
```

**Example - Datasets with partition and exclude**

Bucket structure:
```
test-s3-bucket
├── orders
│   └── year=2022
│       └── month=2
│           ├── 1.parquet
│           └── 2.parquet
└── tmp_orders
    └── year=2021
        └── month=2
            └── 1.parquet


```

Path specs config:
```
path_specs:
    - include: s3://test-s3-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet
      exclude: 
        - **/tmp_orders/**
```
**Example - Datasets of mixed nature**

Bucket structure:
```
test-s3-bucket
├── customers
│   ├── part1.json
│   ├── part2.json
│   ├── part3.json
│   └── part4.json
├── employees.csv
├── food_items.csv
├── tmp_10101000.csv
└──  orders
    └── year=2022
        └── month=2
            ├── 1.parquet
            ├── 2.parquet
            └── 3.parquet

```

Path specs config:
```
path_specs:
    - include: s3://test-s3-bucket/*.csv
      exclude:
        - **/tmp_10101000.csv
    - include: s3://test-s3-bucket/{table}/*.json
    - include: s3://test-s3-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet
```

**Valid path_specs.include**

```python
s3://my-bucket/foo/tests/bar.avro # single file table   
s3://my-bucket/foo/tests/*.* # mulitple file level tables
s3://my-bucket/foo/tests/{table}/*.avro #table without partition
s3://my-bucket/foo/tests/{table}/*/*.avro #table where partitions are not specified
s3://my-bucket/foo/tests/{table}/*.* # table where no partitions as well as data type specified
s3://my-bucket/{dept}/tests/{table}/*.avro # specifying keywords to be used in display name
s3://my-bucket/{dept}/tests/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.avro # specify partition key and value format
s3://my-bucket/{dept}/tests/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.avro # specify partition value only format
s3://my-bucket/{dept}/tests/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # for all extensions
s3://my-bucket/*/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # table is present at 2 levels down in bucket
s3://my-bucket/*/*/{table}/{partition[0]}/{partition[1]}/{partition[2]}/*.* # table is present at 3 levels down in bucket
```

**Valid path_specs.exclude**
- \**/tests/**
- s3://my-bucket/hr/**
- **/tests/*.csv
- s3://my-bucket/foo/*/my_table/**

**Notes**

- {table} represents folder for which dataset will be created.
- include path must end with (*.* or *.[ext]) to represent leaf level.
- if *.[ext] is provided then only files with specified type will be scanned.
- /*/ represents single folder.
- {partition[i]} represents value of partition.
- {partition_key[i]} represents name of the partition.
- While extracting, “i” will be used to match partition_key to partition.
- all folder levels need to be specified in include. Only exclude path can have ** like matching.
- exclude path cannot have named variables ( {} ).
- Folder names should not contain {, }, *, / in their names.
- {folder} is reserved for internal working. please do not use in named variables.



If you would like to write a more complicated function for resolving file names, then a {transformer} would be a good fit.

:::caution

Specify as long fixed prefix ( with out /*/ ) as possible in `path_specs.include`. This will reduce the scanning time and cost, specifically on AWS S3

:::

:::caution

Running profiling against many tables or over many rows can run up significant costs.
While we've done our best to limit the expensiveness of the queries the profiler runs, you
should be prudent about the set of tables profiling is enabled on or the frequency
of the profiling runs.

:::

:::caution

If you are ingesting datasets from AWS S3, we recommend running the ingestion on a server in the same region to avoid high egress costs.

:::

### Compatibility

Profiles are computed with PyDeequ, which relies on PySpark. Therefore, for computing profiles, we currently require Spark 3.0.3 with Hadoop 3.2 to be installed and the `SPARK_HOME` and `SPARK_VERSION` environment variables to be set. The Spark+Hadoop binary can be downloaded [here](https://www.apache.org/dyn/closer.lua/spark/spark-3.0.3/spark-3.0.3-bin-hadoop3.2.tgz).

For an example guide on setting up PyDeequ on AWS, see [this guide](https://aws.amazon.com/blogs/big-data/testing-data-quality-at-scale-with-pydeequ/).