datahub/metadata-ingestion/docs/sources/s3/s3.md

### Path Specs

Path Specs (`path_specs`) is a list of Path Spec (`path_spec`) objects where each individual `path_spec` represents one or more datasets. Include path (`path_spec.include`) represents formatted path to the dataset. This path must end with `*.*` or `*.[ext]` to represent leaf level. If `*.[ext]` is provided then files with only specified extension type will be scanned. "`.[ext]`" can be any of [supported file types](#supported-file-types). Refer [example 1](#example-1---individual-file-as-dataset) below for more details.

All folder levels need to be specified in include path. You can use `/*/` to represent a folder level and avoid specifying exact folder name. To map folder as a dataset, use `{table}` placeholder to represent folder level for which dataset is to be created. For a partitioned dataset, you can use placeholder `{partition_key[i]}` to represent name of `i`th partition and `{partition[i]}` to represent value of `i`th partition. During ingestion, `i` will be used to match partition_key to partition. Refer [example 2 and 3](#example-2---folder-of-files-as-dataset-without-partitions) below for more details.

Exclude paths (`path_spec.exclude`) can be used to ignore paths that are not relevant to current `path_spec`. This path cannot have named variables ( `{}` ). Exclude path can have `**` to represent multiple folder levels. Refer [example 4](#example-4---folder-of-files-as-dataset-with-partitions-and-exclude-filter) below for more details.

Refer [example 5](#example-5---advanced---either-individual-file-or-folder-of-files-as-dataset) if your bucket has more complex dataset representation.

**Additional points to note**
- Folder names should not contain {, }, *, / in their names.
- Named variable {folder} is reserved for internal working. please do not use in named variables.


### Path Specs -  Examples
#### Example 1 - Individual file as Dataset

Bucket structure:

```
test-bucket
├── employees.csv
├── departments.json
└── food_items.csv
```

Path specs config to ingest `employees.csv` and `food_items.csv` as datasets:
```
path_specs:
    - include: s3://test-bucket/*.csv

```
This will automatically ignore `departments.json` file. To include it, use `*.*` instead of `*.csv`.

#### Example 2 - Folder of files as Dataset (without Partitions)

Bucket structure:
```
test-bucket
└──  offers
     ├── 1.avro
     └── 2.avro

```

Path specs config to ingest folder `offers` as dataset:
```
path_specs:
    - include: s3://test-bucket/{table}/*.avro
```

`{table}` represents folder for which dataset will be created.
 
#### Example 3 - Folder of files as Dataset (with Partitions)

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

```

Path specs config to ingest folders `orders` and `returns` as datasets:
```
path_specs:
    - include: s3://test-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet
```

One can also use `include: s3://test-bucket/{table}/*/*/*.parquet` here however above format is preferred as it allows declaring partitions explicitly.

#### Example 4 - Folder of files as Dataset (with Partitions), and Exclude Filter

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


```

Path specs config to ingest folder `orders` as dataset but not folder `tmp_orders`:
```
path_specs:
    - include: s3://test-bucket/{table}/{partition_key[0]}={partition[0]}/{partition_key[1]}={partition[1]}/*.parquet
      exclude: 
        - **/tmp_orders/**
```


#### Example 5 - Advanced - Either Individual file OR Folder of files as Dataset

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

Above config has 3 path_specs and will ingest following datasets
- `employees.csv` - Single File as Dataset
- `food_items.csv` - Single File as Dataset
- `customers` - Folder as Dataset
- `orders` - Folder as Dataset
  and will ignore file `tmp_10101000.csv`

**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/**



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/).

:::caution

From Spark 3.2.0+, Avro reader fails on column names that don't start with a letter and contains other character than letters, number, and underscore. [https://github.com/apache/spark/blob/72c62b6596d21e975c5597f8fff84b1a9d070a02/connector/avro/src/main/scala/org/apache/spark/sql/avro/AvroFileFormat.scala#L158] 
Avro files that contain such columns won't be profiled.
:::