The Data Catalog¶
This section introduces catalog.yml
, the project-shareable Data Catalog. The file is located in conf/base
and is a registry of all data sources available for use by a project; it manages loading and saving of data.
All supported data connectors are available in kedro.extras.datasets.
Using the Data Catalog within Kedro configuration¶
Kedro uses configuration to make your code reproducible when it has to reference datasets in different locations and/or in different environments.
You can copy this file and reference additional locations for the same datasets. For instance, you can use the catalog.yml
file in conf/base/
to register the locations of datasets that would run in production while copying and updating a second version of catalog.yml
in conf/local/
to register the locations of sample datasets that you are using for prototyping your data pipeline(s).
There is built-in functionality for conf/local/
to overwrite conf/base/
detailed here. This means that a dataset called cars
could exist in the catalog.yml
files in conf/base/
and conf/local/
. In code, in src
, you would only call a dataset named cars
and Kedro would detect which definition of cars
dataset to use to run your pipeline - cars
definition from conf/local/catalog.yml
would take precedence in this case.
The Data Catalog also works with the credentials.yml
in conf/local/
, allowing you to specify usernames and passwords that are required to load certain datasets.
The are two ways of defining a Data Catalog through the use of YAML configuration, or programmatically using an API. Both methods allow you to specify:
Dataset name
Dataset type
Location of the dataset using
fsspec
, detailed in the next sectionCredentials needed in order to access the dataset
Load and saving arguments
Whether or not you want a dataset or ML model to be versioned when you run your data pipeline
Specifying the location of the dataset¶
Kedro relies on fsspec
for reading and saving data from a variety of data stores including local file systems, network file systems, cloud object stores, and Hadoop. When specifying a storage location in filepath:
, you should provide a URL using the general form protocol://path/to/data
. If no protocol is provided, the local file system is assumed (same as file://
).
The following prepends are available:
Local or Network File System:
file://
- the local file system is default in the absence of any protocol, it also permits relative paths.Hadoop File System (HDFS):
hdfs://user@server:port/path/to/data
- Hadoop Distributed File System, for resilient, replicated files within a cluster.Amazon S3:
s3://my-bucket-name/path/to/data
- Amazon S3 remote binary store, often used with Amazon EC2, using the library s3fs.S3 Compatible Storage:
s3://my-bucket-name/path/_to/data
- e.g. Minio, using the s3fs library.Google Cloud Storage:
gcs://
- Google Cloud Storage, typically used with Google Compute resource using gcsfs (in development).Azure Blob Storage / Azure Data Lake Storage Gen2:
abfs://
- Azure Blob Storage, typically used when working on an Azure environment.HTTP(s):
http://
orhttps://
for reading data directly from HTTP web servers.
fsspec
also provides other file systems, such as SSH, FTP and WebHDFS. See the documentation for more information.
Data Catalog *_args
parameters¶
Data Catalog accepts two different groups of *_args
parameters that serve different purposes:
fs_args
load_args
andsave_args
The fs_args
is used to configure the interaction with a filesystem.
All the top-level parameters of fs_args
(except open_args_load
and open_args_save
) will be passed in an underlying filesystem class.
Example 1: Provide the project
value to the underlying filesystem class (GCSFileSystem
) to interact with Google Cloud Storage (GCS)
test_dataset:
type: ...
fs_args:
project: test_project
The open_args_load
and open_args_save
parameters are passed to the filesystem’s open
method to configure how a dataset file (on a specific filesystem) is opened during a load or save operation, respectively.
Example 2: Load data from a local binary file using utf-8
encoding
test_dataset:
type: ...
fs_args:
open_args_load:
mode: "rb"
encoding: "utf-8"
load_args
and save_args
configure how a third-party library (e.g. pandas
for CSVDataSet
) loads/saves data from/to a file.
Example 3: Save data to a CSV file without row names (index) using utf-8
encoding
test_dataset:
type: pandas.CSVDataSet
...
save_args:
index: False
encoding: "utf-8"
Using the Data Catalog with the YAML API¶
The YAML API allows you to configure your datasets in a YAML configuration file, conf/base/catalog.yml
or conf/local/catalog.yml
.
Here are some examples of data configuration in a catalog.yml
:
Example 1: Loads / saves a CSV file from / to a local file system
bikes:
type: pandas.CSVDataSet
filepath: data/01_raw/bikes.csv
Example 2: Loads and saves a CSV on a local file system, using specified load and save arguments
cars:
type: pandas.CSVDataSet
filepath: data/01_raw/company/cars.csv
load_args:
sep: ','
save_args:
index: False
date_format: '%Y-%m-%d %H:%M'
decimal: .
Example 3: Loads and saves a compressed CSV on a local file system
boats:
type: pandas.CSVDataSet
filepath: data/01_raw/company/boats.csv.gz
load_args:
sep: ','
compression: 'gzip'
fs_args:
open_args_load:
mode: 'rb'
Example 4: Loads a CSV file from a specific S3 bucket, using credentials and load arguments
motorbikes:
type: pandas.CSVDataSet
filepath: s3://your_bucket/data/02_intermediate/company/motorbikes.csv
credentials: dev_s3
load_args:
sep: ','
skiprows: 5
skipfooter: 1
na_values: ['#NA', NA]
Example 5: Loads / saves a pickle file from / to a local file system
airplanes:
type: pickle.PickleDataSet
filepath: data/06_models/airplanes.pkl
backend: pickle
Example 6: Loads an excel file from Google Cloud Storage
rockets:
type: pandas.ExcelDataSet
filepath: gcs://your_bucket/data/02_intermediate/company/motorbikes.xlsx
fs_args:
project: my-project
credentials: my_gcp_credentials
save_args:
sheet_name: Sheet1
Example 7: Save an image created with Matplotlib on Google Cloud Storage
results_plot:
type: matplotlib.MatplotlibWriter
filepath: gcs://your_bucket/data/08_results/plots/output_1.jpeg
fs_args:
project: my-project
credentials: my_gcp_credentials
Example 8: Loads / saves an HDF file on local file system storage, using specified load and save arguments
skateboards:
type: pandas.HDFDataSet
filepath: data/02_intermediate/skateboards.hdf
key: name
load_args:
columns: [brand, length]
save_args:
mode: w # Overwrite even when the file already exists
dropna: True
Example 9: Loads / saves a parquet file on local file system storage, using specified load and save arguments
trucks:
type: pandas.ParquetDataSet
filepath: data/02_intermediate/trucks.parquet
load_args:
columns: [name, gear, disp, wt]
categories: list
index: name
save_args:
compression: GZIP
file_scheme: hive
has_nulls: False
partition_on: [name]
Example 10: Load / saves a Spark table on S3, using specified load and save arguments
weather:
type: spark.SparkDataSet
filepath: s3a://your_bucket/data/01_raw/weather*
credentials: dev_s3
file_format: csv
load_args:
header: True
inferSchema: True
save_args:
sep: '|'
header: True
Example 11: Loads / saves a SQL table using credentials, a database connection, using specified load and save arguments
scooters:
type: pandas.SQLTableDataSet
credentials: scooters_credentials
table_name: scooters
load_args:
index_col: [name]
columns: [name, gear]
save_args:
if_exists: replace
Example 12: Load a SQL table with credentials, a database connection, and applies a SQL query to the table
scooters_query:
type: pandas.SQLQueryDataSet
credentials: scooters_credentials
sql: select * from cars where gear=4
load_args:
index_col: [name]
When using pandas.SQLTableDataSet or pandas.SQLQueryDataSet you must provide a database connection string. In the example above we pass it using scooters_credentials
key from the credentials (see the details in Feeding in credentials section below). scooters_credentials
must have a top-level key con
containing SQLAlchemy compatible connection string. As an alternative to credentials, you could explicitly put con
into load_args
and save_args
(pandas.SQLTableDataSet
only).
Example 13: Load data from an API endpoint, example US corn yield data from USDA
us_corn_yield_data:
type: api.APIDataSet
url: https://quickstats.nass.usda.gov
credentials: usda_credentials
params:
key: SOME_TOKEN
format: JSON
commodity_desc: CORN
statisticcat_des: YIELD
agg_level_desc: STATE
year: 2000
Note that usda_credientials
will be passed as the auth
argument in the requests
library. Specify the username and password as a list in your credentials.yml
as follows:
usda_credentials:
- username
- password
Example 14: Loading data from Minio (S3 API Compatible Storage)
test:
type: pandas.CSVDataSet
filepath: s3://your_bucket/test.csv # assume `test.csv` is uploaded to the Minio server.
credentials: dev_minio
In credentials.yml
, define the key
, secret
and the endpoint_url
as follows:
dev_minio:
key: token
secret: key
client_kwargs:
endpoint_url : 'http://localhost:9000'
Note
The easiest way to setup MinIO is to run a Docker image. After the following command, you can access to Minio server with http://localhost:9000
and create a bucket and add files as if it is on S3.
docker run -p 9000:9000 -e "MINIO_ACCESS_KEY=token" -e "MINIO_SECRET_KEY=key" minio/minio server /data
Example 15: Loading a model saved as a pickle from Azure Blob Storage
ml_model:
type: pickle.PickleDataSet
filepath: "abfs://models/ml_models.pickle"
versioned: True
credentials: dev_abs
In credentials.yml
, define the account_name
and account_key
as follows:
dev_abs:
account_name: accountname
account_key: key
Example 16: Loading a CSV file stored in a remote location through SSH
Note
This example requires Paramiko to be installed (pip install paramiko
).
cool_dataset:
type: pandas.CSVDataSet
filepath: "sftp:///path/to/remote_cluster/cool_data.csv"
credentials: cluster_credentials
All parameters required to establish the SFTP connection can be defined through fs_args
or in credentials.yml
as follows:
cluster_credentials:
username: my_username
host: host_address
port: 22
password: password
The list of all available parameters is given in the Paramiko documentation.
Creating a Data Catalog YAML configuration file via CLI¶
You can use kedro catalog create command to create a Data Catalog YAML configuration.
It creates a <conf_root>/<env>/catalog/<pipeline_name>.yml
configuration file with MemoryDataSet
datasets for each dataset in a registered pipeline if it is missing from the DataCatalog
.
# <conf_root>/<env>/catalog/<pipeline_name>.yml
rockets:
type: MemoryDataSet
scooters:
type: MemoryDataSet
Adding parameters¶
You can configure parameters for your project and reference them in your nodes. Do this using the add_feed_dict()
method (API documentation). You can use this method to add any other entry / metadata you wish on the DataCatalog
.
Feeding in credentials¶
Before instantiating the DataCatalog
Kedro will first attempt to read the credentials from the project configuration. The resulting dictionary is then passed into DataCatalog.from_config()
as the credentials
argument.
Let’s assume that the project contains the file conf/local/credentials.yml
with the following contents:
dev_s3:
client_kwargs:
aws_access_key_id: key
aws_secret_access_key: secret
scooters_credentials:
con: sqlite:///kedro.db
my_gcp_credentials:
id_token: key
In the example above catalog.yml
contains references to credentials keys dev_s3
and scooters_credentials
. It means that when instantiating motorbikes
dataset, for example, the DataCatalog
will attempt to read top-level key dev_s3
from the received credentials
dictionary, and then will pass its values into the dataset __init__
as credentials
argument. This is essentially equivalent to calling this:
CSVDataSet(
filepath="s3://test_bucket/data/02_intermediate/company/motorbikes.csv",
load_args=dict(sep=",", skiprows=5, skipfooter=1, na_values=["#NA", "NA"]),
credentials=dict(key="token", secret="key"),
)
Loading multiple datasets that have similar configuration¶
You may encounter situations where your datasets use the same file format, load and save arguments, and are stored in the same folder. YAML has a built-in syntax for factorising parts of a YAML file, which means that you can decide what is generalisable across your datasets so that you do not have to spend time copying and pasting dataset configurations in catalog.yml
.
You can see this in the following example:
_csv: &csv
type: spark.SparkDataSet
file_format: csv
load_args:
sep: ','
na_values: ['#NA', NA]
header: True
inferSchema: False
cars:
<<: *csv
filepath: s3a://data/01_raw/cars.csv
trucks:
<<: *csv
filepath: s3a://data/01_raw/trucks.csv
bikes:
<<: *csv
filepath: s3a://data/01_raw/bikes.csv
load_args:
header: False
The syntax &csv
names the following block csv
and the syntax <<: *csv
inserts the contents of the block named csv
. Locally declared keys entirely override inserted ones as seen in bikes
.
Note
It’s important that the name of the template entry starts with a _
so Kedro knows not to try and instantiate it as a dataset.
You can also nest reuseable YAML syntax:
_csv: &csv
type: spark.SparkDataSet
file_format: csv
load_args: &csv_load_args
header: True
inferSchema: False
airplanes:
<<: *csv
filepath: s3a://data/01_raw/airplanes.csv
load_args:
<<: *csv_load_args
sep: ;
In this example the default csv
configuration is inserted into airplanes
and then the load_args
block is overridden. Normally that would replace the whole dictionary. In order to extend load_args
the defaults for that block are then re-inserted.
Transcoding datasets¶
You may come across a situation where you would like to read the same file using two different dataset implementations. Use transcoding when you want to load and save the same file, via its specified filepath
, using different DataSet
implementations.
A typical example of transcoding¶
For instance, parquet files can not only be loaded via the ParquetDataSet
using pandas
, but also directly by SparkDataSet
. This conversion is typical when coordinating a Spark
to pandas
workflow.
To enable transcoding, define two DataCatalog
entries for the same dataset in a common format (Parquet, JSON, CSV, etc.) in your conf/base/catalog.yml
:
my_dataframe@spark:
type: spark.SparkDataSet
filepath: data/02_intermediate/data.parquet
file_format: parquet
my_dataframe@pandas:
type: pandas.ParquetDataSet
filepath: data/02_intermediate/data.parquet
These entries are used in the pipeline like this:
Pipeline(
[
node(func=my_func1, inputs="spark_input", outputs="my_dataframe@spark"),
node(func=my_func2, inputs="my_dataframe@pandas", outputs="pipeline_output"),
]
)
How does transcoding work?¶
In this example, Kedro understands that my_dataframe
is the same dataset in its spark.SparkDataSet
and pandas.ParquetDataSet
formats and helps resolve the node execution order.
In the pipeline, Kedro uses the spark.SparkDataSet
implementation for saving and pandas.ParquetDataSet
for loading, so the first node should output a pyspark.sql.DataFrame
, while the second node would receive a pandas.Dataframe
.
Transforming datasets¶
Transformers are used to intercept the load and save operations on Kedro DataSet
s. Use cases for transformers include:
Data validation
Tracking operation performance
Data format conversion (although we would recommend Transcoding for this)
Applying built-in transformers¶
Here we cover the use case of tracking operation performance by applying built-in transformers to monitor the latency of load and save operations.
Transformers are applied at the DataCatalog
level. To apply the built-in ProfileTimeTransformer
, you need to:
Navigate to
src/<package_name>/hooks.py
Apply
ProfileTimeTransformer
in the hook implementationTransformerHooks.after_catalog_created
Register the hook in your
src/<package_name>/settings.py
# src/<package_name>/hooks.py
from kedro.extras.transformers import ProfileTimeTransformer # new import
from kedro.framework.hooks import hook_impl # new import
from kedro.io import DataCatalog # new import
class TransformerHooks:
@hook_impl
def after_catalog_created(self, catalog: DataCatalog) -> None:
catalog.add_transformer(ProfileTimeTransformer())
# src/<package_name>/settings.py
from <package_name>.hooks import TransformerHooks
HOOKS = (TransformerHooks(),)
Once complete, rerun the pipeline from the terminal and you should see the following logging output:
$ kedro run
...
2019-11-13 15:09:01,784 - kedro.io.data_catalog - INFO - Loading data from `companies` (CSVDataSet)...
2019-11-13 15:09:01,827 - ProfileTimeTransformer - INFO - Loading companies took 0.043 seconds
2019-11-13 15:09:01,828 - kedro.pipeline.node - INFO - Running node: preprocessing_companies: preprocess_companies([companies]) -> [preprocessed_companies]
2019-11-13 15:09:01,880 - kedro_tutorial.nodes.data_engineering - INFO - Running 'preprocess_companies' took 0.05 seconds
2019-11-13 15:09:01,880 - kedro_tutorial.nodes.data_engineering - INFO - Running 'preprocess_companies' took 0.05 seconds
2019-11-13 15:09:01,880 - kedro.io.data_catalog - INFO - Saving data to `preprocessed_companies` (CSVDataSet)...
2019-11-13 15:09:02,112 - ProfileTimeTransformer - INFO - Saving preprocessed_companies took 0.232 seconds
2019-11-13 15:09:02,113 - kedro.runner.sequential_runner - INFO - Completed 1 out of 6 tasks
...
The ProfileTimeTransformer - INFO
log messages report the latency of dataset load and save operations.
Transformer scope¶
You can refine the scope of the transformer by specifying an optional list of the datasets it is applied to in add_transformer
.
For example, the command catalog.add_transformer(profile_time, ["dataset1", "dataset2"])
applies the profile_time
transformer only to the datasets named dataset1
and dataset2
.
This is useful when you need to apply a transformer to just a subset of datasets.
Versioning datasets and ML models¶
Making a simple addition to your Data Catalog allows you to perform versioning of datasets and machine learning models.
Consider the following versioned dataset defined in the catalog.yml
:
cars.csv:
type: pandas.CSVDataSet
filepath: data/01_raw/company/cars.csv
versioned: True
The DataCatalog
will create a versioned CSVDataSet
called cars.csv
. The actual csv file location will look like data/01_raw/company/cars.csv/<version>/cars.csv
, where <version>
corresponds to a global save version string formatted as YYYY-MM-DDThh.mm.ss.sssZ
.
You can run the pipeline with a particular versioned data set with --load-version
flag as follows:
kedro run --load-version="cars.csv:YYYY-MM-DDThh.mm.ss.sssZ"
where --load-version
is dataset name and version timestamp separated by :
.
This section shows just the very basics of versioning, which is described further in the documentation about Kedro IO.
Using the Data Catalog with the Code API¶
The code API allows you to:
configure data sources in code
operate the IO module within notebooks
Configuring a Data Catalog¶
In a file like catalog.py
, you can construct a DataCatalog
object programmatically. In the following, we are using a number of pre-built data loaders documented in the API reference documentation.
from kedro.io import DataCatalog
from kedro.extras.datasets.pandas import (
CSVDataSet,
SQLTableDataSet,
SQLQueryDataSet,
ParquetDataSet,
)
io = DataCatalog(
{
"bikes": CSVDataSet(filepath="../data/01_raw/bikes.csv"),
"cars": CSVDataSet(filepath="../data/01_raw/cars.csv", load_args=dict(sep=",")),
"cars_table": SQLTableDataSet(
table_name="cars", credentials=dict(con="sqlite:///kedro.db")
),
"scooters_query": SQLQueryDataSet(
sql="select * from cars where gear=4",
credentials=dict(con="sqlite:///kedro.db"),
),
"ranked": ParquetDataSet(filepath="ranked.parquet"),
}
)
When using SQLTableDataSet
or SQLQueryDataSet
you must provide a con
key containing SQLAlchemy compatible database connection string. In the example above we pass it as part of credentials
argument. Alternative to credentials
is to put con
into load_args
and save_args
(SQLTableDataSet
only).
Loading datasets¶
You can access each dataset by its name.
cars = io.load("cars") # data is now loaded as a DataFrame in 'cars'
gear = cars["gear"].values
Behind the scenes¶
The following steps happened behind the scenes when load
was called:
The value
cars
was located in the Data CatalogThe corresponding
AbstractDataSet
object was retrievedThe
load
method of this dataset was calledThis
load
method delegated the loading to the underlying pandasread_csv
function
Viewing the available data sources¶
If you forget what data was assigned, you can always review the DataCatalog
.
io.list()
Saving data¶
You can save data using an API similar to that used to load data.
Caution
This use is not recommended unless you are prototyping in notebooks.
Saving data to memory¶
from kedro.io import MemoryDataSet
memory = MemoryDataSet(data=None)
io.add("cars_cache", memory)
io.save("cars_cache", "Memory can store anything.")
io.load("car_cache")
Saving data to a SQL database for querying¶
At this point we may want to put the data in a SQLite database to run queries on it. Let’s use that to rank scooters by their mpg.
import os
# This cleans up the database in case it exists at this point
try:
os.remove("kedro.db")
except FileNotFoundError:
pass
io.save("cars_table", cars)
ranked = io.load("scooters_query")[["brand", "mpg"]]
Saving data in Parquet¶
Finally we can save the processed data in Parquet format.
io.save("ranked", ranked)
Attention
Saving None
to a dataset is not allowed!