kedro_datasets.spark.SparkHiveDataset¶

class kedro_datasets.spark.SparkHiveDataset(*, database, table, write_mode='errorifexists', table_pk=None, save_args=None, metadata=None)[source]¶

SparkHiveDataset loads and saves Spark dataframes stored on Hive. This dataset also handles some incompatible file types such as using partitioned parquet on hive which will not normally allow upserts to existing data without a complete replacement of the existing file/partition.

This Dataset has some key assumptions:

Schemas do not change during the pipeline run (defined PKs must be present for the duration of the pipeline).
Tables are not being externally modified during upserts. The upsert method is NOT ATOMIC to external changes to the target table while executing. Upsert methodology works by leveraging Spark DataFrame execution plan checkpointing.

Example usage for the YAML API:

hive_dataset:
  type: spark.SparkHiveDataset
  database: hive_database
  table: table_name
  write_mode: overwrite

Example usage for the Python API:

 from pyspark.sql import SparkSession
 from pyspark.sql.types import StructField, StringType, IntegerType, StructType

 from kedro_datasets.spark import SparkHiveDataset

 schema = StructType(
...     [StructField("name", StringType(), True), StructField("age", IntegerType(), True)]
... )

 data = [("Alex", 31), ("Bob", 12), ("Clarke", 65), ("Dave", 29)]

 spark_df = SparkSession.builder.getOrCreate().createDataFrame(data, schema)

 dataset = SparkHiveDataset(
...     database="test_database", table="test_table", write_mode="overwrite"
... )
 dataset.save(spark_df)
 reloaded = dataset.load()

 reloaded.take(4)

Attributes

DEFAULT_SAVE_ARGS

Methods

`exists`()	Checks whether a dataset's output already exists by calling the provided _exists() method.
`from_config`(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
`load`()	Loads data by delegation to the provided load method.
`release`()	Release any cached data.
`save`(data)	Saves data by delegation to the provided save method.
`to_config`()	Converts the dataset instance into a dictionary-based configuration for serialization.

DEFAULT_SAVE_ARGS: dict[str, Any] = {}¶

__init__(*, database, table, write_mode='errorifexists', table_pk=None, save_args=None, metadata=None)[source]¶

Creates a new instance of SparkHiveDataset.

Parameters:

database (str) – The name of the hive database.
table (str) – The name of the table within the database.
write_mode (str) – insert, upsert or overwrite are supported.
table_pk (Optional[list[str]]) – If performing an upsert, this identifies the primary key columns used to resolve preexisting data. Is required for write_mode="upsert".
save_args (Optional[dict[str, Any]]) – Optional mapping of any options, passed to the DataFrameWriter.saveAsTable as kwargs. Key example of this is partitionBy which allows data partitioning on a list of column names. Other HiveOptions can be found here: https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html#specifying-storage-format-for-hive-tables
metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

Note

For users leveraging the upsert functionality, a checkpoint directory must be set, e.g. using spark.sparkContext.setCheckpointDir(“/path/to/dir”) or directly in the Spark conf folder.

Raises:: DatasetError – Invalid configuration supplied

exists()[source]¶

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:: bool
Returns:: Flag indicating whether the output already exists.
Raises:: DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)[source]¶

Create a dataset instance using the configuration provided.

Parameters:

name (str) – Data set name.
config (dict[str, Any]) – Data set config dictionary.
load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.
save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()[source]¶

Loads data by delegation to the provided load method.

Return type:: DataFrame
Returns:: Data returned by the provided load method.
Raises:: DatasetError – When underlying load method raises error.

release()[source]¶

Release any cached data.

Raises:: DatasetError – when underlying release method raises error.
Return type:: None

save(data)[source]¶

Saves data by delegation to the provided save method.

Parameters:

data (DataFrame) – the value to be saved by provided save method.

Raises:

DatasetError – when underlying save method raises error.
FileNotFoundError – when save method got file instead of dir, on Windows.
NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None

to_config()[source]¶

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:: dict[str, Any]
Returns:: A dictionary containing the dataset’s type and initialization arguments.