kedro.extras.datasets.spark.SparkHiveDataSet¶
- class kedro.extras.datasets.spark.SparkHiveDataSet(database, table, write_mode='errorifexists', table_pk=None, save_args=None)[source]¶
SparkHiveDataSet
loads and saves Spark dataframes stored on Hive. This data set 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.extras.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) data_set = SparkHiveDataSet(database="test_database", table="test_table", write_mode="overwrite") data_set.save(spark_df) reloaded = data_set.load() reloaded.take(4)
Attributes
Methods
exists
()Checks whether a data set's output already exists by calling the provided _exists() method.
from_config
(name, config[, load_version, ...])Create a data set 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.
- DEFAULT_SAVE_ARGS: Dict[str, Any] = {}¶
- __init__(database, table, write_mode='errorifexists', table_pk=None, save_args=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
oroverwrite
are supported.table_pk (
Optional
[List
[str
]]) – If performing an upsert, this identifies the primary key columns used to resolve preexisting data. Is required forwrite_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
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()¶
Checks whether a data set’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)¶
Create a data set instance using the configuration provided.
- Parameters
name – Data set name.
config – Data set config dictionary.
load_version – Version string to be used for
load
operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.save_version – Version string to be used for
save
operation if the data set is versioned. Has no effect on the data set if versioning was not enabled.
- Returns
An instance of an
AbstractDataset
subclass.- Raises
DatasetError – When the function fails to create the data set from its config.
- load()¶
Loads data by delegation to the provided load method.
- Return type
TypeVar
(_DO
)- Returns
Data returned by the provided load method.
- Raises
DatasetError – When underlying load method raises error.
- release()¶
Release any cached data.
- Raises
DatasetError – when underlying release method raises error.
- Return type
None
- save(data)¶
Saves data by delegation to the provided save method.
- Parameters
data (
TypeVar
(_DI
)) – 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