SparkChain
laktory.models.SparkChain
¤
Bases: BaseChain
The SparkChain
class defines a series of Spark transformation to be
applied to a dataframe. Each transformation is expressed as a node
(SparkChainNode
object) that, upon execution, returns a new dataframe.
Each node is executed sequentially in the provided order. A node may also
be another SparkChain
.
ATTRIBUTE | DESCRIPTION |
---|---|
dataframe_type |
Differentiator to select dataframe chain type
TYPE:
|
nodes |
The list of transformations to be executed.
TYPE:
|
Examples:
import pandas as pd
from laktory import models
df0 = spark.createDataFrame(pd.DataFrame({"x": [1, 2, 3]}))
# Build Chain
sc = models.SparkChain(
nodes=[
{
"with_column": {
"name": "cos_x",
"type": "double",
"expr": "F.cos('x')",
},
},
{
"nodes": [
{
"func_name": "withColumnRenamed",
"func_args": [
"x",
"x_tmp",
],
},
{
"with_column": {
"name": "x2",
"type": "double",
"expr": "F.sqrt('x_tmp')",
},
},
],
},
{
"func_name": "drop",
"func_args": [
"x_tmp",
],
},
]
)
# Execute Chain
df = sc.execute(df0)
# Print result
print(df.toPandas().to_string())
'''
cos_x x2
0 0.540302 1.000000
1 -0.416147 1.414214
2 -0.989992 1.732051
'''
--
laktory.models.SparkChainNode
¤
Bases: BaseChainNode
PolarsChain node that output a dataframe upon execution. As a convenience,
with_column
argument can be specified to create a new column from a
spark or sql expression. Each node is executed sequentially in the
provided order. A node may also be another Spark Chain.
ATTRIBUTE | DESCRIPTION |
---|---|
func_args |
List of arguments to be passed to the spark function. If the function
expects a spark column, its string representation can be provided
with support for |
func_kwargs |
List of keyword arguments to be passed to the spark function. If the
function expects a spark column, its string representation can be
provided with support for |
func_name |
Name of the spark function to build the dataframe. Mutually
exclusive to |
sql_expr |
SQL Expression using |
with_column |
Syntactic sugar for adding a column. Mutually exclusive to
TYPE:
|
with_columns |
Syntactic sugar for adding columns. Mutually exclusive to
TYPE:
|
Examples:
import pandas as pd
from laktory import models
df0 = spark.createDataFrame(pd.DataFrame({"x": [1, 2, 2, 3]}))
node = models.SparkChainNode(
with_column={
"name": "cosx",
"type": "double",
"expr": "F.cos('x')",
},
)
df = node.execute(df0)
node = models.SparkChainNode(
with_column={
"name": "xy",
"type": "double",
"expr": "F.coalesce('x')",
},
)
df = node.execute(df)
print(df.toPandas().to_string())
'''
x cosx xy
0 1 0.540302 1.0
1 2 -0.416147 2.0
2 2 -0.416147 2.0
3 3 -0.989992 3.0
'''
node = models.SparkChainNode(
func_name="drop_duplicates",
func_args=[["x"]],
)
df = node.execute(df)
print(df.toPandas().to_string())
'''
x cosx xy
0 1 0.540302 1.0
1 2 -0.416147 2.0
2 3 -0.989992 3.0
'''
Functions¤
execute
¤
execute(df, udfs=None)
Execute spark chain node
PARAMETER | DESCRIPTION |
---|---|
df |
Input dataframe
TYPE:
|
udfs |
User-defined functions
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
Output dataframe
|
|
Source code in laktory/models/transformers/sparkchainnode.py
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|
--
laktory.models.SparkChainNodeColumn
¤
Bases: BaseChainNodeColumn
Chain node column definition
ATTRIBUTE | DESCRIPTION |
---|---|
name |
Column name
TYPE:
|
type |
Column data type |
unit |
Column units |
expr |
String representation of a polars expression |
sql_expr |
SQL expression |
--
laktory.models.SparkChainNodeSQLExpr
¤
Bases: BaseChainNodeSQLExpr
Chain node SQL expression
ATTRIBUTE | DESCRIPTION |
---|---|
expr |
SQL expression
|