You can use MTable Expander to expand a MTable to a table to facilitate data processing and demonstration.
Supported computing resources
MaxCompute
Apache Flink
Deep Learning Containers (DLC)
Configure the component in the Machine Learning Platform for AI (PAI) console
Input ports
Input port (left-to-right)
Data type
Recommended upstream component
Required
data
None
Yes
Component parameters
Category | Parameter | Required value | |
Field Setting | selectedCol | The name of the computed column. The value of this parameter is STRING in the MTABLE format. | |
reservedCols | The columns to be reserved by the algorithm. | ||
Parameters Setting | Schema | The name and type of the expanded columns. The format is colname coltype[, colname2, coltype2[, ...]], such as f0 string, f1 bigint, f2 double. | |
handleInvalidMethod | The method used to handle invalid values. Valid values:
| ||
Execution Tuning | Choose Running Mode | MaxCompute | Use MaxCompute or Flink computing resources. Fore information about how to configure the number of workers and their memory, see Appendix: How to estimate resource usage. |
Flink | |||
DLC | Use DLC computing resources. Configure the specifications based on the prompts. | ||
Configure the component by coding
You can copy the following code to the code editor of the PyAlink Script component. This allows the PyAlink Script component to function like the MTable Expander component.
import numpy as np
import pandas as pd
from pyalink.alink import *
df_data = pd.DataFrame([
["a1", "11L", 2.2],
["a1", "12L", 2.0],
["a2", "11L", 2.0],
["a2", "12L", 2.0],
["a3", "12L", 2.0],
["a3", "13L", 2.0],
["a4", "13L", 2.0],
["a4", "14L", 2.0],
["a5", "14L", 2.0],
["a5", "15L", 2.0],
["a6", "15L", 2.0],
["a6", "16L", 2.0]
])
input = BatchOperator.fromDataframe(df_data, schemaStr='id string, f0 string, f1 double')
zip = GroupByBatchOp()\
.setGroupByPredicate("id")\
.setSelectClause("id, mtable_agg(f0, f1) as m_table_col")
flatten = FlattenMTableBatchOp()\
.setReservedCols(["id"])\
.setSelectedCol("m_table_col")\
.setSchemaStr('f0 string, f1 int')
zip.linkFrom(input).link(flatten).print()