Data node management

Data nodes get created when scenarios or pipelines are created. Please refer to the Entities' creation section for more details.

In this section, it is assumed that my_config.py module contains a Taipy configuration already implemented.

Data node attributes¶

A DataNode entity is identified by a unique identifier id that Taipy generates. A data node also holds various properties and attributes accessible through the entity:

config_id: The id of the data node config.
scope: The scope of this data node (scenario, pipeline, etc.).
id: The unique identifier of this data node.
name: The user-readable name of the data node.
owner_id: The identifier of the owner (pipeline_id, scenario_id, cycle_id) or None.
last_edit_date: The date and time of the last data modification made through Taipy. Note that only for file-based data nodes (CSV, Excel, pickle, JSON, Parquet, ...), the file's last modification date is used to compute the last_edit_date value. That means if a file is modified manually or by an external process, the last_edit_date value is automatically updated within Taipy.
edits: The ordered list of Edits, representing the successive modifications of the data node.
version: The string indicates the application version of the data node to instantiate. If not provided, the current version is used.
validity_period: The duration since the last edit date for which the data node can be considered up-to-date. Once the validity period has passed, the data node is considered stale and relevant tasks will run even if they are skippable (see the Task management page for more details). If validity_period is set to None, the data node is always up-to-date.
edit_in_progress: The boolean flag signals if the data node is locked for modification.
properties: The dictionary of additional arguments.

Get data node¶

The first method to access a data node is by calling the get() method passing the data node id as parameter:

Example

import taipy as tp
import my_config

scenario = tp.create_scenario(my_config.monthly_scenario_cfg)

data_node = scenario.sales_history
data_node_retrieved = scenario.sales_history
data_node = tp.get(data_node.id)
# data_node == data_node_retrieved

Get data nodes by config_id¶

The data nodes that are part of a scenario, pipeline or task can be directly accessed as attributes by using their config_id:

Example

import taipy as tp
import my_config

# Creating a scenario from a config
scenario = tp.create_scenario(my_config.monthly_scenario_cfg)

# Access the data node 'sales_history' from the scenario
scenario.sales_history

# Access the pipeline 'sales' from the scenario and
# then access the data node 'sales_history' from the pipeline
pipeline = scenario.sales
pipeline.sales_history

# Access the task 'training' from the pipeline and
# then access the data node 'sales_history' from the task
task = pipeline.training
task.sales_history

Data nodes can be retrieved by using get_entities_by_config_id() providing the config_id. This method returns the list of all existing data nodes instantiated from the config_id provided as a parameter.

Example

import taipy as tp
import my_config

# Create 2 scenarios, which will also create 2 trained_model data nodes.
scenario_1 = tp.create_scenario(my_config.monthly_scenario_cfg)
scenario_2 = tp.create_scenario(my_config.monthly_scenario_cfg)

# Get all data nodes by config_id, this will return a list of 2 trained_model data nodes
# created alongside the 2 scenarios.
all_trained_model_dns = tp.get_entities_by_config_id("trained_model")

Get all data nodes¶

All data nodes that are part of a scenario or a pipeline can be directly accessed as attributes:

Example

import taipy as tp
import my_config

# Creating a scenario from a config
scenario = tp.create_scenario(my_config.monthly_scenario_cfg)

# Access all the data nodes from the scenario
scenario.data_nodes

# Access the pipeline 'sales' from the scenario and
# then access all the data nodes from the pipeline
pipeline = scenario.sales
pipeline.data_nodes

All the data nodes can be retrieved using the method get_data_nodes() which returns a list of all existing data nodes.

Example

import taipy as tp

# Retrieve all data nodes
data_nodes = tp.get_data_nodes()

data_nodes #[DataNode 1, DataNode 2, ..., DataNode N]

Read / Write a data node¶

To access the content of a data node you can use the DataNode.read() method. The read method returns the data stored in the data node according to the type of data node:

Example

import taipy as tp
import my_config

# Creating a scenario from a config
scenario = tp.create_scenario(my_config.monthly_scenario_cfg)

# Retrieve a data node
data_node = scenario.sales_history

# Returns the content stored on the data node
data_node.read()

To write some data on the data node, like the output of a task, you can use the DataNode.write() method. The method takes a data object (string, dictionary, lists, NumPy arrays, Pandas dataframes, etc. based on the data node type and its exposed type) as a parameter and writes it on the data node:

Example

import taipy as tp
import my_config

# Creating a scenario from a config
scenario = tp.create_scenario(my_config.monthly_scenario_cfg)

# Retrieve a data node
data_node = scenario.sales_history

data = [{"product": "a", "qty": "2"}, {"product": "b", "qty": "4"}]

# Writes the dictionary on the data node
data_node.write(data)

# returns the new data stored on the data node
data_retrieved = data_node.read()

Pickle¶

When reading from a Pickle data node, Taipy returns whichever data stored in the pickle file.

Pickle data node can write any data object that can be picked, including but not limited to:

integers, floating-point numbers.
strings, bytes, or bytearrays.
tuples, lists, sets, and dictionaries containing only picklable objects.
functions, classes.
instances of classes with picklable properties.

Check out What can be pickled and unpickled? for more details.

CSV¶

When reading from a CSV data node, Taipy returns the data of the CSV file based on the exposed_type parameter. Check out CSV Data Node configuration for more details on exposed_type.

Assume that the content of the sales.csv file is the following.

path/sales.csv

date,nb_sales
12/24/2018,1550
12/25/2018,2315
12/26/2018,1832

The following examples represent the results when reading from a CSV data node with different exposed_type:

data_node.read() returns

exposed_type = "pandas"exposed_type = "modin"exposed_type = "numpy"exposed_type = SaleRow

pandas.DataFrame
(
             date  nb_sales
    0  12/24/2018      1550
    1  12/25/2018      2315
    2  12/26/2018      1832
)

modin.pandas.DataFrame
(
             date  nb_sales
    0  12/24/2018      1550
    1  12/25/2018      2315
    2  12/26/2018      1832
)

numpy.array(
    [
        ["12/24/2018", "1550"],
        ["12/25/2018", "2315"],
        ["12/26/2018", "1832"]
    ],
)

[
    SaleRow("12/24/2018", 1550),
    SaleRow("12/25/2018", 2315),
    SaleRow("12/26/2018", 1832),
]

When writing data to a CSV data node, the CSVDataNode.write() method can take several datatype as the input:

list, numpy array
dictionary, or list of dictionaries
pandas dataframes

The following examples will write to the path of the CSV data node:

data_node.write() examples

listnumpy arraydictionarypandas dataframes

When write a list to CSV data node, each element of a list contains 1 row of data.

# write a list
data_node.write(
    ["12/24/2018", "12/25/2018", "12/26/2018"]
)
# or write a list of lists
data_node.write(
    [
        ["12/24/2018", 1550],
        ["12/25/2018", 2315],
        ["12/26/2018", 1832],
    ]
)

data_node.write(
    np.array([
        ["12/24/2018", 1550],
        ["12/24/2018", 2315],
        ["12/24/2018", 1832],
    ])
)

# "list" form
data_node.write(
    {
        "date": ["12/24/2018", "12/25/2018", "12/26/2018"],
        "nb_sales": [1550, 2315, 1832]
    }
)
# "records" form
data_node.write(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/24/2018", "nb_sales": 2315},
        {"date": "12/24/2018", "nb_sales": 1832},
    ]
)

data = pandas.DataFrame(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/24/2018", "nb_sales": 2315},
        {"date": "12/24/2018", "nb_sales": 1832},
    ]
)

data_node.write(data)

When write a list or numpy array to CSV data node, the column name will be numbered from 1. To write with custom column names, use the CSVDataNode.write_with_column_names() method.

CSVDataNode.write_with_column_names() examples

data_node.write(
    [
        ["12/24/2018", 1550],
        ["12/25/2018", 2315],
        ["12/26/2018", 1832],
    ],
    columns=["date", "nb_sales"]
)

Excel¶

When reading from an Excel data node, Taipy returns the data of the Excel file based on the exposed_type parameter. Check out Excel Data Node configuration for more details on exposed_type.

For the example in this section, assume that sales_history_cfg in my_config.py is an Excel data node configuration with default_path="path/sales.xlsx".

Assume that the content of the sales.xlsx file is the following.

path/sales.xlsx

date	nb_sales
12/24/2018	1550
12/25/2018	2315
12/26/2018	1832

The following examples represent the results when reading from an Excel data node with different exposed_type:

data_node.read() returns

exposed_type = "pandas"exposed_type = "modin"exposed_type = "numpy"exposed_type = SaleRow

pandas.DataFrame
(
             date  nb_sales
    0  12/24/2018      1550
    1  12/25/2018      2315
    2  12/26/2018      1832
)

modin.pandas.DataFrame
(
             date  nb_sales
    0  12/24/2018      1550
    1  12/25/2018      2315
    2  12/26/2018      1832
)

numpy.array(
    [
        ["12/24/2018", "1550"],
        ["12/25/2018", "2315"],
        ["12/26/2018", "1832"]
    ],
)

[
    SaleRow("12/24/2018", 1550),
    SaleRow("12/25/2018", 2315),
    SaleRow("12/26/2018", 1832),
]

When writing data to an Excel data node, the ExcelDataNode.write() method can take several datatype as the input:

list, numpy array
dictionary, or list of dictionaries
pandas dataframes

The following examples will write to the path of the Excel data node:

data_node.write() examples

listnumpy arraydictionarypandas dataframes

When write a list to Excel data node, each element of a list contains 1 row of data.

# write a list
data_node.write(
    ["12/24/2018", "12/25/2018", "12/26/2018"]
)
# or write a list of lists
data_node.write(
    [
        ["12/24/2018", 1550],
        ["12/25/2018", 2315],
        ["12/26/2018", 1832],
    ]
)

data_node.write(
    np.array([
        ["12/24/2018", 1550],
        ["12/25/2018", 2315],
        ["12/26/2018", 1832],
    ])
)

# "list" form
data_node.write(
    {
        "date": ["12/24/2018", "12/25/2018", "12/26/2018"],
        "nb_sales": [1550, 2315, 1832]
    }
)
# "records" form
data_node.write(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/25/2018", "nb_sales": 2315},
        {"date": "12/26/2018", "nb_sales": 1832},
    ]
)

data = pandas.DataFrame(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/25/2018", "nb_sales": 2315},
        {"date": "12/26/2018", "nb_sales": 1832},
    ]
)

data_node.write(data)

When write a list or numpy array to Excel data node, the column name will be numbered from 1. To write with custom column names, use the ExcelDataNode.write_with_column_names() method.

ExcelDataNode.write_with_column_names() examples

data_node.write(
    [
        ["12/24/2018", 1550],
        ["12/25/2018", 2315],
        ["12/26/2018", 1832],
    ],
    columns=["date", "nb_sales"]
)

SQL Table¶

When reading from a SQL Table data node, Taipy returns the data of the SQL Table file based on the exposed_type parameter. Check out SQL Table Data Node configuration for more details on exposed_type.

For the example in this section, assume that sales_history_cfg in my_config.py is a SQL Table data node configuration with table_name="sales".

Assume that the content of the "sales" table is the following.

A selection from the "sales" table

ID	date	nb_sales
1	12/24/2018	1550
2	12/25/2018	2315
3	12/26/2018	1832

The following examples represent the results when reading from a SQL Table data node with different exposed_type:

data_node.read() returns

exposed_type = "pandas"exposed_type = "modin"exposed_type = "numpy"exposed_type = SaleRow

pandas.DataFrame
(
       ID        date  nb_sales
    0   1  12/24/2018      1550
    1   2  12/25/2018      2315
    2   3  12/26/2018      1832
)

modin.pandas.DataFrame
(
       ID        date  nb_sales
    0   1  12/24/2018      1550
    1   2  12/25/2018      2315
    2   3  12/26/2018      1832
)

numpy.array(
    [
        ["1", "12/24/2018", "1550"],
        ["2", "12/25/2018", "2315"],
        ["3", "12/26/2018", "1832"]
    ],
)

[
    SaleRow("12/24/2018", 1550),
    SaleRow("12/25/2018", 2315),
    SaleRow("12/26/2018", 1832),
]

When writing data to a SQL Table data node, the SQLTableDataNode.write() method can take several datatype as the input:

list of lists or list of tuples
numpy array
dictionary, or list of dictionaries
pandas dataframes

Assume that the "ID" column is the auto-increment primary key. The following examples will write to the SQL Table data node:

data_node.write() examples

listnumpy arraydictionarypandas dataframes

# write a list of lists
data_node.write(
    [
        ["12/24/2018", 1550],
        ["12/25/2018", 2315],
        ["12/26/2018", 1832],
    ]
)

# or write a list of tuples
data_node.write(
    [
        ("12/24/2018", 1550),
        ("12/25/2018", 2315),
        ("12/26/2018", 1832),
    ]
)

data = np.array(
    [
        ["12/24/2018", 1550],
        ["12/25/2018", 2315],
        ["12/26/2018", 1832],
    ]
)

data_node.write(data)

# write 1 record to the SQL table
data_node.write(
    {"date": "12/24/2018", "nb_sales": 1550}
)

# write multiple records using a list of dictionaries
data_node.write(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/25/2018", "nb_sales": 2315},
        {"date": "12/26/2018", "nb_sales": 1832},
    ]
)

data = pandas.DataFrame(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/25/2018", "nb_sales": 2315},
        {"date": "12/26/2018", "nb_sales": 1832},
    ]
)

data_node.write(data)

SQL¶

A SQL data node is designed to give the user more flexibility on how to read and write to SQL table (or multiple SQL tables).

Let's consider the orders_cfg in my_config.py which configures a SQL data node.

When reading from a SQL data node, Taipy executes the read query and returns the data of the SQL file based on the exposed_type parameter:

The exposed_type parameter of orders_cfg is undefined, therefore it takes the default value as "pandas". Check out SQL Data Node configuration for more details on exposed_type.

The read_query of orders_cfg is

SELECT orders.ID, orders.date, products.price, orders.number_of_products
FROM orders INNER JOIN products
ON orders.product_id=products.ID

When reading from the SQL data node using data_node.read() method, Taipy will execute the above query and return a pandas.DataFrame represents the "orders" table inner join with the "products" table.

A selection from the "orders" table

ID	date	product_id	number_of_products
1	01/05/2019	2	200
2	01/05/2019	3	450
3	01/05/2019	5	350
4	01/06/2019	1	520
5	01/06/2019	3	250
6	01/07/2019	2	630
7	01/07/2019	4	480

A selection from the "products" table

ID	price	description
1	30	foo product
2	50	bar product
3	25	foo product
4	60	bar product
5	40	foo product

data_node.read() returns

pandas.DataFrame
(
        ID         date   price   number_of_products
    0   1   01/05/2019      50                 200
    1   2   01/05/2019      25                 450
    2   3   01/05/2019      40                 350
    3   4   01/06/2019      30                 520
    4   5   01/06/2019      25                 250
    5   6   01/07/2019      50                 630
    6   7   01/07/2019      60                 480
)

When writing to a SQL data node, Taipy will first pass the data to write_query_builder and then execute a list of queries returned by the query builder:

The write_query_builder parameter of orders_cfg in this example is defined as the write_orders_plan() method.
After being called with the write data as a pd.DataFrame, the write_orders_plan() method will return a list of SQL queries.
The first query deletes all records from "orders" table.
The following query will insert a list of records to the "orders" table according to the data, assume that "ID" column in "orders" table is the auto-increment primary key.

data_node.write()

data = pandas.DataFrame(
    [
        {"date": "01/08/2019", "product_id": 1 "number_of_products": 450},
        {"date": "01/08/2019", "product_id": 3 "number_of_products": 320},
        {"date": "01/08/2019", "product_id": 4 "number_of_products": 350},
    ]
)

data_node.write(data)

The "orders" table after being written:

ID	date	product_id	number_of_products
8	01/08/2019	1	450
9	01/08/2019	3	320
10	01/08/2019	4	350

JSON¶

When reading from a JSON data node, Taipy will return a dictionary or a list based on the format of the JSON file.

When writing data to a JSON data node, the JSONDataNode.write() method can take a list, dictionary, or list of dictionaries as the input.

In JSON, values must be one of the following data types:

A string
A number
An object (embedded JSON object)
An array
A boolean
null

However, the content of a JSON data node can vary. By default, JSON data node provided by Taipy can also encode and decode:

Python enum.Enum.
A datetime.datetime object.
A dataclass object.

For the example in this section, assume that sales_history_cfg in my_config.py is a JSON data node configuration with default_path="path/sales.json".

Read and write from a JSON data node using default encoder and decoder

Write dictionariesWrite datetime.datetimeWrite enum.EnumWrite dataclass object

data = [
    {"date": "12/24/2018", "nb_sales": 1550},
    {"date": "12/25/2018", "nb_sales": 2315},
    {"date": "12/26/2018", "nb_sales": 1832},
]
data_node.write(data)

results in:

[
    {"date": "12/24/2018", "nb_sales": 1550},
    {"date": "12/25/2018", "nb_sales": 2315},
    {"date": "12/26/2018", "nb_sales": 1832},
]

from datetime import datetime

data = [
    {"date": datetime.datetime(2018, 12, 24), "nb_sales": 1550},
    {"date": datetime.datetime(2018, 12, 25), "nb_sales": 2315},
    {"date": datetime.datetime(2018, 12, 26), "nb_sales": 1832},
]
data_node.write(data)

results in:

[
    {"date": {"__type__": "Datetime", "__value__": "2018-12-24T00:00:00"}, "nb_sales": 1550},
    {"date": {"__type__": "Datetime", "__value__": "2018-12-24T00:00:00"}, "nb_sales": 2315},
    {"date": {"__type__": "Datetime", "__value__": "2018-12-24T00:00:00"}, "nb_sales": 1832},
]

The read method will return a list of dictionaries, with "date" are datetime.datetime as data when written.

from enum import Enum

class SaleRank(Enum):
    A = 2000
    B = 1800
    C = 1500
    D = 1200
    F = 1000

data = [
    {"date": "12/24/2018", "nb_sales": SaleRank.C},
    {"date": "12/25/2018", "nb_sales": SaleRank.A},
    {"date": "12/26/2018", "nb_sales": SaleRank.B},
]
data_node.write(data)

results in:

[
    {"date": "12/24/2018", "nb_sales": {"__type__": "Enum-SaleRank-C", "__value__": 1500}},
    {"date": "12/25/2018", "nb_sales": {"__type__": "Enum-SaleRank-A", "__value__": 2000}},
    {"date": "12/26/2018", "nb_sales": {"__type__": "Enum-SaleRank-B", "__value__": 1800}},
]

The read method will return a list of dictionaries, with "nb_sales" are Enum.enum as data when written.

from dataclasses import dataclass

@dataclass
class SaleRow:
    date: str
    nb_sales: int

data = [
    SaleRow("12/24/2018", 1550),
    SaleRow("12/25/2018", 2315),
    SaleRow("12/26/2018", 1832),
]
data_node.write(data)

results in:

[
    {"__type__": "dataclass-SaleRow", "__value__": {"date": "12/24/2018", "nb_sales": 1550}},
    {"__type__": "dataclass-SaleRow", "__value__": {"date": "12/25/2018", "nb_sales": 2315}},
    {"__type__": "dataclass-SaleRow", "__value__": {"date": "12/26/2018", "nb_sales": 1832}},
]

The read method will return a list of SaleRow objects as data when written.

You can also specify custom JSON encoder and decoder to handle different data types. Check out JSON Data Node configuration for more details on how to config custom JSON encoder and decoder.

Parquet¶

When read from a Parquet data node, Taipy returns the data of the Parquet file based on exposed_type parameter. Check out Parquet Data Node configuration for more details on exposed_type.

Assume that the content of the sales.parquet file populates the following table.

path/sales.parquet

date	nb_sales
12/24/2018	1550
12/25/2018	2315
12/26/2018	1832

The following examples represent the results when read from Parquet data node with different exposed_type:

data_node.read() returns

exposed_type = "pandas"exposed_type = "modin"exposed_type = "numpy"exposed_type = SaleRow

pandas.DataFrame
(
             date  nb_sales
    0  12/24/2018      1550
    1  12/25/2018      2315
    2  12/26/2018      1832
)

modin.pandas.DataFrame
(
             date  nb_sales
    0  12/24/2018      1550
    1  12/25/2018      2315
    2  12/26/2018      1832
)

numpy.array(
    [
        ["12/24/2018", "1550"],
        ["12/25/2018", "2315"],
        ["12/26/2018", "1832"]
    ],
)

[
    SaleRow("12/24/2018", 1550),
    SaleRow("12/25/2018", 2315),
    SaleRow("12/26/2018", 1832),
]

When writing data to a Parquet data node, the ParquetDataNode.write() method can take several datatype as the input depending on the exposed type:

pandas dataframes
modin dataframes
numpy arrays
any object, which will be passed to the pd.DataFrame constructor (e.g., list of dictionaries)

The following examples will write to the path of the Parquet data node:

data_node.write() examples

pandas dataframesdictionarymodin dataframes

data = pandas.DataFrame(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/24/2018", "nb_sales": 2315},
        {"date": "12/24/2018", "nb_sales": 1832},
    ]
)

data_node.write(data)

# "list" form
data_node.write(
    {
        "date": ["12/24/2018", "12/25/2018", "12/26/2018"],
        "nb_sales": [1550, 2315, 1832]
    }
)

# "records" form
data_node.write(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/24/2018", "nb_sales": 2315},
        {"date": "12/24/2018", "nb_sales": 1832},
    ]
)

data = modin.pandas.DataFrame(
    [
        {"date": "12/24/2018", "nb_sales": 1550},
        {"date": "12/24/2018", "nb_sales": 2315},
        {"date": "12/24/2018", "nb_sales": 1832},
    ]
)

data_node.write(data)

Additionally, Parquet data node entities also expose two new methods, namely: ParquetDataNode.read_with_kwargs and ParquetDataNode.write_with_kwargs. These two methods may be used to pass additional keyword arguments to the pandas pandas.read_parquet and pandas.DataFrame.to_parquet methods, on top of the arguments which were defined in the ParquetDataNode configuration.

The following examples demonstrate reading and writing to a Parquet data node with additional keyword arguments:

Reading data with ParquetDataNode.read_with_kwargs

columns = ["nb_sales"]
data_node.read_with_kwargs(columns=columns)

Here, the ParquetDataNode.read_with_kwargs method is used to specify a keyword parameter, "columns", which is the list of column names to be read from the Parquet dataset. In this case, only the "nb_sales" column will be read.

Writing data with ParquetDataNode.write_with_kwargs

data_node.write_with_kwargs(index=False)

Here, the ParquetDataNode.write_with_kwargs method is used to specify a keyword parameter, "index", which is a boolean value determining if the index of the DataFrame should be written. In this case, the index will not be not written.

Mongo collection¶

When reading from a Mongo collection data node, Taipy will return a list of objects as instances of a document class defined by custom_document.

When writing data to a Mongo collection data node, the MongoCollectionDataNode.write() method takes a list of objects as instances of a document class defined by custom_document as the input.

By default, Mongo collection data node uses taipy.core.DefaultCustomDocument as the document class. A DefaultCustomDocument can have any attribute, however, the type of the value should be supported by MongoDB, including but not limited to:

Boolean, integers, and floating-point numbers.
String.
Object (embedded document object).
Arrays − arrays or list or multiple values.

For the example in this section, assume that sales_history_cfg in my_config.py is a Mongo collection data node configuration.

Check out MongoDB supported data types for more details.

Read and write from a Mongo collection data node using default document class

from taipy.core import DefaultCustomDocument

data = [
    DefaultCustomDocument(date="12/24/2018", nb_sales=1550),
    DefaultCustomDocument(date="12/25/2018", nb_sales=2315),
    DefaultCustomDocument(date="12/26/2018", nb_sales=1832),
]
data_node.write(data)

will write 3 documents to MongoDB:

[
    {"_id": ObjectId("634cd1b3383279c68cee1c21"), "date": "12/24/2018", "nb_sales": 1550},
    {"_id": ObjectId("634cd1b3383279c68cee1c22"), "date": "12/25/2018", "nb_sales": 2315},
    {"_id": ObjectId("634cd1b3383279c68cee1c23"), "date": "12/26/2018", "nb_sales": 1832},
]

The read method will return a list of DefaultCustomDocument objects, including "_id" attribute.

You can also specify custom document class to handle specific attribute, encode and decode data when reading and writing to the Mongo collection.

Check out Mongo collection Data Node configuration for more details on how to config a custom document class.

Generic¶

A Generic data node has the read and the write functions defined by the user:

When reading from a generic data node, Taipy runs the function defined by read_fct with parameters defined by read_fct_args.
When writing to a generic data node, Taipy runs the function defined by write_fct with parameters defined by write_fct_args.

In memory¶

Since an In memory data node stores data in RAM as a Python variable, the read / write methods are rather straightforward.

When reading from an In memory data node, Taipy returns whichever data stored in RAM corresponding to the data node.

Correspondingly, In memory data node can write any data object that is valid data for a Python variable.

Warning

Since the data is stored in memory, it cannot be used in a multiprocess environment. (See Job configuration for more details).

Filter read results¶

It is also possible to partially read the contents of data nodes, which comes in handy when dealing with large amounts of data. This can be achieved by providing an operator, a Tuple of (field_name, value, comparison_operator), or a list of operators to the DataNode.filter() method:

data_node.filter(
    [("field_name", 14, Operator.EQUAL), ("field_name", 10, Operator.EQUAL)],
    JoinOperator.OR
)

If a list of operators is provided, it is necessary to provide a join operator that will be used to combine the filtered results from the operators.

It is also possible to use pandas style filtering:

temp_data = data_node["field_name"]
temp_data[(temp_data == 14) | (temp_data == 10)]

Warning

For now, the DataNode.filter() method is only implemented for CSVDataNode, ExcelDataNode, SQLTableDataNode, SQLDataNode with "pandas" as the exposed_type value.

Get parent scenarios, pipelines and tasks¶

To get the parent entities of a data node (scenarios or pipelines or tasks) you can use either the method DataNode.get_parents() or the function get_parents(). Both return the parents of the data node.

Example

import taipy as tp
import my_config

# Create a scenario from a config
scenario = tp.create_scenario(my_config.monthly_scenario_cfg)

# Retrieve a data node
data_node = scenario.sales_history

# Retrieve the parent entities of the data node
parent_entities = data_node.get_parents()
# {'scenarios': [Scenario 1], 'pipelines': [Pipeline 1], 'tasks': [Task 1]}

# Retrieve the parent entities of the data node
tp.get_parents(data_node)
# {'scenarios': [Scenario 1], 'pipelines': [Pipeline 1], 'tasks': [Task 1]}

The next section shows the job orchestration and execution.