Simple DLC example

In this tutorial we will be creating a simple Data Load Controller to load data from a Pandas dataframe through RESTful services without a scope.

Importing py_dlc

We can import the library in our python project as follows:

import py_dlc

We will also need the following imports:

Other imports not needed for the DLC, but only for this example:

import pandas as pd
import requests

Session Initialization and Data Gathering

First we will create a new session and initialize a TradeTable Table with two columns.

session = tt.create_session()
session.create_table(
    types={
        "Trade ID": tt.type.STRING,
        "Quantity": tt.type.FLOAT,
    },
    name="TradeTable",
    keys=["Trade ID"],
)

Data Creation

Now we can create some data to be loaded with a Pandas DataFrame.

trade_data_dict = {
    "Trade ID": ["Trade_1", "Trade_2"],
    "Quantity": [1, 2],
}

trades_df = pd.DataFrame(data=trade_data_dict)

Loading Data

Normally we would load our Pandas DataFrame directly into our Cube as follows:

session.tables["TradeTable"].load_pandas(trades_df)

We will continue to use this same logic but instead now we will execute this bit of code within a DataLoadController operation. First we will need to define our DataLoadController and register our Operations.

# Create an instance of the DataLoadController for the current session.
dlc = py_dlc.DataLoadController(session)

Callback Method Definition

Now we can register different operations to execute. Here we will register a Loading operation on our topic “Trades”. We can define the Callback method in one of two ways as can be seen below:

Callback Method Parameter Operation Definition

We can define the Callback Method through a Decorator inline:

# Define Callback inline with the operation definition:
@dlc.operation(topic="Trades", operation_type="LOAD")
def load_trades(session: Session, scope: Optional[Scope]):
    session.tables["TradeTable"].load_pandas(trades_df)

Callback Decorator Operation Definition

We can define the Callback Method as a regular method, then later pass it to the operation definition.

# Define callback:
def load_trades(session: Session, scope: Optional[Scope]):
    session.tables["TradeTable"].load_pandas(trades_df)

# Elsewhere use this callback in the operation definition:
dlc.operation(topic="Trades", operation_type="LOAD")(load_trades)

As we can see the Callback definition is very similar for both designs. We just need to ensure that our Callback method contains the Parameter Objects Session and Optional[Scope]. We can handle the scope however we like, in this example we do not use it.

RESTful Execution

We can initiate the loading of our Trades data by executing a request against the endpoint (The PORT can be resolved by reading the session.port variable):

http://localhost:<PORT>/atoti/pyapi/load-controller/execute

We will execute a POST request with the following JSON body:

{
    "operation": "LOAD",
    "topics": ["Trades"]
}

Or we can execute the request within our Python project with the following:

# Execute a LOAD operation on one of our Topics
json_payload ={
    "operation": "LOAD",
    "topics": ["Trades"]
}

load_response = requests.post(dlc.operation_url, json=json_payload)

This will give us the following response:

{
    "Task Name": "005cfb9caa",
    "Time Taken (MS)": 2569,
    "Status": "SUCCESS",
    "Events": [
        {
            "Data Root": "file:/C:/Users/YourName/AppData/Local/Temp/atoti-zj1dg1gy/tmpwhbybivj.parquet",
            "Source": "PARQUET",
            "Lines Loaded": 2,
            "Duration (MS)": 452,
            "Errors": 0
        }
    ]
}