How to AI with NiFi and Python

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We are all using AI to write code, but when it comes to Apache NiFi, the current landscape often resembles the Wild West. Whether you are generating synthetic data scripts or translating complex machine learning models, Large Language Models (LLMs) are incredible accelerators. However, if you ask an AI to write a native Apache NiFi 2.0 Python processor from scratch, there is a very high probability it will confidently hand you code that instantly breaks your canvas.

NiFi 2.0’s Python API is relatively new, and most AI training data is heavily saturated with legacy NiFi 1.x ExecuteScript solutions (using Jython or Groovy). Even when an AI correctly identifies the 2.0 API, it frequently misconfigures the underlying Java-to-Python bridge, resulting in “ghost” processors with dashed lines and missing routing relationships.

In this post, I am going to share the exact methodology I used to leverage AI for writing custom NiFi processors safely, ensuring my dataflows operate seamlessly with my custom Python logic.

The Input: Example Fraud Python ScriptPermalink

This script’s logic assumes that transactions originating from two specific cities, or those exceeding $10,000, constitute fraud. Traditionally, this fraud model is intended to be deployed on Cloudera Machine Learning (CML) in a Workbench session and invoked in NiFi via the InvokeHTTP processor. I have tested this architecture, and it works flawlessly.

Unfortunately, this integration is often unavailable during local Kubernetes testing (which is the focus of this post) outside of the Cloudera Public Cloud. Therefore, this script serves as a bridge to ensure the same Python responses can be tested natively, allowing downstream test data to flow in non-CML-connected environments.

import cml.models_v1 as models


SUSPICIOUS_CITIES = {
    "Lagos": {"lat": 6.5244, "lon": 3.3792},
    "New Delhi": {"lat": 28.6139, "lon": 77.2090}
}

# 0.45 degrees (~50km) is the exact mathematical net needed to catch all of Steven's regional fraud
TOLERANCE = 0.5

# These 3 accounts have valid data that geographically overlaps with the fraud zones. 
# We whitelist them from the location-based heuristic to ensure a pristine demo.
DEMO_SAFE_ACCOUNTS = []

def is_suspicious_location(lat: float, lon: float) -> str:
    for city, coords in SUSPICIOUS_CITIES.items():
        if (abs(lat - coords["lat"]) <= TOLERANCE) and (abs(lon - coords["lon"]) <= TOLERANCE):
            return city
    return None

@models.cml_model
def detect_fraud(args):
    is_fraud = False
    explanations = {}
    
    # Rule 1: High Amount Threshold (>$10k is ALWAYS flagged)
    if args["amount"] > 10000:
        is_fraud = True
        explanations["amount"] = f"Transaction amount ({args['amount']}) exceeds the 10,000 limit."
        

    # Rule 2: Originates strictly around restricted geographies
    # We skip this check if it's one of the overlapping good accounts
    if args["account_id"] not in DEMO_SAFE_ACCOUNTS:
        suspicious_city = is_suspicious_location(args["lat"], args["lon"])
        if suspicious_city:
            is_fraud = True
            explanations["location"] = f"Transaction originated from a high-risk region near {suspicious_city}."

    if is_fraud:
        return {
            "fraud_score": 0.99,
            "risk_level": "HIGH",
            "decision": "REVIEW",
            "explanations": explanations
        }
    else:
        return {
            "fraud_score": 0.01,
            "risk_level": "LOW",
            "decision": "APPROVE",
            "explanations": {"status": "all heuristic checks passed"}
        }

Rule 1: The AI Writes the Logic, You Own the FrameworkPermalink

The biggest mistake you can make is copying and pasting a complete Python processor generated by an AI directly into your /extensions directory.

AI models often hallucinate complex, aspirational examples that do not function as expected in your specific environment. When an AI provides malformed custom processor code, NiFi will either fail to load the processor entirely or, worse, load it but refuse to display the success and failure relationships in the UI.

The Pro Move: Pin the AI within a strict, proven architectural skeleton for the NiFi wrapper. I am going to show you one right now! By “pin,” I mean I essentially had to wrestle the AI and lock it down using my first processor example. I proved to the AI that my example processor worked, and together we confirmed the baseline processor GenericTransform framework functioned correctly. Finally, we moved forward with constructing the actual custom nifi processor I needed. 💪

Rule 2: Prove the Skeleton FirstPermalink

Before you introduce a single line of AI-generated business logic, deploy a bare-minimum structural template to the canvas. If the skeleton doesn’t load and route data, your complex logic will fail as well. This exercise also proves that you understand how to deliver and iterate versions of a processor for rapid testing in the NiFi UI.

Here is the exact GenericTransform framework I used. It does nothing but pass data through, but it proves the custom processor can compile and expose its relationships natively.

import json
from nifiapi.flowfiletransform import FlowFileTransform, FlowFileTransformResult

class GenericTransformTemplate(FlowFileTransform):
# Mandatory: Registers the processor with the NiFi backend
class Java:
implements = ['org.apache.nifi.python.processor.FlowFileTransform']

class ProcessorDetails:
version = '0.0.1-BASE'
description = 'Bare-minimum framework to test NiFi UI integration.'
tags = ['template', 'framework']

def __init__(self, **kwargs):
# 'pass' is the safest initialization in many containerized environments
pass

def transform(self, context, flowfile):
contents_str = flowfile.getContentsAsBytes().decode('utf-8')
attributes = flowfile.getAttributes()

# Route directly to success without modification
return FlowFileTransformResult(
relationship='success', 
attributes=attributes, 
contents=contents_str
)

Test it: Drop this into your extensions folder. Wait 30 seconds. Drag it onto the canvas. Can you connect the success relationship to a LogAttribute processor? Yes? Now you are ready for the AI code.

Rule 3: Inject Python Logic DefensivelyPermalink

Once your skeleton is proven, prompt your AI to write strictly isolated Python changes within the confines of the processor framework. By this point, the AI should understand your exact architectural approach, making functional Python improvements relatively straightforward.

When injecting new Python logic into your data pipeline, you must code defensively against edge cases:

1. The Array Trap: AI assumes FlowFiles contain a single JSON object. If your upstream generator creates an array of transactions, the AI’s .get() dictionary methods will trigger fatal AttributeErrors. Always wrap your logic to handle both isinstance(payload, list) and single dictionaries.
2. Never Overwrite the Payload: AI scripts often return only the result of their computation. If you replace your FlowFile content with just the ML score, you lose your original transaction_id and break downstream routing. Always append the AI’s output to the existing payload (e.g., payload["ai_response"] = result).
3. Trap Everything: Wrap the AI logic in a try/except block that catches failures, writes the error to an attribute (attributes['python_error'] = str(e)), and safely routes the FlowFile to failure instead of crashing the processor.
4. Anticipate Iteration: Expect to find more edge cases. Keep iterating, and you will get it to work.

Rule 4: Master the Hot-Reload WorkflowPermalink

The NiFi 2.0 Python API features auto-reloading. You do not need to restart your pod or execute manual scripts to test new custom NiFi Python processor logic.

If you are using a local mount (e.g., minikube mount ~/nifi-custom-processors:/extensions):

1. Save your .py file.
2. Wait 30 to 60 seconds. The background thread will detect the file change and recompile it.
3. The UI Catch: The NiFi web canvas aggressively caches UI elements. Refresh your browser and check the processor list for your new version tag to ensure the changes are reflected.

The Example: The Output and Working Custom NiFi Processor

import json
from nifiapi.flowfiletransform import FlowFileTransform, FlowFileTransformResult

class FraudModel(FlowFileTransform):
class Java:
implements = ['org.apache.nifi.python.processor.FlowFileTransform']

class ProcessorDetails:
version = '0.0.4-SNAPSHOT'
description = 'Executes the CML fraud detection model natively in NiFi.'
tags = ['fraud', 'detection', 'cml', 'replacement']

def __init__(self, **kwargs):
pass

# ==========================================
# CML MODEL LOGIC
# ==========================================
SUSPICIOUS_CITIES = {
"Lagos": {"lat": 6.5244, "lon": 3.3792},
"New Delhi": {"lat": 28.6139, "lon": 77.2090}
}
TOLERANCE = 0.5
DEMO_SAFE_ACCOUNTS = []

def is_suspicious_location(self, lat: float, lon: float) -> str:
for city, coords in self.SUSPICIOUS_CITIES.items():
if (abs(lat - coords["lat"]) <= self.TOLERANCE) and (abs(lon - coords["lon"]) <= self.TOLERANCE):
return city
return None

def detect_fraud(self, args: dict) -> dict:
is_fraud = False
explanations = {}

# Rule 1: High Amount Threshold
if args.get("amount", 0) > 10000:
is_fraud = True
explanations["amount"] = f"Transaction amount ({args.get('amount')}) exceeds the 10,000 limit."

# Rule 2: Originates strictly around restricted geographies
if args.get("account_id") not in self.DEMO_SAFE_ACCOUNTS:
suspicious_city = self.is_suspicious_location(args.get("lat", 0.0), args.get("lon", 0.0))
if suspicious_city:
is_fraud = True
explanations["location"] = f"Transaction originated from a high-risk region near {suspicious_city}."

if is_fraud:
return {
"fraud_score": 0.99,
"risk_level": "HIGH",
"decision": "REVIEW",
"explanations": explanations
}
else:
return {
"fraud_score": 0.01,
"risk_level": "LOW",
"decision": "APPROVE",
"explanations": {"status": "all heuristic checks passed"}
}
# ==========================================

def transform(self, context, flowfile):
contents_str = flowfile.getContentsAsBytes().decode('utf-8')
attributes = flowfile.getAttributes()

try:
# Parse incoming JSON
payload = json.loads(contents_str)

# The upstream generator sometimes creates lists of transactions.
# Handle both lists and single dictionaries safely.
if isinstance(payload, list):
for tx in payload:
tx["cml_response"] = self.detect_fraud(tx)
enriched_data = payload
else:
payload["cml_response"] = self.detect_fraud(payload)
enriched_data = payload

return FlowFileTransformResult(
relationship='success', 
attributes=attributes, 
contents=json.dumps(enriched_data)
)

except Exception as e:
# If JSON parsing fails, route to failure and tag the error
attributes['cml_error'] = str(e)
return FlowFileTransformResult(
relationship='failure', 
attributes=attributes, 
contents=contents_str
)

Permalink

The VerdictPermalink

AI is an incredible tool for writing the heavy-lifting logic inside NiFi 2.0 Python processors, but it is a terrible architect for the processor framework itself. By treating my example NiFi API wrapper as a rigid, protected skeleton and carefully injecting Python logic inside of it, I was able to create this processor at lightning speed.

How many times do you think it took me to get this Python processor code to work? The version is 4, so it took me 4 iterations from the start to finish to complete the processor in this excercise.

Now fire up your cluster, open up a Python script, and see if you can transform it into a custom NiFi processor!

ResourcesPermalink

• Custom NiFi Processors with Cloudera Streaming Operators
• NiFi2 Processor Playground
• Cloudera Streaming Operators GitHub Repo
• NiFi Python Developer’s Guide

AppendixPermalink

NiFi 2.0 Custom Python Processor with PandasPermalink

This is written as a complete, copy-paste-ready sample that any engineer can drop into a new environment for immediate testing. No changes to the K8s CR, mount, or pod are required to build this new python processor in the Cloudera Streaming Operator footprint. Similar steps can be duplicated in any appropriate NiFi 2.0 context.

Objective

Create a new, self-contained native Python processor named PandasJSONTransformer:

• Accepts JSON content in a FlowFile (e.g. output from TransactionGenerator).
• Loads it into a Pandas DataFrame.
• Using lon/lat determines distance from home (defined in script).
• Outputs the transformed JSON on the success relationship.

Input Flow File:

[ {
  "ts" : "2026-05-05 14:55:11",
  "account_id" : "943",
  "transaction_id" : "6a9b1242-4892-11f1-b035-3a8bcd2ccadb",
  "amount" : 64,
  "lat" : 44.3568905517,
  "lon" : -0.6186160357,
  "nearest_city" : "Lagos",
  "nearest_country" : "Nigeria"
} ]

Step 1: Create the New Processor File
Navigate to the exact directory where TransactionGenerator.py lives:

cd ~/nifi-custom-processors   # ← adjust only if your local path is different

Create the new file PandasJSONTransformer.py with the following code:

import json
import io
import pandas as pd
import numpy as np
from nifiapi.flowfiletransform import FlowFileTransform, FlowFileTransformResult

class PandasJSONTransformer(FlowFileTransform):
    class Java:
        # Essential: Ensures success and failure relationships appear in NiFi
        implements = ['org.apache.nifi.python.processor.FlowFileTransform']

    class ProcessorDetails:
        version = '1.0.7-FINAL'
        description = 'An example processor using python pandas.'
        tags = ['pandas', 'poc', 'geospatial']
        dependencies = ['pandas', 'numpy'] # NiFi auto-installs these

    def __init__(self, **kwargs):
        # 'pass' is the safest initialization for this environment
        pass

    def transform(self, context, flowfile):
        content_bytes = flowfile.getContentsAsBytes()
        attributes = flowfile.getAttributes()

        # Merritt Island, FL Coordinates
        HOME_LAT, HOME_LON = 28.3181, -80.6660

        try:
            # Step 1: Handle the "Array Trap"
            # Even for single records, we wrap in a list so Pandas creates a proper DataFrame row
            raw_data = json.loads(content_bytes.decode('utf-8'))
            if not isinstance(raw_data, list):
                raw_data = [raw_data]

            df = pd.DataFrame(raw_data)

            # Step 2: Proof of Concept Math
            if 'lat' in df.columns and 'lon' in df.columns:
                df['lat'] = pd.to_numeric(df['lat'], errors='coerce')
                df['lon'] = pd.to_numeric(df['lon'], errors='coerce')

                # Calculate Euclidean distance from Merritt Island:
                # dist = sqrt((lat1 - lat2)^2 + (lon1 - lon2)^2)
                df['dist_from_home'] = np.sqrt(
                    (df['lat'] - HOME_LAT)**2 + (df['lon'] - HOME_LON)**2
                )
                
                # Add a simple flag to show Pandas touched the data
                df['pandas_processed'] = True

            # Step 3: Output Generation
            output_json = df.to_json(orient='records', indent=None)
            
            return FlowFileTransformResult(
                relationship='success',
                contents=output_json.encode('utf-8'),
                attributes={
                    **attributes,
                    'pandas.transformed': 'true',
                    'pandas.version': pd.__version__
                }
            )

        except Exception as e:
            # Rule 3: Defensive failure routing
            return FlowFileTransformResult(
                relationship='failure',
                contents=content_bytes,
                attributes={**attributes, 'pandas.error': str(e)}
            )

Step 2: Deploy & Activate

Ensure the minikube mount is still running:

minikube mount ~/nifi-custom-processors:/extensions --uid 10001 --gid 10001

NiFi 2.0 will automatically detect new/updated .py files in the extensions directory (usually within 10–30 seconds). When testing python changes, increment the version in the code (1.0.1) and re-save the file after each code change — this forces a clean reload. If you are impatient like me you may be refreshing the page to notice new processors.

Step 3: Verification in NiFi UI

• Open NiFi canvas.
• Drag a new processor and search for PandasJSONTransformer.
• The new processor should appear with the exact description and version from the code.
• Simple test flow:
  TransactionGenerator → PandasJSONTransformer. Flow Definition File.
• Run the flow.
• Check output flowfile for the new columns dist_from_home and pandas_processed.

Be patient on first processor attempt after dragging it to the canvas. The processor will indicate dependencies are downloading when it is first introduced to the canvas. The processor must complete this dependency state before allowing you to route Success/Failure.

Step 4: Hand-Off Framework for Any Other Environment

To replicate this exact processor in a different NiFi 2.0 environment:

1. Place PandasJSONTransformer.py in the Python extensions path.
2. Complete the Deployment Steps 1–3 above.
3. Verify pandas are installed by NiFi.
4. Confirm flowfile output is as expected.

Output Flow File:

[ {
  "ts" : "2026-05-05 15:10:13",
  "account_id" : "487",
  "transaction_id" : "xxx84324584-4894-11f1-b035-3a8bcd2ccadb",
  "amount" : 39,
  "lat" : 48.4010217027,
  "lon" : 4.7099962916,
  "dist_from_home" : 87.7062397261,
  "pandas_processed" : true
} ]

Troubleshooting

# Check NiFi pod logs for processor loading
kubectl logs -n cld-streaming mynifi-0 | grep -i pandas

# check pod for python extensions
kubectl exec -n cfm-streaming mynifi-0 -- ls -la /opt/nifi/nifi-current/python/extensions