Monolith vs Microservices — What Works Best for Startups?

[sietrixtechnologies]

Startups often face an important architectural decision early in development: Monolith or Microservices?

Monolithic architectures are usually faster to build, easier to manage, and ideal for small teams aiming to launch quickly. On the other hand, microservices offer scalability, flexibility, and independent deployments as products grow.

From your experience:

• What architecture did you start with?
• At what stage does moving to microservices make sense?
• Have you faced scaling or maintenance challenges with either approach?

Let’s share real-world experiences, lessons learned, and best practices to help startups make smarter technical decisions.

[saitejabandaru-in]

Hi there!

Scikit-learn does not natively implement Gower's distance (e.g., in sklearn.metrics.pairwise_distances or DistanceMetric), but you can easily calculate it and use it with scikit-learn estimators.

Option 1: Using the gower library (Recommended & Easiest)

The standard way is to calculate a precomputed pairwise distance matrix using the gower package and pass it to scikit-learn clustering models (like AgglomerativeClustering or DBSCAN) with metric='precomputed'.

pip install gower

import pandas as pd
import gower
from sklearn.cluster import AgglomerativeClustering

# Mixed data (numerical + categorical)
data = pd.DataFrame({
    'age': [25, 45, 30, 85, 22],
    'income': [50000, 80000, 52000, 20000, 15000],
    'city': ['NYC', 'LA', 'NYC', 'CHI', 'LA'],
    'member': [True, False, True, False, True]
})

# 1. Compute the pairwise Gower distance matrix
gower_matrix = gower.gower_matrix(data)

# 2. Use it in AgglomerativeClustering with precomputed metric
model = AgglomerativeClustering(metric='precomputed', linkage='average', n_clusters=2)
labels = model.fit_predict(gower_matrix)

print("Cluster labels:", labels)

Option 2: Custom Vectorized Numpy Implementation (No third-party deps)

If you want to compute it manually from scratch, Gower similarity is the weighted average of the features' individual similarities. Below is a vectorized implementation for numerical and categorical types:

import numpy as np
import pandas as pd

def gower_distance(df):
    n_rows, n_cols = df.shape
    dist_matrix = np.zeros((n_rows, n_rows))
    
    # Separate numerical and categorical columns
    is_numeric = [pd.api.types.is_numeric_dtype(df[col]) for col in df.columns]
    
    # Precalculate ranges for numerical variables
    ranges = []
    for col in df.columns:
        if pd.api.types.is_numeric_dtype(df[col]):
            col_range = df[col].max() - df[col].min()
            ranges.append(col_range if col_range > 0 else 1.0)
        else:
            ranges.append(0.0)
            
    for i in range(n_rows):
        for j in range(i, n_rows):
            similarities = []
            weights = []
            for col_idx, col in enumerate(df.columns):
                val_i = df.iloc[i, col_idx]
                val_j = df.iloc[j, col_idx]
                
                # Handle missing values
                if pd.isna(val_i) or pd.isna(val_j):
                    continue
                    
                weights.append(1.0)
                if is_numeric[col_idx]:
                    # Numerical feature: distance is absolute difference normalized by range
                    diff = abs(val_i - val_j)
                    similarity = 1.0 - (diff / ranges[col_idx])
                else:
                    # Categorical/Boolean feature: similarity is binary equivalence
                    similarity = 1.0 if val_i == val_j else 0.0
                similarities.append(similarity)
                
            distance = 1.0 - (sum(similarities) / sum(weights))
            dist_matrix[i, j] = distance
            dist_matrix[j, i] = distance
            
    return dist_matrix

Hope this helps! Let me know if you have any questions.