Skip to main content

Application Performance Best Practices

 Application performance is one of the most critical factors in software development. Applications should be designed and implemented with performance objectives in mind. When defining architecture, designing, or implementing code, several best practices should be considered to enhance performance. At the same time, bad practices or anti-patterns should be avoided to prevent performance degradation.

Some best practices and anti-patterns may be universally applicable, while others depend on the specific project context. Architects and project teams should make informed decisions about adopting or rejecting these best practices. However, teams should not dismiss best practices merely on the grounds of subjectivity without thorough evaluation, as many practices can be beneficial despite perceived constraints.


Long-Running Transactions

The throughput of a system (requests processed per second) is negatively affected by long-running transactions. These transactions create wait times, leading to congestion, delays, and locks, which can further escalate into deadlocks.






Strategies to mitigate long-running transactions:

  • Split large transactions into smaller, manageable ones.

  • Keep transactions as short as possible.

  • Use asynchronous processing where applicable.

  • Optimize and tune long-running database queries.

  • Increase thread pool size where necessary.

  • Avoid chatty transactions.

  • Utilize caching to reduce round trips and increase throughput.

Define a Uniform Architecture

A uniform architecture facilitates horizontal scaling. When architectural components have complex interdependencies, scaling out by adding new nodes becomes challenging. Standardized components simplify scalability and maintenance.



Use Caching

Caching improves performance by reducing expensive database queries and minimizing the number of waiting threads, thereby increasing throughput.



Partitioning

Partitioning refers to the division of data to optimize storage, manageability, availability, and performance. While sometimes confused with normalization, partitioning is a physical-level optimization rather than a conceptual one. Partitioning helps overcome database size limitations and improve read/write performance.



Vertical Splitting (Partitioning)

Vertical partitioning involves storing different tables and columns in separate databases. This approach logically splits application data and stores it across multiple databases. The application layer ensures that data is read from and written to the correct partition. Sometimes called "row splitting," this technique distributes columns across multiple databases to reduce the burden on a single database.

Horizontal Splitting (Sharding)

Horizontal partitioning, or sharding, involves distributing rows of a table across multiple database nodes. Some databases, such as Cassandra, HBase, HDFS, and MongoDB, offer native support for sharding. Sharding can be implemented at either the application or database level.

Example: A user table may be split based on geographical location, creating tables such as UsersNorth and UsersSouth to distribute data more effectively.

Challenges of Distributed Data

While partitioning and sharding provide performance benefits, they also introduce challenges:

  • Cross-partition searches: Querying across partitions can be inefficient and complex.

  • Data distribution imbalance: Uneven data distribution, or "hot spots," can limit the effectiveness of sharding.

By applying the correct strategies to address these challenges, application performance can be optimized while maintaining scalability and efficiency.

Comments

Post a Comment

Popular posts from this blog

Virtual environments in python

 Creating virtual environments is essential for isolating dependencies and ensuring consistency across different projects. Here are the main methods and tools available, along with their pros, cons, and recommendations : 1. venv (Built-in Python Virtual Environment) Overview: venv is a lightweight virtual environment module included in Python (since Python 3.3). It allows you to create isolated environments without additional dependencies. How to Use: python -m venv myenv source myenv/bin/activate # On macOS/Linux myenv\Scripts\activate # On Windows Pros: ✅ Built-in – No need to install anything extra. ✅ Lightweight – Minimal overhead compared to other tools. ✅ Works across all platforms . ✅ Good for simple projects . Cons: ❌ No dependency management – You still need pip and requirements.txt . ❌ Not as feature-rich as other tools . ❌ No package isolation per project directory (requires manual activation). Recommendation: Use venv if you need a simple, lightweight solut...
Post a Comment
Read more

Building a Simple Text Generator: A Hands-on Introduction

Introduction Text generation is one of the most exciting applications of Natural Language Processing (NLP) . From autocorrect and chatbots to AI-generated stories and news articles , text generation models help machines produce human-like text. In this blog post, we’ll introduce a simple yet effective text generation method using Markov Chains . Unlike deep learning models like GPT, this approach doesn’t require complex neural networks—it relies on probability-based word transitions to create text. We’ll walk through: ✅ The concept of Markov Chains and how they apply to text generation. ✅ A step-by-step implementation , fetching Wikipedia text and training a basic text generator. ✅ Example outputs and future improvements. The Concept of Markov Chains in Text Generation A Markov Chain is a probabilistic model that predicts future states (or words) based only on the current state (or word), rather than the full sentence history. How it works in text generation: 1️⃣ We analyze a gi...
Post a Comment
Read more

Mastering Trade-Off Analysis in System Architecture: A Strategic Guide for Architects

 In system architecture and design, balancing conflicting system qualities is both an art and a science. Trade-off analysis is a strategic evaluation process that enables architects to make informed decisions that align with business goals and technical constraints. By prioritizing essential system attributes while acknowledging inevitable compromises, architects can craft resilient and efficient solutions. This enhanced guide provides actionable insights and recommendations for architects aiming to master trade-off analysis for impactful architectural decisions. 1. Understanding Trade-Off Analysis Trade-off analysis involves identifying and evaluating the conflicting requirements and design decisions within a system. Architects must balance critical aspects like performance, scalability, cost, security, and maintainability. Since no system can be optimized for every quality simultaneously, prioritization based on project goals is essential. Actionable Insights: Define key quality ...
Post a Comment
Read more