Big O Notation 101
1 – O(1) This is the constant time notation. The runtime remains steady regardless of input size. For example, accessing an element in an array by index and inserting/deleting an element in a hash table.
2 – O(n) Linear time notation. The runtime grows in direct proportion to the input size. For example, finding the max or min element in an unsorted array.
3 – O(log n) Logarithmic time notation. The runtime increases slowly as the input grows. For example, a binary search on a sorted array and operations on balanced binary search trees.
4 – O(n^2) Quadratic time notation. The runtime grows exponentially with input size. For example, simple sorting algorithms like bubble sort, insertion sort, and selection sort.
5 – O(n^3) Cubic time notation. The runtime escalates rapidly as the input size increases. For example, multiplying two dense matrices using the naive algorithm.
6 – O(n logn) Linearithmic time notation. This is a blend of linear and logarithmic growth. For example, efficient sorting algorithms like merge sort, quick sort, and heap sort
7 – O(2^n) Exponential time notation. The runtime doubles with each new input element. For example, recursive algorithms solve problems by dividing them into multiple subproblems.
8 – O(n!) Factorial time notation. Runtime skyrockets with input size. For example, permutation-generation problems.
9 – O(sqrt(n)) Square root time notation. Runtime increases relative to the input’s square root. For example, searching within a range such as the Sieve of Eratosthenes for finding all primes up to n.
How Companies Ship Code
Step 1: The process starts with a product owner creating user stories based on requirements.
Step 2: The dev team picks up the user stories from the backlog and puts them into a sprint for a two-week dev cycle.
Step 3: The developers commit source code into the code repository Git.
Step 4: A build is triggered in Jenkins. The source code must pass unit tests, code coverage threshold, and gates in SonarQube.
Step 5: Once the build is successful, the build is stored in artifactory. Then the build is deployed into the dev environment.
Step 6: There might be multiple dev teams working on different features. The features need to be tested independently, so they are deployed to QA1 and QA2.
Step 7: The QA team picks up the new QA environments and performs QA testing, regression testing, and performance testing.
Steps 8: Once the QA builds pass the QA team’s verification, they are deployed to the UAT environment.
Step 9: If the UAT testing is successful, the builds become release candidates and will be deployed to the production environment on schedule.
Step 10: SRE (Site Reliability Engineering) team is responsible for prod monitoring.
Database Scaling
Database Scaling
How to learn SQL
How to learn SQL
Architecture of a Web Application
1 – It all starts with CI/CD pipelines that deploy code to the server instances. Tools like Jenkins and GitHub help over here.
2 – The user requests originate from the web browser. After DNS resolution, the requests reach the app servers. 3 – Load balancers and reverse proxies (such as Nginx & HAProxy) distribute user requests evenly across the web application servers.
4 – The requests can also be served by a Content Delivery Network (CDN).
5 – The web app communicates with backend services via APIs.
6 – The backend services interact with database servers or distributed caches to provide the data.
7 – Resource-intensive and long-running tasks are sent to job workers using a job queue.
8 – The full-text search service supports the search functionality. Tools like Elasticsearch and Apache Solr can help here.
9 – Monitoring tools (such as Sentry, Grafana, and Prometheus) store logs and help analyze data to ensure everything works fine.
10 – In case of issues, alerting services notify developers through platforms like Slack for quick resolution.
Frontend Performance Cheat Sheet
How to load your websites at lightning speed? Check out these 8 tips to boost frontend performance:
1 – Compression Compress files and minimize data size before transmission to reduce network load.
2 – Selective Rendering/Windowing Display only visible elements to optimize rendering performance. For example, in a dynamic list, only render visible items.
3 – Modular Architecture with Code Splitting Split a bigger application bundle into multiple smaller bundles for efficient loading.
4 – Priority-Based Loading Prioritize essential resources and visible (or above-the-fold) content for a better user experience.
5 – Pre-loading Fetch resources in advance before they are requested to improve loading speed.
6 – Tree Shaking or Dead Code Removal Optimize the final JS bundle by removing dead code that will never be used.
7 – Pre-fetching Proactively fetch or cache resources that are likely to be needed soon.
8 – Dynamic Imports Load code modules dynamically based on user actions to optimize the initial loading times.
Frontend Performance Cheat Sheet
How to load your websites at lightning speed? Check out these 8 tips to boost frontend performance:
1 – Compression Compress files and minimize data size before transmission to reduce network load.
2 – Selective Rendering/Windowing Display only visible elements to optimize rendering performance. For example, in a dynamic list, only render visible items.
3 – Modular Architecture with Code Splitting Split a bigger application bundle into multiple smaller bundles for efficient loading.
4 – Priority-Based Loading Prioritize essential resources and visible (or above-the-fold) content for a better user experience.
5 – Pre-loading Fetch resources in advance before they are requested to improve loading speed.
6 – Tree Shaking or Dead Code Removal Optimize the final JS bundle by removing dead code that will never be used.
7 – Pre-fetching Proactively fetch or cache resources that are likely to be needed soon.
8 – Dynamic Imports Load code modules dynamically based on user actions to optimize the initial loading times.