Java Huffman Compression Engine Reduces File Sizes by 50%

The High Cost of the Wrong Initial Choice I have seen complex computation logic for over a million records take hours to run simply because of inefficient, row-by-row processing that is offered by database stored procedures and traditional java/python code. It is slow, impossible to scale, and eventually kills your product's edge in the market. By moving to vectorized logic with tools like NumPy or Polars, you can turn those hours of computation into milliseconds. This is not just about using newer tools, it is about leveraging modern execution like #SIMD (Single Instruction Multiple Data) and #multithreading to gain a massive competitive advantage. If your backend is computation heavy and you're still stuck in legacy loops, you are leaving performance, market edge and money on the table. References: https://lnkd.in/g8j3A5Bn https://lnkd.in/gaPnFUQm https://lnkd.in/gWt9WHnp #DataEngineering #SystemDesign #NumPy #PerformanceOptimization #Scalability #PythonProgramming #TechStrategy #Vectorization #TechToolChoice

Your API is not slow. Your database is. One of the biggest mistakes I made early in my backend career: Blaming the API layer. Adding async. Changing frameworks. Tweaking code. Nothing worked. The real problem? A single bad query. We had an N+1 query issue that looked harmless in code… But in production? It exploded. 1 request → 100+ DB calls. Fix? • Proper joins • Query optimization • Adding the right indexes Result: API latency dropped from ~1.2s → 150ms 🚀 No new framework. No major rewrite. Just better database thinking. Lesson: If your API is slow, don’t start with code. Start with your queries. Because in backend systems: 👉 Database > API > Framework Curious — what’s the worst DB issue you’ve faced in production? #BackendDevelopment #SystemDesign #Databases #Python #SoftwareEngineering

🧠 Day 192 — Cycle Detection in Directed Graph 🔄➡️ Today solved an important graph problem: Detect Cycle in a Directed Graph. 📌 Problem Goal Given a directed graph: ✔️ Determine if there exists a cycle ✔️ Return true if a cycle is present, else false 🔹 Core Idea Use DFS with path tracking. 👉 Maintain two arrays: • Visited → tracks nodes that are already explored • Path Visited → tracks nodes in the current DFS path 🔹 Cycle Detection Logic While traversing: 1️⃣ If a node is unvisited → continue DFS 2️⃣ If a node is already visited and also in current path → 👉 Cycle detected This indicates a back edge, which forms a cycle in directed graphs. 🔹 Why Path Tracking Works In directed graphs: 👉 A cycle exists if we revisit a node within the same DFS path That’s exactly what pathVisited helps detect. 🔹 Approach 1️⃣ Build adjacency list from edges 2️⃣ Traverse all components 3️⃣ Use DFS with: • visited array • pathVisited array 4️⃣ Backtrack by removing node from path after DFS completes 🧠 Key Learning ✔️ Directed graph cycle detection requires path tracking ✔️ Backtracking is essential to maintain correct DFS path ✔️ Difference from undirected graph → no parent check, use pathVisited 💡 Big Realization Whenever you see: 👉 “Cycle in directed graph” Think: 👉 DFS + Path Visited (Recursion Stack) 🚀 Momentum Status: Graph theory fundamentals getting sharper now. On to Day 193. #DSA #Graphs #DFS #CycleDetection #Java #CodingJourney #LeetCode #ProblemSolving #ConsistencyWins

🛑 Stop failing your production data loads because of duplicate records. The pattern: Idempotent DELETE+INSERT (the "Atomic Switch" pattern). Here is how to implement it: def sync_data(df, target_table, partition_col, partition_val): with connection.cursor() as cursor: cursor.execute(f"DELETE FROM {target_table} WHERE {partition_col} = '{partition_val}'") df.write.mode("append").format("jdbc").save(table=target_table) connection.commit() Why every pipeline needs this: In distributed systems, retries are inevitable. If your pipeline crashes halfway through a write, a simple "append" creates duplicate data, corrupting your analytics. By wrapping a specific partition delete with an insert in a single transaction, you ensure the output is identical whether the job runs once or ten times. We use this for all our daily batch refreshes to guarantee data integrity without complex staging tables. How do you handle idempotency when your source systems don't provide a natural primary key? #DataEngineering #DataPipeline #Python #ETL #BigData

HI CONNECTIONS I recently tackled LeetCode 166, a problem that requires careful handling of edge cases, integer overflows, and the logic of long division. 🔍 The Challenge Given two integers representing a fraction, return the result in string format. If the fractional part repeats, enclose the repeating digit(s) in parentheses. Example: 1 / 2 = 0.5 The Twist: 2 / 3 = 0.(6) 🛠️ My Approach: Long Division with Memory The key to identifying a repeating decimal is realizing that if you encounter the same remainder twice during the division process, the digits will start to repeat from that point onward. Handle Signs & Edge Cases: Determine the sign of the result and handle the case where the numerator is 0. Use long (in Java/C++) to prevent overflow when taking absolute values (e.g., -2147483648). Integer Part: Calculate the whole number part using numerator / denominator. Fractional Part: * Use a Hash Map to store each remainder and its corresponding index in the result string. Multiply the remainder by 10 and continue the division. The Detection: If the remainder is already in the map, insert ( at the stored index and ) at the end of the string, then break. Terminate: If the remainder becomes 0, the decimal is terminating. 📊 Efficiency Time Complexity: O(\text{Denominator}) — In the worst case, the number of digits in the repeating part is less than the denominator. Space Complexity: O(\text{Denominator}) — To store the remainders in the hash map. 💡 Key Takeaway This problem is a great reminder that data structures like Hash Maps aren't just for searching—they are essential for "remembering" states in a process. Recognizing cycles is a fundamental skill in both algorithm design and system monitoring. #LeetCode #AlgorithmDesign #HashMaps #Mathematics #SoftwareEngineering #ProblemSolving

🚀 DSA Challenge Day 1: Linked List Kicking off our Data Structures & Algorithms journey with one of the most fundamental concepts, Linked Lists. A Linked List is a data structure where elements are stored in nodes, and each node contains a reference (pointer) to the next node. This allows data to be stored in non-contiguous memory locations, unlike arrays. 🔹 Why it matters Linked Lists help us understand how data can be dynamically connected and managed in memory, forming the foundation for many advanced data structures. 🔹 Core Components • Head, starting point of the list • Tail, last node in the list • Node, contains data and reference to next node • Size, total number of elements In Collaboration With Justina Jodimuttu Abraham #DSA #Java #CodingJourney #100DaysOfCode #LearningInPublic #Algorithm Google LeetCode

🚀 Day 79 – Database Optimization & Performance Tuning Continuing my journey in the 90 Days of Python Full Stack, today I focused on improving system efficiency through database optimization and performance tuning. As the application grows with more users, alerts, and location data, ensuring fast and efficient data handling becomes essential. 🔹 Work completed today • Optimized database queries to reduce response time • Implemented indexing for faster data retrieval • Reduced redundant database calls • Improved backend performance for handling large data • Enhanced overall system efficiency 🔹 System Workflow User sends request ⬇ Backend processes optimized queries ⬇ Database retrieves data efficiently ⬇ Response sent faster to frontend ⬇ Smooth user experience 🔹 Why this step is important Performance plays a key role in scalability. With this implementation: ✔ Faster response time ✔ Efficient handling of large datasets ✔ Improved real-time system performance ✔ Better user experience and reliability 📌 Day 79 completed — optimized database performance and improved system efficiency. #90DaysOfPython #PythonFullStack #DatabaseOptimization #PerformanceTuning #BackendDevelopment #ScalableSystems #LearningInPublic #DeveloperJourney

Grid Partitioning & Spatial Connectivity: Solving LeetCode 3548 🧩 Solved LeetCode 3548 (Equal Sum Grid Partition II) today—a "Hard" problem that perfectly demonstrates why understanding geometric constraints is just as important as mastering data structures. The Challenge: Partition an  M×N M×N  grid into two sections using a single horizontal or vertical cut. The goal? Equalize the sums of both sections. The catch? You can discount one cell to achieve balance, but only if the remaining section stays connected.The Technical Insight: The "Hard" part isn't the sum—it's the connectivity invariant. 2D Enclosures: If a partition is a rectangle where both dimensions  >1 >1 , removing any single cell will never break connectivity. 1D Strips: If a partition is a  1×N 1×N  or M×1 M×1  strip, connectivity is fragile. You can only discount the endpoint cells. Removing any middle cell splits the section, violating the constraint. My Engineering Approach: Prefix Sums: Precalculated row and column sums to achieve  O(1) O(1)  range-sum queries. Frequency Hashing: Used HashSets to track cell values within potential partitions, allowing  O(1) O(1)  lookups to see if the required "difference" value exists. Connectivity Validation: Implemented a boundary check to ensure that cell removal only happens where mathematically permissible (corners/endpoints for 1D, anywhere for 2D). Result: An optimized  O(M×N) O(M×N)  time and space complexity solution.Problems like these are a great reminder that "Brute Force" is rarely the answer. Success lies in identifying the hidden physical properties of the data structure you are working with. #LeetCode #SoftwareEngineering #Algorithms #DataStructures #ProblemSolving #CodingInterviews #Java #Python #GridDynamics

🚀 Day 20/100: Data Types Deep Dive – Precision, Size & Memory 📊🧠 Today’s learning focused on the science behind data storage in Java. Writing efficient code is not just about logic—it’s about choosing the right data type to optimize memory usage and performance. Here’s a structured breakdown of what I explored: 🏗️ 1. Primitive Data Types – The Core Building Blocks These are predefined types that store actual values directly in memory. 🔢 Numeric (Whole Numbers): byte → 1 byte | Range: -128 to 127 short → 2 bytes | Range: -32,768 to 32,767 int → 4 bytes | Standard integer type long → 8 bytes | Used for large values (L suffix) 🔢 Numeric (Floating-Point): float → 4 bytes | Requires f suffix double → 8 bytes | Default for decimal values 🔤 Non-Numeric: char → 2 bytes | Stores a single Unicode character boolean → JVM-dependent | Represents true or false 🏗️ 2. Non-Primitive Data Types – Reference Types These types store references (memory addresses) rather than actual values: String → Sequence of characters Array → Collection of similar data types Class & Interface → Blueprint for objects 💡 Unlike primitives, their default value is null, and they reside in Heap memory, with references stored in the Stack. 🧠 Key Insight: Primitives → Store actual values (Stack memory) Non-Primitives → Store references to objects (Heap memory) ⚙️ Why This Matters: Choosing the correct data type improves: ✔️ Memory efficiency ✔️ Application performance ✔️ Code reliability at scale 📈 Today reinforced that strong fundamentals in data types are essential for writing optimized, production-ready Java applications. #Day20 #100DaysOfCode #Java #Programming #MemoryManagement #DataTypes #SoftwareEngineering #CodingJourney #JavaDeveloper #10000Coders

Stop treating SCD Type 2 as a storage problem. It’s a query problem. Most SCD implementations follow the same lifecycle: • The MERGE works perfectly • The history accumulates beautifully • Six months later an upstream schema change turns the pipeline into a crime scene Then the real problems start: ❌ A new column appears upstream and the pipeline crashes ❌ A quiet type change (INT → BIGINT) corrupts history or breaks the MERGE ❌ Someone asks: “What did this record look like in October?” and the SQL query is 200 lines long I kept seeing teams rebuild the same fragile logic at every client, so I built a small Python framework to treat SCD tables as a Queryable History Store instead of just an append log. One class. Any Iceberg / Unity Catalog table. It handles: ✅ Automatic Schema Evolution New upstream columns are added on write. ✅ Type Evolution Safe handling of type widening like INT → BIGINT. ✅ Optimized History Writes Insert / close / skip logic handled automatically. And the most important part: history becomes easy to query. Examples: get_records_as_of(date) → instant point-in-time view get_changes_between(start, end) → change window visibility get_record_history(id) → full entity trail When you treat your SCD table as a managed service, you unlock audit and compliance capabilities that data teams actually enjoy using. The data type evolution piece had some nasty edge cases — happy to talk through how I handled those if anyone is building something similar. #DataEngineering #ApacheIceberg #Databricks #SCDType2 #Python #DataArchitecture