arminmokri/StreamProcessing

StreamProcessing is a collection of practical examples for learning stream processing with Java and Kafka Streams, focused on real tasks like filtering, mapping, aggregation, and event handling.

Stream Processing

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This repository provides categorized, real-world examples of stream processing using Java Streams and Kafka Streams.

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Java Streams

1. Basic Stream Operations

1- Filter Even Numbers 🔍

2- Find First Match 🕵️

3- ForEach Print 🖨️

4- Distinct Elements 🆔

5- Skip and Limit ⏭️

2. Mapping & FlatMapping

1- Map to Lengths 🗺️

2- FlatMap Nested Lists 📚

3- Parse CSV to Object 📄

4- Map to Uppercase 🔠

5- FlatMap Optional ❓

3. Reduction & Aggregation

1- Sum Integers ➕

2- Average Salary 📊

3- Find Max Salary 🏆

4- Count Elements 🔢

5- Reduce to Concatenate 🔗

4. Collectors & Conversions

1- Collect To Map 🗺️

2- Join Names 🔗

3- Group By Field 🧩

4- Count Grouped 🎭

5- Collect To Set 🧺

5. Sorting

1- Sort by Salary 💸

2- Multi-field Sort 🧮

3- Reverse Sort 🔄

4- Sort Strings Alphabetically 🔤

5- Sort Custom Objects 🛠️

6. Advanced Transformations

1- Filter + Map + Reduce 🧠

2- Top N Elements 🥇

3- Nested Grouping 🗂️

4- Sliding Window Simulation ⏳

5- Distinct and Sorted ✨

7. Parallel Streams

1- Parallel Sum ⚡

2- Compare Sequential vs Parallel 🆚

3- Thread Safety 🧵

4- Parallel Filter and Collect 🔍

5- Parallel String Transform 🆕

8. Map Stream Operations

1- Stream over Map Entries 🗃️

2- Sort Map by Value 📉

3- Merge Maps 🔀

4- Filter Map by Key 🔑

5- Collect Map to List 📋

9. Primitive Streams

1- IntStream Range 🔢

2- Summary Statistics 📈

3- Boxing/Unboxing 📦

4- DoubleStream Average 🎯

5- LongStream Generate 🚀

10. Exception Handling

1- Handle ParseException 🚫

2- Safe IO in Stream 🧯

3- Recover from Exceptions 🔄

11. Custom Collectors

1- Custom Collector for String Join 🧵

2- Collector to Summary Object 📦

3- Collector with Downstream 🌊

4- Collector for Frequency Map 📊

5- Collector for Partitioned Lists 🎭

12. Real-World Use Cases

1- Group Books by Author 📚

2- Sum Orders per Customer 🧾

3- Filter Active Users ✅

4- Top Selling Products 🥇

5- User Login Statistics 📊

13. Optional Handling

1- Optional from Find ❓

2- Default if Empty 🛡️

3- Map Optional Values 🔁

4- Filter Optional 🔍

5- FlatMap Optional 🔄

14. Debugging with Peek

1- Peek Intermediate Values 🐞

2- Log in Pipeline 📝

3- Side Effects ⚠️

4- Debug with Thread Info 🧵

5- Conditional Peek 🔀

15. Combining Streams

1- Concat Two Streams ➕

2- Zip Two Lists 🧷

3- Merge Stream Sources 🔃

4- Intersect Streams ✨

5- Union Streams 🔗

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Kafka Streams

1. Basics

1- Write and Read to Topic ✍️

2- Word Count 📊

3- Filter and Map Kafka Records 🔎

4- Stateful Transform 🧠

2. Aggregation

1- Group By Key and Count 🧮

2- Sum Values by Window ⏲️

3- Custom Aggregator ⚙️

4- Count Per Key 📈

5- Aggregate to List 📋

3. Joins

1- KStream-KStream Join 🔀

2- KStream-KTable Join 🪢

3- Windowed Joins ⏳

4- Left Join 🧩

5- Outer Join 🌐

4. Windowing

1- Tumbling Windows ⏳

2- Sliding Windows 🎚️

3- Session Windows 🛋️❌

4- Hopping Windows 🔄

5. Other

1- Branching Streams 🌿

2- Processor API Way A (PAPI) ⚙️

3- Processor API Way B (PAPI) ⚙️

4- GlobalKTable Join 🌍

5- Repartitioning Streams 🔄❌

6. Real-World Use Cases

1- Transaction Monitoring 💳❌

2- Clickstream Analytics 🖱️❌

3- IoT Sensor Aggregation 📡❌

4- User Sessionization 👤❌

5- Log Enrichment 🧾❌