Optimizing Code in Java
| Course Details | ||||||||
|---|---|---|---|---|---|---|---|---|
|
||||||||
| Languages | ||||||||
|
||||||||
| Outcomes | ||||||||
|
||||||||
| Price & Terms | ||||||||
|
||||||||
| Technical | ||||||||
|
||||||||
| Specification | ||||||||
|
||||||||
Find and fix slow Java code using Big-O analysis, JVM memory and CPU measurement, multi-threading, caching and lazy initialization.
Finding the parts of a Java application that hold it back, timing them and making them faster is the problem this course works on. You weigh time and space complexity with Big-O notation to choose between lists, sets and maps. You time operations with System.nanoTime() and track memory and CPU use while seeing how garbage collection and Just-In-Time compilation shape speed. Later lessons turn to multi-threading, asynchronous processing, caching and lazy initialization.
What You Will Achieve
- Estimate how a method’s running time and memory grow with input size, and choose the collection type that keeps both low
- Locate the slow step in a Java program by timing its operations and comparing alternative search approaches
- Read memory and CPU figures for a running Java program and explain where the JVM keeps its data
- Run independent tasks at the same time across threads and keep frequently used results in a cache that evicts old entries
Program Overview
Software Performance and Data Structure Fundamentals
Analyzes the cost of code with Big-O notation through small applications such as a contacts manager, a sort checker and a duplicate transaction finder.
- Time and space complexity
- Sets, maps and lists compared
- Contact management and social network exercises
- Text analysis application
Measuring Performance and JVM Memory
Finds bottlenecks by timing code and looks at how the JVM stores data, uses memory and CPU, and compiles code while it runs.
- Operation timing with System.nanoTime()
- JVM memory architecture and memory-efficient strings
- Memory and CPU time tracking
- Garbage collection, JIT and a financial analysis application
Concurrency, Caching and Initialization Patterns
Applies multi-threading and asynchronous processing to concurrent work, then cuts repeated effort with caches and deferred object creation.
- Thread objects and parallel streams for image processing
- Thread pools with ExecutorService
- Asynchronous pipelines with CompletableFuture
- LRU cache, lazy initialization and the Singleton pattern
Certificate Awarded (Paid)
Job Outlook
These skills apply wherever Java applications must handle enterprise-scale workloads, including the design of scalable systems, batch and data processing pipelines, and services that need high throughput.
Good for: Java developers who want exercise-based practice in locating slow code and improving it with better data structures, threading and caching.
Keep in mind: Despite the advanced label, the course is aimed at intermediate Java learners, and each idea is practiced in a short application such as a contacts manager or an LRU cache.
Bottom line: It ties Big-O reasoning, JVM memory behavior and concurrent Java code together through a small program built for each technique.
Frequently Asked Questions
Which Java concurrency tools does the course use?
The threading lessons start with Thread objects and parallel streams, applied to an image processing task, then move on to thread pools built with ExecutorService and asynchronous pipelines written with CompletableFuture.
How is performance measured in the lessons?
Operations are timed with System.nanoTime(), for instance to compare search algorithms. Further exercises track memory use and CPU time and show how garbage collection and Just-In-Time compilation affect the results.
What kinds of applications do the exercises build?
Each technique is practiced in a small program, including a contacts manager, a sort checker, a social network, a duplicate transaction finder, a text analyzer, a financial analysis tool and a batch processor that uses a thread pool.
Which design patterns appear in the course?
The closing lessons cover lazy initialization and the Singleton pattern as initialization patterns that help performance, alongside choosing a cache eviction policy and implementing an LRU cache.
User Reviews
Write your review
You must be logged in to post a review.
Reviews can only be posted by registered learners. Log in or create a free account, then you will return here to post.
There are no reviews yet.