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Java Bloat has been my biggest pet peeve for a long time now and why I started programming in Golang for some of my personal projects. But Jigsaw is a welcome change and a step in the right direction for Java. I think there is a maturity model for the use of the Java Module System... As I typed that, I googled to see if there was someone who had outbeaten me to the idea... and surprise JPMS Maturity Model Thanks Nikolai

2 months ago

So with jlink, will this change how we have traditionally shipped java applications? Thoughts? Much like how we use docker, to deliver products.

2 months ago

Thanks for the link. Very detailed and very well organized.

2 months ago

Deepak Vohra wrote:1. Docker Swarm mode supports Docker Service Stacks for developing microservices that have dependencies between them."

Doesn't Kubernetes support this as well?

2 years ago

With Docker announcing native support for Kubernetes, what do you think their direction with Swarm will be?

2 years ago

Sure, you can do that easy with angular directives. Also have you looked at ng-bootstrap?

2 years ago

Nice, this discussion gives me more tools in my arsenal when I go to the design meetings for our next UI project.

2 years ago

Thanks for all the resources.

So in summary, there is a lot of mixed opinions based on where that information is coming from.

So in summary, there is a lot of mixed opinions based on where that information is coming from.

3 years ago

Why should one go for Kubernetes over Docker Swarm for deploying/clustering containers or could it be a mix of both from a redundancy perspective?

3 years ago

Great answer... Really appreciate the details on it.

Also, Does embracing Docker as an Application Delivery System bring any value to the Open Stack?

Also, Does embracing Docker as an Application Delivery System bring any value to the Open Stack?

3 years ago

Here is the TOC

1.INTRODUCTION TO ALGORITHMS

1.1. Introduction

1.1.1. What you’ll learn about performance

1.1.2. What you’ll learn about solving problems

1.2. Binary Search

1.2.1. A better way to search

1.2.2. Running time

1.3. Big-O-notation

1.3.1. Algorithm running times grow at different rates

1.3.2. Visualizing dfferent Big O run times

1.3.3. Big O establishes a worst-case runtime

1.3.4. Some common Big O run times

1.3.5. The traveling salesperson

1.4. Recap

2. SELECTION SORT

2.1. How Memory Works

2.2. Arrays And Linked Lists

2.2.1. Linked Lists

2.2.2. What arrays are good for

2.2.3. Terminology

2.2.4. More insertions and deletions

2.2.5. Deletions

2.3. Selection Sort

2.4. Recap

3. RECURSION

3.1. Recursion

3.2. Base Case And Recursive Case

3.3. The Stack

3.3.1. The call stack

3.3.2. The call stack with recursion

3.4. Recap

4. QUICKSORT

4.1. Divide And Conquer

4.2. Quicksort

4.3. Big O Notation Revisited

4.3.1. Merge sort vs. quicksort

4.3.2. Average case vs. worst case

4.4. Recap

5. HASH TABLES

5.1. Hash Functions

5.2. Use Cases

5.2.1. Using hash tables for lookups

5.2.2. Preventing duplicate entries

5.2.3. Using hash tables as a cache

5.2.4. Recap

5.3. Collisions

5.4. Performance

5.4.1. Load factor

5.4.2. A good hash function

5.5. Recap

6. BREADTH-FIRST SEARCH

6.1. Introduction To Graphs

6.2. What is a graph?

6.3. Breadth-first Search

6.3.1. Finding the shortest path

6.3.2. Queues

6.4. Implementing The Graph

6.5. Implementing The Algorithm

6.5.1. Running time

6.6. Recap

7. DIJKSTRA’S ALGORITHM

7.1. Working with Dijkstra’s Algorithm

7.2. Terminology

7.3. Trading For A Piano

7.4. Negative Weight Edges

7.5. Implementation

7.6. Recap

8. GREEDY ALGORITHMS

8.1. The Classroom Scheduling Problem

8.2. The Knapsack Problem

8.3. The Set-Covering Problem

8.3.1. Approximation Algorithms

8.4. NP Complete Problems

8.4.1. How do you tell if a problem is NP-Complete?

8.5. Recap

9. DYNAMIC PROGRAMMING

9.1. The Knapsack Problem

9.1.1. The simple solution

9.1.2. Dynamic programming

9.2. Knapsack Problem Faq

9.2.1. What happens if we add an item?

9.2.2. What happens if we change the order of the rows?

9.2.3. Can you fill in the grid column-wise instead of row-wise?

9.2.4. What happens if we add a smaller item?

9.2.5. Can you steal fractions of an item?

9.2.6. Optimizing your travel itinerary

9.2.7. Handling items that depend on each other

9.2.8. Is it possible that the solution will require more than 2 sub-knapsacks?

9.2.9. Is it possible that the best solution doesn't fill the knapsack completely?

9.3. Longest Common Substring

9.3.1. Making the grid

9.3.2. Filling in the grid

9.3.3. The solution

9.3.4. Longest common subsequence

9.3.5. Longest common subsequence — solution

9.4. Recap

10. K NEAREST NEIGHBORS

10.1. Classifying Oranges Vs Grapefruit

10.2. Building A Recommendations System

10.2.1. Feature Extraction

10.2.2. Regression

10.2.3. Picking good features

10.3. Introduction To Machine Learning

10.3.1. OCR

10.3.2. Building a spam filter

10.3.3. Predicting the stock market

10.4. Recap

11. WHERE TO GO NEXT

11.1. Trees

11.2. Inverted Indexes

11.3. The Fourier Transform

11.4. Parallel Algorithms

11.5. Map Reduce

11.5.1. Why are distributed algorithms useful?

11.5.2. The "map" function

11.5.3. The "reduce" function

11.6. Bloom Filters And Hyperloglog

11.6.1. Bloom Filters

11.6.2. Hyperloglog

11.7. The Sha Algorithms

11.7.1. Comparing files

11.7.2. Checking passwords

11.8. Locality Sensitive Hashing

11.9. Diffie-hellman Key Exchange

11.10. Linear Programming

11.11. Epilogue

1.INTRODUCTION TO ALGORITHMS

1.1. Introduction

1.1.1. What you’ll learn about performance

1.1.2. What you’ll learn about solving problems

1.2. Binary Search

1.2.1. A better way to search

1.2.2. Running time

1.3. Big-O-notation

1.3.1. Algorithm running times grow at different rates

1.3.2. Visualizing dfferent Big O run times

1.3.3. Big O establishes a worst-case runtime

1.3.4. Some common Big O run times

1.3.5. The traveling salesperson

1.4. Recap

2. SELECTION SORT

2.1. How Memory Works

2.2. Arrays And Linked Lists

2.2.1. Linked Lists

2.2.2. What arrays are good for

2.2.3. Terminology

2.2.4. More insertions and deletions

2.2.5. Deletions

2.3. Selection Sort

2.4. Recap

3. RECURSION

3.1. Recursion

3.2. Base Case And Recursive Case

3.3. The Stack

3.3.1. The call stack

3.3.2. The call stack with recursion

3.4. Recap

4. QUICKSORT

4.1. Divide And Conquer

4.2. Quicksort

4.3. Big O Notation Revisited

4.3.1. Merge sort vs. quicksort

4.3.2. Average case vs. worst case

4.4. Recap

5. HASH TABLES

5.1. Hash Functions

5.2. Use Cases

5.2.1. Using hash tables for lookups

5.2.2. Preventing duplicate entries

5.2.3. Using hash tables as a cache

5.2.4. Recap

5.3. Collisions

5.4. Performance

5.4.1. Load factor

5.4.2. A good hash function

5.5. Recap

6. BREADTH-FIRST SEARCH

6.1. Introduction To Graphs

6.2. What is a graph?

6.3. Breadth-first Search

6.3.1. Finding the shortest path

6.3.2. Queues

6.4. Implementing The Graph

6.5. Implementing The Algorithm

6.5.1. Running time

6.6. Recap

7. DIJKSTRA’S ALGORITHM

7.1. Working with Dijkstra’s Algorithm

7.2. Terminology

7.3. Trading For A Piano

7.4. Negative Weight Edges

7.5. Implementation

7.6. Recap

8. GREEDY ALGORITHMS

8.1. The Classroom Scheduling Problem

8.2. The Knapsack Problem

8.3. The Set-Covering Problem

8.3.1. Approximation Algorithms

8.4. NP Complete Problems

8.4.1. How do you tell if a problem is NP-Complete?

8.5. Recap

9. DYNAMIC PROGRAMMING

9.1. The Knapsack Problem

9.1.1. The simple solution

9.1.2. Dynamic programming

9.2. Knapsack Problem Faq

9.2.1. What happens if we add an item?

9.2.2. What happens if we change the order of the rows?

9.2.3. Can you fill in the grid column-wise instead of row-wise?

9.2.4. What happens if we add a smaller item?

9.2.5. Can you steal fractions of an item?

9.2.6. Optimizing your travel itinerary

9.2.7. Handling items that depend on each other

9.2.8. Is it possible that the solution will require more than 2 sub-knapsacks?

9.2.9. Is it possible that the best solution doesn't fill the knapsack completely?

9.3. Longest Common Substring

9.3.1. Making the grid

9.3.2. Filling in the grid

9.3.3. The solution

9.3.4. Longest common subsequence

9.3.5. Longest common subsequence — solution

9.4. Recap

10. K NEAREST NEIGHBORS

10.1. Classifying Oranges Vs Grapefruit

10.2. Building A Recommendations System

10.2.1. Feature Extraction

10.2.2. Regression

10.2.3. Picking good features

10.3. Introduction To Machine Learning

10.3.1. OCR

10.3.2. Building a spam filter

10.3.3. Predicting the stock market

10.4. Recap

11. WHERE TO GO NEXT

11.1. Trees

11.2. Inverted Indexes

11.3. The Fourier Transform

11.4. Parallel Algorithms

11.5. Map Reduce

11.5.1. Why are distributed algorithms useful?

11.5.2. The "map" function

11.5.3. The "reduce" function

11.6. Bloom Filters And Hyperloglog

11.6.1. Bloom Filters

11.6.2. Hyperloglog

11.7. The Sha Algorithms

11.7.1. Comparing files

11.7.2. Checking passwords

11.8. Locality Sensitive Hashing

11.9. Diffie-hellman Key Exchange

11.10. Linear Programming

11.11. Epilogue

3 years ago

Would I still need Openstack if I were developing on Docker with an orchestration framework like Kubernetes, Swarm, etc?

3 years ago

Need more than to help you out here. Do you have the source code to share? How are you running the app. From what it seems is you have 2 pieces to the app. The rest backend and the ionic frontend. Are you starting them off separately?

3 years ago

Grails uses Spring under the hood. And you can use servlets within a grails app.

If you are looking for a framework that can scaffold easy and you can get it up and running in no time, go for Grails.

If you are looking for a framework that can scaffold easy and you can get it up and running in no time, go for Grails.

3 years ago

Hey Adam,

Congratulations on your book. I've always wondered about the power of logs produced by the VCS(Version Control Systems) we use, at work or otherwise. And it is really interesting to see a book to that effect.

I have already downloaded Code maat and am gonna have a spin at it.

But we'd love to know what were some of the interesting(most complex) problems that you encountered when working with larger codebases than against those smaller in size with a fewer collaborators?

Thanks!

Sai

Congratulations on your book. I've always wondered about the power of logs produced by the VCS(Version Control Systems) we use, at work or otherwise. And it is really interesting to see a book to that effect.

I have already downloaded Code maat and am gonna have a spin at it.

But we'd love to know what were some of the interesting(most complex) problems that you encountered when working with larger codebases than against those smaller in size with a fewer collaborators?

Thanks!

Sai

3 years ago