Skip to main content

Tell Don't Ask

Tell-Don't-Ask is a principle that helps people remember that object-orientation is about bundling data with the functions that operate on that data. It reminds us that rather than asking an object for data and acting on that data, we should instead tell an object what to do. This encourages to move behavior into an object to go with the data.

Let's clarify with an example. Let's imagine we need to monitor certain values, signaling an alarm should the value rise above a certain limit. If we write this in an "ask" style, we might have a data structure to represent these things…

class AskMonitor...

  private int value;
  private int limit;
  private boolean isTooHigh;
  private String name;
  private Alarm alarm;

  public AskMonitor (String name, int limit, Alarm alarm) {
    this.name = name;
    this.limit = limit;
    this.alarm = alarm;
  }

…and combine this with some accessors to get at this data

class AskMonitor...

  public int getValue() {return value;}
  public void setValue(int arg) {value = arg;}
  public int getLimit() {return limit;}
  public String getName()  {return name;}
  public Alarm getAlarm() {return alarm;}

We would then use the data structure like this

AskMonitor am = new AskMonitor("Time Vortex Hocus", 2, alarm);
am.setValue(3);
if (am.getValue() > am.getLimit()) 
  am.getAlarm().warn(am.getName() + " too high");

"Tell Don't Ask" reminds us to instead put the behavior inside the monitor object itself (using the same fields).

class TellMonitor...

  public void setValue(int arg) {
    value = arg;
    if (value > limit) alarm.warn(name + " too high");
  }

Which would be used like this

TellMonitor tm = new TellMonitor("Time Vortex Hocus", 2, alarm);
tm.setValue(3);

Many people find tell-don't-ask to be a useful principle. One of the fundamental principles of object-oriented design is to combine data and behavior, so that the basic elements of our system (objects) combine both together. This is often a good thing because this data and the behavior that manipulates them are tightly coupled: changes in one cause changes in the other, understanding one helps you understand the other. Things that are tightly coupled should be in the same component. Thinking of tell-don't-ask is a way to help programmers to see how they can increase this co-location.

Comments

Popular posts from this blog

Apache Spark Discretized Streams (DStreams) with Pyspark

Apache Spark Discretized Streams (DStreams) with Pyspark SPARK STREAMING What is Streaming ? Try to imagine this; in every single second , nearly 9,000 tweets are sent , 1000 photos are uploaded on instagram, over 2,000,000 emails are sent and again nearly 80,000 searches are performed according to Internet Live Stats. So many data is generated without stopping from many sources and sent to another sources simultaneously in small packages. Many applications also generate consistently-updated data like sensors used in robotics, vehicles and many other industrial and electronical devices stream data for monitoring the progress and the performance. That’s why great numbers of generated data in every second have to be processed and analyzed rapidly in real time which means “ Streaming ”. DStreams Spark DStream (Discretized Stream) is the basic concept of Spark Streaming. DStream is a continuous stream of data.The data stream receives input from different kind of sources like Kafka, Kinesis...

6 Rules of Thumb for MongoDB Schema Design

“I have lots of experience with SQL and normalized databases, but I’m just a beginner with MongoDB. How do I model a one-to-N relationship?” This is one of the more common questions I get from users attending MongoDB office hours. I don’t have a short answer to this question, because there isn’t just one way, there’s a whole rainbow’s worth of ways. MongoDB has a rich and nuanced vocabulary for expressing what, in SQL, gets flattened into the term “One-to-N.” Let me take you on a tour of your choices in modeling One-to-N relationships. There’s so much to talk about here, In this post, I’ll talk about the three basic ways to model One-to-N relationships. I’ll also cover more sophisticated schema designs, including denormalization and two-way referencing. And I’ll review the entire rainbow of choices, and give you some suggestions for choosing among the thousands (really, thousands) of choices that you may consider when modeling a single One-to-N relationship. Jump the end of the post ...

Khác nhau giữa các chế độ triển khai giữa Local, Standalone và YARN trong Spark

Trong Apache Spark, có ba chế độ triển khai chính: Local, Standalone và YARN. Dưới đây là sự khác biệt giữa chúng: Chế độ triển khai Local: Chế độ triển khai Local là chế độ đơn giản nhất và được sử dụng cho môi trường phát triển và kiểm thử. Khi chạy trong chế độ Local, Spark sẽ chạy trên một máy tính duy nhất bằng cách sử dụng tất cả các luồng CPU có sẵn trên máy đó. Đây là chế độ phù hợp cho các tác vụ nhỏ và không yêu cầu phân tán dữ liệu. Chế độ triển khai Standalone: Chế độ triển khai Standalone cho phép bạn triển khai một cụm Spark độc lập bao gồm nhiều máy tính. Trong chế độ này, một máy tính được chọn làm "Spark Master" và các máy tính khác được kết nối với Spark Master như là "Spark Workers". Spark Master quản lý việc phân phối công việc và quản lý tài nguyên giữa các Spark Workers. Chế độ Standalone phù hợp cho triển khai Spark trên các cụm máy tính riêng lẻ mà không có hệ thống quản lý cụm chuyên dụng. Chế độ triển khai YARN: YARN (Yet Another Resource N...