Massively parallel, cheap cloud computing reduces both costs and time to market. Cloud computing grew out of parallel computing, a concept that many problems can be solved faster by running the computations in parallel.
After parallel algorithms came grid computing, which ran parallel computations on idle desktops. Grid computing is widely adopted by financial companies, which run massive risk calculations. The concept of under-utilized resources, together with the rise of J2EE platform, gave rise to the precursor of cloud computing: application server virtualization.
The idea was to run applications on demand and change what is available depending on the time of day and user activity. These first blocks already empower an unprecedented way of doing large-scale computing, and surely the best is yet to come. Concurrency is one topic engineers notoriously get wrong, and understandibly so, because the brain does juggle many things at a time and in schools linear thinking is emphasized.
Yet concurrency is important in any modern system. Concurrency is about parallelism, but inside the application. The complexity in concurrency programming stems from the fact Threads often needs to operate on the common data. Each Thread has its own sequence of execution, but accesses common data.
One of the most sophisticated concurrency libraries has been developed by Doug Lea and is now part of core Java. No modern web system runs without a cache, which is an in-memory store that holds a subset of information typically stored in the database.
The need for cache comes from the fact that generating results based on the database is costly. User requests fetch data from the cache instead of hitting the database and regenerating the same information.
Caching comes with a cost. Only some subsets of information can be stored in memory. The most common data pruning strategy is to evict items that are least recently used LRU. The prunning needs to be efficient, not to slow down the application.
A lot of modern web applications, including Facebook, rely on a distributed caching system called Memcached , developed by Brad Firzpatrick when working on LiveJournal. The idea was to create a caching system that utilises spare memory capacity on the network.
The idea behind hashing is fast access to data. If the data is stored sequentially, the time to find the item is proportional to the size of the list. For each element, a hash function calculates a number, which is used as an index into the table.
Given a good hash function that uniformly spreads data along the table, the look-up time is constant. Perfecting hashing is difficult and to deal with that hashtable implementations support collision resolution. Beyond the basic storage of data, hashes are also important in distributed systems. The so-called uniform hash is used to evenly allocate data among computers in a cloud database. Memcached similarly uses a hash function. If the Array has four elements, that would result in println To be more mathematically precise, we would say that the runtime complexity of printArray is linear :.
In our second function, this refers exclusively to the size of the Array that is, arr. In Big O notation, there are actually three different qualities of a given piece of code that we can pay attention to:. Generally speaking, in the same sense that a civil engineer is most concerned about the maximum number of vehicles a bridge can support, a software engineer is usually most concerned about worst-case performance.
By looking at printArraySums , you should be able to reason that we can represent its worst-case runtime complexity the number of times println In case it is not clear, we are not just pairing and summing the elements of arrOne and arrTwo at the same indexes, we are literally summing every value of them together in a nested loop.
From here you can start to truly understand the importance of asymptotic runtime and space complexity. In a worst-case scenario, the runtime grows exponentially in a quadratic curve:. Two final notes on this topic: Firstly, if I suddenly made you a bit fearful of nested loops and yes, each nested loop potentially adds another factor of n , then I have done a good job. Even so , understand that if you are certain that n will not exceed a reasonable size even in a function which has exponential growth, then there is not actually a problem.
If you would like to know how to determine if n will have a negative impact on performance, stick around for the last section of this article. Secondly, you may have noticed that all of my examples were about runtime complexity, not memory space complexity. The reason for this is simple: We represent space complexity in exactly the same manner and notation. The term Data Structure, despite what any single teacher may tell you, does not have a singular definition.
Some teachers will emphasize their abstract nature and how we can represent them in mathematics, some teachers will emphasize how they are physically arranged in memory space, and some will emphasize how they are implemented in a particular language specification.
I hate to tell you this, but this is actually a very common problem in computer science and engineering: One word meaning many things and many words meaning one thing, all at the same time. Therefore, rather than trying to make every kind of expert from every kind of academic or professional background happy by using a plethora of technical definitions, let me upset everyone equally by explaining things as clearly as I can in plain English.
Things like user profiles, friends lists, social networks, game states, high scores and so on. When considering DS from the physical perspective of the hardware and operating system, there are two main ways to build a DS. Both ways take advantage of the fact that physical memory is discrete a fancy word for countable , and therefore addressable.
An easy way to imagine this is to think about street addresses and how, depending on which direction you are physically moving and depending on how your country organizes street addresses , the address increases or decreases in value. The first way takes advantage of the fact that we can group pieces of data for example a list of friends in a social media application into a chunk of contiguous physically next to each other memory space.
This turns out to be a very fast and efficient way for a computer to traverse memory space. Instead of giving the computer an n sized list of addresses for each piece of data, we give the computer a single address denoting the start of this DS in physical memory, and the size of that is, n the DS as a single value.
The second way requires each piece of data in the structure itself to contain the address es of the next or previous maybe both? One of the big problems with contiguous memory spaces is that they present problems when it comes to growing adding more elements or shrinking this can fragment the memory space, which I will not explain but suggest a quick google search.
By having each piece of data Link to the other pieces usually just the previous or next one , it becomes largely irrelevant where each piece sits in physical memory space. Therefore, we can grow or shrink the data structure with relative ease. You should be able to reason that since each part of the structure stores not just its own data, but the address of the next or more than that element, then each piece would necessarily require more memory space than with the contiguous Array approach. However, whether it is ultimately more efficient depends on what kind of problem you are trying to solve.
The two approaches I have discussed are generally known as an Array and a Linked List. With very little exception, most of what we care about in the study of DS is how to group collections of data which have some kind of reason to be grouped together, and how best to do that. As I tried to point out, what makes one structure better in a certain situation can make it worse in another.
You should be able to reason from the previous few paragraphs that a Linked List is typically more suitable for a dynamic changing collection, whereas an Array is typically more suitable for a fixed collection — at least with respect to runtime and space efficiency. Do not be misled, however!
It is not always the case that our primary concern is to pick the most efficient DS or algorithm with respect to runtime and memory space. Remember, if n is very small then worrying about a nanosecond or a few bits of memory here and there are not necessarily as important as ease of use and legibility.
Again, I think this is largely due to different experts approaching this from different backgrounds mathematics, digital circuits, low level programming, high level programming and the fact that it is really quite hard to make a verbal definition of one that does not at least partially or entirely describe the other. On the other hand, a data type is defined by and within such a Type system. But I know that Type Theory itself is independent of any particular Type system, so you can hopefully see how tricky it is to say anything concrete about these two terms.
I took quite a long time to explain the previous two topics because they allow me to introduce and motivate this topic rather easily. Before we continue, I must very briefly try to untangle another mess of jargon. For example, if you were to follow a recipe to cook something, then you would be the IPS, the algorithm would be the recipe, and the ingredients and cookware would be the data inputs arguments. Now, by that definition the words function, method, procedure, operation, program, script, subroutine and algorithm all point to the same underlying concept.
This is not by accident — these words all fundamentally mean the same thing. The confusion is that different computer scientists and language designers will implement build the same idea in a slightly different way. Or even more depressingly, they will build them the same way but give a different name. I wish this was not the case, but the best I can do is to warn you. That is all you need to know about algorithms in general, so let us be more specific about how they can help us to write better code.
Recall that our primary concern as software engineers is to write code which is guaranteed to be efficient at least such that it keeps our users happy and safe with respect to limited system resources. Also recall that I previously stated that some DS perform better than others with respect to runtime and memory space, particularly as n gets large.
The same is true of algorithms. Depending on what you are trying to do, different algorithms will perform better than others. It is also worth noting that the DS will tend to shape which algorithms can be applied to the problem, so selecting the right DS and the right algorithm is the true art of software engineering. To finish off this third main topic, we will look at two common but very different ways to solve the one problem: Searching an ordered Array.
By ordered, I mean to say that it is ordered something like least to greatest, greatest to least, or even alphabetically. Also, assume that the algorithm is given some kind of target value as an argument, which is what we use to locate a particular element.
This should become clear in the example in case there is any confusion. The example problem is as follows: We have a collection of Users perhaps loaded from a database or server , which is sorted from least to greatest by a field called userId, which is an Integer value. Suppose that this userId comes from taking the system time look up Unix Time for more info just prior to creating the new User.
However, it is preferable if you have a basic knowledge of Computer, Operating System, and Software. Moreover, if you know what software is and how actual software works for different domains, you will easily and efficiently learn Software Engineering.
In this Software Engineering tutorial, you will learn everything about software engineering starting from introduction, software development lifecycle and its models, various software development architectures, software development tools, how to become a software engineer, interview questions and answers for software engineering, and many more interesting concepts.
Skip to content. Why should you learn Software Engineering? Why is Software Engineering required? Applications of Software Engineering Following are the different Software Engineering applications: Software Development for various domains To perform various operations on the software like testing Maintenance of various software products To apply the knowledge, practices, and technologies to build high-quality software products that enhance productivity in every industry.
Report a Bug. Next Continue. Home Testing Expand child menu Expand. SAP Expand child menu Expand. Web Expand child menu Expand. Must Learn Expand child menu Expand. Software engg. Is the application of engineering to the development of software in a systematic methods.
It is called technology because to develop a software one will have to go from one layer to another. The layers are related are related and each layer demands the fulfillment of previous layer.
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