A data structure is a method of organizing information. These structures include files, lists, arrays, trees, records and tables. Queues are related to ordered lists. With the queue, the new pieces of data are placed at the rear of the data structure, and the deletions are placed at the front. The first piece of data entered into the data structure is the first piece removed from the structure. With queues, data does not remain in the data structure for as long as with stacks. Queues can be compared to lines at the store, where the first person in line is the first person to receive a service.
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While queues are more complex than stacks, the array makes queues easy by placing the newest element at the end and moving each element over one step when one piece of data is removed from the queue. Queues are helpful when multiple consumers share a particular process. For example, a website might only have so much space to allow consumers to download a particular file. With a stack, some consumer might have to wait much longer to download the file than the newest consumers. Queues are also useful when the client does not necessarily receive the data at the same time the data is sent.
Queues can lead to empty spaces in the data structure, since a bigger array is needed than the total number of pieces of data. However, programmers can use circular queues to use the empty space. Programmers can set up time outs where jobs wait until the entry reaches the data queue.
Data queues are a fast method of inter-process communication. Data queues free up jobs from performing some work, which can lead to a better response time and an overall improvement in system performance. Data queues serve as the fastest form of asynchronous communication between two different tasks, since there is less overhead than with database files and data areas.
Queues are flexible, requiring no communications programming. The programmer does not need any knowledge of inter-process communication. Data queues allow computers to handle multiple tasks. The queue can remain active when there are no entries, ready to process data entries when necessary.
Some jobs have performance restraints and cannot handle all the entries, so the data entries are spread out across multiple jobs. For example, only one customer service representative can help a customer at a time, so the queue can spread customer service requests among the representatives, for quicker processing.