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Many applications these days are classified as real-time or batch processing. The question arises of which is the best and why.
Real-time applications require little to no latency. This means they have a request-response time of less than a few seconds.
These applications are typically found in call centers and web 2.0 sites such as Facebook or Google, where the input and output are nearly instantaneous.
However, there are some drawbacks to this type of application, namely the cost associated with running this type of architecture to combat high latency.
For a batch processing architecture, on the other hand, the request-response time can be on the order of several seconds to hundreds of milliseconds.
For example, a typical transaction at a grocery store is at least 3 seconds from beginning to end. While this may not seem like a lot of time, it is usually crucial in a real time data management environment.
A transaction in this environment typically means some action is taking place. If an item was purchased and put into one’s shopping cart, it must be picked up and paid for before being removed from the cart and put into one’s bag.
Real Time Big Data
Big data real time can be described as any application that processes factual information and has real time data software. It includes applications such as call centers and websites where the inputs are nearly instantaneous, but the output is a batch system.
Another typical example would be a live TV or radio broadcast where the program must begin almost instantly but only end when all the input has been processed.
While real time does have its challenges, such as latency, it also comes with its own set of benefits.
In systems with no delay for inputs or outputs, one may run into problems in accuracy because data processing cannot take place fast enough to produce accurate results due to analyst inference.
Batch Processing
A batch processing system is a system that processes one or more input/output streams at a time. It means the system does not respond to requests for output before the input is fully processed.
In this sense, batch processing is synonymous with long-running, loosely coupled interactions between different programs.
Batch processing systems typically use a log file and job queue to store the inputs and produce outputs in a quasi-real-time fashion as batch analytics. These applications are commonly used in environments with many users, batch processing example such as event management systems where producers put in the event information, only to be consumed by consumers who act upon those items after receiving them.
Comparisons Between Batch Processing and Real Time Processing
Here is a detailed comparison between batch processing and real-time processing:
Here is a detailed comparison between batch processing and real-time processing:
1. Postponing
In real-time, the processes start immediately after inputting the commands into the computer. In batch processing, the processes can wait until the computer is accessible and have not many processes running simultaneously.
2. Latency
Latency refers to the time interval between the initiation of a task and its completion. In real-time there is no latency because processes are started immediately after input. At the same time, batch processing is latent because the processes are delayed until they can be completed.
3. Error Handling
In batch processing, error handling is done one at a time. In contrast, in real time, error handling is done simultaneously because processes can start immediately after input without delay due to error handling, thus minimizing downtime due to errors at a particular process.
4. Complete Processing
In real time the tasks are not processed entirely but only partially processed so that inputs for other processes are received and processed, thus completing all inputted tasks which were partially processed previously.
5. Monitoring
In realtime processing the production output is monitored live, while batch processing is done after the complete process has been completed and analyzed.
Examples of Real Time Processes
Here are some examples of real-time processes:
1. Radar Systems
Radar systems are used in many areas, such as military operations, weather forecasting, and other similar applications. The inputs of radar data are continuous, but the output is usually a batch data processing type of system.
In this case, the latency associated with radar systems can be on the order of two seconds or more. This type of system is typically used to predict things in the future as part of a web 2.0 type application where the output is an event happening in the present moment.
2. Call Centers
In call centers, inputs typically include phone conversations between callers and representatives from a business or service, which could be considered an event is occurring in real time.
3. Data Centers
Data centers are used to store and process information. Inputs typically include text files with various data in them, while the output of data centers can be of various types, from a log file to an HTML page.
4. Weather Forecasting
Weather forecasting is typically done using models that take inputs about the current weather conditions and predict what the temperature, precipitation, etc. would be in the future. This is nearly instantaneous in terms of real time processing because it may take several days or weeks for output.
Both batch processing and real time processing have their benefits and trade-offs. It is up to the architect or designer of the application to determine which type of system is best suited for a particular application.
Batch processing tends to be cheaper in terms of hardware cost, while real time applications may require more hardware resources due to latency constraints.
A good rule of thumb is that systems, where accurate time data inputs are required, should be run in a tight loop with little or no latency and use batch systems instead.
