The output of W1 is placed in Q2 where it will wait in Q2 until W2 processes it. This process continues until Wm processes the task at which point the task departs the system. One key advantage of the pipeline architecture is its connected nature, which allows the workers to process tasks in parallel.
This can result in an increase in throughput. As a result, pipelining architecture is used extensively in many systems.
There are several use cases one can implement using this pipelining model. For example, sentiment analysis where an application requires many data preprocessing stages, such as sentiment classification and sentiment summarization. Furthermore, the pipeline architecture is extensively used in image processing, 3D rendering, big data analytics, and document classification domains.
This section provides details of how we conduct our experiments. The workloads we consider in this article are CPU bound workloads. Our initial objective is to study how the number of stages in the pipeline impacts the performance under different scenarios. We use the notation n-stage-pipeline to refer to a pipeline architecture with n number of stages.
To understand the behavior, we carry out a series of experiments. The following are the parameters we vary:. We conducted the experiments on a Core i7 CPU: 2. We use two performance metrics to evaluate the performance, namely, the throughput and the average latency. We define the throughput as the rate at which the system processes tasks and the latency as the difference between the time at which a task leaves the system and the time at which it arrives at the system.
When we compute the throughput and average latency, we run each scenario 5 times and take the average. We implement a scenario using the pipeline architecture where the arrival of a new request task into the system will lead the workers in the pipeline constructs a message of a specific size.
Let us now explain how the pipeline constructs a message using 10 Bytes message. Let us assume the pipeline has one stage i. A request will arrive at Q1 and will wait in Q1 until W1processes it. W2 reads the message from Q2 constructs the second half. We note that the processing time of the workers is proportional to the size of the message constructed. Taking this into consideration, we classify the processing time of tasks into the following six classes:.
When we measure the processing time, we use a single stage and we take the difference in time at which the request task leaves the worker and time at which the worker starts processing the request note: we do not consider the queuing time when measuring the processing time as it is not considered as part of processing. As a result of using different message sizes, we get a wide range of processing times. For example, class 1 represents extremely small processing times while class 6 represents high-processing times.
The following figures show how the throughput and average latency vary under a different number of stages. We clearly see a degradation in the throughput as the processing times of tasks increases. Similarly, we see a degradation in the average latency as the processing times of tasks increases. We expect this behavior because, as the processing time increases, it results in end-to-end latency to increase and the number of requests the system can process to decrease.
Let us now take a look at the impact of the number of stages under different workload classes. The following table summarizes the key observations. We see an improvement in the throughput with the increasing number of stages. Note that there are a few exceptions for this behavior e. Let us now try to reason the behavior we noticed above. It is important to understand that there are certain overheads in processing requests in a pipelining fashion.
For example, when we have multiple stages in the pipeline, there is a context-switch overhead because we process tasks using multiple threads. The context-switch overhead has a direct impact on the performance in particular on the latency.
In addition, there is a cost associated with transferring the information from one stage to the next stage. Transferring information between two consecutive stages can incur additional processing e.
Moreover, there is contention due to the use of shared data structures such as queues which also impacts the performance. When it comes to tasks requiring small processing times e. Therefore, there is no advantage of having more than one stage in the pipeline for workloads. In fact, for such workloads, there can be performance degradation as we see in the above plots. As the processing times of tasks increases e.
For example, we note that for high processing time scenarios, 5-stage-pipeline has resulted in the highest throughput and best average latency. Therefore, for high processing time use cases, there is clearly a benefit of having more than one stage as it allows the pipeline to improve the performance by making use of the available resources i.
Data scientists and data engineering teams can then use the data for the benefit of the enterprise. Data pipelines are comprised of a sequence of data processing steps, facilitated by machine learning, specialized software, and automation. The pipeline determines how, what, and where data is collected and automates the process of extract, transform, load ETL , validates and combines data, then loads it for analysis and visualization.
The pipeline reduces errors, eliminates bottlenecks and latency — enabling data to move much faster and be made useful sooner to the enterprise than through a manual process. Ultimately, data pipelines enable real-time business intelligence that gives the enterprise key insights to make nimble, strategic decisions that improve business outcomes. Data scientists can then use the data to improve targeted functionality use data pipelines to obtain insights into areas such as customer behavior, robotic process automation, user experience, customer journeys, to name a few and to inform the business of key business and customer intelligence.
Raw data comes from multiple sources and there are many challenges in moving data from one location to another and then making it useful.
Issues with latency, data corruption, data source conflicts, and redundant information often make data unclean and unreliable. In order to make data useful, it needs to be clean, easy to move, and trustworthy. Data pipelines remove the manual steps required to solve those issues and create a seamless automated data flow.
Enterprises that use vast amounts of data, depend on real-time data analysis, use cloud data storage, and have siloed data sources typically deploy data pipelines. But having a bunch of data pipelines gets messy. Which is why data pipeline architecture brings structure and order to it. It also helps to improve security, as data pipelines restrict access to data sets, via permission-based access control.
Data pipeline architecture can be broken into various components, such as:. If you have significant data volume, siloed data, need real-time insights, and want to optimize automation across your enterprise, then data pipeline tools will make creating data pipelines easier for your organization.
Batch processing — ideal for moving large amounts of big data on a regular basis, but not in real-time. Open source — built by the open-source community, a common open-source pipelining tool is Apache Kafka. Real-time — ideal for streaming data sources, such as the internet of things IoT , finance, and healthcare.
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