One of the key differences was in their execution model. Spark was a significant improvement over the limitations of Hadoop. Finally, the Task Scheduler picks up these stages and launches them through the cluster manager. The scheduler is also responsible for pipelining the operators together in a stage for greater efficiency. Next, the DAG Scheduler picks up the operator graph on the call of action and splits it into stages of the task:Ī stage basically contains the task based on the partition of the input data. Once we submit our code to Spark, it interprets and converts it to a DAG or operator graph. Here, the vertices represent the RDDs, and the edges represent the operation to be applied to RDD.Ĭonsequently, every edge directs from earlier to later processing in the sequence. Interestingly, the workflow of the data processing within Spark is managed as a DAG. We can process the elements of RDD in parallel with fault tolerance. RDD is basically a fault-tolerant collection of elements distributed over a cluster of machines. At the core of Spark architecture lies the resilient distributed dataset (RDD). Other Practical ApplicationsĪpache Spark is an open-source engine for large-scale data processing with implicit data parallelism and fault tolerance. There are several other real-life scheduling problems like airline routing & scheduling and assembly line scheduling. This is basically a method for the management of large projects and makes use of DAG for representation and finding the critical path. One such scheduling problem with the application of DAG is the program evaluation and review technique (PERT). Similarly, an edge can represent the communication between two tasks and its weight can represent the cost of communication: Here, a vertex can represent the task and its weight can represent the size of the task computation. Let’s take the example of a task scheduling problem. We can represent a scheduling problem using a weighted directed acyclic graph. Depending upon the number of parameters, even a modest-looking problem can become a very difficult problem to solve. It’s interesting to note that many scheduling problems have ordering constraints. Scheduling refers to the problem of assigning work to be done in a system in a manner that optimizes the overall performance while ensuring the correctness of the result. The transitive reduction of a DAG is yet another graph with the same set of nodes and as few edges as possible, retaining the reachability relation of the original graph: The transitive closure of a DAG is another graph with the same set of nodes and as many edges as possible, retaining the reachability relation of the original graph: If we consider all nodes in a DAG as part of a set, its partial order gives us the set ordered by the reachability of the graph. It’s a useful tool for analyzing DAGs.įormally, a binary relation on a set is a partial order if and only if it is reflexive, anti-symmetric, and transitive. In order theory, another branch of mathematics, we can reason about the reachability of a DAG using its partial order relations. Reachability is the ability to get from one node to another within a graph. It can be interesting to understand them better to see why DAGs are useful. For instance, reachability relation, transitive closure, transitive reduction, and topological ordering. Graph theory, a branch of mathematics, defines several properties of DAGs.
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