Apache Spark is an open-source, distributed processing system used for big data workloads. It utilizes in-memory caching and optimized query execution for fast analytic queries against data of any size. Central to Spark’s performance and its ability to perform complex computations is its use of the Directed Acyclic Graph (DAG). Understanding the DAG in Apache Spark is crucial for developers and data engineers who want to optimize their applications and build efficient data pipelines.
What is a Directed Acyclic Graph (DAG)?
A Directed Acyclic Graph (DAG) is a finite directed graph with no directed cycles. That means if you start at any point in the graph, you cannot traverse the edges in the direction they point and return to the starting point. Each node in the DAG represents a data set or a computation, while the edges represent dependencies between these computations.
Role of DAG in Apache Spark
In the context of Apache Spark, the DAG represents a sequence of computations performed on data. Each node in the DAG represents an RDD (Resilient Distributed Dataset – a fundamental data structure of Spark), and each edge represents a transformation that leads to a new RDD. RDD transformations can be operations like map, filter, or reduce. Actions, such as count, collect, or saveAsTextFile, trigger the execution of the DAG.
How Spark utilizes DAG
Apache Spark converts each user program into a DAG of RDD transformations with actions as the final operations that produce results. Spark then translates this graph into a set of stages for execution; these stages are also known as tasks. These stages are groups of tasks that can be executed together without shuffling data between different partitions. The DAG Scheduler divides the operator graph into stages based on the transformations. The stages are then used to create a task set that is submitted to the cluster manager for execution.
DAG Scheduler
The DAG Scheduler is a key component of Apache Spark that constructs the stages from the logical execution plan (the DAG) and then submits them to the Task Scheduler. The DAG Scheduler also handles failures by resubmitting failed stages.
Stages in Spark
In Spark, stages are the physical units of execution. They are composed of tasks, which are the smallest units of execution and correspond to a single data partition. The boundary of a stage is set by operations that shuffle data (like groupByKey or reduceByKey). Shuffles are expensive operations that involve disk I/O and network I/O. Spark tries to minimize the number of shuffles and disk operations by organizing tasks into stages.
Tasks
Tasks are the smallest unit of work sent to the executor. Each task is a single operation that will be run on a single data partition. An RDD can have many partitions, and therefore an RDD operation can have many tasks. The Task Scheduler launches tasks via cluster manager to run on executors.
Job Execution Workflow
The processing of a Spark job can be broken down into these steps:
Step 1: Building the RDD lineage
Every RDD has a lineage or sequence of transformations applied to the initial RDD to get the resulting RDD. Lineage info is used to compute each RDD on demand and to recover lost data if part of the RDD is lost.
Step 2: Converting the lineage to DAG
Once an action is called, the RDD lineage is translated into a DAG. The DAG represents the flow of data and computations at a high level.
Step 3: Splitting the DAG into stages
The DAG is then split into stages at the points where shuffles occur. Stages are then processed in order with shuffles writing intermediate results to disk.
Step 4: Creating tasks for each stage
Each stage consists of multiple tasks, one per partition. These tasks represent the actual computations that will be sent to Spark executors.
Step 5: Executing tasks on executors
The tasks are then distributed across the executors to be executed. Each executor launches its task in separate threads.
Step 6: Handling task failures
If tasks fail, the DAG Scheduler will attempt to rerun them. If too many failures occur, the stage may be aborted, and the job will fail.
Understanding the Transformations and Actions
Transformations in Spark are operations on RDDs that return a new RDD, like map and filter. These transformations are lazy; they are not computed immediately but are recorded in the DAG. Actions, on the other hand, like collect or count, return a non-RDD result. They force the computation of the transformations that were lazily stored in the DAG.
Optimizations Using DAG
Spark uses the DAG to perform optimizations at different levels of operations. These include:
Pipeline Operations
Pipelining refers to the process of executing transformations as a single stage if there’s no shuffle operation between them. This minimizes IO operations.
Stage Planning
Spark uses the DAG to divide the graph into stages of tasks. Tasks within the same stage can be executed in parallel without exchanging data between executors.
Speculative Execution
Sometimes a task might take unusually long to execute due to issues with a machine or other anomalies. Spark can preemptively launch a new copy of the task on another node. This is known as speculative execution, and can often save time for skewed or failing tasks.
Visualizing the DAG
The Spark UI provides a visual representation of the DAG for each job. This view is essential for debugging the performance and execution of your Spark application.
Summary
The use of the DAG in Apache Spark is a fundamental concept that underpins how Spark executes complex workflows efficiently. By breaking down high-level operations into stages and tasks, Spark can optimize the execution flow, minimize redundant data movement, and handle failures effectively. Understanding the intricacies of DAG helps in writing more efficient Spark applications and provides insights into performance tuning and debugging issues in Spark jobs.