When a Data frame is split and again joined with different columns, How many and how are stages created in DAG and how tasks are created in stages.
4. How DAG works in Spark?
The interpreter is the first layer, using a Scala interpreter, Spark interprets the code with some modifications.
Spark creates an operator graph when you enter your code in Spark console.
When we call an Action on Spark RDD at a high level, Spark submits the operator graph to the DAG Scheduler.
Divide the operators into stages of the task in the DAG Scheduler. A stage contains task based on the partition of the input data. The DAG scheduler pipelines operators together. For example, map operators schedule in a single stage.
The stages pass on to the Task Scheduler. It launches task through cluster manager. The dependencies of stages are unknown to the task scheduler.
The Workers execute the task on the slave.
You can get more information from the link: https://data-flair.training/blogs/dag-in-apache-spark/
do tasks in the same stage work simultaneously? if so, the line between partitions in a stage refers to what? example of a DAG
here is a good link for your reading. that explains DAG in detail and few other things that may be of interest. databricks blog on DAG
I can try to explain. as each stage is created it has a set of tasks that are divided. when an action is encountered. Driver sends the task to executors. based on how your data is partitioned N number tasks are invoked on the data in your distributed cluster. so the arrows that you are seeing is execution plan. as in it cannot do map function prior to reading the file. each node that has some data will execute those tasks in order that is provided by the DAG.
I have a Spark Standalone Cluster (which consists of two Workers with 2 cores each). I run an SQLQuery which joins 2 dataframes and shows the result. I have some questions regarding the above simle example.
val df1 = sc.read.text(fn1).toDF()
val df2 = sc.read.text(fn2).toDF()
df1.createOrReplaceTempView("v1")
df2.createOrReplaceTempView("v2")
val df_join = sc.sql("SELECT * FROM v1,v2 WHERE v1.value=v2.value AND v2.value<1500").show()
DAG Scheduler - Number of Tasks
From what i've understood so far when i spark-submit the application, a SparkContext is spawn for the handling of the Job(where job is the printing of result rows). SparkContext creates a Task Scheduler instance which then creates a DAGScheduler. Through a simple event mechanism, the DAGScheduler handles the job for execution(handleJobSubmitted function from the code). SparkSQL query has been transformed into a physical execution plan(through Catalyst Optimizer), and then to an RDD-Graph(with toRdd function). DagScheduler receives the RDD-Graph and recursively creates all the stages.
I do not understand how it finds the Number of Tasks(before the execution of any stage) in the last stage,keeping in mind that the result stage is the one that performs the join(and prints the results). The number of data(and the rdds and the number of their partitions, which define the number of tasks) we have is unknown until the parent stages have ended their execution.
Parallel Execution of Stages
Each one of the two first stages is independent of the other, as it loads data from different files. I have read many posts that say that Stages that do not have dependencies between them MAY be executed in parallel from the cluster. What is the condition that implies that independent stages's tasks are executed in parallel?
Task Dependencies
Finally, i've read that Task Scheduler does not know about Stage Dependencies. If i keep in mind that each Stage in Spark is a TakSet( aka a set of non dependent tasks, each task with same functionality packed up with different partition of data), then TaskScheduler does not know as well the dependencies between tasks of different Stages. As a result, how and when a task knows the data on which it'll execute a function?
If for example, the task knows apriori where to look for its input data, then it could be launched as soon as they become available.
It is mentioned like in " Spark In Action" Book ,
You can access an accumulator’s value only from within the driver. If you try to access it from an executor, an exception will be thrown.
I am learning spark and come across the above. How it could be differentiated or recognize the code which is executed in executor Vs the code executed in driver.
Further, the author referred the above with the following code
https://i.imgur.com/aWx1nAs.png
Transformations run on executors & actions runs on driver other words tasks(transformation) executes on the Workers(Executors) and when action(take/collect) is called it brings back the data at the Driver. to return value.
When any action is called on the RDD, Spark creates the DAG and submits to the DAG scheduler,DAG scheduler divides operators into stages of tasks. A stage is comprised of tasks based on partitions of the input data. The DAG scheduler pipelines operators together.
The Stages are passed on to the Task Scheduler.The task scheduler launches tasks via cluster manager.(Standalone/Yarn/Mesos).
I know that Spark can be operated using Scala, Python and Java. Also, that RDDs are used to store data.
But please explain, what's the architecture of Spark and how does it work internally.
Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset.
Spark translates the RDD transformations into something called DAG (Directed Acyclic Graph) and starts the execution,
At high level, when any action is called on the RDD, Spark creates the DAG and submits to the DAG scheduler.
The DAG scheduler divides operators into stages of tasks. A stage is comprised of tasks based on partitions of the input data. The DAG scheduler pipelines operators together. E.g. many map operators can be scheduled in a single stage. The final result of a DAG scheduler is a set of stages.
The stages are passed on to the Task Scheduler. The task scheduler launches tasks via cluster manager (Spark Standalone/Yarn/Mesos). The task scheduler doesn't know about dependencies of the stages.
The Worker/Slave executes the tasks.
Let's come to how Spark builds the DAG.
At high level, there are two transformations that can be applied onto the RDDs, namely narrow transformation and wide transformation. Wide transformations basically result in stage boundaries.
Narrow transformation - doesn't require the data to be shuffled across the partitions. For example, map, filter, etc.
Wide transformation - requires the data to be shuffled, for example, reduceByKey, etc.
Let's take an example of counting how many log messages appear at each level of severity.
Following is the log file that starts with the severity level:
INFO I'm Info message
WARN I'm a Warn message
INFO I'm another Info message
and create the following Scala code to extract the same:
val input = sc.textFile("log.txt")
val splitedLines = input.map(line => line.split(" "))
.map(words => (words(0), 1))
.reduceByKey{(a,b) => a + b}
This sequence of commands implicitly defines a DAG of RDD objects (RDD lineage) that will be used later when an action is called. Each RDD maintains a pointer to one or more parents along with the metadata about what type of relationship it has with the parent. For example, when we call val b = a.map() on a RDD, the RDD b keeps a reference to its parent a, that's a lineage.
To display the lineage of an RDD, Spark provides a debug method toDebugString() method. For example, executing toDebugString() on splitedLines RDD, will output the following:
(2) ShuffledRDD[6] at reduceByKey at <console>:25 []
+-(2) MapPartitionsRDD[5] at map at <console>:24 []
| MapPartitionsRDD[4] at map at <console>:23 []
| log.txt MapPartitionsRDD[1] at textFile at <console>:21 []
| log.txt HadoopRDD[0] at textFile at <console>:21 []
The first line (from bottom) shows the input RDD. We created this RDD by calling sc.textFile(). See below more diagrammatic view of the DAG graph created from the given RDD.
Once the DAG is built, Spark scheduler creates a physical execution plan. As mentioned above, the DAG scheduler splits the graph into multiple stages, the stages are created based on the transformations. The narrow transformations will be grouped (pipe-lined) together into a single stage. So for our example, Spark will create a two-stage execution as follows:
The DAG scheduler then submits the stages into the task scheduler. The number of tasks submitted depends on the number of partitions present in the textFile. Fox example consider we have 4 partitions in this example, then there will be 4 sets of tasks created and submitted in parallel provided if there are enough slaves/cores. The below diagram illustrates this in bit more detail:
For more detailed information I suggest you to go through the following YouTube videos where the Spark creators give in depth details about the DAG and execution plan and lifetime.
Advanced Apache Spark- Sameer Farooqui (Databricks)
A Deeper Understanding of Spark Internals - Aaron Davidson (Databricks)
Introduction to AmpLab Spark Internals
The diagram below shows how Apache Spark internally working:
Here are some JARGONS from Apache Spark i will be using.
Job:- A piece of code which reads some input from HDFS or local, performs some computation on the data and writes some output data.
Stages:-Jobs are divided into stages. Stages are classified as a Map or reduce stages(Its easier to understand if you have worked on Hadoop and want to correlate). Stages are divided based on computational boundaries, all computations(operators) cannot be Updated in a single Stage. It happens over many stages.
Tasks:- Each stage has some tasks, one task per partition. One task is executed on one partition of data on one executor(machine).
DAG:- DAG stands for Directed Acyclic Graph, in the present context its a DAG of operators.
Executor:- The process responsible for executing a task.
Driver:- The program/process responsible for running the Job over the Spark Engine
Master:- The machine on which the Driver program runs
Slave:- The machine on which the Executor program runs
All jobs in spark comprise a series of operators and run on a set of data. All the operators in a job are used to construct a DAG (Directed Acyclic Graph). The DAG is optimized by rearranging and combining operators where possible. For instance let’s assume that you have to submit a Spark job which contains a map operation followed by a filter operation. Spark DAG optimizer would rearrange the order of these operators, as filtering would reduce the number of records to undergo map operation.
Spark has a small code base and the system is divided in various layers. Each layer has some responsibilities. The layers are independent of each other.
The first layer is the interpreter, Spark uses a Scala interpreter, with some modifications.
As you enter your code in spark console(creating RDD's and applying operators), Spark creates a operator graph.
When the user runs an action(like collect), the Graph is submitted to a DAG Scheduler. The DAG scheduler divides operator graph into (map and reduce) stages.
A stage is comprised of tasks based on partitions of the input data. The DAG scheduler pipelines operators together to optimize the graph. For e.g. Many map operators can be scheduled in a single stage. This optimization is key to Sparks performance. The final result of a DAG scheduler is a set of stages.
The stages are passed on to the Task Scheduler. The task scheduler launches tasks via cluster manager.( Spark Standalone/Yarn/Mesos). The task scheduler doesn't know about dependencies among stages.
The Worker executes the tasks on the Slave. A new JVM is started per JOB. The worker knows only about the code that is passed to it.
Spark caches the data to be processed, allowing it to me 100 times faster than hadoop. Spark is highly configurable, and is capable of utilizing the existing components already existing in the Hadoop Eco-System. This has allowed spark to grow exponentially, and in a little time many organisations are already using it in production.