what is the relation and which one creates other in Spark? RDD lineage DAG,DAG Scheduler,Stages and Task
Hi Friends,
I am confused with the creation of RDD lineage,DAG,DAG Scheduler,Stage and Task.
Please validate my understanding
1) After we submit a job before an action is called...what ever transformation are put in the code before an action is called on RDD ..that RDD will have history of lineage..that is which is the parent RDD and what are transformation has occurred to create this RDD and its dependency..this is called lineage (logical execution plan)
2) When an action is called on RDD,the lineage will be converted into DAG(Physical execution plan).
3)DAG(Physical execution plan) will be submitted to DAG Scheduler which in turn will split the DAG into Stages
4)Each stage will have list of task
5)Each task will run in a executor (One executor will run one task on one partition?)
Also I want to understand where the catalyst optimizer and Tungsten encoder will come into plan?
Is it the responsibility of Catalyst optimizer will convert the RDD lineage into the best optimized execution plan as DAG?
Is it the responsbility of Tungsten encode will convert the Scala code into bytecode?
Please help me to understand the above
Related
Is there a defined standard for effective memory management in Spark
What if I end up creating a couple of DataFrames or RDDs and then keep on reducing that data with joins and aggregations??
Will these DataFrames or RDDs will still be holding resources until the session or job is complete??
No there is not. The lifetime of the main entity in Spark which is the RDD is defined via its lineage. When the your job makes a call to an action then the whole DAG will start getting executed. If the job was executed successfully Spark will release all reserved resources otherwise will try to re-execute the tasks that failed and reconstructing the lost RDDs based on its lineage.
Please check the following resources to get familiar with these concepts:
What is Lineage In Spark?
What is the difference between RDD Lineage Graph and Directed Acyclic Graph (DAG) in Spark?
I have been using Spark + Python to finish some works, it's great, but I have a question in my mind:
Where is the spark job of transformation and action done?
Is transformation job done in Spark Master (or Driver) while action job is done in Workers (Executors), or both of them are done in Workers (Executors)
Workers (aka slaves) are running Spark instances where executors live
to execute tasks.
Transformations are performed at the worker, when the action method is called the computed data is brought back to the driver.
An application in Spark is executed in three steps:
1.Create RDD graph, i.e. DAG (directed acyclic graph) of RDDs to represent entire computation.
2.Create stage graph, i.e. a DAG of stages that is a logical execution plan based on the RDD graph. Stages are created by breaking the RDD graph at shuffle boundaries.
3.Based on the plan, schedule and execute tasks on workers.
Transformations run at executors.
Actions run at executors and driver. Most of the work is still happening in the executors but the final steps like reducing outputs is executed in the driver.
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.
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 tasks(transformation) executes on the Workers(Executors)
and when action(take/collect) is called it brings back the data at the
Driver.
Sparks transformations have to be triggered by calling actions. What does Spark exactly do if no action is called? And which parts or processes are involved in processing a lazy operation (e.g. transformation) before the triggering of its execution?
tl;dr Spark does almost nothing (given what it does in general).
Applying transformations creates a RDD lineage, i.e. a DAG of RDDs. That's how an RDD can meet the R in its name - being resilient and be able to recover in case of missing map outputs. No execution happens on executors, no serialization, sending over the wire, or similar network-related activity. All it does is to create new RDDs out of existing ones building a graph of RDDs.
Every transformation call returns a new RDD. You start with a SparkContext and build a "pipeline" applying transformations.
It's only when an action is called to submit a job when DAGScheduler transforms RDDs into stages of TaskSets/TaskSetManagers that in turn are going to be executed as parallel tasks on executors.
p.s. A couple of transformations, however, trigger a job like sortBy or zipWithIndex. See https://issues.apache.org/jira/browse/SPARK-1021.
My understanding is that before any action is called, Spark is only building the DAG.
Its when you call an Action, it executes the DAG which it has been building so far.
So if you don't call an action, no processing is done. its only building the DAG.
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.
The Spark research paper has prescribed a new distributed programming model over classic Hadoop MapReduce, claiming the simplification and vast performance boost in many cases specially on Machine Learning. However, the material to uncover the internal mechanics on Resilient Distributed Datasets with Directed Acyclic Graph seems lacking in this paper.
Should it be better learned by investigating the source code?
Even i have been looking in the web to learn about how spark computes the DAG from the RDD and subsequently executes the task.
At high level, when any action is called on the RDD, Spark creates the DAG and submits it 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. For 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 executes the tasks on the Slave.
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(). For example executing toDebugString() on the 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 the bottom) shows the input RDD. We created this RDD by calling sc.textFile(). Below is the more diagrammatic view of the DAG graph created from the given RDD.
Once the DAG is build, the 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 two stage execution as follows:
The DAG scheduler will then submit 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 set of tasks created and submitted in parallel provided there are enough slaves/cores. Below diagram illustrates this in 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
Beginning Spark 1.4 visualization of data has been added through the following three components where it also provide a clear graphical representation of DAG.
Timeline view of Spark events
Execution DAG
Visualization of Spark Streaming statistics
Refer to link for more information.