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NEW QUESTION 38
Which of the following statements about executors is correct?
- A. Each node hosts a single executor.
- B. An executor can serve multiple applications.
- C. Executors stop upon application completion by default.
- D. Executors are launched by the driver.
- E. Executors store data in memory only.
Answer: C
Explanation:
Explanation
Executors stop upon application completion by default.
Correct. Executors only persist during the lifetime of an application.
A notable exception to that is when Dynamic Resource Allocation is enabled (which it is not by default). With Dynamic Resource Allocation enabled, executors are terminated when they are idle, independent of whether the application has been completed or not.
An executor can serve multiple applications.
Wrong. An executor is always specific to the application. It is terminated when the application completes (exception see above).
Each node hosts a single executor.
No. Each node can host one or more executors.
Executors store data in memory only.
No. Executors can store data in memory or on disk.
Executors are launched by the driver.
Incorrect. Executors are launched by the cluster manager on behalf of the driver.
More info: Job Scheduling - Spark 3.1.2 Documentation, How Applications are Executed on a Spark Cluster | Anatomy of a Spark Application | InformIT, and Spark Jargon for Starters. This blog is to clear some of the... | by Mageswaran D | Medium
NEW QUESTION 39
Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?
- A. spark.read().schema(fileSchema).parquet(filePath)
- B. spark.read.schema("fileSchema").format("parquet").load(filePath)
- C. spark.read().schema(fileSchema).format(parquet).load(filePath)
- D. spark.read.schema(fileSchema).open(filePath)
- E. spark.read.schema(fileSchema).format("parquet").load(filePath)
Answer: E
Explanation:
Explanation
Pay attention here to which variables are quoted. fileSchema is a variable and thus should not be in quotes.
parquet is not a variable and therefore should be in quotes.
SparkSession.read (here referenced as spark.read) returns a DataFrameReader which all subsequent calls reference - the DataFrameReader is not callable, so you should not use parentheses here.
Finally, there is no open method in PySpark. The method name is load.
Static notebook | Dynamic notebook: See test 1
NEW QUESTION 40
In which order should the code blocks shown below be run in order to read a JSON file from location jsonPath into a DataFrame and return only the rows that do not have value 3 in column productId?
1. importedDf.createOrReplaceTempView("importedDf")
2. spark.sql("SELECT * FROM importedDf WHERE productId != 3")
3. spark.sql("FILTER * FROM importedDf WHERE productId != 3")
4. importedDf = spark.read.option("format", "json").path(jsonPath)
5. importedDf = spark.read.json(jsonPath)
- A. 5, 1, 3
- B. 4, 1, 2
- C. 4, 1, 3
- D. 5, 2
- E. 5, 1, 2
Answer: E
Explanation:
Explanation
Correct code block:
importedDf = spark.read.json(jsonPath)
importedDf.createOrReplaceTempView("importedDf")
spark.sql("SELECT * FROM importedDf WHERE productId != 3")
Option 5 is the only correct way listed of reading in a JSON in PySpark. The option("format", "json") is not the correct way to tell Spark's DataFrameReader that you want to read a JSON file. You would do this through format("json") instead. Also, you can communicate the specific path of the JSON file to the DataFramReader using the load() method, not the path() method.
In order to use a SQL command through the SparkSession spark, you first need to create a temporary view through DataFrame.createOrReplaceTempView().
The SQL statement should start with the SELECT operator. The FILTER operator SQL provides is not the correct one to use here.
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 41
Which of the following describes properties of a shuffle?
- A. Shuffles belong to a class known as "full transformations".
- B. Operations involving shuffles are never evaluated lazily.
- C. In a shuffle, Spark writes data to disk.
- D. Shuffles involve only single partitions.
- E. A shuffle is one of many actions in Spark.
Answer: C
Explanation:
Explanation
In a shuffle, Spark writes data to disk.
Correct! Spark's architecture dictates that intermediate results during a shuffle are written to disk.
A shuffle is one of many actions in Spark.
Incorrect. A shuffle is a transformation, but not an action.
Shuffles involve only single partitions.
No, shuffles involve multiple partitions. During a shuffle, Spark generates output partitions from multiple input partitions.
Operations involving shuffles are never evaluated lazily.
Wrong. A shuffle is a costly operation and Spark will evaluate it as lazily as other transformations. This is, until a subsequent action triggers its evaluation.
Shuffles belong to a class known as "full transformations".
Not quite. Shuffles belong to a class known as "wide transformations". "Full transformation" is not a relevant term in Spark.
More info: Spark - The Definitive Guide, Chapter 2 and Spark: disk I/O on stage boundaries explanation - Stack Overflow
NEW QUESTION 42
Which of the following statements about garbage collection in Spark is incorrect?
- A. In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.
- B. Manually persisting RDDs in Spark prevents them from being garbage collected.
- C. Garbage collection information can be accessed in the Spark UI's stage detail view.
- D. Optimizing garbage collection performance in Spark may limit caching ability.
- E. Serialized caching is a strategy to increase the performance of garbage collection.
Answer: B
Explanation:
Explanation
Manually persisting RDDs in Spark prevents them from being garbage collected.
This statement is incorrect, and thus the correct answer to the question. Spark's garbage collector will remove even persisted objects, albeit in an "LRU" fashion. LRU stands for least recently used.
So, during a garbage collection run, the objects that were used the longest time ago will be garbage collected first.
See the linked StackOverflow post below for more information.
Serialized caching is a strategy to increase the performance of garbage collection.
This statement is correct. The more Java objects Spark needs to collect during garbage collection, the longer it takes. Storing a collection of many Java objects, such as a DataFrame with a complex schema, through serialization as a single byte array thus increases performance. This means that garbage collection takes less time on a serialized DataFrame than an unserialized DataFrame.
Optimizing garbage collection performance in Spark may limit caching ability.
This statement is correct. A full garbage collection run slows down a Spark application. When taking about
"tuning" garbage collection, we mean reducing the amount or duration of these slowdowns.
A full garbage collection run is triggered when the Old generation of the Java heap space is almost full. (If you are unfamiliar with this concept, check out the link to the Garbage Collection Tuning docs below.) Thus, one measure to avoid triggering a garbage collection run is to prevent the Old generation share of the heap space to be almost full.
To achieve this, one may decrease its size. Objects with sizes greater than the Old generation space will then be discarded instead of cached (stored) in the space and helping it to be "almost full".
This will decrease the number of full garbage collection runs, increasing overall performance.
Inevitably, however, objects will need to be recomputed when they are needed. So, this mechanism only works when a Spark application needs to reuse cached data as little as possible.
Garbage collection information can be accessed in the Spark UI's stage detail view.
This statement is correct. The task table in the Spark UI's stage detail view has a "GC Time" column, indicating the garbage collection time needed per task.
In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.
This statement is correct. The G1 garbage collector, also known as garbage first garbage collector, is an alternative to the default Parallel garbage collector.
While the default Parallel garbage collector divides the heap into a few static regions, the G1 garbage collector divides the heap into many small regions that are created dynamically. The G1 garbage collector has certain advantages over the Parallel garbage collector which improve performance particularly for Spark workloads that require high throughput and low latency.
The G1 garbage collector is not enabled by default, and you need to explicitly pass an argument to Spark to enable it. For more information about the two garbage collectors, check out the Databricks article linked below.
NEW QUESTION 43
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