In this post we will dive into topic: PySpark" / Spark collect And collectAsList
Table of Contents
Introduction
Spark Driver
The driver in Apache Spark" is the software that generates the SparkContext" and performs the application’s main function. It is in charge of coordinating task execution throughout the cluster and providing the final output to the user".
The driver programme serves numerous important functions:
- The SparkContext" is the entry point for a Spark application, and it is responsible for connecting to the cluster manager and assigning resources for the application.
- RDDs and DataFrames: The driver software may generate RDDs and DataFrames by importing data from external sources such as HDFS, local file systems, or external databases.
- Performing transformations and actions: To process the data, the driver application can perform transformations and actions on RDDs and DataFrames. Transformations are operations that build a new RDD" or DataFrame", whereas actions return a result or publish data to an external storage system.
- Task scheduling: The driver application schedules tasks to be run on the cluster depending on the transformations and actions done to the RDDs and DataFrames.
- Returning the final result: The driver software displays the application’s final output to the user".
It’s worth noting that the driver application runs on a single workstation and is in charge of coordinating job execution on the worker nodes. When the driver software does a job, it dispatches it to a worker node for execution and then gathers the results from the worker node.
The main component that runs in a Spark application is the driver. It is in charge of building RDDs and DataFrames, executing transformations and actions, scheduling operations, and providing the final output to the user". It operates on a single system and manages job execution among worker nodes.
PySpark / Spark collect And collectAsList
The general syntax of these two methods: collect() and collectAsList() are:
collect() : scala.Array[T] collectAsList() : java.util.List[T]
In PySpark and Spark", the Spark collect() and Spark collectAsList() methods are used to retrieve all the elements of an RDD" (Resilient Distributed Dataset") or DataFrame" and return them as an array or a list, respectively.
collect() method returns all the elements of the RDD" or DataFrame" as an array. This method should be used with caution as it can cause the driver to run out of memory if the RDD" or DataFrame" is too large.
rdd = sc.parallelize([1, 2, 3, 4, 5]) rdd_collected = rdd.collect() print(rdd_collected) # [1, 2, 3, 4, 5]
PySpark" collectAsList() method returns all the elements of the RDD" or DataFrame" as a list. It’s similar to collect() but it returns the data as a list instead of an array.
df = spark.createDataFrame([(1, "a"), (2, "b"), (3, "c")], ["id", "name"]) df_collected = df.collectAsList() print(df_collected) # [Row(id=1, name='a'), Row(id=2, name='b'), Row(id=3, name='c')]
PySpark SQL CollectAsList()
collectAsList() in Spark SQL" is a method for retrieving the results of a SQL" query as a list of Row objects, where each Row object represents a row of the query results.
Here’s an example of using collectAsList() with PySpark SQL":
# create a DataFrame and register it as a table
data = [("AAA", 1), ("BBB", 2), ("CCC", 3)]
df = spark.createDataFrame(data, ["name", "id"])
df.createOrReplaceTempView("people")
# run a SQL query and collect the results as a list
results = spark.sql("SELECT name, id FROM people WHERE id > 1").collectAsList()
# print the results
for row in results:
print(row.name, row.id)
If the id is larger than 1, this will run a SQL" query that picks the name and id columns from the people table, and it will return the results as a list of Row objects. Input will result in:
BBB 2 CCC 3
This approach should be used with caution since it might lead to the driver running out of memory if the query results are too big.
Why You Should Avoid Using Collect() In Spark?
In general, if the DataFrame" or RDD" is too huge, you should avoid using the collect() function in Spark" since it might cause the driver to run out of memory. This is due to the fact that collect() obtains all of the items of the DataFrame" or RDD" and returns them as an array, which might include a considerable amount of data if the DataFrame" or RDD" is big.
Here are some examples of why you should not use collect():
- When working with huge DataFrames or RDDs, remember to: Out of memory mistakes become increasingly common as data size grows.
- When working with distributed data: If your data is spread over numerous nodes, bringing it all to a single workstation might generate network congestion and slow down the task.
- When you simply require a subset of the data: Instead of gathering all of the data, the sample() function may be used to acquire a random sample of the data.
- When you want to do anything with each element: Instead of gathering all of the data, you can use the foreach() function to perform an action on each DataFrame" or RDD" element.
Other approaches, such as take(), foreach(), sample(), or write.format(“file-format>”), are advised in certain cases. save() is used to store data to disc and read it back when needed.
PySpark / Spark Collect Vs Take
To get items from a DataFrame" or RDD", Spark uses both the collect() and take() methods. However, they differ significantly in terms of usage and behaviour:
collect()produces an array containing all of the items in a DataFrame" or RDD". if the DataFrame" or RDD" is too big, this approach should be used with caution since it might cause the driver to run out of memory. When you need to extract a tiny fraction of the data and run some computations on the driver, this approach is beneficial.take(n)produces an array containing the first n elements of a DataFrame" or RDD". This technique is substantially more memory economical than collect() and should be used when just a small fraction of the data has to be retrieved.
Here’s an example of how to use collect() and take(n) methods in Spark:
rdd = sc.parallelize([1, 2, 3, 4, 5]) # Using collect() method rdd_collected = rdd.collect() print(rdd_collected) # [1, 2, 3, 4, 5] # Using take(n) method rdd_taken = rdd.take(3) print(rdd_taken) # [1, 2, 3]
In summary, the PySpark" collect() function returns all of the items of a DataFrame" or RDD" as an array, whereas the take(n) method delivers the first n elements of a DataFrame" or RDD" as an array.
The PySpark" collect() function should be used with caution since it might cause the driver to run out of memory if the DataFrame" or RDD" is too large, whereas the take(n) method is memory efficient and should be used when just a small part of the data has to be retrieved.
PySpark / Spark collect_list Vs collectAsList
Both PySpark" collect_list() and PySpark" collectAsList() are used in Spark" to extract and return the items of a DataFrame" or DataSet" as a list. However, there are notable distinctions in their usage and behaviour:
collect_list()is a function that aggregates a column’s values into a list. If you have numerous groups, this function may be used with a groupBy statement to return a list of values for each group.
df.groupBy("name").agg(collect_list("age")).show()
collectAsList() produces a list of all the components in a DataFrame" or DataSet". if the DataFrame" or DataSet" is too big, this technique should be used with caution since it might cause the driver to run out of memory.
df.collectAsList()
To summarise, collect_list() aggregates the values of a column" into a list, whereas collectAsList() retrieves all the items of a DataFrame" or DataSet" and provides them as a list. When used with a groupBy statement, collect list() provides a list of values for each group, whereas collectAsList() delivers all rows.
if the DataFrame" or DataSet" is too large, use collectAsList() with caution since it might cause the driver to run out of memory.
Summary
To summarise, collect() and collectAsList() are methods for retrieving all of the components in an RDD" or DataFrame". collect() produces an array of all the items, whereas collectAsList() delivers a list of all the elements. Both of these approaches should be used with caution since if the RDD or DataFrame is too big, they might cause the driver to run out of memory.
When working with big DataFrames or RDDs, when working with dispersed data, when you only require a sample of the data, or when you wish to execute an action on each piece, you should avoid using the collect() function in Spark". You can instead use methods like take(), foreach(), sample(), or write. format(“”). save().
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