{"id":19571,"date":"2018-06-30T04:30:44","date_gmt":"2018-06-30T04:30:44","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=19571"},"modified":"2021-05-12T11:09:07","modified_gmt":"2021-05-12T05:39:07","slug":"pyspark-interview-questions","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/","title":{"rendered":"Top 30 PySpark Interview Questions and Answers"},"content":{"rendered":"<p><span style=\"font-weight: 400\">In this <strong>PySpark<\/strong> article, we will go through mostly asked PySpark Interview Questions and Answers. This Interview questions for PySpark will help both freshers and experienced. Moreover, you will get a guide on how to crack PySpark Interview. Follow each link for better understanding.<\/span><\/p>\n<p>So, let\u2019s start PySpark Interview Questions.<\/p>\n<h2><span style=\"font-weight: 400\">PySpark Interview Questions<\/span><\/h2>\n<p><span style=\"font-weight: 400\">Below we are discussing best 30 PySpark Interview Questions:<\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 1. Explain PySpark in brief?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. As Spark is written in Scala so in order to support Python with Spark, Spark Community released a tool, which we call PySpark. In Python programming language, we can also work with RDDs, using PySpark. It is possible due to its library name Py4j. <\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 2. What are the main characteristics of (Py)Spark?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. Some of \u00a0the main characteristics of (Py)Spark are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Here Nodes are abstracted that says no possible to address an individual node.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Also, Network is abstracted, that means there is only implicit communication possible.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Moreover, it is based on Map-Reduce, that means programmer provides a map and a reduce function here.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">And, PySpark is one of the API for Spark.<\/span><\/li>\n<\/ul>\n<p><b>Que 3. Pros of PySpark?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. Some of the benefits of using PySpark are: <\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">For simple problems, it is very simple to write parallelized code.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Also, it handles Synchronization points as well as errors.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Moreover, in Spark, many useful algorithms is already implemented.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Que 4. Cons of PySpark?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. \u00a0\u00a0Some of the limitations on using PySpark are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It is difficult to express a problem in MapReduce fashion sometimes.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Also, Sometimes, it is not as efficient as other programming models.<\/span><\/li>\n<\/ul>\n<p><b>Que 5. Prerequisites to learn PySpark?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. It is being assumed that the readers are already aware of what a programming language and a framework is, before proceeding with the various concepts given in this tutorial. Also, if the readers have some knowledge of Spark and Python in advance, it will be very helpful.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 6. What do you mean by PySpark SparkContext?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. In simple words, an entry point to any spark functionality is what we call SparkContext. While it comes to <strong>PySpark, SparkContext<\/strong> uses Py4J(library) in order to launch a JVM. In this way, it creates a JavaSparkContext. However, PySpark has SparkContext available as \u2018sc\u2019, by default.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 7. Explain PySpark SparkConf?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. Mainly, we use <strong>SparkConf<\/strong> because we need to set a few configurations and parameters to run a Spark application on the local\/cluster. In other words, SparkConf offers configurations to run a Spark application. <\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Code<\/span><\/li>\n<\/ul>\n<pre class=\"EnlighterJSRAW\">class pyspark.SparkConf (\r\n  loadDefaults = True,\r\n  _jvm = None,\r\n  _jconf = None\r\n)<\/pre>\n<p><b>Que 8. Tell us something about PySpark SparkFiles?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. It is possible to upload our files in Apache Spark.<\/span> <span style=\"font-weight: 400\">We do it by using sc.addFile, where sc is our default SparkContext. Also, it helps to get the path on a worker using SparkFiles.get. Moreover, it resolves the paths to files which are added through SparkContext.addFile().<\/span><\/p>\n<p><span style=\"font-weight: 400\">It contains some classmethods, such as \u2212<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">get(filename)<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">getrootdirectory()<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Que 9. Explain get(filename).<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. \u00a0It helps to get the absolute path of a file, which are added through SparkContext.addFile().<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">def get(cls, filename):\r\n              path = os.path.join(SparkFiles.getRootDirectory(), filename)\r\n       return os.path.abspath(path)<\/pre>\n<p><b>Que 10. Explain getrootdirectory().<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. Whereas, it helps to get the root directory which is consist of the files which are added through SparkContext.addFile().<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">def getRootDirectory(cls):\r\n              if cls._is_running_on_worker:\r\n           return cls._root_directory\r\n       else:\r\n           # This will have to change if we support multiple SparkContexts:\r\n           return cls._sc._jvm.org.apache.spark.SparkFiles.getRootDirectory()<\/pre>\n<p><strong>PySpark Interview Questions for freshers &#8211; Q. 1,2,3,4,5,6,7,8<\/strong><\/p>\n<p><strong>PySpark Interview Questions for experienced &#8211; Q. 9,10<\/strong><br \/>\n<b><\/b><\/p>\n<p><b>Que 11. Explain PySpark StorageLevel in brief.<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. Basically, it controls that how an RDD should be stored. Also, it controls if to store \u00a0RDD in the memory or over the disk, or both.\u00a0In addition, even it controls that we need to serialize RDD\u00a0or to replicate RDD partitions.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Code<\/span><\/li>\n<\/ul>\n<pre class=\"EnlighterJSRAW\">class pyspark.StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication = 1)<\/pre>\n<p><b>Que 12. Name different storage levels.<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. There are different <strong>storage levels<\/strong>, which are given below \u2212<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400\">DISK_ONLY StorageLevel(True, False, False, False, 1)<\/span><\/li>\n<li>DISK_ONLY_2 StorageLevel(True, False, False, False, 2)<\/li>\n<li>MEMORY_AND_DISK StorageLevel(True, True, False, False, 1)<\/li>\n<li>MEMORY_AND_DISK_2 StorageLevel(True, True, False, False, 2)<\/li>\n<li>MEMORY_AND_DISK_SER StorageLevel(True, True, False, False, 1)<\/li>\n<li>MEMORY_AND_DISK_SER_2 StorageLevel(True, True, False, False, 2)<\/li>\n<li>MEMORY_ONLY StorageLevel(False, True, False, False, 1)<\/li>\n<li>MEMORY_ONLY_2StorageLevel(False, True, False, False, 2)<\/li>\n<li>MEMORY_ONLY_SER StorageLevel(False, True, False, False, 1)<\/li>\n<li>MEMORY_ONLY_SER_2\u00a0 StorageLevel(False, True, False, False, 2)<\/li>\n<li>OFF_HEAP\u00a0 StorageLevel(True, True, True, False, 1)<\/li>\n<\/ul>\n<p><b>Que 13. What do mean by Broadcast variables?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. In order to save the copy of data across all nodes, we use it.\u00a0<\/span><br \/>\n<span style=\"font-weight: 400\">With SparkContext.broadcast(), a broadcast variable is created.\u00a0<\/span><br \/>\n<span style=\"font-weight: 400\">For Examples:<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">&gt;&gt;&gt; from pyspark.context import SparkContext\r\n&gt;&gt;&gt; sc = SparkContext('local', 'test')\r\n&gt;&gt;&gt; b = sc.broadcast([1, 2, 3, 4, 5])\r\n&gt;&gt;&gt; b.value\r\n[1, 2, 3, 4, 5]\r\n&gt;&gt;&gt; sc.parallelize([0, 0]).flatMap(lambda x: b.value).collect()\r\n[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]\r\n&gt;&gt;&gt; b.unpersist()\r\n&gt;&gt;&gt; large_broadcast = sc.broadcast(range(10000))<\/pre>\n<p><b>Que 14. What are Accumulator variables?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. In order to aggregate the information through associative and commutative operations,\u00a0we use them.\u00a0<\/span><\/p>\n<ul>\n<li>Code<\/li>\n<\/ul>\n<pre class=\"EnlighterJSRAW\">class pyspark.Accumulator(aid, value, accum_param)<\/pre>\n<p><b>Que 15. Explain AccumulatorParam?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. AccumulatorParam is a helper object\u00a0which explains how to accumulate values of a given type.<\/span><br \/>\n<b>class AccumulatorParam(object):<\/b><br \/>\n<b> \u00a0\u00a0\u00a0def zero(self, value):<\/b><br \/>\n<b> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0&#8220;&#8221;&#8221;<\/b><br \/>\n<b>\u00a0 \u00a0 \u00a0 \u00a0 Also,<\/b><br \/>\n<strong>with the provided C{value} (e.g., a zero vector) it\u00a0<\/strong><br \/>\n<strong>Provides a &#8220;zero value&#8221; for the type, compatible in dimensions <\/strong><br \/>\n<b> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0&#8220;&#8221;&#8221;<\/b><br \/>\n<b> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0raise NotImplementedError<\/b><br \/>\n<b> \u00a0\u00a0def addInPlace(self, value1, value2):<\/b><br \/>\n<b><\/b><\/p>\n<p><b>Que 16. Why we need Serializers in PySpark?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. For the purpose of performance tuning, PySpark supports custom serializers, such as\u2212<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">MarshalSerializer<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">PickleSerializer<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><b>Que 17. Explain Marshal Serializer?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. With the help of Python\u2019s Marshal Serializer, it serializes objects. Even if it supports fewer datatypes, it is faster than PickleSerializer.<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">class MarshalSerializer(FramedSerializer):\r\n   def dumps(self, obj):\r\n       return marshal.dumps(obj)\r\n    def loads(self, obj):\r\n       return marshal.loads(obj)<\/pre>\n<p><b>Que 18. Explain Pickel Serializers?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans.\u00a0 This uses Python\u2019s Pickle Serializer to serialize objects.\u00a0It supports nearly any Python object, but in slow speed.<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">class PickleSerializer(FramedSerializer):\r\n  def dumps(self, obj):\r\n       return pickle.dumps(obj, protocol)\r\n   if sys.version &gt;= '3':\r\n       def loads(self, obj, encoding=\"bytes\"):\r\n           return pickle.loads(obj, encoding=encoding)\r\n   else:\r\n      def loads(self, obj, encoding=None):\r\n           return pickle.loads(obj)<\/pre>\n<p><b>Que 19. What do you mean by Status Tracker?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. Status Trackers are Low-level status reporting APIs which helps to monitor job and stage progress.<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">def __init__(self, jtracker):\r\n       self._jtracker = jtracker\r\n<\/pre>\n<p><strong>Que 20. Explain SparkJobinfo?<\/strong><\/p>\n<p><span style=\"font-weight: 400\">Ans. SparkJobinfo exposes information about <strong>Spark Jobs<\/strong>.<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">class SparkJobInfo(namedtuple(\"SparkJobInfo\", \"jobId stageIds status\")):<\/pre>\n<p><strong>PySpark Interview Questions for freshers &#8211; Q. 11,12,13,14,16,17,18,19<\/strong><\/p>\n<p><strong>PySpark Interview Questions for experienced &#8211; Q. 15,20<\/strong><br \/>\n<b><\/b><\/p>\n<p><b>Que 21. Explain SparkStageinfo?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. SparkStageinfo exposes information about Spark Stages<\/span><\/p>\n<pre class=\"EnlighterJSRAW\">class SparkStageInfo(namedtuple(\"SparkStageInfo\",\r\n                               \"stageId currentAttemptId name numTasks numActiveTasks \"\r\n                               \"numCompletedTasks numFailedTasks\")):<\/pre>\n<p><strong>Que 22. Which Profilers do we use in PySpark?<\/strong><br \/>\n<b style=\"font-family: Verdana, Geneva, sans-serif\"><\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans.<\/span> <span style=\"font-weight: 400\">\u00a0Custom profilers are PySpark supported in PySpark to allow for different <strong>Profilers<\/strong> to be used\u00a0an for outputting to different formats than what is\u00a0offered in the BasicProfiler.<\/span><br \/>\n<span style=\"font-weight: 400\">We need to define or inherit the following methods, with a custom profiler:<\/span><\/p>\n<ul>\n<li><span style=\"font-weight: 400\"><strong>profile<\/strong> &#8211; Basically, it produces a system profile of some sort.<\/span><\/li>\n<li><span style=\"font-weight: 400\"><strong>stats<\/strong> &#8211; Well, it returns the collected stats. <\/span><\/li>\n<li><span style=\"font-weight: 400\"><strong>dump<\/strong> &#8211; Whereas, it dumps the profiles to a path.<\/span><\/li>\n<li><span style=\"font-weight: 400\"><strong>add<\/strong> &#8211; Moreover, this method helps to add a profile to the existing accumulated profile<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">Generally, when we create a SparkContext, we choose the profiler class.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 23. Explain Basic Profiler.<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. It is a default profiler, which\u00a0we implement on the basis of cProfile and Accumulator.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 24. Do, we have machine learning API in Python? <\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. As Spark\u00a0provides a <strong>Machine Learning<\/strong> API, MLlib. Similarly, in <strong>Python<\/strong> as well, PySpark has this machine learning API.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 25. Name algorithms supported in PySpark?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. There are several <strong>algorithms in PySpark<\/strong>:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">mllib.classification <\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">mllib.clustering <\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">mllib.fpm <\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">mllib.linalg<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">mllib.recommendation<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">spark.mllib <\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Mllib.regression<\/span><\/li>\n<\/ul>\n<p><b>Que 26. Name parameter of SparkContext?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. The parameters of a SparkContext are:<\/span><\/p>\n<ul>\n<li><strong>Master<\/strong> \u2212 URL of the cluster from which it connects.<\/li>\n<li><strong>appName<\/strong> \u2212 Name of our job.<\/li>\n<li><strong>sparkHome<\/strong> \u2212 Spark installation directory.<\/li>\n<li><strong>pyFiles<\/strong> \u2212 It is the .zip or .py files, in order to send to the cluster and also to add to the PYTHONPATH.<\/li>\n<li><strong>Environment<\/strong> \u2212 Worker nodes environment variables.<\/li>\n<li><strong>Serializer<\/strong> \u2212 RDD serializer.<\/li>\n<li><strong>Conf<\/strong> \u2212 to set all the Spark properties, an object of L{SparkConf}.<\/li>\n<li><strong>JSC<\/strong> \u2212 It is the JavaSparkContext instance.<\/li>\n<\/ul>\n<p><b>Que 27. Which of the parameters of SparkContext we mostly use?<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. Master and app name.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 28. Name attributes of SparkConf.<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans. <strong>Attributes of SparkConf<\/strong> \u2212<\/span><\/p>\n<ol>\n<li><span style=\"font-weight: 400\"> set(key, value) \u2212\u00a0This attribute helps to set a configuration property.<\/span><\/li>\n<li><span style=\"font-weight: 400\"> setMaster(value) \u2212\u00a0It helps to set the master URL.<\/span><\/li>\n<li>setAppName(value) \u2212 This helps to set an application name.<\/li>\n<li><span style=\"font-weight: 400\">get(key, defaultValue=None) \u2212 This\u00a0attribute helps to get a configuration value of a key.<\/span><\/li>\n<li><span style=\"font-weight: 400\"> setSparkHome(value) \u2212 It helps to set Spark installation path on worker nodes.<\/span><\/li>\n<\/ol>\n<p><strong>Que 29.\u00a0Why Profiler?<\/strong><\/p>\n<p><span style=\"font-weight: 400\">Ans. Profilers help us to ensure that the applications do not waste any resources also to spot any problematic code.<\/span><br \/>\n<b><\/b><\/p>\n<p><b>Que 30. State Key Differences in the Python API.<\/b><\/p>\n<p><span style=\"font-weight: 400\">Ans.\u00a0 Differences between the Python and Scala APIs are:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">It is dynamically typed\u00a0hence because of that RDDs can hold objects of multiple types.<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">On comparing with Scala, PySpark does not yet support\u00a0some APIs.<\/span><\/li>\n<\/ul>\n<p><strong>PySpark Interview Questions for freshers &#8211; Q. 21,22,23,25,26,27,28,29<\/strong><\/p>\n<p><strong>PySpark Interview Questions for experienced &#8211; Q. 24,30<\/strong><\/p>\n<p>So, this was all about Pyspark Interview Questions. Hope you like our explanation.<\/p>\n<h2><span style=\"font-weight: 400\">Conclusion &#8211; PySpark Interview Questions<\/span><\/h2>\n<p><span style=\"font-weight: 400\">Hence, in this article of <strong><a href=\"https:\/\/spark.apache.org\/docs\/0.9.0\/python-programming-guide.html\">PySpark<\/a><\/strong> Interview Questions, we went through many questions and answers for the PySpark interview. This mostly asked PySpark Interview Questions will help both freshers as well as experienced. Still, if any doubt regarding PySpark Interview Questions, ask in the comment tab.<\/span><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:1906,&quot;href&quot;:&quot;https:\\\/\\\/spark.apache.org\\\/docs\\\/0.9.0\\\/python-programming-guide.html&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20240710125027\\\/https:\\\/\\\/spark.apache.org\\\/docs\\\/0.9.0\\\/python-programming-guide.html&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-10 07:51:37&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2025-12-14 06:18:29&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2025-12-17 18:29:07&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2025-12-28 15:25:30&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-12 14:08:35&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-16 02:35:18&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-19 16:34:42&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-23 17:06:16&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-01-28 12:55:16&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-02-03 13:29:17&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-02-08 16:29:42&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-02-17 13:27:28&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-02-26 05:57:19&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-03-02 10:21:33&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-03-17 15:04:49&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-03-24 15:43:03&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-03-28 04:15:00&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-01 06:09:00&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-07 18:23:26&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-11 16:40:08&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-24 11:17:40&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-27 14:30:30&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-05-02 16:23:54&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-05-06 02:34:39&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-05-11 11:54:32&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-05-26 09:40:01&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-01 10:46:13&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-06-14 14:44:29&quot;,&quot;http_code&quot;:404}],&quot;broken&quot;:true,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-14 14:44:29&quot;,&quot;http_code&quot;:404},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this PySpark article, we will go through mostly asked PySpark Interview Questions and Answers. This Interview questions for PySpark will help both freshers and experienced. Moreover, you will get a guide on how&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":19859,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[44],"tags":[272,1865,5183,6962,10304,10305,14836],"class_list":["post-19571","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pyspark","tag-advanatges-of-pyspark","tag-best-pyspark-interview-question","tag-guides-for-pyspark-interview","tag-interview-questions-for-pyspark","tag-pyspark-interview-guide","tag-pyspark-interview-questions","tag-top-interview-questions-for-pyspark"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Top 30 PySpark Interview Questions and Answers - DataFlair<\/title>\n<meta name=\"description\" content=\"PySpark Interview questions with answers, crack PySpark Interview,Mostly asked Interview Questions for Pyspark for freshers and experienced\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top 30 PySpark Interview Questions and Answers - DataFlair\" \/>\n<meta property=\"og:description\" content=\"PySpark Interview questions with answers, crack PySpark Interview,Mostly asked Interview Questions for Pyspark for freshers and experienced\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2018-06-30T04:30:44+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2021-05-12T05:39:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/06\/Top-30-PySpark-interview-questions-and-answers-min.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Top 30 PySpark Interview Questions and Answers - DataFlair","description":"PySpark Interview questions with answers, crack PySpark Interview,Mostly asked Interview Questions for Pyspark for freshers and experienced","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/","og_locale":"en_US","og_type":"article","og_title":"Top 30 PySpark Interview Questions and Answers - DataFlair","og_description":"PySpark Interview questions with answers, crack PySpark Interview,Mostly asked Interview Questions for Pyspark for freshers and experienced","og_url":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2018-06-30T04:30:44+00:00","article_modified_time":"2021-05-12T05:39:07+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/06\/Top-30-PySpark-interview-questions-and-answers-min.jpg","type":"image\/jpeg"}],"author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/2c58ecb4f73a39f0ef993f1ddfcd7b89"},"headline":"Top 30 PySpark Interview Questions and Answers","datePublished":"2018-06-30T04:30:44+00:00","dateModified":"2021-05-12T05:39:07+00:00","mainEntityOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/"},"wordCount":1415,"commentCount":0,"publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/#primaryimage"},"thumbnailUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/06\/Top-30-PySpark-interview-questions-and-answers-min.jpg","keywords":["Advanatges of Pyspark","best pyspark interview question","guides for PySpark Interview","Interview questions for PySpark","PySpark Interview Guide","PySpark interview Questions","top interview questions for PySpark"],"articleSection":["PySpark Tutorials"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/","url":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/","name":"Top 30 PySpark Interview Questions and Answers - DataFlair","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/#website"},"primaryImageOfPage":{"@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/#primaryimage"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/#primaryimage"},"thumbnailUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/06\/Top-30-PySpark-interview-questions-and-answers-min.jpg","datePublished":"2018-06-30T04:30:44+00:00","dateModified":"2021-05-12T05:39:07+00:00","description":"PySpark Interview questions with answers, crack PySpark Interview,Mostly asked Interview Questions for Pyspark for freshers and experienced","breadcrumb":{"@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/#primaryimage","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/06\/Top-30-PySpark-interview-questions-and-answers-min.jpg","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/06\/Top-30-PySpark-interview-questions-and-answers-min.jpg","width":1200,"height":628,"caption":"Top 30 PySpark Interview Questions and Answers"},{"@type":"BreadcrumbList","@id":"https:\/\/data-flair.training\/blogs\/pyspark-interview-questions\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Blog Home","item":"https:\/\/data-flair.training\/blogs\/"},{"@type":"ListItem","position":2,"name":"PySpark Tutorials","item":"https:\/\/data-flair.training\/blogs\/category\/pyspark\/"},{"@type":"ListItem","position":3,"name":"Top 30 PySpark Interview Questions and Answers"}]},{"@type":"WebSite","@id":"https:\/\/data-flair.training\/blogs\/#website","url":"https:\/\/data-flair.training\/blogs\/","name":"DataFlair","description":"Learn Today. Lead Tomorrow.","publisher":{"@id":"https:\/\/data-flair.training\/blogs\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/data-flair.training\/blogs\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/data-flair.training\/blogs\/#organization","name":"DataFlair","url":"https:\/\/data-flair.training\/blogs\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/","url":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","contentUrl":"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/07\/Data-Flair.png","width":106,"height":48,"caption":"DataFlair"},"image":{"@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataFlairWS\/","https:\/\/x.com\/DataFlairWS","https:\/\/www.linkedin.com\/company\/dataflair-web-services-pvt-ltd\/","https:\/\/www.youtube.com\/user\/DataFlairWS"]},{"@type":"Person","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/2c58ecb4f73a39f0ef993f1ddfcd7b89","name":"DataFlair Team","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/1ce4a0e3e542444fc73bbebf83e89e8b73e2d95ccb1fcee64da9945f078b97c5?s=96&d=mm&r=g","caption":"DataFlair Team"},"description":"The DataFlair Team provides industry-driven content on programming, Java, Python, C++, DSA, AI, ML, data Science, Android, Flutter, MERN, Web Development, and technology. Our expert educators focus on delivering value-packed, easy-to-follow resources for tech enthusiasts and professionals.","url":"https:\/\/data-flair.training\/blogs\/author\/dfteam2\/"}]}},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/19571","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/comments?post=19571"}],"version-history":[{"count":7,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/19571\/revisions"}],"predecessor-version":[{"id":94253,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/posts\/19571\/revisions\/94253"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media\/19859"}],"wp:attachment":[{"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/media?parent=19571"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/categories?post=19571"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data-flair.training\/blogs\/wp-json\/wp\/v2\/tags?post=19571"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}