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- All major PySpark data structures and types Discussed
- PySpark Control Statements Vs Python Control Statements- Conditional, Loop, Exception Handling, UDFs
- Pyspark Memory Management, Partition & Join Strategy – Scenario Based Questions
- Data Engineer Interview Questions Set5
- SQL Tricky Conceptual Interview Questions
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Category: Pyspark
Absolutely! Let’s walk through all major PySpark data structures and types that are commonly used in transformations and aggregations — especially: 🧱 1. Row — Spark’s Internal Data Holder Example: Used when creating small DataFrames manually. 🏗 2. StructType / StructField — Schema Definition Objects Example: Used with: 🧱 3. struct() — Row-like object inside…
PySpark Control Statements Vs Python Control Statements- Conditional, Loop, Exception Handling, UDFs
Python control statements like if-else can still be used in PySpark when they are applied in the context of driver-side logic, not in DataFrame operations themselves. Here’s how the logic works in your example: Understanding Driver-Side Logic in PySpark Breakdown of Your Example This if-else statement works because it is evaluated on the driver (the main control point of…
Q1.–We are working with large datasets in PySpark, such as joining a 30GB table with a 1TB table or Various Transformation on 30 GB Data, we have 100 cores limit to use per user , what can be best configuration and Optimization strategy to use in pyspark ? will 100 cores are enough or should…
Where to Use Python Traditional Coding in PySpark Scripts Using traditional Python coding in a PySpark script is common and beneficial for handling tasks that are not inherently distributed or do not involve large-scale data processing. Integrating Python with a PySpark script in a modular way ensures that different responsibilities are clearly separated and the…
Spark SQL supports several types of joins, each suited to different use cases. Below is a detailed explanation of each join type, including syntax examples and comparisons. Types of Joins in Spark SQL 1. Inner Join An inner join returns only the rows that have matching values in both tables. Syntax: Example: 2. Left (Outer)…
A quick reference for date manipulation in PySpark:– Function Description Works On Example (Spark SQL) Example (DataFrame API) to_date Converts string to date. String TO_DATE(‘2024-01-15’, ‘yyyy-MM-dd’) to_date(col(“date_str”), “yyyy-MM-dd”) to_timestamp Converts string to timestamp. String TO_TIMESTAMP(‘2024-01-15 12:34:56’, ‘yyyy-MM-dd HH:mm:ss’) to_timestamp(col(“timestamp_str”), “yyyy-MM-dd HH:mm:ss”) date_format Formats date or timestamp as a string. Date, Timestamp DATE_FORMAT(CURRENT_DATE, ‘dd-MM-yyyy’) date_format(col(“date_col”), “dd-MM-yyyy”)…
Table of Contents Introduction to RDDs Resilient Distributed Datasets (RDDs) are the fundamental data structure of Spark. They are: Key Characteristics: RDD Lineage RDD lineage is a graph of all the parent RDDs of an RDD. It’s built as a result of applying transformations to the RDD. Output would show the transformation steps: Creating RDDs…
Window functions in PySpark allow you to perform operations on a subset of your data using a “window” that defines a range of rows. These functions are similar to SQL window functions and are useful for tasks like ranking, cumulative sums, and moving averages. Let’s go through various PySpark DataFrame window functions, compare them with…