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Category: Pyspark
To determine the optimal number of CPU cores, executors, and executor memory for a PySpark job, several factors need to be considered, including the size and complexity of the job, the resources available in the cluster, and the nature of the data being processed. Here’s a general guide: 1. Number of CPU Cores per Executor 2. Number…
Suppose If i am given a maximum of 20 cores to run my data pipeline or ETL framework, i will need to strategically allocate and optimize resources to avoid performance issues, job failures, or SLA breaches. Here’s how you can accommodate within a 20-core limit, explained across key areas: 🔹 1. Optimize Spark Configurations Set…
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…
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…
🚀 PySpark Architecture & Execution Engine — Complete Guide 🔥 1. Spark Evolution Recap ⚔️ 2. Spark vs Hadoop (Core Comparison) Feature Hadoop MapReduce Apache Spark Engine Disk-based In-memory Languages Java-only Scala, Python, R, SQL Iterative Support Poor (writes to disk) Native (in-memory) Speed Slow (I/O bound) Fast (RAM usage) Ecosystem Limited Unified stack 🧱…