pyspark performence tuning

pyspark performence tuning

Comments

Post a Comment

Popular posts from this blog

Prompt Engineering in 2025

Here are some of the best, recent articles you can use as inspiration, reference, or even partially repost (with your own intro and context) on your blog about prompt engineering: 1. **Prompt Engineering in 2025: The Latest Best Practices**      This article explains why prompt engineering is essential, especially for AI product teams, and breaks down current frameworks, templates, and product strategies used by AI companies. It features real-world prompts from successful startups and advice on how prompt iteration can be the key to product success in 2025.[1] 2. **Prompt Engineering Techniques: Top 5 for 2025**      This practical guide lists and explains the top 5 prompt engineering strategies:      - Zero-shot prompting      - Few-shot prompting      - Chain of Thought (CoT) prompting      - Meta prompting      - Self-consistency prompting ...
Read more

Complete DataFrame Analysis and Optimal Spark Submit Configuration

 from pyspark.sql import SparkSession from pyspark.sql.functions import input_file_name, col, size, length import math import time def analyze_dataframe_and_get_optimal_config(df, operation_type="read",                                             target_cluster_cores=None,                                             target_cluster_memory_gb=None):     """     Comprehensive DataFrame analysis with optimal Spark configuration recommendations          Args:         df: PySpark DataFrame to analyze         operation_type: Type of operation ("read", "write", "transform", "ml", "join")         target_cluster_cores: Total cores available in cluster (if known) ...
Read more