Data with Azure

Spark SQL Decimal Precision Loss Understanding Spark SQL's `allowPrecisionLoss` for Decimal Operations When working with high-precision decimal numbers in Apache Spark SQL, especially during arithmetic operations like division, you might encounter situations where the required precision to represent the exact result exceeds Spark's maximum decimal precision (which is typically 38 digits). Spark provides a configuration setting, spark.sql.decimalOperations.allowPrecisionLoss , to control how it handles these situations. Let's explore this setting with two examples using PySpark, and then look at an alternative using Pandas. Example 1 (Spark): `allowPrecisionLoss = true` In this scenario, we explicitly tell Spark that it's okay to potentially lose precision if the result of an operation exceeds the maximum representable precision for decimals. # --- Spark Example 1 Code --- from pyspark.sql import functions as F from decimal import Deci...

The Mystery of the Unexpected Float Value You're coding away, maybe in C++, Java, or Python, and you encounter something strange. You take a perfectly good, large integer value – let's say 2147483647 (the maximum value for a standard 32-bit signed integer) – and you store it in a float variable. #include #include #include int main() { int i = std::numeric_limits ::max(); // i = 2147483647 float f = i; // Cast the large integer to a float std::cout << "Integer i = " << i << std::endl; // Print float with enough precision to see the issue std::cout << "Float f = " << std::fixed << std::setprecision(1) << f << std::endl; return 0; } You run this seemingly simple code, expecting the output for f to be 2147483647.0 . Instead, you get this: Integer i = 2147483647 Float f = 2147483648.0 Wait, what happened? Why did casting 2147483647 to a float magically change its...