When Spark Gets Math Wrong: Understanding Decimal Precision Errors

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 Decimal

# Assuming 'spark' is an active SparkSession

# Set allowPrecisionLoss to TRUE
spark.conf.set("spark.sql.decimalOperations.allowPrecisionLoss", "true")

# --- Create DataFrame ---
# Using Decimal for precise input
df = spark.createDataFrame(
    [(Decimal("772.566432324342"), Decimal("22298328233.43322334535334"))],
    ["A", "B"]
)

# --- Cast Columns to DECIMAL(38, 12) ---
# Values will be cast/rounded to fit 12 decimal places
df_casted = df.withColumn("A", F.col("A").cast("decimal(38, 12)")) \
              .withColumn("B", F.col("B").cast("decimal(38, 12)"))

# --- Add New Column for Division ---
# Perform the division A / B
df_result = df_casted.withColumn("divide_A_and_B", F.col("A") / F.col("B"))

# --- Display the Result ---
print("--- Output for Spark Example 1 (allowPrecisionLoss = true) ---")
df_result.show(truncate=False)

# In a Databricks notebook or similar environment, you'd use:
# display(df_result)

Output for Spark Example 1:


--- Output for Spark Example 1 (allowPrecisionLoss = true) ---
+------------------+-----------------------+---------------+
|A                 |B                      |divide_A_and_B |
+------------------+-----------------------+---------------+
|772.566432324342  |22298328233.4332233454 |0.0000000	   |
+------------------+-----------------------+---------------+

Explanation for Spark Example 1:

  1. The input numbers are cast to DECIMAL(38, 12). Note that the value for column 'B' is rounded to 12 decimal places.
  2. The division operation requires a precision higher than 38.
  3. Because allowPrecisionLoss is true, Spark proceeds, adjusting the result's scale to fit within DECIMAL(38, S), potentially losing precision compared to the true mathematical result.

Example 2 (Spark): `allowPrecisionLoss = false` (Default Behavior)

Now, let's set allowPrecisionLoss to false. Spark will prioritize accuracy and refuse to perform an operation if precision would be lost according to its internal rules.


# --- Spark Example 2 Code ---
from pyspark.sql import functions as F
from decimal import Decimal

# Assuming 'spark' is an active SparkSession

# Set allowPrecisionLoss to FALSE (often the default)
spark.conf.set("spark.sql.decimalOperations.allowPrecisionLoss", "false")

# --- Create DataFrame ---
df = spark.createDataFrame(
    [(Decimal("772.566432324342"), Decimal("22298328233.43322334535334"))],
    ["A", "B"]
)

# --- Cast Columns to DECIMAL(38, 12) ---
df_casted = df.withColumn("A", F.col("A").cast("decimal(38, 12)")) \
              .withColumn("B", F.col("B").cast("decimal(38, 12)"))

# --- Add New Column for Division ---
df_result = df_casted.withColumn("divide_A_and_B", F.col("A") / F.col("B"))

# --- Display the Result ---
print("\n--- Output for Spark Example 2 (allowPrecisionLoss = false) ---")
df_result.show(truncate=False)


# In a Databricks notebook or similar environment, you'd use:
# display(df_result)

Output for Spark Example 2:


--- Output for Spark Example 2 (allowPrecisionLoss = false) ---
+------------------+-----------------------+----------------+
|A                 |B                      |divide_A_and_B  |
+------------------+-----------------------+----------------+
|772.566432324342  |22298328233.4332233454 |null            |
+------------------+-----------------------+----------------+

--- Output for Spark Example 2 (allowPrecisionLoss = false) ---
+------------------+-----------------------+--------------------------+
|A                 |B                      |divide_A_and_B            |
+------------------+-----------------------+--------------------------+
|772.566432324342  |22298328233.4332233454 |0.000000034646118418132300|
+------------------+-----------------------+--------------------------+

(Note: The schema for the null result column might vary.)

Explanation for Spark Example 2:

  1. Casting happens as before.
  2. Spark determines the division requires more than 38 digits of precision.
  3. Because allowPrecisionLoss is false, Spark returns NULL instead of an inaccurate result.

Alternative: Using Pandas and Python's `Decimal` Type

If you're not restricted to Spark or working with smaller datasets locally, you can use the Pandas library along with Python's built-in decimal module for high-precision arithmetic. The `Decimal` type avoids the limitations of standard binary floating-point numbers (`float`).

Python's `Decimal` operates based on a "context" which defines the precision (number of significant digits) for calculations. It doesn't have a direct equivalent to Spark's `allowPrecisionLoss` toggle in the same way. Instead, it performs calculations according to the context precision and returns a `Decimal` result.


# --- Pandas Example Code ---
import pandas as pd
from decimal import Decimal, getcontext

# Set precision for Decimal calculations (e.g., 50 digits)
# This affects NEW Decimals created during operations like division.
getcontext().prec = 50

# --- Create DataFrame ---
# IMPORTANT: Create using Decimal objects directly to maintain precision
data = {
    "A": [Decimal("772.566432324342")],
    "B": [Decimal("22298328233.43322334535334")]
}
df_pandas = pd.DataFrame(data)

# --- Perform Division ---
# Pandas applies the operation element-wise using Python's Decimal arithmetic
df_pandas["divide_A_and_B"] = df_pandas["A"] / df_pandas["B"]

# --- Display the Result ---
print("\n--- Output for Pandas Example ---")
print(df_pandas)
print("\nData types:")
print(df_pandas.dtypes)
print(f"\nDecimal context precision: {getcontext().prec}")

Output for Pandas Example:


--- Output for Pandas Example ---
                              A                                 B                                   divide_A_and_B
0  772.566432324342  22298328233.43322334535334  0.00000003464611841813229973816020408130981335429

Data types:
A                 object
B                 object
divide_A_and_B    object  # Pandas stores Decimal objects as 'object' dtype
dtype: object

Decimal context precision: 50

Explanation for Pandas Example:

  1. We import `pandas` and `Decimal` from the `decimal` module.
  2. We explicitly set the precision for the decimal context using `getcontext().prec`. This determines how many significant digits are kept during calculations.
  3. We create the Pandas DataFrame using `Decimal` objects directly. This is crucial – if you used standard Python floats first, you'd lose precision immediately.
  4. The division `/` is performed directly on the columns. Since the columns contain `Decimal` objects, Python's high-precision `Decimal` division is used.
  5. The result is a `Decimal` object stored in the new column. It maintains precision according to the context setting (`prec=50` in this case). Notice the result has more digits than the Spark `allowPrecisionLoss=true` example, reflecting the higher precision setting.
  6. Pandas doesn't return `NULL` simply because the result requires high precision. It calculates the result based on the context. If you needed to simulate Spark's `allowPrecisionLoss=false`, you would need custom logic *after* the calculation to check if the result exceeds some desired precision/scale limit and then replace it with `None` or `pd.NA`.

Conclusion

Spark's spark.sql.decimalOperations.allowPrecisionLoss configuration is specific to how Spark SQL handles its fixed-precision DECIMAL(P, S) type when calculation results exceed the representable limits:

  • Spark (`allowPrecisionLoss = true`): Calculates and fits the result into `DECIMAL(38, S)`, potentially losing precision.
  • Spark (`allowPrecisionLoss = false`): Returns `NULL` if the exact result cannot be represented within `DECIMAL(38, S)`.
  • Pandas with `Decimal`: Uses Python's `decimal` module. Calculations are performed based on the context's precision setting (`getcontext().prec`). It provides a high-precision result without inherently returning `NULL` due to precision requirements alone. It behaves more like Spark's `allowPrecisionLoss=true` in that it *produces a result*, but that result aims for higher fidelity based on the context, rather than being forced into a `DECIMAL(38,S)` shape.

Choose the tool and approach based on your environment (distributed vs. local), data size, and specific requirements for precision and error handling.

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