Spark: “truncated the string representation of a plan since it was too large.” Warning when using manually created aggregation expression

When using Apache Spark, you might see a warning like this. This just means that Spark has created a very detailed plan to execute your query, but the plan is so big that Spark can’t show all of it in the logs. In this article, we will learn what exactly this issue is, why it happens, and how we can resolve the same.

Table of Contents:

• What does this warning mean?
• How to Fix It?
  • Ignore the warning
  • Check the Query plan
  • Simplify the Query
  • Adjust logging levels
  • Cache intermediate results
• Conclusion
• FAQ’s

What does this warning mean?

When we write code in Spark, it makes a plan for how to process the data. This plan helps Spark run the operations efficiently. When we create complex aggregations in Spark, the query plan can become too large to display fully. This usually happens when there are too many transformations in one step, multiple joins, or groupBy operations that make the execution plan complicated. To keep logs readable, Spark automatically shortens them. So, this warning just means the full plan isn’t shown, but everything is still working fine.

How to Fix It?

Let’s learn how to fix this error with the help of different methods:

1. Ignore the warning

The easiest option is to simply ignore the warning. If your query works fine and you’re not running into performance issues, you don’t need to worry. Spark is just trying to keep the logs from being overwhelming.

2. Check the Query plan

If you’re curious about the detailed plan behind your query, use the explain() function. This will allow us to view the physical and logical query plans, so we can understand exactly how Spark is executing the operations.

df.explain(True)  # True shows both logical and physical plans

This might not completely stop the truncation, but it will give us a clearer view of how Spark is processing the data.

3. Simplify the Query

If you’re manually creating large aggregation expressions, consider breaking the query into smaller, simpler steps. This will reduce the complexity of the plan and might even improve the performance of your job.

Let’s take an example where we’re manually simplifying a complex aggregation:

Original Complex Query

# Complex query with multiple aggregations
agg_result = df.groupBy("column1").agg(
    sum("column2").alias("sum_column2"),
    avg("column3").alias("avg_column3"),
    countDistinct("column4").alias("distinct_count_column4"),
    max("column5").alias("max_column5"),
    min("column6").alias("min_column6")
)
agg_result.explain(True)

The above-mentioned query is an example of a complex query as we have used multiple aggregation operations in a single step, which can make the execution plan large and harder to manage. Now, let us see its simplified query.

Simplified Query:

# Simplified query with individual aggregation steps
agg_sum = df.groupBy("column1").agg(
    sum("column2").alias("sum_column2")
)
agg_avg = df.groupBy("column1").agg(
    avg("column3").alias("avg_column3")
)
agg_count = df.groupBy("column1").agg(
    countDistinct("column4").alias("distinct_count_column4")
)
agg_max = df.groupBy("column1").agg(
    max("column5").alias("max_column5")
)
agg_min = df.groupBy("column1").agg(
    min("column6").alias("min_column6")
)

By breaking down the aggregation like this, we simplify the overall query plan, which can help prevent Spark from generating a plan that’s too large to display.

4. Adjust logging levels

While it might not eliminate the truncation, adjusting the logging level can give us more information. By setting the log level to DEBUG, we can get more detailed output about what’s happening behind the scenes.

spark.sparkContext.setLogLevel("DEBUG")

5. Cache intermediate results:

If your query involves large intermediate results, consider caching them to avoid Spark having to rebuild those results multiple times. This can also help reduce the complexity of your query plan.

df.cache()

Conclusion

The warning just means Spark is shortening the plan to keep logs clean. It doesn’t affect your code. If needed, use `explain()` to see the full plan or simplify your query.

FAQ’s