PySpark - Get the number of rows

spark is a SparkSession object, what does spark.sql().collect() do?

 

ChatGPT said:

Good question 👍

Let’s break it down:

• spark → a SparkSession object.

• spark.sql(query) → runs the given SQL query string and returns a DataFrame (actually a pyspark.sql.DataFrame).

• .collect() → an action on a DataFrame that retrieves all rows of the DataFrame from the cluster’s executors back to the driver program as a list of Row objects.

So:

rows = spark.sql("SELECT * FROM my_table").collect()

will:

1. Parse the SQL into a logical plan.

2. Optimize and execute it across the Spark cluster.

3. Pull all results into the Python driver.

4. rows becomes a Python list of Row objects (each Row is like a named tuple).

⚠️ Caution: .collect() brings all data to the driver, so if the result set is large, it may cause out-of-memory errors.

If you only want a sample, you’d use .show(), .take(n), or .limit(n).collect() instead.

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👉 Do you want me to show you a small example with output of what collect() returns, so you can see how the Row objects look?

 

You said:

Is there a better way to get the number of rows instead of use len(spark.sql().collect())?

 

ChatGPT said:

Yes — using len(spark.sql(...).collect()) is not efficient, because .collect() pulls all the data back to the driver and then counts it in Python. For big tables that can blow up memory.

Better approaches in Spark:

1. Use count() (recommended)

row_count = spark.sql("SELECT * FROM my_table").count()

• .count() is an action that runs a distributed count on the cluster and returns just the number to the driver.

• Much more efficient than .collect().

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2. Do the counting directly in SQL

row_count = spark.sql("SELECT COUNT(*) FROM my_table").collect()[0][0]

• This pushes the aggregation (COUNT(*)) down into Spark’s execution engine.

• The result is a single row with the count.

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✅ Both approaches are good.

• If you’re already in Python DataFrame API: use .count().

• If you’re staying in SQL: use COUNT(*).