Spark SQL Functions for Data Engineers (2026)

Spark 3.5 ships with 1,500+ built-in functions in pyspark.sql.functions. These are the ones that carry production pipelines and the ones interviewers expect on sight: aggregations, window functions, string operations, date handling, and conditionals.

Last updated: July 27, 2026Proudly published by: Jeff Wahl

Spark SQL Aggregation Functions

F.sum()

df.groupBy('dept').agg(F.sum('salary'))

Sums a column within each group.

F.count()

df.groupBy('dept').agg(F.count('*'))

Counts rows per group. Use F.countDistinct() for unique values.

F.avg()

df.groupBy('dept').agg(F.avg('salary').alias('avg_salary'))

Mean value. Alias the output column or it becomes avg(salary).

F.min() / F.max()

df.agg(F.min('created_at'), F.max('created_at'))

Works on numbers, strings, and dates.

F.countDistinct()

df.groupBy('dept').agg(F.countDistinct('employee_id'))

Approximate alternative: F.approx_count_distinct() for large datasets.

F.collect_list() / F.collect_set()

df.groupBy('dept').agg(F.collect_list('name'))

Gathers values into an array. collect_set removes duplicates. Beware: pulls all values to one partition.

Spark SQL Window Functions

F.row_number()

F.row_number().over(Window.partitionBy('dept').orderBy(F.desc('salary')))

Sequential numbering within each partition. The most common window function in interviews.

F.rank() / F.dense_rank()

F.rank().over(Window.partitionBy('dept').orderBy('score'))

rank() leaves gaps on ties; dense_rank() does not.

F.lag() / F.lead()

F.lag('revenue', 1).over(Window.partitionBy('store').orderBy('date'))

Access previous/next row values. Essential for calculating period-over-period changes.

F.sum().over()

F.sum('amount').over(Window.partitionBy('account').orderBy('date').rowsBetween(Window.unboundedPreceding, 0))

Running total. The rowsBetween clause controls the window frame.

Spark SQL String Functions

F.regexp_extract()

F.regexp_extract('url', r'/product/(\d+)', 1)

Extracts a regex capture group. Group 0 returns the full match.

F.concat() / F.concat_ws()

F.concat_ws('-', 'year', 'month', 'day')

concat_ws adds a separator. concat does not.

F.trim() / F.lower() / F.upper()

df.withColumn('clean', F.trim(F.lower('name')))

Chain them for normalization before joins.

F.split()

F.split('tags', ',')

Returns an ArrayType column. Often followed by F.explode().

Spark SQL Date and Timestamp Functions

F.current_date() / F.current_timestamp()

df.withColumn('today', F.current_date())

Evaluated once per query, not per row.

F.datediff() / F.months_between()

F.datediff('end_date', 'start_date')

Returns integer days. months_between returns a decimal.

F.date_trunc()

F.date_trunc('month', 'event_time')

Truncates to the start of the given unit. Useful for time-series grouping.

F.to_date() / F.to_timestamp()

F.to_date('date_str', 'yyyy-MM-dd')

Parses strings into date/timestamp types. The format string matters.

Spark SQL Conditional Functions

F.when() / F.otherwise()

F.when(F.col('score') >= 90, 'A').when(F.col('score') >= 80, 'B').otherwise('C')

Spark's equivalent of CASE WHEN. Chain multiple conditions.

F.coalesce()

F.coalesce('preferred_email', 'work_email', 'personal_email')

Returns the first non-null value. Different from the partition coalesce() method.

F.isnull() / F.isnan()

df.filter(F.isnull('email'))

isnull checks for SQL NULL. isnan checks for NaN (float). They are not the same.

The function catalogue is reference material. What gets graded is knowing which of these push down to the scan and which force a full pass, a distinction drawn out by the PySpark interview questions, and the PySpark practice problems shows which loops weight Spark at all.

Prepare for the interview

01 / Open invite

02min.

Know Spark the way the interviewer who asks it knows it.

a Spark query, the same shape a screen would give you.

The diff against expected. Where ties broke. What you missed.

sandbox

1def sessionize(events):

2 sessions = []

3 for e in events:

4 if gap_minutes(e) > 30:

Execute your solution0.4s avg.

AirbnbInterview question

Solve a Spark problem

Spark SQL Functions FAQ

What is the difference between import pyspark.sql.functions and from pyspark.sql import functions as F?+

They are the same module. The convention is to import as F for brevity: from pyspark.sql import functions as F. Then you call F.col(), F.sum(), etc. This is the standard pattern in production PySpark code and what interviewers expect to see.

Are Spark SQL functions the same as PySpark functions?+

Yes. pyspark.sql.functions maps directly to Spark SQL built-in functions. df.select(F.upper('name')) produces the same execution plan as spark.sql('SELECT upper(name) FROM table'). The Catalyst optimizer treats them identically.

Can I use these functions in Spark Scala too?+

Yes. The same functions exist in org.apache.spark.sql.functions in Scala. The API surface is nearly identical. If you know the PySpark version, the Scala translation is straightforward.

How many built-in functions does Spark have?+

Spark 3.5 (latest stable) ships with 1,500+ built-in functions in pyspark.sql.functions. This covers aggregation, window, string, date, math, collection, and conditional operations. Interviewers expect you to reach for built-in functions before writing a UDF.

Why should I avoid UDFs when a built-in function exists?+

Python UDFs force data serialization between the JVM and Python for every row. This can be 10x to 100x slower than using a native Spark function. The Catalyst optimizer also cannot see inside a UDF, so it cannot apply predicate pushdown or column pruning through one. Pandas UDFs (vectorized) reduce this penalty by operating on Arrow batches, but built-in functions are still faster.

02 / Why practice

Practice Spark SQL Functions

01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
03
5 problem shapes cover 80% of data engineer loops
Parsing and reshaping, sessionization, dedup with tie-breaks, streaming aggregation, top-N-per-group. Writing them by hand turns the unfamiliar into pattern recognition

Open the problems

Related Guides

PySpark Functions Cheat Sheet→

Printable quick reference with the most-used functions

PySpark GroupBy Guide→

groupBy + agg patterns with execution plan analysis

PySpark Interview Questions→

The PySpark questions that come up in DE interviews

The community challenge on real, dirty data→

1 data engineering challenge a week on dirty production-shaped data, scored blind on a hidden dataset. Submit before the freeze; results at the reveal.