https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/ba-p/129142 <H2><STRONG>Introduction</STRONG></H2> <P><SPAN>When building scalable data pipelines in Apache Spark, the way you add or transform columns in a DataFrame can have a dramatic impact on performance. In this post, we’ll dive deep into the technical differences between </SPAN><SPAN>withColumn</SPAN><SPAN> and </SPAN><SPAN>withColumns</SPAN><SPAN>, explore how Spark’s optimizer handles each, and share best practices for writing efficient, production-grade code.</SPAN></P> <H2><STRONG>The Basics: </STRONG><STRONG>withColumn</STRONG><STRONG> and </STRONG><STRONG>withColumns</STRONG></H2> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>withColumn</STRONG><SPAN>: Adds or replaces a single column in a DataFrame. Each call returns a new DataFrame with the updated schema.</SPAN></LI> <LI><STRONG>withColumns</STRONG><SPAN>: Introduced in Spark 3.3, allows you to add or replace multiple columns in a single call by passing a dictionary of column names and expressions.</SPAN></LI> </UL> <H2><STRONG>The Performance Trap: Using </STRONG><STRONG>withColumn</STRONG><STRONG> in a Loop</STRONG></H2> <H2><STRONG>What Happens Under the Hood?</STRONG></H2> <P><SPAN>Every time you call </SPAN><SPAN>withColumn</SPAN><SPAN>, Spark creates a new logical plan with an additional projection node. If you use </SPAN><SPAN>withColumn</SPAN><SPAN> inside a loop to add or modify many columns, Spark’s Catalyst optimizer must re-analyze and re-optimize the entire plan for every new column. This leads to:</SPAN></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>Exponential growth in the logical plan size</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Longer job planning and execution times</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Potential for StackOverflowException or memory errors with hundreds of columns</SPAN><SPAN><BR /></SPAN></LI> </UL> <H2><STRONG>Example: Adding 100 Columns (Inefficient)</STRONG></H2> <LI-CODE lang="python">from pyspark.sql.functions import lit df1 = spark.range(100000) for i in range(100): df1 = df1.withColumn(f"col_{i+1}", lit(i+1)) df1.explain(extended=True) == Parsed Logical Plan == +- Project [id#78033L, col_1#78036, col_2#78042, col_3#78050, col_4#78060, col_5#78072, col_6#78086, col_7#78102, col_8#78120, col_9#78140, 10 AS col_10#78162] +- Project [id#78033L, col_1#78036, col_2#78042, col_3#78050, col_4#78060, col_5#78072, col_6#78086, col_7#78102, col_8#78120, 9 AS col_9#78140] +- Project [id#78033L, col_1#78036, col_2#78042, col_3#78050, col_4#78060, col_5#78072, col_6#78086, col_7#78102, 8 AS col_8#78120] +- Project [id#78033L, col_1#78036, col_2#78042, col_3#78050, col_4#78060, col_5#78072, col_6#78086, 7 AS col_7#78102] +- Project [id#78033L, col_1#78036, col_2#78042, col_3#78050, col_4#78060, col_5#78072, 6 AS col_6#78086] +- Project [id#78033L, col_1#78036, col_2#78042, col_3#78050, col_4#78060, 5 AS col_5#78072] +- Project [id#78033L, col_1#78036, col_2#78042, col_3#78050, 4 AS col_4#78060] +- Project [id#78033L, col_1#78036, col_2#78042, 3 AS col_3#78050] +- Project [id#78033L, col_1#78036, 2 AS col_2#78042] +- Project [id#78033L, 1 AS col_1#78036]</LI-CODE> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>Each iteration creates a new DataFrame and a new logical plan.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>The optimizer must process a much larger and more complex Directed Acyclic Graph (DAG).</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Jobs can be several times slower, and you may hit memory or stack limits.</SPAN></LI> </UL> <H2><STRONG>Why </STRONG><STRONG>withColumns</STRONG><STRONG> (or </STRONG><STRONG>select</STRONG><STRONG>) Is More Efficient</STRONG></H2> <H2><STRONG>Single Expression, Single Plan</STRONG></H2> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>withColumns</STRONG><SPAN> lets you add or modify multiple columns in a single transformation.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>select</STRONG><SPAN> can also be used to achieve the same effect by specifying all columns and their transformations at once.</SPAN><SPAN><BR /></SPAN></LI> </UL> <H2><STRONG>Benefits</STRONG></H2> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>Only one projection node is added to the logical plan, regardless of the number of columns.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>The optimizer has a much simpler plan to analyze and optimize.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>Dramatic reduction in planning and execution time, benchmarks show up to 3-4x speedup for large numbers of columns.</SPAN><SPAN><BR /></SPAN></LI> </UL> <H2><STRONG>Example: Adding 100 Columns Efficiently</STRONG></H2> <LI-CODE lang="python">from pyspark.sql.functions import lit df2 = spark.range(100000) cols = {f"col_{i+1}": lit(i+1) for i in range(100)} df2 = df2.withColumns(cols) df2.explain(extended=True) == Parsed Logical Plan == Project [id#88638L, 1 AS col_1#88740, 2 AS col_2#88741, 3 AS col_3#88742, 4 AS col_4#88743, 5 AS col_5#88744, 6 AS col_6#88745, 7 AS col_7#88746, 8 AS col_8#88747, 9 AS col_9#88748, 10 AS col_10#88749, 11 AS col_11#88750, 12 AS col_12#88751, 13 AS col_13#88752, 14 AS col_14#88753, 15 AS col_15#88754, 16 AS col_16#88755, 17 AS col_17#88756, 18 AS col_18#88757, 19 AS col_19#88758, 20 AS col_20#88759, 21 AS col_21#88760, 22 AS col_22#88761, 23 AS col_23#88762, 24 AS col_24#88763, ... 76 more fields]</LI-CODE> <P><SPAN>Or, using </SPAN><SPAN>select</SPAN><SPAN>:</SPAN></P> <LI-CODE lang="python">df = df.select( *df.columns, *(expr("...").alias(col) for col in columns_to_add)) </LI-CODE> <H2><STRONG>How Spark’s Optimizer Handles These Patterns</STRONG></H2> <H2><STRONG>Multiple </STRONG><STRONG>withColumn</STRONG><STRONG> Calls</STRONG></H2> <H2><STRONG style="font-size: 16px;">1. Logical Plan Growth and Its Ramifications</STRONG></H2> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Plan Expansion</STRONG><SPAN>: Each `withColumn` call produces a new logical plan node (typically a Project node), leading to plan growth that can increase effective operational complexity significantly due to nesting.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Plan Depth and Breadth</STRONG><SPAN>: Repeated transformations result in a logically deep and complex tree, with each new `withColumn` forming a chain of projections.</SPAN></LI> </UL> <P><STRONG>2. Catalyst’s Optimization Overhead</STRONG></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Repeated Parsing and Analysis</STRONG><SPAN>: Catalyst performs checks (e.g., type resolution, rule application) across the </SPAN><I><SPAN>entire</SPAN></I><SPAN> plan for every new logical plan. This becomes slow and memory intensive with plans reaching hundreds or thousands of nodes.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Rule Application Complexity</STRONG><SPAN>: Catalyst applies hundreds of rules in sequential and iterative passes. More nodes and deeper plans increase the comparisons, rewrites, and tree-walking steps, leading to super-linear costs.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Garbage Collection and JVM Overheads</STRONG><SPAN>: Very large logical plans can cause heavy memory allocation and garbage collection pressure, frequently triggering pauses during job planning.</SPAN></LI> </UL> <P><STRONG>3. Physical Plan Creation and Code Generation</STRONG></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Physical Plan Inefficiencies</STRONG><SPAN>: Extremely deep logical trees often translate to deeply nested or redundant physical operators, impacting Spark execution efficiency.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Code Generation (Whole-Stage Codegen)</STRONG><SPAN>: For complex plans, Spark generates large, deeply nested Java code, which can hit JVM limits, causing compilation failures or degraded JIT optimization.</SPAN></LI> </UL> <P><STRONG>4. Extreme-Case Execution Time Effects</STRONG></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Planning Phase Delays</STRONG><SPAN>: The driver side becomes a bottleneck, causing execution delays or indefinite hangs as Catalyst analyzes and rewrites the plan. Users may observe "hung" Spark jobs before data processing begins.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Out of Memory / StackOverflow</STRONG><SPAN>: Repeated `withColumn` calls adding hundreds or thousands of columns can cause the driver to run out of heap space or hit stack overflows due to recursive tree processing.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Execution Runtime Penalty</STRONG><SPAN>: Even if the plan is generated, execution is often suboptimal due to less effective column pruning and pushdown optimizations, ballooning I/O and CPU costs, and less efficient code-generated pipelines. Worker nodes may spend more time interpreting unnecessarily complex execution plans.</SPAN><SPAN><BR /></SPAN></LI> </UL> <P><STRONG>Single </STRONG><STRONG>withColumns</STRONG><STRONG> or </STRONG><STRONG>select</STRONG><STRONG> Call</STRONG></P> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>All transformations are applied in a single logical plan node.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>The optimizer can analyze and optimize the plan much more efficiently.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>The physical plan is simpler and more performant.</SPAN><SPAN><BR /></SPAN></LI> </UL> <P><STRONG>Best Practices and Recommendations</STRONG></P> <UL> <LI style="font-weight: 400;" aria-level="1"><STRONG>Avoid using </STRONG><STRONG>withColumn</STRONG><STRONG> in a loop</STRONG><SPAN> for adding or transforming many columns.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><STRONG>Use </STRONG><STRONG>withColumns</STRONG><STRONG> (Spark 3.3+) or </STRONG><STRONG>select</STRONG><SPAN> to apply all changes in a single transformation.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>For complex dependencies (where each new column depends on the previous one), chaining </SPAN><SPAN>withColumn</SPAN><SPAN> may be necessary, but try to minimize such patterns.</SPAN></LI> </UL> <P><SPAN>Always check the logical and physical plans using </SPAN><SPAN>df.explain()</SPAN><SPAN> to understand the impact of your transformations.</SPAN></P> <P><SPAN><STRONG>Performance Comparison Table (Many Columns)</STRONG></SPAN></P> <TABLE border="1" width="99.7610513739546%"> <TBODY> <TR> <TD width="22.819593787335723%"> <P><STRONG>Method</STRONG></P> </TD> <TD width="20.31063321385902%"> <P><STRONG>Logical Plan Size</STRONG></P> </TD> <TD width="23.297491039426525%"> <P><STRONG>Optimizer Overhead</STRONG></P> </TD> <TD width="17.801672640382318%"> <P><STRONG>Memory Usage</STRONG></P> </TD> <TD width="15.531660692951016%"> <P><STRONG>Performance</STRONG></P> </TD> </TR> <TR> <TD width="22.819593787335723%"> <P>withColumn in loop</P> </TD> <TD width="20.31063321385902%"> <P>Large</P> </TD> <TD width="23.297491039426525%"> <P>High</P> </TD> <TD width="17.801672640382318%"> <P>High</P> </TD> <TD width="15.531660692951016%"> <P>Poor</P> </TD> </TR> <TR> <TD width="22.819593787335723%"> <P>withColumns</P> </TD> <TD width="20.31063321385902%"> <P>Small</P> </TD> <TD width="23.297491039426525%"> <P>Low</P> </TD> <TD width="17.801672640382318%"> <P>Low</P> </TD> <TD width="15.531660692951016%"> <P>Good</P> </TD> </TR> <TR> <TD width="22.819593787335723%"> <P>select</P> </TD> <TD width="20.31063321385902%"> <P>Small</P> </TD> <TD width="23.297491039426525%"> <P>Low</P> </TD> <TD width="17.801672640382318%"> <P>Low</P> </TD> <TD width="15.531660692951016%"> <P>Good</P> </TD> </TR> </TBODY> </TABLE> <H2><STRONG>Benchmark</STRONG></H2> <P><SPAN>A simple experiment on a DataFrame with 100,000 rows found that adding 100 columns in a loop using </SPAN><SPAN>withColumn()</SPAN><SPAN> took 4.16 seconds. Replacing </SPAN><SPAN>withColumn()</SPAN><SPAN> with </SPAN><SPAN>withColumns()</SPAN><SPAN> improved performance by 97%, reducing the time to just 0.13 seconds. The performance gap widens as the number of columns increases.</SPAN></P> <H2><STRONG>Conclusion</STRONG></H2> <UL> <LI style="font-weight: 400;" aria-level="1"><SPAN>For single or a few columns: </SPAN><SPAN>withColumn</SPAN><SPAN> is fine.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1"><SPAN>For many columns: Always prefer </SPAN><SPAN>withColumns</SPAN><SPAN> or </SPAN><SPAN>select</SPAN><SPAN> to avoid performance bottlenecks and memory issues.</SPAN></LI> <LI style="font-weight: 400;" aria-level="1">Understand your logical plan: Use <SPAN>df.explain()</SPAN><SPAN> to see how your code affects Spark’s optimizer.</SPAN></LI> </UL> Mon, 15 Sep 2025 15:35:36 GMT DattaWalake 2025-09-15T15:35:36Z https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/ba-p/129142 <P><SPAN>When building scalable data pipelines in Apache Spark, the way you add or transform columns in a DataFrame can have a dramatic impact on performance. In this post, we’ll dive deep into the technical differences between </SPAN><SPAN>withColumn</SPAN><SPAN> and </SPAN><SPAN>withColumns</SPAN><SPAN>, explore how Spark’s optimizer handles each, and share best practices for writing efficient, production-grade code.</SPAN></P> <P>&nbsp;</P> Mon, 15 Sep 2025 15:35:36 GMT https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/ba-p/129142 DattaWalake 2025-09-15T15:35:36Z https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/131943#M762 <P>Hi&nbsp;<a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/61045">@DattaWalake</a>&nbsp;,</P><P>Thanks for sharing this. This is super important topic. I've seen a lot of folks generating columns within for loops using withColumn approach and as you can guess it was disaster in terms of performance.<BR /><BR /></P> Mon, 15 Sep 2025 09:17:54 GMT https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/131943#M762 szymon_dybczak 2025-09-15T09:17:54Z https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/132001#M763 <P>10/10 as always. Thanks&nbsp;<a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/61045">@DattaWalake</a>!</P> Mon, 15 Sep 2025 14:35:03 GMT https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/132001#M763 smakubi 2025-09-15T14:35:03Z https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/135598#M800 <P><a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/61045">@DattaWalake</a>,&nbsp;great article at the right time. This is useful to mitigate some Spark logical planning issues while migrating the jobs from classic to serverless compute. Thank you.</P> Tue, 21 Oct 2025 22:05:24 GMT https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/135598#M800 Ramana 2025-10-21T22:05:24Z https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/137233#M809 <P>What is the benchmark actually measuring? Batch duration or initial plan time? I&nbsp; don't care much if it is initial plan, but if it influences every batch of my long running process it's quite significant.</P><P>Also surprising that the optimizer is not able to optimise such a trivial case.&nbsp;</P> Sat, 01 Nov 2025 10:49:20 GMT https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/137233#M809 Erik 2025-11-01T10:49:20Z https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/137415#M813 <P>hi&nbsp;<a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/23894">@Erik</a>&nbsp;thanks, good question, its batch duration too. Query planning happens on each batch and generates fresh physical plan.</P> <P>The&nbsp;optimizer&nbsp;can’t&nbsp;fully&nbsp;eliminate&nbsp;the&nbsp;overhead&nbsp;of&nbsp;many&nbsp;chained&nbsp;withColumn&nbsp;calls&nbsp;because&nbsp;each&nbsp;call&nbsp;adds&nbsp;a&nbsp;new&nbsp;projection&nbsp;and&nbsp;attribute&nbsp;aliases&nbsp;that&nbsp;must&nbsp;be&nbsp;preserved&nbsp;for&nbsp;correctness;&nbsp;&nbsp;Catalyst&nbsp;must&nbsp;traverse&nbsp;and&nbsp;optimize&nbsp;a&nbsp;larger&nbsp;tree&nbsp;every&nbsp;time,&nbsp;which&nbsp;increases&nbsp;planning&nbsp;cost.&nbsp;</P> <P>Using&nbsp;withColumns&nbsp;(or&nbsp;a&nbsp;single&nbsp;select)&nbsp;applies&nbsp;all&nbsp;transformations&nbsp;in&nbsp;one&nbsp;projection,&nbsp;keeping&nbsp;the&nbsp;plan&nbsp;shallow&nbsp;and&nbsp;reducing&nbsp;per‑query&nbsp;planning&nbsp;overhead.</P> Mon, 03 Nov 2025 15:29:07 GMT https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/137415#M813 DattaWalake 2025-11-03T15:29:07Z https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/140006#M840 <P>would it be best to simply always use the plural form, to standardize our process? Or is there some drawback?</P> Sat, 22 Nov 2025 15:19:32 GMT https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/140006#M840 natelpeterson1 2025-11-22T15:19:32Z https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/140751#M847 <P>No meaningful performance overhead: using <STRONG>withColumns</STRONG> for a single column performs the same single projection/plan analysis as <STRONG>withColumn</STRONG>, provided you’re on <STRONG>Spark 3.3+</STRONG> (the release that introduced withColumns). The only practical difference is readability.</P> Mon, 01 Dec 2025 14:41:37 GMT https://community.databricks.com/t5/technical-blog/performance-showdown-withcolumn-vs-withcolumns-in-apache-spark/bc-p/140751#M847 DattaWalake 2025-12-01T14:41:37Z