#Joins & Aggregations

Last updated    - V7 (14-Jan-2026)

Key Concepts

S.No	Topic	Sub-Topics
1	Joins	What is a join, Types of joins, Importance, Examples, Use cases
2	Inner Join	Definition, Syntax, Example with RDD, Example with DataFrame, Performance considerations
3	Left Outer Join	Definition, Syntax, Example RDD, Example DataFrame, Handling nulls
4	Right Outer Join	Definition, Syntax, Example RDD, Example DataFrame, Use cases
5	Full Outer Join	Definition, Syntax, Example RDD, Example DataFrame, Null handling
6	Cross Join / Cartesian	Definition, Syntax, Example, Performance considerations, Use cases
7	Self Join	Definition, Syntax, Example RDD, Example DataFrame, Use cases
8	Broadcast Join	Definition, When to use, Example, Performance benefits, Spark configuration
9	Skewed Joins	Definition, Problems caused, Solutions, Salting technique, Performance tips
10	Join on Multiple Columns	Syntax, Example DataFrame, Example SQL, Performance considerations, Best practices
11	Key Considerations in Joins	Partitioning, Shuffling, Data size, Broadcast, Caching
12	Aggregation Overview	What is aggregation, Types, Importance, Syntax, Use cases
13	GroupBy	Definition, Syntax, Example RDD, Example DataFrame, Performance considerations
14	GroupByKey vs ReduceByKey	Definition, Syntax, Performance difference, Example, When to use
15	AggregateByKey	Definition, Syntax, Example, Custom aggregation functions, Performance
16	CountByKey & CountByValue	Definition, Syntax, Example RDD, Example DataFrame, Use cases
17	Sum, Max, Min Aggregations	Syntax, Example DataFrame, Example SQL, Performance, Best practices
18	Average & Mean Aggregations	Syntax, Example RDD, Example DataFrame, Handling nulls, Performance
19	Multiple Aggregations	agg() function, Syntax, Example DataFrame, Example SQL, Performance tips
20	Window Functions for Aggregation	Definition, Syntax, PartitionBy, OrderBy, Example
21	Rollup & Cube	Definition, Syntax, Example DataFrame, Use cases, Performance tips
22	Pivot Aggregations	Definition, Syntax, Example DataFrame, Example SQL, Use cases
23	Approximate Aggregations	approxCountDistinct(), approxQuantile(), Use cases, Syntax, Performance benefits
24	Custom Aggregations	User-defined aggregate functions (UDAF), Syntax, Example, Use cases, Performance tips
25	Combining Joins & Aggregations	Join then aggregate, Aggregate then join, Example DataFrame, SQL example, Best practices
26	Handling Nulls in Joins & Aggregations	Null handling functions, coalesce(), fill(), drop(), Example, Best practices
27	Optimizing Joins	Broadcast join, Partitioning, Caching, Skew handling, Shuffle reduction
28	Optimizing Aggregations	Partitioning, ReduceByKey, AggregateByKey, Caching, Avoid groupByKey for large data
29	Advanced Aggregation Techniques	Window functions, Rollup, Cube, Pivot, Custom UDAFs
30	Real-world Examples	ETL pipelines, Log analytics, Sales aggregation, Customer behavior analysis, Recommendations

Interview question

Basic

• What is a join in Spark?
• What are the types of joins?
• Explain inner join with an example.
• Explain left outer join with an example.
• Explain right outer join with an example.
• Explain full outer join with an example.
• What is a cross join or cartesian product?
• What is a self join?
• Difference between inner join and outer join.
• Difference between left and right outer join.
• Difference between full outer join and inner join.
• How to perform join on multiple columns?
• What is a broadcast join?
• When should you use broadcast join?
• How does Spark handle join shuffles?
• What are skewed joins?
• How to handle nulls in joins?
• What is join on key-value RDDs?
• Explain join using DataFrames.
• Explain join using Spark SQL.
• Difference between join on RDDs and DataFrames.
• What is the role of partitioning in joins?
• What is the impact of join on performance?
• What is a cogroup operation?
• When should you use cogroup over join?

Intermediate

• Explain groupBy in Spark.
• Explain reduceByKey in Spark.
• Difference between groupBy and reduceByKey.
• Explain aggregateByKey.
• Explain combineByKey.
• What is countByKey?
• What is countByValue?
• Explain sum, max, min aggregations.
• Explain average and mean aggregation.
• How to perform multiple aggregations?
• Explain window functions for aggregation.
• Explain rollup aggregation.
• Explain cube aggregation.
• Explain pivot aggregation.
• Explain approxCountDistinct aggregation.
• Explain approxQuantile aggregation.
• What are user-defined aggregate functions (UDAFs)?
• How to perform joins before aggregation?
• How to perform aggregation after join?
• Explain groupBy with multiple columns.
• Explain aggregateByKey vs reduceByKey.
• Explain foldByKey for aggregation.
• Explain subtractByKey.
• Explain join optimizations in aggregations.
• How to cache/join before aggregation for better performance?

Advanced

• Explain shuffle in join and aggregation.
• Explain narrow vs wide dependencies in joins.
• How does Spark optimize joins internally?
• How does Spark optimize aggregations internally?
• What is partitioning and its importance?
• How does partitioning affect join performance?
• How does partitioning affect aggregation performance?
• Explain broadcast join with large datasets.
• Explain handling skewed keys in joins.
• Explain reduce-side join vs map-side join.
• Explain join with multiple RDDs.
• Explain aggregation with multiple RDDs.
• Explain window-based aggregations.
• Explain stateful aggregation in streaming joins.
• Explain streaming joins vs batch joins.
• Explain approximate aggregations for performance.
• Explain advanced pivot operations.
• Explain multi-level aggregations.
• Explain hierarchical rollup and cube.
• Explain combining joins and aggregations for ETL pipelines.
• Explain memory and disk management in join operations.
• Explain partition tuning for large aggregations.
• Explain broadcast variable usage in aggregation.
• Explain accumulators in aggregations.
• Explain best practices for join + aggregation in Spark.

Expert

• Explain shuffle optimization strategies in joins and aggregations.
• Explain Spark Catalyst optimization for DataFrame joins.
• Explain whole-stage code generation for joins & aggregations.
• Explain Tungsten engine role in join and aggregation.
• Explain join strategy selection: sort-merge vs broadcast hash join.
• Explain adaptive query execution (AQE) in joins.
• Explain skew join handling in AQE.
• Explain incremental aggregations for streaming data.
• Explain join/aggregation in structured streaming.
• Explain checkpointing in streaming aggregations.
• Explain watermarking for joins in streaming.
• Explain stateful streaming aggregations.
• Explain memory tuning for large join + aggregation operations.
• Explain tuning shuffle partitions for large datasets.
• Explain optimizing multi-stage aggregation pipelines.
• Explain combining wide & narrow transformations with aggregations.
• Explain caching strategies for repeated joins and aggregations.
• Explain fault-tolerance mechanisms in joins & aggregations.
• Explain Spark UI metrics related to joins and aggregations.
• Explain advanced use cases: ETL, analytics dashboards, ML pipelines.
• Explain differences in join behavior between RDD, DataFrame, and Dataset APIs.
• Explain join performance tuning in distributed clusters.
• Explain aggregation performance tuning in distributed clusters.
• Explain UDAF optimization techniques.
• Explain real-world examples combining joins and aggregations.

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