Welcome to Day 7 of your Spark Mastery journey!
Today is one of the most practical days because joins, unions, and aggregations are used in almost every pipeline you will ever build — be it feature engineering, building fact tables, or aggregating transactional data.
Let’s master the fundamentals with clarity and real-world examples.
🌟 1. Joins in PySpark — The Heart of ETL Pipelines
A join merges two DataFrames based on keys, similar to SQL.
Basic join:
df.join(df2, df.id == df2.id, "inner")
Join on same column name:
df.join(df2, ["id"], "left")
🔹 Types of Joins in Spark
Join Type – Meaning
inner – Matching rows
left – All rows from left, match from right
right – All rows from right
full – All rows from both
left_anti – Rows in left NOT in right
left_semi – Rows in left WHERE match exists in right
cross Cartesian product
Important
left_semi = existence check
left_anti = anti-join / unmatched rows
🌟 2. Union — Stack DataFrames Vertically
Union (same schema, same order)
df.union(df2)
Union by column names:
df.unionByName(df2)
Why important?
Because in real projects you combine:
- monthly files
- daily ingestion datasets
- partitions
🌟 3. GroupBy + Aggregation — Business Logic Layer
This is how reports, fact tables, metrics are built.
Example:
df.groupBy("dept").agg(
sum("salary").alias("total_salary"),
avg("age").alias("avg_age")
)
🔹 count vs countDistinct
df.select(count("id"))
df.select(countDistinct("id"))
🔹 approx_count_distinct (faster!)
df.select(approx_count_distinct("id"))
🌟 4. Real ETL Example — Sales Aggregation
Suppose you have:
- sales table
- product table
Join them:
df_joined = sales.join(products, "product_id", "left")
Aggregate revenue:
df_agg = df_joined.groupBy("category").agg(
sum("amount").alias("total_revenue"),
count("*").alias("transactions")
)
This is EXACTLY how business dashboards are built.
🌟 5. Join Performance Optimization
Use Broadcast Join for small lookup tables:
df.join(broadcast(df_small), "id")
Why?
Avoids shuffle → runs much faster.
🚀 Summary of Day 7
Today we learnt:
- Joins
- Union / UnionByName
- GroupBy
- Aggregations
- broadcast join optimization
Follow for more such content. Let me know if I missed anything in comments. Thank you!!
