Explain the query processing in PySpark

== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[Department#1], functions=[sum(Salary#2L)])
+- Exchange hashpartitioning(Department#1, 200),
ENSURE_REQUIREMENTS, [id=#60]
+- HashAggregate(keys=[Department#1],
functions=[partial_sum(Salary#2L)])
+- InMemoryTableScan [Department#1, Salary#2L]
+- InMemoryRelation [Name#0, Department#1, Salary#2L], StorageLevel(disk, memory, deserialized, 1 replicas)
+- *(1) Scan ExistingRDD[Name#0,Department#1,Salary#2L]


Let's break down what the physical plan is showing you:

1. **AdaptiveSparkPlan:**

The top node, `AdaptiveSparkPlan isFinalPlan=false`, indicates that Spark's Adaptive Query Execution (AQE) is enabled. AQE means Spark can adjust its physical plan at runtime based on the actual data and statistics. Here, it informs you that the current plan is not final and may be optimized further as the job executes.

2. **Final Global Aggregation (HashAggregate):**

The next step is a `HashAggregate` node that groups data by the key `[Department#1]` and uses the aggregation function `sum(Salary#2L)`. This is the final step in computing the total salary per department. Because no grouping keys are passed to the final `groupBy()` (if you had one on the top level, it would be a global aggregation), here it's grouping on the department key to produce the desired result.

3. **Data Exchange (Exchange):**

Before reaching the final aggregation, there's an `Exchange` node. This node handles the data shuffle by redistributing rows across 200 partitions based on hash partitioning of the department column. The exchange ensures that all rows for the same department end up in the same partition so that the subsequent aggregation can compute the final sum correctly. The `ENSURE_REQUIREMENTS` note indicates that Spark is satisfying physical properties (like partitioning) required by the following operators.

4. **Partial Aggregation (HashAggregate):**

Beneath the exchange, another `HashAggregate` node appears. This node computes partial sums of salaries per department. Partial aggregation is a common technique in distributed computing because it reduces the amount of data that has to be shuffled over the network by performing some of the aggregation locally within each partition.

5. **Data Source (InMemoryTableScan and InMemoryRelation):**

The data is ultimately sourced from an `InMemoryTableScan` on the columns `[Department#1, Salary#2L]`. This scan reads data from an `InMemoryRelation`, which is a cached version of your dataset stored in memory (with a storage level that includes disk fallback and a single replica). The `Scan ExistingRDD` at the bottom indicates that this cached DataFrame (or RDD) is being scanned to provide the required columns to the aggregation pipeline.

In summary, the plan shows that Spark is reading data from memory, performing a local (partial) aggregation to compute partial sums of salaries by department, then shuffling the data so that all rows with the same department are grouped together, and finally computing the global sum for each department. This multi-phase strategy (partial then final aggregation) is used to optimize performance and reduce data movement across nodes.