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In Greenplum, the redistribution of the sales table based on the cust_id column involves several steps to ensure that the data is efficiently moved and processed across the segments. Here’s a detailed breakdown of how this redistribution is implemented:

Redistribution Process

1. Query Parsing and Planning:

   • The query dispatcher (QD) on the master node parses the query and generates the query plan. This plan includes the redistribution step necessary to join the sales and customer tables.

2. Redistribute Motion Operator:

   • The query plan includes a Redistribute Motion operator. This operator is responsible for redistributing the sales table across the segments based on the cust_id column.

3. Data Redistribution:

   • Each segment reads its local portion of the sales table.

   • The Redistribute Motion operator redistributes the rows of the sales table to other segments based on the hash value of the cust_id column. This ensures that rows with the same cust_id are sent to the same segment.

4. Execution of Redistribute Motion:

   • The redistribution process involves the following steps:

     • Hash Calculation: Each segment calculates the hash value of the cust_id for each row in the sales table.

     • Data Transfer: Rows are sent to the appropriate segments based on the calculated hash values. This is done in parallel across all segments to maximize efficiency.

5. Local Join Execution:

   • After redistribution, each segment performs a local join between the redistributed sales data and its local customer data. This ensures that the join operation is performed efficiently without the need for further data movement.

Example Query Plan

Here’s an example of what the query plan might look like for the given query:

Gather Motion 4:1  (slice1; segments: 4)->  Hash JoinHash Cond: (s.cust_id = c.cust_id)->  Redistribute Motion 4:4  (slice2; segments: 4)Hash Key: s.cust_id->  Seq Scan on sales s->  Seq Scan on customer c

Detailed Steps in Redistribution

1. Initial Scan:

   • Each segment performs a sequential scan on its local portion of the sales table.

2. Redistribution:

   • The Redistribute Motion operator redistributes the rows of the sales table across all segments based on the cust_id column. This involves:

     • Calculating the hash value of cust_id.

     • Sending rows to the appropriate segments based on the hash value.

3. Local Join:

   • After redistribution, each segment performs a local join between the redistributed sales data and its local customer data.

4. Gathering Results:

   • The results from each segment are gathered back to the master node using a Gather Motion operator. The master node combines the results from all segments to produce the final query result.

Conclusion

The redistribution of the sales table in Greenplum is a critical step in ensuring efficient join operations across distributed data. By redistributing data based on the join key (cust_id), Greenplum leverages its MPP architecture to perform local joins on each segment, thereby maximizing parallel processing and minimizing data movement.