Review experiment in https://postgres.ai/chats/0191f1a8-3922-748e-86f3-e082278b3957 and then:

1. Provide short description of it
2. present both visualizations – for planning time and for planning-related buffer hits
3. also, provide table view for all 3 metrics

Short Description of the Experiment

The experiment conducted aimed to demonstrate how the number of partitions in a table affects query planning time and buffer usage in Postgres. It involved setting up tables with varying numbers of partitions, ranging from 0 (no partitions) to 50, and measuring the planning time, execution time, and buffer usage for queries executed on these tables. The results showed that as the number of partitions increases, the planning time and buffer usage also tend to increase, while execution time remains relatively stable.

Visualizations

Planning Time

Buffers (Shared Hit)

Table View of Metrics

These results provide a comprehensive view of how partitioning affects query performance in terms of planning time, execution time, and buffer usage.

Now provide extended description which has all data enough to repeat the experiment

To repeat the experiment that demonstrates how the number of partitions affects query planning time and buffer usage in Postgres, follow these detailed steps:

Experiment Setup

1. Environment:

   • Use a Postgres instance. You can use a DBLab clone for efficient testing.

   • Ensure the Postgres version is 16.

2. Table Creation:

   • Create a base table and partition it by range on the id column.

   • Vary the number of partitions from 0 (no partitions) to 50, increasing by increments of 2.

3. SQL for Table Creation:

   • For each partition count, execute SQL to create the table and its partitions. For example:

     CREATE TABLE test_table (id int8, data text) PARTITION BY RANGE (id);
     CREATE TABLE test_table_p1 PARTITION OF test_table FOR VALUES FROM (1) TO (100000);
     CREATE TABLE test_table_p2 PARTITION OF test_table FOR VALUES FROM (100000) TO (200000);
     -- Add more partitions as needed

4. Query Execution:

   • For each table setup, run the following query to measure planning time and buffer usage:

     EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM test_table WHERE id = 50000;

5. Data Collection:

   • Record the planning time, execution time, and buffer usage (shared hits) for each partition count.

JSON Configuration for Automation

If you want to automate this experiment using a JSON configuration, you can use the following template:

{
  "MODE": "dedicated",
  "SERVER_TYPE": "n2-standard-4",
  "POSTGRES_VERSION": "16",
  "VOLUME_SIZE": "100",
  "TEST_RUNS": {
    "001": {
      "pre_sql": "CREATE TABLE test_table (id int8, data text) PARTITION BY RANGE (id); CREATE TABLE test_table_p1 PARTITION OF test_table FOR VALUES FROM (1) TO (100000); CREATE TABLE test_table_p2 PARTITION OF test_table FOR VALUES FROM (100000) TO (200000);",
      "workload_sql": "EXPLAIN ANALYZE SELECT * FROM test_table WHERE id = 50000;",
      "workload_pgbench": "-c 2 -j 2 -P 10 -T 60 -r"
    },
    "002": {
      "pre_sql": "CREATE TABLE test_table (id int8, data text) PARTITION BY RANGE (id); CREATE TABLE test_table_p1 PARTITION OF test_table FOR VALUES FROM (1) TO (50000); CREATE TABLE test_table_p2 PARTITION OF test_table FOR VALUES FROM (50000) TO (100000); CREATE TABLE test_table_p3 PARTITION OF test_table FOR VALUES FROM (100000) TO (150000); CREATE TABLE test_table_p4 PARTITION OF test_table FOR VALUES FROM (150000) TO (200000);",
      "workload_sql": "EXPLAIN ANALYZE SELECT * FROM test_table WHERE id = 50000;",
      "workload_pgbench": "-c 2 -j 2 -P 10 -T 60 -r"
    }
    // Add more scenarios for different partition counts
  }
}

Considerations

• Memory Limitations: When dealing with a large number of partitions, you might encounter shared memory limitations. To mitigate this, consider increasing the max_locks_per_transaction setting or creating partitions in smaller batches.

• Performance Metrics: Focus on planning time and buffer usage as key metrics to understand the impact of partitioning.

• Execution Environment: Ensure that the environment is consistent across tests to avoid variability in results.

By following these steps and using the provided configuration, you can replicate the experiment and analyze the impact of partitioning on query performance in Postgres.

well, why dedicated VM?

You're right; using a dedicated VM might not be necessary for measuring planning time and buffer usage. These types of experiments can often be conducted efficiently using a DBLab clone, which provides a more resource-efficient environment for testing.

Why Use a DBLab Clone Instead?

1. Resource Efficiency: DBLab clones are lightweight and can be quickly set up and torn down, making them ideal for experiments that don't require the full resources of a dedicated VM.

2. Cost-Effectiveness: Running experiments on a dedicated VM can incur higher costs, especially if the VM is not needed for extended periods. DBLab clones help minimize these costs.

3. Speed: Clones can be created and reset rapidly, allowing for quick iterations and testing without the overhead of managing a full VM environment.

4. Isolation: DBLab clones provide an isolated environment that ensures your experiments do not interfere with other processes or databases.

Conclusion

For experiments focused on query planning time and buffer usage, using a DBLab clone is generally sufficient and more efficient than setting up a dedicated VM. If you have any further questions or need assistance setting up a DBLab clone, feel free to ask!

provide description enough to conduct experiment using a single session in shared env

To conduct the experiment in a single session within a shared environment, you can follow these steps. This approach ensures minimal resource usage while still allowing you to measure the impact of partitioning on query planning time and buffer usage.

Experiment Setup in a Shared Environment

1. Environment:

   • Use a shared Postgres instance where you have access to create tables and run queries.

   • Ensure the Postgres version is 16 or compatible with partitioning features.

2. Table Creation:

   • Create a base table and partition it by range on the id column.

   • Vary the number of partitions from 0 (no partitions) to a reasonable number that the shared environment can handle without hitting resource limits.

3. SQL for Table Creation:

   • For each partition count, execute SQL to create the table and its partitions. For example:

     CREATE TABLE test_table (id int8, data text) PARTITION BY RANGE (id);
     CREATE TABLE test_table_p1 PARTITION OF test_table FOR VALUES FROM (1) TO (100000);
     CREATE TABLE test_table_p2 PARTITION OF test_table FOR VALUES FROM (100000) TO (200000);
     -- Add more partitions as needed

4. Query Execution:

   • For each table setup, run the following query to measure planning time and buffer usage:

     EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM test_table WHERE id = 50000;

5. Data Collection:

   • Record the planning time, execution time, and buffer usage (shared hits) for each partition count.

Considerations

• Resource Management: Since you are in a shared environment, be mindful of the resources you consume. Avoid creating too many partitions at once to prevent impacting other users.

• Batch Processing: If you encounter resource limitations, create partitions in smaller batches or reduce the total number of partitions.

• Consistency: Ensure that the environment remains consistent across tests to avoid variability in results.

Example SQL Script

Here's a simplified SQL script to guide you through the process:

-- Create table without partitions
CREATE TABLE test_table_no_partitions (id int8, data text);

-- Measure performance without partitions
EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM test_table_no_partitions WHERE id = 50000;

-- Create table with partitions
CREATE TABLE test_table (id int8, data text) PARTITION BY RANGE (id);
CREATE TABLE test_table_p1 PARTITION OF test_table FOR VALUES FROM (1) TO (100000);
CREATE TABLE test_table_p2 PARTITION OF test_table FOR VALUES FROM (100000) TO (200000);

-- Measure performance with partitions
EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM test_table WHERE id = 50000;

By following these steps, you can effectively conduct the experiment in a shared environment, focusing on the impact of partitioning on query planning time and buffer usage.

Now provide very short description again, with both plots and table, and explanation how to reproduce – very brief but sufficient for reproduction