Understanding seq_scan in PostgreSQL's pg_stat_user_tables: A Guide to Optimizing Performance

Understanding seq_scan in PostgreSQL’s pg_stat_user_tables

PostgreSQL provides several system views to monitor and analyze its performance. One such view is pg_stat_user_tables, which contains statistics about the user tables, including scan counts and tuples read. In this article, we will delve into the specifics of the seq_scan column and explore what constitutes a concerning large value.

What are seq_scan and tup_per_scan?

The seq_scan column represents the number of times a table was scanned in the last reset of statistics. A scan is considered a full table scan when no indexes can be used to retrieve data, while partial scans use an index to quickly access specific tuples. The tup_per_scan value, on the other hand, indicates how many tuples were read during each scan.

Understanding seq_scan vs. tup_per_scan

Understanding the relationship between these two statistics is crucial for identifying potential performance bottlenecks in your database. While both values are affected by indexing strategies, they have distinct meanings:

seq_scan: Reflects the number of times a table was scanned without using an index to retrieve data.
tup_per_scan: Represents how many tuples were read during each scan.

## Example Use Case

Let's assume we have two tables: `orders` and `customers`. We run the following query:

```sql
SELECT orders.order_id, customers.customer_name
FROM orders
JOIN customers ON orders.customer_id = customers.customer_id;

Without an index on either column, this query would result in a full table scan. In such cases, both seq_scan and tup_per_scan values would be high.

However, if we add indexes to the columns used in the WHERE clause:

CREATE INDEX idx_orders_customer_id ON orders (customer_id);
CREATE INDEX idx_customers_customer_name ON customers (customer_name);

SELECT orders.order_id, customers.customer_name
FROM orders
JOIN customers ON orders.customer_id = customers.customer_id;

The query would use an index on the orders.customer_id column, reducing both scan counts and tuples read.

Determining a Concerning Large Value for seq_scan

While there’s no strict threshold for a “large” value in seq_scan, understanding its range can help identify potential issues:

seq_scan: A normal range for seq_scan might be between 0 (no scans) to a few dozen or even hundreds, depending on the table size and query frequency. However, these values are highly dependent on the data collection interval.

Data Collection Interval

It’s essential to consider the time frame over which these statistics are collected when evaluating large seq_scan values:

## Example: Understanding the Impact of Statistics Reset

Let's assume we run a query that scans a table and then reset the statistics:

```sql
SELECT * FROM my_table;  -- scan the table
VACUUM (FULL) my_table;  -- reset the statistics

The first seq_scan value would be very high because it represents the last full table scan before resetting. The second value, however, is much lower because it now reflects the time since the last statistics reset.

## Example: Monitoring seq_scan over Time

To better understand the impact of a large `seq_scan`, let's examine its behavior over time:

```sql
SELECT 
       to_char(current_date - interval '1 month') AS "one month ago",
       seq_scan
FROM pg_stat_user_tables;

Running this query reveals that values for seq_scan tend to increase when queries take longer, but they reset every 30 days.

Correlating High seq_scan with Resource Consumption

High seq_scan values can indicate a lack of indexing on a table. This is particularly true if the same statement consistently returns these high scan counts:

## Example: Analyzing Execution Plans for Optimize Queries

To further identify potential issues, we need to analyze the execution plans for queries with high `tup_per_scan` values:

```sql
SELECT 
       sql,
       execution_plan
FROM pg_stat_statements
WHERE total_exec_time > 1000;

We can then inspect the plan using a tool like pg_deployer or simply run it manually to identify the indexes being used (or not):

## Example: Understanding Execution Plans

Running an optimized query will produce different plans than unoptimized ones.

```sql
-- Optimized query with index usage:
SELECT orders.order_id, customers.customer_name
FROM orders
JOIN customers ON orders.customer_id = customers.customer_id
WHERE orders.customer_id IN (SELECT customer_id FROM customers WHERE country='USA');

-- Unoptimized query without index usage:

SELECT orders.order_id, customers.customer_name
FROM orders
JOIN customers ON orders.customer_id = customers.customer_id;

Using indexes on columns used in the WHERE clause can significantly reduce scan counts and improve overall database performance.

Conclusion

While a “large” value for seq_scan doesn’t provide a clear threshold, understanding its context and correlation with other statistics is crucial. Monitoring these values over time, analyzing execution plans, and implementing appropriate indexing strategies are key steps in optimizing PostgreSQL’s performance.

Last modified on 2023-07-03