What are SQL fat pipes?

What are SQL fat pipes?


SQL fat pipes are a performance issue that can occur in SQL Server databases. They occur when a query scans or seeks a large number of rows from a table, causing the query to take a long time to execute.

Symptoms of SQL fat pipes

A few symptoms can indicate that you have a SQL fat pipe issue. These include:

  • Long query execution times
  • High CPU usage
  • High memory usage
  • High I/O wait times

How to identify SQL fat pipes

To identify SQL fat pipes, you can use the SQL Server EXPLAIN PLAN statement. This statement will show you the execution plan of a query, which can help you to identify any potential bottlenecks.

EXPLAIN PLAN FOR <query_statement>

Replace <query_statement> with the SQL query you want to analyze. The EXPLAIN PLAN statement will return a detailed execution plan that shows the steps involved in executing the query.

Execution Plan Components

The execution plan consists of various components, each representing a different step in the query execution process. These components include:

  1. Estimated Row Count: Indicates the estimated number of rows that will be returned by the query.
  2. Table Scan: Indicates that the query is scanning an entire table.
  3. Index Scan: Indicates that the query is using an index to retrieve data.
  4. Join: Indicates that the query is joining two or more tables.
  5. Sort: Indicates that the query is sorting the results.
  6. Filter: Indicates that the query is filtering the results based on a condition.
  7. Aggregate: Indicates that the query is performing aggregation operations.

Interpreting the Execution Plan

To effectively interpret the execution plan, focus on the following aspects:

  1. Step Utilisation: Analyze which steps consume the most time and resource usage.
  2. Index Usage: Identify if appropriate indexes are being utilised to optimise data retrieval.
  3. Join Strategies: Evaluate join strategies for efficient data aggregation.
  4. Filter Conditions: Determine if filter conditions can be rewritten to improve performance.
  5. Sort Operations: Evaluate if sorting operations can be avoided or optimised.

Here are specific aspects to look for when analyzing query execution plans:

  1. Table Scans: Queries that perform full table scans on large tables will likely be fat pipes.
  2. Excessive Sort Operations: Queries involving extensive sorting operations can impact performance if not optimised.
  3. Inefficient Join Strategies: Queries utilizing inefficient join strategies may not use indexes or employ appropriate join algorithms, leading to performance bottlenecks.
  4. Inappropriate Filter Conditions: Complex or poorly-written filter conditions can cause unnecessary data processing, potentially leading to fat pipes.
  5. Missing or Inaccurate Statistics: Incomplete or inaccurate optimizer statistics can hinder the query optimizer’s ability to choose optimal execution plans, increasing the likelihood of fat pipes.

How to fix SQL fat pipes

There are a few ways to fix SQL fat pipes. These include:

  • Indexing: If the table scanned or sought does not have an appropriate index, you can create one. A properly designed index can significantly improve the performance of queries that scan or seek rows from a table.
  • Partitioning: If you have a large table, you can partition it into smaller tables. This can help to improve the performance of queries that scan or seek rows from a table, as the query can only scan or seek a subset of the partitions.
  • Using temporary tables: If you have a query that is scanning or seeking a large number of rows, you can use a temporary table to store the results of the scan or seek. This can help improve the query’s performance, as the query does not need to repeatedly scan or seek the same rows.


SQL fat pipes can be a performance issue that can affect the performance of your SQL Server databases.

By identifying and fixing SQL fat pipes, you can improve the performance of your databases and reduce the risk of performance problems.


Hi, my name is Stephen Finchett. I have been a software engineer for over 30 years and worked on complex, business critical, multi-user systems for all of my career. For the last 15 years, I have been concentrating on web based solutions using the Microsoft Stack including ASP.Net, C#, TypeScript, SQL Server and running everything at scale within Kubernetes.