Analyzing Active Queries and Transactions in MySQL

Analyzing Active Queries and Transactions in MySQL: A Deep Dive into Monitoring and Optimization


Monitoring and analyzing active queries and transactions in a MySQL database is crucial for identifying performance bottlenecks, detecting long-running queries, and understanding the transactional state of the database. In this technical blog, we will explore a powerful query that combines information from the information_schema.processlist and information_schema.innodb_trx tables to provide insights into active queries and transactions. Let’s dive in!

Query Overview

The following query retrieves detailed information about active queries and associated transactions:

t.ID AS 'Process ID',
t.db AS 'Database Name',
t.user AS UserName, AS Host,
t.command AS Command,
t.time AS QueryTime,
t.INFO as Info,
p.trx_state AS 'Transaction State',
p.trx_query AS 'Transaction Query',
p.trx_lock_structs AS 'Lock Structures',
p.trx_tables_locked AS 'Tables Locked',
p.trx_rows_locked AS 'Rows Locked',
p.trx_lock_memory_bytes AS 'Lock Memory Usage (Bytes)',
p.trx_isolation_level AS 'Transaction Isolation Level'
information_schema.processlist AS t
information_schema.innodb_trx AS p ON = p.trx_mysql_thread_id
t.command != 'Sleep'
AND t.time >=10;

Explanation and Use Case:

This query combines data from the information_schema.processlist table, which provides information about active database connections, with the information_schema.innodb_trx table, which contains details about InnoDB transactions. Let’s explore the columns included in the result set and their significance:

  1. Process ID: Unique identifier of the active process or connection.
  2. Database Name: Name of the database associated with the process.
  3. UserName: User executing the query or process.
  4. Host: The host from which the connection originates.
  5. Command: The type of command being executed (e.g., SELECT, UPDATE, INSERT).
  6. QueryTime: Duration of the query or process in seconds.
  7. Info: Additional information about the query or process.
  8. Transaction State: The state of the associated transaction, if applicable.
  9. Transaction Query: The query associated with the transaction, if applicable.
  10. Lock Structures: The number of lock structures used by the transaction.
  11. Tables Locked: The number of tables locked by the transaction.
  12. Rows Locked: The number of rows locked by the transaction.
  13. Lock Memory Usage (Bytes): Amount of memory used by locks in the transaction.
  14. Transaction Isolation Level: The isolation level set for the transaction.

This query helps identify long-running queries (QueryTime >= 10 seconds) and provides insights into the transactional state for active connections. By analyzing the results, you can pinpoint resource-intensive queries, detect potential locking issues, and understand the workload on the database.

Real-life Use Case

Consider a scenario where you are experiencing performance degradation in your MySQL database. By executing this query, you can identify queries that have been running for a significant amount of time (QueryTime >= 10) and investigate their impact on the overall system. The associated transaction information can help identify potential locking conflicts and understand the isolation levels used.


Monitoring and analyzing active queries and transactions in MySQL is crucial for maintaining optimal database performance. By leveraging the combined information from the information_schema.processlist and information_schema.innodb_trx tables, you can gain valuable insights into query execution, transaction states, and locking behavior. This allows you to optimize query performance, troubleshoot issues, and ensure smooth operation of your MySQL database.

Remember to use this query judiciously, as it involves querying system tables and can impact database performance. It’s recommended to run this query during periods of high activity or when troubleshooting specific issues.

We hope this technical blog has provided you with a useful tool for monitoring and analyzing active queries and transactions in MySQL. Happy querying!

About Shiv Iyer 330 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.