WSO2 IS Performance Optimization Avoiding JDBC Userstore Hit

Introduction

In WSO2 Identity Server (IS) deployments, it's common to integrate both Lightweight Directory Access Protocol (LDAP) and Java Database Connectivity (JDBC) userstores. Ensuring optimal performance across these diverse userstores is crucial for maintaining a smooth and responsive identity management system. This article delves into a specific performance challenge related to retrieving user counts from the /scim2/Users endpoint, particularly when the consider_total_records_for_total_results_of_ldap configuration is enabled. We will explore the performance implications, the underlying mechanisms, and strategies to avoid performance hits in JDBC userstores.

Understanding the Challenge: The consider_total_records_for_total_results_of_ldap Configuration

The consider_total_records_for_total_results_of_ldap configuration setting plays a pivotal role in determining how the total number of users is calculated when querying LDAP userstores via the System for Cross-domain Identity Management (SCIM) /scim2/Users endpoint. When enabled, this setting ensures that the totalResults value returned in the response accurately reflects the total number of users matching the applied filter. This is particularly important for scenarios where pagination or other result-set limiting techniques are employed. To retrieve an accurate totalResults value from the GET /scim2/Users endpoint for LDAP userstores, the configuration consider_total_records_for_total_results_of_ldap must be enabled.

However, the implementation details behind this seemingly straightforward feature introduce a performance trade-off. Unlike JDBC userstores, LDAP userstores do not inherently support a single query mechanism to efficiently count users based on a filter. Instead, to accurately determine the total count, all matching users must be loaded into memory. This "load-all-then-count" approach can become a significant performance bottleneck, especially in environments with a large number of users or complex filter criteria. The performance overhead introduced by loading all matching users into memory to compute the count is a crucial consideration, and users must be aware of the potential impact before enabling this configuration.

The Unexpected Impact on JDBC Userstores

The core issue addressed in this article is an unexpected side effect of enabling the consider_total_records_for_total_results_of_ldap configuration. While the primary intention is to ensure accurate user counts for LDAP userstores, its implementation inadvertently extends the same "load-all-then-count" behavior to JDBC userstores as well. When this setting is enabled, JDBC userstores also follow the same behavior—loading all matching usernames into memory to determine the count. This can be seen in the implementation, which means that even though JDBC userstores are capable of efficiently counting users using database-native queries, they are forced to adopt the less performant in-memory counting method when this configuration is active. This is a critical performance regression because JDBC userstores are typically backed by relational databases, which are highly optimized for count operations. For JDBC userstores, a more efficient approach is available via the doCountUsersWithClaims method, which directly issues a COUNT query to the database based on the provided filter.

Diving into the Code: Identifying the Bottleneck

To understand why this performance hit occurs, let's examine the relevant code snippets within WSO2 IS. The key area of concern lies in how the user count is determined when the consider_total_records_for_total_results_of_ldap configuration is enabled. The SCIMUserManager implementation shows that when the configuration is enabled, the system retrieves all matching users and then counts them in memory. While this approach is necessary for LDAP, it bypasses the optimized counting mechanisms available for JDBC. For JDBC userstores, a more efficient approach is available via the doCountUsersWithClaims method, which directly issues a COUNT query to the database based on the provided filter.

In the AbstractUserStoreManager, the implementation reveals that the system retrieves all matching usernames into memory to determine the count when the consider_total_records_for_total_results_of_ldap configuration is enabled. This is where the performance bottleneck manifests for JDBC userstores, as it disregards their ability to perform efficient count operations directly at the database level. For JDBC userstores, the doCountUsersWithClaims method provides a more efficient approach by directly issuing a COUNT query to the database based on the provided filter.

Conversely, the JDBCUserStoreManager implementation showcases the doCountUsersWithClaims method, which efficiently issues a SQL COUNT query to the database. This method leverages the database's built-in counting capabilities, providing a significantly faster way to determine the total number of users matching a filter. The fact that this optimized method is bypassed when the consider_total_records_for_total_results_of_ldap configuration is enabled highlights the performance optimization opportunity.

Steps to Reproduce the Performance Issue

To demonstrate the performance impact, the following steps can be used to reproduce the issue:

1. Set up a WSO2 IS instance with both LDAP and JDBC userstores configured.
2. Populate the JDBC userstore with a substantial number of users (e.g., tens of thousands or more).
3. Enable the consider_total_records_for_total_results_of_ldap configuration.
4. Use the SCIM /scim2/Users endpoint to list users with a filter that matches a significant portion of the users in the JDBC userstore.
5. Observe the response time. It will be notably slower compared to when the configuration is disabled.

Checking user listing and filtering performance on JDBC userstore by enabling consider_total_records_for_total_results_of_ldap config will highlight the performance degradation.

Mitigating the Performance Hit: Strategies and Solutions

Several strategies can be employed to mitigate the performance hit on JDBC userstores when the consider_total_records_for_total_results_of_ldap configuration is enabled:

1. Conditional Configuration: The most straightforward solution is to conditionally enable the consider_total_records_for_total_results_of_ldap configuration only when querying LDAP userstores. This would involve modifying the code to check the userstore type before applying the "load-all-then-count" logic.
2. Optimized Counting for JDBC: Implement a mechanism to leverage the doCountUsersWithClaims method in JDBC userstores, even when the consider_total_records_for_total_results_of_ldap configuration is enabled. This would ensure that JDBC userstores always use their optimized counting capabilities.
3. Configuration per Userstore: Introduce a configuration setting that allows enabling the consider_total_records_for_total_results_of_ldap behavior on a per-userstore basis. This provides fine-grained control over the counting mechanism for each userstore.
4. Asynchronous Counting: For scenarios where the exact totalResults value is not critical, consider implementing an asynchronous counting mechanism. This would allow the SCIM endpoint to return a partial result set quickly, while the total count is calculated in the background. This approach can improve responsiveness, although it may sacrifice immediate accuracy.

Best Practices for Userstore Performance

Beyond the specific issue addressed in this article, several best practices can help ensure optimal userstore performance in WSO2 IS:

• Database Optimization: Regularly optimize the underlying database for JDBC userstores. This includes tasks such as indexing, query optimization, and database tuning.
• LDAP Optimization: For LDAP userstores, ensure that the LDAP server is properly indexed and configured for performance.
• Connection Pooling: Use connection pooling for JDBC userstores to minimize the overhead of establishing database connections.
• Caching: Implement caching strategies to reduce the number of calls to the userstores. WSO2 IS provides caching mechanisms that can be leveraged for user authentication, authorization, and attribute retrieval.
• Monitoring and Profiling: Regularly monitor the performance of userstore operations and use profiling tools to identify bottlenecks.

Conclusion

The consider_total_records_for_total_results_of_ldap configuration in WSO2 IS, while essential for accurate user counts in LDAP userstores, can inadvertently degrade performance in JDBC userstores. By understanding the underlying mechanisms and potential performance implications, administrators and developers can implement strategies to mitigate this issue. By conditionally applying the configuration, leveraging optimized counting methods for JDBC, or adopting asynchronous counting techniques, it's possible to maintain both accurate user counts and optimal performance across diverse userstore environments. Remember that a proactive approach to performance optimization, coupled with adherence to best practices, is key to ensuring a responsive and efficient identity management system.

This article highlights the importance of understanding the nuances of configuration settings and their potential impact on different components within a complex system like WSO2 IS. By carefully evaluating performance trade-offs and implementing appropriate mitigation strategies, organizations can build robust and scalable identity management solutions.