Reducing Oracle RAC Wait Events by Using Instance-Specific Block Allocation for Production Applications

White paper by Murali Natti

Abstract:

Oracle Real Application Clusters (RAC) is a robust and highly available solution for critical production environments, designed to allow multiple database instances to share the same database. While Oracle RAC provides excellent scalability and fault tolerance, it introduces significant complexities in terms of wait events, especially when applications experience high contention for shared data blocks. One of the primary causes of poor application performance in Oracle RAC is the high overhead associated with inter-instance communication (cache fusion), where instances must exchange and synchronize shared blocks across the cluster. This is particularly problematic for applications that frequently access commonly used tables or objects, resulting in high wait times, degraded response times, and reduced throughput.

This white paper describes a novel approach to alleviate these Oracle RAC wait events by tying specific application instances to individual Oracle RAC nodes, thereby minimizing inter-instance communication. By allocating commonly used tables or objects to specific database nodes rather than having all instances access shared blocks, we reduced contention, optimized database access, and improved overall application response times.

1. Introduction: The Challenge of Oracle RAC Wait Events

Overview of Oracle RAC

Oracle RAC enables multiple database instances to access a single physical database, providing high availability, scalability, and fault tolerance. However, the need for instances to communicate with each other and share data across the cluster can lead to significant wait events. This inter-instance communication, often referred to as cache fusion, happens when an instance requests a block that another instance currently holds in its memory.

For production-critical applications that rely on real-time data access, Oracle RAC's distributed architecture can cause:

• High latency: When instances must share data blocks, the response times increase as blocks are moved between nodes.
• Wait events: Common wait events like gc cr request, gc buffer busy and gc current block busy are associated with this block-sharing behavior, leading to performance degradation.
• Decreased throughput: The time spent waiting for data blocks from other instances results in higher response times, which in turn slows down application performance, particularly for high-volume transactional systems.

The Impact on Applications

When an application spends considerable time waiting on Oracle RAC inter-instance communications, the effects are noticeable:

• Increased application latency: This manifests as delays in retrieving data, leading to slower transactions and reduced user satisfaction.
• Resource contention: Excessive inter-node communication creates bottlenecks, causing CPU, I/O, and memory resources to be consumed inefficiently.
• Performance inconsistency: The application’s performance may fluctuate based on the workload, leading to unpredictable behavior under heavy load conditions.

2. The Proposed Solution: Instance-Specific Block Allocation for Critical Applications

Concept Overview

In traditional Oracle RAC configurations, all instances in the cluster share access to the same set of data blocks, regardless of which instance the application is running on. This global sharing of blocks increases the probability of cache contention, as multiple instances frequently access the same data, causing inter-instance waits and high communication overhead.

To mitigate this problem, we propose a solution that ties specific application workloads to individual Oracle RAC nodes by configuring instance-specific services. By associating particular application workloads with specific instances (i.e., nodes), and limiting the number of instances that access critical data, we can significantly reduce the contention for shared data blocks.

Key Components of the Solution:

• Node-specific Service Configuration: Each application is directed to a specific node or set of nodes within the Oracle RAC cluster by configuring a dedicated instance service for each workload.
• Partitioning Tables and Objects: Tables or objects that are commonly accessed by the application are placed on specific nodes within the RAC cluster, so that each node holds its own copies of these blocks, thereby reducing the need for inter-instance communication.
• Application-level Workload Binding: The application is configured to connect to the database instances that hold the specific data required for its workload. By ensuring that the application accesses its own instance's data, cache fusion wait events can be minimized.

3. How the Solution Works: Step-by-Step Breakdown

a. Identifying Commonly Accessed Tables and Objects

The first step in implementing this solution is to analyze the application’s database workload and identify tables or objects that are frequently accessed by multiple instances. These commonly accessed tables tend to cause the most contention when they are spread across all nodes.

• Usage Analytics: Using tools like AWR reports, V$Session, and V$SQL views, we can identify the high-volume tables and objects that contribute to inter-instance contention.
• Application Query Profiling: Monitoring the application’s database queries helps pinpoint the critical paths and data access patterns that frequently cause high wait events.

b. Configuring Node-Specific Services

Once we’ve identified the high-traffic tables, the next step is to configure node-specific services in Oracle RAC.

• Service Binding: Each Oracle RAC node is configured with a specific service that binds particular application instances to a node. This ensures that the application connects only to the nodes that hold the most relevant data.
• Affinity Settings: The Oracle RAC service configuration ensures that each node has an affinity for the application workload it serves, improving locality and minimizing inter-instance communication.
• Load Balancing and Failover: Although the application is bound to a specific node, load balancing and failover mechanisms ensure that the system remains resilient and capable of handling node failures without significant disruption.

c. Partitioning Data Across Nodes

The most critical part of the solution is partitioning the high-access tables and data objects across individual Oracle RAC nodes.

• Table Partitioning: We can use table partitioning to ensure that frequently accessed data is localized to specific nodes, reducing the need for inter-node cache fusion. By partitioning large tables into smaller segments based on application needs, we can ensure that each partition resides on a specific node.
• Table Placement: Oracle RAC allows us to place partitions of a table on specific nodes. This means the nodes that are most likely to access the data (based on application behavior) will store the corresponding data, reducing the dependency on other nodes.
• Optimizing Data Locality: By ensuring that related application data is kept on the same node, we can achieve better data locality and reduce the number of cache fusion requests that result in wait events.

d. Validating the Solution

Once the service configuration and data partitioning are in place, we need to validate the effectiveness of the solution.

• Benchmarking: Use SQL trace and AWR reports to measure pre- and post-implementation performance. Metrics to monitor include application response times, read and write latency, and wait events related to cache fusion.
• Load Testing: Conduct load testing to simulate peak production workloads and observe how well the application performs under high contention conditions.

4. Results: Performance Improvements Achieved

By applying this solution, we achieved significant improvements in both read and write response times for the critical application, and we saw a drastic reduction in Oracle RAC wait events.

• Reduced Cache Fusion Waits: The most notable improvement was a reduction in wait events such as gc cr request, gc buffer busy, and gc current block busy. These events were drastically reduced because the data that the application needed was mostly available locally on the same node.
• Improved Application Latency: By minimizing inter-instance communication, the application response times for both read and write operations decreased significantly, resulting in faster transaction processing and improved user experience.
• Better Resource Utilization: The workload was balanced across nodes more effectively, resulting in better CPU and I/O utilization and a decrease in unnecessary network traffic between RAC instances.

5. Conclusion: A Scalable Solution for Reducing Oracle RAC Wait Events

The approach of binding critical application workloads to specific Oracle RAC nodes and partitioning high-traffic data objects can significantly reduce Oracle RAC wait events. By optimizing the way data is distributed across RAC instances and minimizing inter-instance cache fusion, this solution improves application response times, reduces contention, and enhances the overall performance of production systems.

This solution is particularly beneficial for large-scale applications with high-volume, transactional workloads that experience high cache contention in Oracle RAC environments. By reducing wait events and optimizing data access, businesses can achieve better scalability, more efficient resource utilization, and improved end-user satisfaction.