A potential solution for scalability in centralized logging systems is the use of multiple centralized-logging systems. This approach allows for distributing the logging load across several systems, which can accommodate an increasing volume of logs without degrading performance. When a single centralized system becomes overwhelmed by the sheer amount of data being processed, implementing additional centralized logging systems can alleviate bottlenecks. By creating a multi-tiered architecture where logs can be processed by different systems based on criteria like region, type of log, or application, organizations can ensure that logging remains efficient and scalable.
In contrast, decentralized logging methods can sometimes lead to challenges in managing and correlating data from multiple sources, making it difficult to maintain a unified view. Increased storage capacity is helpful but does not inherently address performance or processing issues related to scalability. Log filtering techniques also improve efficiency but primarily focus on reducing the volume of logs rather than enabling the system to handle a greater load effectively. Thus, utilizing multiple centralized logging systems effectively addresses scalability concerns by distributing both the data processing and storage needs.