Nityananda Gohain
About Nityananda Gohain
Nityananda Gohain is a member of the SigNoz Team who has extensively analyzed and compared the performance of SigNoz with ElasticSearch and Loki across various parameters.
Nityananda Gohain SigNoz Team
Nityananda Gohain is affiliated with the SigNoz team, actively contributing to the development and promotion of their products. SigNoz is a robust observability platform designed to monitor and debug applications, offering an alternative to widely used solutions like ElasticSearch (ELK) and Loki (PLG stack). He has been key in benchmarking and comparing the performance of SigNoz with these other tools, providing valuable insights and data-backed conclusions to help users make informed choices.
Nityananda Gohain Blog Post on SigNoz vs. ELK and Loki
Nityananda Gohain authored a blog post comparing SigNoz with ElasticSearch (ELK stack) and Loki (PLG stack) on various performance metrics, including ingestion, query, and storage. His detailed analysis found that SigNoz performs significantly better in several areas. Specifically, SigNoz was 2.5x faster than ELK for data ingestion and used 50% fewer resources. For aggregation queries, SigNoz outperformed ELK by being 13 times faster. Gohain also highlighted that SigNoz used about half the storage for the same amount of log data compared to ELK.
SigNoz Performance Benchmark by Nityananda Gohain
In his performance benchmark study, Nityananda Gohain utilized flog, a fake log generator, to create logs. The benchmarking deployed four VMs: three for generating logs and one for deploying the logging solution. Gohain's analysis revealed that SigNoz has a superior performance due to its cluster architecture, which includes OTEL collectors on the receiver side. He attributed the efficient storage management of SigNoz to its use of ClickHouse, which employs a Skip Index method, unlike ElasticSearch which indexes everything, leading to a difference in storage resource consumption.
Nityananda Gohain on Loki's Performance Issues
According to Nityananda Gohain, while running his comparisons, he found that Loki does not perform well when it comes to indexing and querying high cardinality data. This insight is crucial for developers and IT professionals who require efficient log management and retrieval for applications that generate large amounts of diverse data. Gohain's evaluation provides a clear understanding of the limits and capabilities of different log management systems.
High Disk Utilization Importance by Nityananda Gohain
Nityananda Gohain emphasized the importance of maintaining high disk utilization to prevent data drop due to backpressure from the disk running at full capacity (100%). He argued that efficient disk utilization is critical for the optimal functioning of observability solutions like SigNoz, ensuring that data ingestion and querying processes run smoothly without any interruptions caused by hardware limitations.