Ankit Anand
About Ankit Anand
Ankit Anand, a member of the SigNoz Team, has conducted extensive benchmarking to compare SigNoz with other logging solutions like ELK and PLG, demonstrating significant performance advantages.
Ankit Anand SigNoz Team Contributions
Ankit Anand is a member of the SigNoz team, where he has played a significant role in improving and benchmarking the performance of the platform. Through rigorous testing and analysis, Anand has demonstrated that SigNoz is 2.5 times faster than the ELK stack (Elasticsearch, Logstash, and Kibana) and consumes 50% less resources during data ingestion. These findings underscore the efficiency and speed benefits of using SigNoz for log management and analysis.
SigNoz vs ELK and PLG Stack Performance Comparison
Ankit Anand conducted extensive performance comparisons between SigNoz, the ELK stack, and the PLG (Promtail, Loki, and Grafana) stack. His research revealed that SigNoz is about 13 times faster than ELK for aggregate queries and uses around half the storage for the same amount of logs. Anand's observations also highlighted that Loki underperforms in scenarios requiring the indexing and querying of high cardinality data, placing SigNoz as a superior solution in these contexts.
Performance Benchmarking with SigNoz Cluster Architecture
Ankit Anand spearheaded the implementation of a SigNoz cluster architecture to benchmark performance against other logging solutions. The benchmark utilized four virtual machines – three dedicated to log generation and one for deploying the logging solution. For log generation, Anand used flog, a fake log generator. This systematic approach enabled a detailed evaluation of CPU, memory, and disk usage during ingestion, providing clear insights into the advantages of SigNoz in a real-world deployment environment.
Evaluating Query Performance in Log Management
Ankit Anand evaluated the query performance of SigNoz using various commonly utilized query types. His assessments were thorough, covering both ingestion and storage efficiency. Anand's work indicated that SigNoz not only handles high ingestion loads effectively but also provides fast and reliable query results, outperforming the ELK stack in both speed and resource utilization. This makes SigNoz a highly competitive option for companies seeking efficient log management solutions.
Importance of Disk Utilization in Log Management
Through his research, Ankit Anand emphasized the crucial role of high disk utilization in log management to prevent data loss due to backpressure. His findings support the idea that effective disk management and resource allocation are essential for maintaining high performance and data integrity in logging systems, reinforcing the importance of choosing efficient solutions like SigNoz for large-scale data operations.