This article shares practical experience from Volcengine's managed Prometheus service and its deep integration with large‑model and autonomous‑driving platforms, explaining what high cardinality is, its impact on monitoring systems, root causes, and a range of design, collection, and analysis techniques to mitigate it.
This article explains the challenges of high‑cardinality GroupBy operations, describes SLS's underlying implementation and session‑based optimizations, and presents three real‑world test cases that demonstrate up to an eight‑fold speed improvement for massive data aggregations.
This article explains why high‑cardinality GroupBy queries are slow, describes SLS's underlying aggregation pipeline, and shows how adjusting session parameters and enabling high‑cardinality optimizations can reduce query times from dozens of seconds to just a few seconds across three real‑world test scenarios.
This article explains why time‑series databases are ideal for massive telemetry data, describes the high‑cardinality problem that degrades performance, and shows how openGemini’s newly introduced columnar engine—combined with sorting and clustering indexes—effectively mitigates those issues while delivering impressive write and query speeds.
This article explains Loki's advantages over Elasticsearch, including low indexing overhead, concurrent query processing with caching, seamless integration with Prometheus and Grafana, detailed architecture components, installation steps, label handling, high‑cardinality challenges, and best practices for efficient log management.
