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The fastest lakehouse SQL engine, replacing Trino/Presto, SparkSQL
Observability and Log Analytics
The most cost-effective alternative to Elasticsearch observability
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Use Case
Observability and Log Analytics
An observability platform collects, stores, and visualizes data like Logs, Traces, and Metrics to analyze a system's internal state. As the data volume increases and the environment becomes more and more complex, the following challenges rise.
Cost Efficiency
High costs for ingesting and storing vast data volumes, while keeping query performance, are a barrier set to rise with business growth.
Semi-Structured Data
JSON in logs and traces needs flexible schemas that also support high-performance storage and analysis.
Diversity and Openness
Diverse tech environments, from on-premises to multi-cloud, and rich ecosystems demand a more open and adaptable observability system.
10x Cost Effective Compared to Elasticsearch
Cut storage volume by 80% while keeping inverted index.
5x ingestion rate with inverted index
2x full-text search performance
Efficient and Flexible JSON Data
VARIANT data type extract JSON fields as sub-columns without ETL
10:1 compression ratio, 3x compared to text or binary JSON
8x analytical performance
Seamless Integration with Observability Ecosystem
Logstash collector and Kibana visualization in ELK
OpenTelemetry exporter and Grafana visualization
AI Observability with Langfuse and OpenLLMetry
Before, our LLM business had a slow, unstable logging system on Loki. Now, Apache Doris manages massive logs, ensuring over 99.9% availability and sub-second query latency on 100 million logs.
At Netease, high storage costs and slow queries on Elastcisearch were issues. Now, Apache Doris has cut storage costs by 70%, let us use SSDs for hot data at no extra cost, and sped up queries 11x with less CPU use.
We generate 14 billion log entries daily, totaling 80TB, with a total archive exceeding 40PB. Our old platform based on Elasticsearch suffered from high storage costs and poor ingestion performance... Switching to Apache Doris cut costs by 50% and sped up queries by 2 to 4 times.
Data Ingestion via HTTP
Logstash and Filebeat output plugins for the ELK ecosystem, OpenTelemetry exporter, Fluentbit output plugin, and RoutineLoad to pull Kafka messages.
High-Performance, Cost-Efficient Unified Storage Engine
Unified storage for logs, traces, and metrics with high compression ratios and fast querying capabilities, including full-text search and aggregation analysis.
Visual Search and Analysis
Compatible with two major visual observability tools, including Kibana in the ELK ecosystem (comming soon) and Grafana. Additionally, a dedicated GUI tool for log discover, VeloDB Studio, is provided.
1. Observability based on VeloDB
2. Elasticsearch vs ClickHouse vs Apache Doris — which powers observability better?
3. Why Apache Doris is a Better Alternative to Elasticsearch for Real-Time Analytics
4. Apache Doris for log and time series data analysis in NetEase, why not Elasticsearch and InfluxDB?
5. Creator of Talkie migrated from Loki and built a PB-scale logging system with Apache Doris
6. What is Observability
7. Demo: Building an Observability Platform with OpenTelemetry, VeloDB, and Grafana
8. Collect, store, and visualize logs using VeloDB Cloud
9. Benchmark: Elasticsearch vs. VeloDB Cloud for log analytics
10. Trace data analytics: OpenTelemetry and VeloDB
