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Use Cases
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.
Archive 5x ingestion rate.
Accelerate full-text search performance by 2x.
Flexible Semi-Structured Data Variant Type
Automatically detect fields in JSON and store them as separated columns.
Archive 3x compression ratio.
Boost analytical performance by 8x with flexible schema adaptation.
Openness and Consistency
Integrate with most popular observability tools like ELK, OpenTelemetry and Grafana.
Provide consistent multi-cloud service on AWS, GCP, and Azure.
Major contributor to Apache Doris open-source project.
Easy to Operate and Use
Easy-to-use visual Cluster Manager for operations.
Support horizontal scaling and auto-balancing.
Easy-to-use standard SQL and MySQL compatible interface for users and developers.
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.
With VeloDB inverted index,VARIANT data type, and tiered storage for hot and cold data, it slashed our total costs by 70% compared to Elasticsearch and supercharged our query performance by 200% to 400%.
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.
Guides, reference manuals, and deep dive - all the technical documentation about observability.
Explore practical applications and gain valuable experiences shared by users across various industries.
Demo tutorials, including how to integrate with OpenTelemetry and how to use VeloDB Studio for log discover.
Join dedicated observability group on Slack and special category on the Forum to ask question and get support.
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