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Surveillance scenarios are changing, but the underlying systems are still stuck in the past
As business evolves, the data accessed by the observable platform is no longer limited to basic monitoring metrics, but also includes application logs, links, user experience, and business data. As data scale, query concurrency, and retention cycles continue to grow, traditional architectures tightly coupled with computing and storage tend to encounter bottlenecks in scaling efficiency and resource utilization.
There are a large number of duplicate queries in dashboards, monitors, and daily troubleshooting. GuanceDB 3.0 identifies query patterns suitable for pre-aggregation, builds aggregated results during the data write phase, reduces the overhead of high-frequency queries repeatedly scanning raw data, and schedules query resources according to actual load.
Traditional MPP architecture vs. GuanceDB 3.0
This is a comparison between the traditional MPP architecture and the GuanceDB 3.0 architecture, highlighting how after decoupling storage and compute, query resources can be independently scheduled according to business loads, offering more flexible choices between performance, cost, and resource isolation.
Traditional MPP architecture
Computing and storage are tightly coupled, with limited scalability. Computing resources are difficult to adjust dynamically, overall utilization is low, and expansion costs are high.
A fully separated storage engine for the AI era
It adopts integrated data lakehouse design and separation of storage-compute, allowing data storage and query computation to expand independently. The system leverages the elasticity of cloud infrastructure to adjust query resources according to business load and optimize resource costs according to different computing power types. For example, computing resources are expanded during weekday peak queries, and idle computing power is reduced during nights and weekends. Actual capacity, query concurrency, and elasticity range are subject to deployment specifications and service quotas.
Tailored to the needs of different enterprises
Through intelligent scheduling, multi-tenant optimization, and elastic computing power provision, GuanceDB 3.0 achieves a comprehensive evolution in performance, cost, and flexibility of the observable data processing architecture, helping enterprises efficiently build more cost-effective data infrastructure.
The system has built multi-level resource scheduling strategies based on different enterprise usage scenarios:
01. High cost-performance users
Priority is given to the use of shared computing pools, and idle computing power in the system can be flexibly called during peak periods to maximize resource reuse
02. Performance-sensitive users
Configure independent query computing clusters to ensure stable and fast response capabilities in any scenario
03. Small and medium-sized teams
Unified access to the shared pool, the system uses concurrency control mechanisms to ensure the query experience while keeping costs controllable
04. Users who prioritize storage
By adopting independent processing paths, computing resources are stripped, further reducing system load and cost
Brand new streaming aggregation acceleration engine
When writing data, it automatically builds queries that need acceleration based on users' historical queries, and preaggregates data in minimal time shards. When the query finds that relevant data already has pre-aggregated results within the current time frame, it directly retrieves data from streaming aggregation rather than the original data.
Transparent acceleration
For indicators and log queries suitable for pre-aggregation, the business side does not need to rewrite existing dashboards and monitors; the system automatically reuses aggregated results
Any data time
Aggregate data based on the time marked by the data itself, and combine write and update mechanisms to handle delayed data reporting
Lower resource usage
Transfers high-frequency queries suitable for pre-aggregation to stream aggregation result responses, reducing the burden of repeated raw data scanning and database query pressure
Advantages of GuanceDB 3.0
Supports a wide range of business scenarios
Different read clusters can be provided for different business scenarios, using different configuration schemes (high concurrency, offline tasks, etc.) and isolating each separately
Real-time queries
Batch reports
Business analysis
Data Mining
GuanceDB 2.0 The privatized version will continue to be served
FAQ
GuanceDB addresses the challenges of storing, querying, aggregating acceleration, and long-term retention of large-scale observable data, suitable for scenarios where logs, metrics, links, RUM, events, and business data continue to grow.
Storage-compute separation allows storage and query resources to scale independently. Teams can adjust compute resources based on query peaks, long-term retention, and cost requirements without having to bind all data and compute into the same cluster.
Stream aggregation acceleration is suitable for high-frequency dashboards, monitors, and fixed analysis paradigms. The system can build pre-aggregated results based on historical queries, reducing the cost of rescanning raw data each time.
