Every database selection meeting eventually produces the same question:
"Isn't distributed inherently more advanced than centralized?"
Not necessarily.
Database architecture should match workload requirements, not technology fashion.
This is particularly important when evaluating GBase Database. Instead of treating centralized and distributed architectures as a simple "old vs. new" comparison, it is more useful to ask what each architecture is designed to optimize.
Centralized vs. Distributed: What Actually Changes?
The two architectures make different engineering trade-offs.
| Dimension | Centralized + Shared Storage | Distributed Multi-Node |
|---|---|---|
| Consistency | Shared data set, strong consistency is straightforward | Multiple replicas with distributed consistency |
| Scalability | Primarily vertical and shared-storage scaling | Horizontal scaling |
| Architecture | Relatively simple and mature | More components and distributed coordination |
| HA focus | Strong consistency, fast failover | Replica availability and distributed recovery |
| Typical workload | Core OLTP, critical transactions | Distributed applications, mixed workloads |
| Main advantage | Predictability and consistency | Elasticity and horizontal expansion |
Neither column is universally better.
The important question is:
Which set of trade-offs matches your workload?
Where Centralized Architecture Makes Sense
A centralized database architecture can be a strong choice when the workload prioritizes:
strong transaction consistency
predictable latency
high concurrency
RPO=0 requirements
fast failover
mature operational processes
For these scenarios, adding distributed nodes is not automatically an improvement.
If the workload is fundamentally transaction-heavy, introducing distributed coordination can add complexity without solving the actual business problem.
This is the architecture direction of GBase Database(GBase 8s), which targets enterprise transaction workloads and supports shared-storage cluster deployments.
Production example
A Shenzhen Metro ACC deployment uses a shared-storage cluster architecture for a high-concurrency transaction workload.
The reported environment covers 16 metro lines and handles peak daily passenger volumes of approximately 8.48 million, with reported concurrency around 30,000 and transaction performance reaching approximately 1.6M TPMC.
The important lesson is not the benchmark number.
It is that a centralized architecture can still be the right engineering choice for a modern, mission-critical system.
Where Distributed Architecture Makes Sense
Distributed database architecture becomes more attractive when the workload requires:
horizontal scaling
increasing node capacity
distributed application deployment
elastic resource expansion
geographically distributed services
mixed application workloads
In these scenarios, distributing data and processing across multiple nodes can provide a better scaling path.
This is the direction of GBase Database(GBase 8c), which is designed for distributed application and mixed-workload scenarios.
Production example
A Huatai Securities deployment uses a three-node distributed cluster.
According to the reported case, the original environment experienced instability as connected devices increased from around 2,000, while the distributed deployment remained stable at approximately 4,000 devices and supported transparent failover in under one minute.
Again, the lesson is architectural:
Distributed architecture becomes valuable when horizontal growth and distributed availability are actual requirements.
And What About Analytical Workloads?
There is another important distinction.
Not every workload should be classified simply as "centralized" or "distributed OLTP."
Large-scale analytical workloads have different requirements:
scanning large datasets
aggregation
parallel execution
columnar storage
compression
high-throughput analytical queries
For these workloads, an MPP architecture can be a better fit.
This is where GBase Database(GBase 8a MPP Cluster) targets analytical processing.
A Sinotruk analytical workload provides one example: reported query response times for approximately 280 GB of metrics data improved from more than 10 seconds to sub-second performance.
The takeaway is not that MPP is always faster.
It is that architecture determines which performance characteristics the database can optimize for.
How to Choose the Right GBase Database Architecture
Before selecting a GBase Database product, classify the workload.
Start with four questions:
1. Is the workload transaction-heavy or analysis-heavy?
Measure actual production activity rather than relying on the application description.
A simple first-level workload test can separate transactional operations from analytical queries:
-- Conceptual workload self-test
SELECT
SUM(CASE
WHEN command IN ('INSERT', 'UPDATE', 'DELETE')
THEN 1 ELSE 0
END) AS tp_ops,
SUM(CASE
WHEN command = 'SELECT'
THEN 1 ELSE 0
END) AS ap_ops
FROM pg_stat_statements;
The exact implementation should be adapted to the database and monitoring environment, but the principle is useful:
Measure first. Choose architecture second.
2. How much horizontal scalability do you actually need?
Ask:
How fast is data growing?
How fast is concurrency growing?
Can the workload scale vertically?
Does the application require adding nodes dynamically?
If horizontal expansion is not a real requirement, distributed architecture may introduce complexity without providing proportional value.
3. What are your HA requirements?
Define:
RPO
RTO
failover behavior
consistency requirements
disaster recovery requirements
A system requiring RPO=0 and extremely predictable transaction behavior may prioritize a different architecture from a cloud-native application designed for elastic expansion.
4. What workload will exist three years from now?
Database selection should not only solve today's problem.
Estimate:
data growth
user growth
transaction growth
analytical demand
deployment changes
Then evaluate whether the architecture still fits the expected workload.
The GBase Database Architecture Map
A practical way to think about the GBase Database family is:
Transaction-heavy → GBase Database(GBase 8s)
Distributed / mixed workloads → GBase Database(GBase 8c)
Analytical / MPP workloads → GBase Database(GBase 8a MPP Cluster)
This does not mean one GBase Database product is universally better than another.
It means the product family covers different architectural requirements.
That is the more useful way to think about database selection.
Stop Asking Which Architecture Is More Advanced
"Centralized vs. distributed" is the wrong first question.
The better sequence is:
Workload → Scale → Consistency → HA → Architecture → Database
A centralized architecture is not obsolete simply because distributed databases are popular.
A distributed architecture is not automatically better simply because it scales horizontally.
And an MPP architecture is not automatically the right answer simply because the dataset is large.
TIL takeaway:
Centralized and distributed databases are not a ranking. They are different engineering trade-offs. Choose the architecture that matches your workload — then choose the GBase Database product that fits that architecture.
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