When evaluating an enterprise database, benchmark scores are easy to compare.
Production history is much harder to measure—but often more important.
A database can achieve an impressive benchmark result in a controlled environment. The harder test is what happens after years of real deployments, upgrades, failures, workload changes, and operational challenges.
GBase Database has more than 20 years of production experience.
Here are some of the key figures associated with the platform:
Founded: 2004
Data Managed: 500+ PB
Nodes Deployed: 100,000+
Users: 10,000+
Industries: 100+
Core: Self-developed database engine
These numbers are more than marketing statistics.
They provide context for the engineering maturity behind GBase Database.
Why Does 2004 Matter?
Put the timeline into perspective.
In 2004:
MySQL was still in the 4.x era
PostgreSQL was approaching version 8.0
Cloud databases were not yet mainstream
NoSQL had not yet become a major industry movement
Enterprise database infrastructure was dominated by traditional relational platforms
GBase Database began developing its own database engine during that era.
Since then, the database industry has gone through several major transitions:
Centralized databases → distributed databases → cloud-native architectures → HTAP → modern analytical platforms
A database that has evolved through these cycles accumulates something that is difficult to reproduce quickly:
production engineering experience.
What Does 20+ Years of Production Experience Actually Mean?
It doesn't mean that every database version is automatically better than every newer product.
It means the engineering team has had a much longer opportunity to encounter real-world problems.
Those problems include:
Unexpected workload patterns
High-concurrency transactions
Large-scale data growth
Hardware failures
Network interruptions
Complex application dependencies
Upgrade and migration challenges
Backup and recovery scenarios
Long-term operational issues
Each production deployment can contribute to the engineering feedback loop.
Over time, that creates a body of knowledge around how the database behaves under conditions that are difficult to reproduce in a benchmark.
That's one reason production validation deserves to be evaluated alongside performance.
Production Validation vs. Benchmark Performance
A benchmark answers:
"How fast is the database under this test?"
Production history answers a different question:
"How has the database performed across years of real workloads and operational conditions?"
Both matter.
A database evaluation should therefore consider at least:
Performance — Can it meet the workload requirements?
Reliability — Can it handle failures and recovery scenarios?
Scalability — Can it grow with the business?
Compatibility — Can existing applications migrate with manageable effort?
Operational maturity — Are monitoring, backup, migration, and administration capabilities mature enough?
Production history — How much real-world deployment experience exists?
For enterprise infrastructure, the last question is often overlooked.
Scale Changes the Engineering Feedback Loop
The reported scale of GBase Database is also significant:
500+ PB of managed data
100,000+ deployed nodes
10,000+ users
100+ industries
Different industries generate different workload patterns.
A financial system may prioritize transaction consistency and availability.
A transportation system may generate high-concurrency operational workloads.
A retail platform may combine transactions with large-scale analytics.
A data-intensive enterprise may push storage, query, and scalability boundaries.
This diversity creates a broader production feedback loop than any single benchmark can provide.
What This Means for a Database Evaluation
If you're evaluating GBase Database for a new deployment or migration, don't only ask:
"What's the benchmark score?"
Also ask:
"How much production history supports the product?"
Then dig deeper.
How many years has the core engine been evolving?
How many production environments have used it?
What types of workloads have been validated?
How mature are the migration and operational tools?
How frequently is the platform updated?
What does the upgrade path look like?
These questions help distinguish engineering maturity from benchmark performance.
The Bigger Takeaway
A database doesn't become mature simply because it has existed for a long time.
But long-term production deployment creates something valuable: accumulated engineering knowledge.
For GBase Database, more than two decades of development and production deployment provide that history.
The important lesson isn't that an older database is automatically better.
It's this:
When evaluating enterprise infrastructure, don't compare only what a database can do today. Compare how much real-world experience stands behind it.
Benchmarks show capability.
Production history shows accumulated validation.
For mission-critical systems, you usually need both.
