Real-World Migration Performance: GBase Database Case Studies in Numbers

Synthetic benchmarks are useful for comparing database engines under controlled conditions.

But when evaluating a real database migration, another type of evidence can be even more valuable:

What happened after the system went into production?

Production environments introduce everything that synthetic benchmarks cannot fully reproduce: legacy SQL, unexpected workloads, operational incidents, concurrency spikes, data-quality issues, and years of accumulated application logic.

The following three GBase Database migration cases illustrate how different architectures performed under real enterprise workloads.

Case 1: Core Banking Migration to GBase Database(GBase 8s)

A major bank migrated four core banking systems from Oracle 11g RAC to GBase Database(GBase 8s) using a shared-storage cluster architecture with SSC and HAC.

The reported production figures include:

Source:              Oracle 11g RAC
Target:              GBase Database(GBase 8s) SSC + HAC
Systems migrated:   4 core banking systems
Continuous operation: 1,000+ days
Peak performance:   20,000 queries/sec
Reported errors:    0 during the referenced peak test
Operations cost:    Reduced by 25%+

The most interesting number here may not be the peak query rate.

It is the 1,000+ days of continuous operation.

A migration is not successful simply because the target database passes a benchmark immediately after deployment. For a core banking system, the real test is whether the platform continues to operate reliably through normal business cycles, peak periods, maintenance operations, and unexpected incidents.

This case also illustrates why architecture matters.

GBase Database(GBase 8s) uses a shared-storage architecture designed for enterprise transactional workloads. For systems coming from an Oracle RAC environment, this type of architecture can reduce the conceptual gap during migration compared with moving directly to a completely different distributed data model.

What this case demonstrates

For enterprise OLTP migrations, evaluate more than peak throughput.

Look at:

• Long-term production stability
• Transaction latency
• Concurrency
• Failover behavior
• Recovery time
• Operational workload
• Migration complexity

A database that performs well for one benchmark but creates significant operational complexity may not produce a better migration outcome.

Case 2: Bank Database Consolidation with GBase Database(GBase 8c)

The second case involves a city commercial bank running a combination of Oracle and multiple MySQL instances.

Instead of maintaining separate database platforms for different workloads, the bank migrated toward a unified GBase Database(GBase 8c) platform.

The reported improvements include:

Nightly batch:       2.5–3 hours → 30 minutes
Data synchronization: 2–3 hours → minutes
Failover:             Manual → automatic, seconds
Core query response:  44% improvement

The nightly batch result is particularly notable.

Reducing a 2.5–3 hour process to approximately 30 minutes represents a reduction of roughly 80–83%, depending on the original runtime.

But the larger lesson is architectural rather than purely computational.

Many enterprise environments accumulate multiple database technologies over time:

Oracle
   +
MySQL
   +
ETL jobs
   +
Synchronization tools
   +
Separate operational procedures

Each individual component may work perfectly well.

The problem appears at the system level.

Every additional platform can introduce another set of:

• Monitoring procedures
• Backup policies
• HA mechanisms
• Database skills
• Synchronization processes
• Upgrade cycles
• Troubleshooting workflows

A unified database platform can potentially reduce this operational complexity.

What this case demonstrates

Database migration should not be evaluated only through license cost.

A more complete calculation includes:

License
+ Hardware
+ Migration
+ Data synchronization
+ Operations
+ Monitoring
+ Backup
+ High availability
+ DBA skills

This is why GBase Database migration should be evaluated as a total operating model rather than a simple database replacement.

Case 3: Automotive Analytics with GBase Database(GBase 8a)

The third case has a very different workload profile.

An automotive manufacturer migrated an analytics workload from Oracle + Hadoop to GBase Database(GBase 8a MPP Cluster).

The reported workload characteristics were:

Source:             Oracle + Hadoop
Target:             GBase Database(GBase 8a)
Data volume:        280 GB metrics
Query latency:      >10 seconds → sub-second
Reported gain:      10x+
Architecture:      Consolidated analytical platform

At first glance, 280 GB may not sound particularly large by modern data-warehouse standards.

That is actually what makes the example useful.

Database performance is not determined by data volume alone.

Query structure, storage format, indexing, partitioning, compression, execution strategy, and parallel processing can all have a major impact.

For analytical workloads, a columnar MPP architecture can process relevant columns in parallel rather than treating the entire dataset like a traditional transactional workload.

This is where GBase Database(GBase 8a) differs fundamentally from the OLTP-oriented GBase Database(GBase 8s).

The optimization target is not simply transaction latency.

It is:

How efficiently can the platform scan, filter, aggregate, and process analytical data?

What These Three GBase Database Cases Have in Common

The three deployments use different products because they solve different problems.

The pattern is important.

There is no single performance number that defines database success.

For OLTP, you may care about:

P99 latency + concurrency + transaction reliability

For OLAP:

Query latency + throughput + scan efficiency

For HTAP:

OLTP latency under analytical load + analytical throughput + data freshness

And for migration projects:

Performance + compatibility + operational cost + stability

Why Production Data Matters

A database can achieve an impressive benchmark result and still fail a migration.

Why?

Because production systems contain things that benchmarks rarely capture:

Legacy SQL
Stored procedures
Unexpected data distributions
Irregular query patterns
Peak-hour concurrency
Long-running transactions
Operational scripts
Backup requirements
Failover procedures
Real user behavior

This is why a serious GBase Database migration should combine benchmark testing with production workload testing.

Start with the actual workload:

1. Collect the most frequently executed SQL.
2. Identify the top resource-consuming queries.
3. Extract stored procedures and critical business logic.
4. Measure concurrency and transaction patterns.
5. Reproduce representative production data volumes.
6. Test normal and peak workloads.
7. Measure performance before and after migration.
8. Rehearse failover and rollback.

The objective is not to prove that the target database wins a benchmark.

The objective is to prove that your application works better—or at least acceptably—after the migration.

The Bigger Lesson from GBase Database Migrations

These three cases point to the same conclusion:

Database migration performance is a production engineering problem, not just a benchmark problem.

GBase Database(GBase 8s), GBase Database(GBase 8c), and GBase Database(GBase 8a) target different workload patterns, so their performance should be evaluated against the requirements of those workloads.

The most useful metrics are therefore not isolated headline numbers.

They are measurable business outcomes:

• How long does the system run reliably?
• How fast are critical queries?
• How much concurrency can it sustain?
• How quickly can it recover?
• How much operational effort does it require?
• How much does the complete migration cost?

That is the difference between asking:

"Is this database fast?"

and asking the more useful question:

"Does this database perform reliably in my production environment?"

For any GBase Database migration, the second question is the one worth benchmarking.