Stop staring at database comparison tables.
Before comparing vendors, answer these 8 questions about your own workload, architecture, and operational requirements.
Once you have the answers, the candidate pool usually becomes much smaller.
Q1: Is My Workload Transaction-Heavy, Analysis-Heavy, or Mixed?
This is the first filter.
Don't rely on intuition. Measure the workload.
If you're using a PostgreSQL-compatible environment with pg_stat_statements, you can start with a simple DML/SELECT split:
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;
This is only a first-level indicator.
A SELECT could be a point lookup or a billion-row aggregation.
So after the initial split, examine:
Query latency
Rows scanned
Query frequency
Concurrency
CPU and I/O consumption
Transaction duration
Aggregation workload
The first decision should be based on workload shape, not database brand.
Q2: How Large Is the Data—and How Fast Is It Growing?
Don't ask only:
"How much data do we have today?"
Ask:
"How much data will we have in three years, and how will applications access it?"
Data volume alone doesn't determine architecture.
A 50 TB transactional database may still be a strong OLTP workload if most queries are selective point lookups.
A smaller database may require distributed analytical processing if queries frequently scan and aggregate large portions of the dataset.
Measure:
Current data volume
Annual growth
Peak storage requirements
Typical scan volume
Query concurrency
Retention period
Data distribution
The key is the combination of scale + growth + access pattern.
Q3: Do You Want One Platform or Multiple Specialized Engines?
This is one of the most important architecture questions.
Suppose you have both heavy transactions and heavy analytics.
You generally have two broad approaches:
Option A
One platform
├── Transaction processing
└── Analytical processing
Option B
Two specialized platforms
├── OLTP database
└── OLAP database
↑
CDC / ETL
The first approach can simplify data movement and reduce synchronization complexity.
The second can allow each database to specialize in its workload.
Neither is automatically better.
The trade-offs include:
Data freshness
Infrastructure cost
Pipeline complexity
Operational overhead
Performance isolation
Skill requirements
Failure domains
This is where an HTAP architecture may become relevant.
For example, GBase Database(GBase 8c) targets mixed transactional and analytical workloads, while GBase Database(GBase 8s) and GBase Database(GBase 8a) focus more directly on transactional and analytical workload patterns.
The architecture should follow the workload—not the other way around.
Q4: What Compliance and Security Requirements Are Non-Negotiable?
Some database requirements aren't optimization targets.
They're hard gates.
Depending on your industry and deployment environment, you may need to evaluate:
Classified protection requirements
Commercial cryptography requirements
Security and reliability evaluations
Data residency
Audit requirements
Encryption
Access control
Security logging
Disaster recovery requirements
For financial institutions and government environments, these requirements can eliminate candidates before performance testing even begins.
So ask:
"What must the database support before we even start the POC?"
Build those requirements into the qualification checklist first.
Q5: Where Will the Database Run?
Deployment architecture can significantly narrow the candidate pool.
Are you planning:
Physical servers?
Virtual machines?
Containers?
Private cloud?
Public cloud?
Hybrid cloud?
Then add the operational constraints:
Kubernetes support
Storage architecture
Network requirements
Resource isolation
Hardware compatibility
Monitoring
Backup infrastructure
High-availability topology
A database that performs well in a lab may not fit your production deployment model.
So don't separate database selection from deployment selection.
They are connected decisions.
Q6: How Mature Is the Migration Toolchain?
Database migration is not just:
Export → Import → Done
The real workflow can include:
Assessment
↓
Schema conversion
↓
SQL / procedure compatibility
↓
Data migration
↓
Incremental synchronization
↓
Object comparison
↓
Application validation
↓
Performance testing
↓
Cutover
↓
Rollback
This is why migration tooling can directly affect project duration and engineering effort.
The source database matters because different migrations create different compatibility problems.
For example:
Oracle → evaluate Oracle-oriented compatibility
MySQL / PostgreSQL → evaluate syntax and application compatibility
Analytical platforms → evaluate analytical SQL and MPP architecture compatibility
For GBase Database, the relevant question isn't simply:
"Does GBase Database support my source database?"
It's:
"How much of the migration workflow can the toolchain automate, validate, and reproduce?"
That is a much more useful question.
Q7: What Do Your HA and Recovery Requirements Actually Mean?
"High availability" is too vague to be useful.
Define the numbers first.
For example:
RPO = 0
means you cannot tolerate data loss.
RTO < 10 seconds
means recovery must happen within a very short window.
Those requirements have direct architectural implications.
You need to evaluate:
Replication mechanism
Shared storage vs distributed replicas
Automatic failover
Split-brain protection
Recovery time
Data consistency
Disaster recovery
Cross-site recovery
Different architectures can achieve similar business objectives through very different mechanisms.
For example, a shared-storage architecture and a multi-replica distributed architecture should not be evaluated using exactly the same assumptions.
The right question is:
"What recovery outcome does the business require?"
Then choose the architecture that can consistently deliver it.
Q8: What Workload Will Your POC Actually Test?
This may be the most important question of all.
Vendor benchmarks can tell you what a platform can achieve under a particular benchmark configuration.
They cannot tell you exactly how your production workload will behave.
A meaningful POC should include:
Your SQL
Use your actual:
Top CPU-consuming SQL
Slow SQL
High-frequency SQL
Stored procedures
Triggers
Application-generated SQL
Your data
Use representative:
Data volume
Data distribution
Data skew
Historical data
Index structure
Your concurrency
Test realistic:
Average concurrency
Peak concurrency
Transaction rate
Analytical concurrency
Your failure scenarios
Don't test only:
"Does it run?"
Also test:
"What happens when something fails?"
Include:
Node failure
Storage failure
Network interruption
Failover
Recovery
Backup and restore
Rollback
A POC should reproduce the conditions that matter to production.
The 8 Questions in One Framework
Before selecting a database, answer:
| Question | What it determines |
|---|---|
| 1. What is the workload shape? | OLTP / HTAP / OLAP direction |
| 2. How large and fast-growing is the data? | Scaling requirements |
| 3. One platform or multiple engines? | Architecture complexity |
| 4. What compliance is mandatory? | Candidate qualification |
| 5. Where will it run? | Deployment compatibility |
| 6. How mature is migration tooling? | Migration effort and risk |
| 7. What are the HA requirements? | Recovery architecture |
| 8. What will the POC test? | Production fit |
Only after answering these should you compare individual products.
Where Does GBase Database Fit?
This framework also provides a practical way to evaluate the GBase Database family.
If your workload is primarily enterprise OLTP, evaluate:
GBase Database(GBase 8s)
If your workload combines significant transactional and analytical processing, evaluate:
GBase Database(GBase 8c)
If your workload is dominated by large-scale analytical processing, evaluate:
GBase Database(GBase 8a)
But don't stop at the product mapping.
Validate:
Workload → Architecture → Compatibility → Migration → POC → Operations
That's the actual selection process.
The One-Liner
Database selection isn't:
"Which brand is the best?"
It's:
"Which architecture fits my workload, constraints, and operational requirements?"
Answer the eight questions first.
Then build the shortlist.
Then run the POC.
Classify first. Compare second. Validate last.
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