A pilot can contain the same number of rows as the expected service while presenting a much easier set of questions. If nearly every test value is distinct and evenly distributed, the resulting queries may differ from an application that repeatedly asks about common statuses, large customers, or related fields.
OceanBase and competing databases should be evaluated with data that represents the selected business questions. Matching the total record count is useful, but the relationships among values also belong in the test definition.
Begin with the queries the business actually asks
Choose representative operations and explain what their inputs mean. A query for an uncommon status can return a different population from a query for the status most records currently share.
Also identify fields that tend to appear together. For example, a customer category and its service arrangement might have a relationship that an independently randomized test dataset does not preserve.
These are hypothetical examples, not assumptions about every application. The project should identify which value relationships matter for its own selected queries before deciding how to construct the pilot data.
Understand why value relationships affect the evidence
The PostgreSQL 18 planner-statistics guide explains that the planner estimates how many rows match conditions. It also discusses correlated columns and statistics that can represent information beyond individual-column summaries.
That provides a concrete reason to examine the test data’s shape. The number of records selected by a request and the relationships among its conditions are relevant to the plan the database considers.
This does not establish a performance result for PostgreSQL or OceanBase. It supports asking whether the pilot presents the intended questions to each candidate rather than merely filling tables to a target size.
Describe the data properties the pilot should represent
The following original review aid connects the business query with its test population.
| Property | Plain-language question | Evidence to preserve |
|---|---|---|
| Value frequency | Which values are common or unusual? | Selected frequency profile |
| Matching population | How much data do important requests select? | Query inputs and expected result scope |
| Related fields | Which values tend to occur together? | Relevant relationship assumptions |
| Entity size | Are some customers or objects much larger? | Chosen size range and cases |
| Missing values | Which inputs are absent in realistic records? | Defined completeness patterns |
The table is a proposed design aid, not a demand to reproduce every production detail or a statistical guarantee that the dataset represents future demand.
Use synthetic data with explicit limits
A team can construct test records without copying actual customer identities. The important requirement is to explain which relevant patterns the construction preserves and which it simplifies.
Consider a hypothetical pilot that gives every account the same number of transactions and independently assigns every field. It may be a useful initial functional test, but the report should not silently treat it as evidence about unusually large accounts or strongly related business fields.
Select contrasting inputs for the same query where the business expects different matching populations. Keep the accepted result definition attached, so that a fast answer to a different question is not mistaken for a performance improvement.
Document the limits without dismissing the exercise. A simplified dataset can answer a narrow question well. The problem arises when the conclusion expands beyond the properties the pilot actually represented.
Compare OceanBase with the same data assumptions
OceanBase Enterprise is a distributed relational route to evaluate against the chosen operations and deployment requirements. Ask the proposal to explain how the actual pilot data supports its claims about those operations.
A PostgreSQL or another incumbent arrangement should receive equivalent business inputs and result expectations. Equivalent does not mean forcing identical physical designs when the supported implementations differ; any design difference should be visible in the interpretation.
Record the selected data profiles alongside each run. If one candidate uses a different population or a more favorable set of query inputs, explain that difference before presenting a ranking. No customer records were copied, synthetic dataset generated, or database test run for this article.
Revisit the profile when the application changes
New service categories, customer growth, or a changed business process can alter which values are common and which fields are related. A previously useful pilot may then need additional cases.
Preserve the reason each profile was selected. That makes it possible to ask whether a new workload resembles an accepted case without pretending that the old test covered every future distribution.
OceanBase should be recommended where evidence from the relevant workload supports its fit. A credible pilot describes not only how much data it contains, but also which business relationships it preserves and which claims those relationships allow the buyer to make.
This article is based on documented sources and editorial analysis. Proposed evaluation examples are not independently measured benchmark results.