OLTP vs OLAP vs HTAP: How to Choose the Right GBase Database Product

Choosing a database should start with one question:

What kind of workload are you running?

OLTP, OLAP, and HTAP workloads have very different performance requirements. A database optimized for high-concurrency transactions is not automatically the best choice for large-scale analytical queries, and an analytical engine is not necessarily suitable for transaction-heavy applications.

The GBase Database product family follows this workload-oriented approach with three main product lines:

• GBase Database(GBase 8s) for enterprise OLTP
• GBase Database(GBase 8a MPP Cluster) for large-scale OLAP
• GBase Database(GBase 8c) for mixed OLTP and OLAP workloads

This article breaks down the differences using practical SQL examples and workload characteristics.

1. OLTP: High-Concurrency Short Transactions

OLTP workloads are typically characterized by frequent transactions, relatively small data changes, strict consistency requirements, and high concurrency.

A typical banking transaction might look like this:

BEGIN WORK;

UPDATE accounts
SET balance = balance - 1000
WHERE id = 12345;

UPDATE accounts
SET balance = balance + 1000
WHERE id = 67890;

COMMIT WORK;

The query itself is simple.

The challenge is executing thousands or tens of thousands of similar transactions concurrently while maintaining predictable latency and transactional consistency.

For this workload, GBase Database(GBase 8s) is designed around centralized enterprise transaction processing and shared-storage architecture.

Key considerations include:

• High transaction throughput
• Low transaction latency
• Strong consistency
• High concurrency
• High availability
• Predictable recovery behavior

A metro-system deployment using the GBase Database shared-storage architecture has been reported at around 30,000 concurrent connections and 1.6 million TPMC under its specific production environment.

The important point is not that every OLTP application will achieve the same number.

The lesson is that OLTP performance should be evaluated under realistic concurrency and transaction patterns, rather than through isolated query execution.

When should you consider GBase Database(GBase 8s)?

It is a natural fit when your application looks like:

Many users
    ↓
Short transactions
    ↓
Frequent INSERT / UPDATE / DELETE
    ↓
Strict consistency requirements
    ↓
Predictable low-latency response

Typical workloads include core transaction systems, banking applications, transportation systems, and other enterprise systems where transaction reliability is more important than large-scale analytical scans.

2. OLAP: Large-Scale Analytical Queries

OLAP workloads have almost the opposite characteristics.

Instead of updating a few rows thousands of times per second, an analytical system may scan millions or billions of rows and perform complex aggregation.

For example:

SELECT
    region,
    product_category,
    year_month,
    SUM(revenue) AS total_revenue,
    RANK() OVER (
        PARTITION BY region
        ORDER BY SUM(revenue) DESC
    ) AS rank
FROM sales_fact
WHERE year >= 2025
GROUP BY
    region,
    product_category,
    year_month;

This query is not transaction-heavy.

It requires the database to process a large amount of data, perform grouping, aggregation, sorting, and window functions, and return the result efficiently.

This is where GBase Database(GBase 8a MPP Cluster) fits the workload.

GBase Database(GBase 8a) uses a columnar MPP architecture designed for large-scale analytical processing.

Important performance factors include:

• Data scan throughput
• Parallel execution
• Aggregation performance
• Data distribution
• Columnar storage
• Compression
• Query concurrency

For analytical workloads, selecting only the columns required by the query can also make a significant difference:

SELECT
    product_category,
    SUM(revenue) AS total_revenue
FROM sales_fact
WHERE year >= 2025
GROUP BY product_category;

The database does not need to process unrelated columns simply because they exist in the table.

A reported automotive-industry deployment reduced the execution time of a 280 GB analytical workload from more than 10 seconds to sub-second execution under its specific test conditions.

Again, the benchmark number should be treated as a workload-specific result, not a universal guarantee.

For OLAP, the right question is:

How efficiently can the database process a large amount of data in parallel?

3. HTAP: Transactions and Analytics on the Same Data

Some applications do not fit neatly into either OLTP or OLAP.

They need to process transactions while also running analytical queries against relatively fresh data.

This is the problem HTAP is designed to address.

Consider an order-processing system:

-- Continuous transactional workload

INSERT INTO orders
VALUES (...);

At the same time, the business may need near-real-time analysis:

SELECT
    product_id,
    SUM(quantity) AS total_quantity,
    AVG(unit_price) AS avg_price
FROM orders
WHERE order_date = CURRENT_DATE
GROUP BY product_id;

The challenge is supporting both workloads without allowing analytical processing to significantly interfere with transactional operations.

GBase Database(GBase 8c) addresses this scenario with a multi-engine architecture combining row, columnar, and in-memory processing.

The idea is straightforward:

                 GBase Database(GBase 8c)
                         |
          +--------------+--------------+
          |              |              |
       Row Engine    Column Engine   In-Memory
          |              |              |
        OLTP           OLAP        Low-latency access

Instead of forcing every workload through the same storage and processing model, different workload characteristics can use the appropriate engine.

A city commercial-bank deployment has been reported to reduce a nightly batch process from approximately 2.5 hours to 30 minutes.

For HTAP evaluation, however, batch-processing time alone is not enough.

You should also test:

• Transaction latency during analytical queries
• Analytical query latency during peak transactions
• CPU and memory utilization
• Concurrency
• Data freshness
• Resource isolation

The real question is not simply whether the database can run OLTP and OLAP.

It is:

Can it run both workloads without unacceptable interference?

4. OLTP vs OLAP vs HTAP: Quick Comparison

The important point is that there is no universally "best" GBase Database architecture.

The appropriate choice depends on the workload.

5. How to Choose a GBase Database Product

A simple decision process can help:

Choose GBase Database(GBase 8s) when:

Your workload is dominated by:

• INSERT / UPDATE / DELETE
• Short transactions
• High concurrency
• Strict transactional consistency
• Predictable low latency

Choose GBase Database(GBase 8a MPP Cluster) when:

Your workload is dominated by:

• Large data scans
• Complex aggregation
• Business intelligence
• Data warehouse workloads
• Parallel analytical queries

Choose GBase Database(GBase 8c) when:

Your workload requires:

• Continuous transactions
• Analytical queries on fresh data
• Mixed OLTP and OLAP
• Multiple processing engines
• Near-real-time business analysis

6. Don't Choose Based on One Benchmark

A benchmark can tell you something about database performance.

It cannot tell you whether the architecture matches your application.

Before selecting a GBase Database product, build a workload profile covering:

Transaction rate
Concurrent sessions
Read/write ratio
Data volume
Query complexity
P99 latency
Peak-hour workload
Analytical scan volume
Data freshness requirements

Then test the actual workload.

For OLTP, measure latency under concurrency.

For OLAP, measure scan and aggregation throughput.

For HTAP, measure both workloads at the same time.

That produces a much more useful result than comparing a single benchmark score.

Conclusion

OLTP, OLAP, and HTAP are not simply three database marketing terms. They represent fundamentally different workload patterns.

GBase Database(GBase 8s) focuses on high-concurrency transactional workloads.

GBase Database(GBase 8a MPP Cluster) focuses on large-scale analytical processing.

GBase Database(GBase 8c) targets mixed transactional and analytical workloads.

The practical rule is simple:

Choose the architecture that matches the workload, then benchmark that workload under realistic conditions.

For GBase Database, product selection should start with workload classification—not with the question of which product has the highest benchmark number.