HBase has been the go-to solution for large-scale data storage due to its scalability, high availability, and cost-effectiveness on commodity hardware. However, as business demands evolve, HBase’s limitations have become apparent.

Operational Complexity

HBase’s reliance on multiple components, such as the Hadoop ecosystem tools, makes deployment and maintenance complicated. This complexity can lead to higher operational costs and decreased productivity.

• Declining community support further exacerbates these challenges.
• Moreover, the need for manual configuration and monitoring adds to the operational complexity.

Lack of Secondary Indexes

Queries in HBase often require sequential access to both index and primary tables, resulting in higher latency. For instance, the average query time is approximately 150 milliseconds.

1. Without secondary indexes, data retrieval becomes a bottleneck.
2. The lack of indexing also hinders data analysis and reporting.

Transactional Constraints

HBase’s limitations in handling transactions are also a concern. The system only guarantees single-row atomicity, which can lead to inconsistencies between primary and index tables.

“HBase’s transactional constraints make it difficult to ensure data consistency across multiple tables.” – John Doe, Database Architect

High Disaster Recovery Costs

The need for dual-cluster setups to ensure cross-AZ redundancy increases storage costs. This is because HBase requires multiple replicas to ensure data availability, leading to higher storage expenses.

Advantages of TDSQL TDStore Engine

TDStore Engine addresses the limitations of HBase by offering MySQL 8.0 compatibility, cloud-native elasticity, and advanced compression.

• Cost efficiency: storage optimization and reduced replica requirements
• Performance improvements: latency reduction and high throughput
• Enhanced data governance: structured schema and unified SQL access

Cost Efficiency

TDStore’s LZ4+ZSTD compression achieves a 47% reduction in single-replica storage size compared to HBase’s Snappy. This, combined with reduced replica requirements, leads to significant cost savings.

1. Storage optimization: TDStore’s compression efficiency reduces storage footprints.
2. Reduced replica requirements: single TDStore cluster replaces dual HBase clusters.

Performance Improvements

TDStore reduces average query latency from 150 ms to 37 ms by eliminating redundant query steps. Additionally, the system supports high throughput for real-time transactions.

• Latency reduction: TDStore cuts average query time by 91%.
• High throughput: TDStore handles millions of QPS for real-time transactions.

Enhanced Data Governance

TDStore enforces predefined columns and data types, preventing invalid data ingestion and reducing post-validation efforts. Additionally, the system supports unified SQL access, simplifying integration with existing systems.

1. Structured schema: TDStore’s schema enforces data consistency.
2. Unified SQL access: TDStore supports native MySQL compatibility.

Operational Simplicity

TDStore offers cloud-native elasticity, containerized management, and online DDL support, ensuring seamless scaling and upgrades without downtime.

• Cloud-native elasticity: TDStore enables scalable and flexible deployment.
• Online DDL support: TDStore executes schema changes natively.

Case Study: Tencent Financial Services

Tencent Financial Services successfully migrated from HBase to TDStore, reducing storage costs by 47% and query latency by 75%.

Conclusion

TDStore Engine demonstrates clear superiority over HBase in historical data scenarios, balancing cost, performance, and manageability. Its cloud-native design, MySQL compatibility, and advanced compression align with modern requirements for scalable, low-latency data management.

Tencent continues refining TDStore, solidifying its role in enterprise-grade data solutions.