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TencentDB TDStore Online DDL: Technological Evolution and Innovations Background & Challenges
a. Introduces to enable (e.g., adding trailing columns, extending fields). Historical data automatically fills default values, ensuring backward compatibility.
a. : Reduces transaction conflicts by ignoring stale writes, improving DDL-DML parallelism.
b. : Divides DDL into three stages ( → → ) to ensure global consistency and smooth transitions.
a. Validates request versions at the storage layer, allowing writes only between adjacent states to eliminate data inconsistency risks.
a. : Splits data into SST files for multi-node parallel ingestion via , bypassing timestamp comparisons to achieve (10 minutes vs. 2.3 hours).
1.
a. (single-node): 16 threads took 2.3 hours.
b. (multi-node): 48 threads completed in 10 minutes, showcasing significant efficiency improvements.
2.
a. : Use partitioning to distribute data evenly, enabling parallel DDL execution.
b. : Combine secondary partitioning for rapid cleanup and elastic scaling.
c. : Align partition keys with frequent query fields; set partition count as multiples of node numbers.
3.
a. : Increase parallel threads (≤ total node CPUs).
b. : Enable mode to unlock multi-node parallel acceleration.
TDStore overcomes traditional OnlineDDL limitations through distributed architecture innovations and engineering practices, delivering for financial-grade scenarios. It empowers enterprises to tackle massive data challenges effectively.
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