This article explains linearizability in distributed systems, compares Raft read strategies, and describes how CB‑SQL uses range leases and node leases to achieve strong consistency while reducing overhead, illustrating the trade‑offs and evolutionary design choices behind its consensus mechanisms.
This article explains how CB‑SQL implements follower reads by using safe (closed) timestamps, describes the CT update mechanism with a Minimum Proposal Tracker, and discusses routing, replica read validation, timestamp forwarding, range split/merge handling, and recovery strategies for consistent distributed reads.
This article explains CB‑SQL's two backup approaches—logical (using DUMP and IMPORT) and physical (using BACKUP and RESTORE)—detailing their mechanisms, supported formats, storage options, performance characteristics, and how they ensure reliable data recovery for large‑scale distributed databases.
The article explains CB‑SQL’s distributed changefeed architecture, its CDC implementation, the challenges of horizontal scalability and transactional ordering, and the innovative RangeFeed mechanism that enables ordered row‑level streams, resolved timestamps, and seamless integration with external systems like Kafka.
The article explains how distributed databases can perform online, asynchronous schema changes without blocking reads or writes by using lease‑based coordination, intermediate delete‑only and write‑only states, and a multi‑step algorithm inspired by Google F1 and implemented in CB‑SQL.
This article introduces CB‑SQL, a MySQL‑compatible elastic database built on CockroachDB and RocksDB, explains RocksDB’s LSM‑tree design, details key configuration parameters, and describes the read/write/clear/file‑operation optimizations employed by CB‑SQL to achieve high scalability and stability.
This article explains how CB‑SQL improves distributed transaction performance by introducing a transaction record table, parallel prepare requests, one‑phase commit for single‑shard writes, latch‑based consistent reads, transaction pipelines, and a staged parallel‑commit mode, dramatically reducing latency and I/O overhead.
