feat: add private cloud practice blog by 唐成

blog/2026-06-05-ivorysql-private-cloud/index.md

@@ -0,0 +1,128 @@
+---
+slug: ivorysql-private-cloud
+title: PostgreSQL/IvorySQL in Private Cloud Practice
+authors: [唐成]
+category: IvorySQL
+image: img/blog/covers/private-cloud.jpg
+tags: [IvorySQL, PostgreSQL, Private Cloud, HOW2026, Cloud Native]
+---
+
+> This article is based on the talk given at HOW 2026 — by Tang Cheng, Senior Database Architect, Founder & CTO of Zqshucheng Technology, and author of "The Way to PostgreSQL Mastery".
+
+Recording: https://www.youtube.com/watch?v=HTJEjcgmBtA
+
+Over the past few years, "full cloud migration" was the dominant direction for database infrastructure. But as core business systems continue to scale, more enterprises are turning their attention back to private cloud architectures — especially in finance, government, and telecom, where databases must balance cloud capabilities with data security, resource control, cost optimization, domestic platform adaptation, high availability, and stability. Database private clouds have evolved from simple "resource virtualization platforms" into full-fledged data infrastructure platforms.
+
+## Why Choose PostgreSQL / IvorySQL for Private Cloud
+
+### Business Challenges
+
+As business scenarios grow more complex and data volumes explode, traditional database architectures reveal several pain points:
+
+1. **Insufficient elasticity**: Long expansion cycles, low resource utilization, unable to adapt to dynamic peaks and valleys.
+2. **Growing operational complexity**: Manual deployment, scaling, and failover can no longer support large-scale cluster management.
+3. **Security & compliance pressure**: Core business data demands higher data sovereignty, access control, and audit oversight.
+4. **Rising database costs**: Commercial license fees, high-end storage costs, and DBA expenses continue to grow — enterprises are paying more attention to TCO.
+
+### Private Cloud Advantages & Database Selection
+
+Private clouds emphasize unified resource management and platform capabilities. In this context, PostgreSQL and IvorySQL have become key choices. PostgreSQL relies on a mature open-source ecosystem, rich extensibility, and enterprise-grade reliability. IvorySQL builds on PostgreSQL compatibility while adding Oracle compatibility, making it especially suitable for domestic substitution and migration scenarios.
+
+### IvorySQL Advantages
+
+As a PG-based open-source database, 100% PG-compatible and highly Oracle-compatible, built for cloud-native environments:
+
+1. **100% PostgreSQL compatible**: Built on the latest PostgreSQL versions with all core features.
+2. **Highly Oracle compatible**: PL/iSQL syntax, data types, functions, stored procedures — dramatically reducing Oracle migration costs.
+3. **Apache 2.0 licensed**: Free to use, modify, and distribute without vendor lock-in.
+4. **Rich cloud-native ecosystem**: Full cloud-native toolchain supporting K8s deployment, elastic scaling, and automated operations.
+5. **Comprehensive Oracle compatibility**: 21 Oracle key features including ROWID, %ROWTYPE, %TYPE, NLS parameters, and case sensitivity.
+6. **Performance & stability**: PG 18.0 async I/O, skip scan, and enterprise-level optimizations.
+
+## Private Cloud Architecture Design
+
+The core of a database private cloud is infrastructure capability.
+
+### Infrastructure — Bare Metal Servers
+
+For high-concurrency, high-IO core workloads, bare metal remains a key deployment option — running databases directly on physical machines avoids virtualization overhead.
+
+
+Key considerations include NVMe SSDs, local high-performance storage, 10GbE/25GbE networking, and RDMA — especially critical for PostgreSQL streaming replication.
+
+
+### Infrastructure — Containers
+
+For systems demanding maximum resource utilization, Kubernetes (K8S) provides an elastic runtime for IvorySQL instances.
+
+
+The Operator pattern is the core component for HA deployment:
+
+- Operator: https://github.com/IvorySQL/ivory-operator/
+- Cloud Frontend: https://github.com/IvorySQL/ivory-cloud-web
+- Cloud Backend: https://github.com/IvorySQL/ivory-cloud
+
+### High Availability
+
+PostgreSQL HA is built on streaming replication. Production environments typically use primary-standby, synchronous replication, and automatic failover.
+
+
+### Database Performance Optimization
+
+Database performance tuning goes beyond parameter tweaking. Key areas include OS, database parameters, connection pooling, SQL execution efficiency, and storage I/O.
+
+
+Storage I/O is often the bottleneck — optimization spans RAID, filesystem, kernel scheduling, and disk partitioning:
+
+- RAID: HDD with cache RAID card (RAID10 preferred); NVMe with software RAID
+- Filesystem: XFS for production, optimized for high concurrency and large file I/O
+- I/O scheduler: `noop` for NVMe, `deadline` for regular disks
+- Read-ahead & alignment: partition alignment with physical sectors, appropriate read-ahead tuning
+
+## Intelligent Monitoring & Operations
+
+### Real-time Monitoring & Alerting
+
+The core goal of monitoring in a database private cloud is rapid problem detection and diagnosis.
+
+
+### Building Intelligent Monitoring
+
+Using open-source tools to upgrade monitoring from reactive alerting to proactive prediction:
+
+- **Metrics & Alerting**: Prometheus Operator with node_exporter and pg_exporter for host and database metrics
+- **Log Management**: EFK or Loki for centralized log collection
+- **Tracing Integration**: Database monitoring integrated with APM (SkyWalking, Jaeger) for end-to-end tracing
+- **Anomaly Detection**: Machine learning on historical metrics for baseline prediction and anomaly detection
+
+### Key Metrics
+
+- Host-level: CPU, memory, disk I/O, network throughput
+- Instance-level: active connections, cache hit ratio, TPS, QPS, lock wait count
+- Object-level: table CRUD frequency, index usage, index bloat, WAL generation rate
+- Session & SQL: slow queries, long transactions, idle transactions
+
+### Getting Database Metrics
+
+PostgreSQL provides built-in system views for granular performance metrics:
+
+- Session waits: `pg_wait_events`, `pg_stat_activity`
+- WAL: `pg_stat_wal`
+- Checkpointer: `pg_stat_checkpointer`
+- Async I/O: `pg_aios`
+- Memory & NUMA: `pg_stat_memory_usage`, `pg_shmem_allocations_numa`
+- Vector operations: `pg_stat_vector_ops`
+- Parallel queries: `pg_stat_parallel_queries`
+
+### Automated Operations
+
+As database scale grows, automation becomes essential — covering infrastructure as code, configuration management, and CI/CD pipelines.
+
+
+## Summary
+
+As enterprise core workloads continue to evolve toward cloud-native, database private clouds are no longer just about "putting databases in the cloud" — they are moving toward platform-centric, intelligent, and cloud-native infrastructure.
+
+PostgreSQL, with its stability, extensibility, and mature open-source ecosystem, has become a key technical foundation for database private clouds. IvorySQL builds on this foundation with enhanced Oracle compatibility and cloud-native capabilities, opening more possibilities for domestic database adoption and smooth migration.
+
+Operations are shifting from manual to automated, from reactive alerting to intelligent prediction, and from single-instance management to unified platform management. As AI-powered operations, self-service database platforms, and multi-modal data capabilities continue to evolve, database private clouds will gradually become complete data infrastructure platforms.

blog/authors.yml

@@ -11,4 +11,6 @@ Yasir Hussain Shah:
   url: https://www.data-bene.io/en/team/yasir-hussain-shah/
 严少安:
   name: 严少安
-  url: https://github.com/shawn0915
+  url: https://github.com/shawn0915
+唐成:
+  name: 唐成

i18n/zh-CN/docusaurus-plugin-content-blog/2026-06-05-ivorysql-private-cloud/index.md

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+---
+slug: ivorysql-private-cloud
+title: PostgreSQL/IvorySQL 私有云中实践
+authors: [唐成]
+category: IvorySQL
+image: img/blog/covers/private-cloud.jpg
+tags: [IvorySQL, PostgreSQL, Private Cloud, HOW2026, Cloud Native]
+---
+
+> 本文整理自 HOW 2026 中国数据库开源发展峰会暨 PostgreSQL 高峰论坛的演讲分享，演讲嘉宾：唐成，资深数据库架构师，中启乘数科技创始人及 CTO，《PostgreSQL修炼之道：从小工到专家》作者。
+
+[HOW 2026 演讲 PPT 下载方式](https://mp.weixin.qq.com/s/PVPEjr4lagmWleBnN34dpw)
+
+该演讲录屏：https://www.bilibili.com/video/BV1jaLx63EZw/
+
+过去几年，"全面上云"一度成为数据库建设的主流方向。但随着核心业务规模不断扩大，越来越多企业开始重新关注私有云架构。尤其在金融、政企、运营商等场景下，数据库不仅需要云化能力，同时还要兼顾数据安全、资源可控、成本优化、国产化适配、高可用与稳定性。数据库私有云，也逐渐从"资源虚拟化平台"演变为完整的数据基础设施平台。
+
+## 为何选择 PostgreSQL / IvorySQL 构建私有云
+
+### 业务发展的挑战
+
+伴随业务场景复杂化、数据体量爆发式增长，传统数据库架构在业务快速增长过程中，逐渐暴露出多个问题：
+
+1. **弹性能力不足**：传统数据库架构扩容周期长，资源利用率低，难以适配业务高峰与低谷之间的动态变化。
+2. **运维复杂度持续提升**：随着数据库实例规模扩大，人工部署、人工扩容、人工切换等传统运维方式已经难以支撑大规模数据库集群管理。
+3. **安全与合规压力增加**：核心业务数据对数据主权、访问控制、审计监管提出更高要求，越来越多行业开始强调数据本地化与资源可控。
+4. **数据库成本持续增长**：商业数据库授权费用、高端存储成本以及 DBA 运维成本不断增加，企业开始更加关注数据库总体拥有成本（TCO）。
+
+### 私有云的优势与数据库选型考量
+
+相比传统数据库部署模式，私有云更强调资源统一管理与平台化能力建设。在这一背景下，PostgreSQL 与 IvorySQL 成为了数据库私有云的重要选择。PostgreSQL 依靠成熟稳定的开源生态、丰富扩展能力以及企业级可靠性，已经成为当前最主流的开源数据库之一。而 IvorySQL 则在兼容 PostgreSQL 生态基础上，进一步增强 Oracle 兼容能力，更适合国产化替代与数据库迁移场景。
+
+### IvorySQL 的优势
+
+作为基于 PG 的开源数据库，100% 兼容 PG 并高度兼容 Oracle，为云原生而生，是企业构建私有云数据库的理想选择：
+
+1. **100% 兼容 PostgreSQL**：IvorySQL 基于最新的 PostgreSQL 版本进行研发，继承了 PG 的所有核心功能与优势。
+2. **高度兼容 Oracle**：提供包括 PL/iSQL 语法、数据类型、函数及存储过程等的 Oracle 兼容性，大幅降低企业从 Oracle 迁移的成本与风险。
+3. **遵循 Apache 2.0 协议**：采用宽松的开源协议，允许用户自由使用、修改和分发，提供了最大的技术自主权。
+4. **丰富的云原生生态**：提供全套云原生工具链，支持在 K8s 等容器平台上快速部署、弹性伸缩和自动化运维。
+5. **全面的 Oracle 兼容性**：新增对 ROWID、%ROWTYPE、%TYPE、NLS 参数、大小写敏感等 21 个 Oracle 关键特性的支持。
+6. **性能与稳定性提升**：基于 PG 18.0 的异步 IO、跳跃扫描等新特性，提供卓越的性能和高并发处理能力。
+
+## 私有云架构设计
+
+数据库私有云的核心，本质上是基础设施能力的构建。
+
+### 基础设施规划——裸金属服务器
+
+对于高并发、高 IO 的核心业务，裸金属依然是数据库部署的重要方案。数据库直接运行在物理机上，可以绕过虚拟化层，获得更低的性能损耗。
+
+![img](11.png)
+
+在实际部署中，通常会重点关注 NVMe SSD、本地高性能存储、万兆 / 25G 网络、RDMA 网络。
+
+![img](22.png)
+
+尤其是在 PostgreSQL 主备复制场景中，网络时延会直接影响同步效率。
+
+### 基础设施规划——容器
+
+对于要求极致资源利用率的系统，采用 Kubernetes（K8S）作为容器编排平台，为 IvorySQL 实例提供一个弹性的运行基座。
+
+![img](33.png)
+
+其中 `Operator` 是实现数据库高可用部署的核心组件。IvorySQL 适配自身数据库特性：
+
+- Operator：https://github.com/IvorySQL/ivory-operator/
+- IvorySQL Cloud 前端：https://github.com/IvorySQL/ivory-cloud-web
+- IvorySQL Cloud 后端：https://github.com/IvorySQL/ivory-cloud
+
+### 高可用体系
+
+PostgreSQL 的高可用体系主要建立在流复制基础之上。在生产环境中，通常采用一主多备、同步复制、自动故障切换等方式保障数据库稳定运行。
+
+![img](44.png)
+
+### 数据库性能优化
+
+数据库性能优化并不是简单"调几个参数"。真正影响数据库性能的，通常包括操作系统、数据库参数、连接池管理、SQL 执行效率、存储 IO。
+
+![img](55.png)
+
+存储 IO 是数据库性能短板高发区，从 RAID 选型、文件系统、内核调度、磁盘分区四个维度落地优化：
+
+- RAID 配置：机械盘配缓存 RAID 卡，优选 RAID10；NVMe 采用软 RAID
+- 文件系统：生产环境统一使用 XFS，适配高并发与大文件 IO 场景
+- I/O 调度器：NVMe 配置 noop，普通磁盘选用 deadline，压低 IO 延迟
+- 预读与对齐：分区对齐物理扇区，合理调控预读参数，削减冗余 IO 损耗
+
+## 智能监控与运维
+
+### 实时监控与告警
+
+数据库私有云环境中，监控体系的核心目标是快速发现问题、定位问题。
+
+![img](66.png)
+
+### 构建智能监控
+
+依托开源工具实现监控从被动告警到主动预判的升级：
+
+- **Metrics 采集与告警**：使用 Prometheus Operator 部署 Prometheus 和 Alertmanager，通过 node_exporter 和 pg_exporter 采集指标
+- **日志平台化管理**：利用 EFK 或 Loki 等日志系统，集中收集和存储所有数据库实例的日志
+- **链路追踪一体化**：将数据库监控与 APM 系统打通，实现从应用端到数据库端的全链路追踪
+- **异常检测与预测**：利用机器学习算法对历史监控指标建模，实现基线预测和异常点检测
+
+### 关键指标监控
+
+全面精准地采集数据库各项指标，重点关注：
+
+- 主机层指标：CPU、内存、磁盘 IO、网络吞吐量
+- 实例层指标：活跃连接数、缓存命中率、TPS、QPS、锁等待数量
+- 对象层指标：表的增删改查频次、索引使用率、表索引膨胀率、WAL 日志产生速度
+- 会话与 SQL 指标：慢查询数量、长事务数量、空闲事务数量
+
+### 获取数据库指标
+
+PostgreSQL 依靠内置系统视图采集细分性能指标：
+
+- 会话等待：`pg_wait_events`、`pg_stat_activity`
+- WAL 运行：`pg_stat_wal`
+- 检查点：`pg_stat_checkpointer`
+- 异步 IO：`pg_aios`
+- 内存与 NUMA：`pg_stat_memory_usage`、`pg_shmem_allocations_numa`
+- 向量运算：`pg_stat_vector_ops`
+- 并行查询：`pg_stat_parallel_queries`
+
+### 自动化运维实践
+
+随着数据库规模不断扩大，自动化运维已经成为数据库私有云的重要组成部分。目前常见实践包括基础设施即代码、配置管理自动化、CI/CD 流水线。
+
+![img](77.png)
+
+## 总结
+
+随着企业核心业务持续向云化演进，数据库私有云建设已经不再只是简单的"数据库上云"。从基础设施规划到高可用体系建设，从性能优化到监控与自动化运维，数据库平台正在逐渐向云原生化、平台化、智能化方向发展。
+
+PostgreSQL 凭借稳定性、扩展能力与成熟开源生态，已经成为数据库私有云的重要技术底座。而 IvorySQL 在兼容 PostgreSQL 生态的基础上，进一步增强 Oracle 兼容能力与云原生适配能力，也为企业数据库国产化与平滑迁移提供了更多可能。
+
+从人工运维转向自动化运维，从被动告警转向智能预警，从单实例管理转向统一平台化管理，数据库运维体系正在发生深刻变化。
+
+未来，随着 AI 运维、自助式数据库服务以及多模数据能力持续发展，数据库私有云也将逐渐演变为完整的数据基础设施平台。

i18n/zh-CN/docusaurus-plugin-content-blog/authors.yml

@@ -11,4 +11,6 @@ Yasir Hussain Shah:
   url: https://www.data-bene.io/en/team/yasir-hussain-shah/
 严少安:
   name: 严少安
-  url: https://github.com/shawn0915
+  url: https://github.com/shawn0915
+唐成:
+  name: 唐成