PGSQL on Pigsty v5.0

被动故障切换

Mon, 01 Jan 0001 00:00:00 +0000

infographic list-row-simple-horizontal-arrow
data
  title 租约过期故障切换流程
  desc 当整个节点宕机，Patroni 无法主动释放租约，只能等待 TTL 过期
  items
    - label 租约过期
      desc Patroni 失联，被动等待主库租约 TTL 过期
      icon mingcute/close-circle-fill
    - label 从库检测
      desc 从库从循环中醒来后发现租约过期，开始竞选
      icon mingcute/key-2-fill
    - label 抢锁提拔
      desc 从库相互比较并抢锁，胜利者提升自己的PG
      icon mingcute/radar-fill
    - label 健康检查
      desc HAPROXY 健康检查发现新主上线，分配流量
      icon mingcute/arrow-up-circle-fill
theme light
  palette antv

RTO 时序图

tooltip: { trigger: axis, axisPointer: { type: shadow }, formatter: $fn:fmt }
legend: { top: 0, itemGap: 12, data: [租约过期, 从库检测, 抢锁提拔, 健康检查] }
grid: { left: 64, right: 24, bottom: 32, top: 40 }
xAxis: { type: value, name: 秒, nameLocation: end, max: 160, axisLine: { show: true }, axisTick: { show: true }, splitLine: { show: true, lineStyle: { type: dashed, opacity: 0.5 } }, minorTick: { show: true, splitNumber: 5 }, minorSplitLine: { show: true, lineStyle: { type: dotted, opacity: 0.2 } } }
yAxis: { type: category, axisLine: { show: true }, axisTick: { show: true }, splitLine: { show: false }, axisLabel: { fontSize: 10, fontFamily: monospace }, data: [wide-max, wide-avg, wide-min, "", safe-max, safe-avg, safe-min, "", norm-max, norm-avg, norm-min, "", fast-max, fast-avg, fast-min] }
series:
  - { name: 租约过期, type: bar, stack: main, barWidth: 20, z: 2, emphasis: { focus: series }, itemStyle: { color: "#e15759" }, data: [120, 110, 100, "-", 60, 55, 50, "-", 30, 27, 25, "-", 20, 17, 15] }
  - { name: 从库检测, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#edc949" }, data: [20, 10, 0, "-", 10, 5, 0, "-", 5, 3, 0, "-", 5, 3, 0] }
  - { name: 抢锁提拔, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#59a14f" }, data: [2, 1, 0, "-", 2, 1, 0, "-", 2, 1, 0, "-", 2, 1, 0] }
  - { name: 健康检查, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#4e79a7" }, data: [8, 6, 4, "-", 6, 5, 3, "-", 4, 3, 2, "-", 2, 2, 1] }
  - { name: RTO总计, type: bar, barGap: "-100%", barWidth: 20, z: 1, itemStyle: { color: "#888", opacity: 0 }, emphasis: { itemStyle: { opacity: 0 } }, data: [150, 127, 104, "-", 78, 66, 53, "-", 41, 34, 27, "-", 29, 23, 16] }
  - { name: RTO预算, type: bar, barGap: "-100%", barWidth: 20, z: 0, itemStyle: { color: "rgba(0,0,0,0.08)" }, emphasis: { itemStyle: { color: "rgba(0,0,0,0.12)" } }, data: [150, 150, 150, "-", 90, 90, 90, "-", 45, 45, 45, "-", 30, 30, 30] }

故障模型

项目	最好	最坏	平均	说明
租约过期	`ttl - loop`	`ttl`	`ttl - loop/2`	最好：即将刷新时宕机最坏：刚刷新完就宕机
从库检测	`0`	`loop`	`loop / 2`	最好：恰好在检测点最坏：刚错过检测点
抢锁提拔	`0`	`2`	`1`	最好：直接抢锁提升最坏：API 超时+Promote
健康检查	`(rise-1) × fastinter`	`(rise-1) × fastinter + inter`	`(rise-1) × fastinter + inter/2`	最好：检查前状态变化最坏：检查后瞬间状态变化

被动故障与主动故障的核心区别：

默认配置模板的参数优化策略说明

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 默认提供了四套场景化参数模板，可以通过 pg_conf 参数指定并使用。

tiny.yml：为小节点、虚拟机、小型演示优化（模板标注为 1-3 核）
oltp.yml：为 OLTP 工作负载和延迟敏感应用优化（4C8GB+）（默认模板）
olap.yml：为 OLAP 工作负载和吞吐量优化（4C8G+）
crit.yml：为数据一致性和关键应用优化（4C8G+）

Pigsty 会针对这四种默认场景，采取不同的参数优化策略，如下所示：

管理 PostgreSQL 数据库集群

Mon, 01 Jan 0001 00:00:00 +0000

速查手册

操作	快捷命令	说明
创建集群	`bin/pgsql-add <cls>`	创建新的 PostgreSQL 集群
扩容集群	`bin/pgsql-add <cls> <ip...>`	为现有集群添加从库副本
缩容集群	`bin/pgsql-rm <cls> <ip...>`	从集群中移除指定实例
销毁集群	`bin/pgsql-rm <cls>`	销毁整个 PostgreSQL 集群
刷新服务	`bin/pgsql-svc <cls> [ip...]`	重载集群的负载均衡配置
刷新HBA	`bin/pgsql-hba <cls> [ip...]`	重载集群的 HBA 访问规则
克隆集群	-	通过备份集群或 PITR 克隆

其他管理任务，请参考：高可用管理，管理用户，管理数据库。

集群实例

Mon, 01 Jan 0001 00:00:00 +0000

根据需求场景选择合适的实例与集群类型，配置出满足需求的 PostgreSQL 数据库集群。

您可以定义不同类型的实例和集群，下面是 Pigsty 中常见的几种 PostgreSQL 实例/集群类型：

读写主库：定义单一实例集群。
只读从库：定义具有一个主库和一个副本的基本 HA 集群。
离线从库：定义专用于 OLAP/ETL/交互式查询的实例
同步备库：启用同步提交以确保没有数据丢失。
法定人数提交：使用多数同步提交获得更高的一致性级别。
备份集群：克隆现有集群并跟随它
延迟集群：克隆现有集群用于紧急数据恢复
Citus集群：定义一个 Citus 分布式数据库集群

读写主库

我们从最简单的情况开始：由一个主库（Primary）组成的单实例集群：

OLTP 模板

Mon, 01 Jan 0001 00:00:00 +0000

oltp.yml 是 Pigsty 的默认配置模板，针对 在线事务处理（OLTP）负载进行了优化。适用于 4-128 核 CPU 的服务器，特点是高并发连接、低延迟响应、高事务吞吐量。

建议同时使用 node_tune = oltp 进行操作系统级别的配套调优。

内核版本

Mon, 01 Jan 0001 00:00:00 +0000

在 Pigsty 中选择"内核"意味着确定 PostgreSQL 大版本、模式/发行版、需要安装的包以及要加载的调优模板。

Pigsty v4.5 当前源码支持 PostgreSQL 14 - 18，默认使用 18。下方内容展示如何通过配置文件完成这些选择。

管理 PostgreSQL 业务用户

Mon, 01 Jan 0001 00:00:00 +0000

快速上手

Pigsty 使用声明式管理方式，首先在 配置清单 中 定义用户，然后使用 bin/pgsql-user <cls> <username> 创建或修改用户。

pg-meta:
  hosts: { 10.10.10.10: { pg_seq: 1, pg_role: primary } }
  vars:
    pg_cluster: pg-meta
    pg_users: [{ name: dbuser_app, password: 'DBUser.App', pgbouncer: true }]  # <--- 在这里定义用户列表！

脚本

bin/pgsql-user <cls> <username>    # 在 <cls> 集群上创建/修改 <username> 用户

剧本

./pgsql-user.yml -l pg-meta -e username=dbuser_app    # 直接使用剧本在 <cls> 集群上创建/修改 <username> 用户

示例

bin/pgsql-user pg-meta dbuser_app    # 在 pg-meta 集群上创建/修改 dbuser_app 用户

关于用户定义参数的完整参考，请查阅 用户配置。角色与权限模型参见 访问控制，认证与凭据管理参见 身份认证。

主动故障检测

Mon, 01 Jan 0001 00:00:00 +0000

infographic list-row-simple-horizontal-arrow
data
  title 崩溃故障切换流程
  desc 当 Patroni 健康，但 PostgreSQL 因故崩溃时的故障切换流程
  items
    - label 故障检测
      desc Patroni 在循环中检测到 PG 崩溃
      icon mingcute/close-circle-fill
    - label 重启超时
      desc Patroni 尝试重启 PG，超时后释放租约
      icon mingcute/refresh-2-fill
    - label 从库检测
      desc 从库从循环中醒来发现租约释放，开始竞选
      icon mingcute/key-2-fill
    - label 抢锁提拔
      desc 从库相互比较并抢锁，胜利者提升自己的 PG
      icon mingcute/radar-fill
    - label 健康检查
      desc HAProxy 健康检查发现新主上线，分配流量
      icon mingcute/arrow-up-circle-fill
theme light
  palette antv

RTO 时序图

tooltip: { trigger: axis, axisPointer: { type: shadow }, formatter: $fn:fmt }
legend: { top: 0, itemGap: 12, data: [故障检测, 重启超时, 从库检测, 抢锁提拔, 健康检查] }
grid: { left: 64, right: 24, bottom: 32, top: 40 }
xAxis: { type: value, name: 秒, nameLocation: end, max: 160, axisLine: { show: true }, axisTick: { show: true }, splitLine: { show: true, lineStyle: { type: dashed, opacity: 0.5 } }, minorTick: { show: true, splitNumber: 5 }, minorSplitLine: { show: true, lineStyle: { type: dotted, opacity: 0.2 } } }
yAxis: { type: category, axisLine: { show: true }, axisTick: { show: true }, splitLine: { show: false }, axisLabel: { fontSize: 10, fontFamily: monospace }, data: [wide-max, wide-avg, wide-min, "", safe-max, safe-avg, safe-min, "", norm-max, norm-avg, norm-min, "", fast-max, fast-avg, fast-min] }
series:
  - { name: 故障检测, type: bar, stack: main, barWidth: 20, z: 2, emphasis: { focus: series }, itemStyle: { color: "#b07aa1" }, data: [20, 10, 0, "-", 10, 5, 0, "-", 5, 3, 0, "-", 5, 3, 0] }
  - { name: 重启超时, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#f28e2c" }, data: [95, 95, 0, "-", 45, 45, 0, "-", 25, 25, 0, "-", 15, 15, 0] }
  - { name: 从库检测, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#edc949" }, data: [20, 10, 0, "-", 10, 5, 0, "-", 5, 3, 0, "-", 5, 3, 0] }
  - { name: 抢锁提拔, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#59a14f" }, data: [2, 1, 0, "-", 2, 1, 0, "-", 2, 1, 0, "-", 2, 1, 0] }
  - { name: 健康检查, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#4e79a7" }, data: [8, 6, 4, "-", 6, 5, 3, "-", 4, 3, 2, "-", 2, 2, 1] }
  - { name: RTO总计, type: bar, barGap: "-100%", barWidth: 20, z: 1, itemStyle: { color: "#888", opacity: 0 }, emphasis: { itemStyle: { opacity: 0 } }, data: [145, 122, 4, "-", 73, 61, 3, "-", 41, 35, 2, "-", 29, 24, 1] }
  - { name: RTO预算, type: bar, barGap: "-100%", barWidth: 20, z: 0, itemStyle: { color: "rgba(0,0,0,0.08)" }, emphasis: { itemStyle: { color: "rgba(0,0,0,0.12)" } }, data: [150, 150, 150, "-", 90, 90, 90, "-", 45, 45, 45, "-", 30, 30, 30] }

故障模型

项目	最好	最坏	平均	说明
故障检测	`0`	`loop`	`loop/2`	最好：PG 恰好在检测前崩溃最坏：PG 刚检测完就崩溃
重启超时	`0`	`start`	`start`	最好：PG 瞬间自愈最坏：等满 start 超时才释放租约
从库检测	`0`	`loop`	`loop/2`	最好：恰好在检测点最坏：刚错过检测点
抢锁提拔	`0`	`2`	`1`	最好：直接抢锁提升最坏：API 超时 + Promote
健康检查	`(rise-1) × fastinter`	`(rise-1) × fastinter + inter`	`(rise-1) × fastinter + inter/2`	最好：检查前状态变化最坏：检查后瞬间状态变化

主动故障与被动故障的核心区别：

OLAP 模板

Mon, 01 Jan 0001 00:00:00 +0000

olap.yml 是针对 在线分析处理（OLAP）负载优化的配置模板。适用于 4-128 核 CPU 的服务器，特点是支持大查询、高并行度、宽松的超时设置和激进的 Vacuum 策略。

建议同时使用 node_tune = olap 进行操作系统级别的配套调优。

别名翻译

Mon, 01 Jan 0001 00:00:00 +0000

PostgreSQL 在不同操作系统上的软件包命名规则存在显著差异：

EL 系统（RHEL/Rocky/Alma/…）使用 pgvector_18，postgis36_18* 这样的格式
Debian/Ubuntu 系统 使用 postgresql-18-pgvector，postgresql-18-postgis-3 这样的格式

这种差异给用户带来了额外的认知负担：您需要记住不同系统的包名规则，还要处理 PostgreSQL 版本号嵌入的问题。

管理 PostgreSQL 业务数据库

Mon, 01 Jan 0001 00:00:00 +0000

快速上手

Pigsty 使用声明式管理方式，首先在 配置清单 中 定义数据库，然后使用 bin/pgsql-db <cls> <dbname> 创建或修改数据库。

pg-meta:
  hosts: { 10.10.10.10: { pg_seq: 1, pg_role: primary } }
  vars:
    pg_cluster: pg-meta
    pg_databases: [{ name: some_db }]  # <--- 在这里定义数据库列表！

脚本

bin/pgsql-db <cls> <dbname>    # 在 <cls> 集群上创建/修改 <dbname> 数据库

剧本

./pgsql-db.yml -l pg-meta -e dbname=some_db    # 直接使用剧本在 <cls> 集群上创建/修改 <dbname> 数据库

示例

bin/pgsql-db pg-meta some_db    # 在 pg-meta 集群上创建/修改 some_db 数据库

关于数据库定义参数的完整参考，请查阅 数据库配置。数据库访问权限见 访问控制：数据库隔离。

CRIT 模板

Mon, 01 Jan 0001 00:00:00 +0000

crit.yml 面向一致性和审计要求较高的事务型业务。它强制启用数据校验和与 Patroni 严格同步模式，增加连接日志，并调整部分 WAL、超时和并行查询参数。

该模板会增加写入延迟，并可能在没有可用同步副本时阻塞写入。使用前应确认一致性目标、故障域、客户端提交设置和可用性要求。

管理 Patroni 高可用

Mon, 01 Jan 0001 00:00:00 +0000

概览

Pigsty 使用 Patroni 管理 PostgreSQL 集群，它可以用来修改集群配置，查看集群状态，执行主从切换，重启集群，重做从库等操作。

要使用 Patroni 进行管理，您需要有以下两种身份之一：

用户/角色

Mon, 01 Jan 0001 00:00:00 +0000

在本文中，“用户”（User）指的是使用 SQL 命令 CREATE USER/ROLE 创建的，数据库集簇内的逻辑对象。

在 PostgreSQL 中，用户直接隶属于数据库集簇而非某个具体的数据库。因此在创建业务数据库和业务用户时，应当遵循"先用户，后数据库"的原则。

管理 PostgreSQL HBA 认证规则

Mon, 01 Jan 0001 00:00:00 +0000

快速上手

Pigsty 使用声明式管理方式，首先在 配置清单 中 定义 HBA 规则，然后使用 bin/pgsql-hba <cls> 刷新规则。

pg-meta:
  hosts: { 10.10.10.10: { pg_seq: 1, pg_role: primary } }
  vars:
    pg_cluster: pg-meta
    pg_hba_rules:                            # <--- 在这里定义 HBA 规则列表！
      - {user: dbuser_app, db: app, addr: intra, auth: pwd, title: 'app access'}
      - {user: dbuser_api, db: all, addr: world, auth: ssl, title: 'api ssl access'}

脚本

bin/pgsql-hba <cls>              # 刷新集群的 PostgreSQL 和 Pgbouncer HBA 规则
bin/pgsql-hba <cls> <ip>...      # 刷新集群中特定实例的 HBA 规则

剧本

./pgsql.yml -l <cls> -t pg_hba,pg_reload                 # 仅刷新 PostgreSQL HBA
./pgsql.yml -l <cls> -t pgbouncer_hba,pgbouncer_reload   # 仅刷新 Pgbouncer HBA
./pgsql.yml -l <cls> -t pg_hba,pg_reload,pgbouncer_hba,pgbouncer_reload  # 同时刷新两者

示例

bin/pgsql-hba pg-meta                      # 刷新 pg-meta 集群的 HBA 规则
bin/pgsql-hba pg-meta 10.10.10.10          # 仅刷新特定实例
bin/pgsql-hba pg-meta 10.10.10.11 10.10.10.12  # 刷新多个实例

关于规则语法，请查阅 HBA 配置；关于认证方法、默认边界与凭据管理，请参考 身份认证。

TINY 模板

Mon, 01 Jan 0001 00:00:00 +0000

tiny.yml 是针对 微型实例 和资源受限环境优化的配置模板。适用于 1-3 核 CPU 的服务器，特点是最小化资源占用、保守的内存分配、禁用并行查询。

建议同时使用 node_tune = tiny 进行操作系统级别的配套调优。

Pgbouncer 连接池管理

Mon, 01 Jan 0001 00:00:00 +0000

概览

Pigsty 使用 Pgbouncer 作为 PostgreSQL 的连接池中间件，默认监听 6432 端口，代理访问本机 5432 端口上的 PostgreSQL 实例。

这是一个 可选组件，如果您并没有海量连接，也不需要事务池化与查询监控指标，可以关闭连接池，直连数据库，或者保留但不使用。

数据库

Mon, 01 Jan 0001 00:00:00 +0000

在本文中，“数据库”（Database）指的是使用 SQL 命令 CREATE DATABASE 创建的，数据库集簇内的逻辑对象。

一组 PostgreSQL 服务器可以同时服务于多个 数据库 （Database）。在 Pigsty 中，你可以在集群配置中定义好所需的数据库。

网络分区

Mon, 01 Jan 0001 00:00:00 +0000

infographic list-row-simple-horizontal-arrow
data
  title 网络分区故障切换流程
  desc 主库与 DCS 网络分区，Patroni 主动降级防止脑裂，等待 TTL 过期后切换
  items
    - label 主库降级
      desc Patroni 重试超时后主动降级 PG
      icon mingcute/shield-fill
    - label 租约过期
      desc Leader Key TTL 过期
      icon mingcute/close-circle-fill
    - label 从库检测
      desc 从库发现租约过期，开始竞选
      icon mingcute/key-2-fill
    - label 抢锁提拔
      desc 从库抢锁并提升为新主库
      icon mingcute/radar-fill
    - label 健康检查
      desc HAProxy 检测新主上线
      icon mingcute/arrow-up-circle-fill
theme light
  palette antv

RTO 时序图

tooltip: { trigger: axis, axisPointer: { type: shadow }, formatter: $fn:fmt }
legend: { top: 0, itemGap: 12, data: [主库降级, 租约过期, 从库检测, 抢锁提拔, 健康检查] }
grid: { left: 64, right: 24, bottom: 32, top: 40 }
xAxis: { type: value, name: 秒, nameLocation: end, max: 160, axisLine: { show: true }, axisTick: { show: true }, splitLine: { show: true, lineStyle: { type: dashed, opacity: 0.5 } }, minorTick: { show: true, splitNumber: 5 }, minorSplitLine: { show: true, lineStyle: { type: dotted, opacity: 0.2 } } }
yAxis: { type: category, axisLine: { show: true }, axisTick: { show: true }, splitLine: { show: false }, axisLabel: { fontSize: 10, fontFamily: monospace }, data: [wide-max, wide-avg, wide-min, "", safe-max, safe-avg, safe-min, "", norm-max, norm-avg, norm-min, "", fast-max, fast-avg, fast-min] }
series:
  - { name: 主库降级, type: bar, stack: main, barWidth: 20, z: 2, emphasis: { focus: series }, itemStyle: { color: "#76b7b2" }, data: [50, 40, 30, "-", 30, 25, 20, "-", 15, 13, 10, "-", 10, 8, 5] }
  - { name: 租约过期, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#e15759" }, data: [70, 70, 70, "-", 30, 30, 30, "-", 15, 15, 15, "-", 10, 10, 10] }
  - { name: 从库检测, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#edc949" }, data: [20, 10, 0, "-", 10, 5, 0, "-", 5, 3, 0, "-", 5, 3, 0] }
  - { name: 抢锁提拔, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#59a14f" }, data: [2, 1, 0, "-", 2, 1, 0, "-", 2, 1, 0, "-", 2, 1, 0] }
  - { name: 健康检查, type: bar, stack: main, z: 2, emphasis: { focus: series }, itemStyle: { color: "#4e79a7" }, data: [8, 6, 4, "-", 6, 5, 3, "-", 4, 3, 2, "-", 2, 2, 1] }
  - { name: RTO总计, type: bar, barGap: "-100%", barWidth: 20, z: 1, itemStyle: { color: "#888", opacity: 0 }, emphasis: { itemStyle: { opacity: 0 } }, data: [150, 127, 104, "-", 78, 66, 53, "-", 41, 34, 27, "-", 29, 23, 16] }
  - { name: RTO预算, type: bar, barGap: "-100%", barWidth: 20, z: 0, itemStyle: { color: "rgba(0,0,0,0.08)" }, emphasis: { itemStyle: { color: "rgba(0,0,0,0.12)" } }, data: [150, 150, 150, "-", 90, 90, 90, "-", 45, 45, 45, "-", 30, 30, 30] }

故障模型

项目	最好	最坏	平均	说明
主库降级	`retry`	`loop + retry`	`loop/2 + retry`	Patroni 检测分区后重试，超时后主动降级
租约过期	`ttl - loop - retry`	`ttl - loop - retry`	`ttl - loop - retry`	降级后剩余的 TTL 时间（近似常数）
从库检测	`0`	`loop`	`loop/2`	最好：恰好在检测点最坏：刚错过检测点
抢锁提拔	`0`	`2`	`1`	最好：直接抢锁提升最坏：API 超时+Promote
健康检查	`(rise-1) × fastinter`	`(rise-1) × fastinter + inter`	`(rise-1) × fastinter + inter/2`	最好：检查前状态变化最坏：检查后瞬间状态变化

网络分区与节点宕机的核心区别：

HBA 规则

Mon, 01 Jan 0001 00:00:00 +0000

概述

HBA（Host-Based Authentication）控制“谁可以从哪里、以什么方式连接到数据库”。认证模型和默认规则说明见 身份认证。 Pigsty 通过 pg_default_hba_rules 与 pg_hba_rules 让 HBA 规则也能以声明式配置形式管理。

参数配置

Mon, 01 Jan 0001 00:00:00 +0000

PostgreSQL 参数可以在多个层级进行配置，不同层级的参数设置具有不同的作用范围和优先级。 Pigsty 支持在四个层级配置 PostgreSQL 参数，从全局到局部依次为：

层级	作用范围	配置方式	存储位置
集群级	整个集群所有实例	Patroni DCS / 调优模板	etcd + `postgresql.conf`
实例级	单个 PostgreSQL 实例	`pg_parameters` / `ALTER SYSTEM`	`postgresql.auto.conf`
数据库级	特定数据库的所有会话	`pg_databases[].parameters`	`pg_db_role_setting`
用户级	特定用户的所有会话	`pg_users[].parameters`	`pg_db_role_setting`

参数优先级从低到高：集群级 < 实例级 < 数据库级 < 用户级 < 会话级（SET 命令）。高优先级的设置会覆盖低优先级的设置。

访问控制

Mon, 01 Jan 0001 00:00:00 +0000

访问控制由角色、对象权限、数据库 ACL 与 HBA 共同决定。本节聚焦配置参数；设计与边界见 访问控制概念。

Pigsty 预置了一套精简的 ACL 模型，通过以下参数描述：

pg_default_roles：系统角色与系统用户。
pg_users：业务用户与角色。
pg_default_privileges：管理员/属主新建对象时的默认权限。
pg_revoke_public、pg_default_schemas、pg_default_extensions：控制 template1 的默认行为。

这些参数应与 HBA 和数据库定义一起管理，形成可复现的访问控制配置。

管理 PostgreSQL 组件服务

Mon, 01 Jan 0001 00:00:00 +0000

概述

Pigsty 的 PGSQL 模块由多个组件构成，每个组件都以 systemd 服务的形式运行在节点上。（pgbackrest 除外）

了解这些组件及其管理方式，对于维护生产环境中的 PostgreSQL 集群非常重要。

管理 PostgreSQL 定时任务

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 使用 crontab 来管理定时任务，用于执行例行备份，冻结老化事务，重整膨胀表索引等维护工作。

速查手册

操作	快捷命令	说明
配置定时任务	`./pgsql.yml -t pg_crontab -l <cls>`	应用 pg_crontab 配置
查看定时任务	`crontab -l`	以 postgres 用户查看
物理备份	`pg-backup [full\|diff\|incr]`	使用 pgBackRest 执行备份
事务冻结	`pg-vacuum [database...]`	冻结老化事务，预防 XID 回卷
膨胀治理	`pg-repack [database...]`	在线重整膨胀的表与索引

其他管理任务，请参考：备份管理，监控系统，高可用管理。

升级 PostgreSQL 大小版本

Mon, 01 Jan 0001 00:00:00 +0000

快速上手

PostgreSQL 版本升级分为两种类型：小版本升级 和 大版本升级，两者的风险和复杂度差异很大。

类型	示例	停机时间	数据兼容性	风险等级
小版本升级	17.2 → 17.3	秒级（滚动重启）	完全兼容	低
大版本升级	17 → 18	分钟级	需要升级数据目录	中

小版本

# 滚动升级：先从库后主库
ansible <cls> -b -a 'yum upgrade -y postgresql17*'
pg restart --role replica --force <cls>
pg switchover <cls>
pg restart <cls> <old-primary> --force

大版本

# 推荐：逻辑复制迁移
bin/pgsql-add pg-new              # 创建新版本集群
# 配置逻辑复制同步数据...
# 切换流量到新集群

扩展

ansible <cls> -b -a 'yum upgrade -y postgis36_17*'
psql -c 'ALTER EXTENSION postgis UPDATE;'

关于在线迁移的详细流程，请参考 在线迁移 文档。

管理 PostgreSQL 扩展插件

Mon, 01 Jan 0001 00:00:00 +0000

快速上手

Pigsty 提供 575 扩展，使用扩展涉及四个步骤：下载、安装、配置、启用。

pg-meta:
  hosts: { 10.10.10.10: { pg_seq: 1, pg_role: primary } }
  vars:
    pg_cluster: pg-meta
    pg_extensions: [ postgis, timescaledb, pgvector ]           # <--- 安装扩展软件包
    pg_libs: 'timescaledb, pg_stat_statements, auto_explain'    # <--- 配置预加载扩展
    pg_databases:
      - name: meta
        extensions: [ postgis, timescaledb, vector ]            # <--- 在数据库中启用

脚本

bin/pgsql-ext <cls>           # 在 <cls> 集群上安装配置中定义的扩展
bin/pgsql-ext <cls> [ext...]  # 在 <cls> 集群上安装命令行参数给出的扩展

剧本

./pgsql.yml -l pg-meta -t pg_ext    # 使用剧本安装扩展

示例

bin/pgsql-ext pg-meta                         # 在 pg-meta 集群上安装定义的扩展
bin/pgsql-ext pg-meta pg_duckdb pg_mooncake   # 安装指定扩展

关于扩展的完整参考，请查阅 扩展插件 章节。关于可用扩展列表，请参考 扩展目录。

PG 高可用

Mon, 01 Jan 0001 00:00:00 +0000

概览

Pigsty 的 PostgreSQL 集群带有开箱即用的高可用方案，由 Patroni、Etcd 和 HAProxy 提供核心能力。

当您的 PostgreSQL 集群含有两个或更多实例时，您无需任何配置即拥有了硬件故障自愈的数据库高可用能力 —— 只要集群中有任意实例存活，集群就可以对外提供完整的服务，而客户端只要连接至集群中的任意节点，即可获得完整的服务，而无需关心主从拓扑变化。

RPO 利弊权衡

Mon, 01 Jan 0001 00:00:00 +0000

RPO（Recovery Point Objective，恢复点目标）定义了在主库发生故障时，允许丢失的最大数据量。

对于金融交易这类数据完整性至关重要的场景，通常要求 RPO = 0，即不允许任何数据丢失；

PGSQL 架构

Mon, 01 Jan 0001 00:00:00 +0000

PGSQL 模块在生产环境中以集群的形式组织，这些集群是由一组通过 主-备 关联的数据库实例组成的 逻辑实体。

概览

PGSQL 模块 包含下列组件，协同提供生产级 PostgreSQL 高可用集群服务：

RTO 利弊权衡

Mon, 01 Jan 0001 00:00:00 +0000

RTO（Recovery Time Objective，恢复时间目标）定义了在主库发生故障时，系统恢复写入能力所需的最长时间。

对于核心交易系统这类可用性至关重要的场景，通常要求 RTO 尽可能短，例如一分钟内。

故障切换模型

Mon, 01 Jan 0001 00:00:00 +0000

Patroni 故障按故障对象分类可以分为以下 10 类，按照检测路径不同，可以进一步归纳为五类，在本节内详细展开。

#	故障场景	描述	最终走哪条路径
1	PG 进程崩溃	crash、OOM killed	主动检测
2	PG 拒绝连接	max_connections	主动检测
3	PG 假活	进程在但无响应	主动检测 (检测超时)
4	Patroni 进程崩溃	kill -9、OOM	被动检测
5	Patroni 假活	进程在但卡住	Watchdog
6	节点宕机	断电、硬件故障	被动检测
7	节点假活	IO hang、CPU 饥饿	Watchdog
8	主库 ↔ DCS 网络中断	防火墙、交换机故障	网络分区
9	存储故障	磁盘坏、磁盘满、挂载失败	主动检测或 Watchdog
10	手动切换	Switchover/Failover	手动触发

但是在 RTO 计算上，最终所有故障都会收敛到两条路径上，本节深入探讨了这两种情况下的 RTO 上下限与均值。

时间点恢复 —— 数据库的时间机器（PITR）

Mon, 01 Jan 0001 00:00:00 +0000

当您不小心删除了数据、表、甚至整个数据库时，时间点恢复（Point-in-Time Recovery，PITR）让您可以回到过去。

—— 这个曾经只有资深 DBA 才能施展的『魔法』，在 Pigsty 的标准配置中零配置开箱即用。

时间点恢复的工作原理

Mon, 01 Jan 0001 00:00:00 +0000

如果把数据库看作一台状态机，那么 WAL（Write-Ahead Log，预写式日志）就是它的完整变更历史 —— PostgreSQL 的每一次写入，都会先以日志记录的形式落盘，然后才应用到数据文件上。这个为崩溃恢复而生的机制带来了一个副产品：只要把某个时刻的数据文件快照保存下来，再持续保留此后产生的 WAL，就可以把数据库重放到这段历史所覆盖的任意时间点。

时间点恢复的实现架构

Mon, 01 Jan 0001 00:00:00 +0000

原理一页就能讲完，工程却没有那么简单：归档不能拖垮主库的写入性能，备份放到对象存储上要加密，主从切换之后备份不能中断，多套集群共用一个仓库时要相互隔离，海量小文件会拖垮备份吞吐……

时间点恢复的策略权衡

Mon, 01 Jan 0001 00:00:00 +0000

备份本质上是一份保险：保费是存储空间、网络带宽与管理成本，保额是灾难来临时能挽回多少数据、多快恢复服务。和所有保险一样，这里没有免费的午餐 —— 更长的恢复窗口意味着更多的空间，更快的恢复意味着更频繁的备份。

声明式恢复

Mon, 01 Jan 0001 00:00:00 +0000

备份系统的全部价值，都在恢复的那一刻兑现。而恢复几乎总是发生在最糟糕的时刻 —— 生产事故、深夜告警、每一分钟都在损失。传统的 PITR 手工流程在这种时刻是残酷的：停 HA、停库、写恢复配置、执行还原、盯日志、验证位点、重建元数据、拉起集群……十几个步骤环环相扣，任何一步出错都可能雪上加霜。

时间点恢复的典型场景

Mon, 01 Jan 0001 00:00:00 +0000

事故发生时，最贵的不是恢复本身，而是 决策时间。恢复的机械步骤已经被 工具编排 好了，真正需要人来回答的只有三个问题： 恢复到哪一刻？原地恢复还是克隆恢复？如何验证数据是对的？

安全合规

Mon, 01 Jan 0001 00:00:00 +0000

数据库通常是信息系统中最敏感的组件：它保存着最有价值的数据，也因此是攻击与故障后果最严重的地方。数据库安全并不是某个可以一键开启的功能，而是一系列问题的答案之和：谁能连进来？连进来能做什么？流量会不会被窃听？操作有没有留痕？数据坏了、丢了、被删了，还能不能恢复？

安全模型

Mon, 01 Jan 0001 00:00:00 +0000

在讨论具体的安全特性之前，值得先回答两个更基本的问题：信任的根在哪里，以及 防线有几道。前者决定了你应该重点保护什么，后者决定了当某一道防线失守时，你还剩下什么。

身份认证

Mon, 01 Jan 0001 00:00:00 +0000

PostgreSQL 使用 pg_hba.conf 进行 基于主机的认证（Host-Based Authentication）：谁（用户）、从哪里（来源地址）、访问什么（数据库）、需要以何种方式证明身份（认证方法）。

访问控制

Mon, 01 Jan 0001 00:00:00 +0000

认证回答“你是谁”，授权回答“你能做什么”。

权限失控很少是因为缺少机制 —— PostgreSQL 的 GRANT 与 REVOKE 足够精细。问题在于缺少一套被默认执行的约定：业务上线时直接把账号设为属主；临时排障授予超级用户后没有及时回收；新表创建后遗漏授权，最终在生产环境触发权限错误。

加密通信

Mon, 01 Jan 0001 00:00:00 +0000

TLS 可以提供三类保护：传输加密、服务端身份验证 与 客户端身份验证。这三项能力需要分别配置：启用服务端 TLS 并不等于客户端已经验证了服务端身份，也不等于服务端要求客户端证书。

数据安全

Mon, 01 Jan 0001 00:00:00 +0000

网络边界、身份认证 和 权限控制 用于降低事件发生的概率；当硬件损坏、口令泄露或误操作已经发生时，还需要依靠数据层机制控制影响并完成恢复。

数据安全要回答四个问题：数据是完整的吗？丢了能恢复吗？被拿走了会泄密吗？发生了什么能查清吗？

合规实践

Mon, 01 Jan 0001 00:00:00 +0000

合规不是一个可以购买的产品，而是一种需要持续证明的状态。它由三部分组成：

配置：安全能力是否启用 —— 这部分由 Pigsty 直接提供；
流程：权限审批、变更管理、恢复演练等制度 —— 需要组织自行建立；
证据：能证明前两者持续有效的记录 —— Pigsty 的 配置清单、运行日志和 监控系统 可以提供其中一部分。

本页从上线前的加固清单开始，给出 Pigsty 安全能力与常见合规框架的映射关系。这些映射用于方案设计和差距分析，不构成等保测评结论、SOC 2 审计意见或法律建议。

快速上手 PostgreSQL

Mon, 01 Jan 0001 00:00:00 +0000

PostgreSQL（简称 PG）是世界上最先进、最流行的开源关系型数据库，你可以用它来存储和检索多模态数据。

本指南面向有基础 Linux 基本命令行操作经验、但对 PostgreSQL 不太熟悉的开发者，带你快速上手 Pigsty 中的 PG。

PostgreSQL Mongo 模式

Mon, 01 Jan 0001 00:00:00 +0000

mongo 配置模板是一个 PostgreSQL 部署模式，而不是独立的 Pigsty 模块。它由以下组件组成：

由标准 PGSQL 模块管理的 PostgreSQL 18
documentdb 扩展及其预加载库
通过 Pigsty Docker APP 工作流部署的无状态 FerretDB 代理

所有数据、高可用、备份、监控与生命周期管理仍由 PostgreSQL 负责；FerretDB 只提供 MongoDB 线协议兼容端点。

运维 SOP 索引

Mon, 01 Jan 0001 00:00:00 +0000

上手路线

顺序	要解决的问题	入口
1	Pigsty 由哪些模块组成？	积木式架构，PGSQL 架构，PGSQL 集群模型
2	怎么先跑起来？	快速上手，图形界面，快速上手 PostgreSQL
3	配置文件该怎么看？	声明式配置，配置清单，配置参数
4	生产部署要准备什么？	架构规划，资源准备，管理机制
5	怎么部署多节点集群？	生产部署，执行剧本，PGSQL 剧本
6	日常怎么管库？	PGSQL 日常管理，集群管理，用户管理，数据库管理
7	怎么验证可靠性？	PG 高可用，Patroni 管理，备份恢复，恢复操作

任务索引

任务	先看	操作入口
准备服务器、磁盘、网络、VIP	资源准备，架构规划，Linux 兼容性	生产部署
准备 SSH、Sudo、管理用户	管理机制	生产部署
本地或云上搭沙箱	沙箱环境	Vagrant，Terraform
单机体验	快速上手	`./configure -g`，`./deploy.yml`
多节点生产部署	部署，生产部署	`./deploy.yml`，`./pgsql.yml`
离线环境部署	离线安装	软件仓库管理
选择配置模板	配置模板，模板列表	`./configure -c <template>`
规划集群名、库名、用户名	PGSQL 集群模型	`pg_cluster`，`pg_databases`，`pg_users`
创建数据库集群	集群实例配置	集群管理，`./pgsql.yml -l <cluster>`
新增业务用户	用户/角色配置	用户管理，`./pgsql-user.yml -l <cluster>`
新增业务数据库	数据库配置	数据库管理，`./pgsql-db.yml -l <cluster>`
配置访问入口	服务/接入	`pg_services`，`pg_default_services`
修改 HBA	HBA 配置	HBA 管理
主从切换	Patroni 管理	`patronictl switchover`
HA 故障演练	PG 高可用，RPO，RTO	3坏2应急处理
配置 VIP	HA 服务接入	配置 PG VIP
配置备份策略	备份策略	备份管理命令
做 PITR 时间点恢复	时间点恢复	恢复操作
误删数据、表、库	误删处理	手工恢复
克隆恢复集群	克隆数据库集群	Fork 实例
使用 Silo 存备份	MINIO 模块	Silo 配置，备份仓库
查看监控告警	监控系统	PGSQL 监控，PGSQL 仪表盘
排查数据库故障	PGSQL 常见问题	故障排查，组件管理
扩容、缩容 PG 集群	集群实例配置	集群管理
升级 PostgreSQL	版本升级	内核版本
安装或启用扩展	扩展插件	扩展管理
迁移已有数据库	数据迁移	迁移剧本
做安全加固	安全考量	访问控制，CA 与证书
管理域名与 Web 入口	域名管理	Nginx 管理
维护基础设施	INFRA 管理预案	`infra.yml`，`infra-rm.yml`
维护 Etcd	ETCD 配置	ETCD 管理，ETCD FAQ
部署应用模板	应用	Docker 模块，`./app.yml`

准备与部署

生产部署先看 架构规划 和 资源准备。这两篇解决节点数量、磁盘、文件系统、网络、VIP、域名、软件源这些问题。

模块：PGSQL

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PGSQL 是 Pigsty 的核心模块：通过 Ansible 清单声明 PostgreSQL 集群，以 Patroni 与 etcd 提供高可用编排，以 pgBackRest 提供备份/PITR，并通过 HAProxy、VIP、DNS、PgBouncer 与完整可观测性栈提供数据库服务。

配置指南

Mon, 01 Jan 0001 00:00:00 +0000

PGSQL 集群模型

Mon, 01 Jan 0001 00:00:00 +0000

PGSQL 模块在生产环境中以集群的形式组织，这些集群是由一组由 主-备 关联的数据库实例组成的 逻辑实体。

每个集群都是一个自治的业务单元，由至少一个 主库实例 组成，并通过服务向外暴露能力。

集群配置

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 是一个“配置驱动”的 PostgreSQL 平台：所有行为都来自 ~/pigsty/conf/*.yml 清单与 PGSQL 参数的组合。

只要写好配置，你就能在几分钟内复刻出一套包含实例、用户、数据库、访问控制、扩展与调优策略的定制集群。

服务/接入

Mon, 01 Jan 0001 00:00:00 +0000

分离读写操作，正确路由流量，稳定可靠地交付 PostgreSQL 集群提供的能力。

服务是一种抽象：它是数据库集群对外提供能力的形式，并封装了底层集群的细节。

服务对于生产环境中的稳定接入至关重要，在高可用集群自动故障时方显其价值，单机用户通常不需要操心这个概念。

PostgreSQL 安全

Mon, 01 Jan 0001 00:00:00 +0000

PostgreSQL 安全由身份认证、权限控制、网络边界、加密通信、数据保护和运维流程共同构成。Pigsty 提供这些机制的配置入口，但部署方仍需根据环境完成加固、验证和持续审计。

用户/角色

Mon, 01 Jan 0001 00:00:00 +0000

在这里的上下文中，用户指的是使用 SQL 命令 CREATE USER/ROLE 创建的，数据库集簇内的逻辑对象。

在 PostgreSQL 中，用户直接隶属于数据库集簇而非某个具体的数据库。因此在创建业务数据库和业务用户时，应当遵循"先用户，后数据库"的原则。

数据库

Mon, 01 Jan 0001 00:00:00 +0000

在这里的上下文中，数据库指的是使用 SQL 命令 CREATE DATABASE 创建的，数据库集簇内的逻辑对象。

一组 PostgreSQL 服务器可以同时服务于多个 数据库 （Database）。在 Pigsty 中，你可以在集群配置中定义好所需的数据库。

服务/接入

Mon, 01 Jan 0001 00:00:00 +0000

分离读写操作，正确路由流量，稳定可靠地交付 PostgreSQL 集群提供的能力。

服务是一种抽象：它是数据库集群对外提供能力的形式，并封装了底层集群的细节。

服务对于生产环境中的稳定接入至关重要，在高可用集群自动故障时方显其价值，单机用户通常不需要操心这个概念。

认证 / HBA

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 中基于主机的身份认证 HBA（Host-Based Authentication）详解。

认证是访问控制与默认权限的基础，PostgreSQL 支持多种认证方法。

这里主要介绍 HBA：Host Based Authentication，HBA 规则定义了哪些用户能够通过哪些方式从哪些地方访问哪些数据库。

访问控制

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 的访问控制文档已按用途拆分：

访问控制概念：角色模型、默认权限、数据库 ACL 和实例隔离边界。
访问控制配置：pg_default_roles、pg_users、pg_default_privileges 等参数。
身份认证：HBA、SCRAM、证书认证与凭据管理。
HBA 配置：PostgreSQL 与 PgBouncer 规则语法。
用户管理：在现有集群中创建、更新和删除用户。

dbrole_offline 只提供独立的只读对象权限，不会自动限制实例范围。若要仅允许其访问离线实例，应为对应 HBA 规则显式设置 role: offline，并验证在线与离线实例生成的 pg_hba.conf。

管理任务

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数据库管理与运维任务

日常管理

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故障排查

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本文档列举了 PostgreSQL 和 Pigsty 中可能出现的故障，以及定位、处理、分析问题的 SOP。

磁盘空间写满

磁盘空间写满是最常见的故障类型。

现象

当数据库所在磁盘空间耗尽时，PostgreSQL 将无法正常工作，可能出现以下现象：数据库日志反复报错"no space left on device"（磁盘空间不足），新数据无法写入，甚至 PostgreSQL 可能触发 PANIC 强制关闭。

误删处理

Mon, 01 Jan 0001 00:00:00 +0000

误删数据

如果是小批量 DELETE 误操作，可以考虑使用 pg_surgery 或者 pg_dirtyread 扩展进行原地手术恢复。

-- 立即关闭此表上的 Auto Vacuum 并中止 Auto Vacuum 本表的 worker 进程
ALTER TABLE public.some_table SET (autovacuum_enabled = off, toast.autovacuum_enabled = off);

CREATE EXTENSION pg_dirtyread;
SELECT * FROM pg_dirtyread('tablename') AS t(col1 type1, col2 type2, ...);

如果被删除的数据已经被 VACUUM 回收，那么使用通用的误删处理流程。

数据迁移

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 内置了一个剧本 pgsql-migration.yml，基于逻辑复制来实现在线数据库迁移。

通过预生成的自动化脚本，应用停机时间可以缩减到几秒内。但请注意，逻辑复制需要 PostgreSQL 10 以上的版本才能工作。

当然如果您有充足的停机时间预算，那么总是可以使用 pg_dump | psql 的方式进行停机迁移。

参考资料

Mon, 01 Jan 0001 00:00:00 +0000

参数配置与参考文档

监控系统

Mon, 01 Jan 0001 00:00:00 +0000

本文介绍了 Pigsty 的监控系统架构，包括监控指标，日志，与目标管理的方式。以及如何监控现有PG集群与远程 RDS服务。

监控概览

Pigsty 使用现代的可观测技术栈对 PostgreSQL 进行监控：

监控面板

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 为 PostgreSQL 提供了诸多开箱即用的 Grafana 监控仪表盘： Demo & Gallery。

当前源码共提供 31 个 PostgreSQL 相关面板：files/grafana/pgsql 中有 29 个 PostgreSQL / PGCAT 面板，files/grafana/app 中另有 2 个 PGLOG 面板。它们按层次分为总览、集群、实例、数据库四大类，按数据来源分为 PGSQL、PGCAT、PGLOG 三类。

指标列表

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PGSQL 模块包含有 638 类可用监控指标。

Metric Name	Type	Labels	Description
ALERTS	Unknown	`category`, `job`, `level`, `ins`, `severity`, `ip`, `alertname`, `alertstate`, `instance`, `cls`	N/A
ALERTS_FOR_STATE	Unknown	`category`, `job`, `level`, `ins`, `severity`, `ip`, `alertname`, `instance`, `cls`	N/A
cls:pressure1	Unknown	`job`, `cls`	N/A
cls:pressure15	Unknown	`job`, `cls`	N/A
cls:pressure5	Unknown	`job`, `cls`	N/A
go_gc_duration_seconds	summary	`job`, `ins`, `ip`, `instance`, `quantile`, `cls`	A summary of the pause duration of garbage collection cycles.
go_gc_duration_seconds_count	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
go_gc_duration_seconds_sum	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
go_goroutines	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of goroutines that currently exist.
go_info	gauge	`version`, `job`, `ins`, `ip`, `instance`, `cls`	Information about the Go environment.
go_memstats_alloc_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes allocated and still in use.
go_memstats_alloc_bytes_total	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total number of bytes allocated, even if freed.
go_memstats_buck_hash_sys_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes used by the profiling bucket hash table.
go_memstats_frees_total	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total number of frees.
go_memstats_gc_sys_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes used for garbage collection system metadata.
go_memstats_heap_alloc_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of heap bytes allocated and still in use.
go_memstats_heap_idle_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of heap bytes waiting to be used.
go_memstats_heap_inuse_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of heap bytes that are in use.
go_memstats_heap_objects	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of allocated objects.
go_memstats_heap_released_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of heap bytes released to OS.
go_memstats_heap_sys_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of heap bytes obtained from system.
go_memstats_last_gc_time_seconds	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of seconds since 1970 of last garbage collection.
go_memstats_lookups_total	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total number of pointer lookups.
go_memstats_mallocs_total	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total number of mallocs.
go_memstats_mcache_inuse_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes in use by mcache structures.
go_memstats_mcache_sys_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes used for mcache structures obtained from system.
go_memstats_mspan_inuse_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes in use by mspan structures.
go_memstats_mspan_sys_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes used for mspan structures obtained from system.
go_memstats_next_gc_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of heap bytes when next garbage collection will take place.
go_memstats_other_sys_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes used for other system allocations.
go_memstats_stack_inuse_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes in use by the stack allocator.
go_memstats_stack_sys_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes obtained from system for stack allocator.
go_memstats_sys_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of bytes obtained from system.
go_threads	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of OS threads created.
ins:pressure1	Unknown	`job`, `ins`, `ip`, `cls`	N/A
ins:pressure15	Unknown	`job`, `ins`, `ip`, `cls`	N/A
ins:pressure5	Unknown	`job`, `ins`, `ip`, `cls`	N/A
patroni_cluster_unlocked	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if the cluster is unlocked, 0 if locked.
patroni_dcs_last_seen	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Epoch timestamp when DCS was last contacted successfully by Patroni.
patroni_failsafe_mode_is_active	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if failsafe mode is active, 0 if inactive.
patroni_is_paused	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if auto failover is disabled, 0 otherwise.
patroni_master	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if this node is the leader, 0 otherwise.
patroni_pending_restart	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if the node needs a restart, 0 otherwise.
patroni_postgres_in_archive_recovery	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if Postgres is replicating from archive, 0 otherwise.
patroni_postgres_running	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if Postgres is running, 0 otherwise.
patroni_postgres_server_version	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Version of Postgres (if running), 0 otherwise.
patroni_postgres_streaming	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if Postgres is streaming, 0 otherwise.
patroni_postgres_timeline	counter	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Postgres timeline of this node (if running), 0 otherwise.
patroni_postmaster_start_time	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Epoch seconds since Postgres started.
patroni_primary	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if this node is the leader, 0 otherwise.
patroni_replica	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if this node is a replica, 0 otherwise.
patroni_standby_leader	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if this node is the standby_leader, 0 otherwise.
patroni_sync_standby	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if this node is a sync standby replica, 0 otherwise.
patroni_up	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
patroni_version	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Patroni semver without periods.
patroni_xlog_location	counter	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Current location of the Postgres transaction log, 0 if this node is not the leader.
patroni_xlog_paused	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Value is 1 if the Postgres xlog is paused, 0 otherwise.
patroni_xlog_received_location	counter	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Current location of the received Postgres transaction log, 0 if this node is not a replica.
patroni_xlog_replayed_location	counter	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Current location of the replayed Postgres transaction log, 0 if this node is not a replica.
patroni_xlog_replayed_timestamp	gauge	`job`, `ins`, `ip`, `instance`, `cls`, `scope`	Current timestamp of the replayed Postgres transaction log, 0 if null.
pg:cls:active_backends	Unknown	`job`, `cls`	N/A
pg:cls:active_time_rate15m	Unknown	`job`, `cls`	N/A
pg:cls:active_time_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:active_time_rate5m	Unknown	`job`, `cls`	N/A
pg:cls:age	Unknown	`job`, `cls`	N/A
pg:cls:buf_alloc_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:buf_clean_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:buf_flush_backend_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:buf_flush_checkpoint_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:cpu_count	Unknown	`job`, `cls`	N/A
pg:cls:cpu_usage	Unknown	`job`, `cls`	N/A
pg:cls:cpu_usage_15m	Unknown	`job`, `cls`	N/A
pg:cls:cpu_usage_1m	Unknown	`job`, `cls`	N/A
pg:cls:cpu_usage_5m	Unknown	`job`, `cls`	N/A
pg:cls:db_size	Unknown	`job`, `cls`	N/A
pg:cls:file_size	Unknown	`job`, `cls`	N/A
pg:cls:ixact_backends	Unknown	`job`, `cls`	N/A
pg:cls:ixact_time_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:lag_bytes	Unknown	`job`, `cls`	N/A
pg:cls:lag_seconds	Unknown	`job`, `cls`	N/A
pg:cls:leader	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:cls:load1	Unknown	`job`, `cls`	N/A
pg:cls:load15	Unknown	`job`, `cls`	N/A
pg:cls:load5	Unknown	`job`, `cls`	N/A
pg:cls:lock_count	Unknown	`job`, `cls`	N/A
pg:cls:locks	Unknown	`job`, `cls`, `mode`	N/A
pg:cls:log_size	Unknown	`job`, `cls`	N/A
pg:cls:lsn_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:members	Unknown	`job`, `ins`, `ip`, `cls`	N/A
pg:cls:num_backends	Unknown	`job`, `cls`	N/A
pg:cls:partition	Unknown	`job`, `cls`	N/A
pg:cls:receiver	Unknown	`state`, `slot_name`, `job`, `appname`, `ip`, `cls`, `sender_host`, `sender_port`	N/A
pg:cls:rlock_count	Unknown	`job`, `cls`	N/A
pg:cls:saturation1	Unknown	`job`, `cls`	N/A
pg:cls:saturation15	Unknown	`job`, `cls`	N/A
pg:cls:saturation5	Unknown	`job`, `cls`	N/A
pg:cls:sender	Unknown	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `cls`	N/A
pg:cls:session_time_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:size	Unknown	`job`, `cls`	N/A
pg:cls:slot_count	Unknown	`job`, `cls`	N/A
pg:cls:slot_retained_bytes	Unknown	`job`, `cls`	N/A
pg:cls:standby_count	Unknown	`job`, `cls`	N/A
pg:cls:sync_state	Unknown	`job`, `cls`	N/A
pg:cls:timeline	Unknown	`job`, `cls`	N/A
pg:cls:tup_deleted_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:tup_fetched_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:tup_inserted_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:tup_modified_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:tup_returned_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:wal_size	Unknown	`job`, `cls`	N/A
pg:cls:xact_commit_rate15m	Unknown	`job`, `cls`	N/A
pg:cls:xact_commit_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:xact_commit_rate5m	Unknown	`job`, `cls`	N/A
pg:cls:xact_rollback_rate15m	Unknown	`job`, `cls`	N/A
pg:cls:xact_rollback_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:xact_rollback_rate5m	Unknown	`job`, `cls`	N/A
pg:cls:xact_total_rate15m	Unknown	`job`, `cls`	N/A
pg:cls:xact_total_rate1m	Unknown	`job`, `cls`	N/A
pg:cls:xact_total_sigma15m	Unknown	`job`, `cls`	N/A
pg:cls:xlock_count	Unknown	`job`, `cls`	N/A
pg:db:active_backends	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:active_time_rate15m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:active_time_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:active_time_rate5m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:age	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:age_deriv1h	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:age_exhaust	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:blk_io_time_seconds_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:blk_read_time_seconds_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:blk_write_time_seconds_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:blks_access_1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:blks_hit_1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:blks_hit_ratio1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:blks_read_1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:conn_limit	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:conn_usage	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:db_size	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:ixact_backends	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:ixact_time_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:lock_count	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:num_backends	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:rlock_count	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:session_time_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:temp_bytes_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:temp_files_1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:tup_deleted_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:tup_fetched_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:tup_inserted_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:tup_modified_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:tup_returned_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:wlock_count	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_commit_rate15m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_commit_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_commit_rate5m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_rollback_rate15m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_rollback_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_rollback_rate5m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_total_rate15m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_total_rate1m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_total_rate5m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xact_total_sigma15m	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:db:xlock_count	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:env:active_backends	Unknown	`job`	N/A
pg:env:active_time_rate15m	Unknown	`job`	N/A
pg:env:active_time_rate1m	Unknown	`job`	N/A
pg:env:active_time_rate5m	Unknown	`job`	N/A
pg:env:age	Unknown	`job`	N/A
pg:env:cpu_count	Unknown	`job`	N/A
pg:env:cpu_usage	Unknown	`job`	N/A
pg:env:cpu_usage_15m	Unknown	`job`	N/A
pg:env:cpu_usage_1m	Unknown	`job`	N/A
pg:env:cpu_usage_5m	Unknown	`job`	N/A
pg:env:ixact_backends	Unknown	`job`	N/A
pg:env:ixact_time_rate1m	Unknown	`job`	N/A
pg:env:lag_bytes	Unknown	`job`	N/A
pg:env:lag_seconds	Unknown	`job`	N/A
pg:env:lsn_rate1m	Unknown	`job`	N/A
pg:env:session_time_rate1m	Unknown	`job`	N/A
pg:env:tup_deleted_rate1m	Unknown	`job`	N/A
pg:env:tup_fetched_rate1m	Unknown	`job`	N/A
pg:env:tup_inserted_rate1m	Unknown	`job`	N/A
pg:env:tup_modified_rate1m	Unknown	`job`	N/A
pg:env:tup_returned_rate1m	Unknown	`job`	N/A
pg:env:xact_commit_rate15m	Unknown	`job`	N/A
pg:env:xact_commit_rate1m	Unknown	`job`	N/A
pg:env:xact_commit_rate5m	Unknown	`job`	N/A
pg:env:xact_rollback_rate15m	Unknown	`job`	N/A
pg:env:xact_rollback_rate1m	Unknown	`job`	N/A
pg:env:xact_rollback_rate5m	Unknown	`job`	N/A
pg:env:xact_total_rate15m	Unknown	`job`	N/A
pg:env:xact_total_rate1m	Unknown	`job`	N/A
pg:env:xact_total_sigma15m	Unknown	`job`	N/A
pg:ins:active_backends	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:active_time_rate15m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:active_time_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:active_time_rate5m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:age	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:blks_hit_ratio1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:buf_alloc_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:buf_clean_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:buf_flush_backend_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:buf_flush_checkpoint_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:ckpt_1h	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:ckpt_req_1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:ckpt_timed_1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:conn_limit	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:conn_usage	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:cpu_count	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:cpu_usage	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:cpu_usage_15m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:cpu_usage_1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:cpu_usage_5m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:db_size	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:file_size	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:fs_size	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:is_leader	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:ixact_backends	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:ixact_time_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:lag_bytes	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:lag_seconds	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:load1	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:load15	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:load5	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:lock_count	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:locks	Unknown	`job`, `ins`, `ip`, `mode`, `instance`, `cls`	N/A
pg:ins:log_size	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:lsn_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:mem_size	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:num_backends	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:rlock_count	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:saturation1	Unknown	`job`, `ins`, `ip`, `cls`	N/A
pg:ins:saturation15	Unknown	`job`, `ins`, `ip`, `cls`	N/A
pg:ins:saturation5	Unknown	`job`, `ins`, `ip`, `cls`	N/A
pg:ins:session_time_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:slot_retained_bytes	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:space_usage	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:status	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:sync_state	Unknown	`job`, `ins`, `instance`, `cls`	N/A
pg:ins:target_count	Unknown	`job`, `cls`, `ins`	N/A
pg:ins:timeline	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:tup_deleted_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:tup_fetched_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:tup_inserted_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:tup_modified_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:tup_returned_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:wal_size	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:wlock_count	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_commit_rate15m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_commit_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_commit_rate5m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_rollback_rate15m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_rollback_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_rollback_rate5m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_total_rate15m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_total_rate1m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_total_rate5m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xact_total_sigma15m	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:ins:xlock_count	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:query:call_rate1m	Unknown	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:query:rt_1m	Unknown	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg:table:scan_rate1m	Unknown	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg_activity_count	gauge	`datname`, `state`, `job`, `ins`, `ip`, `instance`, `cls`	Count of connection among (datname,state)
pg_activity_max_conn_duration	gauge	`datname`, `state`, `job`, `ins`, `ip`, `instance`, `cls`	Max backend session duration since state change among (datname, state)
pg_activity_max_duration	gauge	`datname`, `state`, `job`, `ins`, `ip`, `instance`, `cls`	Max duration since last state change among (datname, state)
pg_activity_max_tx_duration	gauge	`datname`, `state`, `job`, `ins`, `ip`, `instance`, `cls`	Max transaction duration since state change among (datname, state)
pg_archiver_failed_count	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of failed attempts for archiving WAL files
pg_archiver_finish_count	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of WAL files that have been successfully archived
pg_archiver_last_failed_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Time of the last failed archival operation
pg_archiver_last_finish_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Time of the last successful archive operation
pg_archiver_reset_time	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Time at which archive statistics were last reset
pg_backend_count	gauge	`type`, `job`, `ins`, `ip`, `instance`, `cls`	Database backend process count by backend_type
pg_bgwriter_buffers_alloc	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of buffers allocated
pg_bgwriter_buffers_backend	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of buffers written directly by a backend
pg_bgwriter_buffers_backend_fsync	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of times a backend had to execute its own fsync call
pg_bgwriter_buffers_checkpoint	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of buffers written during checkpoints
pg_bgwriter_buffers_clean	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of buffers written by the background writer
pg_bgwriter_checkpoint_sync_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total amount of time that has been spent in the portion of checkpoint processing where files are synchronized to disk, in seconds
pg_bgwriter_checkpoint_write_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total amount of time that has been spent in the portion of checkpoint processing where files are written to disk, in seconds
pg_bgwriter_checkpoints_req	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of requested checkpoints that have been performed
pg_bgwriter_checkpoints_timed	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of scheduled checkpoints that have been performed
pg_bgwriter_maxwritten_clean	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of times the background writer stopped a cleaning scan because it had written too many buffers
pg_bgwriter_reset_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Time at which bgwriter statistics were last reset
pg_boot_time	gauge	`job`, `ins`, `ip`, `instance`, `cls`	unix timestamp when postmaster boot
pg_checkpoint_checkpoint_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint location
pg_checkpoint_elapse	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Seconds elapsed since latest checkpoint in seconds
pg_checkpoint_full_page_writes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s full_page_writes enabled
pg_checkpoint_newest_commit_ts_xid	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s newestCommitTsXid
pg_checkpoint_next_multi_offset	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s NextMultiOffset
pg_checkpoint_next_multixact_id	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s NextMultiXactId
pg_checkpoint_next_oid	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s NextOID
pg_checkpoint_next_xid	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s NextXID xid
pg_checkpoint_next_xid_epoch	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s NextXID epoch
pg_checkpoint_oldest_active_xid	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s oldestActiveXID
pg_checkpoint_oldest_commit_ts_xid	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s oldestCommitTsXid
pg_checkpoint_oldest_multi_dbid	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s oldestMulti’s DB OID
pg_checkpoint_oldest_multi_xid	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s oldestMultiXid
pg_checkpoint_oldest_xid	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s oldestXID
pg_checkpoint_oldest_xid_dbid	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s oldestXID’s DB OID
pg_checkpoint_prev_tli	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s PrevTimeLineID
pg_checkpoint_redo_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s REDO location
pg_checkpoint_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Time of latest checkpoint
pg_checkpoint_tli	counter	`job`, `ins`, `ip`, `instance`, `cls`	Latest checkpoint’s TimeLineID
pg_conf_reload_time	gauge	`job`, `ins`, `ip`, `instance`, `cls`	seconds since last configuration reload
pg_db_active_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time spent executing SQL statements in this database, in seconds
pg_db_age	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Age of database calculated from datfrozenxid
pg_db_allow_conn	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	If false(0) then no one can connect to this database.
pg_db_blk_read_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time spent reading data file blocks by backends in this database, in seconds
pg_db_blk_write_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time spent writing data file blocks by backends in this database, in seconds
pg_db_blks_access	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of times disk blocks that accessed read+hit
pg_db_blks_hit	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of times disk blocks were found already in the buffer cache
pg_db_blks_read	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of disk blocks read in this database
pg_db_cks_fail_time	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time at which the last data page checksum failure was detected in this database
pg_db_cks_fails	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of data page checksum failures detected in this database, -1 for not enabled
pg_db_confl_confl_bufferpin	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of queries in this database that have been canceled due to pinned buffers
pg_db_confl_confl_deadlock	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of queries in this database that have been canceled due to deadlocks
pg_db_confl_confl_lock	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of queries in this database that have been canceled due to lock timeouts
pg_db_confl_confl_snapshot	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of queries in this database that have been canceled due to old snapshots
pg_db_confl_confl_tablespace	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of queries in this database that have been canceled due to dropped tablespaces
pg_db_conflicts	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of queries canceled due to conflicts with recovery in this database
pg_db_conn_limit	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Sets maximum number of concurrent connections that can be made to this database. -1 means no limit.
pg_db_datid	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	OID of the database
pg_db_deadlocks	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of deadlocks detected in this database
pg_db_frozen_xid	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	All transaction IDs before this one have been frozened
pg_db_is_template	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	If true(1), then this database can be cloned by any user with CREATEDB privileges
pg_db_ixact_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time spent idling while in a transaction in this database, in seconds
pg_db_numbackends	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of backends currently connected to this database
pg_db_reset_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time at which database statistics were last reset
pg_db_session_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time spent by database sessions in this database, in seconds
pg_db_sessions	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of sessions established to this database
pg_db_sessions_abandoned	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of database sessions to this database that were terminated because connection to the client was lost
pg_db_sessions_fatal	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of database sessions to this database that were terminated by fatal errors
pg_db_sessions_killed	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of database sessions to this database that were terminated by operator intervention
pg_db_temp_bytes	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Total amount of data written to temporary files by queries in this database.
pg_db_temp_files	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of temporary files created by queries in this database
pg_db_tup_deleted	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows deleted by queries in this database
pg_db_tup_fetched	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows fetched by queries in this database
pg_db_tup_inserted	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows inserted by queries in this database
pg_db_tup_modified	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows modified by queries in this database
pg_db_tup_returned	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows returned by queries in this database
pg_db_tup_updated	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows updated by queries in this database
pg_db_xact_commit	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of transactions in this database that have been committed
pg_db_xact_rollback	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of transactions in this database that have been rolled back
pg_db_xact_total	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of transactions in this database
pg_downstream_count	gauge	`state`, `job`, `ins`, `ip`, `instance`, `cls`	Count of corresponding state
pg_exporter_agent_up	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pg_exporter_last_scrape_time	gauge	`job`, `ins`, `ip`, `instance`, `cls`	seconds exporter spending on scrapping
pg_exporter_query_cache_ttl	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	times to live of query cache
pg_exporter_query_scrape_duration	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	seconds query spending on scrapping
pg_exporter_query_scrape_error_count	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	times the query failed
pg_exporter_query_scrape_hit_count	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	numbers been scrapped from this query
pg_exporter_query_scrape_metric_count	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	numbers of metrics been scrapped from this query
pg_exporter_query_scrape_total_count	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	times exporter server was scraped for metrics
pg_exporter_scrape_duration	gauge	`job`, `ins`, `ip`, `instance`, `cls`	seconds exporter spending on scrapping
pg_exporter_scrape_error_count	counter	`job`, `ins`, `ip`, `instance`, `cls`	times exporter was scraped for metrics and failed
pg_exporter_scrape_total_count	counter	`job`, `ins`, `ip`, `instance`, `cls`	times exporter was scraped for metrics
pg_exporter_server_scrape_duration	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	seconds exporter server spending on scrapping
pg_exporter_server_scrape_error_count	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	N/A
pg_exporter_server_scrape_total_count	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	times exporter server was scraped for metrics
pg_exporter_server_scrape_total_seconds	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	seconds exporter server spending on scrapping
pg_exporter_up	gauge	`job`, `ins`, `ip`, `instance`, `cls`	always be 1 if your could retrieve metrics
pg_exporter_uptime	gauge	`job`, `ins`, `ip`, `instance`, `cls`	seconds since exporter primary server inited
pg_flush_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	primary only, location of current wal syncing
pg_func_calls	counter	`datname`, `funcname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of times this function has been called
pg_func_self_time	counter	`datname`, `funcname`, `job`, `ins`, `ip`, `instance`, `cls`	Total time spent in this function itself, not including other functions called by it, in ms
pg_func_total_time	counter	`datname`, `funcname`, `job`, `ins`, `ip`, `instance`, `cls`	Total time spent in this function and all other functions called by it, in ms
pg_in_recovery	gauge	`job`, `ins`, `ip`, `instance`, `cls`	server is in recovery mode? 1 for yes 0 for no
pg_index_idx_blks_hit	counter	`datname`, `relname`, `job`, `ins`, `relid`, `ip`, `instance`, `cls`, `idxname`	Number of buffer hits in this index
pg_index_idx_blks_read	counter	`datname`, `relname`, `job`, `ins`, `relid`, `ip`, `instance`, `cls`, `idxname`	Number of disk blocks read from this index
pg_index_idx_scan	counter	`datname`, `relname`, `job`, `ins`, `relid`, `ip`, `instance`, `cls`, `idxname`	Number of index scans initiated on this index
pg_index_idx_tup_fetch	counter	`datname`, `relname`, `job`, `ins`, `relid`, `ip`, `instance`, `cls`, `idxname`	Number of live table rows fetched by simple index scans using this index
pg_index_idx_tup_read	counter	`datname`, `relname`, `job`, `ins`, `relid`, `ip`, `instance`, `cls`, `idxname`	Number of index entries returned by scans on this index
pg_index_relpages	gauge	`datname`, `relname`, `job`, `ins`, `relid`, `ip`, `instance`, `cls`, `idxname`	Size of the on-disk representation of this index in pages
pg_index_reltuples	gauge	`datname`, `relname`, `job`, `ins`, `relid`, `ip`, `instance`, `cls`, `idxname`	Estimate relation tuples
pg_insert_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	primary only, location of current wal inserting
pg_io_evictions	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Number of times a block has been written out from a shared or local buffer
pg_io_extend_time	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Time spent in extend operations in seconds
pg_io_extends	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Number of relation extend operations, each of the size specified in op_bytes.
pg_io_fsync_time	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Time spent in fsync operations in seconds
pg_io_fsyncs	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Number of fsync calls. These are only tracked in context normal
pg_io_hits	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	The number of times a desired block was found in a shared buffer.
pg_io_op_bytes	gauge	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	The number of bytes per unit of I/O read, written, or extended. 8192 by default
pg_io_read_time	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Time spent in read operations in seconds
pg_io_reads	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Number of read operations, each of the size specified in op_bytes.
pg_io_reset_time	gauge	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Timestamp at which these statistics were last reset
pg_io_reuses	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	The number of times an existing buffer in reused
pg_io_write_time	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Time spent in write operations in seconds
pg_io_writeback_time	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Time spent in writeback operations in seconds
pg_io_writebacks	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Number of units of size op_bytes which the process requested the kernel write out to permanent storage.
pg_io_writes	counter	`type`, `job`, `ins`, `object`, `ip`, `context`, `instance`, `cls`	Number of write operations, each of the size specified in op_bytes.
pg_is_in_recovery	gauge	`job`, `ins`, `ip`, `instance`, `cls`	1 if in recovery mode
pg_is_wal_replay_paused	gauge	`job`, `ins`, `ip`, `instance`, `cls`	1 if wal play paused
pg_lag	gauge	`job`, `ins`, `ip`, `instance`, `cls`	replica only, replication lag in seconds
pg_last_replay_time	gauge	`job`, `ins`, `ip`, `instance`, `cls`	time when last transaction been replayed
pg_lock_count	gauge	`datname`, `job`, `ins`, `ip`, `mode`, `instance`, `cls`	Number of locks of corresponding mode and database
pg_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	log sequence number, current write location
pg_meta_info	gauge	`cls`, `extensions`, `version`, `job`, `ins`, `primary_conninfo`, `conf_path`, `hba_path`, `ip`, `cluster_id`, `instance`, `listen_port`, `wal_level`, `ver_num`, `cluster_name`, `data_dir`	constant 1
pg_query_calls	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Number of times the statement was executed
pg_query_exec_time	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Total time spent executing the statement, in seconds
pg_query_io_time	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Total time the statement spent reading and writing blocks, in seconds
pg_query_rows	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of rows retrieved or affected by the statement
pg_query_sblk_dirtied	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of shared blocks dirtied by the statement
pg_query_sblk_hit	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of shared block cache hits by the statement
pg_query_sblk_read	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of shared blocks read by the statement
pg_query_sblk_written	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of shared blocks written by the statement
pg_query_wal_bytes	counter	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	Total amount of WAL bytes generated by the statement
pg_receive_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	replica only, location of wal synced to disk
pg_recovery_backup_end_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	Backup end location
pg_recovery_backup_start_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	Backup start location
pg_recovery_min_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	Minimum recovery ending location
pg_recovery_min_timeline	counter	`job`, `ins`, `ip`, `instance`, `cls`	Min recovery ending loc’s timeline
pg_recovery_prefetch_block_distance	gauge	`job`, `ins`, `ip`, `instance`, `cls`	How many blocks ahead the prefetcher is looking
pg_recovery_prefetch_hit	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of blocks not prefetched because they were already in the buffer pool
pg_recovery_prefetch_io_depth	gauge	`job`, `ins`, `ip`, `instance`, `cls`	How many prefetches have been initiated but are not yet known to have completed
pg_recovery_prefetch_prefetch	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of blocks prefetched because they were not in the buffer pool
pg_recovery_prefetch_reset_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Time at which these recovery prefetch statistics were last reset
pg_recovery_prefetch_skip_fpw	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of blocks not prefetched because a full page image was included in the WAL
pg_recovery_prefetch_skip_init	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of blocks not prefetched because they would be zero-initialized
pg_recovery_prefetch_skip_new	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of blocks not prefetched because they didn’t exist yet
pg_recovery_prefetch_skip_rep	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of blocks not prefetched because they were already recently prefetched
pg_recovery_prefetch_wal_distance	gauge	`job`, `ins`, `ip`, `instance`, `cls`	How many bytes ahead the prefetcher is looking
pg_recovery_require_record	gauge	`job`, `ins`, `ip`, `instance`, `cls`	End-of-backup record required
pg_recv_flush_lsn	counter	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Last write-ahead log location already received and flushed to disk
pg_recv_flush_tli	counter	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Timeline number of last write-ahead log location received and flushed to disk
pg_recv_init_lsn	counter	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	First write-ahead log location used when WAL receiver is started
pg_recv_init_tli	counter	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	First timeline number used when WAL receiver is started
pg_recv_msg_recv_time	gauge	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Receipt time of last message received from origin WAL sender
pg_recv_msg_send_time	gauge	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Send time of last message received from origin WAL sender
pg_recv_pid	gauge	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Process ID of the WAL receiver process
pg_recv_reported_lsn	counter	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Last write-ahead log location reported to origin WAL sender
pg_recv_reported_time	gauge	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Time of last write-ahead log location reported to origin WAL sender
pg_recv_time	gauge	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Time of current snapshot
pg_recv_write_lsn	counter	`state`, `slot_name`, `job`, `ins`, `ip`, `instance`, `cls`, `sender_host`, `sender_port`	Last write-ahead log location already received and written to disk, but not flushed.
pg_relkind_count	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`, `relkind`	Number of relations of corresponding relkind
pg_repl_backend_xmin	counter	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	This standby’s xmin horizon reported by hot_standby_feedback.
pg_repl_client_port	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	TCP port number that the client is using for communication with this WAL sender, or -1 if a Unix socket is used
pg_repl_flush_diff	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Last log position flushed to disk by this standby server diff with current lsn
pg_repl_flush_lag	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written and flushed it
pg_repl_flush_lsn	counter	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Last write-ahead log location flushed to disk by this standby server
pg_repl_launch_time	counter	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Time when this process was started, i.e., when the client connected to this WAL sender
pg_repl_lsn	counter	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Current log position on this server
pg_repl_replay_diff	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Last log position replayed into the database on this standby server diff with current lsn
pg_repl_replay_lag	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written, flushed and applied it
pg_repl_replay_lsn	counter	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Last write-ahead log location replayed into the database on this standby server
pg_repl_reply_time	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Send time of last reply message received from standby server
pg_repl_sent_diff	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Last log position sent to this standby server diff with current lsn
pg_repl_sent_lsn	counter	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Last write-ahead log location sent on this connection
pg_repl_state	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Current WAL sender encoded state 0-4 for streaming startup catchup backup stopping
pg_repl_sync_priority	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Priority of this standby server for being chosen as the synchronous standby
pg_repl_sync_state	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Encoded synchronous state of this standby server, 0-3 for async potential sync quorum
pg_repl_time	counter	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Current timestamp in unix epoch
pg_repl_write_diff	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Last log position written to disk by this standby server diff with current lsn
pg_repl_write_lag	gauge	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written it
pg_repl_write_lsn	counter	`pid`, `usename`, `address`, `job`, `ins`, `appname`, `ip`, `instance`, `cls`	Last write-ahead log location written to disk by this standby server
pg_replay_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	replica only, location of wal applied
pg_seq_blks_hit	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`, `seqname`	Number of buffer hits in this sequence
pg_seq_blks_read	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`, `seqname`	Number of disk blocks read from this sequence
pg_seq_last_value	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`, `seqname`	The last sequence value written to disk
pg_setting_block_size	gauge	`job`, `ins`, `ip`, `instance`, `cls`	pg page block size, 8192 by default
pg_setting_data_checksums	gauge	`job`, `ins`, `ip`, `instance`, `cls`	whether data checksum is enabled, 1 enabled 0 disabled
pg_setting_max_connections	gauge	`job`, `ins`, `ip`, `instance`, `cls`	number of concurrent connections to the database server
pg_setting_max_locks_per_transaction	gauge	`job`, `ins`, `ip`, `instance`, `cls`	no more than this many distinct objects can be locked at any one time
pg_setting_max_prepared_transactions	gauge	`job`, `ins`, `ip`, `instance`, `cls`	maximum number of transactions that can be in the prepared state simultaneously
pg_setting_max_replication_slots	gauge	`job`, `ins`, `ip`, `instance`, `cls`	maximum number of replication slots
pg_setting_max_wal_senders	gauge	`job`, `ins`, `ip`, `instance`, `cls`	maximum number of concurrent connections from standby servers
pg_setting_max_worker_processes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	maximum number of background processes that the system can support
pg_setting_wal_log_hints	gauge	`job`, `ins`, `ip`, `instance`, `cls`	whether wal_log_hints is enabled, 1 enabled 0 disabled
pg_size_bytes	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	File size in bytes
pg_slot_active	gauge	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	True(1) if this slot is currently actively being used
pg_slot_catalog_xmin	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	The oldest transaction affecting the system catalogs that this slot needs the database to retain.
pg_slot_confirm_lsn	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	The address (LSN) up to which the logical slot’s consumer has confirmed receiving data.
pg_slot_reset_time	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	When statistics were last reset
pg_slot_restart_lsn	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	The address (LSN) of oldest WAL which still might be required by the consumer of this slot
pg_slot_retained_bytes	gauge	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Size of bytes that retained for this slot
pg_slot_safe_wal_size	gauge	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	bytes that can be written to WAL which will not make slot into lost
pg_slot_spill_bytes	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Bytes that spilled to disk due to logical decode mem exceeding
pg_slot_spill_count	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Xacts that spilled to disk due to logical decode mem exceeding (a xact can be spilled multiple times)
pg_slot_spill_txns	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Xacts that spilled to disk due to logical decode mem exceeding (subtrans included)
pg_slot_stream_bytes	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Bytes that streamed to decoding output plugin after mem exceed
pg_slot_stream_count	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Xacts that streamed to decoding output plugin after mem exceed (a xact can be streamed multiple times)
pg_slot_stream_txns	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Xacts that streamed to decoding output plugin after mem exceed
pg_slot_temporary	gauge	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	True(1) if this is a temporary replication slot.
pg_slot_total_bytes	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Number of decoded bytes sent to the decoding output plugin for this slot
pg_slot_total_txns	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	Number of decoded xacts sent to the decoding output plugin for this slot
pg_slot_wal_status	gauge	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	WAL reserve status 0-3 means reserved,extended,unreserved,lost, -1 means other
pg_slot_xmin	counter	`slot_name`, `job`, `ins`, `ip`, `instance`, `cls`	The oldest transaction that this slot needs the database to retain.
pg_slru_blks_exists	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of blocks checked for existence for this SLRU
pg_slru_blks_hit	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of times disk blocks were found already in the SLRU, so that a read was not necessary
pg_slru_blks_read	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of disk blocks read for this SLRU
pg_slru_blks_written	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of disk blocks written for this SLRU
pg_slru_blks_zeroed	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of blocks zeroed during initializations
pg_slru_flushes	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of flushes of dirty data for this SLRU
pg_slru_reset_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Time at which these statistics were last reset
pg_slru_truncates	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of truncates for this SLRU
pg_ssl_disabled	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of client connection that does not use ssl
pg_ssl_enabled	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of client connection that use ssl
pg_sync_standby_enabled	gauge	`job`, `ins`, `ip`, `names`, `instance`, `cls`	Synchronous commit enabled, 1 if enabled, 0 if disabled
pg_table_age	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Age of this table in vacuum cycles
pg_table_analyze_count	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of times this table has been manually analyzed
pg_table_autoanalyze_count	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of times this table has been analyzed by the autovacuum daemon
pg_table_autovacuum_count	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of times this table has been vacuumed by the autovacuum daemon
pg_table_frozenxid	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	All txid before this have been frozen on this table
pg_table_heap_blks_hit	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of buffer hits in this table
pg_table_heap_blks_read	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of disk blocks read from this table
pg_table_idx_blks_hit	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of buffer hits in all indexes on this table
pg_table_idx_blks_read	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of disk blocks read from all indexes on this table
pg_table_idx_scan	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of index scans initiated on this table
pg_table_idx_tup_fetch	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of live rows fetched by index scans
pg_table_kind	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Relation kind r/table/114
pg_table_n_dead_tup	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Estimated number of dead rows
pg_table_n_ins_since_vacuum	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Estimated number of rows inserted since this table was last vacuumed
pg_table_n_live_tup	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Estimated number of live rows
pg_table_n_mod_since_analyze	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Estimated number of rows modified since this table was last analyzed
pg_table_n_tup_del	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows deleted
pg_table_n_tup_hot_upd	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows HOT updated (i.e with no separate index update required)
pg_table_n_tup_ins	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows inserted
pg_table_n_tup_mod	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows modified (insert + update + delete)
pg_table_n_tup_newpage_upd	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows updated where the successor version goes onto a new heap page
pg_table_n_tup_upd	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of rows updated (includes HOT updated rows)
pg_table_ncols	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of columns in the table
pg_table_pages	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Size of the on-disk representation of this table in pages
pg_table_relid	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Relation oid of this table
pg_table_seq_scan	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of sequential scans initiated on this table
pg_table_seq_tup_read	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of live rows fetched by sequential scans
pg_table_size_bytes	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Total bytes of this table (including toast, index, toast index)
pg_table_size_indexsize	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Bytes of all related indexes of this table
pg_table_size_relsize	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Bytes of this table itself (main, vm, fsm)
pg_table_size_toastsize	gauge	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Bytes of toast tables of this table
pg_table_tbl_scan	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of scans initiated on this table
pg_table_tup_read	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of live rows fetched by scans
pg_table_tuples	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	All txid before this have been frozen on this table
pg_table_vacuum_count	counter	`datname`, `relname`, `job`, `ins`, `ip`, `instance`, `cls`	Number of times this table has been manually vacuumed (not counting VACUUM FULL)
pg_timestamp	gauge	`job`, `ins`, `ip`, `instance`, `cls`	database current timestamp
pg_up	gauge	`job`, `ins`, `ip`, `instance`, `cls`	last scrape was able to connect to the server: 1 for yes, 0 for no
pg_uptime	gauge	`job`, `ins`, `ip`, `instance`, `cls`	seconds since postmaster start
pg_version	gauge	`job`, `ins`, `ip`, `instance`, `cls`	server version number
pg_wait_count	gauge	`datname`, `job`, `ins`, `event`, `ip`, `instance`, `cls`	Count of WaitEvent on target database
pg_wal_buffers_full	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of times WAL data was written to disk because WAL buffers became full
pg_wal_bytes	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total amount of WAL generated in bytes
pg_wal_fpi	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total number of WAL full page images generated
pg_wal_records	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total number of WAL records generated
pg_wal_reset_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	When statistics were last reset
pg_wal_sync	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of times WAL files were synced to disk via issue_xlog_fsync request
pg_wal_sync_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total amount of time spent syncing WAL files to disk via issue_xlog_fsync request, in seconds
pg_wal_write	counter	`job`, `ins`, `ip`, `instance`, `cls`	Number of times WAL buffers were written out to disk via XLogWrite request.
pg_wal_write_time	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total amount of time spent writing WAL buffers to disk via XLogWrite request in seconds
pg_write_lsn	counter	`job`, `ins`, `ip`, `instance`, `cls`	primary only, location of current wal writing
pg_xact_xmax	counter	`job`, `ins`, `ip`, `instance`, `cls`	First as-yet-unassigned txid. txid >= this are invisible.
pg_xact_xmin	counter	`job`, `ins`, `ip`, `instance`, `cls`	Earliest txid that is still active
pg_xact_xnum	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Current active transaction count
pgbouncer:cls:load1	Unknown	`job`, `cls`	N/A
pgbouncer:cls:load15	Unknown	`job`, `cls`	N/A
pgbouncer:cls:load5	Unknown	`job`, `cls`	N/A
pgbouncer:db:conn_usage	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	N/A
pgbouncer:db:conn_usage_reserve	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	N/A
pgbouncer:db:pool_current_conn	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	N/A
pgbouncer:db:pool_disabled	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	N/A
pgbouncer:db:pool_max_conn	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	N/A
pgbouncer:db:pool_paused	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	N/A
pgbouncer:db:pool_reserve_size	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	N/A
pgbouncer:db:pool_size	Unknown	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	N/A
pgbouncer:ins:free_clients	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:free_servers	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:load1	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:load15	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:load5	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:login_clients	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:pool_databases	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:pool_users	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:pools	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer:ins:used_clients	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer_database_current_connections	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	Current number of connections for this database
pgbouncer_database_disabled	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	True(1) if this database is currently disabled, else 0
pgbouncer_database_max_connections	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	Maximum number of allowed connections for this database
pgbouncer_database_min_pool_size	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	Minimum number of server connections
pgbouncer_database_paused	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	True(1) if this database is currently paused, else 0
pgbouncer_database_pool_size	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	Maximum number of server connections
pgbouncer_database_reserve_pool	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `host`, `cls`, `real_datname`, `port`	Maximum number of additional connections for this database
pgbouncer_exporter_agent_up	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
pgbouncer_exporter_last_scrape_time	gauge	`job`, `ins`, `ip`, `instance`, `cls`	seconds exporter spending on scrapping
pgbouncer_exporter_query_cache_ttl	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	times to live of query cache
pgbouncer_exporter_query_scrape_duration	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	seconds query spending on scrapping
pgbouncer_exporter_query_scrape_error_count	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	times the query failed
pgbouncer_exporter_query_scrape_hit_count	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	numbers been scrapped from this query
pgbouncer_exporter_query_scrape_metric_count	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	numbers of metrics been scrapped from this query
pgbouncer_exporter_query_scrape_total_count	gauge	`datname`, `query`, `job`, `ins`, `ip`, `instance`, `cls`	times exporter server was scraped for metrics
pgbouncer_exporter_scrape_duration	gauge	`job`, `ins`, `ip`, `instance`, `cls`	seconds exporter spending on scrapping
pgbouncer_exporter_scrape_error_count	counter	`job`, `ins`, `ip`, `instance`, `cls`	times exporter was scraped for metrics and failed
pgbouncer_exporter_scrape_total_count	counter	`job`, `ins`, `ip`, `instance`, `cls`	times exporter was scraped for metrics
pgbouncer_exporter_server_scrape_duration	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	seconds exporter server spending on scrapping
pgbouncer_exporter_server_scrape_total_count	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	times exporter server was scraped for metrics
pgbouncer_exporter_server_scrape_total_seconds	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	seconds exporter server spending on scrapping
pgbouncer_exporter_up	gauge	`job`, `ins`, `ip`, `instance`, `cls`	always be 1 if your could retrieve metrics
pgbouncer_exporter_uptime	gauge	`job`, `ins`, `ip`, `instance`, `cls`	seconds since exporter primary server inited
pgbouncer_in_recovery	gauge	`job`, `ins`, `ip`, `instance`, `cls`	server is in recovery mode? 1 for yes 0 for no
pgbouncer_list_items	gauge	`job`, `ins`, `ip`, `instance`, `list`, `cls`	Number of corresponding pgbouncer object
pgbouncer_pool_active_cancel_clients	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Client connections that have forwarded query cancellations to the server and are waiting for the server response.
pgbouncer_pool_active_cancel_servers	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Server connections that are currently forwarding a cancel request
pgbouncer_pool_active_clients	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Client connections that are linked to server connection and can process queries
pgbouncer_pool_active_servers	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Server connections that are linked to a client
pgbouncer_pool_cancel_clients	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Client connections that have not forwarded query cancellations to the server yet.
pgbouncer_pool_cancel_servers	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	cancel requests have completed that were sent to cancel a query on this server
pgbouncer_pool_idle_servers	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Server connections that are unused and immediately usable for client queries
pgbouncer_pool_login_servers	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Server connections currently in the process of logging in
pgbouncer_pool_maxwait	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	How long the first(oldest) client in the queue has waited, in seconds, key metric
pgbouncer_pool_maxwait_us	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Microsecond part of the maximum waiting time.
pgbouncer_pool_tested_servers	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Server connections that are currently running reset or check query
pgbouncer_pool_used_servers	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Server connections that have been idle for more than server_check_delay (means have to run check query)
pgbouncer_pool_waiting_clients	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `user`, `cls`, `pool_mode`	Client connections that have sent queries but have not yet got a server connection
pgbouncer_stat_avg_query_count	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Average queries per second in last stat period
pgbouncer_stat_avg_query_time	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Average query duration, in seconds
pgbouncer_stat_avg_recv	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Average received (from clients) bytes per second
pgbouncer_stat_avg_sent	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Average sent (to clients) bytes per second
pgbouncer_stat_avg_wait_time	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time spent by clients waiting for a server, in seconds (average per second).
pgbouncer_stat_avg_xact_count	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Average transactions per second in last stat period
pgbouncer_stat_avg_xact_time	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Average transaction duration, in seconds
pgbouncer_stat_total_query_count	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of SQL queries pooled by pgbouncer
pgbouncer_stat_total_query_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of seconds spent when executing queries
pgbouncer_stat_total_received	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Total volume in bytes of network traffic received by pgbouncer
pgbouncer_stat_total_sent	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Total volume in bytes of network traffic sent by pgbouncer
pgbouncer_stat_total_wait_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Time spent by clients waiting for a server, in seconds
pgbouncer_stat_total_xact_count	gauge	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of SQL transactions pooled by pgbouncer
pgbouncer_stat_total_xact_time	counter	`datname`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of seconds spent when in a transaction
pgbouncer_up	gauge	`job`, `ins`, `ip`, `instance`, `cls`	last scrape was able to connect to the server: 1 for yes, 0 for no
pgbouncer_version	gauge	`job`, `ins`, `ip`, `instance`, `cls`	server version number
process_cpu_seconds_total	counter	`job`, `ins`, `ip`, `instance`, `cls`	Total user and system CPU time spent in seconds.
process_max_fds	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Maximum number of open file descriptors.
process_open_fds	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Number of open file descriptors.
process_resident_memory_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Resident memory size in bytes.
process_start_time_seconds	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Start time of the process since unix epoch in seconds.
process_virtual_memory_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Virtual memory size in bytes.
process_virtual_memory_max_bytes	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Maximum amount of virtual memory available in bytes.
promhttp_metric_handler_requests_in_flight	gauge	`job`, `ins`, `ip`, `instance`, `cls`	Current number of scrapes being served.
promhttp_metric_handler_requests_total	counter	`code`, `job`, `ins`, `ip`, `instance`, `cls`	Total number of scrapes by HTTP status code.
scrape_duration_seconds	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
scrape_samples_post_metric_relabeling	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
scrape_samples_scraped	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
scrape_series_added	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A
up	Unknown	`job`, `ins`, `ip`, `instance`, `cls`	N/A

3坏2应急处理

Sat, 11 Jan 2025 00:00:00 +0000

如果经典3节点高可用部署同时出现两台（多数主体）故障，系统通常无法自动完成故障切换，需要人工介入：

首先判断另外两台服务器的情况，如果短时间内可以拉起，优先选择拉起另外两台服务。否则进入 紧急止血流程

使用 VIP-Manager 为 PostgreSQL 集群配置二层 VIP

Mon, 01 Jan 0001 00:00:00 +0000

您可以在 PostgreSQL 集群上绑定一个可选的 L2 VIP —— 前提条件是：集群中的所有节点都在一个二层网络中。

这个 L2 VIP 强制使用 Master - Backup 模式，Master 始终指向在数据库集群主库实例所在的节点。

Citus 集群部署

Sat, 11 Jan 2025 00:00:00 +0000

Citus 是一个 PostgreSQL 扩展，可以将 PostgreSQL 原地转换为一个分布式数据库，并实现在多个节点上水平扩展，以处理大量数据和大量查询。

Patroni 在 v3.0 后，提供了对 Citus 原生高可用的支持，简化了 Citus 集群的搭建，Pigsty 也对此提供了原生支持。

预置剧本

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 提供了一系列剧本，用于集群上下线扩缩容，用户/数据库管理，监控、备份恢复或迁移已有实例。

剧本	功能
`pgsql.yml`	初始化 PostgreSQL 集群或添加新的从库
`pgsql-rm.yml`	移除 PostgreSQL 集群，或移除某个实例
`pgsql-user.yml`	在现有的 PostgreSQL 集群中添加新的业务用户
`pgsql-db.yml`	在现有的 PostgreSQL 集群中添加新的业务数据库
`pgsql-monitor.yml`	将远程 PostgreSQL 实例纳入监控中
`pgsql-migration.yml`	为现有的 PostgreSQL 集群生成迁移手册和脚本
`pgsql-pitr.yml`	执行 PostgreSQL 时间点恢复 (PITR)

保护机制

使用 PGSQL 剧本时需要 特别注意，剧本 pgsql.yml 与 pgsql-rm.yml 使用不当会有误删数据库的风险！

扩展插件

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 提供 575 个已打包扩展，覆盖时序、地理、向量、全文检索、分析、特性增强等 16 大类别，开箱即用。

在 Pigsty 中使用扩展涉及四个核心步骤：下载、安装、配置/加载 与启用。

pg-meta:
  hosts: { 10.10.10.10: { pg_seq: 1, pg_role: primary } }
  vars:
    pg_cluster: pg-meta
    pg_databases:
      - name: meta
        extensions: [ postgis, timescaledb, vector ]   # 启用：在数据库中创建扩展
    pg_libs: 'timescaledb, pg_stat_statements, auto_explain' # 配置：预加载扩展库
    pg_extensions: [ postgis, timescaledb, pgvector ]  # 安装：安装扩展软件包

PostgreSQL

Mon, 01 Jan 0001 00:00:00 +0000

PostgreSQL 是世界上最先进和最受欢迎的开源数据库。

默认安装 PostgreSQL 18，支持 PostgreSQL 14 ~ 18，并提供 575 个 PG 扩展。

快速开始

使用 pgsql 配置模板安装 Pigsty。

Citus

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 原生支持 Citus。这是一个基于原生 PostgreSQL 内核的分布式水平扩展插件。

安装

Citus 是一个 PostgreSQL 扩展插件，可以按照标准插件安装的流程，在原生 PostgreSQL 集群上加装启用。

./pgsql.yml -t pg_extension -e '{"pg_extensions":["citus"]}'

配置

要定义一个 citus 集群，您需要指定以下参数：

Babelfish

Mon, 01 Jan 0001 00:00:00 +0000

Babelfish 是一个提供 MS SQL Server 线缆协议兼容性的内核分支 + 扩展，由 AWS 开源。

概览

Pigsty 允许您使用 mssql 模式部署 Babelfish 内核，在 PostgreSQL 上提供：

SQL Server 线缆协议兼容（TDS 协议，1433 端口）
T-SQL 语法兼容
与 Pigsty 现有能力（高可用、备份、监控、IaC）统一集成

在 Pigsty v4 中，Babelfish 支持 PostgreSQL 17/18，默认模板使用 pg_version: 17，并已经纳入 Pigsty 标准交付链路。支持所有 Linux 平台。

IvorySQL

Mon, 01 Jan 0001 00:00:00 +0000

IvorySQL 是一个开源的，旨在基于 PG 提供 “Oracle 兼容性” 的 PostgreSQL 内核分支。

概览

Pigsty PGSQL 仓库直接提供 IvorySQL 5.4 软件包，兼容 PostgreSQL 18.4，并覆盖当前支持的 EL、Debian、Ubuntu 与双架构平台。在线安装使用 Pigsty 的 pgsql 仓库；商业版同时提供对应平台的离线交付方案。

PolarDB PG

Mon, 01 Jan 0001 00:00:00 +0000

概览

Pigsty 允许使用 PolarDB 创建带有 “国产化信创资质” 的 PostgreSQL 集群！

PolarDB for PostgreSQL 当前以 PostgreSQL 17 为基线，Pigsty 中的 polar 模板、默认路径与扩展说明也已经同步到 PG17。任何兼容 PostgreSQL 线缆协议的客户端工具都可以访问 PolarDB 集群。

PolarDB Oracle

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 允许使用 PolarDB 创建带有 “国产化信创资质” 的 PolarDB for Oracle 集群！

根据【安全可靠测评结果公告（2023年第1号）】，附表三、集中式数据库。PolarDB v2.0 属于自主可控，安全可靠的国产信创数据库。

PolarDB for Oracle 是基于 PolarDB for PostgreSQL 进行二次开发的 Oracle 兼容版本，两者共用同一套内核，通过 --compatibility-mode 参数进行区分。

Percona

Mon, 01 Jan 0001 00:00:00 +0000

Percona Postgres 是一个带有 pg_tde（透明数据加密）扩展的补丁 Postgres 内核。

Pigsty 从 v4.4.0 起将 Percona PostgreSQL 打包到私有前缀 /usr/pgtde-$v，v4.5.0 延续这一布局（PostgreSQL 18 对应 /usr/pgtde-18）。pgtde 包别名会同时安装内核包与 contrib 包，其中包含 pg_tde、PostGIS、pgvector、wal2json、pg_repack、 pgaudit、pg_stat_monitor 等常用组件。

openHalo

Mon, 01 Jan 0001 00:00:00 +0000

OpenHalo 是一个开源的 PostgreSQL 内核，提供 MySQL 线协议兼容性。

openHalo 基于 PostgreSQL 14.18 内核版本，提供与 MySQL 5.7.32-log / 8.0 版本的线协议兼容性。Pigsty 通过 pg_mode: mysql 与 openhalo 包别名交付。

Pigsty 在所有支持的 Linux 平台上为 OpenHalo 提供部署支持。

Greenplum

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 支持部署 Greenplum 集群，及其衍生发行版 YMatrixDB，并提供了将现有 Greenplum 部署纳入 Pigsty 监控的能力。

概览

Greenplum / YMatrix 集群部署能力仅在专业版本/企业版本中提供，目前不对外开源。

OrioleDB

Mon, 01 Jan 0001 00:00:00 +0000

OrioleDB 是一个 PostgreSQL 存储引擎扩展，声称能够提供 4 倍 OLTP 性能，没有 xid 环绕和表膨胀问题，并具有"云原生"（数据存储在 S3）能力。

OrioleDB 当前在 Pigsty 中支持 PostgreSQL 16、17、18 三个兼容系，当前基线版本为 OrioleDB 1.8 beta16。

Cloudberry

Mon, 01 Jan 0001 00:00:00 +0000

Cloudberry 是一个源自 Greenplum 社区的开源 MPP 数据仓库内核，适合大规模并行分析场景。

概览

在 Pigsty 中，Cloudberry 沿用 gpsql 模式接入，与 Greenplum / MatrixDB 共享一套身份模型、监控逻辑与目录约定。

Neon

Mon, 01 Jan 0001 00:00:00 +0000

Neon 采用了存储与计算分离架构，提供了丝滑的自动扩缩容，Scale to Zero，以及数据库版本分叉等独家能力。

Neon 官网：https://neon.tech/

AgensGraph

Mon, 01 Jan 0001 00:00:00 +0000

AgensGraph 是基于 PostgreSQL 的属性图数据库内核，支持 openCypher 查询，并允许 Cypher 与 SQL 混合使用。

概览

Pigsty 通过 pg_mode: agens 接入 AgensGraph，并保留标准 PostgreSQL 集群的大部分运维体验。

pgEdge

Mon, 01 Jan 0001 00:00:00 +0000

pgEdge 是面向边缘场景的分布式 PostgreSQL 发行版，核心能力建立在 Spock 多主逻辑复制之上。

概览

Pigsty 通过 pg_mode: pgedge 接入 pgEdge，并用标准 PG 集群编排流程交付其核心组件：

DocumentDB

Mon, 01 Jan 0001 00:00:00 +0000

DocumentDB 是微软开源维护的 PostgreSQL 文档数据库扩展，FerretDB 是构建在其上的无状态协议转换代理。两者组合，让标准 PostgreSQL 内核对外提供 MongoDB 线协议兼容端点——使用 MongoDB 驱动的应用程序可以直接对接，请求被转换为对 PostgreSQL 的操作。

内核分支

Sun, 23 Jun 2024 00:00:00 +0000

在 Pigsty 中，您可以使用不同 “风味” 的 PostgreSQL 分支替换 “原生 PG 内核”，实现特殊的功能与效果。

Pigsty 支持多种 PostgreSQL 内核和兼容分支，让您能够在同一套运维体系中获得兼容性、多主复制、图查询、MPP 数仓、透明加密等不同能力。

场景模板

Mon, 01 Jan 0001 00:00:00 +0000

Pigsty 提供四种预置的 Patroni/PostgreSQL 配置模板，针对不同的使用场景进行了参数优化：

模板	CPU 核心	适用场景	特点
`/docs/pgsql/template/oltp.yml`	4-128C	OLTP 事务处理	高并发、低延迟、高吞吐
`/docs/pgsql/template/olap.yml`	4-128C	OLAP 分析处理	大查询、高并行、长事务
`/docs/pgsql/template/crit.yml`	4-128C	一致性优先业务	一致性优先、详细审计
`/docs/pgsql/template/tiny.yml`	1-3C	微型实例	资源受限、低配环境

您可以通过 pg_conf 参数来选择使用哪个配置模板，默认为 /docs/pgsql/template/oltp.yml。

快速开始

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在 Pigsty 中使用扩展需要四个步骤：下载、安装、配置、启用。

下载：将扩展软件包下载到本地仓库（默认本地仓库只保证基础内核与 pgsql-main 包集）
安装：在集群节点上安装扩展软件包
配置：部分扩展需要预加载或配置参数
启用：在数据库中执行 CREATE EXTENSION 创建扩展

声明式配置

在 Pigsty 配置清单中声明扩展，集群初始化时自动完成安装与启用：

扩展简介

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扩展是 PostgreSQL 的灵魂所在。Pigsty 收录了 575 个预编译、开箱即用的扩展插件，充分释放 PostgreSQL 的潜能。

扩展是什么

PostgreSQL 扩展（Extension）是一种模块化机制，允许在不修改核心代码的情况下增强数据库功能。一个扩展通常包含三部分：

软件包

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Pigsty 使用 包别名 机制简化扩展的安装与管理。

包别名机制

管理扩展涉及多个层面的名称映射：

层面	示例 `pgvector`	示例 `postgis`
扩展名	`vector`	`postgis`, `postgis_topology`, …
包别名	`pgvector`	`postgis`
RPM 包名	`pgvector_18`	`postgis36_18*`
DEB 包名	`postgresql-18-pgvector`	`postgresql-18-postgis-3*`

Pigsty 提供 包别名 抽象层，让用户无需关心具体的 RPM/DEB 包名：

下载扩展

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在安装扩展前，需要确保扩展软件包已下载到本地仓库或可从上游获取。

默认行为

Pigsty 默认会把基础 PostgreSQL 18 内核包下载到本地软件仓库。默认额外下载集为 repo_extra_packages_default: [ pgsql-main ]，包含 PostgreSQL 内核、客户端、过程语言，以及 pg_repack、wal2json、pgvector 等基础扩展包。

安装扩展

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Pigsty 使用操作系统的包管理器（yum/apt）安装扩展软件包。

参数	用途	默认行为
`pg_packages`	全局通用软件包	确保存在（不升级）
`pg_extensions`	集群特定扩展	安装最新版本

配置扩展

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部分扩展需要预加载动态库或配置参数后才能使用，本节介绍如何配置扩展。

预加载扩展

大多数扩展安装后可直接使用 CREATE EXTENSION 启用，但部分使用 PostgreSQL Hook 机制的扩展需要 预加载。

启用扩展

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安装扩展软件包后，需要在数据库中执行 CREATE EXTENSION 才能使用扩展功能。

查看可用扩展

安装扩展软件包后，可以查看可用的扩展：

-- 查看所有可用扩展
SELECT * FROM pg_available_extensions;

-- 查看特定扩展
SELECT * FROM pg_available_extensions WHERE name = 'vector';

-- 查看已启用的扩展
SELECT * FROM pg_extension;

创建扩展

使用 CREATE EXTENSION 在数据库中启用扩展：

更新扩展

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扩展更新涉及两个层面：软件包更新（操作系统层面）和 扩展对象更新（数据库层面）。

更新软件包

使用包管理器更新扩展的软件包：

移除扩展

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移除扩展涉及两个层面：删除扩展对象（数据库层面）和 卸载软件包（操作系统层面）。

删除扩展对象

使用 DROP EXTENSION 从数据库中删除扩展：

常见问题

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我当前执行安装的用户为何不能使用 `pg` 管理别名？

从 Pigsty v4.0 开始，使用 pg 管理别名管理全局的 Patroni / PostgreSQL 集群的权限被收紧到了管理节点上的管理员分组（admin）。

默认扩展

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Pigsty 在初始化 PostgreSQL 集群时，会默认安装和启用一些核心扩展。

默认安装的扩展

通过 pg_packages 默认安装的扩展：

扩展	说明
`pg_repack`	在线处理表膨胀，重要的维护工具
`wal2json`	逻辑解码输出 JSON 格式变更，CDC 场景常用
`pgvector`	向量数据类型与索引，默认随 `pgsql-main` 安装

pg_extensions 的默认值为空数组 []。如需安装其他扩展，可以按需声明，例如：

扩展仓库

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Pigsty 提供补充扩展仓库，在 PGDG 官方仓库基础上提供额外的扩展包。

YUM 仓库

适用于 EL 8/9/10 及其兼容系统（RHEL、Rocky、AlmaLinux、CentOS 等）。

其他说明

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服务接入

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分离读写操作，正确路由流量，稳定可靠地交付 PostgreSQL 集群提供的能力。

服务是一种抽象：它是数据库集群对外提供能力的形式，并封装了底层集群的细节。

服务对于生产环境中的稳定接入至关重要，在高可用集群自动故障时方显其价值，单机用户通常不需要操心这个概念。