标签：name -- 解读 WDR snapshot time snap openGauss id

openGauss 的 WDR 报告详细解读

openGauss 数据库自 2020 年 6 月 30 日开源至今已有 10 个月了，在这短短的 10 个月内，openGauss 社区用户下载量已达 13W+、issue 合并 2000+、发行商业版本 6 个。仅 3 月份就有 11 家企业完成 CLA 签署，包括虚谷伟业、云和恩墨、优炫软件、海量数据、柏睿数据、快立方，电信天翼云、东方通、宝兰德、新数科技、深信服，正式加入 openGauss 社区，越来越多的力量参与到社区建设中。4 月 24 日，openGauss 社区理事会筹备会议在深圳大学城召开，邀请到国内著名的数据库技术方向上的多个公司、组织和机构，包括华为、招商银行、中国电信、云和恩墨、海量数据、人大金仓、神舟通用、虚谷伟业、快立方、亚信、超图软件、深信服、哈工大等机构参与，为 openGauss 社区开放治理迈出了新的一步。
openGauss 数据库在经历 1.0.0/1.0.1/1.1.0 三个版本的迭代发布后，于 2021 年 3 月 31 日发布 openGauss 2.0.0 版本，这是 openGauss 社区发布的第一个 Release 版本。深度融合华为在数据库领域多年的经验，结合企业级场景需求，持续构建竞争力特性。
作为一名从 Oracle DBA 转 openGauss 相关工作的“IT 攻城狮”，在遇到性能诊断时念念不忘的还是以前经常使用的 AWR 报告，通过这份报告，DBA 可以较为全面的分析出数据库的性能问题所在范围、为下一步的数据库性能优化和故障诊断提供有力支撑。很高兴在 openGauss 数据库中也看到了类似的功能，那就是 openGauss 的WDR 报告。
本文针对 openGauss 2.0.0 的 WDR 报告进行详细解读，帮助大家梳理 WDR 报告的数据来源以及相关含义，以便在 openGauss 数据库的性能诊断工作中游刃有余。关于性能调优操作方法，由于涉及内容较多，这里就不再复述，再写下去就该被老板炒鱿鱼了。openGauss 数据库归根结底，它的本质还是数据库软件，主流的数据库调优方法在 openGauss 数据库中也基本适用，大家仁者见仁、智者见智，根据 WDR 报告结合自己已有的数据库调优方法，完全可以满足 openGauss 绝大多数的性能调优工作。

干货内容如下：

1. 启用 WDR 报告的 snapshot 收集

$ gs_guc reload -N all -I all -c "enable_wdr_snapshot=on"
postgres=# select name,setting from pg_settings where name like '%wdr%';
            name             | setting
-----------------------------+---------
 enable_wdr_snapshot         | on      -- 开启数据库监控快照功能
 wdr_snapshot_interval       | 60      -- 后台Snapshot线程执行监控快照的时间间隔
 wdr_snapshot_query_timeout  | 100     -- 快照操作相关的sql语句的执行超时时间
 wdr_snapshot_retention_days | 8       -- 系统中数据库监控快照数据的保留天数

2. WDR 信息表

1> snapshot.snapshot 【记录当前系统中存储的 WDR 快照信息】

postgres=# \d snapshot.snapshot
             Table "snapshot.snapshot"
   Column    |           Type           | Modifiers
-------------+--------------------------+-----------
 snapshot_id | bigint                   | not null    -- WDR快照序列号
 start_ts    | timestamp with time zone |             -- WDR快照的开始时间
 end_ts      | timestamp with time zone |             -- WDR快照的结束时间

2> snapshot.tables_snap_timestamp【记录所有表的 WDR 快照信息】

postgres=# \d snapshot.tables_snap_timestamp
       Table "snapshot.tables_snap_timestamp"
   Column    |           Type           | Modifiers
-------------+--------------------------+-----------
 snapshot_id | bigint                   | not null    -- WDR快照序列号
 db_name     | text                     |             -- WDR snapshot对应的database
 tablename   | text                     |             -- WDR snasphot对应的table
 start_ts    | timestamp with time zone |             -- WDR快照的开始时间
 end_ts      | timestamp with time zone |             -- WDR快照的结束时间

3. WDR 数据表

说明：WDR 的数据表保存在 snapshot 这个 schema 下以 snap_开头的表，其数据来源于 dbe_perf 这个 schema 内的视图

postgres=# select relname from pg_class where relname like '%snap_%';
----------------------------------------------------------------------------------------------------------------
snapshot.tables_snap_timestamp                      -- 记录所有存储的WDR快照中数据库、表对象、数据采集的开始、结束时间
snapshot.snapshot                                   -- 记录当前系统中存储的WDR快照数据的索引信息、开始、结束时间
snapshot.snapshot_pkey                              -- snapshot.snapshot表的primary key
snapshot.snap_seq                                   -- 序列
snapshot.snap_global_os_runtime                     -- 操作系统运行状态信息
snapshot.snap_global_os_threads                     -- 线程状态信息
snapshot.snap_global_instance_time                  -- 各种时间消耗信息(时间类型见instance_time视图)
snapshot.snap_summary_workload_sql_count            -- 各数据库主节点的workload上的SQL数量分布
snapshot.snap_summary_workload_sql_elapse_time      -- 数据库主节点上workload（业务）负载的SQL耗时信息
snapshot.snap_global_workload_transaction           -- 各节点上的workload的负载信息
snapshot.snap_summary_workload_transaction          -- 汇总的负载事务信息
snapshot.snap_global_thread_wait_status             -- 工作线程以及辅助线程的阻塞等待情况
snapshot.snap_global_memory_node_detail             -- 节点的内存使用情况
snapshot.snap_global_shared_memory_detail           -- 共享内存上下文的使用情况
snapshot.snap_global_stat_db_cu                     -- 数据库的CU命中情况，可以通过gs_stat_reset()进行清零
snapshot.snap_global_stat_database                  -- 数据库的统计信息
snapshot.snap_summary_stat_database                 -- 汇总的数据库统计信息
snapshot.snap_global_stat_database_conflicts        -- 数据库冲突状态的统计信息
snapshot.snap_summary_stat_database_conflicts       -- 汇总的数据库冲突状态的统计信息
snapshot.snap_global_stat_bad_block                 -- 表、索引等文件的读取失败信息
snapshot.snap_summary_stat_bad_block                -- 汇总的表、索引等文件的读取失败信息
snapshot.snap_global_file_redo_iostat               -- Redo(WAL)相关统计信息
snapshot.snap_summary_file_redo_iostat              -- 汇总的Redo(WAL)相关统计信息
snapshot.snap_global_rel_iostat                     -- 数据对象IO统计信息
snapshot.snap_summary_rel_iostat                    -- 汇总的数据对象IO统计信息
snapshot.snap_global_file_iostat                    -- 数据文件IO统计信息
snapshot.snap_summary_file_iostat                   -- 汇总的数据文件IO统计信息
snapshot.snap_global_replication_slots              -- 复制节点的信息
snapshot.snap_global_bgwriter_stat                  -- 后端写进程活动的统计信息
snapshot.snap_global_replication_stat               -- 日志同步状态信息
snapshot.snap_global_transactions_running_xacts     -- 各节点运行事务的信息
snapshot.snap_summary_transactions_running_xacts    -- 汇总各节点运行事务的信息
snapshot.snap_global_transactions_prepared_xacts    -- 当前准备好进行两阶段提交的事务的信息
snapshot.snap_summary_transactions_prepared_xacts   -- 汇总的当前准备好进行两阶段提交的事务的信息
snapshot.snap_summary_statement                     -- SQL语句的全量信息
snapshot.snap_global_statement_count                -- 当前时刻执行的DML/DDL/DQL/DCL语句统计信息
snapshot.snap_summary_statement_count               -- 汇总的当前时刻执行的DML/DDL/DQL/DCL语句统计信息
snapshot.snap_global_config_settings                -- 数据库运行时参数信息
snapshot.snap_global_wait_events                    -- event等待相关统计信息
snapshot.snap_summary_user_login                    -- 用户登录和退出次数的相关信息
snapshot.snap_global_ckpt_status                    -- 实例的检查点信息和各类日志刷页情况
snapshot.snap_global_double_write_status            -- 实例的双写文件的情况
snapshot.snap_global_pagewriter_status              -- 实例的刷页信息和检查点信息
snapshot.snap_global_redo_status                    -- 实例的日志回放情况
snapshot.snap_global_rto_status                     -- 极致RTO状态信息
snapshot.snap_global_recovery_status                -- 主机和备机的日志流控信息
snapshot.snap_global_threadpool_status              -- 节点上的线程池中工作线程及会话的状态信息
snapshot.snap_statement_responsetime_percentile     -- SQL响应时间P80、P95分布信息
snapshot.snap_global_statio_all_indexes             -- 数据库中的每个索引行、显示特定索引的I/O的统计
snapshot.snap_summary_statio_all_indexes            -- 汇总的数据库中的每个索引行、显示特定索引的I/O的统计
snapshot.snap_global_statio_all_sequences           -- 数据库中每个序列的每一行、显示特定序列关于I/O的统计
snapshot.snap_summary_statio_all_sequences          -- 汇总的数据库中每个序列的每一行、显示特定序列关于I/O的统计
snapshot.snap_global_statio_all_tables              -- 数据库中每个表（包括TOAST表）的I/O的统计
snapshot.snap_summary_statio_all_tables             -- 汇总的数据库中每个表（包括TOAST表）的I/O的统计
snapshot.snap_global_stat_all_indexes               -- 数据库中的每个索引行，显示访问特定索引的统计
snapshot.snap_summary_stat_all_indexes              -- 汇总的数据库中的每个索引行，显示访问特定索引的统计
snapshot.snap_summary_stat_user_functions           -- 汇总的数据库节点用户自定义函数的相关统计信息
snapshot.snap_global_stat_user_functions            -- 用户所创建的函数的状态的统计信息
snapshot.snap_global_stat_all_tables                -- 每个表的一行（包括TOAST表）的统计信息
snapshot.snap_summary_stat_all_tables               -- 汇总的每个表的一行（包括TOAST表）的统计信息
snapshot.snap_class_vital_info                      -- 校验相同的表或者索引的Oid是否一致
snapshot.snap_global_record_reset_time              -- 重置（重启，主备倒换，数据库删除）openGauss统计信息时间
snapshot.snap_summary_statio_indexes_name           -- 表snap_summary_statio_all_indexes的索引
snapshot.snap_summary_statio_tables_name            -- 表snap_summary_statio_all_tables的索引
snapshot.snap_summary_stat_indexes_name             -- 表snap_summary_stat_all_indexes的索引
snapshot.snap_class_info_name                       -- 表snap_class_vital_info的索引
(66 rows)
----------------------------------------------------------------------------------------------------------------

4. WDR 报告创建

4.1 创建 snapshot

-- 当开启enable_wdr_snapshot参数时，数据库默认每小时自动执行一次snapshot操作。
-- 当然特定情况下，也可以手动使用函数创建snapshot，如：select create_wdr_snapshot();
postgres=# select * from snapshot.snapshot offset 20;
 snapshot_id |           start_ts            |            end_ts
-------------+-------------------------------+-------------------------------
          21 | 2021-04-21 05:59:09.337877+08 | 2021-04-21 05:59:10.249162+08
          22 | 2021-04-21 06:59:10.3209+08   | 2021-04-21 06:59:11.229808+08
          23 | 2021-04-21 07:59:10.426882+08 | 2021-04-21 07:59:11.340277+08
          24 | 2021-04-21 08:59:10.534251+08 | 2021-04-21 08:59:11.447762+08
          25 | 2021-04-21 09:59:11.448225+08 | 2021-04-21 09:59:26.121124+08

4.2 查询数据库节点信息

postgres=# select * from pg_node_env;
 node_name |     host     | process | port  | installpath  |     datapath      |          log_directory
-----------+--------------+---------+-------+--------------+-------------------+---------------------------------
 dn_6001   | ***.***.***.*** |    9442 | 26000 | /gaussdb/app | /gaussdb/data/db1 | /gaussdb/log/omm/pg_log/dn_6001

4.3 创建 WDR Report[使用 gsql 客户端生成]

postgres=# \a \t \o WDR_20210421.html      -- 打开格式化输出，输出WDR报告：WDR_20210421.html
postgres=# select generate_wdr_report(24,25,'all','node','dn_6001');    -- 生成WDR报告
postgres=# \o \a \t                        -- 关闭格式化输出

函数说明：generate_wdr_report()

-- 语法
select generate_wdr_report(begin_snap_id bigint, end_snap_id bigint, report_type cstring, report_scope cstring, node_name cstring);
-- 选项：
begin_snap_id：查询时间段开始的snapshot的id（表snapshot.snaoshot中的snapshot_id）
end_snap_id：  查询时间段结束snapshot的id。默认end_snap_id大于begin_snap_id（表snapshot.snaoshot中的snapshot_id）
report_type：  指定生成report的类型。例如，summary/detail/all，其中：summary[汇总数据]/detail[明细数据]/all[包含summary和detail]
report_scope： 指定生成report的范围，可以为cluster或者node，其中：cluster是数据库级别的信息，node是节点级别的信息。
node_name：    当report_scope指定为node时，需要把该参数指定为对应节点的名称。当report_scope为cluster时，该值可以省略或者指定为空或NULL。node[节点名称]、cluster[省略/空/NULL]

5. WDR 报告解读

说明：为了使得 WDR 报告内容不空洞，本次在测试环境使用 BenchmarkSQL 对 openGauss 数据库进行压力测试。本次解读的 WDR 报告样例来自于此时采集的 snapshot 数据。

解读：
这一部分是 WDR 报告的概况信息，从这一部分我们能得到如下信息：

信息分类	信息描述
报告采集类型	Summary + Detail，即汇总数据+明细数据
Snapshot 信息	使用 snapshot_id 为 24 和 25 的快照采集 2021-04-21(08:59 ~ 09:59)的运行信息
硬件配置	1*1c/4g
节点名	dn_6001
openGauss 版本	openGauss 2.0.0

相关代码:

第一部分，Report Type/Report Scope/Report Node内容来源于执行generate_wdr_report函数时输入的参数，详见源码“GenReport::ShowReportType(report_params* params)”
第二部分查询SQL：(变量ld-->snapshot_id)
select snapshot_id as "Snapshot Id",
       to_char(start_ts, 'YYYY-MM-DD HH24:MI:SS') as "Start Time",
       to_char(end_ts, 'YYYY-MM-DD HH24:MI:SS')   as "End Time"
from snapshot.snapshot
where snapshot_id = %ld or snapshot_id = %ld;

第三部分查询SQL：(变量ld-->snapshot_id)
select 'CPUS', x.snap_value
from (select * from pg_node_env) t,
     (select * from snapshot.snap_global_os_runtime) x
      where x.snap_node_name = t.node_name
      and  x.snapshot_id = %ld
      and (x.snap_name = 'NUM_CPUS');

select 'CPU Cores', x.snap_value
from (select * from pg_node_env) t,
     (select * from snapshot.snap_global_os_runtime) x
      where x.snap_node_name = t.node_name
      and x.snapshot_id = %ld
      and x.snap_name = 'NUM_CPU_CORES';

select 'CPU Sockets', x.snap_value
from (select * from pg_node_env) t,
     (select * from snapshot.snap_global_os_runtime) x
      where x.snap_node_name = t.node_name
      and  x.snapshot_id = %ld
      and  x.snap_name = 'NUM_CPU_SOCKETS';

select 'Physical Memory', pg_size_pretty(x.snap_value)
from (select * from pg_node_env) t,
     (select * from snapshot.snap_global_os_runtime) x
where x.snap_node_name = t.node_name
and  x.snapshot_id = %ld
and  x.snap_name = 'PHYSICAL_MEMORY_BYTES';

select node_name as "Host Node Name" from pg_node_env;
select version() as "openGauss Version";

解读：
这一部分是实例的效率百分比，目标值是 100%，即越接近 100%，数据库运行越健康。
Buffer Hit：     即数据库请求的数据在 buffer 中命中的比例，该指标越高代表 openGauss 在 buffer 中查询到目标数据的概率越高，数据读取性能越好。
Effective CPU：       即有效的 CPU 使用比例，该指标偏小则说明 CPU 的有效使用偏低，处于等待状态的比例可能较高。
WalWrite NoWait： 即 WAL 日志写入时不等待的比例，该指标接近 100%，说明 buffer 容量充足，可以满足 WAL 写操作的需求，若指标值偏小则可能需要调大 buffer 容量。
Soft Parse：     即 SQL 软解析的比例，该指标接近 100%，说明当前执行的 SQL 基本都可以在 Buffer 中找到，若指标值偏小则说明存在大量硬解析，需要分析原因，对 DML 语句进行适度优化。
Non-Parse CPU：    即非解析占用的 CPU 比例，该指标接近 100%，说明 SQL 解析并没有占用较多的 CPU 时间。
相关代码：

-- 变量：ld指的是snapshot_id，手动执行以下SQL语句时请自行替换对应的snapshot_id
select
unnest(array['Buffer Hit %%', 'Effective CPU %%', 'WalWrite NoWait %%', 'Soft Parse %%', 'Non-Parse CPU %%']) as "Metric Name",
unnest(array[case when s1.all_reads = 0 then 1 else round(s1.blks_hit * 100 / s1.all_reads) end, s2.cpu_to_elapsd, s3.walwrite_nowait, s4.soft_parse, s5.non_parse]) as "Metric Value"
from
(select (snap_2.all_reads - coalesce(snap_1.all_reads, 0)) as all_reads,
        (snap_2.blks_hit - coalesce(snap_1.blks_hit, 0)) as blks_hit
 from
        (select sum(coalesce(snap_blks_read, 0) + coalesce(snap_blks_hit, 0)) as all_reads,
                coalesce(sum(snap_blks_hit), 0) as blks_hit
         from snapshot.snap_summary_stat_database
         where snapshot_id = %ld) snap_1,
             (select sum(coalesce(snap_blks_read, 0) + coalesce(snap_blks_hit, 0)) as all_reads,
                coalesce(sum(snap_blks_hit), 0) as blks_hit
              from snapshot.snap_summary_stat_database
              where snapshot_id = %ld) snap_2
           ) s1,
          (select round(cpu_time.snap_value * 100 / greatest(db_time.snap_value, 1)) as cpu_to_elapsd
           from
             (select coalesce(snap_2.snap_value, 0) - coalesce(snap_1.snap_value, 0) as snap_value
              from
                (select snap_stat_name, snap_value from snapshot.snap_global_instance_time
                   where snapshot_id = %ld and snap_stat_name = 'CPU_TIME') snap_1,
                (select snap_stat_name, snap_value from snapshot.snap_global_instance_time
                   where snapshot_id = %ld and snap_stat_name = 'CPU_TIME') snap_2) cpu_time,
             (select coalesce(snap_2.snap_value, 0) - coalesce(snap_1.snap_value, 0) as snap_value
              from
                (select snap_stat_name, snap_value from snapshot.snap_global_instance_time
                   where snapshot_id = %ld and snap_stat_name = 'DB_TIME') snap_1,
                (select snap_stat_name, snap_value from snapshot.snap_global_instance_time
                   where snapshot_id = %ld and snap_stat_name = 'DB_TIME') snap_2) db_time
          ) s2,
          (select (bufferAccess.snap_wait - bufferFull.snap_wait) * 100 / greatest(bufferAccess.snap_wait, 1) as walwrite_nowait
           from
             (select coalesce(snap_2.snap_wait) - coalesce(snap_1.snap_wait, 0) as snap_wait
              from
                (select snap_wait from snapshot.snap_global_wait_events
                   where snapshot_id = %ld and snap_event  = 'WALBufferFull') snap_1,
                (select snap_wait from snapshot.snap_global_wait_events
                   where snapshot_id = %ld and snap_event  = 'WALBufferFull') snap_2) bufferFull,
             (select coalesce(snap_2.snap_wait) - coalesce(snap_1.snap_wait, 0) as snap_wait
              from
                (select snap_wait from snapshot.snap_global_wait_events
                   where snapshot_id = %ld and snap_event = 'WALBufferAccess') snap_1,
                (select snap_wait from snapshot.snap_global_wait_events
                   where snapshot_id = %ld and snap_event = 'WALBufferAccess') snap_2) bufferAccess
          ) s3,
          (select round((snap_2.soft_parse - snap_1.soft_parse) * 100 / greatest((snap_2.hard_parse + snap_2.soft_parse)-(snap_1.hard_parse + snap_1.soft_parse), 1)) as soft_parse
           from
             (select sum(snap_n_soft_parse) as soft_parse, sum(snap_n_hard_parse) as hard_parse from snapshot.snap_summary_statement
                where snapshot_id = %ld ) snap_1,
             (select sum(snap_n_soft_parse) as soft_parse, sum(snap_n_hard_parse) as hard_parse from snapshot.snap_summary_statement
                where snapshot_id = %ld ) snap_2
          ) s4,
          (select round((snap_2.elapse_time - snap_1.elapse_time) * 100 /greatest((snap_2.elapse_time + snap_2.parse_time)-(snap_1.elapse_time + snap_1.parse_time), 1)) as non_parse
           from
             (select sum(snap_total_elapse_time) as elapse_time, sum(snap_parse_time) as parse_time from snapshot.snap_summary_statement
                where snapshot_id = %ld ) snap_1,
             (select sum(snap_total_elapse_time) as elapse_time, sum(snap_parse_time) as parse_time from snapshot.snap_summary_statement
                where snapshot_id = %ld ) snap_2
          ) s5;

解读：
这一部分列出了数据库 Top 10 的等待事件、等待次数、总等待时间、平均等待时间、等待事件类型。
等待事件主要分为等待状态、等待轻量级锁、等待 IO、等待事务锁这 4 类，详见下表所示：

等待状态列表

当 wait_status 为 acquire lwlock、acquire lock 或者 wait io 时，表示有等待事件。
正在等待获取 wait_event 列对应类型的轻量级锁、事务锁，或者正在进行 IO。

轻量级锁等待事件列表
当 wait_status 值为 acquire lwlock（轻量级锁）时对应的 wait_event 等待事件类型即为轻量级锁等待。
wait_event 为 extension 时，表示此时的轻量级锁是动态分配的锁，未被监控。

wait_event类型	类型描述
ShmemIndexLock	用于保护共享内存中的主索引哈希表。
OidGenLock	用于避免不同线程产生相同的OID。
XidGenLock	用于避免两个事务获得相同的xid。
ProcArrayLock	用于避免并发访问或修改ProcArray共享数组。
SInvalReadLock	用于避免与清理失效消息并发执行。
SInvalWriteLock	用于避免与其它写失效消息、清理失效消息并发执行。
WALInsertLock	用于避免与其它WAL插入操作并发执行。
WALWriteLock	用于避免并发WAL写盘。
ControlFileLock	用于避免pg_control文件的读写并发、写写并发。
CheckpointLock	用于避免多个checkpoint并发执行。
CLogControlLock	用于避免并发访问或者修改Clog控制数据结构。
SubtransControlLock	用于避免并发访问或者修改子事务控制数据结构。
MultiXactGenLock	用于串行分配唯一MultiXact id。
MultiXactOffsetControlLock	用于避免对pg_multixact/offset的写写并发和读写并发。
MultiXactMemberControlLock	用于避免对pg_multixact/members的写写并发和读写并发。
RelCacheInitLock	用于失效消息场景对init文件进行操作时加锁。
CheckpointerCommLock	用于向checkpointer发起文件刷盘请求场景，需要串行的向请求队列插入请求结构。
TwoPhaseStateLock	用于避免并发访问或者修改两阶段信息共享数组。
TablespaceCreateLock	用于确定tablespace是否已经存在。
BtreeVacuumLock	用于防止vacuum清理B-tree中还在使用的页面。
AutovacuumLock	用于串行化访问autovacuum worker数组。
AutovacuumScheduleLock	用于串行化分配需要vacuum的table。
AutoanalyzeLock	用于获取和释放允许执行Autoanalyze的任务资源。
SyncScanLock	用于确定heap扫描时某个relfilenode的起始位置。
NodeTableLock	用于保护存放数据库节点信息的共享结构。
PoolerLock	用于保证两个线程不会同时从连接池里取到相同的连接。
RelationMappingLock	用于等待更新系统表到存储位置之间映射的文件。
AsyncCtlLock	用于避免并发访问或者修改共享通知状态。
AsyncQueueLock	用于避免并发访问或者修改共享通知信息队列。
SerializableXactHashLock	用于避免对于可串行事务共享结构的写写并发和读写并发。
SerializableFinishedListLock	用于避免对于已完成可串行事务共享链表的写写并发和读写并发。
SerializablePredicateLockListLock	用于保护对于可串行事务持有的锁链表。
OldSerXidLock	用于保护记录冲突可串行事务的结构。
FileStatLock	用于保护存储统计文件信息的数据结构。
SyncRepLock	用于在主备复制时保护xlog同步信息。
DataSyncRepLock	用于在主备复制时保护数据页同步信息。
CStoreColspaceCacheLock	用于保护列存表的CU空间分配。
CStoreCUCacheSweepLock	用于列存CU Cache循环淘汰。
MetaCacheSweepLock	用于元数据循环淘汰。
ExtensionConnectorLibLock	用于初始化ODBC连接场景，在加载与卸载特定动态库时进行加锁。
SearchServerLibLock	用于GPU加速场景初始化加载特定动态库时，对读文件操作进行加锁。
LsnXlogChkFileLock	用于串行更新特定结构中记录的主备机的xlog flush位置点。
ReplicationSlotAllocationLock	用于主备复制时保护主机端的流复制槽的分配。
ReplicationSlotControlLock	用于主备复制时避免并发更新流复制槽状态。
ResourcePoolHashLock	用于避免并发访问或者修改资源池哈希表。
WorkloadStatHashLock	用于避免并发访问或者修改包含数据库主节点的SQL请求构成的哈希表。
WorkloadIoStatHashLock	用于避免并发访问或者修改用于统计当前数据库节点的IO信息的哈希表。
WorkloadCGroupHashLock	用于避免并发访问或者修改Cgroup信息构成的哈希表。
OBSGetPathLock	用于避免对obs路径的写写并发和读写并发。
WorkloadUserInfoLock	用于避免并发访问或修改负载管理的用户信息哈希表。
WorkloadRecordLock	用于避免并发访问或修改在内存自适应管理时对数据库主节点收到请求构成的哈希表。
WorkloadIOUtilLock	用于保护记录iostat，CPU等负载信息的结构。
WorkloadNodeGroupLock	用于避免并发访问或者修改内存中的nodegroup信息构成的哈希表。
JobShmemLock	用于定时任务功能中保护定时读取的全局变量。
OBSRuntimeLock	用于获取环境变量，如GASSHOME。
LLVMDumpIRLock	用于导出动态生成函数所对应的汇编语言。
LLVMParseIRLock	用于在查询开始处从IR文件中编译并解析已写好的IR函数。
CriticalCacheBuildLock	用于从共享或者本地缓存初始化文件中加载cache的场景。
WaitCountHashLock	用于保护用户语句计数功能场景中的共享结构。
BufMappingLock	用于保护对共享缓冲映射表的操作。
LockMgrLock	用于保护常规锁结构信息。
PredicateLockMgrLock	用于保护可串行事务锁结构信息。
OperatorRealTLock	用于避免并发访问或者修改记录算子级实时数据的全局结构。
OperatorHistLock	用于避免并发访问或者修改记录算子级历史数据的全局结构。
SessionRealTLock	用于避免并发访问或者修改记录query级实时数据的全局结构。
SessionHistLock	用于避免并发访问或者修改记录query级历史数据的全局结构。
CacheSlotMappingLock	用于保护CU Cache全局信息。
BarrierLock	用于保证当前只有一个线程在创建Barrier。
dummyServerInfoCacheLock	用于保护缓存加速openGauss连接信息的全局哈希表。
RPNumberLock	用于加速openGauss的数据库节点对正在执行计划的任务线程的计数。
ClusterRPLock	用于加速openGauss的CCN中维护的openGauss负载数据的并发存取控制。
CBMParseXlogLock	Cbm 解析xlog时的保护锁
RelfilenodeReuseLock	避免错误地取消已重用的列属性文件的链接。
RcvWriteLock	防止并发调用WalDataRcvWrite。
PercentileLock	用于保护全局PercentileBuffer
CSNBufMappingLock	保护csn页面
UniqueSQLMappingLock	用于保护uniquesql hash table
DelayDDLLock	防止并发ddl。
CLOG Ctl	用于避免并发访问或者修改Clog控制数据结构
Async Ctl	保护Async buffer
MultiXactOffset Ctl	保护MultiXact offet的slru buffer
MultiXactMember Ctl	保护MultiXact member的slrubuffer
OldSerXid SLRU Ctl	保护old xids的slru buffer
ReplicationSlotLock	用于保护ReplicationSlot
PGPROCLock	用于保护pgproc
MetaCacheLock	用于保护MetaCache
DataCacheLock	用于保护datacache
InstrUserLock	用于保护InstrUserHTAB。
BadBlockStatHashLock	用于保护global_bad_block_stat hash表。
BufFreelistLock	用于保证共享缓冲区空闲列表操作的原子性。
CUSlotListLock	用于控制列存缓冲区槽位的并发操作。
AddinShmemInitLock	保护共享内存对象的初始化。
AlterPortLock	保护协调节点更改注册端口号的操作。
FdwPartitionCaheLock	HDFS分区表缓冲区的管理锁。
DfsConnectorCacheLock	DFSConnector缓冲区的管理锁。
DfsSpaceCacheLock	HDFS表空间管理缓冲区的管理锁。
FullBuildXlogCopyStartPtrLock	用于保护全量Build中Xlog拷贝的操作。
DfsUserLoginLock	用于HDFS用户登录以及认证。
LogicalReplicationSlotPersistentDataLock	用于保护逻辑复制过程中复制槽位的数据。
WorkloadSessionInfoLock	保护负载管理session info内存hash表访问。
InstrWorkloadLock	保护负载管理统计信息的内存hash表访问。
PgfdwLock	用于管理实例向Foreign server建立连接。
InstanceTimeLock	用于获取实例中会话的时间信息。
XlogRemoveSegLock	保护Xlog段文件的回收操作。
DnUsedSpaceHashLock	用于更新会话对应的空间使用信息。
CsnMinLock	用于计算CSNmin。
GPCCommitLock	用于保护全局Plan Cache hash表的添加操作。
GPCClearLock	用于保护全局Plan Cache hash表的清除操作。
GPCTimelineLock	用于保护全局Plan Cache hash表检查Timeline的操作。
TsTagsCacheLock	用于时序tag缓存管理。
InstanceRealTLock	用于保护共享实例统计信息hash表的更新操作。
CLogBufMappingLock	用于提交日志缓存管理。
GPCMappingLock	用于全局Plan Cache缓存管理。
GPCPrepareMappingLock	用于全局Plan Cache缓存管理。
BufferIOLock	保护共享缓冲区页面的IO操作。
BufferContentLock	保护共享缓冲区页面内容的读取、修改。
CSNLOG Ctl	用于CSN日志管理。
DoubleWriteLock	用于双写的管理操作。
RowPageReplicationLock	用于管理行存储的数据页复制。
extension	其他轻量锁。

IO 等待事件列表
当 wait_status 值为 wait io 时对应的 wait_event 等待事件类型即为 IO 等待事件。

wait_event类型	类型描述
BufFileRead	从临时文件中读取数据到指定buffer。
BufFileWrite	向临时文件中写入指定buffer中的内容。
ControlFileRead	读取pg_control文件。主要在数据库启动、执行checkpoint和主备校验过程中发生。
ControlFileSync	将pg_control文件持久化到磁盘。数据库初始化时发生。
ControlFileSyncUpdate	将pg_control文件持久化到磁盘。主要在数据库启动、执行checkpoint和主备校验过程中发生。
ControlFileWrite	写入pg_control文件。数据库初始化时发生。
ControlFileWriteUpdate	更新pg_control文件。主要在数据库启动、执行checkpoint和主备校验过程中发生。
CopyFileRead	copy文件时读取文件内容。
CopyFileWrite	copy文件时写入文件内容。
DataFileExtend	扩展文件时向文件写入内容。
DataFileFlush	将表数据文件持久化到磁盘
DataFileImmediateSync	将表数据文件立即持久化到磁盘。
DataFilePrefetch	异步读取表数据文件。
DataFileRead	同步读取表数据文件。
DataFileSync	将表数据文件的修改持久化到磁盘。
DataFileTruncate	表数据文件truncate。
DataFileWrite	向表数据文件写入内容。
LockFileAddToDataDirRead	读取”postmaster.pid”文件。
LockFileAddToDataDirSync	将”postmaster.pid”内容持久化到磁盘。
LockFileAddToDataDirWrite	将pid信息写到”postmaster.pid”文件。
LockFileCreateRead	读取LockFile文件”%s.lock”。
LockFileCreateSync	将LockFile文件”%s.lock”内容持久化到磁盘。
LockFileCreateWRITE	将pid信息写到LockFile文件”%s.lock”。
RelationMapRead	读取系统表到存储位置之间的映射文件
RelationMapSync	将系统表到存储位置之间的映射文件持久化到磁盘。
RelationMapWrite	写入系统表到存储位置之间的映射文件。
ReplicationSlotRead	读取流复制槽文件。重新启动时发生。
ReplicationSlotRestoreSync	将流复制槽文件持久化到磁盘。重新启动时发生。
ReplicationSlotSync	checkpoint时将流复制槽临时文件持久化到磁盘。
ReplicationSlotWrite	checkpoint时写流复制槽临时文件。
SLRUFlushSync	将pg_clog、pg_subtrans和pg_multixact文件持久化到磁盘。主要在执行checkpoint和数据库停机时发生。
SLRURead	读取pg_clog、pg_subtrans和pg_multixact文件。
SLRUSync	将脏页写入文件pg_clog、pg_subtrans和pg_multixact并持久化到磁盘。主要在执行checkpoint和数据库停机时发生。
SLRUWrite	写入pg_clog、pg_subtrans和pg_multixact文件。
TimelineHistoryRead	读取timeline history文件。在数据库启动时发生。
TimelineHistorySync	将timeline history文件持久化到磁盘。在数据库启动时发生。
TimelineHistoryWrite	写入timeline history文件。在数据库启动时发生。
TwophaseFileRead	读取pg_twophase文件。在两阶段事务提交、两阶段事务恢复时发生。
TwophaseFileSync	将pg_twophase文件持久化到磁盘。在两阶段事务提交、两阶段事务恢复时发生。
TwophaseFileWrite	写入pg_twophase文件。在两阶段事务提交、两阶段事务恢复时发生。
WALBootstrapSync	将初始化的WAL文件持久化到磁盘。在数据库初始化发生。
WALBootstrapWrite	写入初始化的WAL文件。在数据库初始化发生。
WALCopyRead	读取已存在的WAL文件并进行复制时产生的读操作。在执行归档恢复完后发生。
WALCopySync	将复制的WAL文件持久化到磁盘。在执行归档恢复完后发生。
WALCopyWrite	读取已存在WAL文件并进行复制时产生的写操作。在执行归档恢复完后发生。
WALInitSync	将新初始化的WAL文件持久化磁盘。在日志回收或写日志时发生。
WALInitWrite	将新创建的WAL文件初始化为0。在日志回收或写日志时发生。
WALRead	从xlog日志读取数据。两阶段文件redo相关的操作产生。
WALSyncMethodAssign	将当前打开的所有WAL文件持久化到磁盘。
WALWrite	写入WAL文件。
WALBufferAccess	WAL Buffer访问（出于性能考虑，内核代码里只统计访问次数，未统计其访问耗时）。
WALBufferFull	WAL Buffer满时，写wal文件相关的处理。
DoubleWriteFileRead	双写文件读取。
DoubleWriteFileSync	双写文件强制刷盘。
DoubleWriteFileWrite	双写文件写入。
PredoProcessPending	并行日志回放中当前记录回放等待其它记录回放完成。
PredoApply	并行日志回放中等待当前工作线程等待其他线程回放至本线程LSN。
DisableConnectFileRead	HA锁分片逻辑文件读取。
DisableConnectFileSync	HA锁分片逻辑文件强制刷盘。
DisableConnectFileWrite	HA锁分片逻辑文件写入。

事务锁等待事件列表
当 wait_status 值为 acquire lock（事务锁）时对应的 wait_event 等待事件类型为事务锁等待事件。

wait_event 类型	类型描述
relation	对表加锁
extend	对表扩展空间时加锁
partition	对分区表加锁
partition_seq	对分区表的分区加锁
page	对表页面加锁
tuple	对页面上的 tuple 加锁
transactionid	对事务 ID 加锁
virtualxid	对虚拟事务 ID 加锁
object	加对象锁
cstore_freespace	对列存空闲空间加锁
userlock	加用户锁
advisory	加 advisory 锁

相关代码：

-- 说明：变量ld-->snapshot_id, 变量s-->node_name
select snap_event as "Event",
       snap_wait  as "Waits",
       snap_total_wait_time as "Total Wait Times(us)",
       round(snap_total_wait_time/snap_wait) as "Wait Avg(us)",
       snap_type as "Wait Class"
from (
  select snap_2.snap_event as snap_event,
	 snap_2.snap_type  as snap_type,
         snap_2.snap_wait - snap_1.snap_wait as snap_wait,
         snap_2.total_time - snap_1.total_time as snap_total_wait_time
  from
        (select snap_event,
		snap_wait,
		snap_total_wait_time as total_time,
		snap_type
         from snapshot.snap_global_wait_events
         where snapshot_id = %ld
	 and snap_event != 'none'
	 and snap_event != 'wait cmd'
         and snap_event != 'unknown_lwlock_event'
	 and snap_nodename = '%s') snap_1,
        (select snap_event,
		snap_wait,
		snap_total_wait_time as total_time,
		snap_type
         from snapshot.snap_global_wait_events
         where snapshot_id = %ld
	 and snap_event != 'none'
	 and snap_event != 'wait cmd'
         and snap_event != 'unknown_lwlock_event'
	 and snap_nodename = '%s') snap_2
  where snap_2.snap_event = snap_1.snap_event
  order by snap_total_wait_time desc limit 10)
where snap_wait != 0;

解读：

这一部分按照等待类型（STATUS、IO_EVENT、LWLOCK_EVENT、LOCK_EVENT），分类统计等待次数、总等待时间、平均等待时间。
相关代码：

-- 说明： 变量ld-->snapshot_id, 变量s-->node_name
select
    snap_2.type as "Wait Class",
    (snap_2.wait - snap_1.wait) as "Waits",
    (snap_2.total_wait_time - snap_1.total_wait_time) as "Total Wait Time(us)",
    round((snap_2.total_wait_time - snap_1.total_wait_time) / greatest((snap_2.wait - snap_1.wait), 1)) as "Wait Avg(us)"
from
    (select
        snap_type as type,
        sum(snap_total_wait_time) as total_wait_time,
        sum(snap_wait) as wait from snapshot.snap_global_wait_events
    where snapshot_id = %ld
	and snap_nodename = '%s'
	and snap_event != 'unknown_lwlock_event'
	and snap_event != 'none'
    group by snap_type) snap_2
    left join
    (select
        snap_type as type,
        sum(snap_total_wait_time) as total_wait_time,
        sum(snap_wait) as wait
	from snapshot.snap_global_wait_events
    where snapshot_id = %ld
	and  snap_nodename = '%s'
	and  snap_event != 'unknown_lwlock_event' and snap_event != 'none'
    group by snap_type) snap_1
    on snap_2.type = snap_1.type
    order by "Total Wait Time(us)" desc;

解读：
这一部分主机 CPU 的负载情况：CPU 的平均负载、用户使用占比、系统使用占比、IO 等待占比、空闲占比。
相关 SQL：

select
     snap_2.cpus as "Cpus",
     snap_2.cores as "Cores",
     snap_2.sockets as "Sockets",
     snap_1.load as "Load Average Begin",
     snap_2.load as "Load Average End",
     round(coalesce((snap_2.user_time - snap_1.user_time), 0) / greatest(coalesce((snap_2.total_time - snap_1.total_time), 0), 1)  * 100, 2) as "%User",
     round(coalesce((snap_2.sys_time - snap_1.sys_time), 0)   / greatest(coalesce((snap_2.total_time - snap_1.total_time), 0), 1) * 100, 2)  as "%System",
     round(coalesce((snap_2.iowait_time - snap_1.iowait_time), 0) / greatest(coalesce((snap_2.total_time - snap_1.total_time), 0), 1) * 100, 2) as "%WIO",
     round(coalesce((snap_2.idle_time - snap_1.idle_time), 0) / greatest(coalesce((snap_2.total_time - snap_1.total_time), 0), 1) * 100, 2) as "%Idle"
from
	 (select H.cpus, H.cores, H.sockets, H.idle_time, H.user_time, H.sys_time, H.iowait_time,
	 (H.idle_time + H.user_time + H.sys_time + H.iowait_time) AS total_time, H.load  from
     (select C.cpus, E.cores, T.sockets, I.idle_time, U.user_time, S.sys_time, W.iowait_time, L.load from
     (select snap_value as cpus from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'NUM_CPUS')) AS C,
     (select snap_value as cores from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'NUM_CPU_CORES')) AS E,
     (select snap_value as sockets from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'NUM_CPU_SOCKETS')) AS T,
     (select snap_value as idle_time from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'IDLE_TIME')) AS I,
     (select snap_value as user_time from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'USER_TIME')) AS U,
     (select snap_value as sys_time from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'SYS_TIME')) AS S,
     (select snap_value as iowait_time from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'IOWAIT_TIME')) AS W,
     (select snap_value as load from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'LOAD')) AS L ) as H ) as snap_2,

     (select H.cpus, H.cores, H.sockets, H.idle_time, H.user_time, H.sys_time, H.iowait_time,
     (H.idle_time + H.user_time + H.sys_time + H.iowait_time) AS total_time, H.load from
     (select C.cpus, E.cores, T.sockets, I.idle_time, U.user_time, S.sys_time, W.iowait_time, L.load from
     (select snap_value as cpus from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'NUM_CPUS')) AS C,
     (select snap_value as cores from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'NUM_CPU_CORES')) AS E,
     (select snap_value as sockets from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'NUM_CPU_SOCKETS')) AS T,
     (select snap_value as idle_time from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'IDLE_TIME')) AS I,
     (select snap_value as user_time from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'USER_TIME')) AS U,
     (select snap_value as sys_time from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'SYS_TIME')) AS S,
     (select snap_value as iowait_time from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'IOWAIT_TIME')) AS W,
     (select snap_value as load from snapshot.snap_global_os_runtime
      where (snapshot_id = %ld and snap_node_name = '%s' and snap_name = 'LOAD')) AS L ) as H ) as snap_1 ;

解读：
这一部分描述了 openGauss 在快照期间的 IO 负载情况。
Database requests： 即每秒 IO 请求次数，包括请求次数总和、读请求次数、写请求次数.
Database(blocks)： 即每秒 block 请求数量，包含请求的 block 总和数量、读 block 的数量和写 block 的数量.
Database(MB): 即将 block 换算成容量(MB)[如：blocks * 8/1024]，增加数据的可读性。
Redo requests 和 Redo(MB) 分别表示每秒 redo 的写请求次数和 redo 写的数据量。
相关代码：

-- 由于源码中相关SQL融合了C++程序语法，像我这种不做开发的DBA读起来有些难以理解【如：(phyblkwrt * %d) >> 20 这个段没有很好理解】。
-- 但是依旧不影响我们对这些数据采集方法的理解，相关SQL如下：
-- 两个snapshot_id(24和25)期间，数据块的IO统计信息(数值除以3600即换算成以秒为单位的WDR数据)
postgres=# select
               (snap_2.phytotal - snap_1.phytotal) as phytotal,
               (snap_2.phyblktotal - snap_1.phyblktotal) as phyblktotal,
               (snap_2.phyrds - snap_1.phyrds) as phyrds,
               (snap_2.phyblkrd - snap_1.phyblkrd) as phyblkrd,
               (snap_2.phywrts - snap_1.phywrts) as phywrts,
               (snap_2.phyblkwrt - snap_1.phyblkwrt) as phyblkwrt
           from
               (select (snap_phyrds + snap_phywrts) as phytotal,
                   (snap_phyblkwrt + snap_phyblkrd) as phyblktotal,
                   snap_phyrds as phyrds, snap_phyblkrd as phyblkrd,
                   snap_phywrts as phywrts, snap_phyblkwrt as phyblkwrt
                from snapshot.snap_global_rel_iostat
                where snapshot_id = 24 and snap_node_name = 'dn_6001') snap_1,
               (select (snap_phyrds + snap_phywrts) as phytotal,
                   (snap_phyblkwrt + snap_phyblkrd) as phyblktotal,
                   snap_phyrds as phyrds, snap_phyblkrd as phyblkrd,
                   snap_phywrts as phywrts, snap_phyblkwrt as phyblkwrt
                from snapshot.snap_global_rel_iostat
                where snapshot_id = 25 and snap_node_name = 'dn_6001') snap_2;
 phytotal | phyblktotal | phyrds  | phyblkrd | phywrts | phyblkwrt
----------+-------------+---------+----------+---------+-----------
  4626892 |     4626892 | 2955639 |  2955639 | 1671253 |   1671253

-- 两个snapshot_id(24和25)期间，redo的统计信息(数值除以3600即换算成以秒为单位的WDR数据)
postgres=# select
                (snap_2.phywrts - snap_1.phywrts) as phywrts,
                (snap_2.phyblkwrt - snap_1.phyblkwrt) as phyblkwrt
            from
                (select sum(snap_phywrts) as phywrts, sum(snap_phyblkwrt) as phyblkwrt
                from snapshot.snap_global_file_redo_iostat
                where snapshot_id = 24 and snap_node_name = 'dn_6001') snap_1,
                (select sum(snap_phywrts) as phywrts, sum(snap_phyblkwrt) as phyblkwrt
                from snapshot.snap_global_file_redo_iostat
                where snapshot_id = 25 and snap_node_name = 'dn_6001') snap_2;
 phywrts | phyblkwrt
---------+-----------
  132721 |    509414

解读：

这一部分描述了节点内存的变化信息,通过这些变化信息，我们可以了解到在两次快照期间，数据库的内存变化情况，作为数据库性能分析或异常分析的参考。数据来源于 snapshot.snap_global_memory_node_detail。
这部分分别描述了： 内存的类型以及对应的起始大小和终止大小。
这里没有采集到数据的原因： 测试环境内存太小，导致启动时将 memory protect 关闭了，从而导致无法查询 dbe_perf.global_memory_node_detail 视图。而 WDR 的内存统计数据(snapshot.snap_global_memory_node_detail)则来源于该视图。
另外，请确保 disable_memory_protect=off。
关于这部分 Memory Type 常见类型如下：

Memory 类型	说明
max_process_memory	openGauss 实例所占用的内存大小
process_used_memory	进程所使用的内存大小
max_dynamic_memory	最大动态内存
dynamic_used_memory	已使用的动态内存
dynamic_peak_memory	内存的动态峰值
dynamic_used_shrctx	最大动态共享内存上下文
dynamic_peak_shrctx	共享内存上下文的动态峰值
max_shared_memory	最大共享内存
shared_used_memory	已使用的共享内存
max_cstore_memory	列存所允许使用的最大内存
cstore_used_memory	列存已使用的内存大小
max_sctpcomm_memory	sctp 通信所允许使用的最大内存
sctpcomm_used_memory	sctp 通信已使用的内存大小
sctpcomm_peak_memory	sctp 通信的内存峰值
other_used_memory	其他已使用的内存大小
gpu_max_dynamic_memory	GPU 最大动态内存
gpu_dynamic_used_memory	GPU 已使用的动态内存
gpu_dynamic_peak_memory	GPU 内存的动态峰值
pooler_conn_memory	链接池申请内存计数
pooler_freeconn_memory	链接池空闲连接的内存计数
storage_compress_memory	存储模块压缩使用的内存大小
udf_reserved_memory	UDF 预留的内存大小

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
select
    snap_2.snap_memorytype   as "Memory Type",
    snap_1.snap_memorymbytes as "Begin(MB)",
    snap_2.snap_memorymbytes as "End(MB)"
from
    (select snap_memorytype, snap_memorymbytes
     from
     snapshot.snap_global_memory_node_detail
     where (snapshot_id = %ld and snap_nodename = '%s')
     and
    (snap_memorytype = 'max_process_memory'
     or snap_memorytype = 'process_used_memory'
     or snap_memorytype = 'max_shared_memory'
     or  snap_memorytype = 'shared_used_memory'))
    as snap_2
left join
    (select snap_memorytype, snap_memorymbytes
     from
	 snapshot.snap_global_memory_node_detail
	 where (snapshot_id = %ld and snap_nodename = '%s')
	 and (snap_memorytype = 'max_process_memory'
	 or  snap_memorytype = 'process_used_memory'
	 or  snap_memorytype = 'max_shared_memory'
	 or snap_memorytype = 'shared_used_memory'))
    as snap_1
on snap_2.snap_memorytype = snap_1.snap_memorytype;

解读：
这一部分描述了数据库各种状态所消耗的时间，关于 Stat Name 的解释如下：

Stat Name	说明
DB_TIME	作业在多核下的有效时间花销
CPU_TIME	CPU 的时间花销
EXECUTION_TIME	执行器内的时间花销
PARSE_TIME	SQL 解析的时间花销
PLAN_TIME	生成 Plan 的时间花销
REWRITE_TIME	SQL 重写的时间花销
PL_EXECUTION_TIME	plpgsql（存储过程）执行的时间花销
PL_COMPILATION_TIME	plpgsql（存储过程）编译的时间花销
NET_SEND_TIME	网络上的时间花销
DATA_IO_TIME	IO 上的时间花销

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
select     t2.snap_node_name as "Node Name",
	   t2.snap_stat_name as "Stat Name",
	   (t2.snap_value - coalesce(t1.snap_value, 0)) as "Value(us)"
from
      (select * from snapshot.snap_global_instance_time
	where snapshot_id = %ld
	and snap_node_name = '%s') t1
right join
      (select * from snapshot.snap_global_instance_time
	where snapshot_id = %ld
	and   snap_node_name = '%s') t2
on t1.snap_stat_name = t2.snap_stat_name
order by "Value(us)"
desc limit 200;

解读：

这一部分分别从 SQL 执行时间、SQL 消耗 CPU 的时间、SQL 返回的行数、SQL 扫描的行数、SQL 执行的次数、SQL 物理读的次数、SQL 逻辑读的次数等多维度对两次快照期间的 SQL 执行情况进行统计。
关于表中列的含义，如下所示：

列名称	备注
Unique SQL Id	归一化的 SQL ID, 即 SQL 唯一标识
User Name	执行 SQL 的用户
Logical Read	逻辑读，即 Buffer 的块访问次数
Calls	SQL 的调用次数
Min Elapse Time(us)	SQL 在内核中的最小运行时间（单位：微秒）
Max Elapse Time(us)	SQL 在内核中的最大运行时间（单位：微秒）
Total Elapse Time(us)	SQL 在内核中的总运行时间（单位：微秒）
Avg Elapse Time(us)	SQL 在内核中的平均运行时间（单位：微秒）
Returned Rows	SELECT 返回的结果集行数
Tuples Read	SQL 扫描的总行数(包括顺序扫描和随机扫描)
Tuples Affected	SQL 删除的行数
Physical Read	物理读，即下盘读取 block 进入 buffer 的次数
CPU Time(us)	SQL 消耗的 CPU 时间（单位：微秒）
Data IO Time(us)	IO 上的时间花费（单位：微秒）
Sort Count	SQL 排序执行的次数
Sort Time(us)	SQL 排序执行的时间（单位：微秒）
Sort Mem Used(KB)	排序过程中使用的 work memory 大小（单位：KB）
Sort Spill Count	排序过程中发生落盘写文件的次数
Sort Spill Size(KB)	排序过程中发生落盘写文件的大小（单位：KB）
Hash Count	hash 执行的次数
Hash Time(us)	hash 执行的时间（单位：微秒）
Hash Mem Used(KB)	hash 过程中使用的 work memory 大小（单位：KB）
Hash Spill Count	hash 过程中发生落盘写文件的次数
Hash Spill Size(KB)	hash 过程中发生落盘写文件的大小（单位：KB）
SQL Text	SQL 语句内容

Tips：Top200 显得有些冗余，多余的 SQL 信息并没有太大用处，反而降低了可读性，希望将来能优化到 Top20。
相关代码：

-- 由于多个SQL统计信息的SQL语句类似，这里仅列举SQL执行时间的统计SQL，其他的类似。
-- 说明：%s代表node_name，%ld代表snapshot_id
select 	t2.snap_unique_sql_id 												  as "Unique SQL Id",
		t2.snap_user_name 	  												  as "User Name",
		(t2.snap_total_elapse_time - coalesce(t1.snap_total_elapse_time, 0))  as "Total Elapse Time(us)",
		(t2.snap_n_calls - coalesce(t1.snap_n_calls, 0))                      as "Calls",
		round("Total Elapse Time(us)"/greatest("Calls", 1), 0)                as "Avg Elapse Time(us)",
		t2.snap_min_elapse_time 	    				      as "Min Elapse Time(us)",
		t2.snap_max_elapse_time 					      as "Max Elapse Time(us)",
		(t2.snap_n_returned_rows - coalesce(t1.snap_n_returned_rows, 0))      as "Returned Rows",
		((t2.snap_n_tuples_fetched - coalesce(t1.snap_n_tuples_fetched, 0)) +
		(t2.snap_n_tuples_returned - coalesce(t1.snap_n_tuples_returned, 0))) as "Tuples Read",
		((t2.snap_n_tuples_inserted - coalesce(t1.snap_n_tuples_inserted, 0)) +
		(t2.snap_n_tuples_updated - coalesce(t1.snap_n_tuples_updated, 0)) +
		(t2.snap_n_tuples_deleted - coalesce(t1.snap_n_tuples_deleted, 0)))   as "Tuples Affected",
		(t2.snap_n_blocks_fetched - coalesce(t1.snap_n_blocks_fetched, 0))    as "Logical Read",
		((t2.snap_n_blocks_fetched - coalesce(t1.snap_n_blocks_fetched, 0)) -
		(t2.snap_n_blocks_hit - coalesce(t1.snap_n_blocks_hit, 0)))           as "Physical Read",
		(t2.snap_cpu_time - coalesce(t1.snap_cpu_time, 0)) 		      as "CPU Time(us)",
		(t2.snap_data_io_time - coalesce(t1.snap_data_io_time, 0)) 	      as "Data IO Time(us)",
		(t2.snap_sort_count - coalesce(t1.snap_sort_count, 0)) 		      as "Sort Count",
		(t2.snap_sort_time - coalesce(t1.snap_sort_time, 0)) 		      as "Sort Time(us)",
		(t2.snap_sort_mem_used - coalesce(t1.snap_sort_mem_used, 0)) 	      as "Sort Mem Used(KB)",
		(t2.snap_sort_spill_count - coalesce(t1.snap_sort_spill_count, 0))    as "Sort Spill Count",
		(t2.snap_sort_spill_size - coalesce(t1.snap_sort_spill_size, 0))      as "Sort Spill Size(KB)",
		(t2.snap_hash_count - coalesce(t1.snap_hash_count, 0)) 		      as "Hash Count",
		(t2.snap_hash_time - coalesce(t1.snap_hash_time, 0)) 		      as "Hash Time(us)",
		(t2.snap_hash_mem_used - coalesce(t1.snap_hash_mem_used, 0)) 	      as "Hash Mem Used(KB)",
		(t2.snap_hash_spill_count - coalesce(t1.snap_hash_spill_count, 0))    as "Hash Spill Count",
		(t2.snap_hash_spill_size - coalesce(t1.snap_hash_spill_size, 0))      as "Hash Spill Size(KB)",
		LEFT(t2.snap_query, 25) 					      as "SQL Text"
from
		(select * from snapshot.snap_summary_statement where snapshot_id = %ld and snap_node_name = '%s') t1
		right join
		(select * from snapshot.snap_summary_statement where snapshot_id = %ld and snap_node_name = '%s') t2
		on t1.snap_unique_sql_id = t2.snap_unique_sql_id
		and t1.snap_user_id = t2.snap_user_id
		order by "Total Elapse Time(us)"
		desc limit 200;

解读：
这一部分分别从等待时长、等待次数这两个维度对等待事件进行统计。
表格中列的含义即就是列的英文翻译，这里就不再复述了。
具体的等待事件的介绍详见前文：“Top 10 Events by Total Wait Time”部分的内容，这里也不再复述。
相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id，无论从哪个维度统计，基本的SQL语句差异不大，这里仅列举"by wait time"的SQL示例
select     t2.snap_type  as "Type",
	   t2.snap_event as "Event",
	  (t2.snap_total_wait_time - coalesce(t1.snap_total_wait_time, 0)) as "Total Wait Time (us)",
	  (t2.snap_wait - coalesce(t1.snap_wait, 0)) as "Waits",
	  (t2.snap_failed_wait - coalesce(t1.snap_failed_wait, 0)) as "Failed Waits",
	  (case "Waits"
	   when  0 then 0
	   else round("Total Wait Time (us)" / "Waits", 2)
	   end) as "Avg Wait Time (us)",
	   t2.snap_max_wait_time as "Max Wait Time (us)"
from
      (select * from snapshot.snap_global_wait_events
	   where snapshot_id = %ld
	   and snap_nodename = '%s'
	   and snap_event != 'unknown_lwlock_event'
	   and snap_event != 'none') t1
right join
	  (select * from snapshot.snap_global_wait_events
	   where snapshot_id = %ld
	   and snap_nodename = '%s'
	   and snap_event != 'unknown_lwlock_event'
	   and snap_event != 'none') t2
on t1.snap_event = t2.snap_event
order by "Total Wait Time (us)"
desc limit 200;

解读：
这一部分根据 Heap block 的命中率排序统计用户表的 IO 活动状态。
数据来源于 snapshot.snap_global_statio_all_indexes 表和 snapshot.snap_global_statio_all_tables 表。
该表相关列的介绍如下：

列名	描述
DB Name	数据库名
Schema Name	模式名
Table Name	表名
%Heap Blks Hit Ratio	数据块读取缓存命中率=heap_blks_hit/(heap_blks_read+heap_blks_hit)*100
Heap Blks Read	从该表中读取的磁盘块数
Heap Blks Hit	此表缓存命中数
Idx Blks Read	从表中所有索引读取的磁盘块数
Idx Blks Hit	表中所有索引命中缓存数
Toast Blks Read	此表的 TOAST 表读取的磁盘块数（如果存在）
Toast Blks Hit	此表的 TOAST 表命中缓冲区数（如果存在）
Tidx Blks Read	此表的 TOAST 表索引读取的磁盘块数（如果存在）
Tidx Blks Hit	此表的 TOAST 表索引命中缓冲区数（如果存在）

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
SELECT table_io.db_name             as "DB Name",
       table_io.snap_schemaname     as "Schema Name",
       table_io.snap_relname        as "Table Name",
       table_io.heap_blks_hit_ratio as "%Heap Blks Hit Ratio",
       table_io.heap_blks_read      as "Heap Blks Read",
       table_io.heap_blks_hit       as  "Heap Blks Hit",
       idx_io.idx_blks_read         as "Idx Blks Read",
       idx_io.idx_blks_hit          as "Idx Blks Hit",
       table_io.toast_blks_read     as "Toast Blks Read",
       table_io.toast_blks_hit      as "Toast Blks Hit",
       table_io.tidx_blks_read      as "Tidx Blks Read",
       table_io.tidx_blks_hit       as  "Tidx Blks Hit"
FROM
       (select t2.db_name, t2.snap_schemaname , t2.snap_relname ,
       (case
       when ((t2.snap_heap_blks_read - coalesce(t1.snap_heap_blks_read, 0)) + (t2.snap_heap_blks_hit - coalesce(t1.snap_heap_blks_hit, 0))) = 0
       then 0
       else round((t2.snap_heap_blks_hit - coalesce(t1.snap_heap_blks_hit, 0))/
         ((t2.snap_heap_blks_read - coalesce(t1.snap_heap_blks_read, 0)) + (t2.snap_heap_blks_hit - coalesce(t1.snap_heap_blks_hit, 0))) * 100, 2)
       end ) as heap_blks_hit_ratio,
         (t2.snap_heap_blks_read - coalesce(t1.snap_heap_blks_read, 0))   as heap_blks_read,
         (t2.snap_heap_blks_hit - coalesce(t1.snap_heap_blks_hit, 0))     as heap_blks_hit,
         (t2.snap_idx_blks_read - coalesce(t1.snap_idx_blks_read, 0))     as idx_blks_read,
         (t2.snap_idx_blks_hit - coalesce(t1.snap_idx_blks_hit, 0))       as idx_blks_hit,
         (t2.snap_toast_blks_read - coalesce(t1.snap_toast_blks_read, 0)) as toast_blks_read,
         (t2.snap_toast_blks_hit - coalesce(t1.snap_toast_blks_hit, 0))   as toast_blks_hit,
         (t2.snap_tidx_blks_read - coalesce(t1.snap_tidx_blks_read, 0))   as tidx_blks_read,
         (t2.snap_tidx_blks_hit - coalesce(t1.snap_tidx_blks_hit, 0))     as tidx_blks_hit from
         (select * from snapshot.snap_global_statio_all_tables
          where snapshot_id = %ld and snap_node_name = '%s'
          and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
          and snap_schemaname !~ '^pg_toast') t1
        right join
         (select * from snapshot.snap_global_statio_all_tables
          where snapshot_id = %ld
          and snap_node_name = '%s'
          and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
          and snap_schemaname !~ '^pg_toast') t2
         on t1.snap_relid = t2.snap_relid
         and t2.db_name = t1.db_name
         and t2.snap_schemaname = t1.snap_schemaname )  as table_io
LEFT JOIN
	(select t2.db_name , t2.snap_schemaname , t2.snap_relname,
         (t2.snap_idx_blks_read - coalesce(t1.snap_idx_blks_read, 0)) as idx_blks_read,
         (t2.snap_idx_blks_hit - coalesce(t1.snap_idx_blks_hit, 0))   as idx_blks_hit
         from
         (select * from snapshot.snap_global_statio_all_indexes
          where snapshot_id = %ld
		  and snap_node_name = '%s'
		  and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
		  and snap_schemaname !~ '^pg_toast') t1
         right join
         (select * from snapshot.snap_global_statio_all_indexes
          where snapshot_id = %ld
          and snap_node_name = '%s'
          and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
          AND snap_schemaname !~ '^pg_toast') t2
         on t1.snap_relid = t2.snap_relid
         and t2.snap_indexrelid = t1.snap_indexrelid
         and t2.db_name = t1.db_name and t2.snap_schemaname = t1.snap_schemaname) as idx_io
on table_io.db_name = idx_io.db_name
and table_io.snap_schemaname = idx_io.snap_schemaname
and table_io.snap_relname = idx_io.snap_relname
order by "%%Heap Blks Hit Ratio"
asc limit 200;

解读：
这一部分根据索引缓存命中率，统计用户索引 IO 活动信息。
数据来源于 snapshot.snap_global_statio_all_indexes 表。
相关列信息如下：

列名	介绍
DB Name	数据库名
Schema Name	模式名
Table Name	表名
Index Name	索引名
%Idx Blks Hit Ratio	索引缓冲命中率="Idx Blks Hit"/("Idx Blks Hit"+"Idx Blks Read")*100
Idx Blks Read	从索引中读取的磁盘块数
Idx Blks Hit	索引命中缓存数

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
select t2.db_name           as "DB Name",
	   t2.snap_schemaname   as "Schema Name",
	   t2.snap_relname      as "Table Name",
	   t2.snap_indexrelname as "Index Name",
      (case
       when ((t2.snap_idx_blks_read - coalesce(t1.snap_idx_blks_read, 0)) + (t2.snap_idx_blks_hit - coalesce(t1.snap_idx_blks_hit, 0))) = 0 then 0
	   else
       round((t2.snap_idx_blks_hit - coalesce(t1.snap_idx_blks_hit, 0))/((t2.snap_idx_blks_hit - coalesce(t1.snap_idx_blks_hit, 0)) +
      (t2.snap_idx_blks_read - coalesce(t1.snap_idx_blks_read, 0))) * 100, 2)
	   end)                                                         as "%Idx Blks Hit Ratio",
	  (t2.snap_idx_blks_read - coalesce(t1.snap_idx_blks_read, 0))  as "Idx Blks Read",
      (t2.snap_idx_blks_hit - coalesce(t1.snap_idx_blks_hit, 0))    as "Idx Blks Hit"
from
      (select * from snapshot.snap_global_statio_all_indexes
	   where snapshot_id = %ld
	   and snap_node_name = '%s'
	   and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
       and snap_schemaname !~ '^pg_toast') t1
right join
	 (select * from snapshot.snap_global_statio_all_indexes
	  where snapshot_id = %ld
	  and snap_node_name = '%s'
	  and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
      and snap_schemaname !~ '^pg_toast') t2
on t1.snap_relid = t2.snap_relid
and t2.snap_indexrelid = t1.snap_indexrelid
and t2.db_name = t1.db_name
and t2.snap_schemaname = t1.snap_schemaname
order by "%Idx Blks Hit Ratio"
asc limit 200;

解读：
此处存在缺陷，统计的数据有问题，个人认为 SQL 语句需要修改，详见“相关代码”。
这一部分描述的是后台写操作的统计信息，数据来源于 snapshot.snap_global_bgwriter_stat 表。
具体内容如下：

列名	数据获取相关函数	说明
Checkpoints Timed	pg_stat_get_bgwriter_timed_checkpoints()	执行的定期检查点数
Checkpoints Require	pg_stat_get_bgwriter_requested_checkpoints()	执行的需求检查点数
Checkpoint Write Time(ms)	pg_stat_get_checkpoint_write_time()	检查点操作中，文件写入到磁盘消耗的时间（单位：毫秒）
Checkpoint Sync Time(ms)	pg_stat_get_checkpoint_sync_time()	检查点操作中，文件同步到磁盘消耗的时间（单位：毫秒）
Buffers Checkpoint	pg_stat_get_bgwriter_buf_written_checkpoints()	检查点写缓冲区数量
Buffers Clean	pg_stat_get_bgwriter_buf_written_clean()	后端写线程写缓冲区数量
Maxwritten Clean	pg_stat_get_bgwriter_maxwritten_clean()	后端写线程停止清理 Buffer 的次数
Buffers Backend	pg_stat_get_buf_written_backend()	通过后端直接写缓冲区数
Buffers Backend	Fsync pg_stat_get_buf_fsync_backend()	后端不得不执行自己的 fsync 调用的时间数（通常后端写进程处理这些即使后端确实自己写）
Buffers Alloc	pg_stat_get_buf_alloc() 分配的缓冲区数量
Stats Reset	pg_stat_get_bgwriter_stat_reset_time()	这些统计被重置的时间

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
select (snap_2.snap_checkpoints_timed - coalesce(snap_1.snap_checkpoints_timed, 0))         AS "Checkpoints Timed",
       (snap_2.snap_checkpoints_req - coalesce(snap_1.snap_checkpoints_req, 0))             AS "Checkpoints Require",
       (snap_2.snap_checkpoint_write_time - coalesce(snap_1.snap_checkpoint_write_time, 0)) AS "Checkpoint Write Time(ms)",
       (snap_2.snap_checkpoint_sync_time - coalesce(snap_1.snap_checkpoint_sync_time, 0))   AS "Checkpoint Sync Time(ms)",
       (snap_2.snap_buffers_checkpoint - coalesce(snap_1.snap_buffers_checkpoint, 0))       AS "Buffers Checkpoint",
       (snap_2.snap_buffers_clean - coalesce(snap_1.snap_buffers_clean, 0))                 AS "Buffers Clean",
       (snap_2.snap_maxwritten_clean - coalesce(snap_1.snap_maxwritten_clean, 0))           AS "Maxwritten Clean",
       (snap_2.snap_buffers_backend - coalesce(snap_1.snap_buffers_backend, 0))             AS "Buffers Backend",
       (snap_2.snap_buffers_backend_fsync - coalesce(snap_1.snap_buffers_backend_fsync, 0)) AS "Buffers Backend Fsync",
       (snap_2.snap_buffers_alloc - coalesce(snap_1.snap_buffers_alloc, 0))                 AS "Buffers Alloc",
       to_char(snap_2.snap_stats_reset, 'YYYY-MM-DD HH24:MI:SS')                            AS "Stats Reset"
from
       (select * from snapshot.snap_global_bgwriter_stat
	where snapshot_id = %ld
	and snap_node_name = '%s') snap_2
LEFT JOIN
	   (select * from snapshot.snap_global_bgwriter_stat
	    where snapshot_id = %ld
	    and snap_node_name = '%s') snap_1
on snap_2.snapshot_id = snap_1.snapshot_id            --错误点：snap_2.snapshot_id = snap_1.snapshot_id ？ 这其实还是同一个snapshot
and snap_2.snap_node_name = snap_1.snap_node_name
and snap_2.snap_stats_reset = snap_1.snap_stats_reset
limit 200;

-- 统计信息应该是2次snapshot之间的数据，而以上SQL并不能正确输出相关数据。个人觉得可以删除LEFT JOIN连接。
-- 建议修改如下：
select (snap_2.snap_checkpoints_timed - coalesce(snap_1.snap_checkpoints_timed, 0))         AS "Checkpoints Timed",
       (snap_2.snap_checkpoints_req - coalesce(snap_1.snap_checkpoints_req, 0))             AS "Checkpoints Require",
       (snap_2.snap_checkpoint_write_time - coalesce(snap_1.snap_checkpoint_write_time, 0)) AS "Checkpoint Write Time(ms)",
       (snap_2.snap_checkpoint_sync_time - coalesce(snap_1.snap_checkpoint_sync_time, 0))   AS "Checkpoint Sync Time(ms)",
       (snap_2.snap_buffers_checkpoint - coalesce(snap_1.snap_buffers_checkpoint, 0))       AS "Buffers Checkpoint",
       (snap_2.snap_buffers_clean - coalesce(snap_1.snap_buffers_clean, 0))                 AS "Buffers Clean",
       (snap_2.snap_maxwritten_clean - coalesce(snap_1.snap_maxwritten_clean, 0))           AS "Maxwritten Clean",
       (snap_2.snap_buffers_backend - coalesce(snap_1.snap_buffers_backend, 0))             AS "Buffers Backend",
       (snap_2.snap_buffers_backend_fsync - coalesce(snap_1.snap_buffers_backend_fsync, 0)) AS "Buffers Backend Fsync",
       (snap_2.snap_buffers_alloc - coalesce(snap_1.snap_buffers_alloc, 0))                 AS "Buffers Alloc",
       to_char(snap_2.snap_stats_reset, 'YYYY-MM-DD HH24:MI:SS')                            AS "Stats Reset"
from
       (select * from snapshot.snap_global_bgwriter_stat
	where snapshot_id = %ld
	and snap_node_name = '%s') snap_2,
       (select * from snapshot.snap_global_bgwriter_stat
	where snapshot_id = %ld
	and snap_node_name = '%s') snap_1
limit 200;

解读：[本次实验环境是单机，没有复制槽数据]
这一部分描述的是复制槽的相关信息。数据来源于：snapshot.snap_global_replication_slots 表。
信息内容如下所示：

列名	描述
Slot Name	复制槽名称
Slot Type	复制槽类型
DB Name	数据库名称
Active	是否为激活状态
Xmin	事务标识，最早的事务 ID(txid)
Restart Lsn	事务槽开始复制时的 LSN 信息，用来判断哪些事务需要被复制
Dummy Standby	是否为假备

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
SELECT snap_slot_name     as "Slot Name",
       snap_slot_type     as "Slot Type",
       snap_database      as "DB Name",
       snap_active        as "Active",
       snap_x_min         as "Xmin",
       snap_restart_lsn   as "Restart Lsn",
       snap_dummy_standby as "Dummy Standby"
FROM snapshot.snap_global_replication_slots
WHERE snapshot_id = %ld
and snap_node_name = '%s'
limit 200;

解读：[本次实验环境是单机，没有复制槽数据]
这一部分描述事务槽详细的状态信息，数据源于 snapshot.snap_global_replication_stat 表。
信息内容如下所示：

列名	描述
Thread Id	线程 ID
Usesys Id	用户 OID
Usename	用户名
Application Name	应用程序名称
Client Addr	客户端地址
Client Hostname	客户端主机名
Client Port	客户端端口
Backend Start	程序启动时间
State	日志复制状态【追赶状态/一直的流状态】
Sender Sent Location	日志发送的位置
Receiver Write Location	日志接收端 write 的位置
Receiver Flush Location	日志接收端 flush 的位置
Receiver Replay Location	日志接收端 replay 的位置
Sync Priority	同步复制的优先级(0 表示异步复制)
Sync State	同步状态【异步复制/同步复制/潜在同步复制】

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
SELECT snap_pid 			as "Thread Id",
       snap_usesysid 			as "Usesys Id",
       snap_usename 			as "Usename",
       snap_application_name 		as "Application Name",
       snap_client_addr 		as "Client Addr",
       snap_client_hostname 		as "Client Hostname",
       snap_client_port 		as "Client Port",
       snap_backend_start 		as "Backend Start",
       snap_state 			as "State",
       snap_sender_sent_location 	as "Sender Sent Location",
       snap_receiver_write_location     as "Receiver Write Location",
       snap_receiver_flush_location     as "Receiver Flush Location",
       snap_receiver_replay_location    as "Receiver Replay Location",
       snap_sync_priority 		as "Sync Priority",
       snap_sync_state 			 as "Sync State"
FROM  snapshot.snap_global_replication_stat
WHERE snapshot_id = %ld
and snap_node_name = '%s' limit 200;

解读：
这一部分描述用户表状态的统计信息，数据源于 snapshot.snap_global_stat_all_tables 表。
信息内容如下所示：

列名	描述
DB Name	数据库名称
Schema	模式名称
Relname	表名称
Seq Scan	顺序扫描的次数
Seq Tup Read	顺序扫描获取的活跃行数
Index Scan	索引扫描次数
Index Tup Fetch	索引扫描获取的活跃行数
Tuple Insert	插入的行数
Tuple Update	更新的行数
Tuple Delete	删除的行数
Tuple Hot Update	HOT(Heap Only Tuple)更新行数备注：HOT 更新指，如果更新后的新行和旧行位于同一个数据块内，则旧行会有一个指针指向新行，这样就不用更新索引了，通过索引访问到旧行数据，进而访问到新行数据。
Live Tuple	活跃行数(估值)
Dead Tuple	死行数(估值)
Last Vacuum	最后一次手动 Vacuum 的时间(不包含 VACUUM FULL)
Last Autovacuum	最后一次 autovacuum 的时间
Last Analyze	最后一次手动 Analyze 表的时间
Last Autoanalyze	最后一次 autovacuum 线程 Analyze 表的时间
Vacuum Count	手动 vacuum 的次数(不包含 VACUUM FULL)
Autovacuum Count	autovacuum 的次数
Analyze Count	手动 Analyze 的次数
Autoanalyze Count	autovacuum 线程 Analyze 表的次数

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
SELECT 	snap_2.db_name 		as "DB Name",
	snap_2.snap_schemaname 	as "Schema",
	snap_2.snap_relname 	as "Relname",
	(snap_2.snap_seq_scan - coalesce(snap_1.snap_seq_scan, 0)) 		     as "Seq Scan",
	(snap_2.snap_seq_tup_read - coalesce(snap_1.snap_seq_tup_read, 0))           as "Seq Tup Read",
	(snap_2.snap_idx_scan - coalesce(snap_1.snap_idx_scan, 0)) 		     as "Index Scan",
	(snap_2.snap_idx_tup_fetch - coalesce(snap_1.snap_idx_tup_fetch, 0))         as "Index Tup Fetch",
	(snap_2.snap_n_tup_ins - coalesce(snap_1.snap_n_tup_ins, 0)) 		     as "Tuple Insert",
	(snap_2.snap_n_tup_upd - coalesce(snap_1.snap_n_tup_upd, 0)) 		     as "Tuple Update",
	(snap_2.snap_n_tup_del - coalesce(snap_1.snap_n_tup_del, 0)) 		     as "Tuple Delete",
	(snap_2.snap_n_tup_hot_upd - coalesce(snap_1.snap_n_tup_hot_upd, 0)) as "Tuple Hot Update",
	snap_2.snap_n_live_tup 				                             as "Live Tuple",
	snap_2.snap_n_dead_tup 							     as "Dead Tuple",
	to_char(snap_2.snap_last_vacuum, 'YYYY-MM-DD HH24:MI:SS') 		     as "Last Vacuum",
	to_char(snap_2.snap_last_autovacuum, 'YYYY-MM-DD HH24:MI:SS') 		     as "Last Autovacuum",
	to_char(snap_2.snap_last_analyze, 'YYYY-MM-DD HH24:MI:SS') 		     as "Last Analyze",
	to_char(snap_2.snap_last_autoanalyze, 'YYYY-MM-DD HH24:MI:SS') 		     as "Last Autoanalyze",
	(snap_2.snap_vacuum_count - coalesce(snap_1.snap_vacuum_count, 0)) 	     as "Vacuum Count",
	(snap_2.snap_autovacuum_count - coalesce(snap_1.snap_autovacuum_count, 0))   as "Autovacuum Count",
	(snap_2.snap_analyze_count - coalesce(snap_1.snap_analyze_count, 0)) 	     as "Analyze Count",
	(snap_2.snap_autoanalyze_count - coalesce(snap_1.snap_autoanalyze_count, 0)) as "Autoanalyze Count"
FROM
	(SELECT * FROM snapshot.snap_global_stat_all_tables
	 WHERE snapshot_id = %ld
	 and snap_node_name = '%s'
	 and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
	 AND snap_schemaname !~ '^pg_toast') snap_2
LEFT JOIN
	(SELECT * FROM snapshot.snap_global_stat_all_tables
	 WHERE snapshot_id = %ld
	 and snap_node_name = '%s'
	 and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
	 AND snap_schemaname !~ '^pg_toast') snap_1
ON snap_2.snap_relid = snap_1.snap_relid
AND snap_2.snap_schemaname = snap_1.snap_schemaname
AND snap_2.snap_relname = snap_1.snap_relname
AND snap_2.db_name = snap_1.db_name
order by snap_2.db_name, snap_2.snap_schemaname
limit 200;

解读：
这一部分描述用户索引状态的统计信息，数据源于 snapshot.snap_global_stat_all_indexes 表。
信息内容如下所示：

列名	描述
DB Name	数据库名称
Schema	模式名称
Relname	表名称
Index Relname	索引名称
Index Scan	索引扫描次数
Index Tuple Read	索引扫描返回的索引条目数
Index Tuple Fetch	索引扫描获取的活跃行数

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
SELECT 	snap_2.db_name 			as "DB Name",
	snap_2.snap_schemaname 		as "Schema",
	snap_2.snap_relname 		as "Relname",
	snap_2.snap_indexrelname 	as "Index Relname",
	(snap_2.snap_idx_scan - coalesce(snap_1.snap_idx_scan, 0)) 		as "Index Scan",
	(snap_2.snap_idx_tup_read - coalesce(snap_1.snap_idx_tup_read, 0)) 	as "Index Tuple Read",
	(snap_2.snap_idx_tup_fetch - coalesce(snap_1.snap_idx_tup_fetch, 0)) 	as "Index Tuple Fetch"
FROM
	(SELECT * FROM snapshot.snap_global_stat_all_indexes
	 WHERE snapshot_id = %ld
	 and snap_node_name = '%s'
	 and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
	 and snap_schemaname !~ '^pg_toast') snap_2
LEFT JOIN
	(SELECT * FROM snapshot.snap_global_stat_all_indexes
	 WHERE snapshot_id = %ld
	 and snap_node_name = '%s'
	 and snap_schemaname NOT IN ('pg_catalog', 'information_schema', 'snapshot')
	 and snap_schemaname !~ '^pg_toast') snap_1
ON snap_2.snap_relid = snap_1.snap_relid
and snap_2.snap_indexrelid = snap_1.snap_indexrelid
and snap_2.snap_schemaname = snap_1.snap_schemaname
and snap_2.snap_relname = snap_1.snap_relname
and snap_2.snap_indexrelname = snap_1.snap_indexrelname
and snap_2.db_name = snap_1.db_name
order by snap_2.db_name, snap_2.snap_schemaname
limit 200;

解读：
这一部分描述坏块的统计信息，数据源于 snapshot.snap_global_stat_bad_block 表。
信息内容如下所示：

列名	描述
DB Id	数据库 OID
Tablespace Id	表空间 OID
Relfilenode	relation 的 filenode 号
Fork Number	fork 编号
Error Count	error 数量
First Time	坏块第一次出现的时间
Last Time	坏块最近一次出现的时间

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
 SELECT snap_2.snap_databaseid 		AS "DB Id",
	snap_2.snap_tablespaceid 	AS "Tablespace Id",
	snap_2.snap_relfilenode 	AS "Relfilenode",
	snap_2.snap_forknum 		AS "Fork Number",
	(snap_2.snap_error_count - coalesce(snap_1.snap_error_count, 0)) AS "Error Count",
	snap_2.snap_first_time 		AS "First Time",
	snap_2.snap_last_time 		AS "Last Time"
FROM
	(SELECT * FROM snapshot.snap_global_stat_bad_block
	 WHERE snapshot_id = %ld
	 and snap_node_name = '%s') snap_2
LEFT JOIN
	(SELECT * FROM snapshot.snap_global_stat_bad_block
	 WHERE snapshot_id = %ld
         and snap_node_name = '%s') snap_1
ON snap_2.snap_databaseid = snap_1.snap_databaseid
and snap_2.snap_tablespaceid = snap_1.snap_tablespaceid
and snap_2.snap_relfilenode = snap_1.snap_relfilenode
limit 200;

解读：
这一部分描述的是数据库参数配置信息，数据源于 snapshot.snap_global_config_settings 表。
信息内容如下所示：

列名	描述
Name	参数名称
Abstract	参数的简单描述
Type	参数类型(bool/enum/integer/real/string)
Curent Value	参数当前值
Min Value	最小参数值
Max Value	最大参数值
Category	参数逻辑组
Enum Values	参数枚举值
Default Value	启动时的默认参数值
Reset Value	重置时的默认参数值

相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
select 	snap_name 		as "Name",
	snap_short_desc as "Abstract",
	snap_vartype 	as "Type",
	snap_setting 	as "Curent Value",
	snap_min_val 	as "Min Value",
	snap_max_val 	as "Max Value",
	snap_category 	as "Category",
	snap_enumvals 	as "Enum Values",
	snap_boot_val 	as "Default Value",
	snap_reset_val  as "Reset Value"
FROM
	snapshot.snap_global_config_settings
	WHERE snapshot_id = %ld
	and snap_node_name = '%s';

解读：
这一部分描述的是 SQL 语句的详细信息，数据来源于 snapshot.snap_summary_statement 表。
Unique SQL Id： 即 SQL 的唯一识别 ID
SQL Text： 即 SQL 的具体内容。
相关代码：

-- 说明：%s代表node_name，%ld代表snapshot_id
select 	(t2.snap_unique_sql_id) as "Unique SQL Id",
	(t2.snap_query) 	as "SQL Text"
from
	snapshot.snap_summary_statement t2
	where snapshot_id = %ld
        and snap_node_name = '%s';

小结：

关于 openGauss WDR 报告的梳理和解读就到这里，这些内容对于刚刚接触 openGauss 的老 DBA 已经足够了，只要读懂 WDR 报告的数据计算方法和含义即意味着看懂了这份 WDR 报告内容，剩下的数据库优化工作完全可以参照已有的数据库优化经验操作。当然了，“小白”DBA 也完全可以参照网络上大佬们分享的数据库优化案例以及类似 Oracle 等主流数据库的 AWR 报告解读，学习 openGauss 的 WDR 报告，从而执行 openGauss 数据库相关的优化工作。
以上所有观点仅代表个人，如有不正确之处欢迎大佬们指正。

标签：name,--,解读,WDR,snapshot,time,snap,openGauss,id
From： https://www.cnblogs.com/renxyz/p/18080625

openGauss的WDR报告详细解读

openGauss 的 WDR 报告详细解读

干货内容如下：

1. 启用 WDR 报告的 snapshot 收集

2. WDR 信息表

3. WDR 数据表

4. WDR 报告创建

4.1 创建 snapshot

4.2 查询数据库节点信息

4.3 创建 WDR Report[使用 gsql 客户端生成]

5. WDR 报告解读

小结：

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