【GaussDB】深入剖析Insert Select慢的定位全过程
问题描述
insert into t_target select * from t_source;
1、源表和目标表结构一样，只有两个字段，皆为文本类型，行长度30字节以内，主键包括这两个字段。
2、目标表为空表，源表500万行，表大小一百多MB，主键索引大小两百多MB。
3、插入需要10分钟。
4、删掉目标表的主键，插入只需要十几秒。
官方文档调研
阅读官方文档，发现在506.0版本中引入了一个新特性，叫“支持ubtree批量插入索引”
官方文档链接
当以下任何一个场景满足所有的约束条件时，建议打开GUC参数batch_insert_index_types并设置为"rcr_ubtree"以提升批量插入索引性能：
场景1：使用JDBC连接数据库，通过java的批量插入数据接口executeBatch进行批量插入索引；
场景2：执行语句insert into t1 select * from
t2，其中t1和t2表schema相同，t1表包含索引，此语句支持在t1表上批量插入索引；必须在批量插入表数据的前提下才会批量插入索引（explain
verbose命令可以查看是否对表数据做批量插入，Batch
Insert代表是批量插入）。满足这些前提后，打开批量插入索引开关就会执行批量插入索引；场景3：执行语句COPY FROM（示例：COPY t1 FROM ‘file.csv’ delimiter ‘,’ csv
header），其中t1和file.csv文件中的schema相同，t1表包含索引，此语句支持在t1表上批量插入索引。约束1：插入的索引类型为UBTree RCR索引；
约束2：插入数据不递增（插入数据递增会利用缓存，即最后一页，这种情况下批量插入索引无性能提升）；
约束3：不是SMP的批量索引插入。
此特性在以下三个场景会影响性能：
如果批量插入数据是递增的（违反约束2），关闭批量插入功能后，会插入到最右缓存中，性能较好。这种场景下开启批量插入功能会导致性能出现轻微劣化。
在执行持续的批量插入时，索引会逐渐变大，进而导致批量插入性能有所下降。
批量插入数据过于分散导致缓存页面命中率过低，或者运行内存过小的场景下，开启批量插入功能会导致性能出现较大幅度劣化。
如果性能劣化幅度较大，建议设置GUC参数batch_insert_index_types为""，关闭该功能，性能劣化问题即可恢复。如果需要技术支持工程师定位原因，可以设置GUC参数logging_module=‘on(BIINDEX)’，打开批量插入索引的日志打印，执行几分钟批量插入直到日志中出现BIINDEX字符串为止。
其实这个特性的原理简单来说，就是中间加了层索引的缓存页面，可以提前将索引进行排序，加快插入索引的速度（与之相对的其实就是批量数据插入时的单行索引插入）。
1、这个功能在GaussDB中是默认开启的，创建的索引默认就是RCR索引。
2、指定创建PCR索引会报错
GAUSS-40615: Extreme redo does not support pcr’s ubtree.
（Extreme Redo不支持 pcr’s ubtree。）（没找到任何有关Extreme redo的说明）。
3、
batch_insert_index_types默认为rcr_ubtree。
疑问与实验
明明有这个提升性能的特性，但是为什么还是会插入慢呢？
1、尝试在插入前把batch_insert_index_types设置为空，但执行时长没有变化。
2、怀疑源表构造的数据可能是乱序的，如果没有走到索引批量插入的逻辑，或者一批次的索引很少，那么插入的索引不是在最右侧时，对前面的索引可能会有影响，由此带来非常大的性能开销。
3、测试在insert select 的后面，加上order by 主键字段(index only scan)，结果插入只要三十几秒了，其中查询阶段十几秒，插入阶段二十秒。
但这套应用系统里这种场景非常多，不可能每个SQL都去加上排序，而且对大量数据进行排序本来就是开销非常大的，有些表的主键字段非常多，work_mem默认配置也就64MB,会频繁触发落盘。
此时，开发测试人员反馈：
他们在另一套实例里构造了相同的数据的表，执行插入只要40秒。
难道是环境差异导致？
1、对比两个机器硬件配置，执行耗时长的机器，硬件配置还更高。
2、再对比两套数据库的参数配置，并逐个调成一样进行测试，最终发现是shared_buffers过小影响的。
3、在执行耗时长的那个环境中，shared_buffers只有初始的32MB，而正常的那个环境则有10GB。
小型模拟测试用例
尝试复现这个问题：
show shared_buffers; --32MBdrop table if exists t_target;
drop table if existst_source;CREATE TABLE t_target(c1 varchar2(6) ,c2 varchar2(12));CREATE TABLE t_source(c1 varchar2(6) ,c2 varchar2(12));--38MB
insert into t_source
select FLOOR(random() * 1e6)::NUMERIC(6, 0),FLOOR(random() * 1e12)::NUMERIC(12, 0) from pg_catalog.generate_series(1,1000000) ;alter table t_source add primary key (c1,c2);truncate table t_target;
insert into t_target select * from t_source;--3秒truncate table t_target;
alter table t_target add primary key (c1,c2);
insert into t_target select * from t_source;--200秒
有了主键后，插入耗时有两个数量级的差异。
日志分析
在会话中开启set logging_module=‘on(BIINDEX)’，再执行插入，在日志中能看到详细的信息：
2025-07-17 14:43:55.005 primary admin postgres 172.17.0.1
140393958078208 63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor
<Script-22.sql> 00000 19140298416761840 [BIINDEX] LOCATION:
batch_insert_ubtree, storage_ubtree_base.cpp:3068 2025-07-17
14:43:55.005 primary admin postgres 172.17.0.1 140393958078208
63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor <Script-22.sql> 00000
19140298416761840 [BIINDEX] LOG: 00000: [BATCH_INSERT_INDEX_USTORE]
end batch insert index, table oid 315657, index num 1. 2025-07-17
14:43:55.005 primary admin postgres 172.17.0.1 140393958078208
63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor <Script-22.sql> 00000
19140298416761840 [BIINDEX] LOCATION: batch_insert_index,
nbtsort.cpp:2223 2025-07-17 14:43:55.006 primary admin postgres
172.17.0.1 140393958078208 63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor <Script-22.sql> 00000 19140298416761840 [BIINDEX] LOG:
00000: [BATCH_INSERT_INDEX_USTORE] begin batch insert index, table oid
315657, index num 1. 2025-07-17 14:43:55.006 primary admin postgres
172.17.0.1 140393958078208 63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor <Script-22.sql> 00000 19140298416761840 [BIINDEX] LOCATION:
batch_insert_index, nbtsort.cpp:2200 2025-07-17 14:43:55.006 primary
admin postgres 172.17.0.1 140393958078208 63507[0:0#0] 1335387
Mogeaver 23.3.1 - SQLEditor <Script-22.sql> 00000 19140298416761840
[BIINDEX] LOG: 00000: [BATCH_INSERT_INDEX_USTORE] batch insert normal
index t_target_pkey(315660), heap t_target(315657), num_index_tuples:
577. 2025-07-17 14:43:55.006 primary admin postgres 172.17.0.1 140393958078208 63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor
<Script-22.sql> 00000 19140298416761840 [BIINDEX] LOCATION:
open_normal_or_global_index, nbtsort.cpp:2358 2025-07-17 14:43:55.015
primary admin postgres 172.17.0.1 140393958078208 63507[0:0#0] 1335387
Mogeaver 23.3.1 - SQLEditor <Script-22.sql> 00000 19140298416761840
[BIINDEX] LOG: 00000: [BATCH_INSERT_INDEX_USTORE]
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(471, 0, 0, 986), (472, 0, 0, 5378), (473, 0, 0, 3592), (474, 0, 0,
14416), (475, 0, 0, 5941), (476, 0, 0, 13181), (477, 0, 0, 17497),
(478, 0, 0, 3083), (479, 0, 0, 6949), (480, 0, 0, 3532), (481, 0, 0,
15270), (482, 0, 0, 16411), (483, 0, 0, 12420), (484, 0, 0, 19263),
(485, 0, 0, 9903), (486, 0, 0, 15198), (487, 0, 0, 420), (488, 0, 0,
12844), (489, 0, 0, 15230), (490, 0, 0, 16500), (491, 0, 0, 13544),
(492, 0, 0, 3505), (493, 0, 0, 18812), (494, 0, 0, 7896), (495, 0, 0,
8554), (496, 0, 0, 13652), (497, 0, 0, 1830), (498, 0, 0, 10621),
(499, 0, 0, 9082), (500, 0, 0, 12645), (501, 0, 0, 13506), (502, 0, 0,
11277), (503, 0, 0, 8624), (504, 0, 0, 10574), (505, 0, 0, 9260),
(506, 0, 0, 825), (507, 0, 0, 12014), (508, 0, 0, 5056), (509, 0, 0,
2566), (510, 0, 0, 7047), (511, 0, 0, 6787), (512, 0, 0, 6469), (513,
0, 0, 16229), (514, 0, 0, 12805), (515, 0, 0, 8625), (516, 0, 0,
15637), (517, 0, 0, 15343), (518, 0, 0, 8095), (519, 0, 0, 13263),
(520, 0, 0, 453), (521, 0, 0, 9648), (522, 0, 0, 19996), (523, 0, 0,
17766), (524, 0, 0, 16695), (525, 0, 0, 18073), (526, 0, 0, 3441),
(527, 0, 0, 867), (528, 0, 0, 15184), (529, 0, 0, 11414), (530, 0, 0,
20555), (531, 0, 0, 14533), (532, 0, 0, 11369), (533, 0, 0, 15391),
(534, 0, 0, 3331), (535, 0, 0, 3608), (536, 0, 0, 15563), (537, 0, 0,
173), (538, 0, 1, 173), (539, 0, 0, 20859), (540, 0, 0, 7263), (541,
0, 0, 6812), (542, 0, 0, 1537), (543, 0, 0, 19443), (544, 0, 0, 9508),
(545, 0, 0, 16770), (546, 0, 0, 5036), (547, 0, 0, 4070), (548, 0, 0,
4500), (549, 0, 0, 4574), (550, 0, 0, 20569), (551, 0, 0, 21128),
(552, 0, 0, 10645), (553, 0, 0, 20982), (554, 0, 0, 7146), (555, 0, 0,
7316), (556, 0, 0, 15397), (557, 0, 0, 1554), (558, 0, 0, 5545), (559,
0, 0, 3270), (560, 0, 0, 20756), (561, 0, 0, 16300), (562, 0, 0, 702),
(563, 0, 0, 6485), (564, 0, 0, 5636), (565, 0, 0, 20094), (566, 0, 0,
8831), (567, 0, 0, 3506), (568, 0, 0, 7603), (569, 0, 0, 1618), (570,
0, 0, 3488), (571, 0, 0, 17034), (572, 0, 0, 21161), (573, 0, 0,
2852), (574, 0, 0, 3032), (575, 0, 0, 748), (576, 1, 0, 10894), ].
2025-07-17 14:43:55.015 primary admin postgres 172.17.0.1
140393958078208 63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor
<Script-22.sql> 00000 19140298416761840 [BIINDEX] LOCATION:
batch_insert_ubtree, storage_ubtree_base.cpp:3057 2025-07-17
14:43:55.015 primary admin postgres 172.17.0.1 140393958078208
63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor <Script-22.sql> 00000
19140298416761840 [BIINDEX] LOG: 00000: [BATCH_INSERT_INDEX_USTORE]
count is 6, rate is 0.010399. 2025-07-17 14:43:55.015 primary admin
postgres 172.17.0.1 140393958078208 63507[0:0#0] 1335387 Mogeaver
23.3.1 - SQLEditor <Script-22.sql> 00000 19140298416761840 [BIINDEX] LOCATION: batch_insert_ubtree, storage_ubtree_base.cpp:3064
2025-07-17 14:43:55.015 primary admin postgres 172.17.0.1
140393958078208 63507[0:0#0] 1335387 Mogeaver 23.3.1 - SQLEditor
<Script-22.sql> 00000 19140298416761840 [BIINDEX] LOG: 00000:
[BATCH_INSERT_INDEX_USTORE]cache hit rate is low, which may affect actual performance. It is
recommended to set the batch_insert_index_types=‘’. cache hit rate is
low, which may affect actual performance. It is recommended to set the
batch_insert_index_types=’’.
日志里说缓存命中率太低，影响性能，建议设置batch_insert_index_types=‘’
但正如前文所说，这个场景下设置这个没什么用。
尝试把shared_buffers调整为大于这个表的大小，比如75MB，然后重启数据库再测试，不带主键时1秒，带主键时9s。
内存上下文观察
这里要注意一个很容易引起误会的点，gs_shared_memory_detail视图并非指的shared_buffers里的东西，而是动态内存里的共享内存，动态内存包含了 动态共享内存 和 动态会话内存。
dynamic_used_shrctx 对应视图 gs_shared_memory_detail dynamic_used_memory
包含了 dynamic_used_shrctx dynamic_used_memory 是
gs_shared_memory_detail和gs_session_memory_context相加
动态共享内存上下文变化
> select contextname,sum(usedsize) usedsize from
> pg_catalog.gs_shared_memory_detail group by contextname order by 2
> desc;
| contextname | 执行前usedsize |
|---|---|
| ASP unique sql hash table | 10475400 |
| GlobalSysDBCacheEntryMemCxt_13155 | 4759096 |
| ProcessMemory | 2308784 |
| StorageTopMemoryContext | 1321088 |
| GlobalSysDBCacheEntryMemCxt_0 | 358248 |
| TrackStmtContext | 280712 |
| 执行中usedsize | 变化 |
| 12568112 | 2092712 |
| 4760280 | 1184 |
| 2309328 | 544 |
| 1304624 | -16464 |
| 357704 | -544 |
| 282000 | 1288 |
可以发现内存占用大头在ASP unique sql hash table，但这是因为是数据库启动后第一次执行这条SQL，所以才增加了这个内存，后续重复执行这个SQL时，这个内存并不会变，批量插入时似乎并没有额外去申请比较大的动态共享内存。
动态会话内存上下文变化
select * from gs_session_memory_context where sessid like '%.12' order by usedsize desc;
| contextname | level |
|---|---|
| ModifyTable_140544757925632 | 6 |
| BulkMemory | 7 |
| parent | totalsize |
| ExecutorState | 496688 |
| ModifyTable_140544757925632 | 131072 |
| freesize | usedsize |
| 8744 | 487944 |
| 2992 | 128080 |
执行期间，多出了个ExecutorState->ModifyTable->BulkMemory，用量会有轻微的上下浮动，执行完就没了。动态会话内存占用也不大。
shared_buffers对性能的影响
另外调整shared_buffers为不同的值进行相同的插入测试。
xychart-beta
title “shared_buffers对批量索引性能的影响”
x-axis “shared_buffers大小 MB” [32, 40, 45, 50, 55, 60, 65, 70, 75]
y-axis "执行时间 秒 "
line “开启批量插入索引” [200, 92, 66, 47, 27, 17, 11, 10, 9]
line “关闭批量插入索引” [200, 107, 67, 52, 28, 16, 12, 12, 10]
shared_buffers对批量索引性能的影响
可以观察到，在目前这个测试环境中，insert select的插入性能受shared_buffers非常大，而batch_insert_index_types这个参数开启带来的性能提升并不明显。
也就是说，触发批量索引插入时，shared_buffers过小，会非常影响性能，由于可用动态内存是减出来的，因此大部分情况下动态内存是足够进行批量索引插入的。
额外测试
后面加测了下，如果在现场那个正常的那个环境里，插入时也加上order by，插入时间从40秒降到20秒了。
这意味着在特定场景下，先把数据顺序排好，这个批量索引插入的特性也是对性能有优化的，但官方文档说这种场景会导致性能轻微劣化，其实也并不是绝对的。在没有数据库内核源码的情况下，还是得多测试一些不同场景比较才能得到真实情况。
总结
通过对GaussDB中insert into … select …慢的现象进行定位和实验，发现影响批量插入性能的关键因素主要有：
1、主键索引的存在会极大增加插入耗时，尤其在数据量大时，主键索引维护成为瓶颈。
2、shared_buffers参数过小会显著拖慢批量插入性能，建议设置为大于目标表和索引的总大小。
3、batch_insert_index_types参数（批量索引插入）在shared_buffers足够大时提升有限，但shared_buffers过小时无论开关都很慢。
4、数据顺序对性能有影响，提前排序有时能提升插入效率，但并非所有场景都适用。
5、实际批量索引插入动作会消耗会话内存，但shared_buffers依然是性能的决定性因素。
优化建议：
1、生产环境应合理调大shared_buffers，避免默认过小。
2、大批量数据迁移/同步时，必要时可临时去除主键索引，插入后再重建。
3、对于极端慢的场景，可尝试加order by主键字段，但需权衡排序开销。
4、关注数据库内存参数和实际物理资源，必要时与内核研发沟通原理。
参数调优和场景测试是解决数据库性能瓶颈的有效手段，实际效果需结合业务和环境多维度验证。