diff --git "a/content/zh/post/jiajunfeng/\345\210\235\347\252\245-openGauss-\344\271\213\347\264\242\345\274\225\346\216\250\350\215\220Index-advisor.md" "b/content/zh/post/jiajunfeng/\345\210\235\347\252\245-openGauss-\344\271\213\347\264\242\345\274\225\346\216\250\350\215\220Index-advisor.md"
new file mode 100644
index 0000000000000000000000000000000000000000..ebc7bf381fb3b6e1043b40ec0afd1702a0ef078b
--- /dev/null
+++ "b/content/zh/post/jiajunfeng/\345\210\235\347\252\245-openGauss-\344\271\213\347\264\242\345\274\225\346\216\250\350\215\220Index-advisor.md"
@@ -0,0 +1,196 @@
++++
+
+date = "2021-06-04"
+
+tags = ["openGauss核心技术"]
+
+archives = "2021-06"
+
+author = "贾军锋"
+
+summary = "初窥 openGauss 之索引推荐Index-advisor)"
+
+img = "/zh/post/jiajunfeng/title/img4.png"
+
+times = "12:30"
+
++++
+
+# 初窥 openGauss 之索引推荐Index-advisor
+
+TPC-H 是一个面向分析型业务\(AP\)的基准测试，它由一系列热点查询组成，这些热点查询都是高度复杂的，因此执行时间往往都比较长。
+
+在本次实验测试中，将手动向数据库加载TPC-H数据，并保存在名为 tpch 的数据库中。默认TPC-H数据库的表缺少索引，数据库的参数并没有做任何优化，因此执行效率会比较差。
+
+本实验内容比较浅显，使用openGauss的索引推荐\(Index-advisor\)功能，对数据库进行性能优化，同时也让大家对Index-advisor功能有一个初步的了解。
+
+## 环境信息
+
+OS： CentOS Linux release 7.6.1810
+
+openGauss：2.0.0
+
+CPU：1core
+
+Memory：4GB
+
+测试数据脚本清单如下：
+
+```
+[omm@lab01 ~]$ ls -l ~/tpch-kit-back/
+total 1076780
+-rw------- 1 omm dbgrp 24196144 Apr 24 15:39 customer.tbl
+-rw------- 1 omm dbgrp 3814 Apr 24 15:39 dss.ddl
+-rw------- 1 omm dbgrp 753862072 Apr 24 15:39 lineitem.tbl
+-rw------- 1 omm dbgrp 287 May 25 10:52 load.sh
+-rw------- 1 omm dbgrp 2199 Apr 24 15:16 nation.tbl
+-rw------- 1 omm dbgrp 170452161 Apr 24 15:16 orders.tbl
+-rw------- 1 omm dbgrp 10553197 Apr 24 15:11 out0
+-rw------- 1 omm dbgrp 118184616 Apr 24 15:10 partsupp.tbl
+-rw------- 1 omm dbgrp 23935125 Apr 24 15:11 part.tbl
+drwx------ 3 omm dbgrp 4096 Apr 24 15:39 queries
+-rw------- 1 omm dbgrp 384 Apr 24 15:07 region.tbl
+-rw------- 1 omm dbgrp 1399184 Apr 24 15:07 supplier.tbl
+```
+
+## 1. 创建数据库并导入数据
+
+```
+-- 创建数据库tpch
+[omm@lab01 ~]$ gsql -d postgres -p 26000 -c "create database tpch with encoding='UTF-8';"
+-- 创建测试表
+[omm@lab01 ~]$ gsql -d tpch -p 26000 -f ~/tpch-kit-back/dss.ddl
+-- 加载测试数据并统计分析
+[omm@lab01 ~]$ vi load.sh
+---------------------------------------
+for i in `ls *.tbl`; do
+ table=${i/.tbl/}
+ echo "Loading $table..."
+ sed 's/|$//' $i > /tmp/$i
+ gsql -d tpch -p 26000 -c "TRUNCATE $table"
+ gsql -d tpch -p 26000 -c "\\copy $table FROM '/home/omm/tpch-kit-back/$i' CSV DELIMITER '|'"
+ gsql -d tpch -p 26000 -c "ANALYZE $table"
+done
+---------------------------------------
+sh load.sh
+```
+
+## 2. 执行第一次查询测试\(耗时：106s\)
+
+```
+[omm@lab01 ~]$ time gsql -d tpch -p 26000 -f /home/omm/tpch-kit-back/queries/queries.sql -o out0
+total time: 105949 ms
+real 1m46.063s
+user 0m0.707s
+sys 0m0.026s
+```
+
+## 3. 索引信息查询\(当前没有任何索引\)
+
+```
+[omm@lab01 ~]$ gsql -d tpch -p 26000 -r
+tpch=# \d
+ List of relations
+ Schema | Name | Type | Owner | Storage
+--------+----------+-------+-------+----------------------------------
+ public | customer | table | omm | {orientation=row,compression=no}
+ public | lineitem | table | omm | {orientation=row,compression=no}
+ public | nation | table | omm | {orientation=row,compression=no}
+ public | orders | table | omm | {orientation=row,compression=no}
+ public | part | table | omm | {orientation=row,compression=no}
+ public | partsupp | table | omm | {orientation=row,compression=no}
+ public | region | table | omm | {orientation=row,compression=no}
+ public | supplier | table | omm | {orientation=row,compression=no}
+(8 rows)
+tpch=# \di
+No relations found.
+tpch=# select * from pg_indexes where schemaname='public';
+ schemaname | tablename | indexname | tablespace | indexdef
+------------+-----------+-----------+------------+----------
+(0 rows)
+```
+
+## 4. 单条SQL查询索引推荐
+
+```
+-- 未添加索引的查询效率(约4.9s)
+[omm@lab01 ~]$ time gsql -d tpch -p 26000 -c "select * from lineitem where l_orderkey < 100 and l_suppkey > 50;"
+real 0m4.916s
+user 0m0.014s
+sys 0m0.001s
+
+-- 使用索引推荐函数(gs_index_advise)获取优化建议
+tpch=# select *from gs_index_advise('select * from lineitem where l_orderkey < 100 and l_suppkey > 50;');
+ table | column
+----------+--------------
+ lineitem | (l_orderkey)
+
+-- 创建索引
+tpch=# create index idx1 on lineitem(l_orderkey);
+
+-- 查看优化结果(约2.3s)
+[omm@lab01 ~]$ time gsql -d tpch -p 26000 -c "select * from lineitem where l_orderkey < 100 and l_suppkey > 50;"
+real 0m2.337s
+user 0m0.009s
+sys 0m0.007s
+```
+
+## 5. Workload级别索引推荐\(针对一批SQL语句的索引推荐\)
+
+```
+-- 获取推荐索引
+[omm@lab01 ~]$ cd /gauss/app/bin/dbmind/index_advisor/
+[omm@lab01 index_advisor]$ python3 ./index_advisor_workload.py 26000 tpch ~/queries/queries.sql -- 端口：26000 数据库：tpch
+####################################### Generate candidate indexes #######################################
+table: lineitem columns: l_returnflag,l_linestatus
+table: part columns: p_partkey,p_size
+table: supplier columns: s_suppkey,s_nationkey
+table: partsupp columns: ps_partkey,ps_suppkey
+table: nation columns: n_nationkey,n_regionkey
+table: orders columns: o_orderkey,o_custkey
+table: customer columns: c_custkey,c_nationkey
+table: orders columns: o_custkey,o_orderkey
+table: lineitem columns: l_orderkey,l_suppkey
+table: customer columns: c_custkey
+table: part columns: p_partkey,p_type
+table: supplier columns: s_suppkey
+table: lineitem columns: l_suppkey,l_partkey,l_orderkey
+table: part columns: p_partkey
+table: lineitem columns: l_orderkey,l_partkey,l_suppkey
+table: orders columns: o_orderkey
+table: partsupp columns: ps_suppkey
+table: lineitem columns: l_shipdate,l_receiptdate,l_commitdate,l_orderkey
+table: lineitem columns: l_partkey
+######################################## Determine optimal indexes ########################################
+create index ind0 on lineitem(l_shipdate,l_receiptdate,l_commitdate,l_orderkey);
+create index ind1 on lineitem(l_returnflag,l_linestatus);
+create index ind2 on lineitem(l_suppkey,l_partkey,l_orderkey);
+create index ind3 on orders(o_orderkey,o_custkey);
+create index ind4 on partsupp(ps_partkey,ps_suppkey);
+create index ind5 on part(p_partkey,p_size);
+create index ind6 on part(p_partkey,p_type);
+create index ind7 on customer(c_custkey,c_nationkey);
+create index ind8 on supplier(s_suppkey,s_nationkey);
+create index ind9 on nation(n_nationkey,n_regionkey);
+
+-- 创建推荐的索引
+[omm@lab01 ~]$ gsql -d tpch -p 26000 -r
+tpch=# create index ind0 on lineitem(l_shipdate,l_receiptdate,l_commitdate,l_orderkey);
+tpch=# create index ind1 on lineitem(l_returnflag,l_linestatus);
+tpch=# create index ind2 on lineitem(l_suppkey,l_partkey,l_orderkey);
+tpch=# create index ind3 on orders(o_orderkey,o_custkey);
+tpch=# create index ind4 on partsupp(ps_partkey,ps_suppkey);
+tpch=# create index ind5 on part(p_partkey,p_size);
+tpch=# create index ind6 on part(p_partkey,p_type);
+tpch=# create index ind7 on customer(c_custkey,c_nationkey);
+tpch=# create index ind8 on supplier(s_suppkey,s_nationkey);
+tpch=# create index ind9 on nation(n_nationkey,n_regionkey);
+
+-- 测试查询脚本时间(耗时：77s，SQL查询相比之前快了29s)
+[omm@lab01 ~]$ time gsql -d tpch -p 26000 -f /home/omm/tpch-kit-back/queries/queries.sql -o out0
+total time: 77200 ms
+real 1m17.233s
+user 0m0.665s
+sys 0m0.020s
+```
+
diff --git "a/content/zh/post/jiajunfeng/\345\210\235\347\252\245openGauss-\344\271\213\345\217\202\346\225\260\350\207\252\350\260\203\344\274\230X-Tuner.md" "b/content/zh/post/jiajunfeng/\345\210\235\347\252\245openGauss-\344\271\213\345\217\202\346\225\260\350\207\252\350\260\203\344\274\230X-Tuner.md"
new file mode 100644
index 0000000000000000000000000000000000000000..7f3713a546c920074911a13389dba96fbc61af2c
--- /dev/null
+++ "b/content/zh/post/jiajunfeng/\345\210\235\347\252\245openGauss-\344\271\213\345\217\202\346\225\260\350\207\252\350\260\203\344\274\230X-Tuner.md"
@@ -0,0 +1,311 @@
++++
+
+date = "2021-06-04"
+
+tags = ["openGauss核心技术"]
+
+archives = "2021-06"
+
+author = "贾军锋"
+
+summary = "初窥openGauss 之参数自调优X-Tuner)"
+
+img = "/zh/post/jiajunfeng/title/img2.png"
+
+times = "12:30"
+
++++
+
+
+
+# **初窥openGauss 之参数自调优X-Tuner\**
+
+TPC-H 是一个面向分析型业务\(AP\)的基准测试，它由一系列热点查询组成，这些热点查询都是高度复杂的，因此执行时间往往都比较长。 在本次实验测试中，将手动向数据库加载TPC-H数据，并保存在名为 tpch 的数据库中。默认TPC-H数据库的表缺少索引，数据库的参数并没有做任何优化，因此执行效率会比较差。 本实验比较浅显，使用openGauss的参数自调优\(X-Tuner：gs\_xtuner\)功能，对数据库进行参数优化，以提升数据库运行性能，让大家对X-Tuner参数自调优有一个初步的了解。
+
+## **环境信息**
+
+OS：CentOS Linux release 7.6.1810
+
+openGauss：2.0.0
+
+CPU：1core
+
+Memory：4GB
+
+测试数据脚本清单如下：
+
+```
+[omm@lab01 ~]$ ls -l ~/tpch-kit-back/
+total 1076780
+-rw------- 1 omm dbgrp 24196144 Apr 24 15:39 customer.tbl
+-rw------- 1 omm dbgrp 3814 Apr 24 15:39 dss.ddl
+-rw------- 1 omm dbgrp 753862072 Apr 24 15:39 lineitem.tbl
+-rw------- 1 omm dbgrp 287 May 25 10:52 load.sh
+-rw------- 1 omm dbgrp 2199 Apr 24 15:16 nation.tbl
+-rw------- 1 omm dbgrp 170452161 Apr 24 15:16 orders.tbl
+-rw------- 1 omm dbgrp 10553197 Apr 24 15:11 out0
+-rw------- 1 omm dbgrp 118184616 Apr 24 15:10 partsupp.tbl
+-rw------- 1 omm dbgrp 23935125 Apr 24 15:11 part.tbl
+drwx------ 3 omm dbgrp 4096 Apr 24 15:39 queries
+-rw------- 1 omm dbgrp 384 Apr 24 15:07 region.tbl
+-rw------- 1 omm dbgrp 1399184 Apr 24 15:07 supplier.tbl
+```
+
+## 1. 配置pip，并安装setuptools-rust模块
+
+```
+[root@lab01 ~]# wget https://bootstrap.pypa.io/get-pip.py
+[root@lab01 ~]# python3 get-pip.py
+[root@lab01 ~]# pip -V pip 21.1.2 from /usr/local/lib/python3.6/site-packages/
+pip (python 3.6)
+[root@lab01 ~]# pip install setuptools-rust
+```
+
+## 2. 安装依赖包
+
+```
+[omm@lab01 xtuner]$ pip install joblib
+[omm@lab01 xtuner]$ pip install threadpoolctl
+```
+
+## 3. 创建数据库并导入数据
+
+```
+-- 创建数据库tpch
+[omm@lab01 ~]$ gsql -d postgres -p 26000 -c "create database tpch with encoding='UTF-8';"
+-- 创建测试表
+ [omm@lab01 ~]$ gsql -d tpch -p 26000 -f ~/tpch-kit-back/dss.ddl
+ -- 加载测试数据并统计分析
+[omm@lab01 ~]$ vi load.sh
+---------------------------------------
+for i in `ls *.tbl`; do
+table=${i/.tbl/}
+ echo "Loading $table..."
+ sed 's/|$//' $i > /tmp/$i
+ gsql -d tpch -p 26000 -c "TRUNCATE $table"
+ gsql -d tpch -p 26000 -c "\\copy $table FROM '/home/omm/tpch-kit-back/$i' CSV DELIMITER '|'"
+ gsql -d tpch -p 26000 -c "ANALYZE $table"
+done
+ ---------------------------------------
+sh load.sh
+```
+
+## 4. 编辑requirements.txt文件
+
+```
+[omm@lab01 ~]$ cd /gauss/app/bin/dbmind/xtuner/
+[omm@lab01 xtuner]$ vi requirements.txt
+---------------------------------------
+删除:
+ tensorflow>=2.2.0
+ keras-rl2
+ ---------------------------------------
+```
+
+## 5. 生成gs\_xtuner参数调优工具\(需要连接外网\)
+
+```
+[omm@lab01 ~]$ cd /gauss/app/bin/dbmind/xtuner
+[omm@lab01 xtuner]$ python3 setup.py install --user
+```
+
+## 6. 执行快速推荐命令（基于已经作业执行的信息进行推荐，信息来源pg\_stat\_database等）
+
+```
+[omm@lab01 xtuner]$ gs_xtuner recommend --db-name tpch --db-user omm --host 192.168.0.99 --host-user omm --port 26000
+Please input the password of database:
+Please input the password of host:
+Start to recommend knobs. Just a moment, please.
+************************************* Knob Recommendation Report ****************************************
+INFO:
++---------------------------------------+----------------------+
+| Metric | Value |
+ +---------------------------------------+----------------------+
+| workload_type | ap |
+| dirty_background_bytes | 0 |
+| current_locks_count | 0.0 |
+| current_prepared_xacts_count | 0.0 |
+| rollback_commit_ratio | 0.0 |
+| average_connection_age | 0.004575 |
+| checkpoint_proactive_triggering_ratio | 0.00863557858376511 |
+| fetched_returned_ratio | 0.055316264644388206 |
+| cache_hit_rate | 0.5028061903026831 |
+| os_cpu_count | 1 |
+| current_connections | 1.0 |
+| checkpoint_avg_sync_time | 1.07037996545769 |
+| write_tup_speed | 101.161719229361 |
+| used_mem | 131846656.0 |
+| all_database_size | 2292057.41015625 |
+| shared_buffer_heap_hit_rate | 25.917067253117217 |
+| current_free_mem | 3270760 |
+| temp_file_size | 3573.07285767967 |
+| uptime | 38.3688171772222 |
+| os_mem_total | 3879956 |
+| checkpoint_dirty_writing_time_window | 450.0 |
+| read_write_ratio | 47.82294541597867 |
+| read_tup_speed | 4837.86775193848 |
+| max_processes | 503 |
+| track_activity_size | 503.0 |
+| search_modify_ratio | 658741.9884425476 |
+| ap_index | 7.5 |
+| shared_buffer_toast_hit_rate | 76.6304347826087 |
+| block_size | 8.0 |
+| shared_buffer_tidx_hit_rate | 82.7893175074184 |
+| shared_buffer_idx_hit_rate | 97.6601060219748 |
+| enable_autovacuum | True |
+| is_64bit | True |
+| is_hdd | True |
+| load_average | [1.19, 0.82, 0.8] |
++---------------------------------------+----------------------+
+p.s: The unit of storage is kB.
+WARN:
+[0].
+ The number of CPU cores is a little small. Please do not run too high concurrency.
+ You are recommended to set max_connections based on the number of CPU cores.
+ If your job does not consume much CPU, you can also increase it.
+[1].
+ The value of wal_buffers is a bit high. Generally, an excessively large value does not bring better performance.
+ You can also set this parameter to -1.
+ The database automatically performs adaptation.
+*********************************** Recommended Knob Settings **********************************************
++---------------------------+-----------+--------+---------+---------+
+| name | recommend | min | max | restart |
++---------------------------+-----------+--------+---------+---------+
+| shared_buffers | 121256 | 72752 | 139448 | True |
+| max_connections | 134 | 15 | 269 | True |
+| effective_cache_size | 2909967 | 121256 | 2909967 | False |
+| wal_buffers | 3789 | 2048 | 3789 | True |
+| random_page_cost | 3.0 | 2.0 | 3.0 | False |
+| default_statistics_target | 1000 | 100 | 1000 | False |
++---------------------------+-----------+--------+---------+---------+
+注意：修改该推荐值之前，请确保硬件条件满足，否则可能会造成数据库无法启动的问题。
+```
+
+## 7. \[可选\]迭代推荐命令（全局搜索算法，迭代式执行，每轮执行约2分钟）
+
+- 1\> 修改配置文件
+
+```
+vi /home/omm/.local/lib/python3.6/site-packages/openGauss_xtuner-2.0.0-py3.6.egg/tuner/xtuner.conf
+ -------------------------------------------------
+修改如下行：
+ max_iterations = 3 （从100轮改为3）
+ benchmark_path = /home/omm/queries
+------------------------------------------------
+```
+
+- 2\> 执行命令，观察Reward数值变化，粉色输出的轮次为当前较优数值
+
+```
+[omm@lab01 ~]$ time gs_xtuner tune --db-name tpch --db-user omm --host localhost --host-user omm --port 26000
+Please input the password of database:
+Please input the password of host:
+Start to recommend knobs. Just a moment, please.
+WARN: The database may restart several times during tuning, continue or not [yes|no]:yes
+2021-05-26 11:09:12,710: Recorder is starting.
+| iter | target | random... |
+-------------------------------------
+2021-05-26 11:10:58,017: [0] Current reward is -102.935543, knobs: {'random_page_cost': '2.64'}.
+2021-05-26 11:10:58,018: [0] Best reward is -102.935543, knobs: {'random_page_cost': '2.64'}.
+2021-05-26 11:10:58,018: [1] Database metrics: [0.6400000000000001, 0.6007798155874045, 0.65].
+2021-05-26 11:10:58,018: [1] Benchmark score: -102.899098, used mem: 36444544 kB, reward: -102.935543.
+| 1 | -102.9 | 0.6426 |
+2021-05-26 11:12:30,939: [1] Current reward is -91.541441, knobs: {'random_page_cost': '2'}.
+2021-05-26 11:12:30,941: [1] Best reward is -91.541441, knobs: {'random_page_cost': '2'}.
+2021-05-26 11:12:30,941: [2] Database metrics: [0.0, 0.6107552017890537, 2.6].
+2021-05-26 11:12:30,942: [2] Benchmark score: -91.504996, used mem: 36444544 kB, reward: -91.541441.
+| 2 | -91.54 | 0.003251 |
+2021-05-26 11:13:38,617: [2] Current reward is -66.684871, knobs: {'random_page_cost': '2.46'}.
+2021-05-26 11:13:38,618: [2] Best reward is -66.684871, knobs: {'random_page_cost': '2.46'}.
+2021-05-26 11:13:38,618: [3] Database metrics: [0.45999999999999996, 0.621014394376401, 3.47].
+2021-05-26 11:13:38,618: [3] Benchmark score: -66.648426, used mem: 36444544 kB, reward: -66.684871.
+| 3 | -66.68 | 0.4565 |
+2021-05-26 11:14:53,250: [3] Current reward is -73.748742, knobs: {'random_page_cost': '2.9'}.
+2021-05-26 11:14:53,252: [3] Best reward is -66.684871, knobs: {'random_page_cost': '2.46'}.
+2021-05-26 11:14:53,252: [4] Database metrics: [0.8999999999999999, 0.6286889335789447, 3.65].
+2021-05-26 11:14:53,252: [4] Benchmark score: -73.712297, used mem: 36444544 kB, reward: -73.748742.
+| 4 | -73.75 | 0.9016 |
+2021-05-26 11:15:58,798: [4] Current reward is -64.467620, knobs: {'random_page_cost': '2.45'}.
+2021-05-26 11:15:58,799: [4] Best reward is -64.467620, knobs: {'random_page_cost': '2.45'}.
+2021-05-26 11:15:58,799: [5] Database metrics: [0.4500000000000002, 0.633784310797396, 3.45].
+2021-05-26 11:15:58,799: [5] Benchmark score: -64.431175, used mem: 36444544 kB, reward: -64.467620.
+| 5 | -64.47 | 0.4544 |
+2021-05-26 11:16:59,097: [5] Current reward is -59.161970, knobs: {'random_page_cost': '2.43'}.
+2021-05-26 11:16:59,099: [5] Best reward is -59.161970, knobs: {'random_page_cost': '2.43'}.
+2021-05-26 11:16:59,099: [6] Database metrics: [0.43000000000000016, 0.6393591990442545, 3.91].
+2021-05-26 11:16:59,099: [6] Benchmark score: -59.125525, used mem: 36444544 kB, reward: -59.161970.
+| 6 | -59.16 | 0.4304 |
+2021-05-26 11:18:08,157: [6] Current reward is -67.964937, knobs: {'random_page_cost': '2.39'}.
+2021-05-26 11:18:08,158: [6] Best reward is -59.161970, knobs: {'random_page_cost': '2.43'}.
+2021-05-26 11:18:08,158: [7] Database metrics: [0.3900000000000001, 0.6445245622485726, 4.05].
+2021-05-26 11:18:08,158: [7] Benchmark score: -67.928493, used mem: 36444544 kB, reward: -67.964937.
+| 7 | -67.96 | 0.3854 |
+2021-05-26 11:19:11,917: [7] Current reward is -62.842104, knobs: {'random_page_cost': '2.43'}.
+2021-05-26 11:19:11,918: [7] Best reward is -59.161970, knobs: {'random_page_cost': '2.43'}.
+2021-05-26 11:19:11,918: [8] Database metrics: [0.43000000000000016, 0.6489102035318035, 3.5].
+2021-05-26 11:19:11,918: [8] Benchmark score: -62.805659, used mem: 36444544 kB, reward: -62.842104.
+ | 8 | -62.84 | 0.4301 |
+=====================================
+2021-05-26 11:19:11,926: The tuning process is complete. The best reward is -59.161970, best knobs are:
+{'random_page_cost': '2.43'}.
+ ****************************************** Knob Recommendation Report *************************************
+INFO:
++---------------------------------------+-----------------------+
+| Metric | Value |
++---------------------------------------+-----------------------+
+| workload_type | ap |
+| dirty_background_bytes | 0 |
+| current_locks_count | 0.0 |
+| current_prepared_xacts_count | 0.0 |
+| rollback_commit_ratio | 0.0002477694554770677 |
+| average_connection_age | 0.004734 |
+| checkpoint_proactive_triggering_ratio | 0.00938967136150235 |
+| fetched_returned_ratio | 0.09276922373936373 |
+| uptime | 0.224322521666667 |
+| cache_hit_rate | 0.6006356117493342 |
+| os_cpu_count | 1 |
+| current_connections | 1.0 |
+| checkpoint_avg_sync_time | 1.06359368331199 |
+| search_modify_ratio | 1007080.6984163317 |
+| max_processes | 137 |
+| track_activity_size | 137.0 |
+| all_database_size | 2292057.41015625 |
+| temp_file_size | 2694.18229367111 |
+| current_free_mem | 3298680 |
+| shared_buffer_heap_hit_rate | 36.42339765350299 |
+| used_mem | 36444544.0 |
+| os_mem_total | 3879956 |
+| checkpoint_dirty_writing_time_window | 450.0 |
+| ap_index | 7.5 |
+| shared_buffer_toast_hit_rate | 74.11273486430062 |
+| read_tup_speed | 7942.47638202933 |
+| block_size | 8.0 |
+| read_write_ratio | 80.26596656844558 |
+| shared_buffer_tidx_hit_rate | 84.41330998248687 |
+| shared_buffer_idx_hit_rate | 96.54182833084825 |
+| write_tup_speed | 98.9516516216125 |
+| enable_autovacuum | True |
+| is_64bit | True |
+| is_hdd | True |
+| load_average | [0.62, 1.08, 1.13] |
+ +---------------------------------------+-----------------------+
+p.s: The unit of storage is kB.
+WARN:
+[0]. The number of CPU cores is a little small. Please do not run too high concurrency. You are recommended to set max_connections based on the number of CPU cores. If your job does not consume much CPU, you can also increase it.
+BAD:
+[0]. The value of wal_buffers is too high. Generally, a large value does not bring better performance. ********************************************* Recommended Knob Settings ********************************************
++---------------------------+-----------+--------+---------+---------+
+| name | recommend | min | max | restart |
++---------------------------+-----------+--------+---------+---------+
+| random_page_cost | 2.43 | 2.0 | 3.0 | False |
+| shared_buffers | 121256 | 72752 | 139448 | True |
+| max_connections | 134 | 15 | 269 | True |
+| effective_cache_size | 2909967 | 121256 | 2909967 | False |
+| wal_buffers | 3789 | 1894 | 3789 | True |
+| default_statistics_target | 1000 | 100 | 1000 | False |
++---------------------------+-----------+--------+---------+---------+
+real 10m12.961s user 0m6.827s sys 0m1.076s
+```
+
+
+