BenchmarkSQL是一个用于评估数据库性能的开源工具。它模拟TPC-C（Transaction Processing Performance Council）基准测试场景，该场景主要用于衡量数据库在处理大量并发事务时的能力。TPC-C测试场景模拟了一个典型的批发分销商的业务环境，包括新订单、支付、库存查询等操作。
openEuler版本：openEuler 22.03 (LTS-SP4)
安装依赖：
JDK,ANT,R语言
其中安装JDK不在此说明，网上很多教程
安装ANT步骤如下：
将ANT安装包上传至服务器，然后解压：
#tar -xvzf apache-ant-1.10.15-bin.tar.gz
设置环境变量：
编辑/etc/profile文件追以下内容：
export ANT_HOME=/opt/software/ant/apache-ant-1.10.15
export PATH=$PATH:$ANT_HOME/bin
执行：
#source /etc/profile
使环境变量设置生效
验证安装结果：
#ant -version
R语言安装步骤如下：
1.上传压缩包（R-4.4.2.tar.gz）到服务器
2.解压R-4.4.2.tar.gz：
#tar -xvf R-4.4.2.tar.gz
3.进入解压目录，进行编译安装（时间有点长，请耐心等待）：
#./configure && make && make install
4.验证安装结果：
#R --version
安装编译R语言过程中可能会出现下面3个错误：
报错1：configure: error: --with-x=yes (default) and X11 headers/libs are not available
解决方法：yum -y install xorg-x11-server-devel libX11-devel libXt-devel
报错2：configure: error: "liblzma library and headers are required"
解决方法：yum install xz-devel.x86_64
报错3：configure: error: libcurl >= 7.28.0 library and headers are required with support for https
解决方法：yum install libcurl-devel
安装 BenchmarkSQL:
创建数据库和用户
create user benchmarksql with password 'Benchmark123';
create database benchmarksql owner benchmarksql;
给用户授予权限：有建表权限，创建索引权限等等，为了方便修改用户为管理员
alter user benchmarksql with SYSADMIN;
解压benchmarksql
上传benchmark压缩包到服务器,然后解压：
#unzip benchmarksql-5.0.zip
编译benchmarksql
进入benchmark解压目录：
执行命令：
#ant
创建配置文件
进入run目录
#cd run
#cp props.pg my_postgres.properties
修改配置文件
#vi my_postgres.properties
修改内容：
db=postgres
driver=org.postgresql.Driver
conn=jdbc:postgresql://localhost:15000/benchmarksql
user=benchmarksql
password=Benchmark123
osCollectorDevices=net_ens33 blk_sda
- 配置文件详解：
- db=postgres //数据库类型，postgres，mysql
- driver=org.postgresql.Driver //驱动，mysql8.0以上为com.mysql.cj.jdbc.Driver
- conn=jdbc:postgresql://localhost:5432/postgres //PG数据库连接字符串，正常情况下，需要更改localhost为对应PG服务IP、5432位对应PG服务端口、postgres为对应测试数据库名
- user=benchmarksql //数据库用户名，通常建议用默认，这就需要我们提前在数据库中建立benchmarksql用户
- password=PWbmsql //如上用户密码
- warehouses=1 //仓库数量，数量根据实际服务器内存配置，每个仓库 约 100MB，代表一个独立业务单元（TPC-C 标准）
- loadWorkers=4 //用于在数据库中初始化数据的加载进程数量，默认为4，实际使用过程中可以根据实际情况调整，加载速度会随worker数量的增加而有所提升
- terminals=1 //终端数，即并发客户端数量，通常设置为CPU线程总数的2～6倍
- runTxnsPerTerminal=10 //每个终端（terminal）运行的固定事务数量，例如：如果该值设置为10，意味着每个terminal运行10个事务，如果有32个终端，那整体运行320个事务后，测试结束。该参数配置为非0值时，下面的runMins参数必须设置为0
- runMins=0 //要测试的整体时间，单位为分钟，如果runMins设置为60，那么测试持续1小时候结束。该值设置为非0值时，runTxnsPerTerminal参数必须设置为0。这两个参数不能同时设置为正整数，如果设置其中一个，另一个必须为0，主要区别是runMins定义时间长度来控制测试时间；runTxnsPerTerminal定义事务总数来控制时间。
- limitTxnsPerMin=300 //每分钟事务总数限制，该参数主要控制每分钟处理的事务数，事务数受terminals参数的影响，如果terminals数量大于limitTxnsPerMin值，意味着并发数大于每分钟事务总数，该参数会失效，想想也是如此，如果有1000个并发同时发起，那每分钟事务数设置为300就没意义了，上来就是1000个并发，所以要让该参数有效，可以设置数量大于并发数，或者让其失效，测试过程中目前采用的是默认300。
- terminalWarehouseFixed=true //终端和仓库的绑定模式，设置为true时可以运行4.x兼容模式，意思为每个终端都有一个固定的仓库。设置为false时可以均匀的使用数据库整体配置。TPCC规定每个终端都必须有一个绑定的仓库，所以一般使用默认值true。
- //下面五个值的总和必须等于100，默认值为：45, 43, 4, 4 & 4 ，与TPC-C测试定义的比例一致，实际操作过程中，可以调整比重来适应各种场景。
newOrderWeight=45
paymentWeight=43
orderStatusWeight=4
deliveryWeight=4
stockLevelWeight=4 - resultDirectory=my_result_%tY-%tm-%td_%tH%tM%tS //测试数据生成目录，默认无需修改，默认生成在run目录下面，名字形如my_result_xxxx的文件夹。
- osCollectorScript=./misc/os_collector_linux.py //操作系统性能收集脚本，默认无需修改，需要操作系统具备有python环境
- osCollectorInterval=1 //操作系统收集操作间隔，默认为1秒
- //osCollectorSSHAddr=user@dbhost //操作系统收集所对应的主机，如果对本机数据库进行测试，该参数保持注销即可，如果要对远程服务器进行测试，请填写用户名和主机名。
- osCollectorDevices=net_ens33 blk_sda //操作系统中被收集服务器的网卡名称和磁盘名称，例如：使用ifconfig查看操作系统网卡名称，找到测试所走的网卡，名称为ens33，那么下面网卡名设置为net_ens33（net_前缀固定）；使用df -h查看数据库数据目录，名称为（/dev/sdb 33T 18T 16T 54% /hgdata），那么下面磁盘名设置为blk_sdb（blk_前缀固定）
创建数据库模式和加载初始数据
在run目录下执行：
./runDatabaseBuild.sh my_postgres.properties
执行成功后输出信息：
[omm@hostName1 run]$ ./runDatabaseBuild.sh my_postgres.properties
# ------------------------------------------------------------
# Loading SQL file ./sql.common/tableCreates.sql
# ------------------------------------------------------------
create table bmsql_config (
cfg_name varchar(30) primary key,
cfg_value varchar(50)
);
create table bmsql_warehouse (
w_id integer not null,
w_ytd decimal(12,2),
w_tax decimal(4,4),
w_name varchar(10),
w_street_1 varchar(20),
w_street_2 varchar(20),
w_city varchar(20),
w_state char(2),
w_zip char(9)
);
create table bmsql_district (
d_w_id integer not null,
d_id integer not null,
d_ytd decimal(12,2),
d_tax decimal(4,4),
d_next_o_id integer,
d_name varchar(10),
d_street_1 varchar(20),
d_street_2 varchar(20),
d_city varchar(20),
d_state char(2),
d_zip char(9)
);
create table bmsql_customer (
c_w_id integer not null,
c_d_id integer not null,
c_id integer not null,
c_discount decimal(4,4),
c_credit char(2),
c_last varchar(16),
c_first varchar(16),
c_credit_lim decimal(12,2),
c_balance decimal(12,2),
c_ytd_payment decimal(12,2),
c_payment_cnt integer,
c_delivery_cnt integer,
c_street_1 varchar(20),
c_street_2 varchar(20),
c_city varchar(20),
c_state char(2),
c_zip char(9),
c_phone char(16),
c_since timestamp,
c_middle char(2),
c_data varchar(500)
);
create sequence bmsql_hist_id_seq;
create table bmsql_history (
hist_id integer,
h_c_id integer,
h_c_d_id integer,
h_c_w_id integer,
h_d_id integer,
h_w_id integer,
h_date timestamp,
h_amount decimal(6,2),
h_data varchar(24)
);
create table bmsql_new_order (
no_w_id integer not null,
no_d_id integer not null,
no_o_id integer not null
);
create table bmsql_oorder (
o_w_id integer not null,
o_d_id integer not null,
o_id integer not null,
o_c_id integer,
o_carrier_id integer,
o_ol_cnt integer,
o_all_local integer,
o_entry_d timestamp
);
create table bmsql_order_line (
ol_w_id integer not null,
ol_d_id integer not null,
ol_o_id integer not null,
ol_number integer not null,
ol_i_id integer not null,
ol_delivery_d timestamp,
ol_amount decimal(6,2),
ol_supply_w_id integer,
ol_quantity integer,
ol_dist_info char(24)
);
create table bmsql_item (
i_id integer not null,
i_name varchar(24),
i_price decimal(5,2),
i_data varchar(50),
i_im_id integer
);
create table bmsql_stock (
s_w_id integer not null,
s_i_id integer not null,
s_quantity integer,
s_ytd integer,
s_order_cnt integer,
s_remote_cnt integer,
s_data varchar(50),
s_dist_01 char(24),
s_dist_02 char(24),
s_dist_03 char(24),
s_dist_04 char(24),
s_dist_05 char(24),
s_dist_06 char(24),
s_dist_07 char(24),
s_dist_08 char(24),
s_dist_09 char(24),
s_dist_10 char(24)
);
Starting BenchmarkSQL LoadDatadriver=org.postgresql.Driver
conn=jdbc:postgresql://localhost:15000/benchmarksql
user=benchmarksql
password=***********
warehouses=10
loadWorkers=4
fileLocation (not defined)
csvNullValue (not defined - using default 'NULL')Worker 000: Loading ITEM
Worker 001: Loading Warehouse 1
Worker 002: Loading Warehouse 2
Worker 003: Loading Warehouse 3
Worker 000: Loading ITEM done
Worker 000: Loading Warehouse 4
Worker 002: Loading Warehouse 2 done
Worker 002: Loading Warehouse 5
Worker 001: Loading Warehouse 1 done
Worker 001: Loading Warehouse 6
Worker 003: Loading Warehouse 3 done
Worker 003: Loading Warehouse 7
Worker 000: Loading Warehouse 4 done
Worker 000: Loading Warehouse 8
Worker 002: Loading Warehouse 5 done
Worker 002: Loading Warehouse 9
Worker 001: Loading Warehouse 6 done
Worker 001: Loading Warehouse 10
Worker 003: Loading Warehouse 7 done
Worker 000: Loading Warehouse 8 done
Worker 002: Loading Warehouse 9 done
Worker 001: Loading Warehouse 10 done
# ------------------------------------------------------------
# Loading SQL file ./sql.common/indexCreates.sql
# ------------------------------------------------------------
alter table bmsql_warehouse add constraint bmsql_warehouse_pkey
primary key (w_id);
alter table bmsql_district add constraint bmsql_district_pkey
primary key (d_w_id, d_id);
alter table bmsql_customer add constraint bmsql_customer_pkey
primary key (c_w_id, c_d_id, c_id);
create index bmsql_customer_idx1
on bmsql_customer (c_w_id, c_d_id, c_last, c_first);
alter table bmsql_oorder add constraint bmsql_oorder_pkey
primary key (o_w_id, o_d_id, o_id);
create unique index bmsql_oorder_idx1
on bmsql_oorder (o_w_id, o_d_id, o_carrier_id, o_id);
alter table bmsql_new_order add constraint bmsql_new_order_pkey
primary key (no_w_id, no_d_id, no_o_id);
alter table bmsql_order_line add constraint bmsql_order_line_pkey
primary key (ol_w_id, ol_d_id, ol_o_id, ol_number);
alter table bmsql_stock add constraint bmsql_stock_pkey
primary key (s_w_id, s_i_id);
alter table bmsql_item add constraint bmsql_item_pkey
primary key (i_id);
# ------------------------------------------------------------
# Loading SQL file ./sql.common/foreignKeys.sql
# ------------------------------------------------------------
alter table bmsql_district add constraint d_warehouse_fkey
foreign key (d_w_id)
references bmsql_warehouse (w_id);
alter table bmsql_customer add constraint c_district_fkey
foreign key (c_w_id, c_d_id)
references bmsql_district (d_w_id, d_id);
alter table bmsql_history add constraint h_customer_fkey
foreign key (h_c_w_id, h_c_d_id, h_c_id)
references bmsql_customer (c_w_id, c_d_id, c_id);
alter table bmsql_history add constraint h_district_fkey
foreign key (h_w_id, h_d_id)
references bmsql_district (d_w_id, d_id);
alter table bmsql_new_order add constraint no_order_fkey
foreign key (no_w_id, no_d_id, no_o_id)
references bmsql_oorder (o_w_id, o_d_id, o_id);
alter table bmsql_oorder add constraint o_customer_fkey
foreign key (o_w_id, o_d_id, o_c_id)
references bmsql_customer (c_w_id, c_d_id, c_id);
alter table bmsql_order_line add constraint ol_order_fkey
foreign key (ol_w_id, ol_d_id, ol_o_id)
references bmsql_oorder (o_w_id, o_d_id, o_id);
alter table bmsql_order_line add constraint ol_stock_fkey
foreign key (ol_supply_w_id, ol_i_id)
references bmsql_stock (s_w_id, s_i_id);
alter table bmsql_stock add constraint s_warehouse_fkey
foreign key (s_w_id)
references bmsql_warehouse (w_id);
alter table bmsql_stock add constraint s_item_fkey
foreign key (s_i_id)
references bmsql_item (i_id);
# ------------------------------------------------------------
# Loading SQL file ./sql.postgres/extraHistID.sql
# ------------------------------------------------------------
-- ----
-- Extra Schema objects/definitions for history.hist_id in PostgreSQL
-- ----
-- ----
-- This is an extra column not present in the TPC-C
-- specs. It is useful for replication systems like
-- Bucardo and Slony-I, which like to have a primary
-- key on a table. It is an auto-increment or serial
-- column type. The definition below is compatible
-- with Oracle 11g, using a sequence and a trigger.
-- ----
-- Adjust the sequence above the current max(hist_id)
select setval('bmsql_hist_id_seq', (select max(hist_id) from bmsql_history));
-- Make nextval(seq) the default value of the hist_id column.
alter table bmsql_history
alter column hist_id set default nextval('bmsql_hist_id_seq');
-- Add a primary key history(hist_id)
alter table bmsql_history add primary key (hist_id);
# ------------------------------------------------------------
# Loading SQL file ./sql.postgres/buildFinish.sql
# ------------------------------------------------------------
-- ----
-- Extra commands to run after the tables are created, loaded,
-- indexes built and extra's created.
-- PostgreSQL version.
-- ----
vacuum analyze;创建的表和初始化的数据量
运行基准测试
在run目录下执行：
#./runBenchmark.sh my_postgres.properties
如果执行报错：print ",".join([str(x) for x in sysInfo])
这是因为benchmarksql是用python2写的脚本，而openEuler用的是python3，python3的print函数不兼容python2的print函数，需要修改benchmarksql安装目录下的/run/misc/os_collector_linux.py脚本中的print函数用法，具体如何修改见本文最后os_collector_linux.py脚本
如果执行报错：ValueError: can't have unbuffered text I/O
这是因为benchmarksql是用python2写的脚本，而openEuler用的是python3，python3的open函数不兼容python2的open函数，需要修改benchmarksql安装目录下的/run/misc/os_collector_linux.py脚本中的open函数用法，具体如何修改见本文最后os_collector_linux.py脚本
如果执行报错：NameError: name 'lastStatData' is not defined
这是因为benchmarksql是用python2写的脚本，而openEuler用的是python3，python3和python2存在兼容性问题，在 Python 3 中运行时变量作用域可能不同，具体如何修改见本文最后os_collector_linux.py脚本
执行成功后输出信息：
[root@hostName1 run]# ./runBenchmark.sh my_postgres.properties
03:49:54,400 [main] INFO jTPCC : Term-00,
03:49:54,411 [main] INFO jTPCC : Term-00, +-------------------------------------------------------------+
03:49:54,412 [main] INFO jTPCC : Term-00, BenchmarkSQL v5.0
03:49:54,413 [main] INFO jTPCC : Term-00, +-------------------------------------------------------------+
03:49:54,414 [main] INFO jTPCC : Term-00, (c) 2003, Raul Barbosa
03:49:54,416 [main] INFO jTPCC : Term-00, (c) 2004-2016, Denis Lussier
03:49:54,426 [main] INFO jTPCC : Term-00, (c) 2016, Jan Wieck
03:49:54,427 [main] INFO jTPCC : Term-00, +-------------------------------------------------------------+
03:49:54,428 [main] INFO jTPCC : Term-00,
03:49:54,468 [main] INFO jTPCC : Term-00, db=postgres
03:49:54,468 [main] INFO jTPCC : Term-00, driver=org.postgresql.Driver
03:49:54,468 [main] INFO jTPCC : Term-00, conn=jdbc:postgresql://localhost:15000/benchmarksql
03:49:54,469 [main] INFO jTPCC : Term-00, user=benchmarksql
03:49:54,471 [main] INFO jTPCC : Term-00,
03:49:54,471 [main] INFO jTPCC : Term-00, warehouses=10
03:49:54,472 [main] INFO jTPCC : Term-00, terminals=1
03:49:54,481 [main] INFO jTPCC : Term-00, runTxnsPerTerminal=10
03:49:54,481 [main] INFO jTPCC : Term-00, limitTxnsPerMin=300
03:49:54,484 [main] INFO jTPCC : Term-00, terminalWarehouseFixed=true
03:49:54,484 [main] INFO jTPCC : Term-00,
03:49:54,484 [main] INFO jTPCC : Term-00, newOrderWeight=45
03:49:54,485 [main] INFO jTPCC : Term-00, paymentWeight=43
03:49:54,487 [main] INFO jTPCC : Term-00, orderStatusWeight=4
03:49:54,487 [main] INFO jTPCC : Term-00, deliveryWeight=4
03:49:54,487 [main] INFO jTPCC : Term-00, stockLevelWeight=4
03:49:54,487 [main] INFO jTPCC : Term-00,
03:49:54,488 [main] INFO jTPCC : Term-00, resultDirectory=my_result_%tY-%tm-%td_%tH%tM%tS
03:49:54,488 [main] INFO jTPCC : Term-00, osCollectorScript=./misc/os_collector_linux.py
03:49:54,489 [main] INFO jTPCC : Term-00,
03:49:54,559 [main] INFO jTPCC : Term-00, copied my_postgres.properties to my_result_2025-06-19_034954/run.properties
03:49:54,581 [main] INFO jTPCC : Term-00, created my_result_2025-06-19_034954/data/runInfo.csv for runID 11
03:49:54,582 [main] INFO jTPCC : Term-00, writing per transaction results to my_result_2025-06-19_034954/data/result.csv
03:49:54,589 [main] INFO jTPCC : Term-00, osCollectorScript=./misc/os_collector_linux.py
03:49:54,589 [main] INFO jTPCC : Term-00, osCollectorInterval=1
03:49:54,589 [main] INFO jTPCC : Term-00, osCollectorSSHAddr=null
03:49:54,590 [main] INFO jTPCC : Term-00, osCollectorDevices=net_ens33 blk_sda
03:49:54,693 [main] INFO jTPCC : Term-00,
03:49:55,172 [main] INFO jTPCC : Term-00, C value for C_LAST during load: 118
03:49:55,173 [main] INFO jTPCC : Term-00, C value for C_LAST this run: 221
03:49:55,173 [main] INFO jTPCC : Term-00, Ter03:49:58,483 [Thread-1] INFO jTPCC : Term-00, t tpmTOTAL: 96 Memory Usage: 12MB / 118MB
03:49:58,490 [Thread-1] INFO jTPCC : Term-00,
03:49:58,503 [Thread-1] INFO jTPCC : Term-00, Measured tpmC (NewOrders) = 20.24
03:49:58,507 [Thread-1] INFO jTPCC : Term-00, Measured tpmTOTAL = 222.74
03:49:58,510 [Thread-1] INFO jTPCC : Term-00, Session Start = 2025-06-19 03:49:55
03:49:58,512 [Thread-1] INFO jTPCC : Term-00, Session End = 2025-06-19 03:49:58
03:49:58,514 [Thread-1] INFO jTPCC : Term-00, Transaction Count = 10
[root@hostName1 run]#
测试结束后，run目录下会生成一个新目录，它的命名格式为 my_result_%tY-%tm-%td_%tH%tM%tS。
使用 generateReport.sh my_result_* 脚本创建具有图形的 HTML 文件：
例如：
#./generateReport.sh my_result_2025-06-19_034954
使用generateReport.sh，需要安装R语言
注意由于我的openEuler服务器没有安装图形桌面，是没法生成图片的，会报类似下面的错误：Generating my_result_2025-06-19_034954/tpm_nopm.png ... Error in .External2(C_X11, paste0("png::", filename), g$width, g$height, :
将my_result_2025-06-19_034954目录打包下载下来
#zip -r my_result_2025-06-19_034954.zip my_result_2025-06-19_034954/
打开报告文件：report.html，类似下面这样子：
生成的报告怎么看：
（1）整体性能：
Overall tpmC: 124.67
Overall tpmTotal: 280.67
tpmC 是核心事务（NEW_ORDER）每分钟处理的事务数。
tpmTotal 是所有类型的事务（包括NEW_ORDER、Payment、Order Status、Delivery和Stock Level）每分钟的总事务数。
这里的tpmC为124.67，意味着每分钟大约处理124.67个NEW_ORDER事务，这是TPC-C基准测试中最为关键的性能指标。
（2）理论最大值：
理论最大值：12.86 NEW_ORDER 事务/分钟/仓库
TPC-C规范指出，在理想的条件下，每个仓库的NEW_ORDER事务理论最大值为12.86。要达到这个最大值，需要：
完美的负载分配（45%的事务是NEW_ORDER）。
系统在零响应时间下运行。
实际上，由于系统的延迟和负载不平衡，很难达到这个理论最大值。
（3）实际性能 vs 理论性能：
124.67 tpmC 是理论最大值的 969.414%
这意味着系统的实际性能是理论最大值的约9.7倍。也就是说，相较于理想状态，当前的系统性能远超预期，表明该数据库配置在特定负载下的表现非常好。考虑到系统的实际响应时间和负载，这种超出理论最大值的表现通常表明系统在某些方面（如硬件配置、数据库优化、并行处理等）非常高效。
（4）生成的两张图：
每分钟事务数量。
事务延迟。
TPCC表结构
TPC-C测试系统数据库由9张表组成，它们的关系如下图所示，表框里的数字表示该表将要存放多少条记录，仓库数W的调整在测试中能够体现数据库所能够支持的数据规模的能力：
Warehouse（仓库表，W表示仓数，能够体现数据库所能够支持的数据规模）
District（区域表，W*10表每个仓为10个销售点供货）
Customer（用户表，W*30k表示每个供货点为3000个客户提供服务）
Stock（库存表，W*100k表示每个仓库维护10w种商品库存记录）
Order（订单表）
New-Order（新订单表）
Item（商品表，表固定大小为10w）
Order-Line（订单行）
History（历史表）
TPCC事务模型
TPC-C需要处理的交易事务主要为以下几种：
新订单（New-Order，占比45%） ：客户输入一笔新的订货交易；
支付操作（Payment，占比43%） ：更新客户帐户余额以反映其支付状况；
发货（Delivery，占比4%） ：发货（模拟批处理交易）；
订单状态查询（Order-Status，占比4%） ：查询客户最近交易的状态；
库存状态查询（Stock-Level，占比4%） ：查询仓库库存状况，以便能够及时补货。
核心指标解读
附录：
修改run/misc/os_collector_linux.py脚本
#!/usr/bin/env python
# ----------------------------------------------------------------------
# os_collector_linux.py -
#
# Script used to collect OS level resource utilization data like
# CPU usage and disk IO.
#
# This code is used in the jTPCCOSCollect class. It is launched as
# a separate process, possibly via ssh(1) on the remote database
# server. The ability of Python to receive a script to execute on
# stdin allows us to execute this script via ssh(1) on the database
# server without installing any programs/scripts there.
#
# The command line arguments for this script are the runID, the
# interval in seconds at which to collect information and a variable
# number of devices in the form "blk_<devname>" "net_<devname>",
# for example "blk_sda" for the first SCSI disk or "net_eth0".
#
# The output on stdout is one line for CPU/VM info, followed by a
# line for each of the specified devices in CSV format. The first
# set of lines are the CSV headers. The output is prefixed with the
# runID, elapsed_ms and for the devices the blk_ or net_ name that
# was specified on the command line. This format makes it easy to
# load the data into a result database where it can be analyzed
# together with the BenchmarkSQL per transaction results and compared
# to other benchmark runs.
#
# It is the caller's responsibility to split the output lines into
# separate result CSV files.
# ----------------------------------------------------------------------import errno
import math
import os
import sys
import time# ----
# main
# ----
def main(argv):global deviceFDsglobal lastDeviceData# ----# Get the runID and collection interval from the command line# ----runID = int(argv[0])interval = float(argv[1])# ----# Our start time is now. Since most of the information is deltas# we can only produce the first data after the first interval.# ----startTime = time.time()nextDue = startTime + interval# ----# Initialize CPU and vmstat collection and output the CSV header.# ----sysInfo = ['run', 'elapsed', ]sysInfo += initSystemUsage()print(",".join([str(x) for x in sysInfo]))# ----# Get all the devices from the command line.# ----devices = []deviceFDs = {}lastDeviceData = {}for dev in argv[2:]:if dev.startswith('blk_'):devices.append(dev)elif dev.startswith('net_'):devices.append(dev)else:raise Exception("unknown device type '" + dev + "'")# ----# Initialize usage collection per device depending on the type.# Output all the headers in the order, the devices are given.# ----for dev in devices:if dev.startswith('blk_'):devInfo = ['run', 'elapsed', 'device', ]devInfo += initBlockDevice(dev)print(",".join([str(x) for x in devInfo]))elif dev.startswith('net_'):devInfo = ['run', 'elapsed', 'device', ]devInfo += initNetDevice(dev)print(",".join([str(x) for x in devInfo]))# ----# Flush all header lines.# ----sys.stdout.flush()try:while True:# ----# Wait until our next collection interval and calculate the# elapsed time in milliseconds.# ----now = time.time()if nextDue > now:time.sleep(nextDue - now)elapsed = int((nextDue - startTime) * 1000.0)sysInfo = [runID, elapsed, ]sysInfo += getSystemUsage()print(",".join([str(x) for x in sysInfo]))# ----# Collect all device utilization data.# ----for dev in devices:if dev.startswith('blk_'):devInfo = [runID, elapsed, dev, ]devInfo += getBlockUsage(dev, interval)print(",".join([str(x) for x in devInfo]))elif dev.startswith('net_'):devInfo = [runID, elapsed, dev, ]devInfo += getNetUsage(dev, interval)print(",".join([str(x) for x in devInfo]))# ----# Bump the time when we are next due.# ----nextDue += intervalsys.stdout.flush()# ----# Running on the command line for test purposes?# ----except KeyboardInterrupt:print("")return 0# ----# The OSCollector class will just close our stdout on the other# side, so this is expected.# ----except IOError as e:if e.errno == errno.EPIPE:return 0else:raise edef initSystemUsage():global procStatFDglobal procVMStatFDglobal lastStatDataglobal lastVMStatDataprocStatFD = open("/proc/stat", "rb")for line in procStatFD:line = line.decode().split()if line[0] == "cpu":lastStatData = [int(x) for x in line[1:]]breakif len(lastStatData) != 10:raise Exception("cpu line in /proc/stat too short")procVMStatFD = open("/proc/vmstat", "rb")lastVMStatData = {}for line in procVMStatFD:line = line.decode().split()if line[0] in ['nr_dirty', ]:lastVMStatData['vm_' + line[0]] = int(line[1])if len(lastVMStatData.keys()) != 1:raise Exception("not all elements found in /proc/vmstat")return ['cpu_user', 'cpu_nice', 'cpu_system','cpu_idle', 'cpu_iowait', 'cpu_irq','cpu_softirq', 'cpu_steal','cpu_guest', 'cpu_guest_nice','vm_nr_dirty',]def getSystemUsage():global procStatFDglobal procVMStatFDglobal lastStatDataglobal lastVMStatDataprocStatFD.seek(0, 0)for line in procStatFD:line = line.decode().split()if line[0] != "cpu":continuestatData = [int(x) for x in line[1:]]deltaTotal = float(sum(statData) - sum(lastStatData))if deltaTotal == 0:result = [0.0 for x in statData]else:result = []for old, new in zip(lastStatData, statData):result.append(float(new - old) / deltaTotal)lastStatData = statDatabreakprocVMStatFD.seek(0, 0)newVMStatData = {}for line in procVMStatFD:line = line.decode().split()if line[0] in ['nr_dirty', ]:newVMStatData['vm_' + line[0]] = int(line[1])for key in ['vm_nr_dirty', ]:result.append(newVMStatData[key])return resultdef initBlockDevice(dev):global deviceFDsglobal lastDeviceDatadevPath = os.path.join("/sys/block", dev[4:], "stat")deviceFDs[dev] = open(devPath, "rb")line = deviceFDs[dev].readline().decode().split()newData = []for idx, mult in [(0, 1.0), (1, 1.0), (2, 0.5),(4, 1.0), (5, 1.0), (6, 0.5),]:newData.append(int(line[idx]))lastDeviceData[dev] = newDatareturn ['rdiops', 'rdmerges', 'rdkbps', 'wriops', 'wrmerges', 'wrkbps', ]def getBlockUsage(dev, interval):global deviceFDsglobal lastDeviceDatadeviceFDs[dev].seek(0, 0)line = deviceFDs[dev].readline().decode().split()oldData = lastDeviceData[dev]newData = []result = []ridx = 0for idx, mult in [(0, 1.0), (1, 1.0), (2, 0.5),(4, 1.0), (5, 1.0), (6, 0.5),]:newData.append(int(line[idx]))result.append(float(newData[ridx] - oldData[ridx]) * mult / interval)ridx += 1lastDeviceData[dev] = newDatareturn resultdef initNetDevice(dev):global deviceFDsglobal lastDeviceDatadevPath = os.path.join("/sys/class/net", dev[4:], "statistics")deviceData = []for fname in ['rx_packets', 'rx_bytes', 'tx_packets', 'tx_bytes', ]:key = dev + "." + fnamedeviceFDs[key] = open(os.path.join(devPath, fname), "rb")deviceData.append(int(deviceFDs[key].read()))lastDeviceData[dev] = deviceDatareturn ['rxpktsps', 'rxkbps', 'txpktsps', 'txkbps', ]def getNetUsage(dev, interval):global deviceFDsglobal lastDeviceDataoldData = lastDeviceData[dev]newData = []for fname in ['rx_packets', 'rx_bytes', 'tx_packets', 'tx_bytes', ]:key = dev + "." + fnamedeviceFDs[key].seek(0, 0)newData.append(int(deviceFDs[key].read()))result = [float(newData[0] - oldData[0]) / interval,float(newData[1] - oldData[1]) / interval / 1024.0,float(newData[2] - oldData[2]) / interval,float(newData[3] - oldData[3]) / interval / 1024.0,]lastDeviceData[dev] = newDatareturn resultif __name__ == '__main__':sys.exit(main(sys.argv[1:]))
：一阶低通RC滤波器 一阶线性微分方程推导 拉普拉斯域表达（传递函数、频率响应）分析)
