四川网站建设和优化,wordpress清理插件,石家庄模板做网站,网站制作软件OB君#xff1a;好消息#xff01;「 OceanBase 2.2 版本 」正式上线官网啦#xff01;#xff08;点击阅读原文即可直接下载#xff09;OceanBase 2.2版本是成功支撑2019年天猫双11大促的稳定版本#xff0c;同时也是用于TPC-C测试且荣登TPC-C性能榜首的版本。我们将在接…OB君好消息「 OceanBase 2.2 版本 」正式上线官网啦点击阅读原文即可直接下载OceanBase 2.2版本是成功支撑2019年天猫双11大促的稳定版本同时也是用于TPC-C测试且荣登TPC-C性能榜首的版本。我们将在接下来的时间里为大家持续推出 “OceanBase 2.2 手把手系列” 将手把手带大家一起体验OceanBase 2.2的强大功能。欢迎持续关注引言OceanBase 2.2版本近期已通过官网提供下载https://oceanbase.alipay.com/download/resource2.2支持Oracle租户。OceanBase在2019年10月2日荣膺国际事务委员会(TPC)审计发布的TPC-C基准测试榜首用的就是Oracle租户。TPC-C测试使用了207多台阿里云高配ECS服务器是因为TPC-C标准对应用、数据库等规范非常细致严格。一般来说普通企业或个人很难有那样的条件去测试。BenchmarkSQL是开源的TPC-C测试程序它弱化了TPC-C的关键标准(数据分布和应用执行行为方面)使得用几台服务器就可以跑TPC-C成为可能。当然这个结果不能跟官方TPC-C的结果相比较。不过使用BenchmarkSQL来比较不同的数据库的事务处理能力还是有一定参考意义的尤其是相比Sysbench而言。OceanBase测试租户准备1.sys租户参数修改 BenchmarkSQL会加载大量数据短时间内对OceanBase内存消耗速度会很快因此需要针对内存冻结合并和限流参数做一些调优。在sys租户执行 ALTER SYSTEM SET enable_merge_by_turnFALSE;
ALTER SYSTEM set minor_freeze_times100;
ALTER SYSTEM set freeze_trigger_percentage70;
ALTER SYSTEM set writing_throttling_trigger_percentage70 tenantobbmsql;
ALTER SYSTEM set writing_throttling_maximum_duration10m tenantobbmsql;
show parameters where name in (minor_freeze_times,freeze_trigger_percentage);注意业务租户限流参数的修改是在sys租户里需要指定相应的租户名。然后查看确认需要到业务租户里。在业务租户执行SHOW parameters WHERE name IN (writing_throttling_trigger_percentage,writing_throttling_maximum_duration);2. 业务租户参数修改 OceanBase跟Oracle/MySQL相比会有个默认SQL超时和事务超时机制。这个可能会导致后面查看修改数据的SQL报错。所以先修改一下这些参数。set global recyclebinoff;
set global ob_query_timeout1000000000;
set global ob_trx_idle_timeout1200000000;
set global ob_trx_timeout1000000000;此外需要为bmsql准备一个单独的schema(即用户)。drop user tpcc cascade;create user tpcc identified by 123456;
grant all privileges on tpcc.* to tpcc with grant option ;
grant create, drop on *.* to tpcc;3. OBProxy配置修改 OBProxy是OceanBase的访问代理其内部一些参数也可能影响性能。如下面的压缩参数对CPU有一定消耗测试时可以关闭。$ obclient -h127.1 -urootsys#obdemo -P2883 -p123456 -c -A oceanbasealter proxyconfig set enable_compression_protocolFalse;
show proxyconfig like enable_compression_protocol;该参数修改后需要重启obproxy进程[adminxxx /home/admin]
$kill -9 pidof obproxy[adminh07d17167.sqa.eu95 /home/admin]
$cd /opt/taobao/install/obproxy[adminxxx /opt/taobao/install/obproxy]
$bin/obproxy
bin/obproxyBenchmarkSQL准备BenchmarkSQL 官方下载地址是 https://sourceforge.net/projects/benchmarksql/ 下载后请参考 HOW-TO-RUN.txt 中说明先编译安装BenchmarkSQL。然后按下面建议修改部分脚本增加对OceanBase支持。也可以直接下载我编译修改好的BenchmarkSQL地址是https://github.com/obpilot/benchmarksql-5.0 。1. 准备OceanBase驱动文件 BenchmarkSQL是通过jdbc连接各个数据库的。此次OceanBase的测试租户是Oracle类型所以需要借用 lib/oracle 目录然后把相关jar包一并放入其中。其中 oceanbase-client-*.jar 是OceanBase提供的其他jar包可以从互联网获取。[adminxxx /home/admin/benchmarksql-5.0]$ll lib/oracle/
total 3728
-rwxr-xr-x 1 admin admin 52988 Jul 12 2019 commons-cli-1.3.1.jar
-rwxr-xr-x 1 admin admin 245274 Jul 12 2019 commons-lang-2.3.jar
-rwxr-xr-x 1 admin admin 2256213 Jul 12 2019 guava-18.0.jar
-rwxr-xr-x 1 admin admin 54495 Jul 12 2019 json-20160810.jar
-rwxr-xr-x 1 admin admin 1121698 Dec 3 15:04 oceanbase-client-1.0.8.jar
-rwxr-xr-x 1 admin admin 174 Jul 12 2019 README.txt
-rwxr-xr-x 1 admin admin 76997 Jul 12 2019 toolkit-common-logging-1.10.jar2. 准备OB配置文件 $cat props.ob
dboracle
drivercom.alipay.oceanbase.obproxy.mysql.jdbc.Driver
connjdbc:oceanbase://127.0.0.1:2883/tpcc?useUnicodetruecharacterEncodingutf-8
usertpccobbmsql#obdemo
password123456warehouses10
loadWorkers10
//fileLocation/home/t4/tmpterminals10
//To run specified transactions per terminal- runMins must equal zero
runTxnsPerTerminal0
//To run for specified minutes- runTxnsPerTerminal must equal zero
runMins10
//Number of total transactions per minute
limitTxnsPerMin0//Set to true to run in 4.x compatible mode. Set to false to use the
//entire configured database evenly.
terminalWarehouseFixedtrue//The following five values must add up to 100
newOrderWeight45
paymentWeight43
orderStatusWeight4
deliveryWeight4
stockLevelWeight4// Directory name to create for collecting detailed result data.
// Comment this out to suppress.
resultDirectorymy_result_%tY-%tm-%td_%tH%tM%tS
osCollectorScript./misc/os_collector_linux.py
osCollectorInterval1
//osCollectorSSHAddruserdbhost
//osCollectorDevicesnet_eth0 blk_sda注意a. 仓库数(warehouses)决定了数据量。正式的压测仓库数一般在10000以上。b. loadworkers数决定了数据加载的性能。如果OceanBase租户资源很小(尤其是内存资源)那加载速度也不要太快否则容易把租户内存打爆。c. 并发数(terminals)是后期做TPC-C测试的客户端并发数。这个每次测试都可以调整以方便观察不同压力下的性能。d. 压测时间(runMin)是每次测试时间越长测试结果越好且稳定。因为有时候数据访问有个预热过程效果会体现在内存命中率上。3. 创建BenchmarkSQL相关表 1建表脚本该SQL脚本不需要直接执行。create table bmsql_config (cfg_name varchar2(30) primary key,cfg_value varchar2(50)
);create tablegroup tpcc_group partition by hash partitions 12;create table bmsql_warehouse (w_id integer not null,w_ytd decimal(12,2),w_tax decimal(4,4),w_name varchar2(10),w_street_1 varchar2(20),w_street_2 varchar2(20),w_city varchar2(20),w_state char(2),w_zip char(9),primary key(w_id)
)tablegrouptpcc_group partition by hash(w_id) partitions 12;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 varchar2(10),d_street_1 varchar2(20),d_street_2 varchar2(20),d_city varchar2(20),d_state char(2),d_zip char(9),PRIMARY KEY (d_w_id, d_id)
)tablegrouptpcc_group partition by hash(d_w_id) partitions 12;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 varchar2(16),c_first varchar2(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 varchar2(20),c_street_2 varchar2(20),c_city varchar2(20),c_state char(2),c_zip char(9),c_phone char(16),c_since timestamp,c_middle char(2),c_data varchar2(500),PRIMARY KEY (c_w_id, c_d_id, c_id)
)tablegrouptpcc_group use_bloom_filtertrue compress partition by hash(c_w_id) partitions 12;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 varchar2(24)
)tablegrouptpcc_group use_bloom_filtertrue compress partition by hash(h_w_id) partitions 12;create table bmsql_new_order (no_w_id integer not null ,no_d_id integer not null,no_o_id integer not null,PRIMARY KEY (no_w_id, no_d_id, no_o_id)
)tablegrouptpcc_group use_bloom_filtertrue compress partition by hash(no_w_id) partitions 12;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,PRIMARY KEY (o_w_id, o_d_id, o_id)
)tablegrouptpcc_group use_bloom_filtertrue compress partition by hash(o_w_id) partitions 12;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),PRIMARY KEY (ol_w_id, ol_d_id, ol_o_id, ol_number)
)tablegrouptpcc_group use_bloom_filtertrue compress partition by hash(ol_w_id) partitions 12;create table bmsql_item (i_id integer not null,i_name varchar2(24),i_price decimal(5,2),i_data varchar2(50),i_im_id integer,PRIMARY KEY (i_id)
)use_bloom_filtertrue compress localityF,R{all_server}zone1, F,R{all_server}zone2, F,R{all_server}zone3 primary_zonezone1 duplicate_scopecluster;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 varchar2(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),PRIMARY KEY (s_w_id, s_i_id)
)tablegrouptpcc_group use_bloom_filtertrue compress partition by hash(s_w_id) partitions 12;注意a. 建表语句中的分区数目可以根据实际情况调整跟集群节点数有关。如果集群是3台(1-1-1)建议是6个或6的倍数如果集群是6台(2-2-2)建议是12个或12的倍数如果集群是9台(3-3-3)建议是36个或36的倍数。这样是方便后期弹性伸缩测试的时候能尽可能保证每个节点上的分区数均衡。b. 上面bmsql_item使用了【复制表】功能在租户的所有节点上都会有一个副本。当然主副本始终只有一个。有关【复制表】功能介绍请参考《OceanBase事务引擎特性和应用实践分享》。c. 建表语句不包含非主键索引是为了后面加载数据性能更快。2建表./runSQL.sh props.ob ./sql.oceanbase/tableCreates.sql建表后可以查看主副本分布SELECT t1.tenant_id,t1.tenant_name,t2.database_name,t3.table_id,t3.table_Name,t3.tablegroup_id,t3.part_num,t4.partition_Id,t4.zone,t4.svr_ip,t4.role, round(t4.data_size/1024/1024) data_size_mb
from gv$tenant t1join gv$database t2 on (t1.tenant_id t2.tenant_id)join gv$table t3 on (t2.tenant_id t3.tenant_id and t2.database_id t3.database_id and t3.index_type 0)left join __all_virtual_meta_table t4 on (t2.tenant_id t4.tenant_id and ( t3.table_id t4.table_id or t3.tablegroup_id t4.table_id ) and t4.role in (1))
where t1.tenant_id 1001
order by t3.tablegroup_id, t4.partition_Id, t3.table_name ;4. 加载数据 1开始加载数据./runLoader.sh props.ob2观察数据加载性能为了对数据写入速度进行观察可以在sys租户下反复执行下面SQL主要是观察增量内存增速和增量内存总量以及是否接近总增量内存限制。SELECT tenant_id, ip, round(active/1024/1024) active_mb, round(total/1024/1024) total_mb, round(freeze_trigger/1024/1024) freeze_trg_mb, round(mem_limit/1024/1024) mem_limit_mb, freeze_cnt , round((active/freeze_trigger),2) freeze_pct, round(total/mem_limit, 2) mem_usage
FROM gv$memstore
WHERE tenant_id IN (1001)
ORDER BY tenant_id, ip;当然观察数据加载另外一个方法就是使用监控。OCP的监控或者dooba脚本监控。python dooba.py -h 127.1 -urootsys#obdemo -P2883 -p123456dooba 进去后默认是sys租户。按字母小写c选择业务租户。按数字1查看帮助数字2查看租户总览数字3查看租户的机器性能信息按TAB切换当前焦点按字母小写d 删除当前TAB按字母大写R 恢复所有TAB。总览里的NET TAB没有意义可以删除以节省屏幕空间。5. 建索引 索引很少就2条。由于相关表是分区表可以建全局索引或者本地索引。我们建本地索引。$cat ./sql.oceanbase/indexCreates.sql
create index bmsql_customer_idx1on bmsql_customer (c_w_id, c_d_id, c_last, c_first) local;
create index bmsql_oorder_idx1on bmsql_oorder (o_w_id, o_d_id, o_carrier_id, o_id) local;开始建索引。OceanBase建索引很快就会返回索引构建是异步的。./runSQL.sh props.ob ./sql.oceanbase/indexCreates.sql6. 数据校验 检查一下各个表的数据量 obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_CONFIG;
* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_STOCK;----------
| COUNT(*) |
----------
| 4 |
----------
1 row in set (0.06 sec)obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_WAREHOUSE;
----------
| COUNT(*) |
----------
| 10 |
----------
1 row in set (0.06 sec)
obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_DISTRICT;
----------
| COUNT(*) |
----------
| 100 |
----------
1 row in set (0.06 sec)obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_CUSTOMER;
----------
| COUNT(*) |
----------
| 300000 |
----------
1 row in set (0.34 sec)obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_HISTORY;
----------
| COUNT(*) |
----------
| 300000 |
----------
1 row in set (0.10 sec)obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_NEW_ORDER;
----------
| COUNT(*) |
----------
| 90000 |
----------
1 row in set (0.07 sec)obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_OORDER;
----------
| COUNT(*) |
----------
| 300000 |
----------
1 row in set (0.11 sec)obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_ORDER_LINE;
----------
| COUNT(*) |
----------
| 3001782 |
----------
1 row in set (0.27 sec)obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_ITEM;
----------
| COUNT(*) |
----------
| 100000 |
----------
1 row in set (0.08 sec)obclient select /* parallel(16) read_consistency(weak) */ count(*) from TPCC.BMSQL_STOCK;
----------
| COUNT(*) |
----------
| 1000000 |
----------
1 row in set (0.63 sec)为了避免产生的数据不符合规范(如中间报错导致有事务失败)运行下面校验脚本#!/usr/bin/shcc1
SELECT /* no_use_px parallel(8) */ * FROM(SELECT w.w_id, w.w_ytd, d.sum_d_ytdFROM bmsql_warehouse w,(SELECT /* no_use_px parallel(8) */ d_w_id, sum(d_ytd) sum_d_ytd FROM bmsql_district GROUP BY d_w_id) dWHERE w.w_id d.d_w_id
) x
WHERE w_ytd ! sum_d_ytd;cc2
SELECT /* no_use_px parallel(8) */ * FROM(SELECT d.d_w_id, d.d_id, d.d_next_o_id, o.max_o_id, no.max_no_o_idFROM bmsql_district d,(SELECT /* no_use_px parallel(8) */ o_w_id, o_d_id, MAX(o_id) max_o_id FROM bmsql_oorder GROUP BY o_w_id, o_d_id) o,(SELECT /* no_use_px parallel(8) */ no_w_id, no_d_id, MAX(no_o_id) max_no_o_id FROM bmsql_new_order GROUP BY no_w_id, no_d_id) noWHERE d.d_w_id o.o_w_id AND d.d_w_id no.no_w_id AND d.d_id o.o_d_id AND d.d_id no.no_d_id
) x
WHERE d_next_o_id - 1! max_o_id OR d_next_o_id - 1! max_no_o_id;
cc3
SELECT /* no_use_px paratLel(8) */ * FROM(SELECT /* no_use_px parallel(8) */ no_w_id, no_d_id, MAX(no_o_id) max_no_o_id, MIN(no_o_id) min_no_o_id, COUNT(*) count_noFROM bmsql_new_orderGROUP BY no_w_id, no_d_Id
) x
WHERE max_no_o_id - min_no_o_id 1! count_no;
cc4
SELECT /* no_use_px parallel(8) */ * FROM (SELECT o.o_w_id, o.o_d_id, o.sum_o_ol_cnt, ol.count_olFROM (SELECT /* no_use_px parallel(8) */ o_w_id, o_d_id, SUM(o_ol_cnt) sum_o_ol_cnt FROM bmsql_oorder GROUP BY o_w_id, o_d_id) o,(SELECT /* no_use_px parallel(8) */ ol_w_id, ol_d_id, COUNT(*) count_ol FROM bmsql_order_line GROUP BY ol_w_id, ol_d_id) olWHERE o.o_w_id ol.ol_w_id AND o.o_d_id ol.ol_d_id
) x
WHERE sum_o_ol_cnt ! count_ol;
cc5
SELECT /* no_use_px parallel(8) */ * FROM (SELECT o.o_w_id, o.o_d_id, o.o_id, o.o_carrier_id, no.count_noFROM bmsql_oorder o,(SELECT /* no_use_px parallels) */ no_w_id, no_d_id, no_o_id, COUNT(*) count_no FROM bmsql_new_order GROUP BY no_w_id, no_d_id, no_o_id) noWHERE o.o_w_id no.no_w_id AND o.o_d_id no.no_d_id AND o.o_id no.no_o_id
) x
WHERE (o_carrier_id IS NULL AND count_no 0) OR (o_carrier_id IS NOT NULL AND count_no ! 0);
cc6
SELECT /* no_use_px parallel(8) */ * FROM (SELECT o.o_w_id, o.o_d_id, o.o_id, o.o_ol_cnt, ol.count_olFROM bmsql_oorder o,(SELECT /* no_use_px parallel(8) */ ol_w_id, ol_d_id, ol_o_id, COUNT(*) count_ol FROM bmsql_order_line GROUP BY ol_w_id, ol_d_id, ol_o_id) olWHERE o.o_w_id ol.ol_w_id AND o.o_d_id ol.ol_d_id AND o.o_id ol.ol_o_id
) x
WHERE o_ol_cnt ! count_ol;cc7
SELECT /* no_use_px parallel(8) */ * FROM (
SELECT /* no_use_px parallel(8) */ * FROM (SELECT o.o_w_id, o.o_d_id, o.o_id, o.o_ol_cnt, ol.count_olFROM bmsql_oorder o,(SELECT /* no_use_px parallel(8) */ ol_w_id, ol_d_id, ol_o_id, COUNT(*) count_ol FROM bmsql_order_line GROUP BY ol_w_id, ol_d_id, ol_o_id) olWHERE o.o_w_id ol.ol_w_id AND o.o_d_id ol.ol_d_id AND o.o_id ol.ol_o_id
) x
WHERE o_ol_cnt ! count_ol;
cc7
SELECT /* no_use_px parallel(8) */ * FROM (SELECT /* no_use_px parallel(8) */ ol.ol_w_id, ol.ol_d_id, ol.ol_o_id, ol.ol_delivery_d, o.o_carrier_idFROM bmsql_order_line ol, bmsql_oorder oWHERE ol.ol_w_id o.o_w_id ANDol.ol_d_id o.o_d_id ANDol.ol_o_id o.o_id
) x
WHERE (ol_delivery_d IS NULL AND o_carrier_id IS NOT NULL) OR(ol_delivery_d IS NOT NULL AND o_carrier_id IS NULL);
cc8
SELECT /* no_use_px parallel(8) */ * FROM (SELECT w.w_id, w.w_ytd, h.sum_h_amountFROM bmsql_warehouse w,(SELECT /* no_use_px parallel(8) */ h_w_id, SUM(h_amount) sum_h_amount FROM bmsql_history GROUP BY h_w_id) hWHERE w.w_id h.h_w_id) x
WHERE w_ytd ! sum_h_amount;
cc9
SELECT /* no_use_px parallel(8) */ * FROM (SELECT d.d_w_id, d.d_id, d.d_ytd, h.sum_h_amountFROM bmsql_district d,(SELECT /* no_use_px parallel(8) */ h_w_id, h_d_id, SUM(h_amount) sum_h_amount FROM bmsql_history GROUP BY h_w_id, h_d_id) hWHERE d.d_w_id h.h_w_id AND d.d_id h.h_d_id
) x
WHERE d_ytd ! sum_h_amount;
cc_list$cc1|$cc2|$cc3|$cc4|$cc5|$cc6|$cc7|$cc8|$cc9
oldIFS$IFS
IFS|counter0
for sql in $cc_list
dolet counterecho date %F %X cc$counter startobclient -Dtpcc -h127.1 -P2883 -utpccobbmsql#obdemo -p123456 -A -c -e $sql#echo $?if [[ $? -ne 0 ]];thenIFS$oldIFSecho date %F %X cc$counter failedexit 1fiecho date %F %X cc$counter finished
done
IFS$oldIFSBenchmarkSQL TPC-C场景分析1. E-R模型 2. 场景SQL TPC-C 系统需要处理的交易有以下五种场景名场景描述交易占比New-Order客户输入一笔新的订货交易45%Payment更新客户账户余额以反应其支付状况43%Delivery发货批处理交易4%Order-Status查询客户最近交易的状态4%Stock-Level查询仓库库存状况以便能够及时补货。4%场景的比例是在数据库配置文件中定义的。这里是默认值。对于前四种类型的交易要求响应时间在 5 秒以内对于库存状况的查询交易要求响应时间在 20 秒以内。这五种交易作用在图 1 所示的九张表上事务操作类型包括更新插入删除和取消操作。下面是我事先通过OceanBase的全量SQL审计抓出的TPCC的事务SQL(做过去重,但可能不全)。1场景1New-OrderSELECT d_tax, d_next_o_id FROM bmsql_district WHERE d_w_id 778 AND d_id 5 FOR UPDATE;
SELECT c_discount, c_last, c_credit, w_tax FROM bmsql_customer JOIN bmsql_warehouse ON (w_id c_w_id) WHERE c_w_id 778 AND c_d_id 5 AND c_id 2699;
UPDATE bmsql_district SET d_next_o_id d_next_o_id 1 WHERE d_w_id 778 AND d_id 5 ;
INSERT INTO bmsql_oorder ( o_id, o_d_id, o_w_id, o_c_id, o_entry_d, o_ol_cnt, o_all_local) VALUES (5686, 5, 778, 2699, timestamp 2020-01-04 13:49:34.137, 8, 1);
INSERT INTO bmsql_new_order ( no_o_id, no_d_id, no_w_id) VALUES (5686, 5, 778);
SELECT i_price, i_name, i_data FROM bmsql_item WHERE i_id 7752 ; -- 循环8次
SELECT s_quantity, s_data, s_dist_01, s_dist_02, s_dist_03, s_dist_04, s_dist_05, s_dist_06, s_dist_07, s_dist_08, s_dist_09, s_dist_10 FROM bmsql_stock WHERE s_w_id 778 AND s_i_id 7752 FOR UPDATE; -- 循环8次
SHOW VARIABLES WHERE Variable_name tx_read_only;
UPDATE bmsql_stock SET s_quantity 47, s_ytd s_ytd 8, s_order_cnt s_order_cnt 1, s_remote_cnt s_remote_cnt 0 WHERE s_w_id 778 AND s_i_id 7752; -- 循环8次
SHOW VARIABLES WHERE Variable_name tx_read_only;
INSERT INTO bmsql_order_line ( ol_o_id, ol_d_id, ol_w_id, ol_number, ol_i_id, ol_supply_w_id, ol_quantity, ol_amount, ol_dist_info) VALUES (5686, 5, 778, 1, 7752, 778, 8, 589.36, lYvcNHkOvt3iNoBb5W29umGO); -- 循环8次
COMMIT;2场景2New-OrderSELECT c_id FROM bmsql_customer WHERE c_w_id 778 AND c_d_id 2 AND c_last PRICALLYPRES ORDER BY c_first;
SELECT c_first, c_middle, c_last, c_balance FROM bmsql_customer WHERE c_w_id 778 AND c_d_id 2 AND c_id 2694;
SELECT o_id, o_entry_d, o_carrier_id FROM bmsql_oorder WHERE o_w_id 778 AND o_d_id 2 AND o_c_id 2694 AND o_id ( SELECT max(o_id) FROM bmsql_oorder WHERE o_w_id 778 AND o_d_id 2 AND o_c_id 2694 );
SELECT ol_i_id, ol_supply_w_id, ol_quantity, ol_amount, ol_delivery_d FROM bmsql_order_line WHERE ol_w_id 778 AND ol_d_id 2 AND ol_o_id 4494 ORDER BY ol_w_id, ol_d_id, ol_o_id, ol_number;
ROLLBACK;3场景3PaymentUPDATE bmsql_district SET d_ytd d_ytd 4806.11 WHERE d_w_id 778 AND d_id 10;
SELECT d_name, d_street_1, d_street_2, d_city, d_state, d_zip FROM bmsql_district WHERE d_w_id 778 AND d_id 10;
UPDATE bmsql_warehouse SET w_ytd w_ytd 4806.11 WHERE w_id 778;
SELECT w_name, w_street_1, w_street_2, w_city, w_state, w_zip FROM bmsql_warehouse WHERE w_id 778 ;
SELECT c_id FROM bmsql_customer WHERE c_w_id 778 AND c_d_id 10 AND c_last ESEBAROUGHT ORDER BY c_first;
SELECT c_first, c_middle, c_last, c_street_1, c_street_2, c_city, c_state, c_zip, c_phone, c_since, c_credit, c_credit_lim, c_discount, c_balance FROM bmsql_customer WHERE c_w_id 778 AND c_d_id 10 AND c_id 502 FOR UPDATE;
UPDATE bmsql_customer SET c_balance c_balance - 4806.11, c_ytd_payment c_ytd_payment 4806.11, c_payment_cnt c_payment_cnt 1 WHERE c_w_id 778 AND c_d_id 10 AND c_id 502;
INSERT INTO bmsql_history ( h_c_id, h_c_d_id, h_c_w_id, h_d_id, h_w_id, h_date, h_amount, h_data) VALUES (502, 10, 778, 10, 778, timestamp 2020-01-04 13:49:34.148, 4806.11, HfYovpM6 b6aJtf2Xk6);
COMMIT;4场景4SELECT count(*) AS low_stock FROM ( SELECT s_w_id, s_i_id, s_quantity FROM bmsql_stock WHERE s_w_id 778 AND s_quantity 10 AND s_i_id IN ( SELECT ol_i_id FROM bmsql_district JOIN bmsql_order_line ON ol_w_id d_w_id AND ol_d_id d_id AND ol_o_id d_next_o_id - 20 AND ol_o_id d_next_o_id WHERE d_w_id 778 AND d_id 1 ) );
ROLLBACK;5场景5SELECT no_o_id FROM bmsql_new_order WHERE no_w_id 778 AND no_d_id 1 ORDER BY no_o_id ASC;
DELETE FROM bmsql_new_order WHERE no_w_id 778 AND no_d_id 1 AND no_o_id 4488;
UPDATE bmsql_oorder SET o_carrier_id 2 WHERE o_w_id 778 AND o_d_id 1 AND o_id 4488;
SELECT o_c_id FROM bmsql_oorder WHERE o_w_id 778 AND o_d_id 1 AND o_id 4488;
UPDATE bmsql_order_line SET ol_delivery_d timestamp 2020-01-04 13:49:34.181 WHERE ol_w_id 778 AND ol_d_id 1 AND ol_o_id 4488;
SELECT sum(ol_amount) AS sum_ol_amount FROM bmsql_order_line WHERE ol_w_id 778 AND ol_d_id 1 AND ol_o_id 4488;
UPDATE bmsql_customer SET c_balance c_balance 3733.14, c_delivery_cnt c_delivery_cnt 1 WHERE c_w_id 778 AND c_d_id 1 AND c_id 1260;
---循环8次---
commit注意可能还有事务SQL没有找到。3. TPC-C输出指标 TPC-C 的测试结果主要有两个指标流量指标tpmC描述了系统在执行 PaymentOrder-StatusDeliveryStock-level 这四种交易的同时每分钟可以处理的 New-Order交易的数量。流量指标值越大越好。tpm 是 transactions per minute 的简称C 指 TPC 中的 C 基准程序。它的定义是每分钟内系统处理的新订单个数。要注意的是在处理新订单的同时系统还要按图 1 的要求处理其 它 4 类事务 请求。从图 1 可以看出新订单请求不可能超出全部事务请求的 45因此当一个系统的性能为 1000tpmC 时它每分钟实际处理的请求数是 2000 多个。性价比Price/tpmC测试系统价格与流量指标的比值。性价比越小越好。运行BenchmarkSQL TPC-C测试1. OceanBase内存冻结与合并 前面加载了大量数据OceanBase的增量都在内存中需要做一次major freeze以释放增量内存。这个事件分两步。一是冻结操作这个很快。二是合并操作这个跟增量数据量有关通常要几分钟或者几十分钟。每次重复测试的时候都建议做一次major freeze事件以释放内存弊端就是随后测试中内存数据访问又需要一个预热过程。1观察内存增量使用情况select tenant_id, ip, round(active/1024/1024) active_mb, round(total/1024/1024) total_mb, round(freeze_trigger/1024/1024) freeze_trg_mb, round(mem_limit/1024/1024) mem_limit_mb, freeze_cnt, round(total/mem_limit,2) total_pct
from gv$memstore where tenant_id1001 order by tenant_id;2发起内存major freeze事件ALTER SYSTEM major freeze;3 观察合并进度观察合并事件SELECT DATE_FORMAT(gmt_create, %b%d %H:%i:%s) gmt_create_ , module, event, name1, value1, name2, value2, rs_svr_ip
FROM __all_rootservice_event_history
WHERE 1 1 AND module IN (daily_merge)
ORDER BY gmt_create DESC
LIMIT 100;观察合并进度select ZONE,svr_ip,major_version,ss_store_count ss_sc, merged_ss_store_count merged_ss_sc, modified_ss_store_count modified_ss_sc, date_format(merge_start_time, %h:%i:%s) merge_st, date_format(merge_finish_time,%h:%i:%s) merge_ft, merge_process
from __all_virtual_partition_sstable_image_info s
order by major_version, zone, svr_ip ;2. 跑TPC-C测试 1运行测试程序$./runBenchmark.sh props.ob2性能监控注意这个监控界面重点关注QPS/TPS、以及相应的RT、增量内存的增量和总量占比等。此外还能看出测试过程中还是有不少物理读IO。注意这个监控界面里的重点看各个节点的QPS和TPS分布以及远程SQL的数量占总QPS的比例(SRC/SLC)。TPC-C业务定义会有约1%的远程仓库交易事务在OceanBase里这个交易又有一定概率是分布式事务。3TPC-C报告运行结束后会生成结果。从图上看tpmC结果是48204。这个业务租户总资源是20C25G*3。仓库数只有10仓太少了如果机器好一点并做10000仓这个结果应该会更高。运行同时还生成了一个文件夹$ll my_result_2020-01-13_175531/
total 16
drwxrwxr-x 2 admin admin 4096 Jan 13 17:55 data
-rw-rw-r-- 1 admin admin 5130 Jan 13 18:10 report.html
-rwxr-xr-x 1 admin admin 1050 Jan 13 17:55 run.properties以上就是通过BenchmarkSQL跑TPC-C测试程序的完整过程感兴趣的同学也可以按照上述步骤体验。有更多反馈欢迎在文章评论区留言。
