组件压测
压力测试Mysql
安装sysbench
wget https://github.com/akopytov/sysbench/archive/1.0.zip -O "sysbench-1.0.zip"
unzip sysbench-1.0.zip
cd sysbench-1.0
yum install automake libtool –y
./autogen.sh
./configure
#如果出现：configure: error: mysql_config executable not found,需要安装：mariadb-devel (yum install mariadb-devel 安装mysql_config) 可能还要安装mariadb-server包，yum install mariadb-server，但是我没有遇到。
export LD_LIBRARY_PATH=/usr/local/mysql8/include
#这里要换成自己的mysql路径下的include，可以用：which mysql命令查看mysql位置。
make && make install
sysbench --version
#如果报错 error while loading shared libraries: libmysqlclient.so.21: cannot open shared object file: No such file or directory
#添加环境变量
export LD_LIBRARY_PATH=/usr/local/mysql8/lib:$LD_LIBRARY_PATH压测方法
#创建库
CREATE DATABASE testdb;
#测试1张表1000万数据
time sysbench /usr/local/share/sysbench/oltp_read_write.lua --mysql-host=192.168.1.179 --mysql-port=3308 --mysql-user=root --mysql-password='11223344!!' --mysql-db=testdb --table-size=10000000 --tables=1 prepare
#测试10张表100万数据
time sysbench /usr/local/share/sysbench/oltp_read_write.lua --mysql-host=192.168.1.179 --mysql-port=3308 --mysql-user=root --mysql-password='11223344!!' --mysql-db=testdb --table-size=1000000 --tables=10 prepare
#4个进程 测试60秒1张表1000万数据的读写操作
time sysbench /usr/local/share/sysbench/oltp_read_write.lua --mysql-host=192.168.1.179 --mysql-port=3308 --mysql-user=root --mysql-password='11223344!!' --mysql-db=testdb --table-size=10000000 --tables=1 --threads=4 --time=60 run
#测试删除数据1张表1000万数据
time sysbench /usr/local/share/sysbench/oltp_read_write.lua --mysql-host=192.168.1.179 --mysql-port=3308 --mysql-user=root --mysql-password='11223344!!' --mysql-db=testdb --table-size=10000000 --tables=1 cleanup压力测试ES
安装esrally
pip3 install esrally
#创建软连接
ln -s /usr/local/python3/bin/esrally /usr/bin/esrally
#执行命令 验证有没有成功
esrally list tracks
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Available tracks:
Name Description Documents Compressed Size Uncompressed Size Default Challenge All Challenges
----------------------- -------------------------------------------------------------------------------- ----------- ----------------- ------------------- ---------------------------------------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
cohere_vector Benchmark for vector search with Cohere Wikipedia data 32,893,221 186.9 GB 507.0 GB index-and-search index-and-search
dense_vector Benchmark for dense vector indexing and search 10,000,000 7.2 GB 19.5 GB index-and-search index-and-search
elastic/apm Elastic APM benchmark for Rally 19,557 N/A 52.4 MB default default
elastic/endpoint Endpoint track 0 0 bytes 0 bytes default default
elastic/logs Track for simulating logging workloads 16,469,078 N/A N/A logging-indexing cross-clusters-search-and-replication,cross-clusters-search-and-snapshot,logging-disk-usage,logging-indexing-querying,logging-indexing,logging-querying-concurrent-stress,logging-querying,logging-snapshot-mount,logging-snapshot-restore,logging-snapshot,many-shards-quantitative,many-shards-snapshots
elastic/security Track for simulating Elastic Security workloads 77,513,777 N/A N/A security-querying generate-alerts-source-events,security-indexing-querying,security-indexing,security-querying
elser-ingest-speedtest Benchmark weighted terms queries on ELSER tokens on the MS MARCO passage dataset 1,050,000 N/A 2.0 GB ELSER-V2 ELSER-V1,ELSER-V2-PlatformSpecific,ELSER-V2,ELSER-Min-Latency-Multi-Doc-Multi-Parametric-Ingest,ELSER-Size-Search-Multi-Doc-Multi-Parametric-Ingest,ELSER-Multi-Doc-Multi-Parametric-Ingest,ELSER-Multi-Parametric-Ingest
eql EQL benchmarks based on endgame index of SIEM demo cluster 60,782,211 4.5 GB 109.2 GB default default,index-sorting
geonames POIs from Geonames 11,396,503 252.9 MB 3.3 GB append-no-conflicts append-no-conflicts,append-no-conflicts-index-only,append-fast-with-conflicts,significant-text
geopoint Point coordinates from PlanetOSM 60,844,404 482.1 MB 2.3 GB append-no-conflicts append-no-conflicts,append-no-conflicts-index-only,append-fast-with-conflicts
geopointshape Point coordinates from PlanetOSM indexed as geoshapes 60,844,404 470.8 MB 2.6 GB append-no-conflicts append-no-conflicts,append-no-conflicts-index-only,append-fast-with-conflicts
geoshape Shapes from PlanetOSM 84,220,567 17.0 GB 58.7 GB append-no-conflicts append-no-conflicts,append-no-conflicts-big
http_logs HTTP server log data 247,249,096 1.2 GB 31.1 GB append-no-conflicts append-no-conflicts,runtime-fields,append-no-conflicts-index-only,append-sorted-no-conflicts,append-index-only-with-ingest-pipeline,update,append-no-conflicts-index-reindex-only
k8s_metrics Index refresh benchmarks with a Kubernetes pod metrics dataset 8,642,016 2.1 GB 86.1 GB append-no-conflicts-metrics-index-with-refresh append-no-conflicts-metrics-index-with-refresh,append-no-conflicts-metrics-index-with-intermittent-refresh,append-no-conflicts-metrics-index-only,append-no-conflicts-metrics-with-fast-refresh,fast-refresh-index-only,fast-refresh-index-with-search
metricbeat Metricbeat data 1,079,600 87.7 MB 1.2 GB append-no-conflicts append-no-conflicts
msmarco-passage-ranking Benchmark bm25, semantic and hybrid search on the MS MARCO passage dataset 8,841,823 16.3 GB 53.6 GB msmarco-passage-ranking msmarco-passage-ranking
msmarco-v2-vector Benchmark for vector search with msmarco-v2 passage data 138,364,198 1045.0 GB 2980.0 GB index-and-search index-and-search
nested StackOverflow Q&A stored as nested docs 11,203,029 663.3 MB 3.4 GB nested-search-challenge nested-search-challenge,index-only
noaa Global daily weather measurements from NOAA 33,659,481 949.4 MB 9.0 GB append-no-conflicts append-no-conflicts,append-no-conflicts-index-only,aggs,filter-aggs
nyc_taxis Taxi rides in New York in 2015 165,346,708 4.5 GB 74.3 GB append-no-conflicts append-no-conflicts,append-no-conflicts-index-only,append-sorted-no-conflicts-index-only,update,update-only,append-ml,aggs,esql,update-aggs-only,autoscale,autoscale-v2
openai_vector Benchmark for vector search using the OpenAI text-embedding-ada-002 model 2,680,961 31.0 GB 87.3 GB index-and-search index-and-search
percolator Percolator benchmark based on AOL queries 2,000,000 121.1 kB 104.9 MB append-no-conflicts append-no-conflicts
pmc Full text benchmark with academic papers from PMC 574,199 5.5 GB 21.7 GB append-no-conflicts append-no-conflicts,append-no-conflicts-index-only,append-sorted-no-conflicts,append-fast-with-conflicts,indexing-querying
random_vector Benchmarking filtered search on random vectors 0 0 bytes 0 bytes index-and-search index-and-search
so Indexing benchmark using up to questions and answers from StackOverflow 36,062,278 8.9 GB 33.1 GB append-no-conflicts append-no-conflicts,transform,frequent-items
so_vector Benchmark for vector search with StackOverflow data 2,000,000 12.3 GB 32.2 GB index-and-search index-and-search
sql SQL query performance based on NOAA Weather data 33,659,481 949.4 MB 9.0 GB sql sql
tsdb metricbeat information for elastic-app k8s cluster 116,633,698 N/A 123.0 GB append-no-conflicts append-no-conflicts,downsample
tsdb_k8s_queries metricbeat information for elastic-app k8s cluster 17,280,000 N/A 60.3 GB append-no-conflicts append-no-conflicts-rampup,append-no-conflicts,append-no-conflicts-index-only,esql
wikipedia Benchmark for search with Wikipedia data 22,986,185 17.9 GB 76.7 GB index-and-search index-and-search
--------------------------------
[INFO] SUCCESS (took 44 seconds)
--------------------------------
#测试ES 中间会去github上面下载东西 翻墙问题自己解决 我使用的是proxychains4命令 可以看我之前的文档
#注：那个下载文件报错 不用管也行 测试是继续进行的
esrally race --track=geonames --target-hosts=ES地址:9200 --pipeline=benchmark-only --kill-running-processes
压力测试Redis
压测方法
#进入到redis的安装目录bin下
测试一个客户端 1000万数据量
./redis-benchmark -h 127.0.0.1 -p 6379 -c 1 -n 10000000
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