本文主要是分享下对 INFINI Gateway 的压测过程，使用graphite观测压力测试qps的过程。如有什么错漏的地方，还请多多包涵，不多逼逼，进入正题

硬件配置

测试准备

系统调优（所有节点）

修改系统参数

vi /etc/sysctl.conf

net.netfilter.nf_conntrack_max = 262144
net.nf_conntrack_max = 262144
net.ipv4.ip_forward = 1
net.ipv4.conf.default.accept_redirects = 0
net.ipv4.conf.default.rp_filter = 1
net.ipv4.conf.all.accept_redirects = 0
net.ipv4.conf.all.send_redirects = 0
net.ipv4.ip_nonlocal_bind=1
fs.file-max=10485760
net.core.rmem_max=4194304
net.core.wmem_max=4194304
net.ipv4.tcp_tw_reuse=1
net.ipv4.tcp_timestamps=1
net.core.somaxconn=32768
net.ipv4.tcp_syncookies=1
net.ipv4.tcp_max_syn_backlog=65535
net.ipv4.tcp_synack_retries=0
net.core.netdev_max_backlog=65535
net.core.rmem_max=4194304
net.core.wmem_max=4194304
#修改默认的本地端口范围
net.ipv4.ip_local_port_range='1024 65535' 
net.ipv4.tcp_tw_reuse=1
net.ipv4.tcp_timestamps=1

保存并执行 sysctl -p

修改用户单进程的最大文件数，

用户登录时生效

echo '* soft nofile 1048576' >> /etc/security/limits.conf 
echo '* hard nofile 1048576' >> /etc/security/limits.conf 

用户单进程的最大文件数 当前会话生效

ulimit -n 1048576

安装docker，graphite(gateway 安装）

sudo apt-get update
sudo apt-get install docker.io
docker run -d --name graphite --restart=always -p 80:80 -p 2003-2004:2003-2004 -p 2023-2024:2023-2024 -p 8125:8125/udp -p 8126:8126 graphiteapp/graphite-statsd

下载安装gateway

下载最新版infini-gateway(https://github.com/medcl/infini-gateway/releases)，下载后无需安装即可使用

修改配置文件

解压后将gateway.yml配置文件修改成如下：

path.data: data
path.logs: log
entry:
  - name: es_gateway #your gateway endpoint
    enabled: true
   router: not_found #configure your gateway's routing flow
    network:
      binding: 0.0.0.0:8000
#      skip_occupied_port: false
      reuse_port: true #you can start multi gateway instance, they share same port, to full utilize system's resources
    tls:
      enabled: false #if your es is using https, the gateway entrypoint should enable https too
flow:
  - name: not_found #testing flow
    filter:
      - name: not_found
        type: echo
        parameters:
          str: '{"message":"not found"}'
          repeat: 1
  - name: cache_first
    filter: #comment out any filter sections, like you don't need cache or rate-limiter
      - name: get_cache_1
        type: get_cache
        parameters:
          pass_patterns: ["_cat","scroll", "scroll_id","_refresh","_cluster","_ccr","_count","_flush","_ilm","_ingest","_license","_migration","_ml","_rollup","_data_stream","_open", "_close"]
#          hash_factor:
#            header:
#              - "*"
#            path: true
#            query_args:
#              - id
#          must_cache:
#            method:
#              - GET
#            path:
#              - _search
#              - _async_search
      - name: rate_limit_1
        type: rate_limit
        parameters:
          message: "Hey, You just reached our request limit!"
          rules: #configure match rules against request's PATH, eg: /_cluster/health, match the first rule and return
            - pattern: "/(?P<index_name>medcl)/_search" #use index name, will match: /medcl/_search, with limit medcl with max_qps ~=3
              max_qps: 3 #setting max qps after match
              group: index_name #use regex group name to extract the throttle bucket name
            - pattern: "/(?P<index_name>.*?)/_search" #use regex pattern to match index, will match any /$index/_search, and limit each index with max_qps ~=100
              max_qps: 100
              group: index_name
      - name: elasticsearch_1
        type: elasticsearch
        parameters:
          elasticsearch: default  #elasticsearch configure reference name
          max_connection: 1000 #max tcp connection to upstream, default for all nodes
          max_response_size: -1 #default for all nodes
          balancer: weight
          discovery:
            enabled: true
      - name: set_cache_1
        type: set_cache
  - name: request_logging
    filter:
      - name: request_header_filter
        type: request_header_filter
        parameters:
          include:
            CACHE: true
      - name: request_logging_1
        type: request_logging
        parameters:
          queue_name: request_logging
router:
  - name: default
    tracing_flow: request_logging #a flow will execute after request finish
    default_flow: cache_first
    rules: #rules can't be conflicted with each other, will be improved in the future
      - id: 1 # this rule means match every requests, and sent to `cache_first` flow
        method:
          - "*"
        pattern:
          - /
        flow:
          - cache_first # after match, which processing flow will go through
  - name: not_found
    default_flow: not_found
elasticsearch:
- name: default
 enabled: false
  endpoint: http://localhost:9200 # if your elasticsearch is using https, your gateway should be listen on as https as well
  version: 7.6.0 #optional, used to select es adaptor, can be done automatically after connect to es
  index_prefix: gateway_
  basic_auth: #used to discovery full cluster nodes, or check elasticsearch's health and versions
    username: elastic
    password: Bdujy6GHehLFaapFI9uf
statsd:
  enabled: true
  host: 127.0.0.1
  port: 8125
  namespace: gateway.
modules:
- name: elastic
 enabled: false
  elasticsearch: default
  init_template: true
- name: pipeline
  enabled: true
  runners:
    - name: primary
      enabled: true
      max_go_routine: 1
      threshold_in_ms: 0
      timeout_in_ms: 5000
      pipeline_id: request_logging_index
pipelines:
- name: request_logging_index
  start:
    joint: json_indexing
   enabled: false
    parameters:
      index_name: "gateway_requests"
      elasticsearch: "default"
      input_queue: "request_logging"
      num_of_messages: 1
      timeout: "60s"
      worker_size: 6
      bulk_size: 5000
  process: []
queue:
  min_msg_size: 1
  max_msg_size: 500000000
  max_bytes_per_file: 50*1024*1024*1024
  sync_every_in_seconds: 30
  sync_timeout_in_seconds: 10
  read_chan_buffer: 0

开始测试

本文中使用的压测工具http-loader,见附件

为了能充分利用服务器多核资源，测试的时候直接启用多个进程

压测x86 gateway服务器

在gateway1上启动五个gateway

./gateway-amd64&
./gateway-amd64&
./gateway-amd64&
./gateway-amd64&
./gateway-amd64&

测试gateway返回内容

curl http://172.31.18.148:8000

输出

{"message":"not found"}

在gateway2，test机上同时执行（1000个并发压测10分钟）

./http-loader -c 1000 -d 600 http://172.31.18.148:8000

观测gateway1服务器指标

使用 htop 查看系统负载情况，如下图：

这里我们看到cpu基本跑满，说明gateway已经压到极限了

使用iftop查看系统网络流量情况，如下图：

使用netstat，ss查看tcp连接数，如下图：

sudo netstat -n | awk '/^tcp/ {++S[$NF]} END {for(a in S) print a, S[a]}'
ss -s

查看graphite statsd qps指标，如下图：

压测arm gateway服务器

在gateway2上启动五个gateway

./gateway-arm64&
./gateway-arm64&
./gateway-arm64&
./gateway-arm64&
./gateway-arm64&

测试gateway返回内容

curl http://172.31.18.148:8000

输出

{"message":"not found"}

在gateway1，test机上同时执行（1000个并发压测10分钟）

./http-loader -c 1000 -d 600 http://172.31.18.148:8000

观测gateway2服务器指标

使用htop查看系统负载情况，如下图：

使用iftop查看系统网络流量情况，如下图：

使用netstat，ss查看tcp连接数，如下图：

sudo netstat -n | awk '/^tcp/ {++S[$NF]} END {for(a in S) print a, S[a]}'
ss -s

查看graphite statsd qps指标，如下图：

这里我们可以看到qps基本稳定在125万的qps

压测总结

单机压测x86架构机器上能达到95多万的qps, arm架构机器上能达到125万的qps,可见性能还是非常优秀的。由于文章篇幅原因，gateway单进程的压测就不贴了，有兴趣的同学可以自己下载测试下。

#切换该路径下（路径自定）

cd /Users/shiyang/code/elastic/gateway

#下载

wget https://github.com/medcl/infin ... ar.gz

#下载完后解压

tar -zxvf GATEWAY-darwin64.tar.gz

#解压后能看到两个新文件，一个可执行二进制文件，一个yml配置文件

ls

#gateway-darwin64   gateway.yml

#启动 

./gateway-darwin64

curl http://0.0.0.0:8000

1.es防止字段数量爆炸的一些策略

https://medium.com/handy-tech/elasticsearch-field-explosion-ca0a21f97a4a

2.es查找缺失多个字段的文档

https://stackoverflow.com/questions/65125012/elasticsearch-find-documents-missing-two-fields

3.es查询相关性优化（需翻墙）

https://www.youtube.com/watch?v=1iwQFuLVoMk

• 编辑：bsll

• 归档：https://ela.st/cn-daily-all

• 订阅：https://ela.st/cn-daily-sub

• 沙龙：https://ela.st/cn-meetup

• 极限网关 INFINI Gateway 初体验
• INFINI Gateway 的使用方法和使用心得分享
• 性能爆表！INFINI Gateway 性能与压力测试结果
• 四倍索引速度提升, 有点东西

• 索引级别的请求限速
• 重复请求可以缓存
• Kibana 无缝提速
• Elasticsearch 节点自动发现
• 基于权重的节点选择，权重可配置
• 可控制到每个后端节点的最大连接数
• 查询日志统计分析，用于追踪请求排错审计

[root@XXX-ES-01 linux64]# ./esm -s https://elastic:pass@xx.xx.cn:9343 -d https://elastic:pass@xx.xx.cn:8000 -x medcl2 -y medcl23 -r -w 200 --sliced_scroll_size=40 -b 10 -t=30m

[11-12 22:09:38] [INF] [main.go:461,main] start data migration..

Scroll 20377840 / 20387840   99.95% 1m20s

Bulk 20371785 / 20387840   99.92% 1m53s

[11-12 22:11:32] [INF] [main.go:492,main] data migration finished.