# Elasticsearch 部署实践 `Elasticsearch`是目前全文搜索引擎的首选，它可以快速地储存、搜索和分析海量数据；也可以看成是真正分布式的高效数据库集群；`Elastic`的底层是开源库`Lucene`；封装并提供了`REST API`的操作接口。 ## 单节点 docker 测试安装 ``` bash cat > es-start.sh << EOF #!/bin/bash sysctl -w vm.max_map_count=262144 docker run --detach \ --name es01 \ -p 9200:9200 -p 9300:9300 \ -e "discovery.type=single-node" \ -e "bootstrap.memory_lock=true" --ulimit memlock=-1:-1 \ --ulimit nofile=65536:65536 \ --volume /srv/elasticsearch/data:/usr/share/elasticsearch/data \ --volume /srv/elasticsearch/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml \ jmgao1983/elasticsearch:6.4.0 EOF ``` 执行`sh es-start.sh`后，就在本地运行了。 - 验证 docker 镜像运行情况 ``` bash root@docker-ts:~# docker ps -a CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 171f3fecb596 jmgao1983/elasticsearch:6.4.0 "/usr/local/bin/do..." 2 hours ago Up 2 hours 0.0.0.0:9200->9200/tcp, 0.0.0.0:9300->9300/tcp es01 ``` - 验证 es 健康检查 ``` bash root@docker-ts:~# curl http://127.0.0.1:9200/_cat/health epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent 1535523956 06:25:56 docker-es green 1 1 0 0 0 0 0 0 - 100.0% ``` ## 在 k8s 上部署 Elasticsearch 集群 在生产环境下，Elasticsearch 集群由不同的角色节点组成： - master 节点：参与主节点选举，不存储数据；建议3个以上，维护整个集群的稳定可靠状态 - data 节点：不参与选主，负责存储数据；主要消耗磁盘，内存 - client 节点：不参与选主，不存储数据；负责处理用户请求，实现请求转发，负载均衡等功能 这里使用`helm chart`来部署 (https://github.com/helm/charts/tree/master/incubator/elasticsearch) - 1.安装 helm: 以本项目[安全安装helm](../guide/helm.md)为例 - 2.准备 PV: 以本项目[K8S 集群存储](../setup/08-cluster-storage.md)创建`nfs`动态 PV 为例 - 编辑配置文件：roles/cluster-storage/defaults/main.yml ``` bash storage: nfs: enabled: "yes" server: "192.168.1.8" server_path: "/share" storage_class: "nfs-es" provisioner_name: "nfs-provisioner-01" ``` - 创建 nfs provisioner ``` bash $ ansible-playbook /etc/ansible/roles/cluster-storage/cluster-storage.yml # 执行成功后验证 $ kubectl get pod --all-namespaces |grep nfs-prov kube-system nfs-provisioner-01-6b7fbbf9d4-bh8lh 1/1 Running 0 1d ``` - 3.安装 elasticsearch chart ``` bash $ cd /etc/ansible/manifests/es-cluster # 如果你的helm安装没有启用tls证书，请忽略以下--tls参数 $ helm install --tls --name es-cluster --namespace elastic -f es-values.yaml elasticsearch ``` - 4.验证 es 集群 ``` bash # 验证k8s上 es集群状态 $ kubectl get pod,svc -n elastic NAME READY STATUS RESTARTS AGE pod/es-cluster-elasticsearch-client-778df74c8f-7fj4k 1/1 Running 0 2m17s pod/es-cluster-elasticsearch-client-778df74c8f-skh8l 1/1 Running 0 2m3s pod/es-cluster-elasticsearch-data-0 1/1 Running 0 25m pod/es-cluster-elasticsearch-data-1 1/1 Running 0 11m pod/es-cluster-elasticsearch-master-0 1/1 Running 0 25m pod/es-cluster-elasticsearch-master-1 1/1 Running 0 12m pod/es-cluster-elasticsearch-master-2 1/1 Running 0 10m NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/es-cluster-elasticsearch-client NodePort 10.68.157.105 9200:29200/TCP,9300:29300/TCP 25m service/es-cluster-elasticsearch-discovery ClusterIP None 9300/TCP 25m # 验证 es集群本身状态 $ curl $NODE_IP:29200/_cat/health 1539335131 09:05:31 es-on-k8s green 7 2 0 0 0 0 0 0 - 100.0% $ curl $NODE_IP:29200/_cat/indices?v health status index uuid pri rep docs.count docs.deleted store.size pri.store.size root@k8s401:/etc/ansible# curl 10.100.97.41:29200/_cat/nodes? 172.31.2.4 27 80 5 0.09 0.11 0.21 mi - es-cluster-elasticsearch-master-0 172.31.1.7 30 97 3 0.39 0.29 0.27 i - es-cluster-elasticsearch-client-778df74c8f-skh8l 172.31.3.7 20 97 3 0.11 0.17 0.18 i - es-cluster-elasticsearch-client-778df74c8f-7fj4k 172.31.1.5 8 97 5 0.39 0.29 0.27 di - es-cluster-elasticsearch-data-0 172.31.2.5 8 80 3 0.09 0.11 0.21 di - es-cluster-elasticsearch-data-1 172.31.1.6 18 97 4 0.39 0.29 0.27 mi - es-cluster-elasticsearch-master-2 172.31.3.6 20 97 4 0.11 0.17 0.18 mi * es-cluster-elasticsearch-master-1 ``` ### es 性能压测 如上已使用 chart 在 k8s上部署了 **7** 节点的 elasticsearch 集群；各位应该十分好奇性能怎么样；官方提供了压测工具[esrally](https://github.com/elastic/rally)可以方便的进行性能压测，这里省略安装和测试过程；压测机上执行： `esrally --track=http_logs --target-hosts="$NODE_IP:29200" --pipeline=benchmark-only --report-file=report.md` 压测过程需要1-2个小时，部分压测结果如下： ``` bash ------------------------------------------------------ _______ __ _____ / ____(_)___ ____ _/ / / ___/_________ ________ / /_ / / __ \/ __ `/ / \__ \/ ___/ __ \/ ___/ _ \ / __/ / / / / / /_/ / / ___/ / /__/ /_/ / / / __/ /_/ /_/_/ /_/\__,_/_/ /____/\___/\____/_/ \___/ ------------------------------------------------------ | Lap | Metric | Task | Value | Unit | |------:|-------------------------------------:|-------------:|------------:|--------:| ... | All | Min Throughput | index-append | 16903.2 | docs/s | | All | Median Throughput | index-append | 17624.4 | docs/s | | All | Max Throughput | index-append | 19382.8 | docs/s | | All | 50th percentile latency | index-append | 1865.74 | ms | | All | 90th percentile latency | index-append | 3708.04 | ms | | All | 99th percentile latency | index-append | 6379.49 | ms | | All | 99.9th percentile latency | index-append | 8389.74 | ms | | All | 99.99th percentile latency | index-append | 9612.84 | ms | | All | 100th percentile latency | index-append | 9861.02 | ms | | All | 50th percentile service time | index-append | 1865.74 | ms | | All | 90th percentile service time | index-append | 3708.04 | ms | | All | 99th percentile service time | index-append | 6379.49 | ms | | All | 99.9th percentile service time | index-append | 8389.74 | ms | | All | 99.99th percentile service time | index-append | 9612.84 | ms | | All | 100th percentile service time | index-append | 9861.02 | ms | | All | error rate | index-append | 0 | % | | All | Min Throughput | default | 0.66 | ops/s | | All | Median Throughput | default | 0.66 | ops/s | | All | Max Throughput | default | 0.66 | ops/s | | All | 50th percentile latency | default | 770131 | ms | | All | 90th percentile latency | default | 825511 | ms | | All | 99th percentile latency | default | 838030 | ms | | All | 100th percentile latency | default | 839382 | ms | | All | 50th percentile service time | default | 1539.4 | ms | | All | 90th percentile service time | default | 1635.39 | ms | | All | 99th percentile service time | default | 1728.02 | ms | | All | 100th percentile service time | default | 1736.2 | ms | | All | error rate | default | 0 | % | ... ``` 从测试结果看：集群的吞吐可以（k8s es-client pod还可以扩展）；延迟略高一些（因为使用了nfs共享存储）；整体效果不错。 ### 中文分词安装 安装 ik 插件即可，可以自定义已安装ik插件的es docker镜像：创建如下 Dockerfile ``` bash FROM jmgao1983/elasticsearch:6.4.0 RUN /usr/share/elasticsearch/bin/elasticsearch-plugin install \ --batch https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v6.4.0/elasticsearch-analysis-ik-6.4.0.zip \ && cp /usr/share/zoneinfo/Asia/Shanghai /etc/localtime ``` ### 参考阅读 1. [Elasticsearch 入门教程](http://www.ruanyifeng.com/blog/2017/08/elasticsearch.html) 2. [Elasticsearch 压测方案之 esrally 简介](https://segmentfault.com/a/1190000011174694)