压测环境
虚拟机配置
使用阿里云上规格：ecs.u1-c1m4.4xlarge，PL2: 单盘 IOPS 性能上限 10 万 (适用的云盘容量范围：461GiB - 64TiB)
| vCPU | 内存 (GiB) | 磁盘(GB) | 带宽（Gbit/s） | 数量 |
|---|---|---|---|---|
| 16 | 64 | 500 | 5000 | 24 |
Easysearch 配置
7 节点集群，版本：1.9.0
| 实例名 | 内网 IP | 软件 | vCPU | JVM | 磁盘 |
|---|---|---|---|---|---|
| i-2zegn56cijnzklcn2410 | 172.22.75.144 | Easysearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240u | 172.23.15.97 | Easysearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240i | 172.25.230.228 | Easysearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240y | 172.22.75.142 | Easysearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240x | 172.22.75.143 | Easysearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240z | 172.24.250.252 | Easysearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240r | 172.24.250.254 | Easysearch | 16 | 31G | 500GB |
Elasticsearch 配置
7 节点集群，版本：7.10.2
| 实例名称 | 内网 IP | 软件 | vCPU | JVM | 磁盘 |
|---|---|---|---|---|---|
| i-2zegn56cijnzklcn240m | 172.24.250.251 | Elasticsearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240p | 172.22.75.145 | Elasticsearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240o | 172.17.67.246 | Elasticsearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240t | 172.22.75.139 | Elasticsearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240q | 172.22.75.140 | Elasticsearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240v | 172.24.250.253 | Elasticsearch | 16 | 31G | 500GB |
| i-2zegn56cijnzklcn240l | 172.24.250.250 | Elasticsearch | 16 | 31G | 500GB |
监控集群配置
单节点 Easysearch 集群，版本：1.9.0
| 实例名 | 内网 IP | 软件 | vCPU | 内存 | 磁盘 |
|---|---|---|---|---|---|
| i-2zegn56cijnzklcn240f | 172.25.230.226 | 监控集群：Console | 16 | 64G | 500GB |
| i-2zegn56cijnzklcn240j | 172.23.15.98 | 监控集群：Easysearch | 16 | 64G | 500GB |
压测 loadgen 配置
loadgen 版本：1.25.0
4 台压 Easysearch，4 台压 Elasticsearch。
| 实例名 | 内网 IP | 软件 | vCPU | 内存 | 磁盘 |
|---|---|---|---|---|---|
| i-2zegn56cijnzklcn240n | 172.17.67.245 | Loadgen - 压 Easysearch | 16 | 64G | 500GB |
| i-2zegn56cijnzklcn2411 | 172.22.75.141 | Loadgen - 压 Easysearch | 16 | 64G | 500GB |
| i-2zegn56cijnzklcn240k | 172.25.230.227 | Loadgen - 压 Easysearch | 16 | 64G | 500GB |
| i-2zegn56cijnzklcn240e | 172.22.75.138 | Loadgen - 压 Easysearch | 16 | 64G | 500GB |
| i-2zegn56cijnzklcn240h | 172.24.250.255 | Loadgen - 压 Elasticsearch | 16 | 64G | 500GB |
| i-2zegn56cijnzklcn240w | 172.24.251.0 | Loadgen - 压 Elasticsearch | 16 | 64G | 500GB |
| i-2zegn56cijnzklcn240g | 172.24.250.248 | Loadgen - 压 Elasticsearch | 16 | 64G | 500GB |
| i-2zegn56cijnzklcn240s | 172.24.250.249 | Loadgen - 压 Elasticsearch | 16 | 64G | 500GB |
压测索引 Mapping
PUT nginx
{
"mappings": {
"properties": {
"method": {
"type": "keyword"
},
"bandwidth": {
"type": "integer"
},
"service_name": {
"type": "keyword"
},
"ip": {
"type": "ip"
},
"memory_usage": {
"type": "integer"
},
"upstream_time": {
"type": "float"
},
"url": {
"type": "keyword"
},
"response_size": {
"type": "integer"
},
"request_time": {
"type": "float"
},
"request_body_size": {
"type": "integer"
},
"error_code": {
"type": "keyword"
},
"metrics": {
"properties": {
"queue_size": {
"type": "integer"
},
"memory_usage": {
"type": "integer"
},
"thread_count": {
"type": "integer"
},
"cpu_usage": {
"type": "integer"
},
"active_connections": {
"type": "integer"
}
}
},
"cpu_usage": {
"type": "integer"
},
"user_agent": {
"type": "keyword"
},
"connections": {
"type": "integer"
},
"timestamp": {
"type": "date",
"format": "yyyy-MM-dd'T'HH:mm:ss.SSS"
},
"status": {
"type": "integer"
}
}
},
"settings": {
"number_of_shards": 7,
"number_of_replicas": 0,
"refresh_interval": "30s"
}
}
压测方法
每 4 个 loadgen 使用批量写入接口 bulk 轮询压测同一集群的 7 个节点，每个请求写入 10000 个文档。
具体请求如下：
requests:
- request: #prepare some docs
method: POST
runtime_variables:
# batch_no: uuid
runtime_body_line_variables:
# routing_no: uuid
# url: $[[env.ES_ENDPOINT]]/_bulk
url: $[[ip]]/_bulk
body_repeat_times: 10000
basic_auth:
username: "$[[env.ES_USERNAME]]"
password: "$[[env.ES_PASSWORD]]"
body: |
{"index": {"_index": "nginx", "_type": "_doc", "_id": "$[[uuid]]"}}
$[[message]]
压测数据样本
{"method":"DELETE","bandwidth":1955,"service_name":"cart-service","ip":"120.204.26.240","memory_usage":1463,"upstream_time":"1.418","url":"/health","response_size":421,"request_time":"0.503","request_body_size":1737,"error_code":"SYSTEM_ERROR","metrics":{"queue_size":769,"memory_usage":1183,"thread_count":65,"cpu_usage":68,"active_connections":837},"cpu_usage":70,"user_agent":"Mozilla/5.0 (iPad; CPU OS 14_6 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.1","connections":54,"timestamp":"2024-11-16T14:25:21.423","status":500}
{"method":"OPTIONS","bandwidth":10761,"service_name":"product-service","ip":"223.99.83.60","memory_usage":567,"upstream_time":"0.907","url":"/static/js/app.js","response_size":679,"request_time":"1.287","request_body_size":1233,"error_code":"NOT_FOUND","metrics":{"queue_size":565,"memory_usage":1440,"thread_count":148,"cpu_usage":39,"active_connections":1591},"cpu_usage":87,"user_agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.1","connections":354,"timestamp":"2024-11-16T05:37:28.423","status":502}
{"method":"HEAD","bandwidth":10257,"service_name":"recommendation-service","ip":"183.60.242.143","memory_usage":1244,"upstream_time":"0.194","url":"/api/v1/recommendations","response_size":427,"request_time":"1.449","request_body_size":1536,"error_code":"UNAUTHORIZED","metrics":{"queue_size":848,"memory_usage":866,"thread_count":86,"cpu_usage":29,"active_connections":3846},"cpu_usage":71,"user_agent":"Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)","connections":500,"timestamp":"2024-11-16T15:14:30.424","status":403}
压测索引 1 主分片 0 副本
Elastic 吞吐
Elastic 线程及队列
资源消耗
Easysearch 吞吐
Easysearch 线程及队列
资源消耗
对比
| 软件 | 平均集群吞吐 | 平均单节点吞吐 | 最大队列 | 磁盘消耗 |
|---|---|---|---|---|
| Elasticsearch | 5w | 5w | 811 | 10G |
| Easysearch | 7w | 7w | 427 | 4G |
压测索引 1 主分片 1 副本
Elastic 吞吐
Elastic 线程及队列
资源消耗
Easysearch 吞吐
Easysearch 线程及队列
资源消耗
对比
| 软件 | 平均集群吞吐 | 平均单节点吞吐 | 最大队列 | 磁盘消耗(~3000 万文档） |
|---|---|---|---|---|
| Elasticsearch | 10w | 5w | 791 | 22G |
| Easysearch | 14w | 7w | 421 | 7G |
压测索引 7 主分片
Elastic 吞吐
Elastic 线程及队列
资源消耗
网络
单节点平均接收 26MB/s，对应带宽：1456 Mb/s
5 千万文档，总存储 105 GB，单节点 15 GB
Easysearch 吞吐
Easysearch 线程及队列
资源消耗
对比
| 软件 | 平均集群吞吐 | 平均单节点吞吐 | 最大队列 | 磁盘消耗 |
|---|---|---|---|---|
| Elasticsearch | 35w | 5w | 2449 | 105G |
| Easysearch | 60w | 8.5w | 1172 | 36G |
总结
通过对不同场景的压测结果进行对比分析，得出以下结论：
- Easysearch 相比 Elasticsearch 的索引性能显著提升
Easysearch 集群的吞吐性能提升了 40% - 70%，且随着分片数量的增加，性能提升效果更为显著。 - Easysearch 相比 Elasticsearch 的磁盘压缩效率大幅提高
Easysearch 集群的磁盘压缩效率提升了 2.5 - 3 倍，并且随着数据量的增加，压缩效果愈发明显。
此测试结果表明，Easysearch 在日志处理场景中具有更高的性能与存储效率优势，尤其适用于大规模分片与海量数据的使用场景。
如有任何问题，请随时联系我，期待与您交流！
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