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首页 / 关于 Elasticsearch 不同分片设置的压测报告

关于 Elasticsearch 不同分片设置的压测报告

原创 极限实验室 2023-07-17
162

QQ图片20230715153931.png

摘要

为了验证当前集群经常出现索引超时以及请求拒绝的问题，现模拟线上集群环境及索引设置，通过压测工具随机生成测试数据，针对当前的 850 个分片的索引，以及减半之后的索引，以及更小分片索引的写入进行压测，使用不同的并发、不同的批次大小来观察索引的吞吐情况，并记录写入队列的堆积情况，用来分析分片数、批次数对写入的影响，从而确定后续的优化方案。

压测场景

Elasticsearch 版本 v7.7.1, 共有 57 个节点，其中 3 个独立 Master，3 个协调节点，31GB JVM。

压测流程

单索引 850 分片

索引定义

PUT idx-xxxx-xxxxxx
{
    "aliases" : {
      "alias-xxxx-xxxxxx" : { }
    },
    "mappings" : {
      "dynamic" : "strict",
      "_routing" : {
        "required" : true
      },
      "_source" : {
        "excludes" : [
          "isExtract*",
          "batchNo"
        ]
      },
      "properties" : {
        "addxxxx" : {
          "type" : "text",
          "term_vector" : "with_positions_offsets"
        },
        "clxxxx" : {
          "type" : "byte"
        },
        "contxxxx" : {
          "type" : "text",
          "boost" : 4.0,
          "term_vector" : "with_positions_offsets"
        },
        "conxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "con1xxxx" : {
          "type" : "text",
          "boost" : 16.0,
          "term_vector" : "with_positions_offsets",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "normalizer" : "keyword_normalizer"
            }
          },
          "analyzer" : "name_analyzer",
          "search_analyzer" : "keyword_analyzer"
        },
        "contSxxxx" : {
          "type" : "long",
          "index" : false,
          "doc_values" : false
        },
        "contSxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "contTxxxx" : {
          "type" : "short"
        },
        "crtxxxx" : {
          "type" : "date",
          "ignore_malformed" : true,
          "format" : "yyyyMMddHHmmss"
        },
        "duration" : {
          "type" : "long",
          "index" : false,
          "doc_values" : false
        },
        "largeTxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "md5" : {
          "type" : "keyword",
          "index" : false,
          "doc_values" : false
        },
        "orderxxxx" : {
          "type" : "alias",
          "path" : "contName.keyword"
        },
        "ownxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "ownxxxxxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "ownxxxxxxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "ownxxxxxxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "parenxxxxxxxxxx" : {
          "type" : "keyword"
        },
        "pathxx" : {
          "type" : "text",
          "boost" : 8.0,
          "term_vector" : "with_positions_offsets",
          "fields" : {
            "keyword" : {
              "type" : "keyword"
            }
          },
          "analyzer" : "path_analyzer"
        },
        "presexxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "presexxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "presxxxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "prixxxxxx" : {
          "type" : "short",
          "index" : false
        },
        "search_xxxxxx" : {
          "type" : "alias",
          "path" : "contName"
        },
        "servixxxxxx" : {
          "type" : "byte"
        },
        "shotxxxxxx" : {
          "type" : "date",
          "ignore_malformed" : true,
          "format" : "yyyyMMddHHmmss"
        },
        "xxxxxxlThuxxxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "tagxxxxxx" : {
          "type" : "text",
          "term_vector" : "with_positions_offsets"
        },
        "thumxxxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "xxxxxxpdxxxxxx" : {
          "type" : "date",
          "ignore_malformed" : true,
          "format" : "yyyyMMddHHmmss"
        },
        "xxxxxxderAcxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "xxxxxxerAccouxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "xxxxxxerxxxxxxID" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "xxxxxxderNxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        }
      }
    },
    "settings" : {
      "index" : {
        "max_ngram_diff" : "50",
        "refresh_interval" : "1s",
        "number_of_shards" : "850",

        "analysis" : {
          "normalizer" : {
            "keyword_normalizer" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom"
            }
          },
          "analyzer" : {
            "keyword_analyzer" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom",
              "tokenizer" : "keyword"
            },
            "name_analyzer" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom",
              "tokenizer" : "name_tokenizer"
            },
            "path_analyzer" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom",
              "tokenizer" : "path_tokenizer"
            }
          },
          "tokenizer" : {
            "name_tokenizer" : {
              "type" : "ngram",
              "min_gram" : "1",
              "max_gram" : "5"
            },
            "path_tokenizer" : {
              "pattern" : "/",
              "type" : "pattern"
            }
          }
        },
        "number_of_replicas" : "1"

      }
    }
  }

样例数据

POST idx-owncloud-img/_doc/1?routing=1
{
  "ownerxxxxxx" : "002#######0oV",
  "serxxxxxx" : 1,
  "tagxxxxxx" : "",
  "contxxxxxx" : "",
  "xxxxxxAccoxxxxxxe" : "1",
  "presxxxxxx" : "",
  "conxxxxxx" : "jpg",
  "xxxxxxerBxxxxxx" : "6#######573",
  "ownerxxxxxxx" : "13#######62",
  "presxxxxxxL" : "",
  "duxxxxxx" : 0,
  "paxxxxxx" : "00##########################################043",
  "crtxxxxxx" : "20#######45",
  "pxxxxxxtCatxxxxxx" : "001############################043",
  "sxxxxxxThumxxxxxx" : "http://downl#################################################961",
  "uxxxxxxerAxxxxxxt" : "1##############2",
  "uxxxxxxderAccoxxxxxxe" : "1",
  "uxxxxxxderxxxxxxID" : "0#####################V",
  "lxxxxxxhumxxxxxxl" : "http://d###################################D961",
  "thxxxxxxl" : "http://do###############################################################61",
  "axxxxxxss" : "",
  "uxxxxxxm" : "20##############8",
  "cxxxxxx" : 3,
  "coxxxxxx" : 1,
  "prxxxxxx" : 10,
  "coxxxxxx" : "0###################################cm",
  "co2xxxxxx" : 5##############8,
  "shoxxxxxx" : "20##############4",
  "contxxxxxx" : "mm##############g",
  "presxxxxxx" : "",
  "oxxxxxxBmpxxxxxx" : "6#######3",
  "md5" : "7##############1E"
}

loadgen 配置

root@loadgen:/opt/loadgen# cat loadgen.yml
statsd:
  enabled: false
  host: 192.168.3.98
  port: 8125
  namespace: loadgen.
variables:
  - name: ip
    type: file
    path: dict/ip.txt
  - name: message
    type: file
    path: dict/nginx.log
#  - name: user
#    type: file
#    path: dict/user.txt
  - name: id
    type: sequence
  - name: uuid
    type: uuid
  - name: now_local
    type: now_local
  - name: now_utc
    type: now_utc
  - name: now_unix
    type: now_unix
  - name: suffix
    type: range
    from: 12
    to: 12
  - name: bool
    type: range
    from: 0
    to: 1
requests:
  - request:
      method: POST
      runtime_variables:
        batch_no: id
      runtime_body_line_variables:
        routing_no: uuid
      basic_auth:
        username: elastic
        password: ####
      url: https://xxx.elasticsearch.xxx.cn:9243/_bulk
      body_repeat_times: 50
      body: "{ \"create\" : { \"_index\" : \"idx-xxxxxx-xxxxxx\",\"_type\":\"_doc\", \"_id\" : \"$[[uuid]]\" , \"routing\" : \"$[[routing_no]]\" } }\n{ \"ownerxxxxxx\" : \"0011WsjCK0oV\", \"servxxxxxx\" : $[[bool]], \"tagxxxxxx\" : \"\", \"contxxxxxx\" : \"\", \"ownexxxxxxunxxxxxx\" : \"$[[bool]]\", \"prxxxxxxentLxxxxxx\" : \"\", \"conxxxxxx\" : \"jpg\", \"uxxxxxxexxxxxxID\" : \"$[[id]]\", \"owxxxxxxccxxxxxxt\" : \"$[[routing_no]]\", \"prxxxxxxtUxxxxxxL\" : \"\", \"durxxxxxxn\" : 0, \"paxxxxxx\" : \"00019700101000000001/0011WsjCK0oV00019700101000000043\", \"crxxxxxx\" : \"$[[id]]\", \"paxxxxxxntxxxxxxogIxxxxxx\" : \"0011WsjCK0oV00019700101000000043\", \"sxxxxxxThumxxxxxx\" : \"http://xxx.xxx.cn:80/storageWeb/servlet/GetFileByURLServlet?root=/mnt/wfs133&fileid=KB1af35f100578d655b2cfbd7edd2cb50e.jpg&ct=1&type=0&code=80B0EAB7F429F1A32F76EB895F5FF4DE1853D254604FAB67A7C33FDF92BE7220&exp=315&account=MTM2MzgzMTU1NjI=&p=0&ui=0011WsjCK0oV&ci=0011WsjCK0oV06320210812125345tcm&userSiteId=usersite-s&cn=mmexport162592513503...&oprChannel=10000000&dom=D961\", \"xxxxxxderAxxxxxxnt\" : \"$[[routing_no]]\", \"upxxxxxxerAcxxxxxxtype\" : \"$[[bool]]\", \"uploaderNDUserID\" : \"$[[uuid]]\", \"largeThumbnail\" : \"http://xxx.xxx.cn:80/storageWeb/servlet/GetFileByURLServlet?root=/mnt/wfs133&fileid=KB1af35f100578d655b2cfbd7edd2cb50e.jpg&ct=1&type=1&code=80B0EAB7F429F1A32F76EB895F5FF4DE1853D254604FAB67A7C33FDF92BE7220&exp=315&account=MTM2MzgzMTU1NjI=&p=0&ui=0011WsjCK0oV&ci=0011WsjCK0oV06320210812125345tcm&userSiteId=usersite-s&cn=mmexport162592513503...&oprChannel=10000000&dom=D961\", \"xxxxxxil\" : \"http://download.xxx.xxx.com:80/storageWeb/servlet/GetFileByURLServlet?root=/mnt/wfs133&fileid=KB1af35f100578d655b2cfbd7edd2cb50e.jpg&ct=1&type=2&code=80B0EAB7F429F1A32F76EB895F5FF4DE1853D254604FAB67A7C33FDF92BE7220&exp=315&account=MTM2MzgzMTU1NjI=&p=0&ui=0011WsjCK0oV&ci=0011WsjCK0oV06320210812125345tcm&userSiteId=usersite-s&cn=mmexport162592513503...&oprChannel=10000000&dom=D961\", \"adxxxxxx\" : \"\", \"upxxxxxx\" : \"$[[now_unix]]\", \"cxxxxxx\" : 3, \"contxxxxxxe\" : $[[bool]], \"prixxxxxx\" : 10, \"conxxxxxx\" : \"0011WsjCK0oV06320210812125345tcm\", \"contxxxxxx\" : $[[id]], \"shoxxxxxx\" : \"$[[id]]\", \"contxxxxxxe\" : \"mmexport1625925135032.jpg\", \"prxxxxxxtHxxxxxx\" : \"\", \"oxxxxxxrBmxxxxxxID\" : \"$[[id]]\", \"md5\" : \"$[[uuid]]\" }\n"

运行测试

开启 gzip 流量压缩，执行压测：

root@loadgen:/opt/loadgen# ./loadgen-linux-amd64 -config loadgen.yml -d 6000 -c 100  -compress

1 副本 100 并发

screenshot-20230715-154045.png

0 副本 100 并发

screenshot-20230715-154105.png

0 副本 200 并发

screenshot-20230715-154119.png
写入队列已经存在大量堆积和拒绝的现象了：

screenshot-20230715-154143.png

1 副本 200 并发

screenshot-20230715-154158.png

screenshot-20230715-154216.png

1 副本 400 并发

screenshot-20230715-154230.png

1 副本 800 并发

image(8).png

image(9).png

1 副本批次 500 并发 100

image(10).png

image(11).png

1 副本批次 2000 并发 100

image(12).png

image(13).png

1 副本批次 5000 并发 100

image(14).png

1 副本批次 5000 并发 200

image(15).png

image(16).png

单索引 425 分片

索引定义

PUT idx-xxxxxx-xxxxxx-425
{
    "aliases" : {
      "alias-xxxxxx-xxxxxx" : { }
    },
    "mappings" : {
      "dynamic" : "strict",
      "_routing" : {
        "required" : true
      },
      "_source" : {
        "excludes" : [
          "isExtract*",
          "batchNo"
        ]
      },
      "properties" : {
        "addxxxxxx" : {
          "type" : "text",
          "term_vector" : "with_positions_offsets"
        },
        "cxxxxxx" : {
          "type" : "byte"
        },
        "coxxxxxxc" : {
          "type" : "text",
          "boost" : 4.0,
          "term_vector" : "with_positions_offsets"
        },
        "coxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "conxxxxxxe" : {
          "type" : "text",
          "boost" : 16.0,
          "term_vector" : "with_positions_offsets",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "normalizer" : "keyword_normalizer"
            }
          },
          "analyzer" : "name_analyzer",
          "search_analyzer" : "keyword_analyzer"
        },
        "coxxxxxxze" : {
          "type" : "long",
          "index" : false,
          "doc_values" : false
        },
        "conxxxxxxfix" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "coxxxxxxpe" : {
          "type" : "short"
        },
        "cxxxxxxm" : {
          "type" : "date",
          "ignore_malformed" : true,
          "format" : "yyyyMMddHHmmss"
        },
        "duxxxxxxon" : {
          "type" : "long",
          "index" : false,
          "doc_values" : false
        },
        "laxxxxxxbnail" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "md5" : {
          "type" : "keyword",
          "index" : false,
          "doc_values" : false
        },
        "ordxxxxxxNamxxxxxx" : {
          "type" : "alias",
          "path" : "contName.keyword"
        },
        "oxxxxxxccoxxxxxxt" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "owxxxxxxcounxxxxxxpe" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "owxxxxxxpUsxxxxxxD" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "oxxxxxxDUsexxxxxxD" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "pxxxxxxtalxxxxxxD" : {
          "type" : "keyword"
        },
        "patxxxxxx" : {
          "type" : "text",
          "boost" : 8.0,
          "term_vector" : "with_positions_offsets",
          "fields" : {
            "keyword" : {
              "type" : "keyword"
            }
          },
          "analyzer" : "path_analyzer"
        },
        "prxxxxxxntHxxxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "prxxxxxxntLxxxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "prxxxxxxURxxxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "pxxxxxxity" : {
          "type" : "short",
          "index" : false
        },
        "sxxxxxxch_nxxxxxxe" : {
          "type" : "alias",
          "path" : "contName"
        },
        "sexxxxxxeTxxxxxxe" : {
          "type" : "byte"
        },
        "sxxxxxxTm" : {
          "type" : "date",
          "ignore_malformed" : true,
          "format" : "yyyyMMddHHmmss"
        },
        "smxxxxxxThuxxxxxxl" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "taxxxxxxa" : {
          "type" : "text",
          "term_vector" : "with_positions_offsets"
        },
        "txxxxxxnaxxxxxx" : {
          "type" : "keyword",
          "boost" : 8.0,
          "index" : false,
          "doc_values" : false
        },
        "uxxxxxxm" : {
          "type" : "date",
          "ignore_malformed" : true,
          "format" : "yyyyMMddHHmmss"
        },
        "upxxxxxxdexxxxxxount" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "upxxxxxxrAcxxxxxxpe" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "upxxxxxxmpUsxxxxxx" : {
          "type" : "keyword",
          "doc_values" : false
        },
        "uxxxxxxerNDxxxxxxD" : {
          "type" : "keyword",
          "doc_values" : false
        }
      }
    },
    "settings" : {
      "index" : {
        "max_ngram_diff" : "50",
        "refresh_interval" : "1s",
        "number_of_shards" : "425",

        "analysis" : {
          "normalizer" : {
            "keyword_normalizer" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom"
            }
          },
          "analyzer" : {
            "keyword_analyzer" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom",
              "tokenizer" : "keyword"
            },
            "name_analyzer" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom",
              "tokenizer" : "name_tokenizer"
            },
            "path_analyzer" : {
              "filter" : [
                "lowercase"
              ],
              "type" : "custom",
              "tokenizer" : "path_tokenizer"
            }
          },
          "tokenizer" : {
            "name_tokenizer" : {
              "type" : "ngram",
              "min_gram" : "1",
              "max_gram" : "5"
            },
            "path_tokenizer" : {
              "pattern" : "/",
              "type" : "pattern"
            }
          }
        },
        "number_of_replicas" : "1"

      }
    }
  }

1 副本批次 50 并发 100

image(17).png

image(18).png

1 副本批次 50 并发 200

1 副本批次 50 并发 400

1 副本批次 50 并发 800

1 副本批次 500 并发 100

image(19).png

screenshot-20230715-161033.png

1 副本批次 2000 并发 100

screenshot-20230715-161115.png

1 副本批次 5000 并发 100

screenshot-20230715-161131.png

screenshot-20230715-161145.png

单索引 50 分片

1 副本批次 50 并发 100

screenshot-20230715-161158.png

screenshot-20230715-161213.png

1 副本批次 500 并发 100

screenshot-20230715-161228.png

screenshot-20230715-161248.png

1 副本批次 1000 并发 100

screenshot-20230715-161322.png

screenshot-20230715-161335.png

1 副本批次 5000 并发 100

screenshot-20230715-161348.png

screenshot-20230715-161404.png

走网关单索引 425 分片

1 副本批次 50 并发 400>200

screenshot-20230715-161415.png

1 副本批次 500 并发 100

screenshot-20230715-161427.png

1 副本批次 500 并发 200

screenshot-20230715-161850.png

1 副本批次 500 并发 400

screenshot-20230715-161912.png

screenshot-20230715-161932.png

1 副本批次 5000 并发 100

screenshot-20230715-162016.png
screenshot-20230715-162029.png

screenshot-20230715-162043.png

1 副本批次 5000 并发 200

screenshot-20230715-162100.png

screenshot-20230715-162153.png

1 副本批次 5000 并发 400

screenshot-20230715-162206.png

screenshot-20230715-162217.png

走网关单索引 850 分片

1 副本批次 50 并发 400

screenshot-20230715-162227.png

1 副本批次 500 并发 400

screenshot-20230715-162239.png

1 副本批次 5000 并发 400

screenshot-20230715-162252.png

压测结果

索引数 分片数 副本数 批次大小 压测并发 平均写入吞吐(eps)
1 850 1 50 100 10,000
1 850 0 50 100 30,000
1 850 0 50 200 40,000
1 850 1 50 200 18,000
1 850 1 50 400 27,500
1 850 1 50 800 29,700
1 850 1 500 100 30,187
1 850 1 2000 100 68,000
1 850 1 5000 100 98,915
1 850 1 5000 200 78,462
1 425 1 50 100 12,695
1 425 1 500 100 46818
1 425 1 2000 100 100,000
1 425 1 5000 100 130,000
1 50 1 50 100 32,987
1 50 1 500 100 96,207
1 50 1 1000 100 147,719
1 50 1 5000 100 156,961

走网关节点异步合并模式：

索引数 分片数 副本数 批次大小 压测并发 平均写入吞吐(eps)
1 425 1 50 100 500
1 425 1 50 200 1,000
1 425 1 50 400 2,000
1 425 1 500 100 4,800
1 425 1 500 200 9,350
1 425 1 500 400 17,000
1 425 1 5000 100 50,000
1 425 1 5000 200 100,000
1 425 1 5000 400 175,000
1 850 1 50 400 2000
1 850 1 500 400 18,800
1 850 1 5000 400 137,000

结论

大分片索引，850 或者 425，在并发即使只有 100 的情况下就有可能出现占满线程池，出现请求拒绝的情况，单个批次的文档数比较小的情况下，更容易出现。
而同样格式的索引，在 50 个分片的情况下，索引的吞吐是 425 分片的两倍，850 分片的三倍，且线程池基本上没有堆积，或者堆积很快处理完。单次请求的文档数越多，写入的效率越高。
某些场景下索引分片虽然做了 Routing 处理，但是超大分片索引存在严重的转发效率问题，建议按照业务维度，或者当前的 Routing 维度进行索引的划分，将超大索引拆分成若干个子索引，单个索引的分片数尽量不要超过 20 个。

elasticsearchvector墨力计划
最后修改时间：2023-07-17 17:17:23
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目录
  • 摘要
  • 压测场景
  • 压测流程
    • 单索引 850 分片
      • 运行测试
      • 1 副本 100 并发
      • 0 副本 100 并发
      • 0 副本 200 并发
      • 1 副本 200 并发
      • 1 副本 400 并发
      • 1 副本 800 并发
      • 1 副本批次 500 并发 100
      • 1 副本批次 2000 并发 100
      • 1 副本批次 5000 并发 100
      • 1 副本批次 5000 并发 200
    • 单索引 425 分片
      • 1 副本批次 50 并发 100
      • 1 副本批次 50 并发 200
      • 1 副本批次 50 并发 400
      • 1 副本批次 50 并发 800
      • 1 副本批次 500 并发 100
      • 1 副本批次 2000 并发 100
      • 1 副本批次 5000 并发 100
    • 单索引 50 分片
      • 1 副本批次 50 并发 100
      • 1 副本批次 500 并发 100
      • 1 副本批次 1000 并发 100
      • 1 副本批次 5000 并发 100
    • 走网关单索引 425 分片
      • 1 副本批次 50 并发 400>200
      • 1 副本批次 500 并发 100
      • 1 副本批次 500 并发 200
      • 1 副本批次 500 并发 400
      • 1 副本批次 5000 并发 100
      • 1 副本批次 5000 并发 200
      • 1 副本批次 5000 并发 400
    • 走网关单索引 850 分片
      • 1 副本批次 50 并发 400
      • 1 副本批次 500 并发 400
      • 1 副本批次 5000 并发 400
  • 压测结果
  • 结论