Elasticsearch 7.10 向量检索插件opendistro knn性能测试

{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1,
    "knn": true
  },
  "mappings": {
    "dynamic": "false",
    "_source": {
      "enabled": "false"
    },
    "properties": {
      "id": {
        "type": "integer"
      },
      "vector": {
        "type": "knn_vector",
        "dimension":960
      }
    }
  }
}

/doc_knn/_search
/*
* 提示：该行代码过长，系统自动注释不进行高亮。一键复制会移除系统注释 
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0.017100000753998756, 0.034299999475479126, 0.10440000146627426, 0.05310000106692314, 0.005200000014156103, 0.006200000178068876, 0.01209999993443489, 0.011500000022351742, 0.03929999843239784, 0.0697999969124794, 0.10480000078678131, 0.16290000081062317, 0.04520000144839287, 0.09139999747276306, 0.1193000003695488, 0.11640000343322754, 0.007199999876320362, 0.03400000184774399, 0.07320000231266022, 0.030899999663233757, 0.005400000140070915, 0.008500000461935997, 0.01979999989271164, 0.013899999670684338, 0.030700000002980232, 0.02160000056028366, 0.10180000215768814, 0.07959999889135361, 0.03449999913573265, 0.04540000110864639, 0.0957999974489212, 0.0869000032544136, 0.009600000455975533, 0.01889999955892563, 0.026399999856948853, 0.03610000014305115, 0.014700000174343586, 0.011699999682605267, 0.00839999970048666, 0.03440000116825104, 0.032499998807907104, 0.03269999846816063, 0.054999999701976776, 0.06849999725818634, 0.04899999871850014, 0.03020000085234642, 0.04089999943971634, 0.13590000569820404, 0.01590000092983246, 0.016200000420212746, 0.012799999676644802, 0.05389999970793724, 0.015399999916553497, 0.013000000268220901, 0.015200000256299973, 0.06840000301599503, 0.03099999949336052, 0.02889999933540821, 0.049300000071525574, 0.1177000030875206, 0.046799998730421066, 0.0560000017285347, 0.0674000009894371, 0.20250000059604645, 0.026900000870227814, 0.015799999237060547, 0.018400000408291817, 0.06769999861717224, 0.013299999758601189, 0.009700000286102295, 0.009600000455975533, 0.03319999948143959, 0.023499999195337296, 0.024700000882148743, 0.06599999964237213, 0.04699999839067459, 0.022299999371170998, 0.06840000301599503, 0.08169999718666077, 0.12200000137090683, 0.010700000450015068, 0.0142000000923872, 0.01860000006854534, 0.041999999433755875, 0.009200000204145908, 0.006599999964237213, 0.01549999974668026, 0.01730000041425228, 0.02410000003874302, 0.02810000069439411, 0.10459999740123749, 0.07100000232458115, 0.020400000736117363, 0.09690000116825104, 0.15240000188350677, 0.09470000118017197, 0.009499999694526196, 0.013299999758601189, 0.039400000125169754, 0.03909999877214432, 0.007000000216066837, 0.012900000438094139, 0.01360000018030405, 0.00989999994635582, 0.05000000074505806, 0.10570000112056732, 0.09459999948740005, 0.08479999750852585, 0.0430000014603138, 0.1177000030875206, 0.09070000052452087, 0.08169999718666077, 0.008799999952316284, 0.018200000748038292, 0.06419999897480011, 0.04859999939799309, 0.011699999682605267, 0.022700000554323196, 0.05469999834895134, 0.03720000013709068, 0.06459999829530716, 0.093299999833107, 0.15809999406337738, 0.15289999544620514, 0.0494999997317791, 0.05950000137090683, 0.12929999828338623, 0.11240000277757645, 0.011300000362098217, 0.03680000081658363, 0.13169999420642853, 0.07530000060796738, 0.026799999177455902, 0.013000000268220901, 0.021800000220537186, 0.03460000082850456, 0.027499999850988388, 0.04529999941587448, 0.06939999759197235, 0.12280000001192093, 0.05299999937415123, 0.08389999717473984, 0.11110000312328339, 0.06790000200271606, 0.021400000900030136, 0.05640000104904175, 0.07660000026226044, 0.023800000548362732, 0.03959999978542328, 0.01640000008046627, 0.02319999970495701, 0.055799998342990875, 0.03889999911189079, 0.0357000008225441, 0.08150000125169754, 0.10930000245571136, 0.04780000075697899, 0.06700000166893005, 0.10379999876022339, 0.14419999718666077, 0.03739999979734421, 0.021299999207258224, 0.019700000062584877, 0.060100000351667404, 0.0142000000923872, 0.01730000041425228, 0.03280000016093254, 0.02070000022649765, 0.029999999329447746, 0.05119999870657921, 0.11420000344514847, 0.05050000175833702, 0.03370000049471855, 0.07190000265836716, 0.1242000013589859, 0.10620000213384628, 0.023399999365210533, 0.012400000356137753, 0.06030000001192093, 0.08659999817609787], "k": "10"}}}}
*/

# Clients 100
/opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 100 -c 0 -i 20 -o /tmp/result.esknn_100.txt 10.0.0.5 9200
# Clients 500
/opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 500 -c 0 -i 20 -o /tmp/result.esknn_500.txt 10.0.0.5 9200
# Clients 1000
/opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 1000 -c 0 -i 20 -o /tmp/result.esknn_1000.txt 10.0.0.5 9200
# Clients 1500
/opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 1500 -c 0 -i 20 -o /tmp/result.esknn_1500.txt 10.0.0.5 9200

-s 180：运行时间为180秒，默认为 60，-1 代表永远
-n 1500：1500个客户端进行并发搜索，默认为 10
-c 0：不等待客户端返回结果，直接发送下一个查询请求，默认为 1000，建议留空
-i 20：在前20个查询中忽略延迟（即不计入性能测试结果），以便进行预热，默认为 0
-q：指定查询文件，由make-queries.py生成
-P：使用HTTP POST方法发送请求
-H：指定POST消息体的头信息，为JSON格式

/opt/vespa/bin/vespa-fbench -P -H "Content-Type:application/json" -H "Authorization: Basic $(echo -n 'elastic:changeme' | base64)" -q data/elastic/knn_queries.txt -s 180 -n 1500 -c 1000 -i 20 -o /tmp/result.esknn_1500.txt 10.0.0.5 9200

# 添加yum源仓库
[root@centos ~]# yum-config-manager --add-repo \
https://copr.fedorainfracloud.org/coprs/g/vespa/vespa/repo/epel-7/group_vespa-vespa-epel-7.repo
[root@centos ~]# yum -y install epel-release centos-release-scl
# 安装vespa
[root@centos ~]# yum -y install vespa

[root@centos ~]# git clone https://github.com/jobergum/dense-vector-ranking-performance.git
Cloning into 'dense-vector-ranking-performance'...
remote: Enumerating objects: 149, done.
remote: Counting objects: 100% (2/2), done.
remote: Compressing objects: 100% (2/2), done.
remote: Total 149 (delta 0), reused 0 (delta 0), pack-reused 147
Receiving objects: 100% (149/149), 532.09 MiB | 725.00 KiB/s, done.
Resolving deltas: 100% (56/56), done.
[root@centos ~]# cd dense-vector-ranking-performance
[root@centos dense-vector-ranking-performance]# ll
total 52
drwxr-xr-x 5 root root  4096 May 10 13:45 bin
drwxr-xr-x 5 root root  4096 May 10 13:45 config
drwxr-xr-x 5 root root  4096 May 10 13:45 data
-rw-r--r-- 1 root root   187 May 10 13:45 Dockerfile.elastic
-rw-r--r-- 1 root root   344 May 10 13:45 Dockerfile.opendistroforelasticsearch
-rw-r--r-- 1 root root   102 May 10 13:45 Dockerfile.vespa
-rw-r--r-- 1 root root 11357 May 10 13:45 LICENSE
-rw-r--r-- 1 root root 15017 May 10 13:45 README.md
[root@centos ~]#

[root@centos dense-vector-ranking-performance]# wget http://ann-benchmarks.com/gist-960-euclidean.hdf5

[root@VM-16-2-centos ~]# coscmd upload gist-960-euclidean.hdf5 /
Upload gist-960-euclidean.hdf5   =>   cos://knn-1253240642/gist-960-euclidean.hdf5
100%|███████████████████████████████████████| 3.58G/3.58G [00:23<00:00, 162MB/s]
[root@centos ~]# 

[root@centos dense-vector-ranking-performance]# cat config/elastic/index_knn.json 
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1,
    "knn": true
  },
  "mappings": {
    "dynamic": "false",
    "_source": {
      "enabled": "false"
    },
    "properties": {
      "id": {
        "type": "integer"
      },
      "vector": {
        "type": "knn_vector",
        "dimension":960 
      }
    }
  }
}
[root@centos dense-vector-ranking-performance]# 

[root@centos dense-vector-ranking-performance]# cat create_knn-index.sh 
#!/bin/sh
curl -s -X PUT "http://10.0.0.16:9200/doc_knn?pretty" -H "Content-Type:application/json" -d @config/elastic/index_knn.json
[root@centos dense-vector-ranking-performance]# 

[root@centos dense-vector-ranking-performance]# cat bin/make-feed.py 
import h5py
import sys
import concurrent.futures 
import requests

file= sys.argv[1]
train = h5py.File(file, 'r')['train']

def feed_to_es_and_vespa(data):
  docid,vector = data
  vector = vector.tolist()
  vespa_body = {
    "fields": {
      'vector': {
        'values': vector 
      },
      'id': docid
    }
  }
  es_body={
    'id': docid,
    'vector': vector 
  }

  response = requests.post('http://10.0.0.5:9200/doc_knn/_doc/%i' %docid, json=es_body)
  response.raise_for_status()

nthreads=32
with concurrent.futures.ThreadPoolExecutor(max_workers=nthreads) as executor:
  futures = [executor.submit(feed_to_es_and_vespa,data) for data in enumerate(train)]
  for result in concurrent.futures.as_completed(futures):
    pass

[root@centos dense-vector-ranking-performance]# 

[root@centos dense-vector-ranking-performance-master]# cat bin/make-queries.py 
import numpy as np
import json
import h5py
import sys

file= sys.argv[1]
test= h5py.File(file, 'r')['test']

esknn_queries = open('data/elastic/knn_queries.txt', 'w')

for v in test:
  query_vector = v.tolist()

  esknn_script_query = {
    'knn': {
      'vector': {
        'vector': query_vector,
        'k': '10'
      }
    }
  }
  esknn_body={
    'size': 10,
    'timeout': '15s',
    'stored_fields': '_none_',
    'docvalue_fields': ['_id'],
    'query': esknn_script_query
  }
  esknn_queries.write('/doc_knn/_search\n')
  esknn_queries.write(json.dumps(esknn_body) + '\n')

[root@centos dense-vector-ranking-performance]# 

[root@VM-10-4-centos dense-vector-ranking-performance]# cat bin/check-recall.py 
import numpy as np
import json
import h5py
import sys
import requests

file= sys.argv[1]
data = h5py.File(file, 'r')

def get_elastic_result(query_vector):
  esknn_script_query = {
    'knn': {
      'vector': {
        'vector': query_vector,
        'k': '10'
      }
    }
  }
  esknn_body={
    'size': 10,
    'timeout': '15s', 
    'query': esknn_script_query
  }
  response = requests.post('http://10.0.0.5:9200/doc_knn/_search', json=esknn_body)
  response.raise_for_status()
  hits=[]
  for h in response.json()['hits']['hits']:
    id = h['_id']
    score = h['_score']  
    hits.append(int(id))
  return hits

def compute_recall(real_neighbors,computed_neighbors, n=10):
  real_neighbors = real_neighbors[0:n]
  recalled = 0 
  for r_n in real_neighbors:
    if r_n in computed_neighbors:
      recalled=recalled+1
  return recalled/n

average_recall_elastic = []
average_recall_vespa = []
average_recall_opendistroforelastic = []
for i,vector in enumerate(data['test'][0:1000]):
  real_neighbors = data['neighbors'][i]
  distances = data['distances'][i]
  vector=vector.tolist()

  computed_neighbors_es = get_elastic_result(vector)

  recall_elastic = compute_recall(real_neighbors, computed_neighbors_es)
  average_recall_elastic.append(recall_elastic)

print('Average recall Elastic KNN = %f' % np.average(average_recall_elastic))

[root@centos dense-vector-ranking-performance]# bash create_knn-index.sh 
{
  "acknowledged" : true,
  "shards_acknowledged" : true,
  "index" : "doc_knn"
}

[root@centos dense-vector-ranking-performance]# python3 ./bin/make-feed.py gist-960-euclidean.hdf5

[root@centos dense-vector-ranking-performance]# curl -XPOST -s '10.0.0.5:9200/doc_knn/_forcemerge?max_num_segments=1'

[root@centos dense-vector-ranking-performance]# python3 ./bin/make-queries.py gist-960-euclidean.hdf5

[root@centos dense-vector-ranking-performance]# /opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 100 -c 1000 -i 20 -o /tmp/result.esknn_100.txt 10.0.0.5 9200

[root@centos dense-vector-ranking-performance]# python3 bin/check-recall.py gist-960-euclidean.hdf5

# Clients 100
/opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 100 -c 0 -i 20 -o /tmp/result.esknn_100.txt 10.0.0.5 9200
# Clients 500
/opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 500 -c 0 -i 20 -o /tmp/result.esknn_500.txt 10.0.0.5 9200
# Clients 1000
/opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 1000 -c 0 -i 20 -o /tmp/result.esknn_1000.txt 10.0.0.5 9200
# Clients 1500
/opt/vespa/bin/vespa-fbench -P -H Content-Type:application/json -q data/elastic/knn_queries.txt -s 180 -n 1500 -c 0 -i 20 -o /tmp/result.esknn_1500.txt 10.0.0.5 9200

============================100============================
Starting clients...
[dummydate]: PROGRESS: vespa-fbench: Seconds left 180
[dummydate]: PROGRESS: vespa-fbench: Seconds left 120
[dummydate]: PROGRESS: vespa-fbench: Seconds left 60
Stopping clients
Clients stopped.

Clients Joined.
*** HTTP keep-alive statistics ***
connection reuse count -- 272922
***************** Benchmark Summary *****************
clients:                     100
ran for:                     180 seconds
cycle time:                    0 ms
lower response limit:          0 bytes
skipped requests:              0
failed requests:               0
successful requests:      271022
cycles not held:          271022
minimum response time:      6.62 ms
maximum response time:    209.89 ms
average response time:     65.89 ms
25   percentile:             50.30 ms
50   percentile:             65.20 ms
75   percentile:             80.70 ms
90   percentile:             94.60 ms
95   percentile:            103.30 ms
98   percentile:            113.40 ms
99   percentile:            120.70 ms
99.5 percentile:            128.40 ms
99.6 percentile:            130.80 ms
99.7 percentile:            133.70 ms
99.8 percentile:            138.10 ms
99.9 percentile:            145.30 ms
actual query rate:       1516.34 Q/s
utilization:               99.91 %
zero hit queries:              0
zero hit percentage:        0.00 %
http request status breakdown:
       200 :   273022 

============================500============================
Starting clients...
[dummydate]: PROGRESS: vespa-fbench: Seconds left 180
[dummydate]: PROGRESS: vespa-fbench: Seconds left 120
[dummydate]: PROGRESS: vespa-fbench: Seconds left 60
Stopping clients
Clients stopped.

Clients Joined.
*** HTTP keep-alive statistics ***
connection reuse count -- 274650
***************** Benchmark Summary *****************
clients:                     500
ran for:                     180 seconds
cycle time:                    0 ms
lower response limit:          0 bytes
skipped requests:              0
failed requests:               0
successful requests:      265150
cycles not held:          265150
minimum response time:      6.95 ms
maximum response time:    748.57 ms
average response time:    328.76 ms
25   percentile:            220.30 ms
50   percentile:            382.90 ms
75   percentile:            429.50 ms
90   percentile:            540.00 ms
95   percentile:            591.70 ms
98   percentile:            629.30 ms
99   percentile:            646.90 ms
99.5 percentile:            659.80 ms
99.6 percentile:            663.40 ms
99.7 percentile:            667.80 ms
99.8 percentile:            674.30 ms
99.9 percentile:            682.19 ms
actual query rate:       1520.57 Q/s
utilization:               99.98 %
zero hit queries:              0
zero hit percentage:        0.00 %
http request status breakdown:
       200 :   275150 

============================1000============================
Starting clients...
[dummydate]: PROGRESS: vespa-fbench: Seconds left 180
[dummydate]: PROGRESS: vespa-fbench: Seconds left 120
[dummydate]: PROGRESS: vespa-fbench: Seconds left 60
Stopping clients
Clients stopped.

Clients Joined.
*** HTTP keep-alive statistics ***
connection reuse count -- 284687
***************** Benchmark Summary *****************
clients:                    1000
ran for:                     180 seconds
cycle time:                    0 ms
lower response limit:          0 bytes
skipped requests:              0
failed requests:               0
successful requests:      265687
cycles not held:          265687
minimum response time:     60.49 ms
maximum response time:   1127.86 ms
average response time:    637.18 ms
25   percentile:            562.80 ms
50   percentile:            631.50 ms
75   percentile:            707.50 ms
90   percentile:            779.20 ms
95   percentile:            823.00 ms
98   percentile:            874.70 ms
99   percentile:            906.20 ms
99.5 percentile:            932.30 ms
99.6 percentile:            939.20 ms
99.7 percentile:            947.00 ms
99.8 percentile:            958.20 ms
99.9 percentile:            974.73 ms
actual query rate:       1569.25 Q/s
utilization:               99.99 %
zero hit queries:              0
zero hit percentage:        0.00 %
http request status breakdown:
       200 :   285240 
       429 :      447 

============================1500============================
Starting clients...
[dummydate]: PROGRESS: vespa-fbench: Seconds left 180
[dummydate]: PROGRESS: vespa-fbench: Seconds left 120
[dummydate]: PROGRESS: vespa-fbench: Seconds left 60
Stopping clients
Clients stopped.

Clients Joined.
*** HTTP keep-alive statistics ***
connection reuse count -- 334485
***************** Benchmark Summary *****************
clients:                    1500
ran for:                     180 seconds
cycle time:                    0 ms
lower response limit:          0 bytes
skipped requests:              0
failed requests:               0
successful requests:      305985
cycles not held:          305985
minimum response time:      6.26 ms
maximum response time:   1826.55 ms
average response time:    813.62 ms
25   percentile:            790.00 ms
50   percentile:            882.80 ms
75   percentile:            957.10 ms
90   percentile:           1030.10 ms
95   percentile:           1092.90 ms
98   percentile:           1199.20 ms
99   percentile:           1272.70 ms
99.5 percentile:           1336.80 ms
99.6 percentile:           1355.01 ms
99.7 percentile:           1378.50 ms
99.8 percentile:           1401.40 ms
99.9 percentile:           1445.50 ms
actual query rate:       1843.47 Q/s
utilization:               99.99 %
zero hit queries:              0
zero hit percentage:        0.00 %
http request status breakdown:
       200 :   281304 
       429 :    54681