Elastic search performance testing finding
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Setup1 Node cluster on my local laptop: 8core, Xms=8G, Xmx=8G
Indexing performance (Single index):
10 million payments, each one about 5KB, with batch size = 10000. Each batch takes roughly 2.5 s → 4 s, total time to index 10 million payment is around 50 min
Indexing performance (Multiple indices):
20 separate indices store totally 10 million payments. Indexing execution is slightly faster than single index case. Each batch takes roughly 1.7 s → 3.8 s, total time to index 10 million payment is around 38 min
Parameters required for bulk load operation
Elasticsearch config: http.max_content_length: 500mb
Client time out adjustment:
RestClient.builder(HttpHost("localhost", 9200))
.setRequestConfigCallback
it.apply
this.setConnectTimeout(5000)
this.setSocketTimeout(60000)
.setMaxRetryTimeoutMillis(60000))Initially batch size is set to 100000, elastic search server becomes unstable with high GC frequency, occupying a large percent of CPU time. So larger batch size does not always imply higher performance
Query aggregation performance:
Test query: real aggregation query used by rule engine
"aggregations":
"date_range":
"range":
"field": "createdAt",
"ranges": [
"key": "LAST_7_DAYS",
"from": 1544400968485,
"to": 1545005768486
],
"keyed": false
,
"aggregations":
"filter_aggregator":
"filters":
"filters":
"602c7d66-e990-4dfb-b6e2-72b62ff159d5":
"terms":
"beneficiaryId.keyword": [
"602c7d66-e990-4dfb-b6e2-72b62ff159d5"
],
"boost": 1
,
"67cab0c8-2510-443d-8f00-bce19c04815e":
"terms":
"bankAccountUserId.keyword": [
"67cab0c8-2510-443d-8f00-bce19c04815e"
],
"boost": 1
,
"8da52e51-eabf-4f6c-b9f0-e222933c1cb7":
"terms":
"payerId.keyword": [
"8da52e51-eabf-4f6c-b9f0-e222933c1cb7"
],
"boost": 1
,
"8da52e51-eabf-4f6c-b9f0-e222933c1cb7_602c7d66-e990-4dfb-b6e2-72b62ff159d5":
"bool":
"filter": [
"terms":
"payerId.keyword": [
"8da52e51-eabf-4f6c-b9f0-e222933c1cb7"
],
"boost": 1
,
"terms":
"beneficiaryId.keyword": [
"602c7d66-e990-4dfb-b6e2-72b62ff159d5"
],
"boost": 1
],
"adjust_pure_negative": true,
"boost": 1
,
"9a1b4bad-ccf5-4c67-8718-02696cb351e4":
"terms":
"clientId.keyword": [
"9a1b4bad-ccf5-4c67-8718-02696cb351e4"
],
"boost": 1
,
"9a1b4bad-ccf5-4c67-8718-02696cb351e4_602c7d66-e990-4dfb-b6e2-72b62ff159d5":
"bool":
"filter": [
"terms":
"clientId.keyword": [
"9a1b4bad-ccf5-4c67-8718-02696cb351e4"
],
"boost": 1
,
"terms":
"beneficiaryId.keyword": [
"602c7d66-e990-4dfb-b6e2-72b62ff159d5"
],
"boost": 1
],
"adjust_pure_negative": true,
"boost": 1
,
"9a1b4bad-ccf5-4c67-8718-02696cb351e4_8da52e51-eabf-4f6c-b9f0-e222933c1cb7":
"bool":
"filter": [
"terms":
"clientId.keyword": [
"9a1b4bad-ccf5-4c67-8718-02696cb351e4"
],
"boost": 1
,
"terms":
"payerId.keyword": [
"8da52e51-eabf-4f6c-b9f0-e222933c1cb7"
],
"boost": 1
],
"adjust_pure_negative": true,
"boost": 1
,
"9a1b4bad-ccf5-4c67-8718-02696cb351e4_8da52e51-eabf-4f6c-b9f0-e222933c1cb7_602c7d66-e990-4dfb-b6e2-72b62ff159d5":
"bool":
"filter": [
"terms":
"clientId.keyword": [
"9a1b4bad-ccf5-4c67-8718-02696cb351e4"
],
"boost": 1
,
"terms":
"payerId.keyword": [
"8da52e51-eabf-4f6c-b9f0-e222933c1cb7"
],
"boost": 1
,
"terms":
"beneficiaryId.keyword": [
"602c7d66-e990-4dfb-b6e2-72b62ff159d5"
],
"boost": 1
],
"adjust_pure_negative": true,
"boost": 1
,
"other_bucket": false,
"other_bucket_key": "_other_"
,
"aggregations":
"beneficiary_amount":
"stats":
"field": "beneficiaryAmountUsd"
,
"payer_amount":
"stats":
"field": "payerAmountUsd"
,
"distinct_count_beneficiary":
"cardinality":
"field": "beneficiaryId.keyword"
,
"distinct_count_payer":
"cardinality":
"field": "payerId.keyword"
,
"distinct_count_client":
"cardinality":
"field": "clientId.keyword"
,
"distinct_count_bank_acc":
"cardinality":
"field": "bankAccountUserId.keyword"
,
"distinct_count_bene_country":
"cardinality":
"field": "beneficiaryCountry.keyword"
,
"distinct_count_payer_country":
"cardinality":
"field": "payerCountry.keyword"
,
"distinct_count_bene_currency":
"cardinality":
"field": "beneficiaryCurrency.keyword"
,
"distinct_count_payer_currency":
"cardinality":
"field": "payerCurrency.keyword"
,
"structured_payment_amount_personal":
"range":
"field": "payerAmountUsd",
"ranges": [
"from": 9000,
"to": 9999.999
],
"keyed": false
,
"structured_payment_amount_company":
"range":
"field": "payerAmountUsd",
"ranges": [
"from": 112500000,
"to": 124999999.999
],
"keyed": false
Test result : (Single Index)
| Scenario | Number of run | Execution times | Min | Max | Average |
|---|---|---|---|---|---|
| Single thread<br/>Search result hit<br/>Result size unset<br/> | 10 | Text |
Conclusion:
Aggregation performance hinges on the number of documents that matches the aggregation?
Result size parameter has significant impact on aggregation performance. not only because it skipped returning hit documents, but also because it enables caching for aggregation result, otherwise, you have to force result caching by explicitly setting?request_cache=true
https://www.elastic.co/guide/en/elasticsearch/reference/6.6/shard-request-cache.html
Executing query concurrently can also have negative impact on performance
Increasing number of indices have positive impact on index speed but have large negative impact on aggregation if the aggregation is performed across indices
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