Does “from” parameter in ElasticSearch Impact the ElasticSearch Cluster?
I have a large number of documents(around 34719074 documents) in a type of an index(ES 2.4.4). While searching, my ES Cluster seems to be in high impact(Search Latency, CPU Usage, JVM Memory and Load Average) when the "from" parameter is high(greater than 100000, "size" parameter being constant). Any specific reason for it? My query looks like:
"explain": false,
"size": 100,
"from": <>,
"_source":
"excludes": ,
"includes": [
<around 850 fields>
]
,
"sort": [
<sorting from an string field>
]
elasticsearch lucene
asked Nov 14 '18 at 6:30
Yashasvi Raj PantYashasvi Raj Pant
2731315
add a comment |
I have a large number of documents(around 34719074 documents) in a type of an index(ES 2.4.4). While searching, my ES Cluster seems to be in high impact(Search Latency, CPU Usage, JVM Memory and Load Average) when the "from" parameter is high(greater than 100000, "size" parameter being constant). Any specific reason for it? My query looks like:
"explain": false,
"size": 100,
"from": <>,
"_source":
"excludes": ,
"includes": [
<around 850 fields>
]
,
"sort": [
<sorting from an string field>
]
elasticsearch lucene
asked Nov 14 '18 at 6:30
Yashasvi Raj PantYashasvi Raj Pant
2731315
1
here is the explanation for that behavior
– Nirit Levi
Nov 14 '18 at 9:08
add a comment |
I have a large number of documents(around 34719074 documents) in a type of an index(ES 2.4.4). While searching, my ES Cluster seems to be in high impact(Search Latency, CPU Usage, JVM Memory and Load Average) when the "from" parameter is high(greater than 100000, "size" parameter being constant). Any specific reason for it? My query looks like:
"explain": false,
"size": 100,
"from": <>,
"_source":
"excludes": ,
"includes": [
<around 850 fields>
]
,
"sort": [
<sorting from an string field>
]
elasticsearch lucene
asked Nov 14 '18 at 6:30
Yashasvi Raj PantYashasvi Raj Pant
2731315
I have a large number of documents(around 34719074 documents) in a type of an index(ES 2.4.4). While searching, my ES Cluster seems to be in high impact(Search Latency, CPU Usage, JVM Memory and Load Average) when the "from" parameter is high(greater than 100000, "size" parameter being constant). Any specific reason for it? My query looks like:
"explain": false,
"size": 100,
"from": <>,
"_source":
"excludes": ,
"includes": [
<around 850 fields>
]
,
"sort": [
<sorting from an string field>
]
elasticsearch lucene
elasticsearch lucene
asked Nov 14 '18 at 6:30
Yashasvi Raj PantYashasvi Raj Pant
2731315
asked Nov 14 '18 at 6:30
Yashasvi Raj PantYashasvi Raj Pant
2731315
asked Nov 14 '18 at 6:30
Yashasvi Raj PantYashasvi Raj Pant
2731315
asked Nov 14 '18 at 6:30
Yashasvi Raj PantYashasvi Raj Pant
2731315
asked Nov 14 '18 at 6:30
Yashasvi Raj PantYashasvi Raj Pant
2731315
2731315
1
here is the explanation for that behavior
– Nirit Levi
Nov 14 '18 at 9:08
add a comment |
1
here is the explanation for that behavior
– Nirit Levi
Nov 14 '18 at 9:08
1
1
here is the explanation for that behavior
– Nirit Levi
Nov 14 '18 at 9:08
here is the explanation for that behavior
– Nirit Levi
Nov 14 '18 at 9:08
add a comment |
1 Answer
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oldest
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This is a classic problem of deep pagination. You may read the link on pagination in Elasticsearch. Essentially, to get the next set documents after skipping 100000 documents would be an memory intensive task because to attain a result set of 100000+ documents, 100000+ documents need to fetched from each shard and then processed (ranking, sorting, etc.). Ranking/Sorting over a smaller result set takes lesser time that doing that on a larger result set.
answered Nov 14 '18 at 12:19
Abhilash BollaAbhilash Bolla
548
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
This is a classic problem of deep pagination. You may read the link on pagination in Elasticsearch. Essentially, to get the next set documents after skipping 100000 documents would be an memory intensive task because to attain a result set of 100000+ documents, 100000+ documents need to fetched from each shard and then processed (ranking, sorting, etc.). Ranking/Sorting over a smaller result set takes lesser time that doing that on a larger result set.
answered Nov 14 '18 at 12:19
Abhilash BollaAbhilash Bolla
548
add a comment |
This is a classic problem of deep pagination. You may read the link on pagination in Elasticsearch. Essentially, to get the next set documents after skipping 100000 documents would be an memory intensive task because to attain a result set of 100000+ documents, 100000+ documents need to fetched from each shard and then processed (ranking, sorting, etc.). Ranking/Sorting over a smaller result set takes lesser time that doing that on a larger result set.
answered Nov 14 '18 at 12:19
Abhilash BollaAbhilash Bolla
548
add a comment |
This is a classic problem of deep pagination. You may read the link on pagination in Elasticsearch. Essentially, to get the next set documents after skipping 100000 documents would be an memory intensive task because to attain a result set of 100000+ documents, 100000+ documents need to fetched from each shard and then processed (ranking, sorting, etc.). Ranking/Sorting over a smaller result set takes lesser time that doing that on a larger result set.
answered Nov 14 '18 at 12:19
Abhilash BollaAbhilash Bolla
548
This is a classic problem of deep pagination. You may read the link on pagination in Elasticsearch. Essentially, to get the next set documents after skipping 100000 documents would be an memory intensive task because to attain a result set of 100000+ documents, 100000+ documents need to fetched from each shard and then processed (ranking, sorting, etc.). Ranking/Sorting over a smaller result set takes lesser time that doing that on a larger result set.
answered Nov 14 '18 at 12:19
Abhilash BollaAbhilash Bolla
548
answered Nov 14 '18 at 12:19
Abhilash BollaAbhilash Bolla
548
answered Nov 14 '18 at 12:19
Abhilash BollaAbhilash Bolla
548
answered Nov 14 '18 at 12:19
Abhilash BollaAbhilash Bolla
548
548
add a comment |
add a comment |
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1
here is the explanation for that behavior
– Nirit Levi
Nov 14 '18 at 9:08