Hardware related issue

Hi Guys, We were planning on using 7 physical servers for our solr node with 64 VCPUs 2GHZ and 128 GRAM each but due to some constrains we had to use virtual environment that does not has the same amount of cpus. We were suggested to use less cpus with higher ghz. Do we need to look for L1-L3

RE: Filtering large amount of values

e.org/jira/browse/LUCENE-9328 for related problem. Make sure you obtain them once per segment, when leaf reader is injected. Recently there are some new method(s) for {!terms} I'm wondering if any of them might solve the problem. On Thu, May 14, 2020 at 2:36 PM Rudenko, Artur wrote: >

Filtering large amount of values

Hi, We have a requirement of implementing a boolean filter with up to 500k values. We took the approach of post filter. Our environment has 7 servers of 128gb ram and 64cpus each server. We have 20-40m very large documents. Each solr instance has 64 shards with 2 replicas and JVM memory xms

RE: Possible performance bug - JSON facet - numBuckets:true

hile the original solr implementation claims there are 2671 buckets (11 more) The result of both query were compared with comparing tool and except of QTime, different limit value and numbuckets value all were the same (I decided not to pace all the buckets response but all were the same = 2660 and no

RE: Possible performance bug - JSON facet - numBuckets:true

Guys? Artur Rudenko -Original Message- From: Rudenko, Artur Sent: Saturday, February 15, 2020 12:50 PM To: solr-user@lucene.apache.org Subject: RE: Possible performance bug - JSON facet - numBuckets:true Promoting my question Thanks, Artur Rudenko From: Rudenko, Artur Sent

RE: Slow quires and facets

Promoting my question Artur Rudenko -Original Message- From: Rudenko, Artur Sent: Wednesday, February 12, 2020 10:33 PM To: solr-user@lucene.apache.org Subject: Slow quires and facets Hello everyone, I'm am currently investigating a performance issue in our environment: 20M large

RE: Possible performance bug - JSON facet - numBuckets:true

Promoting my question Thanks, Artur Rudenko From: Rudenko, Artur Sent: Wednesday, February 12, 2020 9:48 PM To: solr-user@lucene.apache.org Subject: Possible performance bug - JSON facet - numBuckets:true Hello everyone, I'm am currently investigating a performance issue in our environment

Slow quires and facets

Hello everyone, I'm am currently investigating a performance issue in our environment: 20M large PARENT documents and 800M nested small CHILD documents. The system inserts about 400K PARENT documents and 16M CHILD documents per day. (Currently we stopped the calls insertion to investigate the

Possible performance bug - JSON facet - numBuckets:true

Hello everyone, I'm am currently investigating a performance issue in our environment and it looks like we found a performance bug. Our environment: 20M large PARENT documents and 800M nested small CHILD documents. The system inserts about 400K PARENT documents and 16M CHILD documents per day.

RE: Possible performance issue in my environment setup

> On Feb 11, 2020, at 6:17 AM, Rudenko, Artur wrote: > > I'm am currently investigating a performance issue in our environment (20M > large PARENT documents and 800M nested small CHILD documents). The system > inserts about 400K PARENT documents and 16M CHILD documents per day. &

Possible performance issue in my environment setup

I'm am currently investigating a performance issue in our environment (20M large PARENT documents and 800M nested small CHILD documents). The system inserts about 400K PARENT documents and 16M CHILD documents per day. This is a solr cloud 8.3 environment with 7 servers (64 VCPU 128 GB RAM each,

Solr fact response strange behaviour

I'm trying to parse facet response, but sometimes the count returns as Long type and sometimes as Integer type(on different environments), The error is: "java.lang.ClassCastException: java.lang.Integer cannot be cast to java.lang.Long" Can you please explain why this happenes? Why it not

Type of auto suggest feature

Hi, I am quite new to solr and I am interested in implementing a sort of auto terms suggest (not auto complete) feature based on the user query. Users builds some query (on multiple fields) and I am trying to help him refining his query by suggesting to add more terms based on his current query.