Thanks Emir. I’m unfortunately already using a routing key that needs to be at
the top level, since I’m collapsing on that field.
Adding a sub-key won’t help much if my theory is correct, as even a single
shard (distrib=false) showed serious performance degradation, and query latency
is the max(shard latency). I’d need a routing scheme that assured that a given
shard has *only* A’s, or *only* B’s.
Even if I could use “permissions” as the top-level routing key though, this is
a very low cardinality field, so I’d expect to end up with very large
differences between the sizes of the shards in that case. That’s fine from a
SolrCloud query perspective of course, but it makes for more difficult resource
provisioning.
On 8/12/16, 1:39 AM, "Emir Arnautovic" <emir.arnauto...@sematext.com> wrote:
Hi Jeff,
I will not comment on your theory (will let that to guys more familiar
with Lucene code) but will point to one alternative solution: routing.
You can use routing to split documents with different permission to
different shards and use composite hash routing to split "A" (and maybe
"B" as well) documents to multiple shards. That will make sure all doc
with the same permission are on the same shard and on query time only
those will be queried (less shards to query) and there is no need to
include term query or filter query at all.
Here is blog explaining benefits of composite hash routing:
https://sematext.com/blog/2015/09/29/solrcloud-large-tenants-and-routing/
Regards,
Emir
--
Monitoring * Alerting * Anomaly Detection * Centralized Log Management
Solr & Elasticsearch Support * http://sematext.com/
On 11.08.2016 19:39, Jeff Wartes wrote:
> This isn’t really a question, although some validation would be nice.
It’s more of a warning.
>
> Tldr is that the insert order of documents in my collection appears to
have had a huge effect on my query speed.
>
>
> I have a very large (sharded) SolrCloud 5.4 index. One aspect of this
index is a multi-valued field (“permissions”) that for 90% of docs contains one
particular value, (“A”) and for 10% of docs contains another distinct value.
(“B”) It’s intended to represent something like permissions, so more values are
possible in the future, but not present currently. In fact, the addition of
docs with value B to this index was very recent, previously all docs had value
“A”. All queries, in addition to various other Boolean-query type restrictions,
have a terms query on this field, like {!terms f=permissions v=A} or {!terms
f=permissions v=A,B}
>
> Last week, I tried to re-index the whole collection from scratch, using
source data. Query performance on the resulting re-index proved to be abysmal,
I could get barely 10% of my previous query throughput, and even that was at
latencies that were orders of magnitude higher than what I had in production.
>
> I hooked up some CPU profiling to a server that had shards from both the
old and new version of the collection, and eventually it looked like the
significant difference in processing the two collections was coming from
ConstantWeight.scorer()
> Specifically, this line
>
https://github.com/apache/lucene-solr/blob/0a1dd10d5262153f4188dfa14a08ba28ec4ccb60/solr/core/src/java/org/apache/solr/search/SolrConstantScoreQuery.java#L102
> was far more expensive in my re-indexed collection. From there, the call
chain goes through an LRUQueryCache, down to a BulkScorer, and ends up with the
extra work happening here:
>
https://github.com/apache/lucene-solr/blob/0a1dd10d5262153f4188dfa14a08ba28ec4ccb60/lucene/core/src/java/org/apache/lucene/search/Weight.java#L169
>
> I don’t pretend to understand all that code, but the difference in my
re-index appears to have something to do either with that cache, or the
aggregate docIdSets that need weights generated is simply much bigger in my
re-index.
>
>
> But the queries didn’t change, and the data is basically the same, what
else could have changed?
>
> The documents with the “B” distinct value were added recently to the
high-performance collection, but the A’s and the B’s were all mixed up in the
source data dump I used to re-index. On a hunch, I manually ordered the docs
such that the A’s were all first and re-indexed again, and performance is great!
>
> Here’s my theory: Using TieredMergePolicy, the vast quantity of the
documents in an index are contained in the largest segments. I’m guessing
there’s an optimization somewhere that says something like “This segment only
has A’s”. By indexing all the A’s first, those biggest segments only contain
A’s, and only the smallest, newest segments are unable to make use of that
optimization.
>
> Here’s the scary part: Although my re-index is now performing well, if
this theory is right, some random insert (or a deliberate optimize) at some
random point in the future could cascade a segment merge such that the largest
segment(s) now contain both A’s and B’s, and performance suddenly goes over a
cliff. I have no way to prevent this possibility except to stop doing inserts.
>
> My current thinking is that I need to pull the terms-query part out of
the query and do a filter query for it instead. Probably as a post-filter,
since I’ve had bad luck with very large filter queries and the filter cache.
I’d tested this originally (when I only had A’s), but found the performance was
a bit worse than just leaving it in the query. I’ll take a bit worse and
predictability over a bit better and a time bomb though, if those are my
choices.
>
>
> If anyone has any comments refuting or supporting this theory, I’d
certainly like to hear it. This is the first time I’ve encountered anything
about insert order mattering from a performance perspective, and it becomes a
general-form question around how to handle low-cardinality fields.
>
