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.
