Timothy Potter created SOLR-15036:
-------------------------------------
Summary: Use plist automatically for executing a facet expression
against a collection alias backed by multiple collections
Key: SOLR-15036
URL: https://issues.apache.org/jira/browse/SOLR-15036
Project: Solr
Issue Type: Improvement
Security Level: Public (Default Security Level. Issues are Public)
Components: streaming expressions
Reporter: Timothy Potter
Assignee: Timothy Potter
Attachments: relay-approach.patch
For analytics use cases, streaming expressions make it possible to compute
basic aggregations (count, min, max, sum, and avg) over massive data sets.
Moreover, with massive data sets, it is common to use collection aliases over
many underlying collections, for instance time-partitioned aliases backed by a
set of collections, each covering a specific time range. In some cases, we can
end up with many collections (think 50-60) each with 100's of shards. Aliases
help insulate client applications from complex collection topologies on the
server side.
Let's take a basic facet expression that computes some useful aggregation
metrics:
{code:java}
facet(
some_alias,
q="*:*",
fl="a_i",
sort="a_i asc",
buckets="a_i",
bucketSorts="count(*) asc",
bucketSizeLimit=10000,
sum(a_d), avg(a_d), min(a_d), max(a_d), count(*)
)
{code}
Behind the scenes, the {{FacetStream}} sends a JSON facet request to Solr which
then expands the alias to a list of collections. For each collection, the
top-level distributed query controller gathers a candidate set of replicas to
query and then scatters {{distrib=false}} queries to each replica in the list.
For instance, if we have 60 collections with 200 shards each, then this results
in 12,000 shard requests from the query controller node to the other nodes in
the cluster. The requests are sent in an async manner (see {{SearchHandler}}
and {{HttpShardHandler}}) In my testing, we’ve seen cases where we hit 18,000
replicas and these queries don’t always come back in a timely manner. Put
simply, this also puts a lot of load on the top-level query controller node in
terms of open connections and new object creation.
Instead, we can use {{plist}} to send the JSON facet query to each collection
in the alias in parallel, which reduces the overhead of each top-level
distributed query from 12,000 to 200 in my example above. With this approach,
you’ll then need to sort the tuples back from each collection and do a rollup,
something like:
{code:java}
select(
rollup(
sort(
plist(
select(facet(coll1,q="*:*", fl="a_i", sort="a_i asc", buckets="a_i",
bucketSorts="count(*) asc", bucketSizeLimit=10000, sum(a_d), avg(a_d),
min(a_d), max(a_d), count(*)),a_i,sum(a_d) as the_sum, avg(a_d) as the_avg,
min(a_d) as the_min, max(a_d) as the_max, count(*) as cnt),
select(facet(coll2,q="*:*", fl="a_i", sort="a_i asc", buckets="a_i",
bucketSorts="count(*) asc", bucketSizeLimit=10000, sum(a_d), avg(a_d),
min(a_d), max(a_d), count(*)),a_i,sum(a_d) as the_sum, avg(a_d) as the_avg,
min(a_d) as the_min, max(a_d) as the_max, count(*) as cnt)
),
by="a_i asc"
),
over="a_i",
sum(the_sum), avg(the_avg), min(the_min), max(the_max), sum(cnt)
),
a_i, sum(the_sum) as the_sum, avg(the_avg) as the_avg, min(the_min) as
the_min, max(the_max) as the_max, sum(cnt) as cnt
)
{code}
One thing to point out is that you can’t just avg. the averages back from each
collection in the rollup. It needs to be a *weighted avg.* when rolling up the
avg. from each facet expression in the plist. However, we have the count per
collection, so this is doable but will require some changes to the rollup
expression to support weighted average.
While this plist approach is doable, it’s a pain for users to have to create
the rollup / sort over plist expression for collection aliases. After all,
aliases are supposed to hide these types of complexities from client
applications!
The point of this ticket is to investigate the feasibility of auto-wrapping the
facet expression with a rollup / sort / plist when the collection argument is
an alias with multiple collections; other stream sources will be considered
after facet is proven out.
Lastly, I also considered an alternative approach of doing a parallel relay on
the server side. The idea is similar to {{plist}} but instead of this being
driven on the client side, the {{FacetModule}} can create intermediate queries
(I called them {{relay}} queries in my impl.) that help distribute the load. In
my example above, there would be 60 such relay queries, each sent to a replica
for each collection in the alias, which then sends the {{distrib=false}}
queries to each replica. The relay query response handler collects the facet
responses from each replica before sending back to the top-level query
controller for final results.
I have this approach working in the attached patch ([^relay-approach.patch])
but it feels a little invasive to the {{FacetModule}} (and the distributed
query inner workings in general). To me, the auto- {{plist}} approach feels
like a better layer to add this functionality vs. deeper down in the facet
module code. Moreover, we may be able to leverage the {{plist}} solution for
other stream sources whereas the relay approach required me to change logic in
the {{FacetModule}} directly, so is likely not as reusable for other types of
queries. It's possible the relay approach could be generalized but I'm not
clear how useful that would be beyond streaming expression analytics use cases;
feedback on this point welcome of course.
I also think {{plist}} should try to be clever and avoid sending the top-level
(per collection) request to the same node if it can help it.
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