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https://issues.apache.org/jira/browse/SOLR-15036?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17261539#comment-17261539
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Joel Bernstein edited comment on SOLR-15036 at 1/8/21, 7:25 PM:
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One thing we could do to remove any risks and move faster with this would be to
make the default behavior be the standard non-plist approach. Then we could
turn it on with a named param or a system param.
{code}
facet(alias, tiered="true")
{code}
The tiered parameter would turn on the tiered aggregations.
was (Author: joel.bernstein):
One thing we could do to remove any risks and move faster with this would be to
make the default behavior be the standard non-plist approach. Then we could
turn it on with a named param or a system param.
> 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
> Priority: Major
> Attachments: relay-approach.patch
>
> Time Spent: 1h 20m
> Remaining Estimate: 0h
>
> 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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