Multi-queries, or query batches, let you send multiple queries to Manticore in one go (more formally, one network request).

Two API methods that implement multi-query mechanism are AddQuery() and RunQueries(). You can also run multiple queries with SphinxQL, see Multi-statement queries. (In fact, regular Query() call is internally implemented as a single AddQuery() call immediately followed by RunQueries() call.) AddQuery() captures the current state of all the query settings set by previous API calls, and memorizes the query. RunQueries() actually sends all the memorized queries, and returns multiple result sets. There are no restrictions on the queries at all, except just a sanity check on a number of queries in a single batch (see max_batch_queries).

Why use multi-queries? Generally, it all boils down to performance. First, by sending requests to searchd in a batch instead of one by one, you always save a bit by doing less network roundtrips. Second, and somewhat more important, sending queries in a batch enables searchd to perform certain internal optimizations. As new types of optimizations are being added over time, it generally makes sense to pack all the queries into batches where possible, so that simply upgrading Manticore to a new version would automatically enable new optimizations. In the case when there aren’t any possible batch optimizations to apply, queries will be processed one by one internally.

Why (or rather when) not use multi-queries? Multi-queries requires all the queries in a batch to be independent, and sometimes they aren’t. That is, sometimes query B is based on query A results, and so can only be set up after executing query A. For instance, you might want to display results from a secondary index if and only if there were no results found in a primary index. Or maybe just specify offset into 2nd result set based on the amount of matches in the 1st result set. In that case, you will have to use separate queries (or separate batches).

There are two major optimizations to be aware of: common query optimization and common subtree optimization.

Common query optimization means that searchd will identify all those queries in a batch where only the sorting and group-by settings differ, and only perform searching once. For instance, if a batch consists of 3 queries, all of them are for “ipod nano”, but 1st query requests top-10 results sorted by price, 2nd query groups by vendor ID and requests top-5 vendors sorted by rating, and 3rd query requests max price, full-text search for “ipod nano” will only be performed once, and its results will be reused to build 3 different result sets.

So-called faceted searching is a particularly important case that benefits from this optimization. Indeed, faceted searching can be implemented by running a number of queries, one to retrieve search results themselves, and a few other ones with same full-text query but different group-by settings to retrieve all the required groups of results (top-3 authors, top-5 vendors, etc). And as long as full-text query and filtering settings stay the same, common query optimization will trigger, and greatly improve performance.

Common subtree optimization is even more interesting. It lets searchd exploit similarities between batched full-text queries. It identifies common full-text query parts (subtrees) in all queries, and caches them between queries. For instance, look at the following query batch:

donald trump president
donald trump barack obama john mccain
donald trump speech

There’s a common two-word part (“donald trump”) that can be computed only once, then cached and shared across the queries. And common subtree optimization does just that. Per-query cache size is strictly controlled by subtree_docs_cache and subtree_hits_cache directives (so that caching all sixteen gazillions of documents that match “i am” does not exhaust the RAM and instantly kill your server).

Here’s a code sample (in PHP) that fire the same query in 3 different sorting modes:

require ( "sphinxapi.php" );
$cl = new ManticoreClient ();
$cl->SetMatchMode ( SPH_MATCH_EXTENDED );

$cl->SetSortMode ( SPH_SORT_RELEVANCE );
$cl->AddQuery ( "the", "lj" );
$cl->SetSortMode ( SPH_SORT_EXTENDED, "published desc" );
$cl->AddQuery ( "the", "lj" );
$cl->SetSortMode ( SPH_SORT_EXTENDED, "published asc" );
$cl->AddQuery ( "the", "lj" );
$res = $cl->RunQueries();

How to tell whether the queries in the batch were actually optimized? If they were, respective query log will have a “multiplier” field that specifies how many queries were processed together:

[Sun Jul 12 15:18:17.000 2009] 0.040 sec x3 [ext/0/rel 747541 (0,20)] [lj] the
[Sun Jul 12 15:18:17.000 2009] 0.040 sec x3 [ext/0/ext 747541 (0,20)] [lj] the
[Sun Jul 12 15:18:17.000 2009] 0.040 sec x3 [ext/0/ext 747541 (0,20)] [lj] the

Note the “x3” field. It means that this query was optimized and processed in a sub-batch of 3 queries. For reference, this is how the regular log would look like if the queries were not batched:

[Sun Jul 12 15:18:17.062 2009] 0.059 sec [ext/0/rel 747541 (0,20)] [lj] the
[Sun Jul 12 15:18:17.156 2009] 0.091 sec [ext/0/ext 747541 (0,20)] [lj] the
[Sun Jul 12 15:18:17.250 2009] 0.092 sec [ext/0/ext 747541 (0,20)] [lj] the

Note how per-query time in multi-query case was improved by a factor of 1.5x to 2.3x, depending on a particular sorting mode. In fact, for both common query and common subtree optimizations, there were reports of 3x and even more improvements, and that’s from production instances, not just synthetic tests.