Sorting and ranking

Full-text queries return matches sorted by default. If nothing is specified, they are sorted by relevance, which is equivalent to ORDER BY weight() DESC in SQL format.

Non-full-text queries do not perform any sorting by default.

Extended mode

ORDER BY weight() DESC, price ASC, id DESC

Extended mode is automatically enabled when you explicitly provide sorting rules by adding the ORDER BY clause in SQL format or using the sort option via HTTP JSON.

In the sort clause, you can use any combination of up to 5 columns, each followed by asc or desc. Functions and expressions are not allowed as arguments for the sort clause, except for the weight() and random() functions (the latter can only be used via SQL in the form of ORDER BY random()). However, you can use any expression in the SELECT list and sort by its alias.

  • SQL
select *, a + b alias from test order by alias desc;
| id   | a    | b    | f        | alias |
|    1 |    2 |    3 | document |     5 |


Sorting by attributes

Query results can be sorted by one or more attributes. Example:

    "match": { "title": "what was" }
  "sort": [ "_score", "id" ]

"sort" specifies an array of attributes and/or additional properties. Each element of the array can be an attribute name or "_score" if you want to sort by match weights. In that case, the sort order defaults to ascending for attributes and descending for _score.

You can also specify the sort order explicitly. Example:

  { "price":"asc" },
  • asc: sort in ascending order
  • desc: sort in descending order

You can also use another syntax and specify the sort order via the order property:

  { "gid": { "order":"desc" } }

Sorting by MVA attributes is also supported in JSON queries. Sorting mode can be set via the mode property. The following modes are supported:

  • min: sort by minimum value
  • max: sort by maximum value


  { "attr_mva": { "order":"desc", "mode":"max" } }

When sorting on an attribute, match weight (score) calculation is disabled by default (no ranker is used). You can enable weight calculation by setting the track_scores property to true:

  "query": { "match": { "title": "what was" } },
  "sort": [ { "gid": { "order":"desc" } } ]

Ranking overview

Ranking (also known as weighting) of search results can be defined as a process of computing a so-called relevance (weight) for every given matched document regards to a given query that matched it. So relevance is, in the end, just a number attached to every document that estimates how relevant the document is to the query. Search results can then be sorted based on this number and/or some additional parameters, so that the most sought-after results would appear higher on the results page.

There is no single standard one-size-fits-all way to rank any document in any scenario. Moreover, there can never be such a way, because relevance is subjective. As in, what seems relevant to you might not seem relevant to me. Hence, in general cases, it's not just hard to compute; it's theoretically impossible.

So ranking in Manticore is configurable. It has a notion of a so-called ranker. A ranker can formally be defined as a function that takes a document and a query as its input and produces a relevance value as output. In layman's terms, a ranker controls exactly how (using which specific algorithm) Manticore will assign weights to the documents.

Available built-in rankers

Manticore ships with several built-in rankers suited for different purposes. Many of them use two factors: phrase proximity (also known as LCS) and BM25. Phrase proximity works on keyword positions, while BM25 works on keyword frequencies. Essentially, the better the degree of phrase match between the document body and the query, the higher the phrase proximity (it maxes out when the document contains the entire query as a verbatim quote). And BM25 is higher when the document contains more rare words. We'll save the detailed discussion for later.

The currently implemented rankers are:

  • proximity_bm25, the default ranking mode that uses and combines both phrase proximity and BM25 ranking.
  • bm25, a statistical ranking mode that uses BM25 ranking only (similar to most other full-text engines). This mode is faster but may result in worse quality for queries containing more than one keyword.
  • none, a no-ranking mode. This mode is obviously the fastest. A weight of 1 is assigned to all matches. This is sometimes called boolean searching, which just matches the documents but does not rank them.
  • wordcount, ranking by the keyword occurrences count. This ranker computes the per-field keyword occurrence counts, then multiplies them by field weights, and sums the resulting values.
  • proximity returns the raw phrase proximity value as a result. This mode is internally used to emulate SPH_MATCH_ALL queries.
  • matchany returns rank as it was computed in SPH_MATCH_ANY mode earlier and is internally used to emulate SPH_MATCH_ANY queries.
  • fieldmask returns a 32-bit mask with the N-th bit corresponding to the N-th full-text field, numbering from 0. The bit will only be set when the respective field has any keyword occurrences satisfying the query.
  • sph04 is generally based on the default 'proximity_bm25' ranker, but additionally boosts matches when they occur at the very beginning or the very end of a text field. Thus, if a field equals the exact query, sph04 should rank it higher than a field that contains the exact query but is not equal to it. (For instance, when the query is "Hyde Park", a document titled "Hyde Park" should be ranked higher than one titled "Hyde Park, London" or "The Hyde Park Cafe".)
  • expr allows you to specify the ranking formula at runtime. It exposes several internal text factors and lets you define how the final weight should be computed from those factors. You can find more details about its syntax and a reference of available factors in a subsection below.

The ranker name is case-insensitive. Example:

SELECT ... OPTION ranker=sph04;

Quick summary of the ranking factors

Name Level Type Summary
max_lcs query int maximum possible LCS value for the current query
bm25 document int quick estimate of BM25(1.2, 0)
bm25a(k1, b) document int precise BM25() value with configurable K1, B constants and syntax support
bm25f(k1, b, {field=weight, ...}) document int precise BM25F() value with extra configurable field weights
field_mask document int bit mask of matched fields
query_word_count document int number of unique inclusive keywords in a query
doc_word_count document int number of unique keywords matched in the document
lcs field int Longest Common Subsequence between query and document, in words
user_weight field int user field weight
hit_count field int total number of keyword occurrences
word_count field int number of unique matched keywords
tf_idf field float sum(tf*idf) over matched keywords == sum(idf) over occurrences
min_hit_pos field int first matched occurrence position, in words, 1-based
min_best_span_pos field int first maximum LCS span position, in words, 1-based
exact_hit field bool whether query == field
min_idf field float min(idf) over matched keywords
max_idf field float max(idf) over matched keywords
sum_idf field float sum(idf) over matched keywords
exact_order field bool whether all query keywords were a) matched and b) in query order
min_gaps field int minimum number of gaps between the matched keywords over the matching spans
lccs field int Longest Common Contiguous Subsequence between query and document, in words
wlccs field float Weighted Longest Common Contiguous Subsequence, sum(idf) over contiguous keyword spans
atc field float Aggregate Term Closeness, log(1+sum(idf1idf2pow(distance, -1.75)) over the best pairs of keywords

Document-level Ranking Factors

A document-level factor is a numeric value computed by the ranking engine for every matched document with regards to the current query. So it differs from a plain document attribute in that the attribute does not depend on the full text query, while factors might. These factors can be used anywhere in the ranking expression. Currently implemented document-level factors are:

  • bm25 (integer), a document-level BM25 estimate (computed without keyword occurrence filtering).
  • max_lcs (integer), a query-level maximum possible value that the sum(lcs*user_weight) expression can ever take. This can be useful for weight boost scaling. For instance, MATCHANY ranker formula uses this to guarantee that a full phrase match in any field ranks higher than any combination of partial matches in all fields.
  • field_mask (integer), a document-level 32-bit mask of matched fields.
  • query_word_count (integer), the number of unique keywords in a query, adjusted for the number of excluded keywords. For instance, both (one one one one) and (one !two) queries should assign a value of 1 to this factor, because there is just one unique non-excluded keyword.
  • doc_word_count (integer), the number of unique keywords matched in the entire document.

Field-level Ranking Factors

A field-level factor is a numeric value computed by the ranking engine for every matched in-document text field regards to the current query. As more than one field can be matched by a query, but the final weight needs to be a single integer value, these values need to be folded into a single one. To achieve that, field-level factors can only be used within a field aggregation function, they can not be used anywhere in the expression. For example, you cannot use (lcs+bm25) as your ranking expression, as lcs takes multiple values (one in every matched field). You should use (sum(lcs)+bm25) instead, that expression sums lcs over all matching fields, and then adds bm25 to that per-field sum. Currently implemented field-level factors are:

  • lcs (integer), the length of a maximum verbatim match between the document and the query, counted in words. LCS stands for Longest Common Subsequence (or Subset). Takes a minimum value of 1 when only stray keywords were matched in a field, and a maximum value of query keywords count when the entire query was matched in a field verbatim (in the exact query keywords order). For example, if the query is 'hello world' and the field contains these two words quoted from the query (that is, adjacent to each other, and exactly in the query order), lcs will be 2. For example, if the query is 'hello world program' and the field contains 'hello world', lcs will be 2. Note that any subset of the query keyword works, not just a subset of adjacent keywords. For example, if the query is 'hello world program' and the field contains 'hello (test program)', lcs will be 2 just as well, because both 'hello' and 'program' matched in the same respective positions as they were in the query. Finally, if the query is 'hello world program' and the field contains 'hello world program', lcs will be 3. (Hopefully that is unsurprising at this point.)

  • user_weight (integer), the user specified per-field weight (refer to OPTION field_weights in SQL). The weights default to 1 if not specified explicitly.

  • hit_count (integer), the number of keyword occurrences that matched in the field. Note that a single keyword may occur multiple times. For example, if 'hello' occurs 3 times in a field and 'world' occurs 5 times, hit_count will be 8.

  • word_count (integer), the number of unique keywords matched in the field. For example, if 'hello' and 'world' occur anywhere in a field, word_count will be 2, regardless of how many times both keywords occur.

  • tf_idf (float), the sum of TF/IDF over all the keywords matched in the field. IDF is the Inverse Document Frequency, a floating point value between 0 and 1 that describes how frequent the keyword is (basically, 0 for a keyword that occurs in every document indexed, and 1 for a unique keyword that occurs in just a single document). TF is the Term Frequency, the number of matched keyword occurrences in the field. As a side note, tf_idf is actually computed by summing IDF over all matched occurrences. That's by construction equivalent to summing TF*IDF over all matched keywords.

  • min_hit_pos (integer), the position of the first matched keyword occurrence, counted in words

    Therefore, this is a relatively low-level, "raw" factor that you'll likely want to adjust before using it for ranking. The specific adjustments depend heavily on your data and the resulting formula, but here are a few ideas to start with: (a) any min_gaps-based boosts could be simply ignored when word_count<2;

    (b) non-trivial min_gaps values (i.e., when word_count>=2) could be clamped with a certain "worst-case" constant, while trivial values (i.e., when min_gaps=0 and word_count<2) could be replaced by that constant;

    (c) a transfer function like 1/(1+min_gaps) could be applied (so that better, smaller min_gaps values would maximize it, and worse, larger min_gaps values would fall off slowly); and so on.

  • lccs (integer). Longest Common Contiguous Subsequence. The length of the longest subphrase common between the query and the document, computed in keywords.

    The LCCS factor is somewhat similar to LCS but more restrictive. While LCS can be greater than 1 even if no two query words are matched next to each other, LCCS will only be greater than 1 if there are exact, contiguous query subphrases in the document. For example, (one two three four five) query vs (one hundred three hundred five hundred) document would yield lcs=3, but lccs=1, because although the mutual dispositions of 3 keywords (one, three, five) match between the query and the document, no 2 matching positions are actually adjacent.

    Note that LCCS still doesn't differentiate between frequent and rare keywords; for that, see WLCCS.

  • wlccs (float). Weighted Longest Common Contiguous Subsequence. The sum of IDFs of the keywords of the longest subphrase common between the query and the document.

    WLCCS is calculated similarly to LCCS, but every "suitable" keyword occurrence increases it by the keyword IDF instead of just by 1 (as with LCS and LCCS). This allows ranking sequences of rarer and more important keywords higher than sequences of frequent keywords, even if the latter are longer. For example, a query (Zanzibar bed and breakfast) would yield lccs=1 for a (hotels of Zanzibar) document, but lccs=3 against (London bed and breakfast), even though "Zanzibar" is actually somewhat rarer than the entire "bed and breakfast" phrase. The WLCCS factor addresses this issue by using keyword frequencies.

  • atc (float). Aggregate Term Closeness. A proximity-based measure that increases when the document contains more groups of more closely located and more important (rare) query keywords.

    WARNING: you should use ATC with OPTION idf='plain,tfidf_unnormalized' (see below); otherwise, you may get unexpected results.

    ATC essentially operates as follows. For each keyword occurrence in the document, we compute the so-called term closeness. To do this, we examine all the other closest occurrences of all the query keywords (including the keyword itself) to the left and right of the subject occurrence, calculate a distance dampening coefficient as k = pow(distance, -1.75) for these occurrences, and sum the dampened IDFs. As a result, for every occurrence of each keyword, we obtain a "closeness" value that describes the "neighbors" of that occurrence. We then multiply these per-occurrence closenesses by their respective subject keyword IDF, sum them all, and finally compute a logarithm of that sum.

In other words, we process the best (closest) matched keyword pairs in the document, and compute pairwise "closenesses" as the product of their IDFs scaled by the distance coefficient:

pair_tc = idf(pair_word1) * idf(pair_word2) * pow(pair_distance, -1.75)

We then sum such closenesses, and compute the final, log-dampened ATC value:

atc = log(1+sum(pair_tc))

Note that this final dampening logarithm is precisely the reason you should use OPTION idf=plain because, without it, the expression inside the log() could be negative.

Having closer keyword occurrences contributes much more to ATC than having more frequent keywords. Indeed, when the keywords are right next to each other, distance=1 and k=1; when there's just one word in between them, distance=2 and k=0.297, with two words between, distance=3 and k=0.146, and so on. At the same time, IDF attenuates somewhat slower. For example, in a 1 million document collection, the IDF values for keywords that match in 10, 100, and 1000 documents would be respectively 0.833, 0.667, and 0.500. So a keyword pair with two rather rare keywords that occur in just 10 documents each but with 2 other words in between would yield pair_tc = 0.101 and thus barely outweigh a pair with a 100-doc and a 1000-doc keyword with 1 other word between them and pair_tc = 0.099. Moreover, a pair of two unique, 1-doc keywords with 3 words between them would get a pair_tc = 0.088 and lose to a pair of two 1000-doc keywords located right next to each other and yielding a pair_tc = 0.25. So, basically, while ATC does combine both keyword frequency and proximity, it still somewhat favors proximity.

Ranking factor aggregation functions

A field aggregation function is a single-argument function that accepts an expression with field-level factors, iterates over all matched fields, and computes the final results. The currently implemented field aggregation functions include:

  • sum, which adds the argument expression over all matched fields. For example sum(1) should return the number of matched fields.
  • top, which returns the highest value of the argument across all matched fields.

Formula expressions for all the built-in rankers

Most other rankers can actually be emulated using the expression-based ranker. You just need to provide an appropriate expression. While this emulation will likely be slower than using the built-in, compiled ranker, it may still be interesting if you want to fine-tune your ranking formula starting with one of the existing ones. Additionally, the formulas describe the ranker details in a clear, readable manner.

  • proximity_bm25 (default ranker) = sum(lcs*user_weight)*1000+bm25
  • bm25 = sum(user_weight)*1000+bm25
  • none = 1
  • wordcount = sum(hit_count*user_weight)
  • proximity = sum(lcs*user_weight)
  • matchany = sum((word_count+(lcs-1)*max_lcs)*user_weight)
  • fieldmask = field_mask
  • sph04 = sum((4*lcs+2*(min_hit_pos==1)+exact_hit)*user_weight)*1000+bm25

Configuration of IDF formula

The historically default IDF (Inverse Document Frequency) in Manticore is equivalent to OPTION idf='normalized,tfidf_normalized', and those normalizations may cause several undesired effects.

First, idf=normalized causes keyword penalization. For instance, if you search for the | something and the occurs in more than 50% of the documents, then documents with both keywords the and[something will get less weight than documents with just one keyword something. Using OPTION idf=plain avoids this.

Plain IDF varies in [0, log(N)] range, and keywords are never penalized; while the normalized IDF varies in [-log(N), log(N)] range, and too frequent keywords are penalized.

Second, idf=tfidf_normalized causes IDF drift over queries. Historically, we additionally divided IDF by query keyword count, so that the entire sum(tf*idf) over all keywords would still fit into [0,1] range. However, that means that queries word1 and word1 | nonmatchingword2 would assign different weights to the exactly same result set, because the IDFs for both word1 and nonmatchingword2 would be divided by 2. OPTION idf='tfidf_unnormalized' fixes that. Note that BM25, BM25A, BM25F() ranking factors will be scale accordingly once you disable this normalization.

IDF flags can be mixed; plain and normalized are mutually exclusive;tfidf_unnormalized and tfidf_normalized are mutually exclusive; and unspecified flags in such a mutually exclusive group take their defaults. That means that OPTION idf=plain is equivalent to a complete OPTION idf='plain,tfidf_normalized' specification.

Pagination of search results

Manticore Search returns the top 20 matched documents in the result set by default.


In SQL, you can navigate through the result set using the LIMIT clause.

LIMIT can accept either one number as the size of the returned set with a zero offset, or a pair of offset and size values.


When using HTTP JSON, the nodes offset and limit control the offset of the result set and the size of the returned set. Alternatively, you can use the pair size and from instead.

  • SQL
  • JSON
SELECT  ... FROM ...  [LIMIT [offset,] row_count]
SELECT  ... FROM ...  [LIMIT row_count][ OFFSET offset]

Result set window

By default, Manticore Search uses a result set window of 1000 best-ranked documents that can be returned in the result set. If the result set is paginated beyond this value, the query will end in error.

This limitation can be adjusted with the query option max_matches.

Increasing the max_matches to very high values should only be done if it's necessary for the navigation to reach such points. A high max_matches value requires more memory and can increase the query response time. One way to work with deep result sets is to set max_matches as the sum of the offset and limit.

Lowering max_matches below 1000 has the benefit of reducing the memory used by the query. It can also reduce the query time, but in most cases, it might not be a noticeable gain.

  • SQL
  • JSON
SELECT  ... FROM ...   OPTION max_matches=<value>

Distributed searching

Manticore is designed to scale effectively through its distributed searching capabilities. Distributed searching is beneficial for improving query latency (i.e., search time) and throughput (i.e., max queries/sec) in multi-server, multi-CPU, or multi-core environments. This is crucial for applications that need to search through vast amounts of data (i.e., billions of records and terabytes of text).

The primary concept is to horizontally partition the searched data across search nodes and process it in parallel.

Partitioning is done manually. To set it up, you should:

  • Set up multiple instances of Manticore on different servers
  • Distribute different parts of your dataset to different instances
  • Configure a special distributed table on some of the searchd instances
  • Route your queries to the distributed table

This type of table only contains references to other local and remote tables - so it cannot be directly reindexed. Instead, you should reindex the tables that it references.

When Manticore receives a query against a distributed table, it performs the following steps:

  1. Connects to the configured remote agents
  2. Sends the query to them
  3. Simultaneously searches the configured local tables (while the remote agents are searching)
  4. Retrieves the search results from the remote agents
  5. Merges all the results together, removing duplicates
  6. Sends the merged results to the client

From the application's perspective, there are no differences between searching through a regular table or a distributed table. In other words, distributed tables are fully transparent to the application, and there's no way to tell whether the table you queried was distributed or local.

Learn more about remote nodes.