Você pode conseguir um melhor desempenho pesquisando primeiro nas linhas com frequências mais altas. Isso pode ser obtido "granulando" as frequências e, em seguida, percorrendo-as processualmente, por exemplo, da seguinte maneira:

--testbed e lexikondados fictícios:

begin;
set role dba;
create role stack;
grant stack to dba;
create schema authorization stack;
set role stack;
--
create table lexikon( _id serial, 
                      word text, 
                      frequency integer, 
                      lset integer, 
                      width_granule integer);
--
insert into lexikon(word, frequency, lset) 
select word, (1000000/row_number() over(order by random()))::integer as frequency, lset
from (select 'word'||generate_series(1,1000000) word, generate_series(1,1000000) lset) z;
--
update lexikon set width_granule=ln(frequency)::integer;
--
create index on lexikon(width_granule, lset);
create index on lexikon(lset);
-- the second index is not used with the function but is added to make the timings 'fair'

granuleanálise (principalmente para informação e ajuste):

create table granule as 
select width_granule, count(*) as freq, 
       min(frequency) as granule_start, max(frequency) as granule_end 
from lexikon group by width_granule;
--
select * from granule order by 1;
/*
 width_granule |  freq  | granule_start | granule_end
---------------+--------+---------------+-------------
             0 | 500000 |             1 |           1
             1 | 300000 |             2 |           4
             2 | 123077 |             5 |          12
             3 |  47512 |            13 |          33
             4 |  18422 |            34 |          90
             5 |   6908 |            91 |         244
             6 |   2580 |           245 |         665
             7 |    949 |           666 |        1808
             8 |    349 |          1811 |        4901
             9 |    129 |          4926 |       13333
            10 |     47 |         13513 |       35714
            11 |     17 |         37037 |       90909
            12 |      7 |        100000 |      250000
            13 |      2 |        333333 |      500000
            14 |      1 |       1000000 |     1000000
*/
alter table granule drop column freq;
--

função para escanear altas frequências primeiro:

create function f(p_lset_low in integer, p_lset_high in integer, p_limit in integer)
       returns setof lexikon language plpgsql set search_path to 'stack' as $$
declare
  m integer;
  n integer := 0;
  r record;
begin 
  for r in (select width_granule from granule order by width_granule desc) loop
    return query( select * 
                  from lexikon 
                  where width_granule=r.width_granule 
                        and lset>=p_lset_low and lset<=p_lset_high );
    get diagnostics m = row_count;
    n = n+m;
    exit when n>=p_limit;
  end loop;
end;$$;

resultados (tempos provavelmente devem ser tomados com uma pitada de sal, mas cada consulta é executada duas vezes para combater qualquer cache)

primeiro usando a função que escrevemos:

\timing on
--
select * from f(20000, 30000, 5) order by frequency desc limit 5;
/*
 _id |   word    | frequency | lset  | width_granule
-----+-----------+-----------+-------+---------------
 141 | word23237 |      7092 | 23237 |             9
 246 | word25112 |      4065 | 25112 |             8
 275 | word23825 |      3636 | 23825 |             8
 409 | word28660 |      2444 | 28660 |             8
 418 | word29923 |      2392 | 29923 |             8
Time: 80.452 ms
*/
select * from f(20000, 30000, 5) order by frequency desc limit 5;
/*
 _id |   word    | frequency | lset  | width_granule
-----+-----------+-----------+-------+---------------
 141 | word23237 |      7092 | 23237 |             9
 246 | word25112 |      4065 | 25112 |             8
 275 | word23825 |      3636 | 23825 |             8
 409 | word28660 |      2444 | 28660 |             8
 418 | word29923 |      2392 | 29923 |             8
Time: 0.510 ms
*/

e, em seguida, com uma simples varredura de índice:

select * from lexikon where lset between 20000 and 30000 order by frequency desc limit 5;
/*
 _id |   word    | frequency | lset  | width_granule
-----+-----------+-----------+-------+---------------
 141 | word23237 |      7092 | 23237 |             9
 246 | word25112 |      4065 | 25112 |             8
 275 | word23825 |      3636 | 23825 |             8
 409 | word28660 |      2444 | 28660 |             8
 418 | word29923 |      2392 | 29923 |             8
Time: 218.897 ms
*/
select * from lexikon where lset between 20000 and 30000 order by frequency desc limit 5;
/*
 _id |   word    | frequency | lset  | width_granule
-----+-----------+-----------+-------+---------------
 141 | word23237 |      7092 | 23237 |             9
 246 | word25112 |      4065 | 25112 |             8
 275 | word23825 |      3636 | 23825 |             8
 409 | word28660 |      2444 | 28660 |             8
 418 | word29923 |      2392 | 29923 |             8
Time: 51.250 ms
*/
\timing off
--
rollback;

Dependendo de seus dados do mundo real, você provavelmente desejará variar o número de grânulos e a função usada para colocar linhas neles. A distribuição real de frequências é fundamental aqui, assim como os valores esperados para a limitcláusula e o tamanho dos lsetintervalos procurados.

Configurar

Estou desenvolvendo a configuração de @Jack para facilitar o acompanhamento e a comparação das pessoas. Testado com PostgreSQL 9.1.4 .

CREATE TABLE lexikon (
   lex_id    serial PRIMARY KEY
 , word      text
 , frequency int NOT NULL  -- we'd need to do more if NULL was allowed
 , lset      int
);

INSERT INTO lexikon(word, frequency, lset) 
SELECT 'w' || g  -- shorter with just 'w'
     , (1000000 / row_number() OVER (ORDER BY random()))::int
     , g
FROM   generate_series(1,1000000) g

A partir daqui sigo um caminho diferente:

ANALYZE lexikon;

mesa auxiliar

Esta solução não adiciona colunas à tabela original, apenas precisa de uma pequena tabela auxiliar. Coloquei no schema public, use qualquer schema de sua preferência.

CREATE TABLE public.lex_freq AS
WITH x AS (
   SELECT DISTINCT ON (f.row_min)
          f.row_min, c.row_ct, c.frequency
   FROM  (
      SELECT frequency, sum(count(*)) OVER (ORDER BY frequency DESC) AS row_ct
      FROM   lexikon
      GROUP  BY 1
      ) c
   JOIN  (                                   -- list of steps in recursive search
      VALUES (400),(1600),(6400),(25000),(100000),(200000),(400000),(600000),(800000)
      ) f(row_min) ON c.row_ct >= f.row_min  -- match next greater number
   ORDER  BY f.row_min, c.row_ct, c.frequency DESC
   )
, y AS (   
   SELECT DISTINCT ON (frequency)
          row_min, row_ct, frequency AS freq_min
        , lag(frequency) OVER (ORDER BY row_min) AS freq_max
   FROM   x
   ORDER  BY frequency, row_min
   -- if one frequency spans multiple ranges, pick the lowest row_min
   )
SELECT row_min, row_ct, freq_min
     , CASE freq_min <= freq_max
         WHEN TRUE  THEN 'frequency >= ' || freq_min || ' AND frequency < ' || freq_max
         WHEN FALSE THEN 'frequency  = ' || freq_min
         ELSE            'frequency >= ' || freq_min
       END AS cond
FROM   y
ORDER  BY row_min;

A tabela fica assim:

row_min | row_ct  | freq_min | cond
--------+---------+----------+-------------
400     | 400     | 2500     | frequency >= 2500
1600    | 1600    | 625      | frequency >= 625 AND frequency < 2500
6400    | 6410    | 156      | frequency >= 156 AND frequency < 625
25000   | 25000   | 40       | frequency >= 40 AND frequency < 156
100000  | 100000  | 10       | frequency >= 10 AND frequency < 40
200000  | 200000  | 5        | frequency >= 5 AND frequency < 10
400000  | 500000  | 2        | frequency >= 2 AND frequency < 5
600000  | 1000000 | 1        | frequency  = 1

Como a coluna condserá usada no SQL dinâmico mais abaixo, você deve tornar esta tabela segura . Sempre qualifique o esquema da tabela se não puder ter certeza de um current apropriado search_pathe revogue os privilégios de gravação de public(e qualquer outra função não confiável):

REVOKE ALL ON public.lex_freq FROM public;
GRANT SELECT ON public.lex_freq TO public;

A tabela lex_freqserve a três propósitos:

• Crie índices parciais necessários automaticamente.
• Forneça etapas para a função iterativa.
• Metainformações para ajuste.

Índices

Esta DOinstrução cria todos os índices necessários:

DO
$$
DECLARE
   _cond text;
BEGIN
   FOR _cond IN
      SELECT cond FROM public.lex_freq
   LOOP
      IF _cond LIKE 'frequency =%' THEN
         EXECUTE 'CREATE INDEX ON lexikon(lset) WHERE ' || _cond;
      ELSE
         EXECUTE 'CREATE INDEX ON lexikon(lset, frequency DESC) WHERE ' || _cond;
      END IF;
   END LOOP;
END
$$

Todos esses índices parciais juntos abrangem a tabela uma vez. Eles têm aproximadamente o mesmo tamanho de um índice básico em toda a tabela:

SELECT pg_size_pretty(pg_relation_size('lexikon'));       -- 50 MB
SELECT pg_size_pretty(pg_total_relation_size('lexikon')); -- 71 MB

Apenas 21 MB de índices para tabela de 50 MB até agora.

Eu crio a maioria dos índices parciais em arquivos (lset, frequency DESC). A segunda coluna só ajuda em casos especiais. Mas como as duas colunas envolvidas são do tipo integer, devido às especificidades do alinhamento de dados em combinação com MAXALIGN no PostgreSQL, a segunda coluna não aumenta o índice. É uma pequena vitória por quase nenhum custo.

Não faz sentido fazer isso para índices parciais que abrangem apenas uma única frequência. Esses são apenas (lset). Os índices criados têm a seguinte aparência:

CREATE INDEX ON lexikon(lset, frequency DESC) WHERE frequency >= 2500;
CREATE INDEX ON lexikon(lset, frequency DESC) WHERE frequency >= 625 AND frequency < 2500;
-- ...
CREATE INDEX ON lexikon(lset, frequency DESC) WHERE frequency >= 2 AND frequency < 5;
CREATE INDEX ON lexikon(lset) WHERE freqency = 1;

Função

A função é um pouco semelhante em estilo à solução de @Jack:

CREATE OR REPLACE FUNCTION f_search(_lset_min int, _lset_max int, _limit int)
  RETURNS SETOF lexikon
$func$
DECLARE
   _n      int;
   _rest   int := _limit;   -- init with _limit param
   _cond   text;
BEGIN 
   FOR _cond IN
      SELECT l.cond FROM public.lex_freq l ORDER BY l.row_min
   LOOP    
      --  RAISE NOTICE '_cond: %, _limit: %', _cond, _rest; -- for debugging
      RETURN QUERY EXECUTE '
         SELECT * 
         FROM   public.lexikon 
         WHERE  ' || _cond || '
         AND    lset >= $1
         AND    lset <= $2
         ORDER  BY frequency DESC
         LIMIT  $3'
      USING  _lset_min, _lset_max, _rest;

      GET DIAGNOSTICS _n = ROW_COUNT;
      _rest := _rest - _n;
      EXIT WHEN _rest < 1;
   END LOOP;
END
$func$ LANGUAGE plpgsql STABLE;

Principais diferenças:

• SQL dinâmico com RETURN QUERY EXECUTE.
  À medida que percorremos as etapas, um plano de consulta diferente pode ser o beneficiário. O plano de consulta para SQL estático é gerado uma vez e reutilizado - o que pode economizar alguma sobrecarga. Mas neste caso a consulta é simples e os valores são bem diferentes. O SQL dinâmico será uma grande vitória.

• Dynamic LIMIT for every query step.
  This helps in multiple ways: First, rows are only fetched as needed. In combination with dynamic SQL this may also generate different query plans to begin with. Second: No need for an additional LIMIT in the function call to trim the surplus.

Benchmark

Setup

I picked four examples and ran three different tests with each. I took the best of five to compare with warm cache:

1. The raw SQL query of the form:

   SELECT * 
   FROM   lexikon 
   WHERE  lset >= 20000
   AND    lset <= 30000
   ORDER  BY frequency DESC
   LIMIT  5;
   

2. The same after creating this index

   CREATE INDEX ON lexikon(lset);
   

   Needs about the same space as all my partial indexes together:

   SELECT pg_size_pretty(pg_total_relation_size('lexikon')) -- 93 MB
   

3. The function

   SELECT * FROM f_search(20000, 30000, 5);
   

Results

SELECT * FROM f_search(20000, 30000, 5);

1: Total runtime: 315.458 ms
2: Total runtime: 36.458 ms
3: Total runtime: 0.330 ms

SELECT * FROM f_search(60000, 65000, 100);

1: Total runtime: 294.819 ms
2: Total runtime: 18.915 ms
3: Total runtime: 1.414 ms

SELECT * FROM f_search(10000, 70000, 100);

1: Total runtime: 426.831 ms
2: Total runtime: 217.874 ms
3: Total runtime: 1.611 ms

SELECT * FROM f_search(1, 1000000, 5);

1: Total runtime: 2458.205 ms
2: Total runtime: 2458.205 ms -- for large ranges of lset, seq scan is faster than index.
3: Total runtime: 0.266 ms

Conclusion

As expected, the benefit from the function grows with bigger ranges of lset and smaller LIMIT.

With very small ranges of lset, the raw query in combination with the index is actually faster. You'll want to test and maybe branch: raw query for small ranges of lset, else function call. You could even just build that into the function for a "best of both worlds" - that's what I would do.

Depending on your data distribution and typical queries, more steps in lex_freq may help performance. Test to find the sweet spot. With the tools presented here, it should be easy to test.

Não vejo razão para incluir a palavra coluna no índice. Então este índice

CREATE INDEX lexikon_lset_frequency ON lexicon (lset, frequency DESC)

fará com que sua consulta seja executada rapidamente.

UPD

Atualmente não há maneiras de criar um índice de cobertura no PostgreSQL. Houve uma discussão sobre esse recurso na lista de discussão do PostgreSQL http://archives.postgresql.org/pgsql-performance/2012-06/msg00114.php

Using a GIST index

Is there a way to make it perform fast, regardless of whether the range (@Low, @High) is wide or narrow and regardless of whether the top frequency words are luckily in the (narrow) selected range?

It depends on what you mean when you fast: you obviously have to visit every row in the range because your query is ORDER freq DESC. Shy of that the query planner already covers this if I understand the question,

Here we create a table with 10k rows of (5::int,random()::double precision)

CREATE EXTENSION IF NOT EXISTS btree_gin;
CREATE TABLE t AS
  SELECT 5::int AS foo, random() AS bar
  FROM generate_series(1,1e4) AS gs(x);

We index it,

CREATE INDEX ON t USING gist (foo, bar);
ANALYZE t;

We query it,

EXPLAIN ANALYZE
SELECT *
FROM t
WHERE foo BETWEEN 1 AND 6
ORDER BY bar DESC
FETCH FIRST ROW ONLY;

We get a Seq Scan on t. This is simply because our selectivity estimates let pg conclude heap access is faster than scanning an index and rechecking. So we make it more juicy by inserting 1,000,000 more rows of (42::int,random()::double precision) that don't fit our "range".

INSERT INTO t(foo,bar)
SELECT 42::int, x
FROM generate_series(1,1e6) AS gs(x);

VACUUM ANALYZE t;

And then we requery,

EXPLAIN ANALYZE
SELECT *
FROM t
WHERE foo BETWEEN 1 AND 6
ORDER BY bar DESC
FETCH FIRST ROW ONLY;

You can see here we complete in 4.6 MS with an Index Only Scan,

                                                                 QUERY PLAN                                                                  
---------------------------------------------------------------------------------------------------------------------------------------------
 Limit  (cost=617.64..617.64 rows=1 width=12) (actual time=4.652..4.652 rows=1 loops=1)
   ->  Sort  (cost=617.64..642.97 rows=10134 width=12) (actual time=4.651..4.651 rows=1 loops=1)
         Sort Key: bar DESC
         Sort Method: top-N heapsort  Memory: 25kB
         ->  Index Only Scan using t_foo_bar_idx on t  (cost=0.29..566.97 rows=10134 width=12) (actual time=0.123..3.623 rows=10000 loops=1)
               Index Cond: ((foo >= 1) AND (foo <= 6))
               Heap Fetches: 0
 Planning time: 0.144 ms
 Execution time: 4.678 ms
(9 rows)

Expanding the range to include the whole table, produces another seq scan -- logically, and growing it with another billion rows would produce another Index Scan.

So in summary,

• It'll perform fast, for the quantity of data.
• Fast is relative to the alternative, if the range isn't selective enough a sequential scan may be as fast as you can get.