All indexes in LightDB
are secondary indexes, meaning that each index is
stored separately from the table's main data area (which is called the
table's heap
in LightDB terminology). This means that
in an ordinary index scan, each row retrieval requires fetching data from
both the index and the heap. Furthermore, while the index entries that
match a given indexable WHERE
condition are usually
close together in the index, the table rows they reference might be
anywhere in the heap. The heap-access portion of an index scan thus
involves a lot of random access into the heap, which can be slow,
particularly on traditional rotating media. (As described in
Section 12.5, bitmap scans try to alleviate
this cost by doing the heap accesses in sorted order, but that only goes
so far.)
To solve this performance problem, LightDB supports index-only scans, which can answer queries from an index alone without any heap access. The basic idea is to return values directly out of each index entry instead of consulting the associated heap entry. There are two fundamental restrictions on when this method can be used:
The index type must support index-only scans. B-tree indexes always do. Other index types have no support. The underlying requirement is that the index must physically store, or else be able to reconstruct, the original data value for each index entry. As a counterexample, GIN indexes cannot support index-only scans because each index entry typically holds only part of the original data value.
The query must reference only columns stored in the index. For
example, given an index on columns x
and y
of a table that also has a
column z
, these queries could use index-only scans:
SELECT x, y FROM tab WHERE x = 'key'; SELECT x FROM tab WHERE x = 'key' AND y < 42;
but these queries could not:
SELECT x, z FROM tab WHERE x = 'key'; SELECT x FROM tab WHERE x = 'key' AND z < 42;
(Expression indexes and partial indexes complicate this rule, as discussed below.)
If these two fundamental requirements are met, then all the data values required by the query are available from the index, so an index-only scan is physically possible. But there is an additional requirement for any table scan in LightDB: it must verify that each retrieved row be “visible” to the query's MVCC snapshot, as discussed in Chapter 14. Visibility information is not stored in index entries, only in heap entries; so at first glance it would seem that every row retrieval would require a heap access anyway. And this is indeed the case, if the table row has been modified recently. However, for seldom-changing data there is a way around this problem. LightDB tracks, for each page in a table's heap, whether all rows stored in that page are old enough to be visible to all current and future transactions. This information is stored in a bit in the table's visibility map. An index-only scan, after finding a candidate index entry, checks the visibility map bit for the corresponding heap page. If it's set, the row is known visible and so the data can be returned with no further work. If it's not set, the heap entry must be visited to find out whether it's visible, so no performance advantage is gained over a standard index scan. Even in the successful case, this approach trades visibility map accesses for heap accesses; but since the visibility map is four orders of magnitude smaller than the heap it describes, far less physical I/O is needed to access it. In most situations the visibility map remains cached in memory all the time.
In short, while an index-only scan is possible given the two fundamental requirements, it will be a win only if a significant fraction of the table's heap pages have their all-visible map bits set. But tables in which a large fraction of the rows are unchanging are common enough to make this type of scan very useful in practice.
To make effective use of the index-only scan feature, you might choose to
create a covering index, which is an index
specifically designed to include the columns needed by a particular
type of query that you run frequently. Since queries typically need to
retrieve more columns than just the ones they search
on, LightDB allows you to create an index
in which some columns are just “payload” and are not part
of the search key. This is done by adding an INCLUDE
clause listing the extra columns. For example, if you commonly run
queries like
SELECT y FROM tab WHERE x = 'key';
the traditional approach to speeding up such queries would be to create
an index on x
only. However, an index defined as
CREATE INDEX tab_x_y ON tab(x) INCLUDE (y);
could handle these queries as index-only scans,
because y
can be obtained from the index without
visiting the heap.
Because column y
is not part of the index's search
key, it does not have to be of a data type that the index can handle;
it's merely stored in the index and is not interpreted by the index
machinery. Also, if the index is a unique index, that is
CREATE UNIQUE INDEX tab_x_y ON tab(x) INCLUDE (y);
the uniqueness condition applies to just column x
,
not to the combination of x
and y
.
(An INCLUDE
clause can also be written
in UNIQUE
and PRIMARY KEY
constraints, providing alternative syntax for setting up an index like
this.)
It's wise to be conservative about adding non-key payload columns to an index, especially wide columns. If an index tuple exceeds the maximum size allowed for the index type, data insertion will fail. In any case, non-key columns duplicate data from the index's table and bloat the size of the index, thus potentially slowing searches. And remember that there is little point in including payload columns in an index unless the table changes slowly enough that an index-only scan is likely to not need to access the heap. If the heap tuple must be visited anyway, it costs nothing more to get the column's value from there. Other restrictions are that expressions are not currently supported as included columns, and that only B-tree indexes currently support included columns.
Before LightDB had
the INCLUDE
feature, people sometimes made covering
indexes by writing the payload columns as ordinary index columns,
that is writing
CREATE INDEX tab_x_y ON tab(x, y);
even though they had no intention of ever using y
as
part of a WHERE
clause. This works fine as long as
the extra columns are trailing columns; making them be leading columns is
unwise for the reasons explained in Section 12.3.
However, this method doesn't support the case where you want the index to
enforce uniqueness on the key column(s).
Suffix truncation always removes non-key
columns from upper B-Tree levels. As payload columns, they are
never used to guide index scans. The truncation process also
removes one or more trailing key column(s) when the remaining
prefix of key column(s) happens to be sufficient to describe tuples
on the lowest B-Tree level. In practice, covering indexes without
an INCLUDE
clause often avoid storing columns
that are effectively payload in the upper levels. However,
explicitly defining payload columns as non-key columns
reliably keeps the tuples in upper levels
small.
In principle, index-only scans can be used with expression indexes.
For example, given an index on f(x)
where x
is a table column, it should be possible to
execute
SELECT f(x) FROM tab WHERE f(x) < 1;
as an index-only scan; and this is very attractive
if f()
is an expensive-to-compute function.
However, LightDB's planner is currently not
very smart about such cases. It considers a query to be potentially
executable by index-only scan only when all columns
needed by the query are available from the index. In this
example, x
is not needed except in the
context f(x)
, but the planner does not notice that and
concludes that an index-only scan is not possible. If an index-only scan
seems sufficiently worthwhile, this can be worked around by
adding x
as an included column, for example
CREATE INDEX tab_f_x ON tab (f(x)) INCLUDE (x);
An additional caveat, if the goal is to avoid
recalculating f(x)
, is that the planner won't
necessarily match uses of f(x)
that aren't in
indexable WHERE
clauses to the index column. It will
usually get this right in simple queries such as shown above, but not in
queries that involve joins. These deficiencies may be remedied in future
versions of LightDB.
Partial indexes also have interesting interactions with index-only scans. Consider the partial index shown in Example 12.3:
CREATE UNIQUE INDEX tests_success_constraint ON tests (subject, target) WHERE success;
In principle, we could do an index-only scan on this index to satisfy a query like
SELECT target FROM tests WHERE subject = 'some-subject' AND success;
But there's a problem: the WHERE
clause refers
to success
which is not available as a result column
of the index. Nonetheless, an index-only scan is possible because the
plan does not need to recheck that part of the WHERE
clause at run time: all entries found in the index necessarily
have success = true
so this need not be explicitly
checked in the plan. LightDB versions 9.6
and later will recognize such cases and allow index-only scans to be
generated, but older versions will not.