November 2019

The details on how tables and indexes work. Rows,
pages, the heap and indexes are all covered, both on-disk layout and

SQL (or relational) databases are some of oldest and probably still are the
single most commonly used database technology – both in database server form
(such as postgresql) or in library form (sqlite).

Despite this, SQL databases’ internal operations can be mysterious to a
surprisingly large number of people – even those who work with them daily.

Unlike other database technologies, such key-value stores or graph
databases, relational databases are not based on a single data structure but a
combination of several different data structures. Understanding this
combination is not difficult and can help a lot when working with a SQL

The relation

Programmers most often work with just two primary data types: arrays and
associative arrays.

Arrays allow for lists of objects, providing a way of
grouping multiple objects together.

diagram of an array
Example array holding cats in an imaginary animal shelter

Array implementation details vary widely but in general: an item can be
added to an array at the end in a constant amount of time no matter the size of
the array already. Searching for an given item is different: each entry in the
array must be checked and this takes a length of time proportional to the size
of the array at present (deletion has similar performance characteristics).

Operation Time (worst-case)
Insert Constant / O(1)
Search/Delete Linear / O(n)

Associative arrays (often called “hash-tables” after the
most common implementation) allow for reference to items via a “key”.

diagram of an associate array

Example hash table holding the same cats, “keyed” by their
registration number.

This key must be unique for each item but associate arrays typically allow
for fast insert, search and delete so long as the key is being used.
If the key isn’t being used for the operation, the entire data structure must
be traversed, just as with arrays.

Operation Time (worst-case)
Insert Constant / O(1)
Search/Delete (by key) Constant / O(1)
Search/Delete (otherwise) Linear / O(n)

SQL databases use a different type: the relation.

A relation is a generalisation of both of these data types. A relation is a
set of tuples. Each tuple can hold the details of a single item but the
individual fields are split out and accessible on the relation level. Within a
relation, the tuple form is exactly the same for each tuple.

diagram of a relation

Example relation holding the same cats.

Note that properties of the cats are broken out and available at the
relation level.

Relations are useful because they expose the internal fields of what was
previously an opaque object. Fields like age and breed are now open to
operations on the relation level itself. This means that more complicated
operations are possible on the relation without having to write specific
functions to ask questions of objects.

Being able to operate on fields directly also allows for easy definition of
“indexes”, which speed up access and search within the database.

SQL databases’ implementation of relations varies a little but are mostly a
combination of two different underlying data structures: the heap
and b-trees. Consequently the operation times
are generally logarithmic – meaning that they grow slowly as the size of the
data-set grows.

Operation Time (worst-case)
Insert Logarithmic / O(log n)
Search/Delete Logarithmic / O(log n)

The rest of this discussion will be about how these two underlying data
structures work.

The row

SQL databases aren’t a perfect representation of relations. Instead of
relations and tuples they represent “tables” and “rows”. Rows are functionally
the same as tuples but tables have a quirk – duplicate rows are allowed. This
does matter for writing queries but doesn’t change the fundamentals of how
things work so I’ll treat them as the same and will use SQL terminology from
now on.

Rows are typically represented, both on-disk and in memory, as a short
header followed a concatenation of row values.

diagram of a row

Example row holding data on Alfred.

If we assume that 4 bytes (32 bits) are used to store an int and
strings use one byte per character (as is the case in ASCII) and are
terminated in a “NUL” byte the length of row data here is 4 7 4
18 = 33 bytes. Rows are typically much longer in practice.

The size of the header varies by database system but is usually kept
as small as possible (27 bytes in postgresql).

The header will often contain information about the total length of the row.
Depending on the specific implementation it might also contain other
information, especially information about concurrency (ie: which transactions a
given row is visible in).

The rest of the row is just the values, one after another.

There are two kinds of row values: those that are fixed length and those
that are variable length. A 32-bit integer or a 64 bit float is an example of a
fixed length value those can be represented directly. Strings, binary blobs,
JSON, XML and other types can vary in length can be handled in one of two

One common method is to use a special “terminator” character (often NULL or
). This method means that null either cannot be allowed to
appear in the string or that NULLs have some special handling to avoid
ambiguity. The other alternative is to have a a leading length indicator to
specify how many bytes long the string is.

The order that the values of the row appear in could be included in the
header but usually is table level metadata to avoid having to waste space
repeatedly including information about what the order of the row values is.

The heap

Rows are stored in what is commonly called “the heap”. This is where the
main body of table data is held. The heap is broken down further into pages.
Pages are short (often 4 or 8 kilobyte) collections of rows, again with a small
header to indicate how many rows are present.

diagram of a heap page

Example page. Note that the page has a fixed size and that free
space is at the end.

In practice many systems will make it possible for a row to cross

Pages are a common pattern in systems which manage data that is moved
frequently between disk and main memory. For exmaple, operating systems use
pages in their virtual memory implementations.

diagram of a heap page

Example heap. Just a header and then page after page. The heap can
grow to an unlimited size. Note that there is a gap where Page 3 has
been deleted.

Working through pages, operations on the heap are simple. To perform a
write, the relevant page is read from disk into memory if necessary, then
edited and then written back to disk. Updates are similar. Pages can be
appended to until there is no more space for any additional rows. Deletion is
similarly simple: the page is rewritten without the unwanted row.

Use of pages instead of storing rows directly in a file has a few
advantages. The first is that pages are highly amenable to IO buffering, which
helps performance. In many IO systems reading a whole page from disk takes the
same amount of time as would reading a 40 byte row directly. Once a page has
been read into memory it can be kept there as a cache until there is a shortage
of memory and it has to be evicted from memory.

The second reason is that access to pages by number is fast – the database
system seeks to the start of the page ((page size * page number) 1) and then
reads forward as many bytes as the page size. This makes the heap and page
system very efficient if there is an external reference to the page number.

The third benefit is that pages allow fragmentation to be dealt with
piecewise. Fragmentation happens when rows are deleted, creating gaps. If rows
were written directly, these gaps would be difficult to fill – only a row of
the same or equal size as the deleted row could be placed in the gap. With
pages, fragmentation is limited as pages are written whole with the gaps moved
to the end of the page. Perhaps when a page is completely empty a page from the
end of the heap can be moved and written in this location instead.

Heaps overall have great performance for insertion. A free space has to be
found and then the row written into it. Depending on the method for finding
this can be as simple as writing to the last page and calling it a day.

The problem heaps don’t solve is search. When you want to find all rows
where a=1, you need to work through every page to find it. That’s often called
a “full table scan” and performs very poorly. Narrowing the number of pages
that must be consulted improves performance hugely. What is required for this
is an index.

The index

The crucial addition on top of the heap is the index. SQL databases
overwhelmingly use B-trees (or minor variations) as indexes.

B-trees were invented at Boeing in the 1970s. Despite allusions in the name,
they aren’t binary trees – though they are balanced. B-trees work in the
following way: each node holds an ordered list. Each element in that list is
either a value itself or a reference to another node that holds the value in
question (or which holds yet another reference to that value).

A node will usually fill a database page – so the ordered list will contain
hundreds or thousands of elements. This makes b-trees very dense per-page and
hopefully very shallow – ideal for looking up a key with the minimum of
dereferences (each dereference could cause a page load). Some database systems
use the same heap structure for heap pages and index pages.

diagram of a b-tree

Example b-tree. In reality they are likely to be thousands of
entries wide – however they will usually be similarly shallow – often
just 2 or 3 levels.

All operations on B-trees have logarithmic complexity: execution time grows
slowly as the number of items in the index increases. This means that they
remain fast even with large database sizes.

For many it can be surpising the indices aren’t hash tables. After all,
don’t hash tables have faster access and insert times (constant) than b-trees?
There are a few reasons for this.

Firstly, a hash table has a fixed size: if you have a hash function that
outputs a two byte hash your hash table is limited to 65535 distinct
locations1. To deal with rising occupancy in your
hashtable you have to “rebalance” – that is, increase the hash size and
recalculcate for all entries. Unfortunately rebalancing means having to
recalcuate every hash and hold both the new and old table in memory for a

Second is that hash tables are relatively sparse in memory. A 32-bit hash
table requires 2GB of ram. Assuming that a rebalance occurs once occupancy is
greater than 75% that means that at least 500mb of memory is wasted –
likely more. This sparseness means they compete with the heap for memory.

The third reason is that hashing can’t really help with operations where you
want all values between two numbers – say, all cats adopted last week – as hash
tables don’t maintain order. This also causes hassle for the query optimiser as
an estimate of rows returned cannot be provided until the query is under

The combination in practice

A single table will have a single heap and multiple indexes. Each
index will document, for each entry, in which page (or pages) a the relevant
row can be found. This allows for general logarithmic access by any key if the
relevant index exists.

Further reading

  1. Chapter
    12 of the Postgres manual
    gives a good overview of the disk layout
    of their page and heap system.

  2. SQL performance
    gives a great overview of how querying works in detail
    and what you can do to improve your queries.

  3. Transaction
    Processing: Concepts and Techniques
    covers the same material but in
    a lot more detail (and much more besides).

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