Documentation Source Text

doc {SQLite As An Application File Format} {appfileformat.html} {
  This article advocates using SQLite as an application file format
  in place of XML or JSON or a "pile-of-file".
}
doc {Well Known Users} {famous.html} {
  This page lists a small subset of the many thousands of devices
  and application programs that make use of SQLite.
}
doc {35% Faster Than The Filesystem} {fasterthanfs.html} {
  This article points out that reading blobs out of an SQLite database
  is often faster than reading the same blobs from individual files in
  the filesystem.
}


###############################################################################
heading {Technical and Design Documentation} technical {
  These documents are oriented toward describing the internal
  implementation details and operation of SQLite.  

<title>35% Faster Than The Filesystem</title>
<tcl>hd_keywords {faster than the filesystem}</tcl>

<table_of_contents>

<h1>Summary</h1>

<p>10 kilobyte blobs (for example, thumbnail
images) can be read out of an SQLite database about 35% faster
than they can be read from individual files on disk.

<p>Furthermore, a single SQLite database holding many thousands
of thumbnail-sized blobs uses about 20% less disk space than
storing the blobs in individual files.

<p>The performance difference arises (we believe) because when
reading from an SQLite database, the open() and close() system calls
only need to be invoked once, whereas
open() and close() must be invoked once for each blob
when reading the blobs from individual files.  In other words, the
overhead of calling open() and close() is greater than the overhead
of using the database.  The size reduction arises from the fact that
individual files are padded out to the next multiple of the filesystem
block size, whereas the blobs are packed more tightly into an SQLite
database.

<h1>How Performance Was Measuremented</h1>

<p>The performance comparison is accomplished using the
[https://www.sqlite.org/src/file/test/kvtest.c|kvtest.c] program
found in the SQLite source tree.
To compile the test program, first gather the kvtest.c source file
into a directory with the [amalgamation|SQLite amalgamation] source
files "sqlite3.c" and "sqlite3.h".  Then on unix, run a command like
the following:

<codeblock>
gcc -Os -I. -DSQLITE_DIRECT_OVERFLOW_READ kvtest.c sqlite3.c \
    -o kvtest -ldl -lpthread
</codeblock>

<p>
Generate a test database with 100,000 random blobs, each 10,000 bytes in
size using a command like this:

<codeblock>
./kvtest init test1.db --count 100k --size 10k
</codeblock>

<p>
Next, make copies of all the blobs into individual files in a directory
using commands like this:

<codeblock>
mkdir test1.dir
./kvtest export test1.db test1.dir
</codeblock>

<p>
Measure the performance for reading blobs from the database and from
individual files using these commands:

<codeblock>
./kvtest run test1.db --count 100k --blob-api
./kvtest run test1.dir --count 100k
</codeblock>

<p>
Depending on your platform, you should see that reads from the test1.db
database file are about 35% faster than reads from individual files in
the test1.dir folder.

<h2>Variations</h2>

<p>The [-DSQLITE_DIRECT_OVERFLOW_READ] compile-time option causes SQLite
to bypass its page cache when reading content from overflow pages.  This
helps database reads of 10K blobs run a little faster, but not all that much
faster.  SQLite still holds a speed advantage over direct filesystem reads
without the SQLITE_DIRECT_OVERFLOW_READ compile-time option.

<p>Other compile-time options such as using -O3 instead of -Os or
using [-DSQLITE_THREADSAFE=0] and/or some of the other
[recommended compile-time options] might help SQLite to run even faster
relative to direct filesystem reads.

<p>When constructing the test data, trying varying the size of the blob.
The performance advantage will shift toward direct filesystem reads as
the size of blobs increase, since the cost of invoking open() and close()
will be amortized over more bytes transferred using read().  The break-even
point, the point where it becomes faster to read directly from the filesystem,
will vary from one system to another.

<p>The --blob-api option causes database reads to occur using the
[sqlite3_blob_open()], [sqlite3_blob_reopen()], and [sqlite3_blob_read()]
interfaces instead of using SQL statements.  Without the --blob-api
option, a separate SQL statement is run for reading each blob and
the performance of reading from the database is approximately
the same as the performance from reading directly from files.
This is still a significant finding, however, since few people would
expect an SQL database to run as fast as a direct read from a file
(much less faster) and yet it does.

<p>If the --random option is added to the "run" command, that causes the
blobs to be read in a random order.  This causes a noticable decrease in
the performance of database reads, since it forces more movement of B-Tree
cursors.  When --random is used and --blob-api is omitted, reading directly
from files on disk is generally a little faster, but reads from the database
are still competitive.

<h1>Other Considerations</h1>

<p>Other SQL database engines advise applications to store blobs in separate
files and then store the filename in the database.  In that case, where
the database must first be consulted to find the filename before opening
and reading the file, simply storing the entire blob in the database is
gives much faster read performance with SQLite.
See the [Internal Versus External BLOBs] article for more information.

<p>This report only looks at the performance of reads, not writes.
Because SQLite implements [atomic commit|power-safe ACID transactions]
we expect that write performance into SQLite will be slower than writing
directly to individual files.  However, if ACID transactions are disabled
via [PRAGMA journal_mode|PRAGMA journal_mode=OFF]
(thus putting SQLite on equal footing with the filesystem) and the
[sqlite3_blob_write()] interface is used, SQLite might well be competitive
or even faster than writes to separate files on disk.  That is an
experiment we have not yet run.