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A parallel map/reduce execution framework for sqlite
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A parallel map/reduce execution framework for sqlite databases

Dr Paul Brewer - Economic and Financial Technology Consulting LLC -

multicoresql speeds up sql by executing queries on the multiple cores in a single computer across multiple database shards. It has been tested on 2/4/8 core consumer hardware and the 16 and 32 core rentals available at Amazon EC2 (e.g. c4-8xlarge).

The primary consideration for good performance is that the dataset fit into Linux cache memory.
multicoresql does not require that datasets fit into memory and will happily slog through larger datasets, although performance will be limited by disk bottlenecks. The time required reading disk vs memory can be 10-20x

multicoresql does not at this time distribute tasks across multiple machines.

License: The MIT License

Test Drive with Docker without Installation

If you have the docker container service running on a Linux host, you can test drive multicoresql without installing it permanently. multicoresql does not otherwise use or require docker.

The test drive will always fetch the latest code from this github site.

Run either of these docker commands for a test:

clang build and test: docker run -it drpaulbrewer/multicoresql-test-clang:latest

gcc build and test: docker run -it drpaulbrewer/multicoresql-test-gcc:latest

These containers are located on the public dockerhub and will be automatically downloaded by docker, then run the build and a test script and finally exit.

The test script gives several examples of map and reduce queries and expected results against a sharded table of integers from 1 to 1,000,000. This exercises creation of the shards from a csv file and the map/reduce capability. Among the tests are math queries that sum series for log(2) and PI.

For more control, run manually by appending /bin/bash to the docker run command. Then, at the root prompt for the container, cd /opt/github and ./ to run the build and test script.


multicoresql uses and requires as pre-requsities the SQLite database engine and the scons build system

multicoresql prefers to compile under clang but will also compile under gcc and will install into /usr/local.

Install script for bare Debian and related distros such as Ubuntu:

sudo apt-get install git scons clang sqlite3
git clone
cd multicoresql
# make the build directory where the compiled libraries and executables will be written
mkdir ./build
# scons version of classic "make"
# scons version of classic "make install"
sudo scons install


After running sudo scons install the following will be installed in /usr/local:

/usr/local/lib/ -- shared library of multicoresql functions

/usr/local/bin/3sqls -- query runner that always allocates queries over 3 Linux sqlite3 processes

/usr/local/bin/sqls -- query runner that allocates queries over a selectable number of processes, defaulting to 1 per core

/usr/local/bin/sqlsfromcsv -- from a csv data file and a schema, builds a directory containing sqlite3 database shards

/usr/local/bin/sqlsfromsqlite -- from an existing sqlite3 database table with a shardid column, builds a directory containing sqlite3 database shards

Importing Data

from CSV


is used to create, from a single CSV file, a collection of sqlite3 shard databases suitable for multicoresql.

Running sqlsfromcsv without parameters provides this reminder message:

Usage: sqlsfromcsv csvfile skiplines schemafile tablename dbDir shardcount
Example: sqlsfromcsv example.csv 1 createmytable.sql mytable ./mytable 100

csvfile String, is the /path/to/csvfile.csv

skiplines Number, is the number of lines to skip for omitting CSV header. Usually 1 or 0.

schemafile String, gives a filename or a string containing an sqlite create table statement for the table data

tablename String, is the tablename to create.

dbDir String, is the /path/to/shards for a directory where sqlsfromcsv should output the sqlite3 database shards

shardcount Number, is the number of sqlite3 database shards that should be created from the data in the csv file

Each data row from the csv file is sharded randomly to a shard using a random number generator to select the shard.

from existing SQLite Database


is used to create, from a table in an existing sqlite3 database with a shardid column, a collection of sqlite3 shards.

Running sqlsfromsqlite without parameters provides this reminder message:

usage: sqlsfromsqlite <dbname> <tablename> <dbdir> 

<dbname> String, is the /path/to/an/existing/sqlite3.db

<tablename> String, is the source table for extracting data. Currently each run is restricted to a single table.

<dbDir> String, is the /path/to/shards for a directory where sqlsfromsqlite should output the sqlite3 db shards

shard databases are created and named from the distinct values of the shardid column of the input table.

Allowed characters in the shardid column are [0-9][A-Z][a-z].-_ alphanumeric, dot, dash, and underscore; except that dot is illegal as the first character of a shardid.

Running Queries


Quick Example: Summing a column

sqls -d ./myshards -m "select sum(n) as partsum from mytable;"  -r "select sum(partsum) as fullsum from maptable;"

Notice here:

-d is used to identify a directory containing sqlite3 databases (shards)

-m is used to identify a map query that is run on each shard database;

plus, optionally

-r is used to identify a reduce query that is run on the data collected from the map query.

The map and reduce queries can be sql file names instead of statements.

Multiple sql statements are allowed, separated by ;. Multiple sql files are not allowed.

The reduce query is always written against the table maptable.

maptable is created by multicoresql as the collected results of running the map query on each shard.

Other options not shown:

-c number specifies how many Linux processes to use for the map query. The default is to create a number of processes equal to the number of cpu cores.

-v verbose. prints settings before executing query

Map Only

For a map query only the

-d (sqlite3 db shard directory) and -m (mapped sqlite3 query string or sql filename)

parameters are required.

Output formats

Queries are interpreted by the sqlite3 command line shell, and therefore all output formats of sqlite3 defined in the sqlite3 .mode command are supported:

From the sqlite3 documentation:

.mode MODE ?TABLE? Set output mode where MODE is one of:

ascii Columns/rows delimited by 0x1F and 0x1E

csv Comma-separated values

column Left-aligned columns. (See .width)

html HTML <table> code

insert SQL insert statements for TABLE

line One value per line

list Values delimited by .separator strings

tabs Tab-separated values

tcl TCL list elements

See for a complete list of special commands.

Sqlite3 .mode commands would typically be given in the -r reduce query.

You may provide multiple sql commands by using ";" as a separator


Environment Variables

MULTICORE_SQLITE3_BIN specify the /path/to/usr/local/bin/sqlite3 path to the sqlite3 command shell. The default is to search PATH. Useful if you have multiple sqlite3 executables or need a special version.

MULTICORE_SQLITE3_EXTENSIONS a space-separated list of libraries to be loaded by multicoresql via the sqlite3 .load command

Temp Directories

multicoresql creates a temporary directories while running, in /tmp/multicoresql-XXXXXX where X is an alphanumeric character.

Temporary directories are typically removed on successful completion of a query or command, but are left behind by failed queries and commands. This is by design, and allows for post-failure inspection.

C API Example

Warning: The API is not yet stable. Functions exposed by libmulticoresql are subject to change.

Running a query from "C" currently looks like this:

# include "multicoresql.h"
struct mu_DBCONF *db = mu_opendb("/path/to/sqlite3/database/shards");
# test db!=NULL
struct mu_QUERY *Q = mu_create_query(mapsql_or_fname,
char *result = mu_run_query(db, Q);
if (result) {
    printf("%s\n", result);
} else {
    printf("Error: %s\n", mu_error_string());
    # do not free mu_error_string

./src/multicoresql.h is documented with doxygen-style comments documenting the public functions

FAQ Frequently Asked Questions

FAQ 1. Why can't I run sqls, sqlsfromcsv or other executables from the build directory?

Because by default the linker doesn't know to use the shared libraries that are in the build directory, the current directory, etc.

What you did:

cd /path/to/multicoresql
mkdir ./build
cd ./build
# try to run anything in the build directory

Message: ./sqls: error while loading shared libraries: cannot open shared object file: No such file or directory

Issue: The linker can not load the shared library because it doesn't know where to look


export LD_LIBRARY_PATH=/path/to/multicoresql/build
# dont type /path/to/... literally, instead substitute the full name of the build directory
# now run the executables, e.g. sqls, in the ./build subdirectory and they should work

Note: permanently setting LD_LIBRARY_PATH, e.g., on login or shell startup is considered a bad practice.
It is expected that you only want to run tests or temporarily use the executables from the build.

See also:

FAQ 2. Why can't I run sqls, sqlsfromcsv or other executables after sudo scons install ?

You didn't run sudo ldconfig to update the config file to include the new install in your system's libraries.

Message: Similar to FAQ #1

error while loading shared libraries: cannot open shared object file: No such file or directory

Issue: The linker can not load the shared library /usr/local/lib/ because it doesn't know that it is available for system wide use.

Fix: Once the files are installed in /usr/local, then as root refresh the ldconfig

sudo ldconfig

See also:

FAQ 3. Why did I get a warning telling me to run sudo ldconfig ?

This warning is always generated. You haven't made an error, nor is there an error in the software.

Because multicoresql includes a shared library, I have included a reminder to run sudo ldconfig so that your system will be configured properly to load /usr/local/ when running the multicoresql binaries.

See: FAQ2 for what happens should you fail to run sudo ldconfig as required

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