Musoq lets you use SQL-like queries on files, directories, images and other data sources without a database. It's designed to ease life for developers.
To try out Musoq, follow the instructions in CLI repository.
- Query files and directories using familiar SQL-like syntax
- Analyze data in place, without importing into a database
- Extend functionality with plugins for various data sources
- Run on Windows, Linux, and Docker (MacOS support planned)
- Create custom data source plugins to fit your needs
Musoq aims to make data exploration easier, whether you're analyzing log files, searching through directories, or extracting information from CSVs. It's a tool built to save time and reduce complexity in everyday data tasks.
- OS: Query your filesystem, processes, and system metadata - from file contents to EXIF data
- Archives: Treat ZIP and other archive files as queryable tables
- FlatFile: Work with any text-based files as database tables
- SeparatedValues: Handle CSV, TSV and other delimited files with SQL capabilities
- Git: Query Git repositories - analyze commits, diffs, branches and more
- Roslyn: Analyze C# code structure, metrics and patterns using SQL
- Docker: Query containers, images and Docker resources (experimental)
- Kubernetes: Interact with K8s clusters, pods and services (experimental)
- Postgres: Query PostgreSQL databases directly (experimental)
- Sqlite: Work with SQLite databases (experimental)
- Airtable: Access Airtable bases through SQL interface
- Json: Query JSON files with SQL syntax
- OpenAI: Enhance queries with GPT models for text extraction and analysis
- Ollama: Use open-source LLMs for data extraction and processing
- CANBus: Analyze CAN bus data and DBC files for automotive applications
- Time: Work with time-series data and schedules
- System: Core utilities including ranges, dual tables and common functions
Musoq is designed to simplify data querying across various sources using SQL-like syntax. To help you decide if Musoq is right for your needs, here's what you should know:
- Quick, ad-hoc querying of diverse data sources (files, CSVs, archives, etc.)
- Providing SQL-like syntax for non-database data
- Simplifying complex queries with innovative syntax features
- Handling small to medium-sized datasets efficiently
- Full SQL standard compliance (I prioritize user-friendly syntax over strict standards)
- High-performance processing of large datasets
- A mature, unchanging API
- Your feedback and contributions are welcome to shape the project's future
If Musoq aligns with your needs, I'm excited to have you on board! If not, I appreciate your interest and welcome any suggestions for improvement.
Look at the documentation for Musoq at https://puchaczov.github.io/Musoq/. What's inside:
- How to run this tool
- Practical examples
- Available Tables & Methods
Musoq might be using in various places, including:
-- How many commits does the repositroy have
select
Count(1) as CommitsCount
from #git.repository('D:\repos\efcore') r
cross apply r.Commits c
group by 'fake'
-- Top 10 authors by number of commits
select
c.AuthorEmail,
Count(c.Sha) as CommitCount
from #git.repository('/path/to/repo') r
cross apply r.Commits c
group by c.AuthorEmail
having Count(c.Sha) > 10
order by Count(c.Sha) desc
take 10-- Extract all SQL queries from tests from loaded solution
select
p.RowNumber() as RowNumber,
p.Name,
c.Name,
m.Name,
g.ToBase64(g.GetBytes(g.LlmPerform('You are C# developer. Your task is to extract SQL query without any markdown characters. If no sql, then return empty string', m.Body))) as QueryBase64
from #csharp.solution('/some/path/Musoq.sln') s
inner join #openai.gpt('gpt-4o') g on 1 = 1
cross apply s.Projects p
cross apply p.Documents d
cross apply d.Classes c
cross apply c.Attributes a
cross apply c.Methods m
where a.Name = 'TestClassAttribute'
-- How many lines of code does the project contains?
select
Sum(c.LinesOfCode) as TotalLinesOfCode,
Sum(c.MethodsCount) as TotalMethodsCount
from #csharp.solution('/some/path/Musoq.sln') s
cross apply s.Projects p
cross apply p.Documents d
cross apply d.Classes c
group by 'fake'
-- Top 3 methods with highest complexity
select
c.Name as ClassName,
m.Name as MethodName,
Max(m.CyclomaticComplexity) as HighestComplexity
from #csharp.solution('/some/path/Musoq.sln') s
cross apply s.Projects p
cross apply p.Documents d
cross apply d.Classes c
cross apply c.Methods m
group by c.Name, m.Name
order by Max(m.CyclomaticComplexity) desc
take 3-- Look for files greater than 1 gig
SELECT
FullName
FROM #os.files('/some/path', true)
WHERE ToDecimal(Length) / 1024 / 1024 / 1024 > 1
-- Look for how many space does the extensions occupies within some directory
SELECT
Extension,
Round(Sum(Length) / 1024 / 1024 / 1024, 1) as SpaceOccupiedInGB,
Count(Extension) as HowManyFiles
FROM #os.files('/some/directory', true)
GROUP BY Extension
HAVING Round(Sum(Length) / 1024 / 1024 / 1024, 1) > 0
-- Query your images folder, filter to include only .jpg files and show it's EXIF metadata
SELECT
f.Name,
m.DirectoryName,
m.TagName,
m.Description
FROM #os.files('./Images', false) f CROSS APPLY #os.metadata(f.FullName) m
WHERE f.Extension = '.jpg'
-- Get first, last 5 bits from files and consecutive 10 bytes of file with offset of 5 from tail
SELECT
ToHex(Head(5), '|'),
ToHex(Tail(5), '|'),
ToHex(GetFileBytes(10, 5), '|')
FROM #os.files('/some/directory', false)
-- Diff between two folders
SELECT
(CASE WHEN SourceFile IS NOT NULL
THEN SourceFileRelative
ELSE DestinationFileRelative
END) AS FullName,
(CASE WHEN State = 'TheSame'
THEN 'The Same'
ELSE State
END) AS Status
FROM #os.dirscompare('E:\DiffDirsTests\A', 'E:\DiffDirsTests\B')
-- Compute Sha on files
SELECT
FullName,
f.Sha256File()
FROM #os.files('@qfs/', false) f-- Query .csv files from archive file
table PeopleDetails {
Name 'System.String',
Surname 'System.String',
Age 'System.Int32'
};
couple #separatedvalues.comma with table PeopleDetails as SourceOfPeopleDetails;
with Files as (
select
a.Key as InZipPath
from #archives.file('./Files/Example2/archive.zip') a
where
a.IsDirectory = false and
a.Contains(a.Key, '/') = false and
a.Key like '%.csv'
)
select
f.InZipPath,
b.Name,
b.Surname,
b.Age
from #archives.file('./Files/Example2/archive.zip') a
inner join Files f on f.InZipPath = a.Key
cross apply SourceOfPeopleDetails(a.GetStreamContent(), true, 0) as b;-- Describe images using AI
SELECT
llava.DescribeImage(photo.Base64File()),
photo.FullName
FROM #os.files('/path/to/directory', false) photo
INNER JOIN #ollama.models('llava:13b', 0.0) llava ON 1 = 1
-- Count tokens in Markdown and C files
SELECT
SUM(gpt.CountTokens(f.GetFileContent())) AS TokensCount
FROM #os.files('/path/to/directory', true) f
INNER JOIN #openai.gpt('gpt-4') gpt ON 1 = 1
WHERE f.Extension IN ('.md', '.c')
-- Extract data from recipe image
select s.Shop, s.ProductName, s.Price from #stdin.image('OpenAi', 'gpt-4o') s
-- Compute sentiment on a comments
SELECT
csv.PostId,
csv.Comment,
gpt.Sentiment(csv.Comment) as Sentiment,
csv.Date
FROM #separatedvalues.csv('/home/somebody/comments_sample.csv', true, 0) csv
INNER JOIN #openai.gpt('gpt-4-1106-preview') gpt on 1 = 1-- Extract imports from proto file:
-- import "some/some_message_1"
-- ant turn them into:
-- some/SomeMessage1
with Events as (
select
Replace(
Replace(
Line,
'import "',
''
),
'.proto";',
''
) as Namespace
from #flat.file('/path/to/file.proto') f
where
Length(Line) > 6 and
Head(Line, 6) = 'import' and
IndexOf(Line, 'some') <> -1
)
select
Choose(
0,
Split(e.Namespace, '/')
) +
'/' +
Replace(
ToTitleCase(
Choose(
1,
Split(e.Namespace, '/')
)
),
'_',
''
) as Events
from Events e
-- Count word frequencies within text
with p as (
select
Replace(Replace(ToLowerInvariant(w.Value), '.', ''), ',', '') as Word
from #flat.file('/some/path/to/text/file.txt') f cross apply f.Split(f.Line, ' ') w
)
select
Count(p.Word, 1) as AllWordsCount,
Count(p.Word) as SpecificWordCount,
Round(ToDecimal((Count(p.Word) * 100)) / Count(p.Word, 1), 2) as WordFrequencies,
Word
from p group by p.Word having Count(p.Word) > 1-- Extract structured data from unstructured text
select s.Who, s.Age from #stdin.text('Ollama', 'llama3.1') s where ToInt32(s.Age) > 26 and ToInt32(s.Age) < 75-- Count occurrences of each name in a table with headers
select t.Name, Count(t.Name) from #stdin.table(true) t group by t.Name having Count(t.Name) > 1select
m.Id,
m.Name,
m.DLC,
m.Transmitter,
m.Comment as MessageComment,
m.CycleTime,
s.Name,
s.StartBit,
s.Length,
s.ByteOrder,
s.InitialValue,
s.Factor,
s.IsInteger,
s.Offset,
s.Minimum,
s.Maximum,
s.Unit,
s.Comment as SignalsComment
from #can.messages('@qfs/Model3CAN.dbc') m cross apply m.Signals s
Musoq supports a rich set of SQL-like features:
- Parameterizable sources
- Optional query reordering (FROM ... WHERE ... GROUP BY ... HAVING ... SELECT ... SKIP N TAKE N2)
- Use of
*to select all columns - GROUP BY and HAVING operators
- SKIP & TAKE operators
- Set operators (UNION, UNION ALL, EXCEPT, INTERSECT)
- LIKE / NOT LIKE operator
- RLIKE / NOT RLIKE operator (regex)
- CONTAINS operator
- CTE expressions
- IN operator
- INNER, LEFT OUTER, RIGHT OUTER JOIN operator
- ORDER BY operator
- CROSS / OUTER APPLY operator
The order is accidental. I will work on things that are the most urgent from the perspective of my current or near future work I will be using it with.
- Comprehensive documentation
- Roslyn data source
- Improve runtime efficiency
- Parallelize query execution
- Recursive CTE
- Rework JSON & XML support
- Subqueries
- More tests & better handling of syntax / runtime exceptions
If you think something might be important for the project to broaden its capabilities, feel free to submit a feature request.
Musoq is an evolving project designed primarily for querying and analyzing smaller datasets, with a focus on user-friendly and efficient operations. Here's an overview of its current state:
-
Primary Use Case: Musoq serves as a tool for ad-hoc querying data and manipulation tasks. It intentionally support only reads. It excels at handling smaller datasets where its SQL-like syntax can provide more intuitive and efficient data operations.
-
Innovative SQL Syntax: I introduce new SQL syntax variants to simplify some complex queries and reduce the effort required for specific operations. This approach prioritizes user efficiency and ease of use, even if it means deviating from standard SQL in some cases.
-
Development Stage: Musoq is in active development, continuously improving its core functionality and expanding its syntax to better serve its primary use case. This includes introduction of new syntax features sometimes.
-
Dataset Size: At the current stage, Musoq is best suited for smaller to medium-sized datasets. For very large datasets or big data scenarios, traditional big data tools will be more appropriate.
-
Real-World Usage: As the project creator, I use Musoq in various workplaces to facilitate my daily tasks and improve my workflow efficiency. It has proven to be a valuable tool in real-world scenarios, helping me perform data operations more effectively across different professional environments.
-
API and Syntax Stability: The core functionality is stable. These changes are always aimed at improving usability and efficiency. While I strive for backwards compatibility, new syntax features may be introduced regularly.
-
Project Suitability: Musoq is well-suited for projects that involve data analysis, file system operations, and other tasks typically handled by scripting languages. It's designed to be a reliable and efficient tool for these scenarios, especially where its unique syntax features can simplify complex operations.
I'm commited to improving Musoq within its intended scope, with a particular focus on innovative SQL syntax that makes data querying tasks easier. I welcome feedback, bug reports, and contributions from the community, especially those that align with the goal of simplifying complex data operations through clever syntax innovations.
Musoq offers a plugin API that all sources use. To learn how to implement your own plugin, you should examine how existing plugins are created.
I hate loops. Developed out of a need for a versatile tool that could query various data sources with SQL syntax, without those horrible loops, Musoq aims to minimize the effort and time required for data querying and analysis.
Musoq is licensed under the MIT License - see the LICENSE file for details.
Note: While Musoq uses SQL-like syntax, it may not be fully SQL compliant. Some differences may appear, and Musoq implements some experimental syntax and behaviors that are not used by traditional database engines and this is intended!