dsq
spyql
Our great sponsors
dsq | spyql | |
---|---|---|
20 | 23 | |
3,603 | 902 | |
3.9% | - | |
4.3 | 0.0 | |
7 months ago | over 1 year ago | |
Go | Jupyter Notebook | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dsq
-
Tracking SQLite Database Changes in Git
You might want to look at tsv-utils, or a similar project: https://github.com/eBay/tsv-utils
For the SQL part, but maybe a lot heavier, you can use one of the projects listed on this page: https://github.com/multiprocessio/dsq (No longer maintained, but has links to lots of other projects)
-
DuckDB: Querying JSON files as if they were tables
Welcome to the gang! :)
- Ask HN: Programs that saved you 100 hours? (2022 edition)
-
Command-line data analytics made easy
SPyQL is really cool and its design is very smart, with it being able to leverage normal Python functions!
As far as similar tools go, I recommend taking a look at DataFusion[0], dsq[1], and OctoSQL[2].
DataFusion is a very (very very) fast command-line SQL engine but with limited support for data formats.
dsq is based on SQLite which means it has to load data into SQLite first, but then gives you the whole breath of SQLite, it also supports many data formats, but is slower at the same time.
OctoSQL is faster, extensible through plugins, and supports incremental query execution, so you can i.e. calculate a running group by + count while tailing a log file. It also supports normal databases, not just file formats, so you can i.e. join with a Postgres table.
[0]: https://github.com/apache/arrow-datafusion
[1]: https://github.com/multiprocessio/dsq
[2]: https://github.com/cube2222/octosql
Disclaimer: Author of OctoSQL
-
Jq Internals: Backtracking
> dsq registers go-sqlite3-stdlib so you get access to numerous statistics, url, math, string, and regexp functions that aren't part of the SQLite base. (https://github.com/multiprocessio/dsq#standard-library)
Ah, I wondered if they rolled their own SQL parser, but no, I now see the sqlite.go in the repo and all is made clear
-
Run SQL on CSV, Parquet, JSON, Arrow, Unix Pipes and Google Sheet
I am currently evaluating dsq and its partner desktop app DataStation. AIUI, the developer of DataStation realised that it would be useful to extract the underlying pieces into a standalone CLI, so they both support the same range of sources.
dsq CLI - https://github.com/multiprocessio/dsq
- multiprocessio / dsq :
-
OctoSQL allows you to join data from different sources using SQL
OctoSQL is an awesome project and Kuba has a lot of great experience to share from building this project I'm excited to learn from.
And while building a custom database engine does allow you to do pretty quick queries, there are a few issues.
First, the SQL implemented is nonstandard. As I was looking for documentation and it pointed me to `SELECT * FROM docs.functions fs`. I tried to count the number of functions but octosql crashed (a Go panic) when I ran `SELECT count(1) FROM docs.functions fs` and `SELECT count() FROM docs.functions fs` which is what I lazily do in standard SQL databases. (`SELECT count(fs.name) FROM docs.function fs` worked.)
This kind of thing will keep happening because this project just doesn't have as much resources today as SQLite, Postgres, DuckDB, etc. It will support a limited subset of SQL.
Second, the standard library seems pretty small. When I counted the builtin functions there were only 29. Now this is an easy thing to rectify over time but just noting about the state today.
And third this project only has builtin support for querying CSV and JSON files. Again this could be easy to rectify over time but just mentioning the state today.
octosql is a great project but there are also different ways to do the same thing.
I build dsq [0] which runs all queries through SQLite so it avoids point 1. It has access to SQLite's standard builtin functions plus* a battery of extra statistic aggregation, string manipulation, url manipulation, date manipulation, hashing, and math functions custom built to help this kind of interactive querying developers commonly do [1].
And dsq supports not just CSV and JSON but parquet, excel, ODS, ORC, YAML, TSV, and Apache and nginx logs.
A downside to dsq is that it is slower for large files (say over 10GB) when you only want a few columns whereas octosql does better in some of those cases. I'm hoping to improve this over time by adding a SQL filtering frontend to dsq but in all cases dsq will ultimately use SQLite as the query engine.
You can find more info about similar projects in octosql's Benchmark section but I also have a comparison section in dsq [2] and an extension of the octosql benchmark with different set of tools [3] including duckdb.
Everyone should check out duckdb. :)
[0] https://github.com/multiprocessio/dsq
[1] https://github.com/multiprocessio/go-sqlite3-stdlib
-
GitHub Actions are down again
What's annoying about this is that the PR doesn't even say it's trying to run tests. It says everything is passing and just doesn't list the actions.
For a second I thought someone must have deleted the actions yaml files.
This is a dangerous failure mode.
-
Xlite: Query Excel, Open Document spreadsheets (.ods) as SQLite virtual tables
This is a cool project! But if you query Excel and ODS files with dsq you get the same thing plus a growing standard library of functions that don't come built into SQLite such as best-effort date parsing, URL parsing/extraction, statistical aggregation functions, math functions, string and regex helpers, hashing functions and so on [1].
spyql
-
Fq: Jq for Binary Formats
I prefer a SQL-like format. Itβs not as complete but it cover most of the day-to-day use cases. Take a look at https://github.com/dcmoura/spyql (I am the author). Congrats on fq!
-
Command-line data analytics made easy with SPyQL
SPyQL documentation: spyql.readthedocs.io
-
This Week In Python
spyql β Query data on the command line with SQL-like SELECTs powered by Python expressions
- Command-line data analytics made easy
-
Jc β JSONifies the output of many CLI tools
This is great!
I am the author of SPyQL [1]. Combining JC with SPyQL you can easily query the json output and run python commands on top of it from the command-line :-) You can do aggregations and so forth in a much simpler and intuitive way than with jq.
I just wrote a blogpost [2] that illustrates it. It is more focused on CSV, but the commands would be the same if you were working with JSON.
- The fastest command-line tools for querying large JSON datasets
-
Working with more than 10gb csv
You can import the data into a PostgreSQL/MySQL/SQLite/... database and then query the database. However, even with the right choice of indexes, it might take a while to run queries on a table with hundreds of millions of records. You can easily import your data to these databases with SpyQL: $ spyql "SELECT * FROM csv TO sql(table=my_table_name) | sqlite3 my.db" (you would need to create the table my_table_name before running the command).
-
ClickHouse Cloud is now in Public Beta
https://github.com/dcmoura/spyql/blob/master/notebooks/json_...
And ClickHouse looks like a normal relational database - there is no need for multiple components for different tiers (like in Druid), no need for manual partitioning into "daily", "hourly" tables (like you do in Spark and Bigquery), no need for lambda architecture... It's refreshing how something can be both simple and fast.
- A SQLite extension for reading large files line-by-line
-
I want to convert a large JSON file into Tabular Format.
I thought this library was pretty nifty for json. It's also relatively fast compared to most json parsers: https://github.com/dcmoura/spyql
What are some alternatives?
go-duckdb - go-duckdb provides a database/sql driver for the DuckDB database engine.
prql - PRQL is a modern language for transforming data β a simple, powerful, pipelined SQL replacement
querycsv - QueryCSV enables you to load CSV files and manipulate them using SQL queries then after you finish you can export the new values to a CSV file
malloy - Malloy is an experimental language for describing data relationships and transformations.
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
tresql - Shorthand SQL/JDBC wrapper language, providing nested results as JSON and more
q - q - Run SQL directly on delimited files and multi-file sqlite databases
Preql - An interpreted relational query language that compiles to SQL.
xlite - Query Excel spredsheets (.xlsx, .xls, .ods) using SQLite
prosto - Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby
serviceq - Super fault-tolerant HTTP load balancer & queue. White paper for reference - https://github.com/gptankit/serviceq-paper
pxi - π§ pxi (pixie) is a small, fast, and magical command-line data processor similar to jq, mlr, and awk.