sq is a command line tool that provides jq-style access to structured data sources: SQL databases, or document formats like CSV or Excel. It is the lovechild of sql+jq.

sq executes jq-like queries, or database-native SQL. It can join across sources: join a CSV file to a Postgres table, or MySQL with Excel.

sq outputs to a multitude of formats including JSON, Excel, CSV, HTML, Markdown and XML, and can insert query results directly to a SQL database.

sq can also inspect sources to view metadata about the source structure (tables, columns, size). You can use sq diff to compare tables, or entire databases. sq has commands for common database operations to copy, truncate, and drop tables.

Find out more at sq.io.

brew install neilotoole/sq/sq

/bin/sh -c "$(curl -fsSL https://sq.io/install.sh)"

scoop bucket add sq https://github.com/neilotoole/sq
scoop install sq

go install github.com/neilotoole/sq

The ghcr.io/neilotoole/sq image is preloaded with sq and a handful of related tools like jq.

# Shell into a one-time container.
$ docker run -it ghcr.io/neilotoole/sq zsh

# Start detached (background) container named "sq-shell".
$ docker run -d --name sq-shell ghcr.io/neilotoole/sq
# Shell into that container.
$ docker exec -it sq-shell zsh 

Running sq in a Kubernetes environment is useful for DB migrations, as well as general data wrangling.

# Start pod named "sq-shell".
$ kubectl run sq-shell --image ghcr.io/neilotoole/sq
# Shell into the pod.
$ kubectl exec -it sq-shell -- zsh 

See other install options.

Use sq help to see command help. Docs are over at sq.io. Read the overview, and tutorial. The cookbook has recipes for common tasks, and the query guide covers sq's query language.

The major concept is: sq operates on data sources, which are treated as SQL databases (even if the source is really a CSV or XLSX file etc.).

In a nutshell, you sq add a source (giving it a handle), and then execute commands against the source.

Initially there are no sources.

$ sq ls

Let's add a source. First we'll add a SQLite database, but this could also be Postgres, SQL Server etc., or a document source such Excel or CSV.

Download the sample DB, and sq add the source.

$ wget https://sq.io/testdata/sakila.db

$ sq add ./sakila.db
@sakila  sqlite3  sakila.db

$ sq ls -v
HANDLE   ACTIVE  DRIVER   LOCATION                         OPTIONS
@sakila  active  sqlite3  sqlite3:///Users/demo/sakila.db

$ sq ping @sakila
@sakila       1ms  pong

$ sq src
@sakila  sqlite3  sakila.db

The sq ping command simply pings the source to verify that it's available.

sq src lists the active source, which in our case is @sakila. You can change the active source using sq src @other_src. When there's an active source specified, you can usually omit the handle from sq commands. Thus you could instead do:

$ sq ping
@sakila  1ms  pong

$ sq add https://acme.s3.amazonaws.com/sales.csv

Fundamentally, sq is for querying data. The jq-style syntax is covered in detail in the query guide.

The above query selected some rows from the actor table. You could also use native SQL, e.g.:

But we're flying a bit blind here: how did we know about the actor table?

sq inspect is your friend.

Use sq inspect -v to see more detail. Or use -j to get JSON output:

Combine sq inspect with jq for some useful capabilities. Here's how to list all the table names in the active source:

$ sq inspect -j | jq -r '.tables[] | .name'
actor
address
category
city
country
customer
[...]

And here's how you could export each table to a CSV file:

$ sq inspect -j | jq -r '.tables[] | .name' | xargs -I % sq .% --csv --output %.csv
$ ls
actor.csv     city.csv	    customer_list.csv  film_category.csv  inventory.csv  rental.csv		     staff.csv
address.csv   country.csv   film.csv	       film_list.csv	  language.csv	 sales_by_film_category.csv  staff_list.csv
category.csv  customer.csv  film_actor.csv     film_text.csv	  payment.csv	 sales_by_store.csv	     store.csv

Note that you can also inspect an individual table:

Read more about sq inspect.

Use sq diff to compare metadata, or row data, for sources, or individual tables.

The default behavior is to diff table schema and row counts. Table row data is not compared in this mode.

Use --data to compare row data.

There are many more options available. See the diff docs.

sq query results can be output in various formats (text, json, csv, etc.). Those results can also be "outputted" as an insert into a database table.

That is, you can use sq to insert results from a Postgres query into a MySQL table, or copy an Excel worksheet into a SQLite table, or a push a CSV file into a SQL Server table etc.

Here we query a CSV file, and insert the results into a Postgres table.

sq can perform the usual joins. Here's how you would join tables actor, film_actor, and film:

$ sq '.actor | join(.film_actor, .actor_id) | join(.film, .film_id) | .first_name, .last_name, .title'

But sq can also join across data sources. That is, you can join an Excel worksheet with a Postgres table, or join a CSV file with MySQL, and so on.

This example joins a Postgres database, an Excel worksheet, and a CSV file.

Read more about cross-source joins in the query guide.

sq provides several handy commands for working with tables: tbl copy, tbl truncate and tbl drop. Note that these commands work directly against SQL database sources, using their native SQL commands.

$ sq tbl copy .actor .actor_copy
Copied table: @sakila.actor --> @sakila.actor_copy (200 rows copied)

$ sq tbl truncate .actor_copy
Truncated 200 rows from @sakila.actor_copy

$ sq tbl drop .actor_copy
Dropped table @sakila.actor_copy

For file-based sources (such as CSV or XLSX), you can sq add the source file, but you can also pipe it:

$ cat ./example.xlsx | sq .Sheet1

Similarly, you can inspect:

$ cat ./example.xlsx | sq inspect

sq knows how to deal with a data source type via a driver implementation. To view the installed/supported drivers:

$ sq driver ls
DRIVER     DESCRIPTION                          
sqlite3    SQLite                               
postgres   PostgreSQL                           
sqlserver  Microsoft SQL Server / Azure SQL Edge
mysql      MySQL                                
csv        Comma-Separated Values               
tsv        Tab-Separated Values                 
json       JSON                                 
jsona      JSON Array: LF-delimited JSON arrays 
jsonl      JSON Lines: LF-delimited JSON objects
xlsx       Microsoft Excel XLSX                 

sq has many output formats:

• --text: Text
• --json: JSON
• --jsona: JSON Array
• --jsonl: JSON Lines
• --csv / --tsv : CSV / TSV
• --xlsx: XLSX (Microsoft Excel)
• --html: HTML
• --xml: XML
• --yaml: YAML
• --markdown: Markdown
• --raw: Raw (bytes)

See CHANGELOG.md.

• Thanks to Diego Souza for creating the Arch Linux package, and @icp for creating the Void Linux package.
• Much inspiration is owed to jq.
• See go.mod for a list of third-party packages.
• Additionally, sq incorporates modified versions of:
  • olekukonko/tablewriter
  • segmentio/encoding for JSON encoding.
• The Sakila example databases were lifted from jOOQ, which in turn owe their heritage to earlier work on Sakila.
• Date rendering via ncruces/go-strftime.
• A modified version dolmen-go/contextio is incorporated into the codebase.
• djherbis/buffer is used for caching.
• A forked version of nightlyone/lockfile is incorporated.
• The human-friendly text log format handler is a fork of lmittmann/tint.

• usql
• textql
• golang-migrate
• octosql
• rq
• miller
• jsoncolor is a JSON colorizer created for sq.
• streamcache is a Go in-memory byte cache mechanism created for sq.
• fifomu is a FIFO mutex, used by streamcache, and thus upstream in sq.
• tailbuf is a fixed-size object tail buffer created for sq.
• oncecache is an in-memory object cache created for sq.