sq is a command line tool that provides jq-style access to
structured data sources such as SQL databases,
or document formats like CSV or Excel.
sq can perform cross-source joins,
execute database-native SQL, and output to a multitude of formats including JSON,
Excel, CSV, HTML, Markdown and XML, or insert directly to a SQL database.
sq can also inspect sources to view metadata about the source structure (tables,
columns, size) and has commands for common database operations such as copying
or dropping tables.
For other installation options, see here.
It is strongly advised to install shell completion.
brew tap neilotoole/sq && brew install sqscoop bucket add sq https://github.com/neilotoole/sq
scoop install sq
curl -fsSLO https://github.com/neilotoole/sq/releases/latest/download/sq-linux-amd64.deb && sudo apt install -y ./sq-linux-amd64.deb && rm ./sq-linux-amd64.debsudo rpm -i https://github.com/neilotoole/sq/releases/latest/download/sq-linux-amd64.rpmyum localinstall -y https://github.com/neilotoole/sq/releases/latest/download/sq-linux-amd64.rpmShell completion is available for bash, zsh, fish, and powershell.
It is strongly recommended to install.
Execute sq completion --help for installation instructions.
Use sq help to see command help. The tutorial is the best place to start.
The cookbook has recipes for common actions.
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, Excel, etc. Download the sample DB, and sq add the source. We
use -h to specify a handle to use.
$ wget https://sq.io/testdata/sakila.db
$ sq add ./sakila.db -h @sakila_sl3
@sakila_sl3 sqlite3 sakila.db
$ sq ls -v
HANDLE DRIVER LOCATION OPTIONS
@sakila_sl3* sqlite3 sqlite3:/root/sakila.db
$ sq ping @sakila_sl3
@sakila_sl3 1ms pong
$ sq src
@sakila_sl3 sqlite3 sakila.dbThe 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_sl3.
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_sl3 1ms pongFundamentally, sq is for querying data. Using our jq-style syntax:
$ sq '.actor | .actor_id < 100 | .[0:3]'
actor_id first_name last_name last_update
1 PENELOPE GUINESS 2020-02-15T06:59:28Z
2 NICK WAHLBERG 2020-02-15T06:59:28Z
3 ED CHASE 2020-02-15T06:59:28ZThe above query selected some rows from the actor table. You could also
use native SQL, e.g.:
$ sq sql 'SELECT * FROM actor WHERE actor_id < 100 LIMIT 3'
actor_id first_name last_name last_update
1 PENELOPE GUINESS 2020-02-15T06:59:28Z
2 NICK WAHLBERG 2020-02-15T06:59:28Z
3 ED CHASE 2020-02-15T06:59:28ZBut we're flying a bit blind here: how did we know about the actor table?
sq inspect is your friend (output abbreviated):
$ sq inspect
HANDLE DRIVER NAME FQ NAME SIZE TABLES LOCATION
@sakila_sl3 sqlite3 sakila.db sakila.db/main 5.6MB 21 sqlite3:///root/sakila.db
TABLE ROWS TYPE SIZE NUM COLS COL NAMES COL TYPES
actor 200 table - 4 actor_id, first_name, last_name, last_update numeric, VARCHAR(45), VARCHAR(45), TIMESTAMP
address 603 table - 8 address_id, address, address2, district, city_id, postal_code, phone, last_update int, VARCHAR(50), VARCHAR(50), VARCHAR(20), INT, VARCHAR(10), VARCHAR(20), TIMESTAMP
category 16 table - 3 category_id, name, last_updateUse --json (-j) to output in JSON (output abbreviated):
$ sq inspect -j
{
"handle": "@sakila_sl3",
"name": "sakila.db",
"driver": "sqlite3",
"db_version": "3.31.1",
"location": "sqlite3:///root/sakila.db",
"size": 5828608,
"tables": [
{
"name": "actor",
"table_type": "table",
"row_count": 200,
"columns": [
{
"name": "actor_id",
"position": 0,
"primary_key": true,
"base_type": "numeric",
"column_type": "numeric",
"kind": "decimal",
"nullable": false
}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.csvNote that you can also inspect an individual table:
$ sq inspect @sakila_sl3.actor
TABLE ROWS TYPE SIZE NUM COLS COL NAMES COL TYPES
actor 200 table - 4 actor_id, first_name, last_name, last_update numeric, VARCHAR(45), VARCHAR(45), TIMESTAMP
sq query results can be output in various formats (JSON, XML, CSV, etc), and can also be "outputted" as an insert into database sources.
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.
Note: If you want to copy a table inside the same (database) source, use
sq tbl copyinstead, which uses the database's native table copy functionality.
For this example, we'll insert an Excel worksheet into our @sakila_sl3 SQLite database. First, we download the XLSX file, and sq add it as a source.
$ wget https://sq.io/testdata/xl_demo.xlsx
$ sq add ./xl_demo.xlsx --opts header=true
@xl_demo_xlsx xlsx xl_demo.xlsx
$ sq @xl_demo_xlsx.person
uid username email address_id
1 neilotoole [email protected] 1
2 ksoze [email protected] 2
3 kubla [email protected] NULL
[...]Now, execute the same query, but this time sq inserts the results into a new table (person) in @sakila_sl3:
$ sq @xl_demo_xlsx.person --insert @sakila_sl3.person
Inserted 7 rows into @sakila_sl3.person
$ sq inspect @sakila_sl3.person
TABLE ROWS TYPE SIZE NUM COLS COL NAMES COL TYPES
person 7 table - 4 uid, username, email, address_id INTEGER, TEXT, TEXT, INTEGER
$ sq @sakila_sl3.person
uid username email address_id
1 neilotoole [email protected] 1
2 ksoze [email protected] 2
3 kubla [email protected] NULL
[...]sq has rudimentary support for cross-source joins. That is, you can join an Excel worksheet with a CSV file, or Postgres table, etc.
Note: The current mechanism for these joins is highly naive:
sqcopies the joined table from each source to a "scratch database" (SQLite by default), and then performs the JOIN using the scratch database's SQL interface. Thus, performance is abysmal for larger tables. There are massive optimizations to be made, but none have been implemented yet.
See the tutorial for further details, but given an Excel source @xl_demo and a CSV source @csv_demo, you can do:
$ sq '@csv_demo.data, @xl_demo.address | join(.D == .address_id) | .C, .city'
C city
[email protected] Washington
[email protected] Ulan Bator
[email protected] Washington
[email protected] Ulan Bator
[email protected] Washingtonsq provides several handy commands for working with tables. Note that these commands work directly against SQL database sources, using their native SQL commands.
$ sq tbl copy .actor .actor_copy
Copied table: @sakila_sl3.actor --> @sakila_sl3.actor_copy (200 rows copied)
$ sq tbl truncate .actor_copy
Truncated 200 rows from @sakila_sl3.actor_copy
$ sq tbl drop .actor_copy
Dropped table @sakila_sl3.actor_copyFor 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 .Sheet1Similarly, you can inspect:
$ cat ./example.xlsx | sq inspectsq knows how to deal with a data source type via a driver implementation. To view the installed/supported drivers:
$ sq drivers
DRIVER DESCRIPTION USER-DEFINED DOC
sqlite3 SQLite false https://github.com/mattn/go-sqlite3
postgres PostgreSQL false https://github.com/jackc/pgx
sqlserver Microsoft SQL Server false https://github.com/denisenkom/go-mssqldb
mysql MySQL false https://github.com/go-sql-driver/mysql
csv Comma-Separated Values false https://en.wikipedia.org/wiki/Comma-separated_values
tsv Tab-Separated Values false https://en.wikipedia.org/wiki/Tab-separated_values
json JSON false https://en.wikipedia.org/wiki/JSON
jsona JSON Array: LF-delimited JSON arrays false https://en.wikipedia.org/wiki/JSON
jsonl JSON Lines: LF-delimited JSON objects false https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON
xlsx Microsoft Excel XLSX false https://en.wikipedia.org/wiki/Microsoft_Excelsq has many output formats:
--table: Text/Table--json: JSON--jsona: JSON Array--jsonl: JSON Lines--csv/--tsv: CSV / TSV--xlsx: XLSX (Microsoft Excel)--html: HTML--xml: XML--markdown: Markdown--raw: Raw (bytes)
- Much inspiration is owed to jq.
- See
go.modfor a list of third-party packages. - Additionally,
sqincorporates modified versions of:olekukonko/tablewritersegmentio/encodingfor JSON encoding.
- The Sakila example databases were lifted from jOOQ, which in turn owe their heritage to earlier work on Sakila.