# sq: swiss army knife for data `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. ## Install For other installation options, see [here](https://github.com/neilotoole/sq/wiki/Home#Install). ### macOS ```shell script brew tap neilotoole/sq && brew install sq ``` ### Windows ``` scoop bucket add sq https://github.com/neilotoole/sq scoop install sq ``` ### Linux #### apt ```shell script 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.deb ``` #### rpm ```shell script sudo rpm -i https://github.com/neilotoole/sq/releases/latest/download/sq-linux-amd64.rpm ``` #### yum ```shell script yum localinstall -y https://github.com/neilotoole/sq/releases/latest/download/sq-linux-amd64.rpm ``` ## Quickstart Use `sq help` to see command help. The [tutorial](https://github.com/neilotoole/sq/wiki/Tutorial) is the best place to start. The [cookbook](https://github.com/neilotoole/sq/wiki/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. ### Sources Initially there are no sources. ```sh $ 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. ```sh $ 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.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_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: ```sh $ sq ping @sakila_sl3 1ms pong ``` ### Query Fundamentally, `sq` is for querying data. Using our jq-style syntax: ```sh $ 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 2020-02-15T06:59:28Z ``` The above query selected some rows from the `actor` table. You could also use native SQL, e.g.: ```sh $ 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 2020-02-15T06:59:28Z ``` But we're flying a bit blind here: how did we know about the `actor` table? ### Inspect `sq inspect` is your friend (output abbreviated): ```sh $ 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_update ``` Use `--json` (`-j`) to output in JSON (output abbreviated): ```shell $ 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](https://stedolan.github.io/jq/) for some useful capabilities. Here's how to [list](https://github.com/neilotoole/sq/wiki/Cookbook#list-name-of-each-table-in-a-source) all the table names in the active source: ```sh $ sq inspect -j | jq -r '.tables[] | .name' actor address category city country customer [...] ``` And here's how you could [export](https://github.com/neilotoole/sq/wiki/Cookbook#export-all-tables-to-csv) each table to a CSV file: ```sh $ 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: ```sh $ 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 ``` ### Insert Output Into Database Source `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 copy` instead, 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. ```sh $ 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 neilotoole@apache.org 1 2 ksoze kaiser@soze.org 2 3 kubla kubla@khan.mn NULL [...] ``` Now, execute the same query, but this time `sq` inserts the results into a new table (`person`) in `@sakila_sl3`: ```shell $ 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 neilotoole@apache.org 1 2 ksoze kaiser@soze.org 2 3 kubla kubla@khan.mn NULL [...] ``` ### Cross-Source Join `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: `sq` copies 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](https://github.com/neilotoole/sq/wiki/Tutorial#join) for further details, but given an Excel source `@xl_demo` and a CSV source `@csv_demo`, you can do: ```sh $ sq '@csv_demo.data, @xl_demo.address | join(.D == .address_id) | .C, .city' C city neilotoole@apache.org Washington kaiser@soze.org Ulan Bator nikola@tesla.rs Washington augustus@caesar.org Ulan Bator plato@athens.gr Washington ``` ### Table Commands `sq` provides several handy commands for working with tables. Note that these commands work directly against SQL database sources, using their native SQL commands. ```sh $ 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_copy ``` ### UNIX Pipes For file-based sources (such as CSV or XLSX), you can `sq add` the source file, but you can also pipe it: ```shell $ cat ./example.xlsx | sq .Sheet1 ``` Similarly, you can inspect: ```shell $ cat ./example.xlsx | sq inspect ``` ## Data Source Drivers `sq` knows how to deal with a data source type via a _driver_ implementation. To view the installed/supported drivers: ```sh $ 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_Excel ``` ## Output Formats `sq` has many output formats: - `--csv`: 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) ## Acknowledgements - Much inspiration is owed to [jq](https://stedolan.github.io/jq/). - See [`go.mod`](https://github.com/neilotoole/sq/blob/master/go.mod) for a list of third-party packages. - Additionally, `sq` incorporates modified versions of: - [`olekukonko/tablewriter`](https://github.com/olekukonko/tablewriter) - [`segmentio/encoding`](https://github.com/segmentio/encoding) for JSON encoding. - The [_Sakila_](https://dev.mysql.com/doc/sakila/en/) example databases were lifted from [jOOQ](https://github.com/jooq/jooq), which in turn owe their heritage to earlier work on Sakila. ## Similar / Related / Noteworthy Projects - [usql](https://github.com/xo/usql) - [textql](https://github.com/dinedal/textql) - [golang-migrate](https://github.com/golang-migrate/migrate) - [octosql](https://github.com/cube2222/octosql) - [rq](https://github.com/dflemstr/rq)