While development may continue in the future with a different architecture, for the moment you should probably instead use DuckDB, ClickHouse-local, or GlareDB (based on DataFusion).

These are built on stronger analytics foundations than projects like dsq based on SQLite. For example, column-oriented storage and vectorized execution, let alone JIT-compiled expression evaluation, are possible with these other projects.

More here.

Since Github doesn't provide a great way for you to learn about new releases and features, don't just star the repo, join the mailing list.

• About
• Install
  • macOS Homebrew
  • Binaries on macOS, Linux, WSL
  • Binaries on Windows (not WSL)
  • Build and install from source
• Usage
  • Pretty print
  • Piping data to dsq
  • Multiple files and joins
  • SQL query from file
  • Transforming data to JSON without querying
  • Array of objects nested within an object
    • Multiple Excel sheets
    • Limitation: nested arrays
  • Nested object values
    • Caveat: PowerShell, CMD.exe
    • Nested objects explained
    • Limitation: whole object retrieval
  • Nested arrays
    • JSON operators
  • REGEXP
  • Standard Library
  • Output column order
  • Dumping inferred schema
  • Caching
  • Interactive REPL
  • Converting numbers in CSV and TSV files
• Supported Data Types
• Engine
• Comparisons
• Benchmark
  • Notes
• Third-party integrations
• Community
• How can I help?
• License

This is a CLI companion to DataStation (a GUI) for running SQL queries against data files. So if you want the GUI version of this, check out DataStation.

Binaries for amd64 (x86_64) are provided for each release.

dsq is available on macOS Homebrew:

$ brew install dsq

On macOS, Linux, and WSL you can run the following:

$ VERSION="v0.23.0"
$ FILE="dsq-$(uname -s | awk '{ print tolower($0) }')-x64-$VERSION.zip"
$ curl -LO "https://github.com/multiprocessio/dsq/releases/download/$VERSION/$FILE"
$ unzip $FILE
$ sudo mv ./dsq /usr/local/bin/dsq

Or install manually from the releases page, unzip and add dsq to your $PATH.

Download the latest Windows release, unzip it, and add dsq to your $PATH.

If you are on another platform or architecture or want to grab the latest release, you can do so with Go 1.18+:

$ go install github.com/multiprocessio/dsq@latest

dsq will likely work on other platforms that Go is ported to such as AARCH64 and OpenBSD, but tests and builds are only run against x86_64 Windows/Linux/macOS.

You can either pipe data to dsq or you can pass a file name to it. NOTE: piping data doesn't work on Windows.

If you are passing a file, it must have the usual extension for its content type.

For example:

$ dsq testdata.json "SELECT * FROM {} WHERE x > 10"

Or:

$ dsq testdata.ndjson "SELECT name, AVG(time) FROM {} GROUP BY name ORDER BY AVG(time) DESC"

By default dsq prints ugly JSON. This is the most efficient mode.

$ dsq testdata/userdata.parquet 'select count(*) from {}'
[{"count(*)":1000}
]

If you want prettier JSON you can pipe dsq to jq.

$ dsq testdata/userdata.parquet 'select count(*) from {}' | jq
[
  {
    "count(*)": 1000
  }
]

Or you can enable pretty printing with -p or --pretty in dsq which will display your results in an ASCII table.

$ dsq --pretty testdata/userdata.parquet 'select count(*) from {}'
+----------+
| count(*) |
+----------+
|     1000 |
+----------+

When piping data to dsq you need to set the -s flag and specify the file extension or MIME type.

For example:

$ cat testdata.csv | dsq -s csv "SELECT * FROM {} LIMIT 1"

Or:

$ cat testdata.parquet | dsq -s parquet "SELECT COUNT(1) FROM {}"

You can pass multiple files to DSQ. As long as they are supported data files in a valid format, you can run SQL against all files as tables. Each table can be accessed by the string {N} where N is the 0-based index of the file in the list of files passed on the commandline.

For example this joins two datasets of differing origin types (CSV and JSON).

$ dsq testdata/join/users.csv testdata/join/ages.json \
  "select {0}.name, {1}.age from {0} join {1} on {0}.id = {1}.id"
[{"age":88,"name":"Ted"},
{"age":56,"name":"Marjory"},
{"age":33,"name":"Micah"}]

You can also give file-table-names aliases since dsq uses standard SQL:

$ dsq testdata/join/users.csv testdata/join/ages.json \
  "select u.name, a.age from {0} u join {1} a on u.id = a.id"
[{"age":88,"name":"Ted"},
{"age":56,"name":"Marjory"},
{"age":33,"name":"Micah"}]

As your query becomes more complex, it might be useful to store it in a file rather than specify it on the command line. To do so replace the query argument with --file or -f and the path to the file.

$ dsq data1.csv data2.csv -f query.sql

As a shorthand for dsq testdata.csv "SELECT * FROM {}" to convert supported file types to JSON you can skip the query and the converted JSON will be dumped to stdout.

For example:

$ dsq testdata.csv
[{...some csv data...},{...some csv data...},...]

DataStation and dsq's SQL integration operates on an array of objects. If your array of objects happens to be at the top-level, you don't need to do anything. But if your array data is nested within an object you can add a "path" parameter to the table reference.

For example if you have this data:

$ cat api-results.json
{
  "data": {
    "data": [
      {"id": 1, "name": "Corah"},
      {"id": 3, "name": "Minh"}
    ]
  },
  "total": 2
}

You need to tell dsq that the path to the array data is "data.data":

$ dsq --pretty api-results.json 'SELECT * FROM {0, "data.data"} ORDER BY id DESC'
+----+-------+
| id | name  |
+----+-------+
|  3 | Minh  |
|  1 | Corah |
+----+-------+

You can also use the shorthand {"path"} or {'path'} if you only have one table:

$ dsq --pretty api-results.json 'SELECT * FROM {"data.data"} ORDER BY id DESC'
+----+-------+
| id | name  |
+----+-------+
|  3 | Minh  |
|  1 | Corah |
+----+-------+

You can use either single or double quotes for the path.

Excel files with multiple sheets are stored as an object with key being the sheet name and value being the sheet data as an array of objects.

If you have an Excel file with two sheets called Sheet1 and Sheet2 you can run dsq on the second sheet by specifying the sheet name as the path:

$ dsq data.xlsx 'SELECT COUNT(1) FROM {"Sheet2"}'

You cannot specify a path through an array, only objects.

It's easiest to show an example. Let's say you have the following JSON file called user_addresses.json:

$ cat user_addresses.json
[
  {"name": "Agarrah", "location": {"city": "Toronto", "address": { "number": 1002 }}},
  {"name": "Minoara", "location": {"city": "Mexico City", "address": { "number": 19 }}},
  {"name": "Fontoon", "location": {"city": "New London", "address": { "number": 12 }}}
]

You can query the nested fields like so:

$ dsq user_addresses.json 'SELECT name, "location.city" FROM {}'

And if you need to disambiguate the table:

$ dsq user_addresses.json 'SELECT name, {}."location.city" FROM {}'

On PowerShell and CMD.exe you must escape inner double quotes with backslashes:

> dsq user_addresses.json 'select name, \"location.city\" from {}'
[{"location.city":"Toronto","name":"Agarrah"},
{"location.city":"Mexico City","name":"Minoara"},
{"location.city":"New London","name":"Fontoon"}]

Nested objects are collapsed and their new column name becomes the JSON path to the value connected by .. Actual dots in the path must be escaped with a backslash. Since . is a special character in SQL you must quote the whole new column name.

You cannot query whole objects, you must ask for a specific path that results in a scalar value.

For example in the user_addresses.json example above you CANNOT do this:

$ dsq user_addresses.json 'SELECT name, {}."location" FROM {}'

Because location is not a scalar value. It is an object.

Nested arrays are converted to a JSON string when stored in SQLite. Since SQLite supports querying JSON strings you can access that data as structured data even though it is a string.

So if you have data like this in fields.json:

[
  {"field1": [1]},
  {"field1": [2]},
]

You can request the entire field:

$ dsq fields.json "SELECT field1 FROM {}" | jq
[
  {
    "field1": "[1]"
  },
  {
    "field1": "[2]",
  }
]

You can get the first value in the array using SQL JSON operators.

$ dsq fields.json "SELECT field1->0 FROM {}" | jq
[
  {
    "field1->0": "1"
  },
  {
    "field1->0": "2"
  }
]

Since DataStation and dsq are built on SQLite, you can filter using x REGEXP 'y' where x is some column or value and y is a REGEXP string. SQLite doesn't pick a regexp implementation. DataStation and dsq use Go's regexp implementation which is more limited than PCRE2 because Go support for PCRE2 is not yet very mature.

$ dsq user_addresses.json "SELECT * FROM {} WHERE name REGEXP 'A.*'"
[{"location.address.number":1002,"location.city":"Toronto","name":"Agarrah"}]

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.

View that project docs for all available extended functions.

When emitting JSON (i.e. without the --pretty flag) keys within an object are unordered.

If order is important to you you can filter with jq: dsq x.csv 'SELECT a, b FROM {}' | jq --sort-keys.

With the --pretty flag, column order is purely alphabetical. It is not possible at the moment for the order to depend on the SQL query order.

For any supported file you can dump the inferred schema rather than dumping the data or running a SQL query. Set the --schema flag to do this.

The inferred schema is very simple, only JSON types are supported. If the underlying format (like Parquet) supports finer-grained data types (like int64) this will not show up in the inferred schema. It will show up just as number.

For example:

$ dsq testdata/avro/test_data.avro --schema --pretty
Array of
  Object of
    birthdate of
      string
    cc of
      Varied of
        Object of
          long of
            number or
        Unknown
    comments of
      string
    country of
      string
    email of
      string
    first_name of
      string
    gender of
      string
    id of
      number
    ip_address of
      string
    last_name of
      string
    registration_dttm of
      string
    salary of
      Varied of
        Object of
          double of
            number or
        Unknown
    title of
      string

You can print this as a structured JSON string by omitting the --pretty flag when setting the --schema flag.

Sometimes you want to do some exploration on a dataset that isn't changing frequently. By turning on the --cache or -C flag DataStation will store the imported data on disk and not delete it when the run is over.

With caching on, DataStation calculates a SHA1 sum of all the files you specified. If the sum ever changes then it will reimport all the files. Otherwise when you run additional queries with the cache flag on it will reuse that existing database and not reimport the files.

Since without caching on DataStation uses an in-memory database, the initial query with caching on may take slightly longer than with caching off. Subsequent queries will be substantially faster though (for large datasets).

For example, in the first run with caching on this query might take 30s:

$ dsq some-large-file.json --cache 'SELECT COUNT(1) FROM {}'

But when you run another query it might only take 1s.

$ dsq some-large-file.json --cache 'SELECT SUM(age) FROM {}'

Not because we cache any result but because we cache importing the file into SQLite.

So even if you change the query, as long as the file doesn't change, the cache is effective.

To make this permanent you can export DSQ_CACHE=true in your environment.

Use the -i or --interactive flag to enter an interactive REPL where you can run multiple SQL queries.

$ dsq some-large-file.json -i
dsq> SELECT COUNT(1) FROM {};
+----------+
| COUNT(1) |
+----------+
|     1000 |
+----------+
(1 row)
dsq> SELECT * FROM {} WHERE NAME = 'Kevin';
(0 rows)

CSV and TSV files do not allow to specify the type of the individual values contained in them. All values are treated as strings by default.

This can lead to unexpected results in queries. Consider the following example:

$ cat scores.csv
name,score
Fritz,90
Rainer,95.2
Fountainer,100

$ dsq scores.csv "SELECT * FROM {} ORDER BY score"
[{"name":"Fountainer","score":"100"},
{"name":"Fritz","score":"90"},
{"name":"Rainer","score":"95.2"}]

Note how the score column contains numerical values only. Still, sorting by that column yields unexpected results because the values are treated as strings, and sorted lexically. (You can tell that the individual scores were imported as strings because they're quoted in the JSON result.)

Use the -n or --convert-numbers flag to auto-detect and convert numerical values (integers and floats) in imported files:

$ dsq ~/scores.csv --convert-numbers "SELECT * FROM {} ORDER BY score"
[{"name":"Fritz","score":90},
{"name":"Rainer","score":95.2},
{"name":"Fountainer","score":100}]

Note how the scores are imported as numbers now and how the records in the result set are sorted by their numerical value. Also note that the individual scores are no longer quoted in the JSON result.

To make this permanent you can export DSQ_CONVERT_NUMBERS=true in your environment. Turning this on disables some optimizations.

Under the hood dsq uses DataStation as a library and under that hood DataStation uses SQLite to power these kinds of SQL queries on arbitrary (structured) data.

Not included:

• clickhouse-local: fastest of anything listed here but so gigantic (over 2GB) that it can't reasonably be considered a good tool for any environment
• sqlite3: requires multiple commands to ingest CSV, not great for one-liners
• datafusion-cli: very fast (slower only than clickhouse-local) but requires multiple commands to ingest CSV, so not great for one-liners

This benchmark was run June 19, 2022. It is run on a dedicated bare metal instance on OVH with:

• 64 GB DDR4 ECC 2,133 MHz
• 2x450 GB SSD NVMe in Soft RAID
• Intel Xeon E3-1230v6 - 4c/8t - 3.5 GHz/3.9 GHz

It runs a SELECT passenger_count, COUNT(*), AVG(total_amount) FROM taxi.csv GROUP BY passenger_count query against the well-known NYC Yellow Taxi Trip Dataset. Specifically, the CSV file from April 2021 is used. It's a 200MB CSV file with ~2 million rows, 18 columns, and mostly numerical values.

The script is here. It is an adaptation of the benchmark that the octosql devs run.

* While dsq and q are built on top of sqlite3 there is not a builtin way in sqlite3 to cache ingested files without a bit of scripting

Not included:

• clickhouse-local: faster than any of these but over 2GB so not a reasonable general-purpose CLI
• datafusion-cli: slower only than clickhouse-local but requires multiple commands to ingest CSV, can't do one-liners
• sqlite-utils: takes minutes to finish

OctoSQL, duckdb, and SpyQL implement their own SQL engines. dsq, q, trdsql, and textql copy data into SQLite and depend on the SQLite engine for query execution.

Tools that implement their own SQL engines can do better on 1) ingestion and 2) queries that act on a subset of data (such as limited columns or limited rows). These tools implement ad-hoc subsets of SQL that may be missing or differ from your favorite syntax. On the other hand, tools that depend on SQLite have the benefit of providing a well-tested and well-documented SQL engine. DuckDB is exceptional since there is a dedicated company behind it.

dsq also comes with numerous useful functions (e.g. best-effort date parsing, URL parsing/extraction, statistics functions, etc.) on top of SQLite builtins.

• ob-dsq

Join us at #dsq on the Multiprocess Discord.

Download dsq and use it! Report bugs on Discord.

If you're a developer with some Go experience looking to hack on open source, check out GOOD_FIRST_PROJECTS.md in the DataStation repo.

This software is licensed under an Apache 2.0 license.