Commandline tool for running SQL queries against JSON, CSV, Excel, Parquet, and more
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- About
- Install
- Usage
- Pretty print
- Piping data to dsq
- Multiple files and joins
- Transforming data to JSON without querying
- Array of objects nested within an object
- Nested object values
- Nested arrays
- 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
- Third-party integrations
- Community
- How can I help?
- License
About
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.
Install
Binaries for amd64 (x86_64) are provided for each release.
macOS Homebrew
dsq
is available on macOS Homebrew:
$ brew install dsq
Binaries on macOS, Linux, WSL
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
.
Binaries on Windows (not WSL)
Download the latest Windows
release, unzip it,
and add dsq
to your $PATH
.
Build and install from source
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.
Usage
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"
Pretty print
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 |
+----------+
Piping data to dsq
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 {}"
Multiple files and joins
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"}]
Transforming data to JSON without querying
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...},...]
Array of objects nested within an object
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.
Multiple Excel sheets
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"}'
Limitation: nested arrays
You cannot specify a path through an array, only objects.
Nested object values
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 {}'
Caveat: PowerShell, CMD.exe
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 explained
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.
Limitation: whole object retrieval
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
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]",
}
]
JSON operators
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"
}
]
REGEXP
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"}]
Standard Library
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.
Output column order
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.
Dumping inferred schema
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.
Caching
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.
Interactive REPL
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)
Converting numbers in CSV and TSV files
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.
Supported Data Types
Name | File Extension(s) | Mime Type | Notes | |
---|---|---|---|---|
CSV | csv |
text/csv |
||
TSV | tsv , tab |
text/tab-separated-values |
||
JSON | json |
application/json |
Must be an array of objects or a path to an array of objects. | |
Newline-delimited JSON | ndjson , jsonl |
application/jsonlines |
||
Concatenated JSON | cjson |
application/jsonconcat |
||
ORC | orc |
orc |
||
Parquet | parquet |
parquet |
||
Avro | avro |
application/avro |
||
YAML | yaml , yml |
application/yaml |
||
Excel | xlsx , xls |
application/vnd.ms-excel |
If you have multiple sheets, you must specify a sheet path. | |
ODS | ods |
application/vnd.oasis.opendocument.spreadsheet |
If you have multiple sheets, you must specify a sheet path. | |
Apache Error Logs | NA | text/apache2error |
Currently only works if being piped in. | |
Apache Access Logs | NA | text/apache2access |
Currently only works if being piped in. | |
Nginx Access Logs | NA | text/nginxaccess |
Currently only works if being piped in. | |
LogFmt Logs | logfmt |
text/logfmt |
Engine
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.
Comparisons
Name | Link | Caching | Engine | Supported File Types | Binary Size |
---|---|---|---|---|---|
dsq | Here | Yes | SQLite | CSV, TSV, a few variations of JSON, Parquet, Excel, ODS (OpenOffice Calc), ORC, Avro, YAML, Logs | 49M |
q | http://harelba.github.io/q/ | Yes | SQLite | CSV, TSV | 82M |
textql | https://github.com/dinedal/textql | No | SQLite | CSV, TSV | 7.3M |
octoql | https://github.com/cube2222/octosql | No | Custom engine | JSON, CSV, Excel, Parquet | 18M |
csvq | https://github.com/mithrandie/csvq | No | Custom engine | CSV | 15M |
sqlite-utils | https://github.com/simonw/sqlite-utils | No | SQLite | CSV, TSV | N/A, Not a single binary |
trdsql | https://github.com/noborus/trdsql | No | SQLite, MySQL or PostgreSQL | Few variations of JSON, TSV, LTSV, TBLN, CSV | 14M |
spysql | https://github.com/dcmoura/spyql | No | Custom engine | CSV, JSON, TEXT | N/A, Not a single binary |
duckdb | https://github.com/duckdb/duckdb | ? | Custom engine | CSV, Parquet | 35M |
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
Benchmark
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.
Program | Version | Mean [s] | Min [s] | Max [s] | Relative |
---|---|---|---|---|---|
dsq | 0.20.1 (caching on) | 1.151 ± 0.010 | 1.131 | 1.159 | 1.00 |
duckdb | 0.3.4 | 1.723 ± 0.023 | 1.708 | 1.757 | 1.50 ± 0.02 |
octosql | 0.7.3 | 2.005 ± 0.008 | 1.991 | 2.015 | 1.74 ± 0.02 |
q | 3.1.6 (caching on) | 2.028 ± 0.010 | 2.021 | 2.055 | 1.76 ± 0.02 |
sqlite3 * | 3.36.0 | 4.204 ± 0.018 | 4.177 | 4.229 | 3.64 ± 0.04 |
trdsql | 0.10.0 | 12.972 ± 0.225 | 12.554 | 13.392 | 11.27 ± 0.22 |
dsq | 0.20.1 (default) | 15.030 ± 0.086 | 14.895 | 15.149 | 13.06 ± 0.13 |
textql | fca00ec | 19.148 ± 0.183 | 18.865 | 19.500 | 16.63 ± 0.21 |
spyql | 0.6.0 | 16.985 ± 0.105 | 16.854 | 17.161 | 14.75 ± 0.16 |
q | 3.1.6 (default) | 24.061 ± 0.095 | 23.954 | 24.220 | 20.90 ± 0.20 |
* 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
Notes
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.
Third-party integrations
Community
Join us at #dsq on the Multiprocess Discord.
How can I help?
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.
License
This software is licensed under an Apache 2.0 license.