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Querying files

TL;DR

How to query a file?

This feature is only available in the shell mode. If you want similar features in the MySQL server, you need to create a vtable.

JSON

Run SELECT * FROM read_json('path/to/file.json') in your terminal.

CSV

Run SELECT * FROM read_csv('path/to/file.csv') in your terminal.

Parquet

Run SELECT * FROM read_parquet('path/to/file.parquet') in your terminal.

YAML

Run SELECT * FROM read_yaml('path/to/file.yaml') in your terminal.

TOML

Run SELECT * FROM read_toml('path/to/file.toml') in your terminal.

Introduction

Anyquery is able to run SQL queries on JSON, CSV, Parquet, YAML, and TOML files. The shell mode provides syntactic sugar to query these files. In the MySQL server, you need to create a vtable to query these files which is explained here.

Listing all the packages from Homebrew
SELECT full_name FROM read_json('https://formulae.brew.sh/api/formula.json');

Remote files

While you can query local files, you can also query remote files. You can query files from HTTP, HTTPS, S3, and GCS. The syntax is the same as querying local files.

HTTPS

SELECT * FROM read_json('https://example.com/file.json');

S3

The aws_access_key_id, aws_access_key_secret, region (optional), and version (optional) can be passed as query parameters. Anyquery will also attempt to read them from the environment variables and ~/.aws/config.

SELECT * FROM read_json('s3::https://s3.amazonaws.com/bucket-name/file.json?aws_access_key_id=your-access-key&aws_access_key_secret=your-secret-key&region=us-west-1');

GCS

To query a file from GCS, you need to set GOOGLE_APPLICATION_CREDENTIALS to the path of your service account key or GOOGLE_CREDENTIALS to the content of your service account key.

SELECT * FROM read_json('gcs::https://www.googleapis.com/storage/v1/bucket/file.json');

All files will be cached in the local filesystem to avoid downloading them multiple times. To clear the cache, you can use the clear_file_cache function.

SELECT clear_file_cache();

Stdin

You can also query files from stdin. The syntax is the same as querying local files.

Querying JSON from stdin
curl https://formulae.brew.sh/api/formula.json | anyquery -q "SELECT full_name, \"desc\", license FROM read_json('stdin');"
cat file.json | anyquery -q "SELECT * FROM read_json('-');"

File formats

JSON

To query a JSON file, you need to use the read_json function. The function takes one or two arguments. The first argument is the path to the JSON file. The second argument is optional and is the JSON path to the data you want to query.

-- Query the whole JSON file
SELECT * FROM read_json('path/to/file.json');
-- Query a specific path in the JSON file
SELECT * FROM read_json('path/to/file.json', '$.items[*]');

You can also specify the parameters with named arguments.

SELECT * FROM read_json(path='path/to/file.json', json_path='$.items[*]');

Shapes supported

The following shapes are supported:

records:

[
{"id": 1, "name": "Alice"},
{"id": 2, "name": "Bob"}
]

columns:

{
"id": [1, 2],
"name": ["Alice", "Bob"]
}

object:

In this case, there is only one row, and each key is a column.

{
"id": 1,
"name": "Alice"
}

CSV

To query a CSV file, you need to use the read_csv function. The function takes one, two, or three arguments. The first argument is the path to the CSV file. The second argument is optional and is if the first row is a header. The third argument is optional and is the delimiter.

-- Query the whole CSV file
SELECT * FROM read_csv('path/to/file.csv');
-- Query a CSV file with a header
SELECT * FROM read_csv('https://csvbase.com/meripaterson/stock-exchanges.csv', header=true);
-- Query a CSV file with a header and a custom delimiter
SELECT * FROM read_csv('path/to/file.csv', header=true, delimiter=';');

You also specify a schema for the CSV file, and any query will make the best effort to parse the file according to the schema.

Querying a CSV file with a schema
SELECT * FROM read_csv('https://csvbase.com/rmirror/us-covid-cases', schema='CREATE TABLE csv (id int, date varchar, cases int)', header=true) LIMIT 30;

TSV

To query a TSV file, use the read_csv function with the delimiter set to \t.

SELECT * FROM read_csv('path/to/file.tsv', delimiter='\t');

HTML

You can query HTML tables using the read_html function. The function takes two arguments. The first argument is the URL of the HTML page. The second argument is the selector of the table.

Analyzing disk prices using SQL
SELECT * FROM read_html('https://diskprices.com', '#diskprices');

If the CSS selector points to an element that is not a table, it will return all elements that match the selector.

Extracting all "th
anyquery> SELECT * FROM read_html('https://diskprices.com', 'th');
+----------+----------------+----------------------------------------------------+
| tag_name | content | attributes |
+----------+----------------+----------------------------------------------------+
| th | Price per GB | [{"Namespace":"","Key":"class","Val":"price-per-gb |
| | | hidden"}] |
| th | Price per TB | [{"Namespace":"","Key":"class","Val":"price-per-tb |
| | | "}] |
| th | Price | null |
| th | Capacity | null |
| th | Warranty | null |
| th | Form Factor | null |
| th | Technology | null |
| th | Condition | null |
| th | Affiliate Link | null |
+----------+----------------+----------------------------------------------------+
9 results

To extract an attribute from an element, you can use the ->> operator to access the JSON object’s attribute.

Extracting all links mentionning Amazon on the page
SELECT
value ->> 'Val' as link
FROM
read_html ('https://diskprices.com', 'a'),
json_each (attributes)
WHERE
value ->> 'Key' = 'href'
AND value ->> 'Val' LIKE '%amazon%';

Parquet

To query a Parquet file, you need to use the read_parquet function. The function takes one argument which is the path to the Parquet file.

SELECT * FROM read_parquet('https://csvbase.com/calpaterson/english-womens-football-matches.parquet');

YAML

To query a YAML file, you need to use the read_yaml function. The function takes one argument, which is the path to the YAML file.

SELECT * FROM read_yaml('path/to/file.yaml');

Each key in the YAML file represents a column. Therefore, only one row is returned. This structure is similar to the objects shape in JSON.

TOML

To query a TOML file, you need to use the read_toml function. The function takes one argument, which is the path to the TOML file.

SELECT * FROM read_toml('path/to/file.toml');

Each key in the TOML file is a column. Therefore, only one row is returned. It’s similar to the objects shape in JSON.

MySQL server

To query files in the MySQL server, you need to create a virtual table. It’s a table that points to the file. The virtual table is created using the CREATE VIRTUAL TABLE statement. It uses the same arguments as the shell mode. Each table named read_file_format in the shell has a corresponding file_format_reader table in the MySQL server. file_format_reader tables are also available in the shell mode.

Read a JSON file
CREATE VIRTUAL TABLE my_json_table USING json_reader('path/to/file.json');
SELECT * FROM my_json_table;
DROP TABLE my_json_table;
Read a TSV file
CREATE VIRTUAL TABLE my_tsv_table USING csv_reader('path/to/file.tsv', separator='\t');
SELECT * FROM my_tsv_table;
DROP TABLE my_tsv_table;

Limitations

  • You cannot observe the schema of the file using the PRAGMA table_info or DESCRIBE statement. This is due to anyquery rewriting your query on the fly to create a temporary virtual table for the duration of the query. To observe the schema, you need to create a virtual table as specified in the MySQL server section.
  • You cannot use the CREATE VIEW statement with the read_* functions.