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mirror of https://github.com/lana-k/sqliteviz.git synced 2025-12-06 18:18:53 +08:00

21 Commits

Author SHA1 Message Date
lana-k
001f400142 update home page, fix links #100 2023-06-10 19:07:48 +02:00
lana-k
932a8b20a1 Docs: Loading remote database and inquiries #109 2023-06-10 18:42:06 +02:00
lana-k
140eda15c3 Merge branch 'docs' of github.com:lana-k/sqliteviz into docs 2023-06-09 00:15:58 +02:00
lana-k
1bdc528dff Broken links in documentation #100 2023-06-09 00:15:24 +02:00
lana-k
edcf104b1a Update Installation.md 2022-07-10 22:35:53 +02:00
lana-k
0a18dca5bd Update How-to-migrate-to-sqliteviz-dot-com.md 2022-07-10 18:43:58 +02:00
lana-k
d249e0ac02 Update How-to-migrate-to-sqliteviz-dot-com.md 2022-07-10 18:42:41 +02:00
lana-k
5c53d24ef7 Update How-to-migrate-to-sqliteviz-dot-com.md 2022-07-10 16:54:23 +02:00
lana-k
7660689c27 fix typo 2022-07-01 17:37:28 +02:00
lana-k
092a77a544 rename 2022-07-01 17:34:38 +02:00
lana-k
a268941f01 about migration 2022-07-01 17:27:09 +02:00
lana-k
17514249e7 fix image 2022-07-01 17:04:03 +02:00
lana-k
f574ead7ea Update index.md 2022-07-01 16:49:02 +02:00
lana-k
9de01dbe2e sidebar settings 2022-01-07 18:46:18 +01:00
lana-k
2397ea6fb3 add index 2022-01-07 12:47:12 +01:00
lana-k
27bb23ac3d Add headers; fix images and links 2022-01-06 22:34:07 +01:00
lana-k
0698522fa5 fix link 2022-01-06 22:16:05 +01:00
lana-k
73849ee820 remove versions 2022-01-06 22:04:25 +01:00
lana-k
7dde645fbc fix images in Basic usage 2022-01-06 21:38:29 +01:00
lana-k
b273d7677d add docs 2022-01-06 21:32:52 +01:00
lana-k
f9561b8678 clear branch 2022-01-06 21:29:46 +01:00
276 changed files with 653 additions and 46699 deletions

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> 1%
last 2 versions
not dead

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[*.{js,jsx,ts,tsx,vue}]
indent_style = space
indent_size = 2
trim_trailing_whitespace = true
insert_final_newline = true

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module.exports = {
root: true,
env: {
node: true,
es2022: true
},
extends: ['eslint:recommended', 'plugin:vue/vue3-recommended', 'prettier'],
rules: {
'no-console': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
'no-debugger': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
'no-case-declarations': 'off',
'max-len': [2, 100, 4, { ignoreUrls: true }],
'vue/multi-word-component-names': 'off',
'vue/no-mutating-props': 'warn',
'vue/no-reserved-component-names': 'warn',
'vue/no-v-model-argument': 'off',
'vue/require-default-prop': 'off',
'vue/custom-event-name-casing': ['error', 'camelCase'],
'vue/attribute-hyphenation': ['error', 'never']
},
overrides: [
{
files: ['**/__tests__/*.{j,t}s?(x)', '**/tests/**/*.spec.{j,t}s?(x)'],
env: {
mocha: true
}
}
]
}

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module.exports = {
dataSource: 'milestones',
ignoreIssuesWith: ['wontfix', 'duplicate'],
milestoneMatch: 'v{{tag_name}}',
template: {
issue: '- {{name}} [{{text}}]({{url}})',
changelogTitle: '',
release: '{{body}}'
},
groupBy: {
Enhancements: ['enhancement', 'internal'],
'Bug fixes': ['bug']
}
}

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name: Create release
on:
workflow_dispatch:
push:
tags:
- '*'
jobs:
deploy:
name: Create release
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Use Node.js
uses: actions/setup-node@v1
with:
node-version: 18.x
- name: Update npm
run: npm install -g npm@10
- name: npm install and build
run: |
npm install
npm run build
- name: Create archives
run: |
cd dist
zip -9 -r ../dist.zip . -x "*.map"
zip -9 -r ../dist_map.zip .
- name: Create Release Notes
run: |
npm install github-release-notes@0.16.0 -g
gren changelog --generate --config="/.github/workflows/config.grenrc.cjs"
env:
GREN_GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Create release
uses: ncipollo/release-action@v1
with:
artifacts: 'dist.zip,dist_map.zip'
token: ${{ secrets.GITHUB_TOKEN }}
bodyFile: 'CHANGELOG.md'

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name: Run tests
on:
workflow_dispatch:
push:
branches:
- 'master'
pull_request:
branches:
- 'master'
jobs:
test:
name: Run tests
runs-on: ubuntu-24.04
steps:
- uses: actions/checkout@v2
- name: Use Node.js
uses: actions/setup-node@v1
with:
node-version: 18.x
- name: Install browsers
run: |
export DEBIAN_FRONTEND=noninteractive
sudo add-apt-repository -y ppa:mozillateam/ppa
sudo apt-get update
sudo apt-get install -y chromium-browser firefox-esr
- name: Update npm
run: npm install -g npm@10
- name: Install the project
run: npm install
- name: Run lint
run: npm run lint -- --no-fix
- name: Run karma tests
run: npm run test

23
.gitignore vendored
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.DS_Store
node_modules
/dist
/coverage
# local env files
.env.local
.env.*.local
# Log files
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
# Editor directories and files
.idea
.vscode
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?

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{
"trailingComma": "none",
"tabWidth": 2,
"semi": false,
"singleQuote": true,
"arrowParens": "avoid"
}

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Basic-usage.md Normal file
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# Basic usage
## Choose SQLite database or CSV file
You can choose a database or a CSV file right on the welcom page (fig. 1). The
supported file extentions: `.csv`, `.db`,`.sqlite` and `.sqlite3`.
![Fig. 1: Welcome page](./img/Screenshot_welcome.png)
If you choose a CSV file it will be parsed. Then sqliteviz creates a new
database with data from the CSV. You can change parsing settings and table name
in the dialog which is shown automatically if you choose a CSV file (fig. 2).
![Fig. 2: CSV import dialog](./img/Screenshot_csv.png)
If you don't have a database or CSV press `Create empty database` button and
then fill an empty database using DDL and DML statements. A tab with an example
of how to do it will be opened automatically (fig. 3).
![Fig. 3: Workspace (empty database)](./img/Screenshot_editor.png)
Database tables, columns and their types are shown in the left panel of the
`Workspace` (fig. 4).
![Fig. 4: Workspace (not empty database)](./img/Screenshot_editor_with_db.png)
## Create inquiry
### Open new tab
Press `Create` button in the top toolbar or use `Ctrl+b` (`Cmd+b` for MacOS)
keyboard shortcut to open a new tab for an inquiry. An inquiry consists of three
parts: SQL query, result set (result of the query execution) and visualisation
of the result set. A tab consists of two panels. Each of them can show one of
the three parts of inquiry. By default the top panel shows SQL query editor and
the bottom panel shows the result set. You can switch any panel to any other
view with corresponding buttons in the right side toolbar.
* ![](./img/sql.svg) Switch the panel to an SQL query editor. In that
editor you can specify and run not only a `SELECT` statement for getting data
but also DDL/DML statements for modifying the database.
* ![](./img/table.svg) Switch the panel to the result set. Here you
can see the result of the query execution.
* ![](./img/visualisation.svg) Switch the panel to visualisation. This
panel allows building a pivot table and charts from the result set.
> **Note:** The query editor can show you hints: SQL keywords, table and column
> names. In a common case column names are shown in the hint if you specify the
> table name, e.g. `SELECT table_name.`. But if there is only one table in your
> database column names will be always available in the hint. Press `Ctrl+Space`
> to show a hint explicitly.
### Run query
Press ![](./img/run.svg) button in the right side toolbar of an SQL
editor panel or use `Ctrl+r` or `Ctrl+Enter` (`Cmd+r` or `Cmd+Enter` for MacOS)
keyboard shortcut to execute a query in the current opened tab.
> **Note:** Running is not available if a query for the current tab is not
> specified.
The query result will be displayed in the result panel (fig. 4).
### Create visualisation
After running a query switch any panel to the visualisation. Switch to the
disired type of visualisation with buttons in the right side toolbar of the
visualisation panel.
* ![](./img/chart.svg) Switch to a chart type visualisation.
* ![](./img/pivot.svg) Switch to a pivot type visualisation.
> **Note:** All unsaved changes in the current visualisation will be lost when
> you switch to the other visualisation type.
A pivot table can be represented as a regular or multi-header table or
visualised with a chart.
Read more details of working with pivot in [Pivot tables][2].
All customised charts in sqliteviz are created with a `react-chart-editor`
component (fig. 5). The same component with some additional features is used in
Plotly Chart Studio. Explore its [documentation][1] to learn how to build charts
with `react-chart-editor`.
![Fig. 5: Visualisation with a chart](./img/Screenshot_chart.png)
You can save any visualisation as an image by clicking ![](./img/camera.svg).
## Save inquiry
Press `Save` button in the top toolbar or use `Ctrl+s` (`Cmd+s` for MacOS)
keyboard shortcut to save an inquiry in the current opened tab to local storage
of your browser.
After that, the inquiry will be in the list on `Inquiries` page.
> **Note:** Only the text of the SQL query and the visualisation settings will
> be saved. The result of the query execution won't be saved.
[1]: https://plotly.com/chart-studio-help/tutorials/#basic
[2]: ../Pivot-table

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Diagnostic-information.md Normal file
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# Diagnostic information
SQLite running in sqliteviz is compiled with particular [options][1] that can
enable or disable some SQLite features. You can get a list of them in `App
info` dialog (fig. 1) by clicking on ![](./img/info.svg) icon in the top
toolbar.
![Fig. 1: App info dialog](./img/Screenshot_app_info.png)
[1]: https://sqlite.org/compile.html

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# An easy way to run tests locally without Nodejs installed:
#
# docker build -t sqliteviz/test -f Dockerfile.test .
#
FROM node:12.22-bullseye
RUN set -ex; \
apt update; \
apt install -y chromium firefox-esr; \
npm install -g npm@7
WORKDIR /tmp/build
COPY package.json package-lock.json ./
COPY lib lib
RUN npm install
COPY . .
RUN set -ex; \
sed -i 's/browsers: \[.*\],/browsers: ['"'FirefoxHeadlessTouch'"'],/' karma.conf.js
RUN npm run lint -- --no-fix && npm run test

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# Export current database
Sqliteviz allows running not only `SELECT` statements but DML/DDL statements
too. You can save database modifications into a `.sqlite` file by clicking on
icon ![](./img/file-export.svg) next to the database name on `Workspace`
page.

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Home.md Normal file
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Welcome to the sqliteviz wiki!
# For users
1. [Installation][0]
2. [Basic usage][1]
3. [Multiple CSV file import][9]
4. [Manage inquiries][2]
5. [Export current database][4]
6. [Pivot table][3]
7. [Predefined inquiries][5]
8. [Sharing][11]
9. [Diagnostic information][6]
## Examples and tutorials
1. [How to rename tables and columns after CSV import][8]
2. [How to build a pivot table in SQL(ite)][10]
3. [How to rename tables and columns after CSV import][12]
# For developers
1. [Integrate predefined inquiries][7]
[0]: Installation
[1]: Basic-usage
[2]: Manage-inquiries
[3]: Pivot-table
[4]: Export-current-database
[5]: Predefined-inquiries
[6]: Diagnostic-information
[7]: Integrate-predefined-inquiries
[8]: How-to-rename-tables-and-columns-after-CSV-import
[9]: Multiple-CSV-file-import
[10]: How-to-build-a-pivot-table-in-SQLite
[11]: Sharing
[12]: How-to-rename-tables-and-columns-after-csv-import

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# How to build a pivot table in SQLite
This how-to explores how to build pivot tables in SQLite, which doesn't have a
special constructs like `PIVOT` or `CROSSTAB` in its SQL dialect.
## Static-column pivot table
If the columns of a pivot table are known beforehand, it's possible to write a
standard, say SQL-92, query that would produce a pivot table in its result set.
This example uses World Bank [country indicators][1]. This query calculates
average fertility and life expectancy for a few country groups.
```sql
SELECT
"Country Name",
AVG(IIF(
FLOOR(year / 10) = 196 AND "Indicator Name" LIKE 'Fertility rate%',
value,
NULL
)) AS "FR 196x",
AVG(IIF(
FLOOR(year / 10) = 196 AND "Indicator Name" LIKE 'Life expectancy%',
value,
NULL
)) AS "LE 196x",
AVG(IIF(
FLOOR(year / 10) = 197 AND "Indicator Name" LIKE 'Fertility rate%',
value,
NULL
)) AS "FR 197x",
AVG(IIF(
FLOOR(year / 10) = 197 AND "Indicator Name" LIKE 'Life expectancy%',
value,
NULL
)) AS "LE 197x",
AVG(IIF(
FLOOR(year / 10) = 198 AND "Indicator Name" LIKE 'Fertility rate%',
value,
NULL
)) AS "FR 198x",
AVG(IIF(
FLOOR(year / 10) = 198 AND "Indicator Name" LIKE 'Life expectancy%',
value,
NULL
)) AS "LE 198x",
AVG(IIF(
FLOOR(year / 10) = 199 AND "Indicator Name" LIKE 'Fertility rate%',
value,
NULL
)) AS "FR 199x",
AVG(IIF(
FLOOR(year / 10) = 199 AND "Indicator Name" LIKE 'Life expectancy%',
value,
NULL
)) AS "LE 199x",
AVG(IIF(
FLOOR(year / 10) = 200 AND "Indicator Name" LIKE 'Fertility rate%',
value,
NULL
)) AS "FR 200x",
AVG(IIF(
FLOOR(year / 10) = 200 AND "Indicator Name" LIKE 'Life expectancy%',
value,
NULL
)) AS "LE 200x"
FROM country_indicators
WHERE "Country Name" IN(
'Arab World',
'Central Europe and the Baltics',
'East Asia & Pacific',
'European Union',
'Latin America & Caribbean',
'High income',
'Middle income',
'Low income'
)
GROUP BY 1
ORDER BY
CASE "Country Name"
WHEN 'High income' THEN 1
WHEN 'Middle income' THEN 2
WHEN 'Low income' THEN 3
WHEN 'European Union' THEN 4
WHEN 'Central Europe and the Baltics' THEN 5
WHEN 'East Asia & Pacific' THEN 6
WHEN 'Latin America & Caribbean' THEN 7
WHEN 'Arab World' THEN 8
ELSE 99
END
```
## Dynamic-column pivot table
SQLite in sqliteviz is built with [pivot_vtab][2] extension. The same result set
can be produced with this, arguably simpler and more maintainable, query.
```sql
CREATE VIRTUAL TABLE temp.pivot USING pivot_vtab(
(
WITH t(country_name) AS (VALUES
('High income'),
('Middle income'),
('Low income'),
('European Union'),
('Central Europe and the Baltics'),
('East Asia & Pacific'),
('Latin America & Caribbean'),
('Arab World')
)
SELECT country_name FROM t
),
(
SELECT
FLOOR(year / 10) || '|' || "Indicator Name" column_key,
CASE
WHEN "Indicator Name" LIKE 'Fertility rate%' THEN 'FR'
WHEN "Indicator Name" LIKE 'Life expectancy%' THEN 'LE'
END || ' ' || FLOOR(year / 10) || 'x' column_name
FROM country_indicators
WHERE
"Indicator Name" LIKE 'Fertility rate%'
OR "Indicator Name" LIKE 'Life expectancy%'
GROUP BY 1
),
(
SELECT AVG(value)
FROM country_indicators
WHERE
"Country Name" = :country_name
AND FLOOR(year / 10) || '|' || "Indicator Name" = :column_key
)
);
SELECT * FROM pivot
```
[1]: https://github.com/plotly/datasets/blob/master/country_indicators.csv
[2]: https://github.com/jakethaw/pivot_vtab

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# How to migrate to sqliteviz.com
All upcoming sqliteviz releases will be deployed on sqliteviz.com instead of
[Github Pages](https://lana-k.github.io/sqliteviz/#/). The saved inquiries that
you still care about can be imported to sqliteviz.com as usual.
## Step 1: export inquiries
- Click `Inquiries` in the main top menu
- Select all inquiries by clicking the checkbox in the table header
- Click `Export` and save your inquiries in a JSON file
## Step 2: uninstall sqliteviz
If you installed sqliteviz as PWA uninstall it before install the new version.
- Go to `chrome://apps/` in your browser
- Right click on sqliteviz icon
- Choose `Remove from Chrome`
## Step 3: import inquiries
- Go to [https://sqliteviz.com/app/#/workspace](https://sqliteviz.com/app/#/workspace)
- Click `Inquiries` in the main top menu
- Click `Import`
- Choose the file where you exported your inquiries

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# How to rename tables and columns after CSV import
## Rename columns
If sqliteviz parses CSV without `Use first row as column headers` option then
it will name the columns like `col1`, `col2` etc. You can easily rename the
columns after import with `ALTER TABLE` statements like this:
```sql
ALTER TABLE your_table_name
RENAME COLUMN current_column_name TO new_column_name;
```
### Column rename example
There is a table `dots` with columns `col1`, `col2`, `col3`. Here are the steps
to rename the columns to `x`, `y` and `z` respectively:
- Click `Create` in the top toolbar
- In the opened query editor write a script
```sql
ALTER TABLE dots
RENAME COLUMN col1 TO x;
ALTER TABLE dots
RENAME COLUMN col2 TO y;
ALTER TABLE dots
RENAME COLUMN col3 TO z;
```
- Click ![](./img/run.svg) to run the script
## Rename table
```sql
ALTER TABLE current_table_name
RENAME TO new_table_name;
```
### Table rename example
There is a table `dots`. Here are the steps to rename it to `point`:
- Click `Create` in the top toolbar
- In the opened query editor write a script
```sql
ALTER TABLE dots RENAME TO point
```
- Click ![](./img/run.svg) to run the script

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Installation.md Normal file
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# Installation
The latest release of sqliteviz is running on [sqliteviz.com/app][1].
Basically, you don't need to install sqliteviz. But if you want you can install
it and use it as a regular desktop application with the following steps:
1. Open sqliteviz with [Chrome browser][2]
2. At the top right of the address bar, click `Install`
3. Confirm installation
See more about PWA on [Google Chrome Help][3].
[1]: https://sqliteviz.com/app/
[2]: https://www.google.com/chrome
[3]: https://support.google.com/chrome/answer/9658361?hl=en&ref_topic=7439636

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# Integrate predefined inquiries
If you run sqliteviz on your own server you can specify predefined inquiries.
These inquiries will appear in `Inquiries` list for all users working with
sqliteviz on your server.
To create a list of predefined inquiries choose inquiries in `Inquiries` list
and export them to `inquiries.json`. Then (re)place this file on the server in
the same directory as `index.html`.
A user can't edit, rename or delete a predefined inquiry. The rest operations
are available.

201
LICENSE
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@@ -1,201 +0,0 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
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other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
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including but not limited to software source code, documentation
source, and configuration files.
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# Manage inquiries
## Organise
You can find all inquiries that you saved in `Inquiries` (fig. 1).
![Fig. 1: Inquiries](./img/Screenshot_my_inquiries.png)
To manipulate one inquiry hover the cursor over the row with the inquiry and
choose the action:
* ![](./img/rename.svg) rename an inquiry
* ![](./img/copy.svg) duplicate an inquiry
* ![](./img/file-export.svg) export an inquiry to JSON file
* ![](./img/delete.svg) delete an inquiry
To edit a query or visualisation settings of an inquiry click on the respective
row. You will be redirected to `Workspace` where the chosen inquiry will be
opened in a tab.
> **Note:** After opening an inquiry there will be no visualisation for it even
> if you specified it and saved. That is so because there is no data to build
> the visualisation. Run the query and all saved chart settings will be applied.
You can also delete or export a group of inquiries to a JSON file. Select
inquiries with checkboxes and press `Delete`/`Export` button above the grid
(fig. 2).
![Fig. 2: Inquiries: a group of inquiries is selected](./img/Screenshot_group.png)
> **Note:** Some operations are not available for predefined inquiries. Read
> [Predefined inquiries][1] for details.
## Import
Click `Import` button on `Inquiries` page to import inquiries from a JSON file
generated by export.
[1]: ../Predefined-inquiries

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# Multiple CSV file import
Sometimes it's useful to import several CSV files as tables in one database. For
example, to be able to `JOIN` them in SQL.
In sqliteviz you can not only create a database from a CSV file, but also add
another table from CSV to the existing database.
- Click ![](./img/add-csv.svg) icon in the schema panel on `Workspace`
page
- Choose a CSV file
- Import it with `CSV import` dialog.

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# Pivot table
## Pivot table UI
Sqliteviz allows building pivot tables and visualizing them. To build a pivot
run a query to get data. Then open visualisation panel by clicking ![](./img/visualisation.svg)
in any of the two side toolbars and choose a pivot mode by clicking ![](./img/pivot.svg).
A pivot visualisation has the following settings:
- Columns choose one or more column names from the result set. The values in
the chosen columns will be column names of the pivot table.
- Rows choose one or more column names from the result set. The values in the
chosen columns will be row names of the pivot table.
- Order of columns and rows.
- Aggregator and its arguments a function which will be used for pivot cell
calculation. An aggregator can have from zero to two arguments. An aggregator
argument is one of the columns of the result set.
- View pivot table visualisation. It can be a table, a heatmap, a chart,
etc. See some examples of different views of the same pivot table below.
![Fig. 1: Table](./img/Screenshot_pivot_table.png)
![Fig. 2: Heatmap](./img/Screenshot_pivot_heatmap.png)
![Fig. 3: Horizontal Stacked Bar Chart](./img/Screenshot_pivot_barchart.png)
There are several built-in chart views for a pivot. But you can build your own
with `Custom chart` view (fig. 4).
![Fig. 4: Custom Chart](./img/Screenshot_pivot_custom_chart.png)
> **Note:** You can switch to other pivot views and back to `Custom chart`
> all your custom chart settings will be remembered. But if you switch the
> visualisation mode from pivot to any other mode, unsaved changes will be lost.
You can save any visualisation as an image by clicking ![](./img/camera.svg).
## Pivot table SQL
Pivot table (in the form of a result set) can be built on the SQL-level and,
technically speaking, can be visualised as any other result set. Practically
though there are a couple of challenges with that:
1. Visualising a dataset of long/tall shape is much more convenient in Plotly
(chart editor) rather than one of wide/fat shape.
2. SQLite doesn't have a special constructs like `PIVOT` or `CROSSTAB` in
its SQL dialect.
[How to build a pivot table in SQL(ite)][1] explores two options with static
(or beforehand-known) and dynamic columns.
[1]: ../How-to-build-a-pivot-table-in-SQ-Lite

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# Predefined inquiries
Predefined inquiries come out of the box on the sqliteviz instance. In sqliteviz
deployed on [Github Pages][1] there are no predefined inquiries, but you may
see them if you use sqliteviz integrated into another app.
These inquiries are shown in `Inqueries` list with a special label on the mouse
over (fig. 1).
![Fig. 1: Predefined inquiry](./img/Screenshot_predefined.png)
As a user, you can't rename or delete a predefined inquiry. You can't save
changes in a predefined inquiry, but you can save them as a new inquiry. The
rest operations, copy and export, are available.
[1]: https://lana-k.github.io/sqliteviz

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<p align="center">
<img src="src/assets/images/Logo.svg"/>
</p>
# sqliteviz
Sqliteviz is a single-page offline-first PWA for fully client-side visualisation
of SQLite databases, CSV, JSON or NDJSON files.
With sqliteviz you can:
- run SQL queries against a SQLite database and create [Plotly][11] charts and pivot tables based on the result sets
- import a CSV/JSON/NDJSON file into a SQLite database and visualize imported data
- export result set to CSV file
- manage inquiries and run them against different databases
- import/export inquiries from/to a JSON file
- export a modified SQLite database
- use it offline from your OS application menu like any other desktop app
https://user-images.githubusercontent.com/24638357/128249848-f8fab0f5-9add-46e0-a9c1-dd5085a8623e.mp4
## Quickstart
The latest release of sqliteviz is deployed on [sqliteviz.com/app][6].
## Wiki
For user documentation, check out sqliteviz [documentation][7].
## Motivation
It's a kind of middleground between [Plotly Falcon][1] and [Redash][2].
## Components
It is built on top of [react-chart-editor][3], [PivotTable.js][12], [sql.js][4] and [Vue-Codemirror][8] in [Vue.js][5]. CSV parsing is performed with [Papa Parse][9].
[1]: https://github.com/plotly/falcon
[2]: https://github.com/getredash/redash
[3]: https://github.com/plotly/react-chart-editor
[4]: https://github.com/sql-js/sql.js
[5]: https://github.com/vuejs/vue
[6]: https://sqliteviz.com/app/
[7]: https://sqliteviz.com/docs
[8]: https://github.com/surmon-china/vue-codemirror#readme
[9]: https://www.papaparse.com/
[10]: https://github.com/lana-k/sqliteviz/wiki/Predefined-queries
[11]: https://github.com/plotly/plotly.js
[12]: https://github.com/nicolaskruchten/pivottable

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# Sharing
You can generate a share link. That allows to share not only inquiries but also
a database and inquiry display settings. Sqliteviz will automatically load
database and inquiries, open them and run the query for the first inquiry.
The share link can have the following query parameters:
| Parameter | Values | Description |
|--------------|----------------------------------|-------------|
| `data_url` | | A URL to an SQLite database file. If not provided sqliteviz will run inquiries agains an empty database.|
| `data_format`|`sqlite` | Currently share links support only "sqlite" data format.
| `inquiry_url`| | A URL to an inquiry JSON file (you can make that file with inquiry export see [Manage inquiries][3]).|
| `inquiry_id` | | If `inquiry_id` is provided (can occure multiple times) sqliteviz will load only inquiries with provided IDs. If not provided it will get them all.|
| `maximize` | `table`, `sqlEditor`, `dataView` | Specify which panel should be maximised for each inquiry. If not provided the inquiries will be opened in the default state: SQL editor on top and result set at the bottom.|
> **Note:** the server where you host your database or inquiry files must allow
cross-origin access. For example you can place your files on [GitHub Gist][1].
You can check your URLs with [CORS tester][2].
Use the following form to generate a share link:
[1]: https://gist.github.com/
[2]: https://cors-test.codehappy.dev/?origin=https%3A%2F%2Fsqliteviz.com%2F&method=get
[3]: ../Manage-inquiries

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module.exports = {
presets: ['@vue/cli-plugin-babel/preset']
}

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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
<meta name="viewport" content="width=device-width,initial-scale=1.0" />
<link rel="icon" href="favicon.png" />
<link rel="manifest" href="manifest.webmanifest" />
<title>sqliteviz</title>
<style>
#sqliteviz-loading-wrapper {
position: fixed;
width: 100%;
height: 100%;
left: 0;
top: 0;
background-color: white;
}
#sqliteviz-loading-text {
display: block;
position: absolute;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
color: #506784;
font-family: sans-serif;
font-size: 20px;
}
#sqliteviz-loading-wrapper svg {
display: block;
position: absolute;
left: 50%;
top: 50%;
transform: translate(-50%, -50%);
}
#sqliteviz-loading-wrapper circle {
position: absolute;
left: 0;
right: 0;
top: 0;
bottom: 0;
fill: none;
stroke-width: 5px;
stroke-linecap: round;
stroke: #119dff;
}
#sqliteviz-loading-wrapper circle.bg {
stroke: #c8d4e3;
}
#sqliteviz-loading-wrapper circle.front {
stroke-dasharray: 402px;
animation: sqliteviz-loading 2s linear 0s infinite;
}
@keyframes sqliteviz-loading {
0% {
stroke-dasharray: 100px 402px;
stroke-dashoffset: 0;
}
50% {
stroke-dasharray: 251px;
stroke-dashoffset: -251px;
}
100% {
stroke-dasharray: 100px 402px;
stroke-dashoffset: -502px;
}
}
</style>
<!-- head extention slot start -->
<!-- head extention slot end -->
</head>
<body>
<noscript>
<strong>
We're sorry but this app doesn't work properly without JavaScript
enabled. Please enable it to continue.
</strong>
</noscript>
<div id="app">
<div id="sqliteviz-loading-wrapper">
<div id="sqliteviz-loading-text">LOADING</div>
<svg height="170" width="170" viewBox="0 0 170 170">
<circle class="bg" cx="85" cy="85" r="80" />
<circle class="front" cx="85" cy="85" r="80" />
</svg>
</div>
</div>
<!-- extention slot start -->
<!-- extention slot end -->
<script type="module" src="/src/main.js"></script>
</body>
</html>

13
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# Introduction
Sqliteviz is a single-page offline-first PWA for fully client-side visualisation of SQLite databases or CSV files.
With sqliteviz you can:
- run SQL queries against a SQLite database and create Plotly charts and pivot tables based on the result sets
- import a CSV file into a SQLite database and visualize imported data
- export result set to CSV file
- manage inquiries and run them against different databases
- import/export inquiries from/to a JSON file
- export a modified SQLite database
- use it offline from your OS application menu like any other desktop app

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@@ -1,10 +0,0 @@
{
"include": ["src/**/*", "tests/**/*"],
"exclude": ["node_modules", "dist"],
"compilerOptions": {
"baseUrl": "./",
"paths": {
"@\/*": ["./src/*"]
}
}
}

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@@ -1,122 +0,0 @@
module.exports = function (config) {
config.set({
vite: {
config: {
resolve: {
alias: {
vue: 'vue/dist/vue.esm-bundler.js'
}
},
server: {
preTransformRequests: false
}
},
coverage: {
enable: true,
include: 'src/*',
exclude: ['node_modules', 'src/components/svg/*'],
extension: ['.js', '.vue'],
requireEnv: false
}
},
// base path that will be used to resolve all patterns (eg. files, exclude)
basePath: '',
// frameworks to use
// available frameworks: https://npmjs.org/browse/keyword/karma-adapter
frameworks: ['mocha', 'sinon-chai', 'vite'],
// list of files / patterns to load in the browser
files: [
{
pattern: 'test.setup.js',
type: 'module',
watched: false,
served: false
},
{
pattern: 'tests/**/*.spec.js',
type: 'module',
watched: false,
served: false
},
{
pattern: 'src/assets/styles/*.css',
type: 'css',
watched: false,
served: false
}
],
plugins: [
'karma-vite',
'karma-mocha',
'karma-sinon-chai',
'karma-firefox-launcher',
'karma-chrome-launcher',
'karma-spec-reporter',
'karma-coverage'
],
// list of files / patterns to exclude
exclude: [],
// test results reporter to use
// possible values: 'dots', 'progress'
// available reporters: https://npmjs.org/browse/keyword/karma-reporter
reporters: ['spec', 'coverage'],
coverageReporter: {
dir: 'coverage',
reporters: [{ type: 'lcov', subdir: '.' }, { type: 'text-summary' }]
},
// web server port
port: 9876,
// enable / disable colors in the output (reporters and logs)
colors: true,
// level of logging
// possible values: config.LOG_DISABLE || config.LOG_ERROR || config.LOG_WARN ||
// config.LOG_INFO || config.LOG_DEBUG
logLevel: config.LOG_INFO,
// enable / disable watching file and executing tests whenever any file changes
autoWatch: false,
customLaunchers: {
FirefoxHeadlessTouch: {
base: 'FirefoxHeadless',
prefs: {
'dom.w3c_touch_events.enabled': 1,
'dom.events.asyncClipboard.clipboardItem': true
}
}
},
// start these browsers
// available browser launchers: https://npmjs.org/browse/keyword/karma-launcher
browsers: ['ChromiumHeadless', 'FirefoxHeadlessTouch'],
// Continuous Integration mode
// if true, Karma captures browsers, runs the tests and exits
singleRun: true,
// Concurrency level
// how many browser should be started simultaneous
concurrency: 2,
client: {
captureConsole: true,
mocha: {
timeout: 7000
}
},
browserConsoleLogOptions: {
terminal: true,
level: ''
}
})
// Fix the timezone
process.env.TZ = 'Europe/Amsterdam'
}

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@@ -1,2 +0,0 @@
benchmark
dist

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FROM emscripten/emsdk:3.0.1
WORKDIR /tmp/build
COPY configure.py .
RUN python3.8 configure.py
COPY build.py .
RUN python3.8 build.py

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@@ -1,108 +0,0 @@
# SQLite WebAssembly build
This directory contains Docker-based build script, `make.sh`, that builds
a custom version of [sql.js][1]. It allows sqliteviz to have more recent
version of SQLite build with a number of useful extensions.
`Makefile` from [sql.js][1] is rewritten as more comprehensible `configure.py`
and `build.py` Python scripts that run in `emscripten/emsdk` Docker container.
## Extension
SQLite [amalgamation][2] extensions included:
1. [FTS5][4] -- virtual table module that provides full-text search
functionality
2. [FTS3/FTS4][15] -- older virtual table modules for full-text search
3. [JSON1][16] -- scalar, aggregate and table-valued functions for managing JSON data
SQLite [contribution extensions][17]:
1. [extension-functions][18] -- mathematical and string extension functions for SQL queries.
Math: `acos`, `asin`, `atan`, `atn2`, `atan2`, `acosh`, `asinh`, `atanh`, `difference`,
`degrees`, `radians`, `cos`, `sin`, `tan`, `cot`, `cosh`, `sinh`, `tanh`, `coth`,
`exp`, `log`, `log10`, `power`, `sign`, `sqrt`, `square`, `ceil`, `floor`, `pi`.
String: `replicate`, `charindex`, `leftstr`, `rightstr`, `ltrim`, `rtrim`, `trim`,
`replace`, `reverse`, `proper`, `padl`, `padr`, `padc`, `strfilter`.
Aggregate: `stdev`, `variance`, `mode`, `median`, `lower_quartile`, `upper_quartile`.
SQLite [miscellaneous extensions][3] included:
1. `generate_series` table-valued [series function][6] ([series.c][7])
2. `transitive_closure` virtual table for
[Querying Tree Structures in SQLite][11] ([closure.c][8])
3. `uuid`, `uuid_str` and `uuid_blob` RFC-4122 UUID functions ([uuid.c][9])
4. `regexp` (hence `REGEXP` operator) and `regexpi` functions ([regexp.c][10])
5. `percentile` function ([percentile.c][13])
6. `decimal`, `decimal_cmp`, `decimal_add`, `decimal_sub` and `decimal_mul` functions
([decimal.c][14])
SQLite 3rd party extensions included:
1. [pivot_vtab][5] -- a pivot virtual table
2. `pearson` correlation coefficient function extension from [sqlean][21]
(which is part of [squib][20])
3. [sqlitelua][22] -- a virtual table `luafunctions` which allows to define custom scalar,
aggregate and table-valued functions in Lua
To ease the step to have working clone locally, the build is committed into
the repository.
Examples of queries involving these extensions can be found in the test suite in
[sqliteExtensions.spec.js][19].
## Build method
Basically it's extended amalgamation and `SQLITE_EXTRA_INIT` concisely
described in [this message from SQLite Forum][12]:
> Simply append it to the end of the amalgamation file. The real problem is
> how you get the init function called. The easiest way (to me at any rate) is
> to append a function (after the extensions you want to add are all appended)
> that adds the init function for each extension to the auto extension list
> for new connections, and set the pre-processor symbol SQLITE_EXTRA_INIT to
> the name of this function. [...]
>
> An example `SQLITE_EXTRA_INIT` function looks like this:
>
> ```
> int core_init(const char* dummy)
> {
> int nErr = 0;
>
> nErr += sqlite3_auto_extension((void*)sqlite3_autobusy_init);
> nErr += sqlite3_auto_extension((void*)sqlite3_ipaddress_init);
>
> return nErr ? SQLITE_ERROR : SQLITE_OK;
> }
> ```
>
> so you would then define `SQLITE_EXTRA_INIT=core_init` when compiling the
> amalgamation code and the extensions would thereafter be automatically
> initialized on each connection.
[1]: https://github.com/sql-js/sql.js
[2]: https://sqlite.org/amalgamation.html
[3]: https://sqlite.org/src/dir?ci=trunk&name=ext/misc
[4]: https://sqlite.org/fts5.html
[5]: https://github.com/jakethaw/pivot_vtab
[6]: https://sqlite.org/series.html
[7]: https://sqlite.org/src/file/ext/misc/series.c
[8]: https://sqlite.org/src/file/ext/misc/closure.c
[9]: https://sqlite.org/src/file/ext/misc/uuid.c
[10]: https://sqlite.org/src/file/ext/misc/regexp.c
[11]: https://charlesleifer.com/blog/querying-tree-structures-in-sqlite-using-python-and-the-transitive-closure-extension/
[12]: https://sqlite.org/forum/forumpost/6ad7d4f4bebe5e06?raw
[13]: https://sqlite.org/src/file/ext/misc/percentile.c
[14]: https://sqlite.org/src/file/ext/misc/decimal.c
[15]: https://sqlite.org/fts3.html
[16]: https://sqlite.org/json1.html
[17]: https://sqlite.org/contrib/
[18]: https://sqlite.org/contrib//download/extension-functions.c?get=25
[19]: https://github.com/lana-k/sqliteviz/blob/master/tests/lib/database/sqliteExtensions.spec.js
[20]: https://github.com/mrwilson/squib/blob/master/pearson.c
[21]: https://github.com/nalgeon/sqlean/blob/incubator/src/pearson.c
[22]: https://github.com/kev82/sqlitelua

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@@ -1,4 +0,0 @@
/lib/build-*
/lib/dist
/build-*-result.json
/sample.csv

View File

@@ -1,17 +0,0 @@
FROM node:20.14-bookworm
RUN set -ex; \
apt-get update; \
apt-get install -y firefox-esr; \
apt-get install -y chromium
WORKDIR /tmp/build
COPY package.json ./
COPY lib/dist lib/dist
COPY lib/package.json lib/package.json
RUN npm install
COPY . .
CMD npm run benchmark

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@@ -1,25 +0,0 @@
# SQLite WebAssembly build micro-benchmark
This directory contains a micro-benchmark for evaluating SQLite WebAssembly
builds performance on read and write SQL queries, run from `make.sh` script. If
the script has permission to `nice` processes and [Procpath][1] is installed,
e.g. it is run with `sudo -E env PATH=$PATH ./make.sh`, it'll `renice` all
processes running inside the benchmark containers. It can also serve as a smoke
test (e.g. for memory leaks).
The benchmark operates on a set of SQLite WebAssembly builds expected in
`lib/build-$NAME` directories each containing `sql-wasm.js` and
`sql-wasm.wasm`. Then it creates a Docker image for each, and runs the
benchmark in Firefox and Chromium using Karma in the container.
After successful run, the benchmark produces the following per each build:
- `build-$NAME-result.json`
- `build-$NAME.sqlite` (if Procpath is installed)
- `build-$NAME.svg` (if Procpath is installed)
These files can be analysed using `result-analysis.ipynb` Jupyter notebook.
The SVG is a chart with CPU and RSS usage of each test container (i.e. Chromium
run, then Firefox run per container).
[1]: https://pypi.org/project/Procpath/

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@@ -1,57 +0,0 @@
module.exports = function (config) {
const timeout = 15 * 60 * 1000
config.set({
frameworks: ['mocha'],
files: [
'suite.js',
{
pattern: 'node_modules/sql.js/dist/sql-wasm.wasm',
served: true,
included: false
},
{ pattern: 'sample.csv', served: true, included: false }
],
reporters: ['progress', 'json-to-file'],
singleRun: true,
customLaunchers: {
ChromiumHeadlessNoSandbox: {
base: 'ChromiumHeadless',
flags: ['--no-sandbox']
}
},
browsers: ['ChromiumHeadlessNoSandbox', 'FirefoxHeadless'],
concurrency: 1,
browserDisconnectTimeout: timeout,
browserNoActivityTimeout: timeout,
captureTimeout: timeout,
browserSocketTimeout: timeout,
pingTimeout: timeout,
client: {
captureConsole: true,
mocha: { timeout: timeout }
},
logLevel: config.LOG_INFO,
browserConsoleLogOptions: { terminal: true, level: config.LOG_INFO },
preprocessors: { 'suite.js': ['webpack'] },
webpack: {
mode: 'development',
module: {
noParse: [__dirname + '/node_modules/benchmark/benchmark.js']
},
node: { fs: 'empty' }
},
proxies: {
'/sql-wasm.wasm': '/base/node_modules/sql.js/dist/sql-wasm.wasm'
},
jsonToFileReporter: { outputPath: '.', fileName: 'suite-result.json' }
})
}

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@@ -1,5 +0,0 @@
{
"name": "sql.js",
"main": "./dist/sql-wasm.js",
"private": true
}

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@@ -1,47 +0,0 @@
#!/bin/bash -e
cleanup () {
rm -rf lib/dist "$renice_flag_file"
docker rm -f sqljs-benchmark-run 2> /dev/null || true
}
trap cleanup EXIT
if [ ! -f sample.csv ]; then
wget --header="accept-encoding: gzip" -q -O- \
https://github.com/plotly/datasets/raw/547090bd/wellspublic.csv \
| gunzip -c > sample.csv
fi
PLAYBOOK=procpath/karma_docker.procpath
# for renice to work run like "sudo -E env PATH=$PATH ./make.sh"
test_ni=$(nice -n -5 nice)
if [ $test_ni == -5 ]; then
renice_flag_file=$(mktemp)
fi
{
while [ -f $renice_flag_file ]; do
procpath --logging-level ERROR play -f $PLAYBOOK renice:watch
done
} &
shopt -s nullglob
for d in lib/build-* ; do
rm -rf lib/dist
cp -r $d lib/dist
sample_name=$(basename $d)
docker build -t sqliteviz/sqljs-benchmark .
docker rm sqljs-benchmark-run 2> /dev/null || true
docker run -d -it --cpus 2 --name sqljs-benchmark-run sqliteviz/sqljs-benchmark
{
rm -f ${sample_name}.sqlite
procpath play -f $PLAYBOOK -o database_file=${sample_name}.sqlite track:record
procpath play -f $PLAYBOOK -o database_file=${sample_name}.sqlite \
-o plot_file=${sample_name}.svg track:plot
} &
docker attach sqljs-benchmark-run
docker cp sqljs-benchmark-run:/tmp/build/suite-result.json ${sample_name}-result.json
docker rm sqljs-benchmark-run
done

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@@ -1,23 +0,0 @@
{
"name": "sqlite-webassembly-microbenchmark",
"private": true,
"dependencies": {
"@babel/core": "^7.14.8",
"babel-loader": "^8.2.2",
"benchmark": "^2.1.4",
"lodash": "^4.17.4",
"papaparse": "^5.3.1",
"mocha": "^9.0.3",
"karma": "^6.3.4",
"karma-chrome-launcher": "^3.1.0",
"karma-firefox-launcher": "^2.1.1",
"karma-json-to-file-reporter": "^1.0.1",
"karma-mocha": "^2.0.1",
"karma-webpack": "^4.0.2",
"webpack": "^4.46.0",
"sql.js": "file:./lib"
},
"scripts": {
"benchmark": "karma start karma.conf.js"
}
}

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@@ -1,28 +0,0 @@
# This command may run when "sqljs-benchmark-run" does not yet exist or run
[renice:watch]
interval: 2
repeat: 30
environment:
ROOT_PID=docker inspect -f "{{.State.Pid}}" sqljs-benchmark-run 2> /dev/null || true
query:
PIDS=$..children[?(@.stat.pid in [$ROOT_PID])]..pid
command:
echo $PIDS | tr , '\n' | xargs --no-run-if-empty -I{} -- renice -n -5 -p {}
# Expected input arguments: database_file
[track:record]
interval: 1
stop_without_result: 1
environment:
ROOT_PID=docker inspect -f "{{.State.Pid}}" sqljs-benchmark-run
query:
$..children[?(@.stat.pid == $ROOT_PID)]
pid_list: $ROOT_PID
# Expected input arguments: database_file, plot_file
[track:plot]
moving_average_window: 5
title: Chromium vs Firefox (№1 RSS, №2 CPU)
custom_query_file:
procpath/top2_rss.sql
procpath/top2_cpu.sql

View File

@@ -1,29 +0,0 @@
WITH diff_all AS (
SELECT
record_id,
ts,
stat_pid,
stat_utime + stat_stime - LAG(stat_utime + stat_stime) OVER (
PARTITION BY stat_pid
ORDER BY record_id
) tick_diff,
ts - LAG(ts) OVER (
PARTITION BY stat_pid
ORDER BY record_id
) ts_diff
FROM record
), diff AS (
SELECT * FROM diff_all WHERE tick_diff IS NOT NULL
), one_time_pid_condition AS (
SELECT stat_pid
FROM record
GROUP BY 1
ORDER BY SUM(stat_utime + stat_stime) DESC
LIMIT 2
)
SELECT
ts,
stat_pid pid,
100.0 * tick_diff / (SELECT value FROM meta WHERE key = 'clock_ticks') / ts_diff value
FROM diff
JOIN one_time_pid_condition USING(stat_pid)

View File

@@ -1,13 +0,0 @@
WITH one_time_pid_condition AS (
SELECT stat_pid
FROM record
GROUP BY 1
ORDER BY SUM(stat_rss) DESC
LIMIT 2
)
SELECT
ts,
stat_pid pid,
stat_rss / 1024.0 / 1024 * (SELECT value FROM meta WHERE key = 'page_size') value
FROM record
JOIN one_time_pid_condition USING(stat_pid)

View File

@@ -1,218 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"source": [
"import json\n",
"from pathlib import Path\n",
"\n",
"import pandas\n",
"from IPython.display import display, IFrame, Markdown\n",
"from plotly import express"
],
"outputs": [],
"execution_count": null,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": true
}
},
{
"cell_type": "code",
"source": [
"render_format = 'svg'\n",
"benchmark_path = Path()\n",
"df_dict = {}\n",
"stat_dict = {}\n",
"for p in benchmark_path.glob('build-*-result.json'):\n",
" build_name = p.stem.split('-', 2)[1]\n",
" for browser_data in json.loads(p.read_text()):\n",
" browser_name = f'{browser_data[\"browser\"][\"name\"]} {browser_data[\"browser\"][\"major\"]}'\n",
" browser_name = browser_name.lower().replace('chrome headless', 'chromium')\n",
" for result in (r for i, r in browser_data['result'].items() if i.isdigit()):\n",
" key = build_name, browser_name, result['name']\n",
" df_dict[key] = result['stats']['sample']\n",
" stat_dict[key] = result['stats']\n",
"\n",
"min_sample_size = min(len(v) for v in df_dict.values())\n",
"df_dict = {k: v[:min_sample_size] for k, v in df_dict.items()}\n",
" \n",
"wide_df = pandas.DataFrame(df_dict).reset_index()\n",
"df = pandas.melt(\n",
" wide_df, \n",
" id_vars='index', \n",
" var_name=['build', 'browser', 'test'], \n",
" value_name='duration',\n",
")\n",
"df = df.rename(columns={'index': 'run'})\n",
"df.sort_values(['build', 'run'], inplace=True)"
],
"outputs": [],
"execution_count": null,
"metadata": {
"inputHidden": true,
"outputExpanded": false
}
},
{
"cell_type": "markdown",
"source": [
"# sql.js build comparison\n",
"\n",
"<style>\n",
"@page {\n",
" size: 215mm 297mm;\n",
" margin: 0;\n",
"}\n",
"</style>"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"!du -b lib | head -n 2"
],
"outputs": [],
"execution_count": null,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": true
}
},
{
"cell_type": "code",
"source": [
"stat_df = pandas.DataFrame(stat_dict)\n",
"stat_df = stat_df.loc[['mean', 'rme']].transpose()\n",
"stat_df.index = stat_df.index.set_names(['build', 'browser', 'test'])\n",
"stat_df = stat_df.reset_index().sort_values(['test', 'browser'], ascending=False)\n",
"for index, row in stat_df.iterrows():\n",
" print('\\t'.join([\n",
" row['build'],\n",
" row['browser'],\n",
" row['test'],\n",
" f'{row[\"mean\"]:.2f} s ± {row[\"rme\"]:.1f}%'\n",
" ]))"
],
"outputs": [],
"execution_count": null,
"metadata": {
"inputHidden": true,
"outputExpanded": false
}
},
{
"cell_type": "code",
"source": [
"fig = express.box(df, x='browser', y='duration', points='all', color='build', facet_row='test')\n",
"fig.update_yaxes(matches=None)\n",
"fig.show(render_format)"
],
"outputs": [],
"execution_count": null,
"metadata": {
"inputHidden": true,
"outputExpanded": false
}
},
{
"cell_type": "code",
"source": [
"fig = express.line(\n",
" df, x='run', y='duration', color='build', facet_col='browser', facet_row='test'\n",
")\n",
"fig.update_yaxes(matches=None)\n",
"fig.show(render_format)"
],
"outputs": [],
"execution_count": null,
"metadata": {
"inputHidden": true,
"outputExpanded": false
}
},
{
"cell_type": "code",
"source": [
"plot_df = df.groupby(['browser', 'build', 'test'])['duration'].mean().reset_index()\n",
"plot_df['pct'] = (\n",
" plot_df\n",
" .groupby(['browser', 'test'])['duration']\n",
" .pct_change()\n",
" .fillna('')\n",
" .map(lambda v: '{:.2%}'.format(v) if v else v)\n",
")\n",
"fig = express.bar(\n",
" plot_df, \n",
" x='browser', \n",
" y='duration', \n",
" color='build', \n",
" text='pct', \n",
" barmode='overlay', \n",
" facet_row='test',\n",
")\n",
"fig.update_yaxes(matches=None)\n",
"fig.show(render_format)"
],
"outputs": [],
"execution_count": null,
"metadata": {
"inputHidden": true,
"outputExpanded": false
}
},
{
"cell_type": "code",
"source": [
"for p in sorted(benchmark_path.glob('build-*.svg')):\n",
" display(Markdown(p.stem))\n",
" display(IFrame(p, 640, 480))"
],
"outputs": [],
"execution_count": null,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": true,
"outputExpanded": false
}
}
],
"metadata": {
"kernelspec": {
"name": "stats",
"language": "python",
"display_name": "Stats (Python 3.10)"
},
"language_info": {
"name": "python",
"version": "3.10.14",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
},
"kernel_info": {
"name": "stats"
},
"nteract": {
"version": "0.14.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@@ -1,137 +0,0 @@
import benchmark from 'benchmark'
import initSqlJs from 'sql.js'
import lodash from 'lodash'
import Papa from 'papaparse'
import useragent from 'ua-parser-js'
describe('SQLite build benchmark', function () {
let parsedCsv
let sqlModule
let selectDb
before(async function () {
parsedCsv = await parseCsv('http://localhost:9876/base/sample.csv')
sqlModule = await initSqlJs()
selectDb = new sqlModule.Database()
importToTable(selectDb, parsedCsv)
})
function benchmarkImport() {
const db = new sqlModule.Database()
try {
importToTable(db, parsedCsv)
} finally {
db.close()
}
}
function benchmarkSelect() {
const result = selectDb.exec(`
SELECT county, AVG(avg_depth) avg_depth_c
FROM (
SELECT s.county, s.town, COUNT(*) cnt, AVG(s.DrilledDepth) avg_depth
FROM csv_import s
JOIN csv_import USING(hole)
WHERE s.town IS NOT NULL
GROUP BY 1, 2
ORDER BY 4 DESC
)
GROUP BY 1
ORDER BY 2 DESC
`)
console.assert(result.values.length == 56, 'Unexpected size of result set')
}
it('run', async function () {
const suite = createSuite()
suite.add('import', { initCount: 3, minSamples: 50, fn: benchmarkImport })
suite.add('select', { initCount: 3, minSamples: 50, fn: benchmarkSelect })
await run(suite)
})
})
function importToTable(db, parsedCsv, chunkSize = 1024) {
const columnListString = parsedCsv.meta.fields.join(', ')
db.exec(`CREATE TABLE csv_import(${columnListString})`)
const params = parsedCsv.meta.fields.map(name => '?').join(', ')
const insertStmt = db.prepare(`INSERT INTO csv_import VALUES(${params})`)
chunkArray(parsedCsv.data, chunkSize).map(function (chunk) {
db.exec('BEGIN')
chunk.map(row => insertStmt.run(Object.values(row)))
db.exec('COMMIT')
})
}
class PromiseWrapper {
constructor() {
this.promise = new Promise((resolve, reject) => {
this.reject = reject
this.resolve = resolve
})
}
}
function parseCsv(url) {
return new Promise((resolve, reject) => {
Papa.parse(url, {
header: true,
download: true,
skipEmptyLines: 'greedy',
complete: results => resolve(results),
error: (error, file) => reject(error)
})
})
}
function chunkArray(arr, size) {
return arr.reduce(function (result, value, index) {
const chunkIndex = Math.floor(index / size)
if (!(chunkIndex in result)) {
result[chunkIndex] = []
}
result[chunkIndex].push(value)
return result
}, [])
}
function createSuite() {
// Combined workaround from:
// - https://github.com/bestiejs/benchmark.js/issues/106
// - https://github.com/bestiejs/benchmark.js/issues/237
// Benchmark could not pick up lodash otherwise
const bm = benchmark.runInContext({ _: lodash })
// Avoid `ReferenceError: Benchmark is not defined` error because Benchmark is assumed
// to be in window
window.Benchmark = bm
return new bm.Suite()
}
function run(suite) {
const suiteResult = new PromiseWrapper()
suite
.on('cycle', function (event) {
console.info(String(event.target))
})
.on('complete', function () {
console.log(
JSON.stringify({
browser: useragent(navigator.userAgent).browser,
result: this.filter('successful')
})
)
suiteResult.resolve()
})
.on('error', function (event) {
console.error('Benchmark failed', String(event.target))
suiteResult.reject()
})
.run({ async: true })
return suiteResult.promise
}

View File

@@ -1,99 +0,0 @@
import logging
import subprocess
from pathlib import Path
# See the setting descriptions on these pages:
# - https://emscripten.org/docs/optimizing/Optimizing-Code.html
# - https://github.com/emscripten-core/emscripten/blob/main/src/settings.js
cflags = (
# SQLite configuration
'-DSQLITE_DEFAULT_CACHE_SIZE=-65536', # 64 MiB
'-DSQLITE_DEFAULT_MEMSTATUS=0',
'-DSQLITE_DEFAULT_SYNCHRONOUS=0',
'-DSQLITE_DISABLE_LFS',
'-DSQLITE_DQS=0',
'-DSQLITE_ENABLE_FTS3',
'-DSQLITE_ENABLE_FTS3_PARENTHESIS',
'-DSQLITE_ENABLE_FTS5',
'-DSQLITE_ENABLE_NORMALIZE',
'-DSQLITE_EXTRA_INIT=extra_init',
'-DSQLITE_OMIT_DEPRECATED',
'-DSQLITE_OMIT_LOAD_EXTENSION',
'-DSQLITE_OMIT_SHARED_CACHE',
'-DSQLITE_THREADSAFE=0',
# Compile-time optimisation
'-Os', # reduces the code size about in half comparing to -O2
'-flto',
'-Isrc', '-Isrc/lua',
)
emflags = (
# Base
'--memory-init-file', '0',
'-s', 'ALLOW_TABLE_GROWTH=1',
# WASM
'-s', 'WASM=1',
'-s', 'ALLOW_MEMORY_GROWTH=1',
'-s', 'ENVIRONMENT=web,worker',
# Link-time optimisation
'-Os',
'-flto',
# sql.js
'-s', 'EXPORTED_FUNCTIONS=@src/sqljs/exported_functions.json',
'-s', 'EXPORTED_RUNTIME_METHODS=@src/sqljs/exported_runtime_methods.json',
'--pre-js', 'src/sqljs/api.js',
)
def build(src: Path, dst: Path):
out = Path('out')
out.mkdir()
logging.info('Building LLVM bitcode for sqlite3.c')
subprocess.check_call([
'emcc',
*cflags,
'-c', src / 'sqlite3.c',
'-o', out / 'sqlite3.o',
])
logging.info('Building LLVM bitcode for extension-functions.c')
subprocess.check_call([
'emcc',
*cflags,
'-c', src / 'extension-functions.c',
'-o', out / 'extension-functions.o',
])
logging.info('Building LLVM bitcode for SQLite Lua extension')
subprocess.check_call([
'emcc',
*cflags,
'-shared',
*(src / 'lua').glob('*.c'),
*(src / 'sqlitelua').glob('*.c'),
'-o', out / 'sqlitelua.o',
])
logging.info('Building WASM from bitcode')
subprocess.check_call([
'emcc',
*emflags,
out / 'sqlite3.o',
out / 'extension-functions.o',
out / 'sqlitelua.o',
'-o', out / 'sql-wasm.js',
])
logging.info('Post-processing build and copying to dist')
(out / 'sql-wasm.wasm').rename(dst / 'sql-wasm.wasm')
with (dst / 'sql-wasm.js').open('w') as f:
f.write((src / 'sqljs' / 'shell-pre.js').read_text())
f.write((out / 'sql-wasm.js').read_text())
f.write((src / 'sqljs' / 'shell-post.js').read_text())
if __name__ == '__main__':
logging.basicConfig(level='INFO', format='%(asctime)s %(levelname)s %(name)s %(message)s')
src = Path('src')
dst = Path('dist')
dst.mkdir()
build(src, dst)

View File

@@ -1,150 +0,0 @@
import logging
import re
import shutil
import subprocess
import sys
import tarfile
import zipfile
from io import BytesIO
from pathlib import Path
from urllib import request
amalgamation_url = 'https://sqlite.org/2025/sqlite-amalgamation-3500300.zip'
# Extension-functions
# ===================
# It breaks amalgamation if appended as other extension because it redefines
# several functions, so build it separately. Note that sql.js registers these
# extension functions by calling ``registerExtensionFunctions`` itself.
contrib_functions_url = 'https://sqlite.org/contrib/download/extension-functions.c?get=25'
extension_urls = (
# Miscellaneous extensions
# ========================
('https://sqlite.org/src/raw/e212edb2?at=series.c', 'sqlite3_series_init'),
('https://sqlite.org/src/raw/5559daf1?at=closure.c', 'sqlite3_closure_init'),
('https://sqlite.org/src/raw/5bb2264c?at=uuid.c', 'sqlite3_uuid_init'),
('https://sqlite.org/src/raw/388e7f23?at=regexp.c', 'sqlite3_regexp_init'),
('https://sqlite.org/src/raw/72e05a21?at=percentile.c', 'sqlite3_percentile_init'),
('https://sqlite.org/src/raw/228d47e9?at=decimal.c', 'sqlite3_decimal_init'),
# Third-party extension
# =====================
('https://github.com/jakethaw/pivot_vtab/raw/e7705f34/pivot_vtab.c', 'sqlite3_pivotvtab_init'),
('https://github.com/nalgeon/sqlean/raw/95e8d21a/src/pearson.c', 'sqlite3_pearson_init'),
# Third-party extension with own dependencies
# ===========================================
('https://github.com/kev82/sqlitelua/raw/db479510/src/main.c', 'sqlite3_luafunctions_init'),
)
lua_url = 'http://www.lua.org/ftp/lua-5.3.5.tar.gz'
sqlitelua_url = 'https://github.com/kev82/sqlitelua/archive/db479510.zip'
sqljs_url = 'https://github.com/sql-js/sql.js/archive/refs/tags/v1.7.0.zip'
def _generate_extra_init_c_function(init_function_names):
auto_ext_calls = '\n'.join([
'nErr += sqlite3_auto_extension((void*){});'.format(init_fn)
for init_fn in init_function_names
])
return '''
int extra_init(const char* dummy)
{
int nErr = 0;
%s
return nErr ? SQLITE_ERROR : SQLITE_OK;
}
''' % auto_ext_calls
def _get_amalgamation(tgt: Path):
logging.info('Downloading and extracting SQLite amalgamation %s', amalgamation_url)
archive = zipfile.ZipFile(BytesIO(request.urlopen(amalgamation_url).read()))
archive_root_dir = zipfile.Path(archive, archive.namelist()[0])
for zpath in archive_root_dir.iterdir():
with zpath.open() as fr, (tgt / zpath.name).open('wb') as fw:
shutil.copyfileobj(fr, fw)
def _get_lua(tgt: Path):
# Library definitions from lua/Makefile
lib_str = '''
CORE_O= lapi.o lcode.o lctype.o ldebug.o ldo.o ldump.o lfunc.o lgc.o llex.o \
lmem.o lobject.o lopcodes.o lparser.o lstate.o lstring.o ltable.o \
ltm.o lundump.o lvm.o lzio.o
LIB_O= lauxlib.o lbaselib.o lbitlib.o lcorolib.o ldblib.o liolib.o \
lmathlib.o loslib.o lstrlib.o ltablib.o lutf8lib.o loadlib.o linit.o
LUA_O= lua.o
'''
header_only_files = {'lprefix', 'luaconf', 'llimits', 'lualib'}
lib_names = set(re.findall(r'(\w+)\.o', lib_str)) | header_only_files
logging.info('Downloading and extracting Lua %s', lua_url)
archive = tarfile.open(fileobj=BytesIO(request.urlopen(lua_url).read()))
(tgt / 'lua').mkdir()
for tarinfo in archive:
tarpath = Path(tarinfo.name)
if tarpath.match('src/*') and tarpath.stem in lib_names:
with (tgt / 'lua' / tarpath.name).open('wb') as fw:
shutil.copyfileobj(archive.extractfile(tarinfo), fw)
logging.info('Downloading and extracting SQLite Lua extension %s', sqlitelua_url)
archive = zipfile.ZipFile(BytesIO(request.urlopen(sqlitelua_url).read()))
archive_root_dir = zipfile.Path(archive, archive.namelist()[0])
(tgt / 'sqlitelua').mkdir()
for zpath in (archive_root_dir / 'src').iterdir():
if zpath.name != 'main.c':
with zpath.open() as fr, (tgt / 'sqlitelua' / zpath.name).open('wb') as fw:
shutil.copyfileobj(fr, fw)
def _get_contrib_functions(tgt: Path):
request.urlretrieve(contrib_functions_url, tgt / 'extension-functions.c')
def _get_extensions(tgt: Path):
init_functions = []
sqlite3_c = tgt / 'sqlite3.c'
with sqlite3_c.open('ab') as f:
for url, init_fn in extension_urls:
logging.info('Downloading and appending to amalgamation %s', url)
with request.urlopen(url) as resp:
f.write(b'\n')
shutil.copyfileobj(resp, f)
init_functions.append(init_fn)
logging.info('Appending SQLITE_EXTRA_INIT to amalgamation')
f.write(_generate_extra_init_c_function(init_functions).encode())
def _get_sqljs(tgt: Path):
logging.info('Downloading and extracting sql.js %s', sqljs_url)
archive = zipfile.ZipFile(BytesIO(request.urlopen(sqljs_url).read()))
archive_root_dir = zipfile.Path(archive, archive.namelist()[0])
(tgt / 'sqljs').mkdir()
for zpath in (archive_root_dir / 'src').iterdir():
with zpath.open() as fr, (tgt / 'sqljs' / zpath.name).open('wb') as fw:
shutil.copyfileobj(fr, fw)
def configure(tgt: Path):
_get_amalgamation(tgt)
_get_contrib_functions(tgt)
_get_lua(tgt)
_get_extensions(tgt)
_get_sqljs(tgt)
subprocess.check_call(['emcc', '--version'])
if __name__ == '__main__':
if sys.version_info < (3, 8):
print('Python 3.8 or higher is expected', file=sys.stderr)
sys.exit(1)
logging.basicConfig(level='INFO', format='%(asctime)s %(levelname)s %(name)s %(message)s')
src = Path('src')
src.mkdir()
configure(src)

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View File

@@ -1,9 +0,0 @@
#!/bin/bash -e
docker build -t sqliteviz/sqljs .
rm -r dist || true
CONTAINER=$(docker create sqliteviz/sqljs)
docker cp $CONTAINER:/tmp/build/dist .
docker rm $CONTAINER

View File

@@ -1,5 +0,0 @@
{
"name": "sql.js",
"main": "./dist/sql-wasm.js",
"private": true
}

19569
package-lock.json generated

File diff suppressed because it is too large Load Diff

View File

@@ -1,85 +0,0 @@
{
"name": "sqliteviz",
"version": "0.27.1",
"license": "Apache-2.0",
"private": true,
"type": "module",
"scripts": {
"dev": "vite",
"build": "vite build",
"serve": "vite preview",
"test": "karma start karma.conf.cjs",
"lint": "eslint --ext .js,.vue --ignore-path .gitignore --fix src",
"format": "prettier . --write"
},
"dependencies": {
"@sigma/export-image": "^3.0.0",
"buffer": "^6.0.3",
"codemirror": "^5.65.18",
"codemirror-editor-vue3": "^2.8.0",
"core-js": "^3.6.5",
"dataurl-to-blob": "^0.0.1",
"graphology": "^0.26.0",
"graphology-layout": "^0.6.1",
"graphology-layout-forceatlas2": "^0.10.1",
"html2canvas": "^1.1.4",
"jquery": "^3.6.0",
"nanoid": "^3.1.12",
"papaparse": "^5.4.1",
"pivottable": "^2.23.0",
"plotly.js": "^2.35.2",
"promise-worker": "^2.0.1",
"react": "^16.14.0",
"react-chart-editor": "^0.46.1",
"react-dom": "^16.14.0",
"seedrandom": "^3.0.5",
"sigma": "^3.0.1",
"sql.js": "file:./lib/sql-js",
"tiny-emitter": "^2.1.0",
"veaury": "^2.5.1",
"vue": "^3.5.11",
"vue-final-modal": "^4.5.5",
"vue-multiselect": "^3.0.0-beta.3",
"vue-router": "^4.4.5",
"vuejs-paginate-next": "^1.0.2",
"vuex": "^4.1.0"
},
"devDependencies": {
"@babel/core": "^7.25.7",
"@vitejs/plugin-vue": "^5.2.1",
"@vue/eslint-config-standard": "^8.0.1",
"@vue/test-utils": "^2.4.6",
"chai": "^4.1.2",
"chai-as-promised": "^8.0.1",
"eslint": "^8.57.1",
"eslint-config-prettier": "^10.1.1",
"eslint-plugin-import": "^2.20.2",
"eslint-plugin-node": "^11.1.0",
"eslint-plugin-promise": "^4.2.1",
"eslint-plugin-standard": "^4.0.0",
"eslint-plugin-vue": "^9.28.0",
"flush-promises": "^1.0.2",
"karma": "^6.4.4",
"karma-coverage": "^2.2.1",
"karma-coverage-istanbul-reporter": "^3.0.3",
"karma-firefox-launcher": "^2.1.3",
"karma-mocha": "^1.3.0",
"karma-spec-reporter": "^0.0.36",
"karma-vite": "^1.0.5",
"mocha": "^5.2.0",
"prettier": "3.5.3",
"process": "^0.11.10",
"url-loader": "^4.1.1",
"vite": "^5.4.14",
"vite-plugin-istanbul": "^5.0.0",
"vite-plugin-node-polyfills": "^0.23.0",
"vite-plugin-pwa": "^0.21.1",
"vite-plugin-static-copy": "^2.2.0",
"vue-cli-plugin-ui-karma": "^0.2.5"
},
"overrides": {
"karma-vite": {
"vite-plugin-istanbul": "$vite-plugin-istanbul"
}
}
}

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[]

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{
"background_color": "white",
"description": "Sqliteviz is a single-page application for fully client-side visualisation of SQLite databases, CSV, JSON or NDJSON.",
"display": "fullscreen",
"icons": [
{
"src": "favicon.png",
"sizes": "32x32",
"type": "image/png"
},
{
"src": "Logo48x48.png",
"sizes": "48x48",
"type": "image/png"
},
{
"src": "Logo192x192.png",
"sizes": "192x192",
"type": "image/png"
},
{
"src": "Logo512x512.png",
"sizes": "512x512",
"type": "image/png"
}
],
"name": "sqliteviz",
"short_name": "sqliteviz",
"start_url": "index.html"
}

32
sidebar.json Normal file
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[{
"sections": [{
"title": "For users",
"items": [
"/docs/",
"/docs/installation/",
"/docs/basic-usage/",
"/docs/multiple-csv-file-import/",
"/docs/manage-inquiries/",
"/docs/export-current-database/",
"/docs/pivot-table/",
"/docs/predefined-inquiries/",
"/docs/sharing/",
"/docs/diagnostic-information/"
]
},
{
"title": "Examples and tutorials",
"items": [
"/docs/how-to-migrate-to-sqliteviz-dot-com/",
"/docs/how-to-build-a-pivot-table-in-sq-lite/",
"/docs/how-to-rename-tables-and-columns-after-csv-import/"
]
},
{
"title": "For developers",
"items": [
"/docs/integrate-predefined-inquiries/"
]
}
]
}]

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<template>
<div id="app">
<router-view />
</div>
</template>
<script>
import storedInquiries from '@/lib/storedInquiries'
export default {
computed: {
inquiries() {
return this.$store.state.inquiries
}
},
watch: {
inquiries: {
deep: true,
handler() {
storedInquiries.updateStorage(this.inquiries)
}
}
},
created() {
this.$store.commit('setInquiries', storedInquiries.getStoredInquiries())
addEventListener('storage', event => {
if (event.key === storedInquiries.myInquiriesKey) {
this.$store.commit('setInquiries', storedInquiries.getStoredInquiries())
}
})
}
}
</script>
<style>
#app,
.dialog,
input,
label,
button,
.plotly_editor * {
font-family: 'Open Sans', Helvetica, Arial, sans-serif;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
body {
margin: 0;
}
.CodeMirror-hints {
z-index: 999 !important;
}
</style>

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