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mirror of https://github.com/lana-k/sqliteviz.git synced 2025-12-07 02:28:54 +08:00

63 Commits

Author SHA1 Message Date
lana-k
a2464d839f #115 fix version number 2024-01-07 13:55:38 +01:00
lana-k
316e603c3c #115 style fixes 2024-01-07 13:37:21 +01:00
lana-k
88466eca5e #115 fix lint 2024-01-07 12:31:53 +01:00
lana-k
5123e39a60 #115 version 2024-01-07 12:14:08 +01:00
lana-k
4c8401f32f #115 scroll record to beginning 2024-01-06 20:36:43 +01:00
lana-k
d949629ee4 #115 fix new lines - use pre 2024-01-06 18:55:45 +01:00
lana-k
7a18e415c8 #115 add styles for blob and null 2024-01-06 16:51:35 +01:00
lana-k
878689b3f7 fix svg button state 2024-01-06 12:03:06 +01:00
lana-k
42f040975d #115 tests 2024-01-06 11:23:23 +01:00
lana-k
78e9ca2120 #115 fix tests 2024-01-03 18:26:07 +01:00
lana-k
96af391f20 #115 clear message 2024-01-02 13:57:42 +01:00
lana-k
f58b62eb0c #115 add messages 2023-12-27 23:00:05 +01:00
lana-k
b17040d3ef #115 copy cell value 2023-12-27 22:22:49 +01:00
lana-k
bc6154b9ad #115 add icons 2023-12-27 21:30:43 +01:00
lana-k
3aea8c951b #115 update value when switch row 2023-12-26 20:45:11 +01:00
lana-k
1e982a1196 #115 unselect on paging 2023-10-31 22:27:47 +01:00
lana-k
6ecbde7fd3 #115 style fixes 2023-10-31 20:48:30 +01:00
lana-k
5ee881432a #115 select cell between modes; pass record number 2023-10-29 20:01:51 +01:00
lana-k
735e4ec7f6 #115 record and row navigator 2023-10-28 22:51:28 +02:00
lana-k
07d31dbfe9 #115 unselect 2023-10-28 19:48:36 +02:00
lana-k
ac1f7de62c #115 formats and call selections 2023-10-27 22:50:54 +02:00
lana-k
96877de532 #115 move focus 2023-10-27 18:47:45 +02:00
lana-k
b60fc28e47 #115 json view 2023-10-27 17:14:14 +02:00
lana-k
bec3d9c737 #115 add split in result set 2023-10-25 20:43:22 +02:00
lana-k
8aac7af481 update package.json 2023-07-03 23:33:52 +02:00
lana-k
6982204e68 Update currentTab when close tabs #112 2023-07-03 23:13:09 +02:00
lana-k
41e0ae7332 fix test for firefox #110 2023-06-29 23:14:08 +02:00
lana-k
ebb5af4f10 send event when sharing 2023-06-29 22:57:39 +02:00
lana-k
ae26358b25 add test #110 2023-06-29 22:28:41 +02:00
lana-k
d9ee702b8e update papaparse #111 2023-06-29 22:14:28 +02:00
lana-k
446045fa55 Catch parsing errors in compete #110 2023-06-29 22:13:56 +02:00
lana-k
1a9d1b308b check data format #109 2023-06-10 20:05:42 +02:00
lana-k
014ecf145e update version 2023-06-10 19:11:15 +02:00
lana-k
0044d82b6f Loading remote database and inquiries #109 2023-06-05 22:31:39 +02:00
lana-k
998e8d66f7 Tab refactor 2023-06-01 14:42:51 +02:00
lana-k
db3dbdf993 Merge branch 'master' of github.com:lana-k/sqliteviz 2023-05-17 21:41:17 +02:00
lana-k
4e13a16e33 No blocking while loading predifined #107 2023-05-17 21:37:41 +02:00
saaj
9c0103fd05 SQLite 3.41.0 and pearson correlation extension function (#106)
* Build SQLite 3.41.0

* Update pivot_vtab

* Add Pearson correlation coefficient function extension, build

* Add an easy way to running test locally without Nodejs

* Use RSS sum to pick top2 processes for the report

* Try previous Ubuntu LTS as a workaround for Firefox worker not starting
2023-03-04 22:51:25 +01:00
saaj
e4b117ffb9 Sqljs upgrade and benchmark improvements (#103)
* Update to sql.js 1.7.0

* Update to emsdk 3.0.1, replace/remove deprecated/irrelevant settings

- Renamed .bc extension to .o
- Remove deprecated INLINING_LIMIT setting
- Remove SINGLE_FILE

* Update SQLite to 3.39.3

* Collect and plot CPU and RSS charts from the benchmark containers

* Move procpath commands to a playbook, plot only top 2 RSS & CPU usage

* Optimise for size, put -flto for both compile and link
2023-03-04 17:00:46 +01:00
lana-k
6320f818cb fix undefined in tests and chart metrics 2022-07-30 16:42:30 +02:00
lana-k
3c456ef135 fix sql hint: read properties of undefined #99 2022-07-29 15:27:01 +02:00
lana-k
c713c713b7 fix paths #97 2022-07-20 23:15:14 +02:00
lana-k
babf0074c0 add artifact with source map #97 2022-07-20 22:49:26 +02:00
lana-k
e71e6700c1 improve events 2022-07-20 22:47:40 +02:00
lana-k
84e66b8167 Update README.md 2022-07-10 23:18:52 +02:00
lana-k
9e84cf269e Merge branch 'master' of github.com:lana-k/sqliteviz 2022-07-10 22:54:09 +02:00
lana-k
e897b4913b cancel deploying to github pages; remove banner 2022-07-10 22:53:23 +02:00
saaj
0646f58ca0 Build SQLite 3.39.0 (#95) 2022-07-10 22:46:39 +02:00
lana-k
c674bf11e3 update version number 2022-07-10 22:29:20 +02:00
lana-k
2d8a91675e Remove console.log 2022-07-10 22:25:11 +02:00
lana-k
45b1021559 links to website 2022-07-10 21:26:26 +02:00
lana-k
7216e996d1 add banner 2022-07-10 21:25:51 +02:00
lana-k
6eae9a0f2d remove empty lines 2022-07-10 16:02:04 +02:00
lana-k
7486b32bd1 add head extention slot 2022-07-03 15:38:41 +02:00
lana-k
2c564767f8 Merge branch 'master' of github.com:lana-k/sqliteviz 2022-07-01 21:01:07 +02:00
lana-k
289a727cbe fix icon width 2022-07-01 21:01:02 +02:00
saaj
5f2b8ba5a9 Upgrade to SQLite 3.38.5 (#91)
* Update to SQLite 3.37 and latest generate_series extension

* Expect column types in upper case

* Rebuild newer SQLite, 3.38.5
2022-06-27 17:35:40 +02:00
lana-k
f0a4212e2b fix tests 2022-06-26 21:06:24 +02:00
lana-k
c8deff32c1 fix lint 2022-06-25 22:38:22 +02:00
lana-k
d56604a7d6 events refactor 2022-06-25 22:37:09 +02:00
lana-k
48e311bff8 minor change 2022-06-24 22:58:43 +02:00
lana-k
518b22b489 events 2022-06-24 21:29:40 +02:00
lana-k
a20dd7f849 forbid index minifying 2022-06-11 19:29:42 +02:00
253 changed files with 67424 additions and 622 deletions

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.browserslistrc Normal file
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> 1%
last 2 versions
not dead

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.editorconfig Normal file
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@@ -0,0 +1,5 @@
[*.{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
},
extends: [
'plugin:vue/essential',
'@vue/standard'
],
parserOptions: {
parser: 'babel-eslint'
},
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 }]
},
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: 10.x
- name: Update npm
run: npm install -g npm@7
- name: npm install and build
run: |
npm install
npm run build
- name: Create archives
run: |
cd dist
zip -9 -r ../dist.zip . -x "js/*.map" -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.js"
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-20.04
steps:
- uses: actions/checkout@v2
- name: Use Node.js
uses: actions/setup-node@v1
with:
node-version: 10.x
- name: Install browsers
run: |
export DEBIAN_FRONTEND=noninteractive
sudo apt-get update
sudo apt-get install -y chromium-browser firefox
- name: Update npm
run: npm install -g npm@7
- name: Install the project
run: npm install
- name: Run lint
run: npm run lint -- --no-fix
- name: Run karma tests
run: npm run test

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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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# 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
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
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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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. [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]
# 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

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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
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.

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Apache License
Version 2.0, January 2004
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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-SQLite

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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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README.md Normal file
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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 or CSV 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 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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module.exports = {
presets: [
'@vue/cli-plugin-babel/preset'
]
}

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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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karma.conf.js Normal file
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// Karma configuration
'use strict'
const path = require('path')
const VueLoaderPlugin = require('vue-loader/lib/plugin')
function resolve (dir) {
return path.join(__dirname, dir)
}
module.exports = function (config) {
config.set({
// 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'],
// list of files / patterns to load in the browser
files: [
'./karma.files.js',
{
pattern: 'node_modules/sql.js/dist/sql-wasm.wasm',
watched: false,
included: false,
served: true,
nocache: false
}
],
// list of files / patterns to exclude
exclude: [],
// preprocess matching files before serving them to the browser
// available preprocessors: https://npmjs.org/browse/keyword/karma-preprocessor
preprocessors: {
'./karma.files.js': ['webpack']
},
// 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' }]
},
// !!DONOT delete this reporter, or vue-cli-addon-ui-karma doesnot work
jsonResultReporter: {
outputFile: 'report/karma-result.json',
isSynchronous: true
},
junitReporter: {
outputDir: 'report', // results will be saved as $outputDir/$browserName.xml
// if included, results will be saved as $outputDir/$browserName/$outputFile
outputFile: undefined,
suite: '', // suite will become the package name attribute in xml testsuite element
useBrowserName: true, // add browser name to report and classes names
// function (browser, result) to customize the name attribute in xml testcase element
nameFormatter: undefined,
// function (browser, result) to customize the classname attribute in xml testcase element
classNameFormatter: undefined,
properties: {} // key value pairs add to the <properties> section of the report
},
// web server port
port: 9876,
// enable / disable colors in the output (reporters and logs)
colors: true,
// level of logging
// possible values: config.LOG_DISABLE || config.LOG_ERROR || config.LOG_WARN ||
// config.LOG_INFO || config.LOG_DEBUG
logLevel: config.LOG_INFO,
// enable / disable watching file and executing tests whenever any file changes
autoWatch: false,
customLaunchers: {
FirefoxHeadlessTouch: {
base: 'FirefoxHeadless',
prefs: {
'dom.w3c_touch_events.enabled': 1,
'dom.events.asyncClipboard.clipboardItem': true
}
}
},
// start these browsers
// available browser launchers: https://npmjs.org/browse/keyword/karma-launcher
browsers: ['ChromiumHeadless', 'FirefoxHeadlessTouch'],
// Continuous Integration mode
// if true, Karma captures browsers, runs the tests and exits
singleRun: true,
// Concurrency level
// how many browser should be started simultaneous
concurrency: 2,
client: {
captureConsole: true,
mocha: {
timeout: 7000
}
},
browserConsoleLogOptions: {
terminal: true,
level: ''
},
webpack: {
mode: 'development',
entry: './src/main.js',
resolve: {
extensions: ['.js', '.vue', '.json'],
alias: {
vue$: 'vue/dist/vue.esm.js',
'@': resolve('src')
}
},
module: {
rules: [
{
test: /\.js$/,
exclude: /(node_modules|bower_components)/,
use: [
{
loader: 'babel-loader'
}
]
},
{
test: /\.js$/,
include: /src/,
exclude: /(node_modules|bower_components|\.spec\.js$)/,
use: [
{
loader: 'istanbul-instrumenter-loader',
options: {
esModules: true
}
}
]
},
{
test: /worker\.js$/,
loader: 'worker-loader'
},
{
test: /\.(png|jpe?g|gif|svg)(\?.*)?$/,
loader: 'url-loader'
},
{
test: /\.vue$/,
loader: 'vue-loader',
options: {
loaders: {
js: 'babel-loader'
},
postLoaders: {
js: 'istanbul-instrumenter-loader?esModules=true'
}
}
},
{
test: /\.css$/,
use: ['vue-style-loader', 'css-loader']
},
{
test: /\.scss$/,
use: ['vue-style-loader', 'css-loader', 'sass-loader']
},
{
test: /\.(woff2?|eot|ttf|otf)(\?.*)?$/,
loader: 'url-loader',
options: {
limit: 10000,
name: resolve('fonts/[name].[hash:7].[ext]')
}
}
]
},
plugins: [new VueLoaderPlugin()],
node: {
fs: 'empty'
}
},
webpackMiddleware: {
watchOptions: {
ignored: /node_modules/
}
},
proxies: {
'/_karma_webpack_/sql-wasm.wasm': '/base/node_modules/sql.js/dist/sql-wasm.wasm',
'/base/sql-wasm.wasm': '/base/node_modules/sql.js/dist/sql-wasm.wasm'
}
})
// Fix the timezone
process.env.TZ = 'Europe/Amsterdam'
}

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karma.files.js Normal file
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import Vue from 'vue'
import { VuePlugin } from 'vuera'
import VModal from 'vue-js-modal'
Vue.use(VuePlugin)
Vue.use(VModal)
Vue.config.productionTip = false
// require all test files (files that ends with .spec.js)
const testsContext = require.context('./tests', true, /\.spec.js$/)
// Read more about why we need to call testContext:
// https://www.npmjs.com/package/require-context#context-api
testsContext.keys().forEach(testsContext)
// require all src files except main.js and router/index.js for coverage.
// you can also change this to match only the subset of files that
// you want coverage for.
// We don't include router/index.js to avoid installing VueRouter globally in tests
const srcContext = require.context('./src', true, /^\.\/(?!(main|(router(\/)?(index)?))(\.js)?$)/)
srcContext.keys().forEach(srcContext)

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lib/sql-js/.dockerignore Normal file
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benchmark
dist

9
lib/sql-js/Dockerfile Normal file
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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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# 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])
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

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/lib/build-*
/lib/dist
/build-*-result.json
/sample.csv

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FROM node:14-bullseye
RUN set -ex; \
echo 'deb http://deb.debian.org/debian unstable main' \
> /etc/apt/sources.list.d/unstable.list; \
apt-get update; \
apt-get install -y -t unstable firefox; \
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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# 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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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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{
"name": "sql.js",
"main": "./dist/sql-wasm.js",
"private": true
}

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#!/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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{
"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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@@ -0,0 +1,28 @@
# 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

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@@ -0,0 +1,29 @@
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)

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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)

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{
"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": [
"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": "3.10.7",
"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
}

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@@ -0,0 +1,140 @@
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
}

88
lib/sql-js/build.py Normal file
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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',
)
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 WASM from bitcode')
subprocess.check_call([
'emcc',
*emflags,
out / 'sqlite3.o',
out / 'extension-functions.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)

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import logging
import shutil
import subprocess
import sys
import zipfile
from io import BytesIO
from pathlib import Path
from urllib import request
amalgamation_url = 'https://sqlite.org/2023/sqlite-amalgamation-3410000.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/8d79354f?at=series.c', 'sqlite3_series_init'),
('https://sqlite.org/src/raw/dbfd8543?at=closure.c', 'sqlite3_closure_init'),
('https://sqlite.org/src/raw/5bb2264c?at=uuid.c', 'sqlite3_uuid_init'),
('https://sqlite.org/src/raw/5853b0e5?at=regexp.c', 'sqlite3_regexp_init'),
('https://sqlite.org/src/raw/b9086e22?at=percentile.c', 'sqlite3_percentile_init'),
('https://sqlite.org/src/raw/09f967dc?at=decimal.c', 'sqlite3_decimal_init'),
# Third-party extension
# =====================
('https://github.com/jakethaw/pivot_vtab/raw/9323ef93/pivot_vtab.c', 'sqlite3_pivotvtab_init'),
('https://github.com/nalgeon/sqlean/raw/95e8d21a/src/pearson.c', 'sqlite3_pearson_init'),
)
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_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:
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_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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#!/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

5
lib/sql-js/package.json Normal file
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{
"name": "sql.js",
"main": "./dist/sql-wasm.js",
"private": true
}

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{
"name": "sqliteviz",
"version": "0.24.1",
"license": "Apache-2.0",
"private": true,
"scripts": {
"serve": "vue-cli-service serve",
"build": "NODE_OPTIONS=--max_old_space_size=4096 vue-cli-service build",
"test": "vue-cli-service karma",
"lint": "vue-cli-service lint"
},
"dependencies": {
"codemirror": "^5.57.0",
"core-js": "^3.6.5",
"dataurl-to-blob": "^0.0.1",
"html2canvas": "^1.1.4",
"jquery": "^3.6.0",
"nanoid": "^3.1.12",
"papaparse": "^5.4.1",
"pivottable": "^2.23.0",
"plotly.js": "^1.58.4",
"promise-worker": "^2.0.1",
"react": "^16.13.1",
"react-chart-editor": "^0.45.0",
"react-dom": "^16.13.1",
"sql.js": "file:./lib/sql-js",
"vue": "^2.6.11",
"vue-codemirror": "^4.0.6",
"vue-js-modal": "^2.0.0-rc.6",
"vue-multiselect": "^2.1.6",
"vue-router": "^3.2.0",
"vue2-teleport": "^1.0.1",
"vuejs-paginate": "^2.1.0",
"vuera": "^0.2.7",
"vuex": "^3.4.0"
},
"devDependencies": {
"@vue/cli-plugin-babel": "^4.4.0",
"@vue/cli-plugin-eslint": "^4.4.0",
"@vue/cli-plugin-router": "^4.4.0",
"@vue/cli-plugin-vuex": "^4.4.0",
"@vue/cli-service": "^4.4.0",
"@vue/eslint-config-standard": "^5.1.2",
"@vue/test-utils": "^1.1.2",
"babel-eslint": "^10.1.0",
"chai": "^4.1.2",
"chai-as-promised": "^7.1.1",
"eslint": "^6.7.2",
"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": "^6.2.2",
"flush-promises": "^1.0.2",
"karma": "^3.1.4",
"karma-firefox-launcher": "^2.1.0",
"karma-webpack": "^4.0.2",
"vue-cli-plugin-ui-karma": "^0.2.5",
"vue-template-compiler": "^2.6.11",
"workbox-webpack-plugin": "^6.1.5",
"worker-loader": "^3.0.8"
}
}

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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="<%= BASE_URL %>favicon.png">
<link rel="manifest" href="<%= BASE_URL %>manifest.webmanifest">
<title><%= htmlWebpackPlugin.options.title %></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%);
}
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[{
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},
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