Huey is a browser-based application that lets you explore tabular datasets. Huey supports reading from multiple file formats, like .csv, .parquet, .json data files as well as .duckdb database files.
Try Huey now online https://rpbouman.github.io/huey/src/index.html
- An intuitive and responsive pivot table that supports filtering and (sub)totals
- Supports many different aggregate functions for reporting and data exploration
- Automatic breakdown of date/time columns into separate parts (year, month, quarter etc) for reporting
- Supports reading .parquet, .csv, .json and .duckdb database files. (Support for reading MS Excel .xlsx files and .sqlite is planned)
- Export of results and/or SQL queries to file or clipboard
- Blazing fast, even for large files - courtesy of DuckDB
- Zero install. Download or checkout the source tree, and open src/index.html in your browser - no server required. Note that although Huey can run locally, there is nothing that keeps you from serving it from a webserver if you want to.
- It's free! Huey is released under the MIT license, just like DuckDB.
- Huey is based on DuckDB WASM. DuckDB is awesome! However, the WASM runtime imposes some limits which result in a poorer performance as compared to native DuckDB. That said, DuckDB WASM is still incredibly fast when compared to any in-browser alternative.
- Checkout or Download from github
- Open index.html in your web browser. Note that although Huey runs locally, it depends on resources served by jsdelivr.com, so make sure you're connected to the internet.
- Register one or more files or urls, and start analyzing!
Huey uses DuckDb WASM to read and analyze data files. Due to general security policy, the web browser can not simply read files from your local computer: you need to explicitly select and register files in DuckDB WASM's virtual file system.
To register one or more files, you can either
- Click the 'Upload...' button
.
The upload button is always available as the leftmost button on the toolbar at the top of the page. The upload action will pop up a file browser dialog that lets you browse and choose one or more files from your local filesystem.
In the file browser dialog, navigate to the file or files that you want to explore, select them and then confirm the dialog by clicking the 'Ok' button. - Drag 'n Drop one or multiple files unto the "Datasources" tab in the sidebar.
Either action will open the Upload dialog. The upload dialog will show a progress bar for each file that is being registered. Additional progress items may appear in case a duckdb extension needs to be installed and/or loaded.
After completion of the upload process, the upload dialog is updated to indicate the status of the uploads (or the extension installation, if applicable).
Items that encountered an error are indicated by red progressbars. In case of errors, the item is expanded to reveal any information that might help to remedy the issue.
Successful actions are indicated by green progressbars. Succesfully loaded files are available in the Datasources tab, from where you can start exploring their contents by clicking the explore button 
. As a convenience, the explore button is also present in the upload dialog.
Huey will attempt to group files having similar column signature. The group appears as a separate top-level node in the Datasources tab, with its individual files indented below it. A file group has its own explore button, so that you can not only explore the individual files, but also the UNION of all Files in the group:
Files that cannot be grouped appear in a separate Miscellanous Files group.
In addition to local files, you can also register URLs. To register a URL, click the "Load data from URL" button on the toolbar 
. You will be prompted to enter the URL:
After confirming, the upload dialog appears just like when uploading local files.
Note that loading data from URL is subject to certain restrictions due to browser security policies. Typically the URL needs to be either in the same domain as from where Huey is served, or the remote server needs to pass CORS headers to overcome the same-origin policy.
Apart from reading data files directly, Huey can also open existing duckdb files and access its tables and views. The process for accessing duckdb files is exactly the same as for accessing data files. Just make sure you give your duckdb file a '.duckdb' extension - that's how Huey knows it's a duckdb file. (DuckDB data files are not required to have any particular name or extension, but Huey currently cannot detect that, so it relies on a file extension convention instead.) Successfully loaded .duckdb files will appear in the DuckDb Folder, which appears at the top of the DataSources tab.
The schemas in the duckdb database file are presented as folders below the duckdb file entry, and any tables or views in the schema are presented below the schema folder. Each table or view has an explore button which you can click to explore the data.
Note: We ran into a limitation - when the duckdb file itself refers to external files, then it's likely that Huey (or rather, DuckDB WASM) won't be able to find them. But native duckdb tables, as well as views based on duckdb base tables work marvelously and are quite a bit faster than querying bare data files.
The Datasources have an explore button
. After clicking it, the sidebar switches to the Attributes tab, which is then is populated with a list of the Attributes of the selected Datasource.
You can think of Attributes as a list of values (a column) that can be extracted from the Datasource and presented along the axes of the pivot table.
The pivot table has two axes for placing attribute values:
- Attributes appearing on the horizontal axis are used to generate column headers. For this reason the horizontal axis is also known as the 'columns'-axis.
- attributes appearing on the vertical axis are used to generate row headers. For this reason the vertical axis is also known as the 'rows'-axis.
The selection of attributes and their placement on the axis is represented by the Query interface. The following screenshot showing the Attribute Sidebar (left), the Query Interface (top right), and the pivot table (bottom right) may help to explain:
The screenshot shows a simple query, with one attribute "hvfhs_license_num" placed on the columns axis of the Query interface. Placing the attribute on the Columns axis causes its values to be shown as column headings of the pivot table.
Likewise, the attribute "dispatching_base_num" is placed on the Rows axis, and this causes its values to show as row headings in the pivot table.
Finally, the generic "count" aggregator is placed on the cells axis. This causes the value of the aggregate to be computed for each combination of values of the rows- and columns-headings. The aggregated value are placed in the cells at the intersection of the corresponding row and column.
By default, the cell headers appear on the Columns axis, below the last Column Axis item (if any).
The cell headers can also by placed on the Rows axis, in which case they appear right to the values of the last row axis item:
(Note that for this particular example, which has only one aggregator on the cells-axis, its placement on either cells or rows doesn't make much difference.)
Attributes can be placed either by clicking one of the desired axis-placement buttons, which appear to the left of the attribute name. Alternatively, you can drag attributes form the Attributes sidebar to the desired position on the axis in the Query interface.
Once the items are placed in the rows and column axes, you can move and flip the axes by clicking on the axis icon that appears right before the "Rows" and "Columns" axis header text. Clicking on the axis icon of the Cells axis will affect the placement of the cell headers on either of the Rows- and Columns- axes.
After changing the Query, it must be executed so the pivot table may be updated.
If the "Autorun query" checkbox on the toolbar is checked, this will happen automatically.
If the "Autorun query" checkbox is not checked, then you can execute the query by clicking the "play" button that appears just in front of the checkbox label:
Right before the attribute item, there is a widget to expand the Attribute so its derived Attributes and Aggregates are revealed.
You can think of a derived attribute as an expression (formulae) that calculates some aspect from a single value from the attribute upon which it is based. For example, from an attribute that represents timestamp values, we can extract only the date part, or only the time part, or even the individual parts like year, month, and so on. The values that are thus derived from the original attribute values can be thought of as a 'virtual' column and can appear on wither of the pivot table axes.
Aggregates are special expressions that calculate a result on a group of attribute values. Aggregates cannot be placed on the horizontal or vertical axes of the pivot table. Rather, they can used to create cells appearing at the intersection of the row and column headers.
The query editor supports a special Filters axis. Items placed on the filters axis represent conditions that are applied on the underlying dataset. Items can appear independently on the filter axis: they are not automatically visible in the query result, but items that appear on the filter axis may also (additionally) be placed on the rows or columns axis.
Immediately after placing a new item on the Filters axis, the Filter Dialog pops up right below the Filter axis item. The Filter dialog lets you choose values and operators to filter the query results.
The Filter type dropdown appears in the top of the Filter Dialog. Here you choose the operator that should be used to filter the data. The options are:
- Include: the values in the data must match any of the filter values exactly
- Exclude: rows from the data appear only when the value from the respective item does not match any of the filter values.
- Like: the values in the data must match the pattern of one or more filter values
- Not Like: the values in the data must not match the pattern of any of the filter values
- Between: the values of the data must be between the filter value-ranges
- Not Between: the values of the data must not be between any of the filter value-ranges
In the top of the Filter Dialog there is an input where you can type a value that will be used as a pattern for retrieving values from the respective item.
The pattern is used in a LIKE comparison to retrieve the items values. LIKE patterns support two wildcards:
'%'(percent sign) is a wildcard for any character sequence, of any length;'_'(underscore) is a wildcard for any single character.
The input supports multiple patterns. To enter multiple patterns, separate them by a semi-colon. You can also paste delimited data (such as a range of cells from an Excel workbook) and paste them directly into the input.
The retrieved values are placed in the Picklist appearing below the input.
Right above the input, there appear two checkboxes:
- Apply all filters: when checked, the Filter Picklist will be populated with values from only rows that (in addition to the pattern in the input) also respect all other filter items (if present)
- Auto-wildcards: when checked, texts entered or pasted into the input are automatically pre- and postfixed with the
'%'wildcard, effectively finding all values that contain the entered text.
Filter values can be applied to the filter item by finding them in the Filter Value Picklist and clicking them. Alternatively, values in the input may be applied explicitly by clicking the button next to the input, or by hitting the Enter button on the keyboard. Applying a value in this way while an already applied value is selected will overwrite the applied value with the new one.
Applied values may be removed by selecting them and then hitting the Clear Highlighted dialog button, or the Delete key on the keyboard. Hitting the Clear All button will remove all applied values.
- The Apply button will actually apply the chosen values to the filter and close the Filter Dialog.
- The Remove button will remove the filter item entirely from the Filter axis and close the Filter Dialog
- The Cancel button will close the Filter Dialag without changing the state of the filter item.
Items on the row or columns axis have a "totals" toggle-icon. When enabled, totals for that item will be displayed in a totals row or column.
Everytime you make a change to your query it will be encoded and appended to the URL as fragment (a.k.a. hash or anchor). You can bookmark the url and revisit it later, or you can copy the url from your browser's address bar and share it.
You can load queries simply by navigating to the respective url (including the fragment). The fragment includes the query and a reference to the datasource - not the actual data itself.
When restoring the query, Huey checks if there is currently a datasource present that matches the referenced datasource's name and column signature. If so, it will use it. If there is currently no datasource that matches the referenced one, Huey will prompt you so you can upload it.
If the referenced datasource does not exist, but there are other datasources that could satisfy the query (based on whether it includes all attributes mentioned in the query), then the prompt will offer those datasources too as alternatives:
If the datasource is built on a URL, Huey will attempt to access it directly. If that succeeds, you won't be prompted to confirm: Huey will simply load the remote datasource and restore the query.
Because the query state updates the page url, you can use the browser's standard Back and Forward buttons to browse between different versions of your query.
Hitting the Clone button on the toolbar will open a new instance of Huey in a new browser tab, while preserving the existing data sources as well as the current query.
Huey provides export capabilities so you can use the results of your analysis outside huey. The export dialog lets you export query results by downloading it as csv, parquet, or JSON file, or you can choose to have your results copied to your operating system clipboard.
Apart from the result data, Huey also lets you export the SQL statements that would produce the query result.
The settings dialog lets you control Huey's behavior. You can open the settings dialog by clicking the "gear" icon, which is on the right in the top toolbar:
.
Settings are persisted in the browser's local storage.
Settings are organized in separate tabs:
This lets you control the behavior of datasources.
- The UNION loose typing checkbox controls how Huey detects whether datasources have the same column signature. When unchecked, exact data type matching is used; when checked, a more loose typing rule is applied.
This tab bundles all settings that controls the default appearance of values
- NULL-value label: a string that is used to symbolize NULL-values.
- Totals label: a string that is used to indicate the value is the total of the item indicated by the header.
- Use default locale: when checked, the Browser's default locale(s) are used to format numbers. When unchecked, the Locale setting becomes editable.
- Locale: the custom locale to use. You need to uncheck the Use default locale checkbox to edit this setting
- Min. integer digits: the minimal number of digits to use to denote the integer part of numbers
- Min. fraction digits: the minimal number of fractional digits to denote
- Max. fraction digits: the maximum number of fractional digits to denote
Controls the behavior of the query editor.
- Autorun: whether queries execute automatically after editing the query
Settings to control the Query's filter behavior
- Search timeout (ms): the number of milliseconds to wait after user input before running a query to populate the filter picklist.
- Picklist pagesize: the number of rows to fetch per query to populate the filter picklist.
Settings that control the appearance and behavior of the Pivot Table
- Max. cellwidth (ch): Columns grow according to the largest value, up to this value. The unit is ch - the number of characters.
- Themes dropdown: a dropdown showing the various themes/color schemes.
You can embed huey inside a frame on your own webpage and control the application by sending it commands using the postMessage()-method.
Currently this experimental feature is under development and not documented in detail. Please checkout src/PostMessageInterface/PostMessageTestbed.html for an example that illustrates this feature.
