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A JavaScript OLAP database for realtime data analysis and visualization

Ozone is a special-purpose database for filtering and counting. It's good for answering questions of the form "how many... in each...?" And it's particularly good as a client-side back end for data visualization libraries such as D3. Because Ozone runs in the web browser, data visualizations can avoid querying the server when adding or removing filters. Ozone consists of two components:

  • An API for OLAP-style (counting/filtering/partitioning) queries from JavaScript, similar in style to jQuery and D3—and intended for use with D3 or jQuery.

  • Back-end TypeScript interfaces which separate the views of the underlying data from the data format. Well-defined interfaces make it easy to convert data from one format to another, or to provide multiple query implementations. Multiple implementations simplify support for browsers with different features or performance characteristics. This component is typically used with node.js to generate Ozone-ready data files.

There are also things Ozone is not designed to do. Ozone is column-oriented, rather than row-oriented: it is not made for accessing individual records. Ozone is also not well suited for data that can't be summarized easily, such as names or identifiers. In general, it's best at handling large columns with a small number of individual values, such as answers to multiple-choice questions.

The major differences between Ozone and a traditional SQL database (most of these are not unusual for OLAP databases):

  • Ozone uses its own jQuery-like JavaScript API, not SQL or MDX.

  • Ozone is read-only; if any record changes, the entire database should be regenerated.

  • Ozone does not explicitly support primary keys. Since Ozone is primarily used for counting and summarizing, access to individual records is not optimized.

  • Ozone does not support multiple tables within a single database. A database is treated as one giant table, equivalent to joining several tables together.


Summer Olympics Medals, using D3

Simpler D3 demo

jQuery demo

Querying Ozone

Start with data, such as a CSV file:

var ozone = require("ozone-db");                     // This line is only needed if you use require.js or node
var db = ozone.serialization.buildFromCsv(rawData);  // rawData is a string with comma-separated values

Filter or partition to create a subset of the database, which can be treated as its own database:

var dbOfWomen = db.filter('Gender', 'F');
var dbOfMaleGermans = db.filter('Gender', 'M').filter('Country', 'Germany');
var numberOfMaleGermans = dbOfMaleGermans.size();

// A partition filters on all values for a particular field

var genderDbs = db.partition('Gender');
if (genderDbs['F'].size() === dbOfWomen.size()) {
   console.log("It works!");
if (genderDbs['M'].filter('Country', 'Germany').size() === dbOfMaleGermans.size()) {
   console.log("It works!");

Field objects can be used to describe the data, and also as identifiers for filters. Fields from filtered views can be used interchangeably with fields from the original database.

var genderField = db.field('Gender');

var itWorks = genderField.identifier  === 'Gender'  &&
              genderField.displayName === 'Participant Gender' &&
              genderField.typeOfValue === 'string';

var filteringByFieldWorks = dbOfWomen.size() === db.filter(genderField, 'F').size() &&
                            dbOfWomen.size() === db.filter(dbOfMaleGermans.field('Gender', 'F')).size();

NOTE: in version 0.1.12 and earlier, size is a value rather than a function. Also, db.size() is not necessarily the same as the number of rows. See Aggregation below.

Use field.distinctValueEstimate() to determine how to present the fields

var fields = db.fields();
for (var i=0; i<fields.length; i++) {
    var field = fields[i];
    var selector;
    if (field.distinctValueEstimate() < 5) {
       selector = "checkbox";
    else if (field.distinctValueEstimate() < 100 ) {
       selector = "drop-down menu";
    else {
       selector = "search field";
    console.log("I will use a "+ selector +" to choose the "+ field.displayName);

You cannot access rows directly. Instead, filter down to the size that you want and then iterate over the rows via the eachRow function.

var nameField = db.field('Name');
var q8Field   = db.field('Question8Responses');  // In this example we are analyzing responses from a survey
dbOfMaleGermans.eachRow(function(row) {
    console.log( nameField.value(row) + " has these responses to question 8:");

    // Question 8 is multiple choice with multiple selections allowed, so q8Field doesn't have a value function.

    var responses = q8Field.values(row);
    for (var i=0 i<responses.length; i++) {

Getting data into Ozone

Although Ozone can read multiple data formats, querying is done from its native column-oriented format. For more than a trivial amount of data, convert the data server-side, so that the browser downloads the native JSON format.

Node.js users can convert CSV files into Ozone's format; see demo/buildDataStore.js for an example. Ozone's JSON format is documented in TypeScript in case you wish to write JSON directly rather than via Node.

For example, in node:

    var       fs = require('fs');
    var    ozone = require('ozone-db'); 
    var filename = "MyData.csv";
    var       db = ozone.serialization.buildFromCsv(fs.readFileSync(filename, {encoding: 'utf-8'}));
    var   dbJson = JSON.stringify(ozone.serialization.writeStore(db));
    fs.writeFileSync("MyOzoneData.json", dbJson, {encoding: 'utf-8'});

Then in the browser:

   var db;
   $.getJSON( "MyOzoneData.json", function( data ) {  // If you like jQuery...
       db = ozone.serialization.readStore(data);
   $http.get("MyOzoneData.json")                      // If you like angular.js
            db = ozone.serialization.readStore(data);
   d3.json("MyOzoneData.json", function(data) {       // ...or if you prefer D3
       db = ozone.serialization.readStore(data);

You can pass in metadata as a second parameter to ozone.serialization.buildFromCsv() to alter how the fields are created. See demo/buildDataStore.js for an example, or the FieldDescribing interface to see what's possible.

Internally, Ozone converts CSV into a database that it can iterate over but not query (called a RowStore, since data is stored in rows), and uses that to produce its native ColumnStore. If your data is already a JavaScript array, you can skip the conversion to CSV by calling and presenting your data as RowStore data.

To minimize file size and memory usage, sort the rows on at least one column before converting to Ozone format.


Ozone lets you combine rows that are not meaningfully different. This is especially useful if you are removing columns you don't need. Aggregation creates (or adds to) a field that keeps track of the per-row size, so that size() returns the same value as before. By default, that field is named 'Records'. To find out how many rows there are, call rowCount() instead of size().

A field may be designated as a count, so that its values are added together during aggregation. See aggregationRule in the FieldDescribing interface for details.

See demo/buildDataStore.js for an example of ozone.transform.aggregate(), or the transform.ts source comments to see the options.


Ozone currently targets ECMAScript 5, so if you need to support IE 8 or lower, you will need to use es5-shim.js.


Ozone is low latency and memory efficient. It allows larger data sets than would otherwise be possible in a web browser. However, JavaScript is a harsh environment for big datasets. The language provides little support for memory management, it's easy to accidentally keep things in closure scope longer than needed, and different browsers on different devices have vastly different capabilities. Test your code carefully under memory-constrained conditions on all supported platforms!

Ozone will never be a "Big Data" tool, if by Big Data you mean it should handle more data than will fit on a single computer. However, Ozone can work on subsets of data provided by a server-side Big Data back end.


Ozone is an TypeScript rewrite of portions of Vocal Laboratories' proprietary JVM-based reporting engine.


A JavaScript database for realtime data analysis and visualization




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