Datavore is a small in-browser database engine written in JavaScript. Datavore enables you to perform fast aggregation queries within web-based analytics or visualization applications. Datavore consists of an in-memory column-oriented database implemented using standard JavaScript arrays. The system provides support for filtering and group-by aggregation queries. When run within an optimized JavaScript environment, Datavore can complete queries over million element data tables at interactive (sub-100ms) rates.

Simply reference the script dv.js within your web page to import Datavore. The included example files include demonstrations of Datavore's functionality along with performance benchmarks. The profile example shows how Datavore can be used to support high-performance brushing and linking among visualizations using the D3 framework.

A Datavore table is simply a collection of data columns, each realized as a JavaScript array. To create a table instance, you can either initialize the full table through the constructor or add columns one-by-one. For instance:

var colA = ["a","a","b","b","c"];
var colB = [0,1,2,3,4];

// create a table in one call by bundling up columns
var tab1 = dv.table([
    {name:"A", values:colA, type:dv.type.nominal},
    {name:"B", values:colB, type:dv.type.numeric}
]);

// create a table adding one column at a time
// the resulting 'tab2' should be identical to 'tab1'
var tab2 = dv.table();
tab2.addColumn("A", colA, dv.type.nominal);
tab2.addColumn("B", colB, dv.type.numeric);

In addition to the column name and array of values, each column must have a specified data type, one of dv.type.nominal, dv.type.ordinal, dv.type.numeric, or dv.type.unknown. Numeric means the column contains numbers that can be aggregated (e.g., summed, averaged, etc). Nominal values are category labels without a meaningful sort order, while ordinal values can be meaningfully sorted.

Datavore treats nominal and ordinal data in a special way: it recodes the input array values as zero-based integers (much like a star schema). The unique values in the input array are sorted and placed into a lookup table. Mapping strings and other data types to integer codes enables faster query performance.

You can access values within a Datavore table directly via array indices or through the table get method. For nominal or ordinal types, direct access will return coded integers. The get method always returns the original value.

// both array indices and the "get" method use (column, row) ordering
alert(tab1[0][1]);    // 1st column, 2nd row, coded   --> prints "0"
alert(tab1.get(0,1)); // 1st column, 2nd row, uncoded --> prints "a"

// directly accessing the lookup table (lut) to decode a value
// included for demo purposes only; use the "get" method instead!
// 1st column, 2nd row, uncoded --> prints "a"
alert(tab1[0].lut[tab1[0][1]]);

You can either access columns by their numerical index (as above) or by name:

// accessing table values by column name
alert(tab1["A"][1]);    // 1st column, 2nd row, coded   --> prints "0"
alert(tab1.get("A",1)); // 1st column, 2nd row, uncoded --> prints "a"

WARNING: Datavore column names should NOT be numbers. If you use column names that JavaScript can interpret as integer values ("00") you will likely experience unexpected (and undesirable) behavior.

Datavore tables support two kinds of queries: filtering operations and group-by aggregation. Filtering queries simply filter table contents according to a predicate function; these are similar to simple SQL queries with a WHERE clause. The filtering function takes a table instance and row number as arguments and returns a new Datavore table instance.

// creates a new table with 3 rows: [["b","b","c"], [2,3,4]]
var filtered_table = tab1.where(function(table, row) {
    return table.get("B", row) > 1;
});

NOTE: To ensure that tables created by various filtering queries are compatible with each other, nominal and ordinal columns within the result tables will always have the same lookup table as the original table, even if some unique values have been completely filtered out. As a result you may see some unexpected zero values returned when running dense aggregation queries on filtered tables.

The primary use case for Datavore is running aggregation queries. These queries allow you to calculate counts, sums, averages, standard deviations, and minimum or maximum values for a column, optionally grouped according to nominal or ordinal dimensions. These queries are similar to SQL queries with group-by clauses.

// count all rows in the table -> returns [[5]]
var counts = tab1.query({vals:[dv.count()]});

// count rows and sum values in 2nd column, grouped by 1st column
// returns -> [["a","b","c"], [2,2,1], [1,5,4]]]
var groups = tab1.query({dims:[0], vals:[dv.count(), dv.sum(1)]});

// same as before, but now with extra parameter "code:true"
// nominal/ordinal types remain coded integers, NOT original values
// returns -> [[0,1,2], [2,2,1], [1,5,4]]]
var uncode = tab1.query({dims:[0], vals:[dv.count(), dv.sum(1)], code:true});

// count all table rows where first column != "a"
// returns -> [["a","b","c"], [0,2,1]]
var filter = tab1.query({dims:[0], vals:[dv.count()], where:
    function(table, row) { return table.get("A",row) != "a"; }
});

The return value of the query method is an array of arrays. Note that the return value is not a Datavore table object. The input to the query method should be a JavaScript object with up to four parameters: vals (required), dims, where, and code.

The vals parameter indicates the aggregation functions to run. The available operators are dv.count, dv.sum, dv.min, dv.max, dv.avg, dv.variance, and dv.stdev. All aggregation operators accept a single column index or name as input (except for dv.count, which ignores any input).

The dims parameter indicates the dimensions to group by. This should be an array containing column indices, column names or special dimension query operators (dv.bin or dv.quantile).

The where parameter specifies a predicate function for filtering the table (as in where queries). Filtering is performed prior to aggregation.

If true, the code parameter indicates that nominal and ordinal values should be left as coded integers. If false (the default), coded integers are mapped back to the original values in the query result arrays.

The standard aggregate query uses a dense representation of the resulting data space. What this means is that all dimensions are realized, even if the resulting aggregate values are zero. So if you group by columns A and B, and column A has 3 unique values and column B has 4 unique values, then the resulting aggregate table will have 3*4=12 rows, including zero values.

Datavore also supports a sparse representation that does not include rows for zero values. To use a sparse representation, use the sparse_query function, like so:

// non-zero counts of all table rows where first column != "a"
// returns -> [["b","c"], [2,1]]
var sparse = tab1.sparse_query({dims:[0], vals:[dv.count()], where:
    function(table, row) { return table.get("A",row) != "a"; }
});

So why the different query types? Dense queries can be calculated faster – by "materializing" the full dimensionality of the aggregated data one can use an array to store all the intermediate results. The sparse representation instead uses an associative array (a JavaScript object instance), which induces a higher overhead for object value lookups. On the other hand, dense queries over high-dimensional data can produce very large result arrays; sometimes these can be too large to fit in the browser's memory footprint. So, if you are dealing with high-dimensional aggregates (concretely, if the product of the set sizes of your group-by dimensions is > 100,000 rows) you should consider using sparse_query. However, if the total number of aggregate rows is reasonable (as is typically the case), or you want to explicitly include zero-valued cells, use the normal query method for faster performance.

NOTE: Dense queries are processed by the dense_query function. The query function is simply an alias for dense_query.

Datavore can be extended with new dimensional and (with some effort) aggregate operators. To create your own dimensional operator, view the source code for dv.bin and dv.quantile, and follow their example. Adding new aggregate operators is possible but more complex. You will need to add a new module (following in the foot steps of dv.sum, dv.avg, etc) and add new logic to the inner loop of the query processor (for both dense and sparse queries). This is not for the faint of heart! The query processor avoids making function calls within its inner loop — this helps make Datavore much faster, but at some cost to extensibility. You will have to modify the guts of the engine to add new aggregate operators.