Tablesaw is a high-performance, in-memory data table in Java.

Its design is driven by two ideas: First, few people need distributed analytics. On a single server, Tablesaw lets you work interactively with a 2,000,000,000 row table. (I plan to raise that ceiling, btw.) Second, it should be super easy to use: To that end I happily steal ideas from everything from spreadsheets to specialized column stores like KDB.

###On Maven Central:

<dependency>
    <groupId>com.github.lwhite1</groupId>
    <artifactId>tablesaw</artifactId>
    <version>0.7.1</version>
</dependency>

###Getting started?:

• You can find an introductory tutorial here: https://jtablesaw.wordpress.com/an-introduction/ The early drafts of a User Guide are also available on that site
• The JavaDoc can be found here: http://lwhite1.github.io/tablesaw/apidocs/
• If you have questions of any kind: Ask them in the Issues section of this Repo.

A 1.0 release is planned for early September.

• Import data from RDBMS and CSV files, local or remote (http, S3, etc.)
• Add and remove columns
• Sort
• Filter
• Group
• Map and reduce operations
• Descriptive stats (mean, min, max, median, etc.)
• Store tables in a very-fast, compressed columnar storage format

• Integrated machine learning
• More specialized column types and operations: (lat/lon, time interval, money)

I'm starting to add plot support to Tablesaw, because you can't understand a dataset unless you can see it. We will gradually increase both the number of plot types and the supported options. Meanwhile, here's an example where each dot shows the starting location of a single tornado:

The goal is to support discovery by seamlessly integrating Tablesaw's data manipulation facilities with plotting libraries to make visualization as easy as possible. We'll take the same approach when it comes to integrating machine learning tools.

In its current state, some areas of Tablesaw perform better than others. To give you a sense of where we're going, you can now load a 500,000,000 row, 4 column csv file (35GB on disk) entirely into about 10 GB of memory. If it's in Tablesaw's .saw format, you can load it in 22 seconds. You can query that table in 1-2 ms: fast enough to use as a cache for a Web app.

BTW, those numbers were achieved on a laptop.

The goal in this example is to identify the production shifts with the worst performance. These few lines demonstrate data import, column-wise operations (differenceInSeconds()), filters (isInQ2()) grouping and aggegating (median() and .by()), and (top(n)) calculations.

    Table ops = Table.create("data/operations.csv");                             // load data
    
    LongColumn duration = start.differenceInSeconds(end);                        // calc duration
    duration.setName("Duration");
    ops.addColumn(duration);
    
    Table filtered = ops.selectWhere(                                            // filter
          allOf
              (column("date").isInQ2(),
              (column("SKU").startsWith("429")),
              (column("Operation").isEqualTo("Assembly"))));
   
    Table summary = filtered.median("Duration").by("Facility", "Shift");         // group medians
    FloatArrayList tops = summary.floatColumn("Median").top(5);                  // get "slowest"

If you see something that can be improved, let us know.