About univocity-parsers

Flexibility

Map records to simple Strings:

    String[] row;
    while ((row = parser.parseNext()) != null) {
        System.out.println(Arrays.toString(row));
    }

Or convert them to Objects:

    rowProcessor
            .convertFields(Conversions.toDate("yyyy-MMM-dd"))
            .set("dateOfBirth", "dateJoined");

Or even fields i n JavaBeans, using annotations:

    @Trim
    @LowerCase
    @BooleanString(falseStrings = { "no", "n", "null" }, trueStrings = { "yes", "y" })
    @Parsed
    private Boolean pending;

You can easily define custom processors to execute all sorts of operations over the records, and even write your own parser for your specific format.

Field selection

Don't waste resources and processing power parsing unwanted values. You can select specific fields on an input text and get only values from these, in the order you specify.

For example, using the following CSV:

    Year,Make,Model,Description,Price
    1997,Ford,E350,"ac, abs, moon",3000.00
    1999,Chevy,"Venture ""Extended Edition""","",4900.00

You can select some fields:

    parserSettings.selectFields("Price", "Year", "Make");

To produce:

    3000.00, 1997, Ford
    4900.00, 1999, Chevy

When writing, simply define what values you have for each record, in whatever order you need, and univocity-parsers will write all records in the correct format.

    writer.writeRow(3000.00, 1997, "Ford");
    writer.writeRow(4900.00, 1999, "Chevy");

The result will be:

    Year,Make,Model,Description,Price
    1997,Ford,,,3000.00
    1999,Chevy,,,4900.00

Parellel input buffering

Parsers written on top of univocity-parsers architecture will buffer input files' content using a separate thread to optimize the processing of large inputs even further.

Check our tutorials, documentation and source code.