Package csvplus extends the standard Go encoding/csv package with fluent interface, lazy stream processing operations, indices and joins.

The library is primarily designed for ETL-like processes. It is mostly useful in places where the more advanced searching/joining capabilities of a fully-featured SQL database are not required, but the same time the data transformations needed still include SQL-like operations.

Simple sequential processing:

people := csvplus.FromFile("people.csv").SelectColumns("name", "surname", "id")

err := csvplus.Take(people).
	Filter(csvplus.Like(csvplus.Row{"name": "Amelia"})).
	Map(func(row csvplus.Row) csvplus.Row { row["name"] = "Julia"; return row }).
	ToCsvFile("out.csv", "name", "surname")

if err != nil {
	return err
}

More involved example:

customers := csvplus.FromFile("people.csv").SelectColumns("id", "name", "surname")
custIndex, err := csvplus.Take(customers).UniqueIndexOn("id")

if err != nil {
	return err
}

products := csvplus.FromFile("stock.csv").SelectColumns("prod_id", "product", "price")
prodIndex, err := csvplus.Take(products).UniqueIndexOn("prod_id")

if err != nil {
	return err
}

orders := csvplus.FromFile("orders.csv").SelectColumns("cust_id", "prod_id", "qty", "ts")
iter := csvplus.Take(orders).Join(custIndex, "cust_id").Join(prodIndex)

return iter(func(row csvplus.Row) error {
	// prints lines like:
	//	John Doe bought 38 oranges for £0.03 each on 2016-09-14T08:48:22+01:00
	_, e := fmt.Printf("%s %s bought %s %ss for £%s each on %s\n",
		row["name"], row["surname"], row["qty"], row["product"], row["price"], row["ts"])
	return e
})

The package functionality is based on the operations on the following entities:

• type Row
• type DataSource
• type Index

Row represents one row from a DataSource. It is a map from column names to the string values under those columns on the current row. The package expects a unique name assigned to every column at source. Compared to using integer indices this provides more convenience when complex transformations get applied to each row during processing.

Type DataSource represents any source of zero or more rows, like .csv file. This is a function that when invoked feeds the given callback with the data from its source, one Row at a time. The type also has a number of operations defined on it that provide for easy composition of the operations on the DataSource, forming so called fluent interface. All these operations are 'lazy', i.e. they are not performed immediately, but instead each of them returns a new DataSource.

There is also a number of convenience operations that actually invoke the DataSource function to produce a specific type of output:

• IndexOn to build an index on the specified column(s);
• UniqueIndexOn to build a unique index on the specified column(s);
• ToCsv to serialise the DataSource to the given io.Writer in .csv format;
• ToCsvFile to store the DataSource in the specified file in .csv format;
• ToJSON to serialise the DataSource to the given io.Writer in JSON format;
• ToJSONFile to store the DataSource in the specified file in JSON format;
• ToRows to convert the DataSource to a slice of Rows.

Index is a sorted collection of rows. The sorting is performed on the columns specified when the index is created. Iteration over an index yields a sorted sequence of rows. An Index can be joined with a DataSource. The type has operations for finding rows and creating sub-indices in O(log(n)) time. Another useful operation is resolving duplicates. Building an index takes O(n*log(n)) time. It should be noted that the Index building operation requires the entire dataset to be read into the memory, so certain care should be taken when indexing huge datasets. An index can also be stored to, or loaded from a disk file.

For more details see the documentation.

The project is in a usable state usually called "beta". Tested on Linux Mint 18.3 using Go version 1.10.2.