Run this function on each item after the foreach function

This is basically a foreach but without forcing evaluation of this LazySeq.

Same as after() but takes a Resource (i.e. can use it for something like logging)

Assert that this reader is in sorted order

Assert that this reader is in sorted order AND unique

Run this function on each item before the foreach function

This is basically a foreach but without forcing evaluation of this LazySeq.

Same as before() but takes a Resource (i.e. can use it for something like logging)

Split the LazySeq into num buckets of equal size using a round-robin algorithm

Creates an asynchronous buffer that spins up a producer thread which feeds data into a BlockingQueue that is read using the resulting LazySeq.

This is the method that sub-classes must implement

Mostly standard group by implementation that uses tmp files to store the values of the HashMap

A cross between grouped and groupBy that allows you to specify a key to be used (like in groupBy) instead of a fixed count (like in grouped). All elements next to each other with the same key get returned in each group.

e.g. LazySeq.wrap(Seq(1,1,1,2,2,1)).groupedBy{ a => a }.toIndexedSeq => Vector((1,Vector(1, 1, 1)), (2,Vector(2, 2)), (1,Vector(1)))

Merge corresponding records from this sorted read with that sorted reader given a method to get a common key that can be compared.

Execute the method on the first element of the LazySeq whenever it is evaluated.

Note: The first element is still call via foreach

Same as onFirst except for the last element whenever it is evaluated

Performs a parallel flat map maintaining ordered output

Performs a parallel flat map maintaining ordered output

A Parallel foreach

A Parallel foreach

Performs a parallel map maintaining ordered output

Performs a parallel map maintaining ordered output

Standard partition implementation using LazySeqs

Collapse elements with the same key by applying a binary operator.

Should be similar to doing something like: reader.groupBy(key).values.flatMap{ _.reduce(op) }