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## Background
-Python was a bit too slow, even when parallelized (with GNU parallel), e.g. for
-generating clusters of similar documents or to do verification. An option for
-the future would be to resort to [Cython](https://cython.org/). Parts of
+Python was a bit too slow, even when parallelized (with the great [GNU
+parallel](https://www.gnu.org/software/parallel/), e.g. for generating clusters
+of similar documents or to do verification. An option for the future would be
+to resort to [Cython](https://cython.org/) or
+[pyspark](https://spark.apache.org/docs/latest/api/python/index.html). Parts of
[fuzzycat](https://git.archive.org/webgroup/fuzzycat) has been ported into this
project for performance (and we saw a 25x speedup for certain tasks).
@@ -19,8 +21,8 @@ project for performance (and we saw a 25x speedup for certain tasks).
## Overview
We follow a map-reduce style approach (on a single machine): We extract
-specific keys from data. We group items (via sort) with the same *key* together
-and apply some computation on these groups.
+specific **keys** from data. We **group** items (via *sort*) with the same
+*key* together and **apply** a function over these groups.
Mapper is defined as function type, mapping a blob of data (e.g. a single JSON
object) to a number of fields (e.g. key, value).