  Web archives, query and proxy logs, and so on, can all be very large and
highly repetitive; and are accessed only sporadically and partially, rather
than continually and holistically. This type of data is ideal for
compression-based archiving, provided that random-access to small fragments of
the original data can be achieved without needing to decompress everything. The
recent RLZ (relative Lempel Ziv) compression approach uses a semi-static model
extracted from the text to be compressed, together with a greedy factorization
of the whole text encoded using static integer codes. Here we demonstrate more
precisely than before the scenarios in which RLZ excels. We contrast RLZ with
alternatives based on block-based adaptive methods, including approaches that
"prime" the encoding for each block, and measure a range of implementation
options using both hard-disk (HDD) and solid-state disk (SSD) drives. For HDD,
the dominant factor affecting access speed is the compression rate achieved,
even when this involves larger dictionaries and larger blocks. When the data is
on SSD the same effects are present, but not as markedly, and more complex
trade-offs apply.
