  Coming high-cadence wide-field optical telescopes will image hundreds of
thousands of sources per minute. Besides inspecting the near real-time data
streams for transient and variability events, the accumulated data archive is a
wealthy laboratory for making complementary scientific discoveries.
  The goal of this work is to optimise column-oriented database techniques to
enable the construction of a full-source and light-curve database for
large-scale surveys, that is accessible by the astronomical community.
  We adopted LOFAR's Transients Pipeline as the baseline and modified it to
enable the processing of optical images that have much higher source densities.
The pipeline adds new source lists to the archive database, while
cross-matching them with the known cataloged sources in order to build a full
light-curve archive. We investigated several techniques of indexing and
partitioning the largest tables, allowing for faster positional source look-ups
in the cross matching algorithms. We monitored all query run times in long-term
pipeline runs where we processed a subset of IPHAS data that have image source
density peaks over $170,000$ per field of view ($500,000$ deg$^{-2}$).
  Our analysis demonstrates that horizontal table partitions of declination
widths of one-degree control the query run times. Usage of an index strategy
where the partitions are densily sorted according to source declination yields
another improvement. Most queries run in sublinear time and a few (<20%) run in
linear time, because of dependencies on input source-list and result-set size.
We observed that for this logical database partitioning schema the limiting
cadence the pipeline achieved with processing IPHAS data is 25 seconds.
