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We present a flexible template-based photometric redshift estimationframework, implemented in C#, that can be seamlessly integrated into a SQLdatabase (or DB) server and executed on-demand in SQL. The DB integrationeliminates the need to move large photometric datasets outside a database forredshift estimation, and utilizes the computational capabilities of DBhardware. The code is able to perform both maximum likelihood and Bayesianestimation, and can handle inputs of variable photometric filter sets andcorresponding broad-band magnitudes. It is possible to take into account thefull covariance matrix between filters, and filter zero points can beempirically calibrated using measurements with given redshifts. The list ofspectral templates and the prior can be specified flexibly, and the expensivesynthetic magnitude computations are done via lazy evaluation, coupled with acaching of results. Parallel execution is fully supported. For large upcomingphotometric surveys such as the LSST, the ability to perform in-place photo-zcalculation would be a significant advantage. Also, the efficient handling ofvariable filter sets is a necessity for heterogeneous databases, for examplethe Hubble Source Catalog, and for cross-match services such as SkyQuery. Weillustrate the performance of our code on two reference photo-z estimationtesting datasets, and provide an analysis of execution time and scalabilitywith respect to different configurations. The code is available for download athttps://github.com/beckrob/Photo-z-SQL.

Photo-z-SQL: integrated, flexible photometric redshift computation in a database