  Read-optimized columnar databases use differential updates to handle writes
by maintaining a separate write-optimized delta partition which is periodically
merged with the read-optimized and compressed main partition. This merge
process introduces significant overheads and unacceptable downtimes in update
intensive systems, aspiring to combine transactional and analytical workloads
into one system. In the first part of the paper, we report data analyses of 12
SAP Business Suite customer systems. In the second half, we present an
optimized merge process reducing the merge overhead of current systems by a
factor of 30. Our linear-time merge algorithm exploits the underlying high
compute and bandwidth resources of modern multi-core CPUs with
architecture-aware optimizations and efficient parallelization. This enables
compressed in-memory column stores to handle the transactional update rate
required by enterprise applications, while keeping properties of read-optimized
databases for analytic-style queries.
