  We present BLITZCRANK, a high-speed semantic compressor designed for OLTP
databases. Previous solutions are inadequate for compressing row-stores: they
suffer from either low compression factor due to a coarse compression
granularity or suboptimal performance due to the inefficiency in handling
dynamic data sets. To solve these problems, we first propose novel semantic
models that support fast inferences and dynamic value set for both discrete and
continuous data types. We then introduce a new entropy encoding algorithm,
called delayed coding, that achieves significant improvement in the decoding
speed compared to modern arithmetic coding implementations. We evaluate
BLITZCRANK in both standalone microbenchmarks and a multicore in-memory
row-store using the TPC-C benchmark. Our results show that BLITZCRANK achieves
a sub-microsecond latency for decompressing a random tuple while obtaining high
compression factors. This leads to an 85% memory reduction in the TPC-C
evaluation with a moderate (19%) throughput degradation. For data sets larger
than the available physical memory, BLITZCRANK help the database sustain a high
throughput for more transactions before the l/O overhead dominates.
