  In-memory databases (IMDBs) are gaining increasing popularity in big data
applications, where clients commit updates intensively. Specifically, it is
necessary for IMDBs to have efficient snapshot performance to support certain
special applications (e.g., consistent checkpoint, HTAP). Formally, the
in-memory consistent snapshot problem refers to taking an in-memory consistent
time-in-point snapshot with the constraints that 1) clients can read the latest
data items and 2) any data item in the snapshot should not be overwritten.
Various snapshot algorithms have been proposed in academia to trade off
throughput and latency, but industrial IMDBs such as Redis adhere to the simple
fork algorithm. To understand this phenomenon, we conduct comprehensive
performance evaluations on mainstream snapshot algorithms. Surprisingly, we
observe that the simple fork algorithm indeed outperforms the state-of-the-arts
in update-intensive workload scenarios. On this basis, we identify the
drawbacks of existing research and propose two lightweight improvements.
Extensive evaluations on synthetic data and Redis show that our lightweight
improvements yield better performance than fork, the current industrial
standard, and the representative snapshot algorithms from academia. Finally, we
have opensourced the implementation of all the above snapshot algorithms so
that practitioners are able to benchmark the performance of each algorithm and
select proper methods for different application scenarios.
