Scaling-out a database system typically requires partitioning the
\ndatabase across multiple servers. If applications do not partition
\nperfectly, then transactions accessing multiple partitions end up
\nbeing distributed, which has well-known scalability challenges. To
\naddress them, we describe a high-performance transaction mechanism
\nthat uses optimistic concurrency control on a multi-versioned
\ntree-structured database stored in a shared log. The system scales
\nout by adding servers, without partitioning the database.
\nOur solution is modeled on the Hyder architecture, published by
\nBernstein, Reid, and Das at CIDR 2011. We present the design and
\nevaluation of the first full implementation of that architecture. The
\ncore of the system is a log roll-forward algorithm, called meld, that
\ndoes optimistic concurrency control. Meld is inherently sequential
\nand is therefore the main bottleneck. Our main algorithmic contributions
\nare optimizations to meld that significantly increase transaction
\nthroughput. They use a pipelined design that parallelizes
\nmeld onto multiple threads. The slowest pipeline stage is much
\nfaster than the original meld algorithm, yielding a 3x improvement
\nof system throughput over the original meld algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"
Scaling-out a database system typically requires partitioning the database across multiple servers. If applications do not partition perfectly, then transactions accessing multiple partitions end up being distributed, which has well-known scalability challenges. To address them, we describe a high-performance transaction mechanism that uses optimistic concurrency control on a multi-versioned tree-structured database stored in a shared 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