TAILIEUCHUNG - Column-Stores vs. Row-Stores: How Different Are They Really?

Distributed transactions have historically been implemented by the database community in the manner pioneered by the architects of System R* [22] in the 1980s. The primary mechanism by which System R*-style distributed transactions impede throughput and extend latency is the requirement of an agreement protocol between all participating machines at commit time to ensure atomicity and durability. To ensure isolation, all of a transaction’s locks must be held for the full duration of this agreement protocol, which is typi- cally two-phase commit. The problem with holding locks during the agreement protocol is that two-phase commit requiresmultiplenetworkround-tripsbe- tween all participating machines, and therefore the time required to run the protocol can often be. | Column-Stores vs. Row-Stores How Different Are They Really Daniel J. Abadi Yale University New Haven CT Usa dna@ Samuel R. Madden MIT Cambridge MA USA madden@ Nabil Hachem AvantGarde Consulting LLC Shrewsbury MA USA nhachem@ ABSTRACT There has been a significant amount of excitement and recent work on column-oriented database systems column-stores . These database systems have been shown to perform more than an order of magnitude better than traditional row-oriented database systems row-stores on analytical workloads such as those found in data warehouses decision support and business intelligence applications. The elevator pitch behind this performance difference is straightforward column-stores are more I O efficient for read-only queries since they only have to read from disk or from memory those attributes accessed by a query. This simplistic view leads to the assumption that one can obtain the performance benefits of a column-store using a row-store either by vertically partitioning the schema or by indexing every column so that columns can be accessed independently. In this paper we demonstrate that this assumption is false. We compare the performance of a commercial row-store under a variety of different configurations with a column-store and show that the row-store performance is significantly slower on a recently proposed data warehouse benchmark. We then analyze the performance difference and show that there are some important differences between the two systems at the query executor level in addition to the obvious differences at the storage layer level . Using the column-store we then tease apart these differences demonstrating the impact on performance of a variety of column-oriented query execution techniques including vectorized query processing compression and a new join algorithm we introduce in this paper. We conclude that while it is not impossible for a row-store to achieve some of the performance advantages of a

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