TAILIEUCHUNG - Lookup Tables: Fine-Grained Partitioning for Distributed Databases

This is all possible because the TKFORMAT subsystem is installed inside the DBMS. In fact, TKFORMAT supports more than just SAS formats that are defined using PROC FORMAT. SAS ships with hundreds of formats already defined. Formats supplied by SAS are deployed into the DBMS as a small set of runtime modules. Formats created by PROC FORMAT, in contrast, can export these user-written format definitions as XML. These XML streams can then be stored inside the DBMS as database objects. The TKFORMAT components know how to identify a user-written format and find its XML definition inside the DBMS. The details. | Lookup Tables Fine-Grained Partitioning for Distributed Databases Aubrey L. Tatarowicz 1 Carlo Curino 2 Evan P. C. Jones Sam Madden 4 Massachusetts Institute of Technology USA 1altat@ 2 krl@ 3 evanj@ 4 madden@ Abstract The standard way to scale a distributed OLTP DBMS is to horizontally partition data across several nodes. Ideally this results in each query transaction being executed at just one node to avoid the overhead of distribution and allow the system to scale by adding nodes. For some applications simple strategies such as hashing on primary key provide this property. Unfortunately for many applications including social networking and order-fulfillment simple partitioning schemes applied to many-to-many relationships create a large fraction of distributed queries transactions. What is needed is a finegrained partitioning where related individual tuples . cliques of friends are co-located together in the same partition. Maintaining a fine-grained partitioning requires storing the location of each tuple. We call this metadata a lookup table. We present a design that efficiently stores very large tables and maintains them as the database is modified. We show they improve scalability for several difficult to partition database workloads including Wikipedia Twitter and TPC-E. Our implementation provides 40 to 300 better throughput on these workloads than simple range or hash partitioning. I. INTRODUCTION Partitioning is an essential strategy for scaling database workloads. In order to be effective for web and OLTP workloads a partitioning strategy should minimize the number of nodes involved in answering a query or transaction 1 thus limiting distribution overhead and enabling efficient scale-out. In web and OLTP databases the most common strategy is to horizontally partition the database using hash or range partitioning. This is used by commercially available distributed databases and it works well in many .

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