Modular Data Storage with Anvil Mike Mammarella Shant Hovsepian Eddie Kohler UCLA UCLA UCLA/Meraki
[email protected] [email protected] [email protected] http://www.read.cs.ucla.edu/anvil/ ABSTRACT age strategies and behaviors. We intend Anvil configura- Databases have achieved orders-of-magnitude performance tions to serve as single-machine back-end storage layers for improvements by changing the layout of stored data – for databases and other structured data management systems. instance, by arranging data in columns or compressing it be- The basic Anvil abstraction is the dTable, an abstract key- fore storage. These improvements have been implemented value store. Some dTables communicate directly with sta- in monolithic new engines, however, making it difficult to ble storage, while others layer above storage dTables, trans- experiment with feature combinations or extensions. We forming their contents. dTables can represent row stores present Anvil, a modular and extensible toolkit for build- and column stores, but their fine-grained modularity of- ing database back ends. Anvil’s storage modules, called dTa- fers database designers more possibilities. For example, a bles, have much finer granularity than prior work. For ex- typical Anvil configuration splits a single “table” into sev- ample, some dTables specialize in writing data, while oth- eral distinct dTables, including a log to absorb writes and ers provide optimized read-only formats. This specialization read-optimized structures to satisfy uncached queries. This makes both kinds of dTable simple to write and understand. split introduces opportunities for clean extensibility – for Unifying dTables implement more comprehensive function- example, we present a Bloom filter dTable that can slot ality by layering over other dTables – for instance, building a above read-optimized stores and improve the performance read/write store from read-only tables and a writable journal, of nonexistent key lookup.