Template B+ trees: an index scheme for fast data streams with distributed append-only stores

Distributed systems are now commonly used to manage massive data flooding from the physical world, such as user-generated content from online social media and communication records from mobile phones. The new generation of distributed data management systems, such as HBase, Cassandra and Riak, are designed to perform queries and tuple insertions only. Other database operations such as deletions and updates are simulated by appending the keys associated with the target tuples to operation logs. Such an append-only store architecture maximizes the processing throughput on incoming data, but potentially incurs higher costs during query processing, because additional computation is needed to generate consistent snapshots of the database. Indexing is the key to enable efficient query processing by fast data retrieval and aggregation under such a system architecture. This thesis presents a new in-memory indexing scheme for distributed append-only stores. Our new scheme utilizes traditional index structures based on B+ trees and their variants to create an efficient in-memory template-based tree without the overhead of expensive node splits. We also propose the use of optimized domain partitioning and multi-thread insertion techniques to exploit the advantages of the template B+ tree structure. Our empirical evaluations show that insertion throughput is five times higher with template B+ trees than with HBase, on a variety of real and synthetic workloads

Deferred lightweight indexing for log-structured key-value stores

The recent shift towards write-intensive workload on bigdata (e.g., financial trading, social user-generated data streams)has pushed the proliferation of log-structured key-value stores, represented by Google's BigTable [1], Apache HBase [2] andCassandra [3]. While providing key-based data access with aPut/Get interface, these key-value stores do not support value-based access methods, which significantly limits their applicability in modern web and database applications. In this paper, we present DELI, a DEferred Lightweight Indexing scheme on the log-structured key-value stores. To index intensively updated bigdata in real time, DELI aims at making the index maintenance as lightweight as possible. The key idea is to apply an append-only design for online index maintenance and to collect index garbage at carefully chosen time. DELI optimizes the performance of index garbage collection through tightly coupling its execution with a native routine process called compaction. The DELI'ssystem design is fault-tolerant and generic (to most key-valuestores), we implemented a prototype of DELI based on HBasewithout internal code modification. Our experiments show that the DELI offers significant performance advantage for the write-intensive index maintenance