Inspired by Google's BigTable, a variety of scalable, semi-structured, weak-semantic table stores have been developed and optimized for different priorities such as query speed, ingest speed, availability, and interactivity. As these systems mature, performance benchmarking will advance from measuring the rate of simple workloads to understanding and debugging the performance of advanced features such as ingest speed-up techniques and function shipping filters from client to servers. This paper describes YCSB++, a set of extensions to the Yahoo! Cloud Serving Benchmark (YCSB) to improve performance understanding and debugging of these advanced features. YCSB++ includes multi-tester coordination for increased load and eventual consistency measurement, multi-phase workloads to quantify the consequences of work deferment and the benefits of anticipatory configuration optimization such as B-tree pre-splitting or bulk loading, and abstract APIs for explicit incorporation of advanced features in benchmark tests. To enhance performance debugging, we customized an existing cluster monitoring tool to gather the internal statistics of YCSB++, table stores, system services like HDFS, and operating systems, and to offer easy post-test correlation and reporting of performance behaviors. YCSB++ features are illustrated in case studies of two BigTable-like table stores, Apache HBase and Accumulo, developed to emphasize high ingest rates and finegrained security.

Data-intensive applications fall into two computing styles: Internet services (cloud computing) or high-performance computing (HPC). In both categories, the underlying file system is a key component for scalable application performance. In this paper, we explore the similarities and differences between PVFS, a parallel file system used in HPC at large scale, and HDFS, the primary storage system used in cloud computing with Hadoop. We integrate PVFS into Hadoop and compare its performance to HDFS using a set of data-intensive computing benchmarks. We study how HDFS-specific optimizations can be matched using PVFS and how consistency, durability, and persistence tradeoffs made by these file systems affect application performance. We show how to embed multiple replicas into a PVFS file, including a mapping with a complete copy local to the writing client, to emulate HDFS's file layout policies. We also highlight implementation issues with HDFS's dependence on disk bandwidth and benefits from pipelined replication.