Parallel shared-nothing data management systems have been widely used to exploit a cluster of machines for efficient and scalable data processing. When a cluster needs to be dynamically scaled in or out, data must be efficiently rebalanced. Ideally, data rebalancing should have a low data movement cost, incur a small overhead on data ingestion and query processing, and be performed online without blocking reads or writes. However, existing parallel data management systems often exhibit certain limitations and drawbacks in terms of efficient data rebalancing. In this paper, we introduce DynaHash, an efficient data rebalancing approach that combines dynamic bucketing with extendible hashing for shared-nothing OLAP-style parallel data management systems. DynaHash dynamically partitions the records into a number of buckets using extendible hashing to achieve good a load balance with small rebalancing costs. We further describe an end-to-end implementation of the proposed approach inside an open-source Big Data Management System (BDMS), Apache AsterixDB. Our implementation exploits the out-of-place update design of LSM-trees to efficiently rebalance data without blocking concurrent reads and writes. Finally, we have conducted performance experiments using the TPC-H benchmark and we present the results here.

Exploiting a storage hierarchy is critical to cost-effective data management. However, most systems are challenged when data is not in cache because of the additional I/O to move data between SSD and main memory. To improve both cost and performance, some systems use a log structured store to write a batch of pages instead of a "block-at-a-time". However, host-based log structuring incurs the additional cost and complexity of garbage collection and recovery, duplicating similar SSD FTL functionality. In prior work, we presented a customized SSD controller implementation for an Open-Channel SSD to enable host computers to write batches of fixed size pages. This current work is a major redesign to support a batched write interface with variable size pages. Variable size pages can enable easy support of data compression and encryption, as well as reducing internal page storage fragmentation, e.g, within a B-tree. Thus it further improves I/O performance while making it easier and more efficient to support these capabilities.

Modern storage devices do not support update-in-place. Rather, flash and shingled disks, are forms of log structured stores. Such a store writes a number of diverse and non-contiguous logical pages into a unit of contiguous storage we call a segment instead of using a write I/O to update each page in place. The result is that pages need to be relocated and remapped on every write. Log structuring was invented for and used initially to improve performance in file systems. Segments need to be garbage collected, but can be only when they no longer house any current pages. A process of "cleaning" produces an empty segment by, when necessary, moving (re-writing) still current pages of the segment to another location. Cleaning effectiveness has a major impact on the performance of modern storage devices, and for flash, impacts the rate of wear and hence the lifetime of the device. We analyze cleaning performance and introduce a cleaning strategy that uses a new way to prioritize the order in which segments are cleaned. Our cleaning strategy approximates an "optimal cleaning strategy". Simulation studies confirm the results of the analysis. This strategy is a significant improvement over previous cleaning strategies.

Business organizations regularly collect customer data to improve their services. Organizations may want to share data within themselves or even with third-parties to maximize data utility. Since business data contain lots of customer data, organizations must respect customers' privacy expounded by privacy laws. In this paper, we present PSpec-SQL, a distributed data analytics system that automatically enforces privacy compliance for SQL queries. Our system provides a high-level language PSpec for the data owner to specify her data usage policy. As usual, the data analyst queries data to perform data analysis, but our system checks each query to ensure only policy-compliant queries are executed. We have implemented a prototype of PSpec-SQL on top of Spark-SQL, and carried out a case study on the TPC benchmarks. The results show the practicability of our system with negligible overhead over query processing.

The log-structured merge-tree (LSM-tree) [16, 18] is widely used in modern NoSQL systems. Different from traditional update-in-place structures, an LSM-tree first buffers all writes in memory, which are subsequently flushed to disk when memory is full. The on-disk components are usually organized into levels of exponentially increasing sizes, where a smaller level is merged into the adjacent larger level when it fills up. To bound the temporary disk space occupied by merges, modern LSM-tree implementations often range partition a disk component into many fixed-size SSTables

Log-Structured Merge-trees (LSM-trees) have been widely used in modern NoSQL systems. Due to their out-of-place update design, LSM-trees have introduced memory walls among the memory components of multiple LSM-trees and between the write memory and the buffer cache. Optimal memory allocation among these regions is non-trivial because it is highly workload-dependent. Existing LSM-tree implementations instead adopt static memory allocation schemes due to their simplicity and robustness, sacrificing performance. In this paper, we attempt to break down these memory walls in LSM-based storage systems. We first present a memory management architecture that enables adaptive memory management. We then present a partitioned memory component structure with new flush policies to better exploit the write memory to minimize the write cost. To break down the memory wall between the write memory and the buffer cache, we further introduce a memory tuner that tunes the memory allocation between these two regions. We have conducted extensive experiments in the context of Apache AsterixDB using the YCSB and TPC-C benchmarks and we present the results here.

Summary Traditional relational database systems handle data by dividing their memory into sections such as a buffer cache and working memory, assigning a memory budget to each section to efficiently manage a limited amount of overall memory. They also assign memory budgets to memory‐intensive operators such as sorts and joins and control the allocation of memory to these operators; each memory‐intensive operator attempts to maximize its memory usage to reduce disk I/O cost. Implementing such memory‐intensive operators requires a careful design and application of appropriate algorithms that properly utilize memory. Today's Big Data management systems need the ability to handle large amounts of data similarly, as it is unrealistic to assume that truly big data will fit into memory. In this article, we share our memory management experiences in Apache AsterixDB, an open‐source Big Data management software platform that scales out horizontally on shared‐nothing commodity computing clusters. We describe the implementation of AsterixDB's memory‐intensive operators and their designs related to memory management. We also discuss memory management at the global (cluster) level. We conducted an experimental study using several synthetic and real datasets to explore the impact of this work. We believe that future Big Data management system builders can benefit from these experiences.

The rising demands of real-time analytics have emphasized the need for Hybrid Transactional and Analytical Processing (HTAP) systems, which can handle both fast transactions and analytics concurrently. Wildfire is such a large-scale HTAP system prototyped at IBM Research - Almaden, with many techniques developed in this project incorporated into the IBM’s HTAP product offering. To support both workloads efficiently, Wildfire organizes data differently across multiple zones, with more recent data in a more transaction-friendly zone and older data in a more analytics-friendly zone. Data evolve from one zone to another, as they age. In fact, many other HTAP systems have also employed the multi-zone design, including SAP HANA, MemSQL, and SnappyData. Providing a unified index on the large volumes of data across multiple zones is crucial to enable fast point queries and range queries, for both transaction processing and real-time analytics. However, due to the scale and evolving nature of the data, this is a highly challenging task. In this paper, we present Umzi, the multi-version and multi-zone LSM-like indexing method in the Wildfire HTAP system. To the best of our knowledge, Umzi is the first indexing method to support evolving data across multiple zones in an HTAP system, providing a consistent and unified indexing view on the data, despite the constantly on-going changes underneath. Umzi employs a flexible index structure that combines hash and sort techniques together to support both equality and range queries. Moreover, it fully exploits the storage hierarchy in a distributed cluster environment (memory, SSD, and distributed shared storage) for index efficiency. Finally, all index maintenance operations in Umzi are designed to be non-blocking and lock-free for queries to achieve maximum concurrency, while only minimum locking overhead is incurred for concurrent index modifications.

In recent years, the Log Structured Merge (LSM) tree has been widely adopted by NoSQL and NewSQL systems for its superior write performance. Despite its popularity, however, most existing work has focused on LSM-based key-value stores with only a single LSM-tree; auxiliary structures, which are critical for supporting ad-hoc queries, have received much less attention. In this paper, we focus on efficient data ingestion and query processing for general-purpose LSM-based storage systems. We first propose and evaluate a series of optimizations for efficient batched point lookups, significantly improving the range of applicability of LSM-based secondary indexes. We then present several new and efficient maintenance strategies for LSM-based storage systems. Finally, we have implemented and experimentally evaluated the proposed techniques in the context of the Apache AsterixDB system, and we present the results here.

The Log-Structured Merge-Tree (LSM-tree) has been widely adopted for use in modern NoSQL systems for its superior write performance. Despite the popularity of LSM-trees, they have been criticized for suffering from write stalls and large performance variances due to the inherent mismatch between their fast in-memory writes and slow background I/O operations. In this paper, we use a simple yet effective two-phase experimental approach to evaluate write stalls for various LSM-tree designs. We further explore the design choices of LSM merge schedulers to minimize write stalls given an I/O bandwidth budget. We have conducted extensive experiments in the context of the Apache AsterixDB system and we present the results here.