Text analytical tasks like word embedding, phrase mining and topic modeling, are placing increasing demands as well as challenges to existing database management systems. In this paper, we provide a novel algebraic approach based on associative arrays. Our data model and algebra can bring together relational operators and text operators, which enables interesting optimization opportunities for hybrid data sources that have both relational and textual data. We demonstrate its expressive power in text analytics using several real-world tasks.
more »
« less
CH2: A Hybrid Operational/Analytical Processing Benchmark for NoSQL
Database systems with hybrid data management support, referred to as HTAP or HOAP architectures, are gaining popularity. These first appeared in the relational world, and the CH-benCHmark (CH) was proposed in 2011 to evaluate such relational systems. Today, one finds NoSQL database systems gaining adoption for new applications. In this paper we present CH2, a new benchmark – created with CH as its starting point – aimed at evaluating hybrid data platforms in the document data management world. Like CH, CH2 borrows from and extends both TPC-C and TPC-H. Differences from CH include a document-oriented schema, a data generation scheme that creates a TPC-H-like history, and a “do over” of the CH queries that is more in line with TPC-H. This paper details shortcomings that we uncovered in CH, the design of CH2, and preliminary results from running CH2 against Couchbase Server 7.0 (whose Query and Analytics services provide HOAP support for NoSQL data). The results provide insight into the performance isolation and horizontal scalability properties of Couchbase Server 7.0 as well as demonstrating the efficacy of CH2 for evaluating such platforms.
more »
« less
- NSF-PAR ID:
- 10351638
- Editor(s):
- Nambiar, R; Poess, M.
- Date Published:
- Journal Name:
- Proc. 13th TPC Technology Conf. on Performance Evaluation & Benchmarking (TPC TC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
From the United States’ Health Insurance Portability and Accountability Act (HIPAA) to the European Union’s General Data Protection Regulation (GDPR), there has been an increased focus on individual data privacy protection. Because multiple enforcement agencies (such as legal entities and external governing bodies) have jurisdiction over data governance, it is possible for the same data value to be subject to multiple (and potentially conflicting) policies. As a result, managing and enforcing all applicable legal requirements has become a complex task. In this paper, we present a comprehensive overview of the steps to integrating data retention and purging into a database management system (DBMS). We describe the changes necessary at each step of the data lifecycle management, the minimum functionality that any DBMS (relational or NoSQL) must support, and the guarantees provided by this system. Our proposed solution is 1) completely transparent from the perspective of the DBMS user; 2) requires only a minimal amount of tuning by the database administrator; 3) imposes a negligible performance overhead and a modest storage overhead; and 4) automates the enforcement of both retention and purging policies in the database.more » « less
-
null (Ed.)Analytic workloads on terabyte data-sets are often run in the cloud, where application and storage servers are separate and connected via network. In order to saturate the storage bandwidth and to hide the long storage latency, such a solution requires an expensive server cluster with sufficient aggregate DRAM capacity and hardware threads. An alternative solution is to push the query computation into storage servers. In this paper we present an in-storage Analytics QUery Offloading MAchiNe (AQUOMAN) to “offload” most SQL operators, including multi-way joins, to SSDs. AQUOMAN executes Table Tasks, which apply a static dataflow graph of SQL operators to relational tables to produce an output table. Table Tasks use a streaming computation model, which allows AQUOMAN to process queries with a reasonable amount of DRAM for intermediate results. AQUOMAN is a general analytic query processor, which can be integrated in the database software stack transparently. We have built a prototype of AQUOMAN in FPGAs, and using TPC-H benchmarks on 1TB data sets, shown that a single instance of 1TB AQUOMAN disk, on average, can free up 70% CPU cycles and reduce DRAM usage by 60%. One way to visualize this saving is to think that if we run queries sequentially and ignore inter-query page cache reuse, MonetDB running on a 4-core, 16GB-DRAM machine with AQUOMAN augmented SSDs performs, on average, as well as a MonetDB running on a 32-core, 128GB-DRAM machine with standard SSDs.more » « less
-
Stefanidis, K. ; Golab, L. (Ed.)Secondary indexes in relational database systems are traditionally built under the assumption that one data record maps to one indexed value. Nowadays, particularly in NoSQL systems, single data records can hold collections of values that users want to access efficiently in an ad-hoc manner. Multi-valued indexes aim to give users the best of both worlds: (i) to keep a more natural data model of records with collections of values, and (ii) to reap the benefits of a secondary index. In this paper, we detail the steps taken to realize multi-valued indexes in AsterixDB, a Big Data management system with a structured query language operating over a collection of docu- ments. This includes (a) creating the specification language for such indexes, (b) illustrating data flows for bulk-loading and maintaining an index, and (c) discussing query plans to take advantage of multi-valued indexes for use in predicates with existential and universal quantification. We conclude with ex- periments that compare AsterixDB multi-valued indexes against similar indexes in MongoDB and Couchbase Query.more » « less
-
Embedded database libraries provide developers with a com- mon and convenient data persistence layer. They have spread to many systems, including interactive devices like smart- phones, appearing in all major mobile systems. Their perfor- mance affects the response times and resource consumption of millions of phone apps and billions of phone users. It is thus critical that we better understand how they work, so they can be used more efficiently, and so developers can make faster libraries. Mobile databases differ significantly from server-class storage in terms of platform, usage, and measurement. Phones are multi-tenant, end-user devices that the database must share with other apps. Contrary to traditional database design goals, workloads on phones are single-app, bursty, and rarely saturate the CPU. We argue that mobile storage design should refocus on what matters on the mobile platform: latency and energy. As accurate per- formance measurement tools are necessary to evaluation of good database design, this uncovers another issue: Tradi- tional database benchmarking methods produce misleading results when applied to mobile devices, due to evaluating performance at saturation. Development of databases and measurements specifically designed for the mobile platform is necessary to optimize user experience of the most common database usage in the world.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-94437-7_5
