Approximate Aggregates in Oracle 12C

Su, Hong; Zaït, Mohamed; Barrière, Vladimir; Torres, J.L. Rueda; Menck, Andre

doi:10.1145/2983323.2983353

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…AQP Systems Many commercial business-critical systems support AQP, e.g., Oracle [102] and Windows Azure [19]. The challenge of building AQP system is to design effective offline indexes or summaries (by analyzing data on distributed systems), devising effective query plan during the query-time and finding effective algorithms.…”

Section: Comparison Of Aqp Methodsmentioning

confidence: 99%

Approximate Query Processing: What is New and Where to Go?

2018

Data Sci. Eng.

112

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Online analytical processing (OLAP) is a core functionality in database systems. The performance of OLAP is crucial to make online decisions in many applications. However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance requirement. To alleviate this problem, approximate query processing (AQP) has been proposed, which aims to find an approximate answer as close as to the exact answer efficiently. Existing AQP techniques can be broadly categorized into two categories. (1) Online aggregation: select samples online and use these samples to answer OLAP queries. (2) Offline synopses generation: generate synopses offline based on a-priori knowledge (e.g., data statistics or query workload) and use these synopses to answer OLAP queries. We discuss the research challenges in AQP and summarize existing techniques to address these challenges. In addition, we review how to use AQP to support other complex data types, e.g., spatial data and trajectory data, and support other applications, e.g., data visualization and data cleaning. We also introduce existing AQP systems and summarize their advantages and limitations. Lastly, we provide research challenges and opportunities of AQP. We believe that the survey can help the partitioners to understand existing AQP techniques and select appropriate methods in their applications.

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Section: Comparison Of Aqp Methodsmentioning

confidence: 99%

Approximate Query Processing: What is New and Where to Go?

2018

Data Sci. Eng.

112

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“…A method to perform approximate queries on granulated data summaries of large datasets is shown in [52]. Approximated techniques are now available on some widely used systems [54,8]. The VerdictDB [45] system provides a handy way to support AQP for SQL-based systems by adding a query/result-rewriting middle layer between the client and the DBMS.…”

Section: State Of the Artmentioning

confidence: 99%

Overlay Indexes: Efficiently Supporting Aggregate Range Queries and Authenticated Data Structures in Off-the-Shelf Databases

Pennino

Pizzonia

Papi³

2019

IEEE Access

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Commercial off-the-shelf DataBase Management Systems (DBMSes) are highly optimized to process a wide range of queries by means of carefully designed indexing and query planning. However, many aggregate range queries are usually performed by DBMSes using sequential scans, and certain needs, like storing Authenticated Data Structures (ADS), are not supported at all. Theoretically, these needs could be efficiently fulfilled adopting specific kinds of indexing, which however are normally ruled-out in DBMSes design.We introduce the concept of overlay index : an index that is meant to be stored in a standard database, alongside regular data and managed by regular software, to complement DBMS capabilities. We show a data structure, that we call DB-tree, that realizes an overlay index to support a wide range of custom aggregate range queries as well as ADSes, efficiently. All DB-trees operations can be performed by executing a small number of queries to the DBMS, that can be issued in parallel in one or two query rounds, and involves a logarithmic amount of data. We experimentally evaluate the efficiency of DB-trees showing that our approach is effective, especially if data updates are limited.

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“…Similar to Oracle 12c [68], the approximation settings in Ver-dictDB (e.g., I/O budget, accuracy requirements) can be set either on a per-query basis or at the system/connection level. The latter allows VerdictDB to be used in a transparent mode for speeding up (legacy) applications that are not designed to use AQP features, e.g., the DBA can choose appropriate settings on behalf of the application.…”

Section: User Interfacementioning

confidence: 99%

“…Despite its long history in academic research [69], approximate query processing (AQP) has had little success in terms of industrial adoption [45]. Only recently, a few vendors have started to include limited forms of approximation features in their products, e.g., Facebook's Presto [4], Infobright's IAQ [2], Yahoo's Druid [1], SnappyData [5], and Oracle 12C [68]. While there are several factors contributing to this slow adoption, one of the ways to quickly widen the reach of this technology is to offer a Universal AQP (UAQP): an AQP strategy that could work with all existing platforms without requiring any modifications to existing databases.…”

Section: Introductionmentioning

confidence: 99%

VerdictDB

Park

Mozafari

Sorenson

et al. 2018

Proceedings of the 2018 International Conference on Management of Data

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Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases-an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a databaseagnostic approximation engine that will widen the reach of this emerging technology across various platforms.Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171× speedup (18.45× on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.

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Approximate Aggregates in Oracle 12C

Cited by 5 publications

References 16 publications

Approximate Query Processing: What is New and Where to Go?

Approximate Query Processing: What is New and Where to Go?

Overlay Indexes: Efficiently Supporting Aggregate Range Queries and Authenticated Data Structures in Off-the-Shelf Databases

VerdictDB

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