Fast updates on read-optimized databases using multi-core CPUs

Krueger, Jens; Kim, Changkyu; Grund, Martin; Satish, Nadathur; Schwalb, David; Chhugani, Jatin; Plattner, Hasso; Dubey, Pradeep; Zeier, Alexander

doi:10.14778/2047485.2047491

Cited by 110 publications

(76 citation statements)

References 28 publications

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“…OLTP systems, especially enterprise OLTP systems that handle high-profile data (e.g., financial and order processing systems), also need to be scalable but cannot give up strong transactional and consistency requirements [27]. The only option previously available for these organizations was to purchase more powerful single-node machines or develop custom middleware that distributes queries over traditional DBMS nodes [41].…”

Section: Introductionmentioning

confidence: 99%

Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Pavlo

Curino

Zdonik

2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

228

172

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The advent of affordable, shared-nothing computing systems portends a new class of parallel database management systems (DBMS) for on-line transaction processing (OLTP) applications that scale without sacrificing ACID guarantees [7,9]. The performance of these DBMSs is predicated on the existence of an optimal database design that is tailored for the unique characteristics of OLTP workloads [43]. Deriving such designs for modern DBMSs is difficult, especially for enterprise-class OLTP systems, since they impose extra challenges: the use of stored procedures, the need for load balancing in the presence of time-varying skew, complex schemas, and deployments with larger number of partitions.To this purpose, we present a novel approach to automatically partitioning databases for enterprise-class OLTP systems that significantly extends the state of the art by: (1) minimizing the number distributed transactions, while concurrently mitigating the effects of temporal skew in both the data distribution and accesses, (2) extending the design space to include replicated secondary indexes, (4) organically handling stored procedure routing, and (3) scaling of schema complexity, data size, and number of partitions. This effort builds on two key technical contributions: an analytical cost model that can be used to quickly estimate the relative coordination cost and skew for a given workload and a candidate database design, and an informed exploration of the huge solution space based on large neighborhood search. To evaluate our methods, we integrated our database design tool with a high-performance parallel, main memory DBMS and compared our methods against both popular heuristics and a state-of-the-art research prototype [17]. Using a diverse set of benchmarks, we show that our approach improves throughput by up to a factor of 16× over these other approaches.

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Section: Introductionmentioning

confidence: 99%

Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Pavlo

Curino

Zdonik

2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

228

172

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“…Enterprise Data As shown by Krueger et al [5], enterprise data consists of many sparse columns. The domain of values is often limited, because there is a limited number of underlying options in the business processes.…”

Section: Column Stores With a Read-optimized Partitionmentioning

confidence: 99%

“…Inserts and updates to the compressed column are handled by a delta partition, thereby avoiding to re-encode the column for each insert [5]. The delta partition is stored uncompressed and extended by a CSB+ tree index to allow for fast lookups.…”

Section: Length Of Address In Main Partition Bitsmentioning

confidence: 99%

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Fast Column Scans: Paged Indices for In-Memory Column Stores

Faust

Schwalb

Krueger

2015

Lecture Notes in Computer Science

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Abstract. Commodity hardware is available in configurations with huge amounts of main memory and it is viable to keep large databases of enterprises in the RAM of one or a few machines. Additionally, a reunification of transactional and analytical systems has been proposed to enable operational reporting on the most recent data. In-memory column stores appeared in academia and industry as a solution to handle the resulting mixed workload of transactional and analytical queries. Therein queries are processed by scanning whole columns to evaluate the predicates on non-key columns. This leads to a waste of memory bandwidth and reduced throughput. In this work we present the Paged Index, an index tailored towards dictionary-encoded columns. The indexing concept builds upon the availability of the indexed data at high speeds, a situation that is unique to in-memory databases. By reducing the search scope we achieve up to two orders of magnitude of performance increase for the column scan operation during query runtime.

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“…Although having a record counter introduces a penalty on re-encrypting a non-updated attribute within that record, this penalty is insignificant because most workload is read-intensive rather than write-intensive. The update-intensive workload also requires extensive read operations to search for appropriate records to write [81].…”

Section: Seed Componentsmentioning

confidence: 99%

A novel architecture for secure database processing in cloud computing

Chen¹

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Security, particularly data privacy, is one of the biggest barriers to the adoption of Database-as-a-Service (DBaaS) in Cloud Computing. Recent security breaches demonstrate that a more powerful protection mechanism is needed to protect data confidentiality from any honest-but-curious administrator. Typical prior e ort on addressing this security problem is either prohibitively slow or highly restrictive in operation.In this thesis, a novel cloud system architecture CypherDB, which makes use of a secure processor, is proposed to protect the confidentiality of outsourced database processing. To achieve this, a framework is developed to use these secure processors in the cloud for secure database processing. This framework allows distributed and parallel processing of the encrypted data and exhibits virtualization features in Cloud Computing. The CypherDB architecture also relies on two major components to protect the privacy of an outsourced database against any honest-but-curious administrator of high performance.Firstly, a novel database encryption scheme is developed to protect the outsourced database which can be executed under a CypherDB secure processor with high performance. Our proposed scheme makes use of custom instructions to hide the encryption latency from the program execution. This scheme is extensively validated through an integration with SQLite, a practical database application program.Secondly, a novel secure processor architecture is also developed to provide architectural support to our proposed database encryption scheme and e cient protection mechanism to secure all intermediate data generated onthe-fly during query execution. The e ciency, robustness and the cost of our novel processor architecture are validated and evaluated through extensive simulations and implementation on a FPGA platform.

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Fast updates on read-optimized databases using multi-core CPUs

Cited by 110 publications

References 28 publications

Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Fast Column Scans: Paged Indices for In-Memory Column Stores

A novel architecture for secure database processing in cloud computing

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