Efficient index compression in DB2 LUW

Bhattacharjee, Bishwaranjan; Lim, Lipyeow; Malkemus, Timothy; Mihaila, George A.; Ross, Kenneth A.; Lau, Sherman; McArthur, Cathy; Tóth, Zoltán; Sherkat, Reza

doi:10.14778/1687553.1687573

Cited by 31 publications

(25 citation statements)

References 20 publications

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“…Bhattacharjee et al [5] have introduced index compression techniques used in DB2. Ordulu and Tolmer [6] have proposed the adaptive padding method to reduce the compression overhead for insert-intensive workloads.…”

Section: Related Workmentioning

confidence: 99%

A Selective Compression Strategy for Performance Improvement of Database Compression

Lee

2015

KIPS Transactions on Software and Data Engineering

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The Internet of Things (IoT) significantly increases the amount of data. Database compression is important for big data because it can reduce costs for storage systems and save I/O bandwidth. However, it could show low performance for write-intensive workloads such as OLTP due to the updates of compressed pages. In this paper, we present practical guidelines for the performance improvement of database compression. Especially, we propose the SELECTIVE strategy, which compresses only tables whose space savings are close to the expected space savings calculated by the compressed page size. Experimental results using the TPC-C benchmark and MySQL show that the strategy can achieve 1.1 times better performance than the uncompressed counterpart with 17.3% space savings.

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Section: Related Workmentioning

confidence: 99%

A Selective Compression Strategy for Performance Improvement of Database Compression

Lee

2015

KIPS Transactions on Software and Data Engineering

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“…Poess and Potapov [7] have presented compression techniques used in Oracle for data warehouses and OLAP systems where the majority of the accesses are read only. Bhattacharjee et al [2] have introduced index compression schemes used in DB2. Aghav [1] has introduced various compression techniques for difference domains of databases.…”

Section: Related Workmentioning

confidence: 99%

Performance Improvement of Database Compression for OLTP Workloads

Lee

2014

IEICE Trans. Inf. & Syst.

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SUMMARYAs data volumes explode, data storage costs become a large fraction of total IT costs. We can reduce the costs substantially by using compression. However, it is generally known that database compression is not suitable for write-intensive workloads. In this paper, we provide a comprehensive solution to improve the performance of compressed databases for write-intensive OLTP workloads. We find that storing data too densely in compressed pages incurs many future page splits, which require exclusive locks. In order to avoid lock contention, we reduce page splits by sacrificing a couple of percent of space savings. We reserve enough space in each compressed page for future updates of records and prevent page merges that are prone to incur page splits in the near future. The experimental results using TPC-C benchmark and MySQL/InnoDB show that our method gives 1.5 times higher throughput with 33% space savings compared with the uncompressed counterpart and 1.8 times higher throughput with only 1% more space compared with the state-of-the-art compression method developed by Facebook.

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“…In such a case, the Indirection method needs to traverse only one index, and update one LID-to-RID mapping. 13 In contrast, the base method needs to traverse and update HDDresident pages for every index. Figure 10 shows that the performance improvement approaches 20X as the number of indexes increases from 1 to 16.…”

Section: Gist Implementationmentioning

confidence: 99%

“…These capabilities are often important for operational data stores [35]. For example, it is not uncommon to find tens of indexes to improve analytical and decision-making queries even in TPC benchmarks [20,19] or enterprise resource planning (ERP) scenarios [14,13].…”

Section: Introductionmentioning

confidence: 99%

Making updates disk-I/O friendly using SSDs

Sadoghi¹,

Ross

Canim³

et al. 2013

Proc. VLDB Endow.

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Multiversion databases store both current and historical data. Rows are typically annotated with timestamps representing the period when the row is/was valid. We develop novel techniques for reducing index maintenance in multiversion databases, so that indexes can be used effectively for analytical queries over current data without being a heavy burden on transaction throughput. To achieve this end, we re-design persistent index data structures in the storage hierarchy to employ an extra level of indirection. The indirection level is stored on solid state disks that can support very fast random I/Os, so that traversing the extra level of indirection incurs a relatively small overhead.The extra level of indirection dramatically reduces the number of magnetic disk I/Os that are needed for index updates, and localizes maintenance to indexes on updated attributes. Further, we batch insertions within the indirection layer in order to reduce physical disk I/Os for indexing new records. By reducing the index maintenance overhead on transactions, we enable operational data stores to create more indexes to support queries. We have developed a prototype of our indirection proposal by extending the widely used Generalized Search Tree (GiST) open-source project, which is also employed in PostgreSQL. Our working implementation demonstrates that we can significantly reduce index maintenance and/or query processing cost, by a factor of 3. For insertions of new records, our novel batching technique can save up to 90% of the insertion time.

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Efficient index compression in DB2 LUW

Cited by 31 publications

References 20 publications

A Selective Compression Strategy for Performance Improvement of Database Compression

A Selective Compression Strategy for Performance Improvement of Database Compression

Performance Improvement of Database Compression for OLTP Workloads

Making updates disk-I/O friendly using SSDs

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