Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

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Bitmap indexes must be compressed to reduce input/output costs and minimize CPU usage. To accelerate logical operations (AND, OR, XOR) over bitmaps, we use techniques based on run-length encoding (RLE), such as Word-Aligned Hybrid (WAH) compression. These techniques are sensitive to the order of the rows: a simple lexicographical sort can divide the index size by 9 and make indexes several times faster. We investigate reordering heuristics based on computed attribute-value histograms. Simply permuting the columns of the table based on these histograms can increase the sorting efficiency by 40%.

Tecnologia Diversão e humor

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

Histogram-Aware Sorting for Enhanced Word-Aligned Compression in Bitmap Indexes

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In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.

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We consider the ubiquitous technique of VByte compression, which represents each integer as a variable length sequence of bytes. The low 7 bits of each byte encode a portion of the integer, and the high bit of each byte is reserved as a continuation flag. This flag is set to 1 for all bytes except the last, and the decoding of each integer is complete when a byte with a high bit of 0 is encountered. VByte decoding can be a performance bottleneck especially when the unpredictable lengths of the encoded integers cause frequent branch mispredictions. Previous attempts to accelerate VByte decoding using SIMD vector instructions have been disappointing, prodding search engines such as Google to use more complicated but faster-to-decode formats for performance-critical code. Our decoder (Masked VByte) is 2 to 4 times faster than a conventional scalar VByte decoder, making the format once again competitive with regard to speed. Jeff Plaisance, Nathan Kurz, Daniel Lemire, Vectorized VByte Decoding, International Symposium on Web Algorithms 2015, 2015. http://arxiv.org/pdf/1503.07387.pdf

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CNIC Information System with Pakdata Cf In Pakistan

CNIC Information System with Pakdata Cf In Pakistan

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"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

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Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

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Platformless Horizons for Digital Adaptability

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Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

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TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

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DBX First Quarter 2024 Investor Presentation

DBX First Quarter 2024 Investor Presentation

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Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

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Introduction to Multilingual Retrieval Augmented Generation (RAG)

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Why Teams call analytics are critical to your entire business

Why Teams call analytics are critical to your entire business