English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Parallel shared-nothing data management systems have been widely used toexploit a cluster of machines for efficient and scalable data processing. Whena cluster needs to be dynamically scaled in or out, data must be efficientlyrebalanced. Ideally, data rebalancing should have a low data movement cost,incur a small overhead on data ingestion and query processing, and be performedonline without blocking reads or writes. However, existing parallel datamanagement systems often exhibit certain limitations and drawbacks in terms ofefficient data rebalancing.  In this paper, we introduce DynaHash, an efficient data rebalancing approachthat combines dynamic bucketing with extendible hashing for shared-nothingOLAP-style parallel data management systems. DynaHash dynamically partitionsthe records into a number of buckets using extendible hashing to achieve good aload balance with small rebalancing costs. We further describe an end-to-endimplementation of the proposed approach inside an open-source Big DataManagement System (BDMS), Apache AsterixDB. Our implementation exploits theout-of-place update design of LSM-trees to efficiently rebalance data withoutblocking concurrent reads and writes. Finally, we have conducted performanceexperiments using the TPC-H benchmark and we present the results here.

DynaHash: Efficient Data Rebalancing in Apache AsterixDB (Extended Version)