Ring-Based NoSQL Systems: Reconfigurations and Key-Value Stores
"Explore the advancements in ring-based NoSQL systems, focusing on reconfiguration operations and key-value stores like Apache Cassandra and Dynamo. Learn how systems like Parqua facilitate online reconfigurations seamlessly. Dive into the phases of the Parqua reconfiguration process for efficient data management." (Total characters: 358)
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Presentation Transcript
Parqua Online Reconfigurations in Ring-Based NoSQL Systems Yosub Shin, Mainak Ghosh, Indranil Gupta University of Illinois at Urbana-Champaign 1 DPRG: http://dprg.cs.uiuc.edu
Ring-Based Key-value Stores Growing segment in Industry (21% CAGR, $3.4 B by 2020) Replacing traditional databases Fast and highly available Apache Cassandra (Facebook), Voldemort (LinkedIn), Riak (Basho), Dynamo (Amazon) Used widely Amazon shopping carts (Dynamo) Netflix (Cassandra) 2
Reconfiguration Operations Major pain point: Reconfiguration operations Change that affects a lot of data in tables at once Examples Change key, e.g., SSN Employee ID [MongoDB JIRA Server-4000] Scale out/in Today s solution: export DB, shut down DB, change configuration, re- import and re-start DB 3
Parqua System Allows ring-based key-value stores to perform reconfiguration in online manner Answering reads, writes and queries normally While transferring data in the background among servers and committing 4
Reconfiguration: Parqua Phases I. Prepare Phase Create new table Column family (CF) Old CF continues to serve reads/writes 6
Reconfiguration: Parqua Phases II. Execute Phase Each node Hashes its data Sends it to new node in new configuration Appropriate replica 7
Reconfiguration: Parqua Phases III. Commit Phase Atomically swap between old and new configs Schemas SSTables Writes locked briefly 8
Reconfiguration: Parqua Phases IV. Recovery Phase Nodes catch up with R/W received during Execute phase Activate new config. 9
Experiments Parqua: 1. Maintains high availability under reconfiguration multiple 9 s of availability 2. Has minimal impact on latency 50th percentile about the same Tail slightly longer 3. Scales with database size and cluster size Read (%) Write (%) Read only 99.17 Uniform 99.27 99.01 Latest 96.07 98.92 Zipfian 99.02 98.92 10 DPRG: http://dprg.cs.uiuc.edu
Experiments Effect on Read Latency 12
Experiments Scalability (Database size)
Experiments Scalability (Cluster size) 14
