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Identifier 000441652
Title Balancing Garbage Collection vs I/O Amplification using hybrid Key-Value Placement in LSM-based Key-Value Stores
Alternative Title Ισορροπώντας το κόστος της ανάκτησης χώρου και του αυξημένου I/O σε συστήματα κλειδιού-τιμής βασισμένα στο LSM δέντρο με την υβριδική τοποθέτηση ζευγαριών κλειδιού-τιμής
Author Ξανθάκης, Γιώργος Ι.
Thesis advisor Μπίλας, Άγγελος
Reviewer Μαγκούτης, Κωνσταντίνος
Πρατικάκης, Πολύβιος
Abstract Key-value (KV) separation is a technique that introduces randomness in the I/O access patterns to reduce I/O amplification in LSM-based key-value stores for fast storage devices (NVMe). KV separation has a significant drawback that makes it less attractive: Delete and especially update operations that are important in modern workloads result in frequent and expensive garbage collection (GC) in the value log. In this thesis, we design and implement Parallax, which proposes hybrid KV placement that reduces GC overhead significantly and maximizes the benefits of using a log. We first model the benefits of KV separation for different KV pair sizes. We use this model to classify KV pairs in three categories small, medium, and large. Then, Parallax uses different approaches for each KV category: It always places large values in a log and small values in place. For medium values it uses a mixed strategy that combines the benefits of using a log and eliminates GC overhead as follows: It places medium values in a log for all but the last few (typically one or two) levels in the LSM structure, where it performs a full compaction, merges values in place, and reclaims log space without the need for GC. We evaluate Parallax against RocksDB that places all values in place and BlobDB that always performs KV separation. We find that Parallax increases throughput by up to 12.4x and 17.83x, decreases I/O amplification by up to 27.1x and 26x, and increases CPU efficiency by up to 18.7x and 28x respectively, for all but scan-based YCSB workloads.
Language English
Subject Fast storage devices
KV separation
LSM tree
Issue date 2021-07-30
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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