TY - GEN
T1 - Lethe
T2 - 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
AU - Sarkar, Subhadeep
AU - Papon, Tarikul Islam
AU - Staratzis, Dimitris
AU - Athanassoulis, Manos
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/6/14
Y1 - 2020/6/14
N2 - Data-intensive applications fueled the evolution of log structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as a second-class citizen. A delete inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion. Further, LSM engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written. We highlight that fast persistent deletion without affecting read performance is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on the right-to-be-forgotten, and (iii) en masse cloud deployment of data systems that makes storage a precious resource. To address these challenges, in this paper, we build a new key-value storage engine, Lethe, that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show that Lethe supports any user-defined threshold for the delete persistence latency offering higher read throughput (1.17-1.4x) and lower space amplification (2.1-9.8x), with a modest increase in write amplification (between 4% and 25%). In addition, Lethe supports efficient range deletes on a secondary delete key by dropping entire data pages without sacrificing read performance nor employing a costly full tree merge.
AB - Data-intensive applications fueled the evolution of log structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as a second-class citizen. A delete inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion. Further, LSM engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written. We highlight that fast persistent deletion without affecting read performance is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on the right-to-be-forgotten, and (iii) en masse cloud deployment of data systems that makes storage a precious resource. To address these challenges, in this paper, we build a new key-value storage engine, Lethe, that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show that Lethe supports any user-defined threshold for the delete persistence latency offering higher read throughput (1.17-1.4x) and lower space amplification (2.1-9.8x), with a modest increase in write amplification (between 4% and 25%). In addition, Lethe supports efficient range deletes on a secondary delete key by dropping entire data pages without sacrificing read performance nor employing a costly full tree merge.
KW - compaction
KW - compaction policy
KW - compaction trigger
KW - delete key
KW - delete persistence
KW - delete persistence latency
KW - delete persistence threshold
KW - fade
KW - interweaved storage layout
KW - kiwi
KW - lethe
KW - LSM-trees
KW - out-of-place deletes
KW - sort key
KW - TTL-driven compaction
UR - https://www.scopus.com/pages/publications/85086273485
UR - https://www.scopus.com/pages/publications/85086273485#tab=citedBy
U2 - 10.1145/3318464.3389757
DO - 10.1145/3318464.3389757
M3 - Conference contribution
AN - SCOPUS:85086273485
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 893
EP - 908
BT - SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PB - Association for Computing Machinery
Y2 - 14 June 2020 through 19 June 2020
ER -