TY - GEN
T1 - DoublefaceAd
T2 - 21st International Middleware Conference, Middleware 2020
AU - Zhang, Shungeng
AU - Wang, Qingyang
AU - Kanemasa, Yasuhiko
AU - Liu, Jianshu
AU - Pu, Calton
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - The broad adoption of fanout queries on distributed datastores has made asynchronous event-driven datastore drivers a natural choice due to reduced multithreading overhead. However, through extensive experiments using the latest datastore drivers (e.g., MongoDB, HBase, DynamoDB) and YCSB benchmark, we show that an asynchronous datastore driver can cause unexpected performance degradation especially in fanout-query scenarios. For example, the default MongoDB asynchronous driver adopts the latest Java asynchronous I/O library, which uses a hidden on-demand JVM level thread pool to process fanout query responses, causing a surprising multithreading overhead when the query response size is large. A second instance is the traditional wisdom of modular design of an application server and the embedded asynchronous datastore driver can cause an imbalanced workload between the two components due to lack of coordination, incurring frequent unnecessary system calls. To address the revealed problems, we introduce DoubleFaceAD-a new asynchronous datastore driver architecture that integrates the management of both upstream and downstream workload traffic through a few shared reactor threads, with fanout-query-aware priority-based scheduling to reduce the overall query waiting time. Our experimental results on two representative application scenarios (YCSB and DBLP) show DoubleFaceAD outperforms all other types of datastore drivers up to 34% on throughput and 1.9× faster on 99th percentile response time.
AB - The broad adoption of fanout queries on distributed datastores has made asynchronous event-driven datastore drivers a natural choice due to reduced multithreading overhead. However, through extensive experiments using the latest datastore drivers (e.g., MongoDB, HBase, DynamoDB) and YCSB benchmark, we show that an asynchronous datastore driver can cause unexpected performance degradation especially in fanout-query scenarios. For example, the default MongoDB asynchronous driver adopts the latest Java asynchronous I/O library, which uses a hidden on-demand JVM level thread pool to process fanout query responses, causing a surprising multithreading overhead when the query response size is large. A second instance is the traditional wisdom of modular design of an application server and the embedded asynchronous datastore driver can cause an imbalanced workload between the two components due to lack of coordination, incurring frequent unnecessary system calls. To address the revealed problems, we introduce DoubleFaceAD-a new asynchronous datastore driver architecture that integrates the management of both upstream and downstream workload traffic through a few shared reactor threads, with fanout-query-aware priority-based scheduling to reduce the overall query waiting time. Our experimental results on two representative application scenarios (YCSB and DBLP) show DoubleFaceAD outperforms all other types of datastore drivers up to 34% on throughput and 1.9× faster on 99th percentile response time.
KW - Asynchronous
KW - Distributed datastores
KW - Fanout queries
KW - Performance
UR - http://www.scopus.com/inward/record.url?scp=85098506610&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098506610&partnerID=8YFLogxK
U2 - 10.1145/3423211.3425684
DO - 10.1145/3423211.3425684
M3 - Conference contribution
AN - SCOPUS:85098506610
T3 - Middleware 2020 - Proceedings of the 2020 21st International Middleware Conference
SP - 430
EP - 444
BT - Middleware 2020 - Proceedings of the 2020 21st International Middleware Conference
PB - Association for Computing Machinery, Inc
Y2 - 7 December 2020 through 11 December 2020
ER -