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1 Vol. 0 No MyCassandra MyCassandra MyCassandra Cluster MyCassandra Cluster MyCassandra. Cassandra Cassandra 11.6% 6.53 A Cloud Storage Supporting both Read- Intensive and Write-Intensive Workloads Shunsuke Nakamura and Kazuyuki Shudo A cloud storage with persistence shows solid performance only with a read-heavy or writeheavy workload. There is a trade-off between read-optimized and write-optimized design of a cloud storage. It is dominated by the storage engine, which is a software component managing stored data on memory and disk. A storage engine is an independent component and it can be pluggable with an adequate software design though today s cloud storages are not always modular. We have developed a modular cloud storage MyCassandra to demonstrate that a cloud storage can be read-optimized and write-optimized by a modular design. Various types of storage engines can be introduced into MyCassandra and they determine with what workload the cloud storage performs well. MyCassandra proved that such a modular design enables a cloud storage to adapt to workloads. In this paper, we present a method to build a cloud storage performing well with both readheavy and write-heavy workloads. A MyCassandra Cluster consists of MyCassandra nodes with different types of storage engines, read-optimized, write-optimized and on-memory (readand-write-optimized). A query is routed to nodes which process the query efficiently. A cluster maintains consistency between data replicas with a quorum protocol. A MyCassandra Cluster showed comparable performance with the original Cassandra with write-heavy workloads, and it showed considerably better performance with read-heavy workloads. In comparison with Cassandra, its read latency was 11.6% and its read throughput was 6.53 times. 1. RDBMS NoSQL key-value store documentoriented database 1) GraphDB 2) Tokyo Institute of Technology RDBMS key-value master/worker 1
2 / Apache Cassandra 3)4) Apache HBase 5) Yahoo Sherpa 6) sharded MySQL 7) (MySQL sharding ) MyCassandra 8) MyCassandra MyCassandra Cluster My- Cassandra Apache Cassandra MyCassandra MyCassandra Cluster YCSB 2. I/O I/O I/O 9) 1 Consistent hashing Fig. 1 Consistent hashing / MyCassandra MyCassandra / MyCassandra Cluster Apache Cassandra MyCassandra. 2.1 Apache Cassandra Apache Cassandra Facebook Apache Project Consistent Hashing Cassandra consistent hashing ID 1 ID (key1) Q ID Q ID V consistent hashing
3 Vol. 0 No Cassandra Fig. 2 Write process in a Cassandra node 4 Fig. 4 MyCassandra Storage Engine Interface Storage Engine Interface of MyCassandra key SSTable 3 Cassandra Fig. 3 Write/Read performance for Cassandra gossip ID IP Bigtable Cassandra Log Structured Merge Tree 10) Google Bigtable 11) Commit Log MemTable SSTable ( 2) ( 1 ) Commit Log ( 2 ) MemTable ( 3 ) MemTable SSTable ( ) Commit Log I/O Commit Log SSTable 3 Cassandra Cassandra ID N Q V F N 2.2 MyCassandra MyCassandra Cassandra 4 Cassandra MyCassandra My- Cassandra Cassandra Storage Engine Interface (connect, put, get) My- Cassandra MyCassandra Bigtable MySQL Redis
4 Fig. 5 Write latency for each storage engine 6 Fig. 6 Read latency for each storage engine 5, 6 YCSB (4.1 ) 6 Bigtable MySQL Redis MyCassandra Bigtable Cassandra MySQL JDBC MySQL InnoDB SQL Redis 12) memcached key/value key-value store. Redis API 3. / MyCassandra Cluster My- Cassandra Cluster 7 MyCassandra Cluster Fig. 7 Basic idea of MyCassandra Cluster 7-(a) Cassandra quorum ( ) protocol Cassandra MyCassandra Cluster 3 N 3 N=3 R W 2 quorum MyCassandra Cluster (b)
5 Vol. 0 No Fig. 8 Host, node and storage engine placement on the ring MyCassandra Cassandra consistent hashing ( ) ID ID N 3 N 8 8-(3) 3 MyCassandra 2 8-(1) 1 1) 2). 1) 2) 8-(2) 1 ID 13) 1 9 (N=3 ) Fig. 9 Flows of read and write processes (in case of N = 3) N N 3.2 MyCassandra Cluster Cassandra gossip Cassandra gossip MyCassandra Cluster MyCassandra Cluster / quorum N W R /.
6 W ACK -a N W -b W ACK. R -a) -b R N R Cassandra ReadRepair Cassandra Cassandra MyCassandra Cluster gossip 4. MyCassandra Cluster : Bigtable (Cassandra 0.6.2) : MySQL ( alpha) : Redis (2.0.4) Yahoo! s Cloud Serving Benchmark (YCSB) 14) 4.1 YCSB YCSB Yahoo! Research YCSB YCSB YCSB 3 ( 1 ) load phase: ( 2 ) warmup phase: ( 3 ) transaction phase: 1 4 Write-Only Write-Heavy Read-Only Read-Heavy Write-Only Read-Only Zipf Zipf
7 Vol. 0 No YCSB Table 1 YCSB workloads Workload App Ex Read Write Write-Only Log 0% 100% Write-Heavy Session 50% 50% Read-Heavy Photo Tagging 95% 5% Read-Only Cache 100% 0% 2 3 N=3 MyCassandra Cluster =18 Cassandra ,000 MyCassandra Cluster Cassandra MyCassanra Cluster Cassandra Read-Heavy Cassandra 14.8% Read-Only 11.5% MyCassandra Cluster Write- Only Cassandra MyCassandra Cluster Cassandra Read-Heavy 1.49 Read-Only 6.53 Write-Heavy (read:write=50:50) 2 Table 2 Experiment parameters quorum key value Table ,000 (N,W,R)=(3,2,2) Byte 10 1KB 3 Machine configurations OS Linux fc14.x86 64 CPU 2.40 GHz Xeon E Memory 32GB RAM Disk 1TB SATA HDD 2 ( 4.2 ) JVM Java SE 6 Update Fig. 10 Write latency for each workload 11 Fig. 11 Read latency for each workload Cassandra MyCassandra Cluster Write-Heavy MyCassadra Cluster Cassandra MySQL 1) 2) 4.3 SSD I/O HDD. SSD SSD 1 HDD SSD I/O
8 Fig. 12 Throughput for each workload 13 Cassandra (HDD/SSD) Fig. 13 Throughput for Cassandra on HDD vs. SSD 4 HDD SSD Table 4 Disk configurations HDD SSD vender Western Digital Crucial model WDC WD1002FBYS CTFDDAC128MAG capacity 1TB 128GB seq. write 86,277 qps 96,401 qps seq. read 108,914 qps 216,099 qps rand. write 2,485 qps 29,045 qps rand. read 926 qps 21,751 qps 14 MyCassandra Cluster (HDD/SSD) Fig. 14 Throughput for MyCassandra Cluster on HDD vs. SSD IOzone 15) SSD 2 Cassandra CommitLog SSTable MyCassandra Cluster I/O Bigtable SSTable MySQL YCSB Cassandra Read-Only SSD HDD 3.37 MyCassandra Cluster Read-Heavy SSD HDD 1.50 SSD Cassandra HDD SSD Bigtable 5. MyCassandra Cluster 5.1 Cassandra Read- Repair HintedHandoff ReadRepair Cassandra MyCassandra Cluster HintedHandoff HintedHandoff 1) 2)ReadRepair 5.2 / Bigtable I/O 1 3
9 Vol. 0 No. 0 9 MyCassandra 7. / Cassandra Amazon EC2 Cassandra 6. Anvil 16) dtable Cloudy 17) Amazon Dynamo 13) Amazon key-value store BDB MySQL Dynamo MyCassandra 8) MyCassandra FD-Tree 18) SSD B+-tree LSM-tree SSD Fractal-Tree MySQL TokuDB 19) B-Trees I/O Fractal- Tree I/O I/O Fractal-Tree B-Trees LSM-Tree Cassandra / 1 / MyCassandra Cluster ) 10gen: MongoDB, (2009). 2) Neo Technology: Neo4j, (2010). 3) The Apache Software Foundation: Cassandra, (2010). 4) Lakshman, A. and Malik, P.: Cassandra - A Decentralized Structured Storage System, Proc. LADIS 09 (2009). 5) The Apache Software Foundation: HBase, (2010). 6) Cooper, B. F., Ramakrishnan, R., Srivastava, U., Silberstein, A., Bohannon, P., Jacobsen, H.,
10 Puz, N., Weaver, D. and Yerneni, R.: PNUTS: Yahoo! s Hosted Data Serving Platform, Proc. VLDB 08 (2008). 7) Oracle: MySQL, 8), :,, 2011-OS-116(-10) (2011). 9) Sears, R., Callaghan, M. and Brewer, E.: Rose: compressed, log-structured replication, Proceedings of the VLDB Endowment, Vol. 1 (1), pp (2008). 10) O Neil, P., Cheng, E., Gawlick, D. and O Neil, E.: The Log-Structed Merge-Tree (LSM-Tree), Acta Informatica, Vol. 33 (2), pp (1996). 11) Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A. and Gruber, R. E.: Bigtable: A Distributed Storage System for Structured Data, Proc. OSDI 06, Vol. 7, pp (2006). 12) Redis: Redis, (2010). 13) DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P. and Vogels, W.: Dynamo: Amazon s Highly Available Keyvalue Store, Proc. SOSP 07 (2007). 14) Cooper, B. F., Silberstein, A., Tam, E., Ramakrishnan, R. and Sears, R.: Benchmarking Cloud Serving Systems with YCSB, Proc. SOCC 10 (2010). 15) Don Capps: IOzone Filesystem Benchmark, 16) Mammarella, M., Hovsepian, S. and Kohler, E.: Modular Data Storage with Anvil, Proc. SOSP 09 (2009). 17) Kossmann, D., Kraska, T., Loesing, S., Merkli, S., Mittal, R. and Pfaffhauser, F.: Cloudy: A Modular Cloud Storage System, Proc. VLDB 10 (2010). 18) Li, Y., He, B., Yang, R. J., Qiong, L. and Ke, Y.: Tree Indexing on Solid State Drives, VLDB 10 (2010). 19) Tokutek: TokuDB, (2011).
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