© 2013 Mellanox Technologies 1 NoSQL DB Benchmarking with high performance Networking solutions WBDB, Xian, July 2013

© 2013 Mellanox Technologies 2 Leading Supplier of End-to-End Interconnect Solutions Host/Fabric SoftwareICsSwitches/GatewaysAdapter CardsCables Comprehensive End-to-End InfiniBand and Ethernet Portfolio Virtual Protocol Interconnect Storage Front / Back-End Server / Compute Switch / Gateway 56G IB & FCoIB 56G InfiniBand 10/40/56GbE & FCoE 10/40/56GbE Fibre Channel Virtual Protocol Interconnect

© 2013 Mellanox Technologies 3 Motivation to Accelerate Data Analytics  Data Analysis Requires Faster Network Hadoop Map Reduce Framework is a network intensive workload - Mapped data is shuffled between nodes in the cluster Data Replication - A high availability event triggers Multi-Tera of data movement  Provide Higher Data Value Expose SSD’s low latency capabilities Better server/CPU utilization * Data Source: Intersect360 Research, 2012, IT and Data scientists survey Big Data Applications Require High Bandwidth and Low Latency Interconnect

© 2013 Mellanox Technologies 4  Cassandra Database enables update capabilities  Latency factors Commit-log settings Workload Cassandra, Update Latency

© 2013 Mellanox Technologies 5  Cassandra Database Read  Latency factors Media used Workload Cassandra, Read Latency

© 2013 Mellanox Technologies 6  5 Nodes in the Ring  64GB RAM 8 x 8GB DDR3 1333MHz  2 x E Cores per socket  5 x Seagate® Constellation® ES SATA 6Gb/s 2TB Hard Drive 7200 RPM  NIC: Mellanox Technologies MT27500 Family [ConnectX-3] 10Gb Ethernet FW_VER=  Switch SX1036  OS: RH 6.3 MLNX_OFED_LINUX  Apache Cassandra , 2 seeds System Used for Cassandra Benchmark

© 2013 Mellanox Technologies 7  SSDs Become De-Facto standard in HDFS deployment Read capability is a critical factor for application performance  E-DFSIO, Part of Intel’s HiBench test suite, profiles aggregated throughput on the cluster 1GbE network impede any performance benefit from SSD deployment Unlocking the Power of SSDs In Hadoop Environment E-DFSIO, Showing the Power of HDFS

© 2013 Mellanox Technologies 8  Updates are made to server memory Extreme low latency for HBase - Java GC policy hurting on large throughput HBase Benchmarking, Update Latency

© 2013 Mellanox Technologies 9  Hitting the media capabilities HBase Benchmarking, Read Latency

© 2013 Mellanox Technologies 10  4 Region servers, 1 Master, 3 Zookeeper quorum servers  64GB RAM 8 x 8GB DDR3 1333MHz  2 x E Cores per socket  5 x Seagate® Constellation® ES SATA 6Gb/s 2TB Hard Drive 7200 RPM  NIC: Mellanox Technologies MT27500 Family [ConnectX-3] 10Gb Ethernet FW_VER=  Switch SX1036  OS: RH 6.3 MLNX_OFED_LINUX  Apache Hbase , Zookeeper 3.4.5, Apache Hadoop System Used for HBase Benchmarks

© 2013 Mellanox Technologies 11  EMC 1000-Node Analytic Platform  Accelerates Industry's Hadoop Development  24 PetaByte of physical storage  Mellanox VPI Solutions Test Drive Your Big Data 2X Faster Hadoop Job Run-Time Hadoop Acceleration Hadoop Acceleration High Throughput, Low Latency, RDMA Critical for ROI

© 2013 Mellanox Technologies 12 The Great Things in Hadoop Distributed File System HDFS is a block storage solution Block size can be modified to provide efficient solutions for very large files Inherent reliability, no need for high end storage solution to make sure data is there! Tuned for Hadoop work loads, write one and read many

© 2013 Mellanox Technologies 13 The Less Great Things in HDFS It’s hard to manage the different setting to get the right nodes into the right capabilities. Ingress and extraction of data requires additional tools. Small files or latency sensitiveDefault 3x Replication Metadata Server Failure

© 2013 Mellanox Technologies 14 Local Disks – The Common Practice

© 2013 Mellanox Technologies 15 Other Distributed Storage Solution for Hadoop, Really?!

© 2013 Mellanox Technologies 16 OrangeFS as Hadoop Storage Solution

© 2013 Mellanox Technologies 17 Lustre as Hadoop Storage Solution Source: Map/Reduce on Lustre, Hadoop Performance in HPC Environments, Nathan Rutman, Senior Architect, Networked Storage Solutions, Xyratex

© 2013 Mellanox Technologies 18 CEPH as Hadoop Storage Solution  Generating lot of Interest since the Ceph kernel client was pulled into Linux kernel Object-based parallel file system Scalable metadata server Each file can specify it’s own striping strategy and object size Automatic rebalancing of data with minimal data movement Hadoop module for integrating Ceph has been in development since 0.12 release  Benchmarks on Ceph is still WIP We are currently working on using running benchmarks on Ceph – Stay tuned!!

© 2013 Mellanox Technologies 19 Thank You