1 / 19

NoSQL DB Benchmarking with high performance Networking solutions

NoSQL DB Benchmarking with high performance Networking solutions. WBDB, Xian, July 2013. Leading Supplier of End-to-End Interconnect Solutions . Storage Front / Back-End. Server / Compute. Switch / Gateway. Virtual Protocol Interconnect. Virtual Protocol Interconnect. 56 G IB & FCoIB.

lexine
Download Presentation

NoSQL DB Benchmarking with high performance Networking solutions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. NoSQL DB Benchmarking with high performance Networking solutions WBDB, Xian, July 2013

  2. Leading Supplier of End-to-End Interconnect Solutions Storage Front / Back-End Server / Compute Switch / Gateway Virtual Protocol Interconnect Virtual Protocol Interconnect 56G IB & FCoIB 56G InfiniBand 10/40/56GbE & FCoE 10/40/56GbE Fibre Channel Comprehensive End-to-End InfiniBand and Ethernet Portfolio ICs Adapter Cards Switches/Gateways Cables Host/Fabric Software

  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 Big Data Applications Require High Bandwidth and Low Latency Interconnect * Data Source: Intersect360 Research, 2012, IT and Data scientists survey

  4. Cassandra, Update Latency • Cassandra Database enables update capabilities • Latency factors • Commit-log settings • Workload

  5. Cassandra, Read Latency • Cassandra Database Read • Latency factors • Media used • Workload

  6. System Used for Cassandra Benchmark • 5 Nodes in the Ring • 64GB RAM • 8 x 8GB DDR3 1333MHz • 2 x E5-2670 • 8 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=2.11.500 • Switch SX1036 • OS: RH 6.3 • MLNX_OFED_LINUX-1.5.3 • Apache Cassandra 1.1.12, 2 seeds

  7. Unlocking the Power of SSDs In Hadoop Environment • 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 E-DFSIO, Showing the Power of SSD @ HDFS

  8. HBase Benchmarking, Update Latency • Updates are made to server memory • Extreme low latency for HBase • Java GC policy hurting on large throughput

  9. HBase Benchmarking, Read Latency • Hitting the media capabilities

  10. System Used for HBase Benchmarks • 4 Region servers, 1 Master, 3 Zookeeper quorum servers • 64GB RAM • 8 x 8GB DDR3 1333MHz • 2 x E5-2670 • 8 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=2.11.500 • Switch SX1036 • OS: RH 6.3 • MLNX_OFED_LINUX-1.5.3 • Apache Hbase 0.94.9, Zookeeper 3.4.5, Apache Hadoop 1.1.2

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

  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

  13. The Less Great Things in HDFS Metadata Server Failure Default 3x Replication Small files or latency sensitive 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.

  14. Local Disks – The Common Practice

  15. Other Distributed Storage Solution for Hadoop, Really?!

  16. OrangeFS as Hadoop Storage Solution

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

  18. CEPH as Hadoop Storage Solution • Generating lot of Interest since the Ceph kernel client was pulled into Linux kernel 2.6.34 • 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!!

More Related