Benchmarking Cassandra on Violin

Size: px
Start display at page:

Download "Benchmarking Cassandra on Violin"

Transcription

1 Technical White Paper Report Technical Report Benchmarking Cassandra on Violin Accelerating Cassandra Performance and Reducing Read Latency With Violin Memory Flash-based Storage Arrays Version 1.0 Abstract This technical report presents the results of a benchmark test performed by Locomatix, Inc. on Apache s Cassandra open source distributed database management system utilizing the Violin Memory 6000 Series flash-based memory array for primary storage.

2 Contents 1 Introduction Cassandra Benchmark NoSQL Workload Performance Analysis Data Ingestion Results Observations Data Analysis Workload Observations Yahoo Cloud Serving Benchmark Insert Only Update Heavy 50% Reads and 50% Updates Read Heavy 95% Reads and 5% Updates Read Only 100% Reads Conclusion

3 1 Introduction Recently, Locomatix, Inc. performed benchmark testing on Apache s Cassandra open source distributed database management system. The benchmarks consisted of a series of comparisons between a hard disk drive (HDD) based platform and one utilizing a single Violin flash Memory Array. The testing confirmed that Violin flash Memory Arrays offer significant performance gains in Cassandra environments. Most noteworthy was the 10-40x latency reduction achieved during mixed read/write workloads. The Cassandra benchmarks demonstrate the potential of storage at the speed of memory and also the limitations of both mechanical disks and current software architectures designed around HDD behaviors. When the data blocks were small, bandwidth requirements relatively low, and traffic patterns sequential, Cassandra performance differences between the Violin flash Memory Array and the HDD platform were predictably small. But as bandwidth requirements increased and especially as the input/output (I/O) patterns grew more random, disk performance suffered and soon stalled, while Violin flash Memory Arrays delivered. 2 Cassandra Cassandra is a popular NoSQL distributed key-value store designed to scale to a large (100s to 1000s) number of nodes and have no single point of failure. It was initially developed at Facebook to deal with terabytes of inbox system data. From a technology perspective, Cassandra uses three key concepts to achieve high performance: columnoriented data layout, data de-normalization, and distributed hashing. Collectively, these allow it to perform O(1) lookup on simple key-value operations, scale near-linearly on single pass based analytic algorithms, and achieve high speeds when appending new data. The fast data append feature of Cassandra distinguishes it from other similar systems and thus Cassandra is especially interesting as a data management solution for analytic applications that need to ingest large amounts of data very quickly. In Cassandra, the data is organized as a Column Family. A unique key identifies each data item in a column family. Several column families can co-exist in a single key space. A column family can be loosely compared to a table in a relational database management system (RDMS) and key space to a database in RDMS, as illustrated in Figure 1. 3 Benchmark Goals The main goal of the benchmark efforts was to compare and analyze the performance of Cassandra on two Figure 1: Cassandra Data Model platforms: a system with traditional HDD and a more modern system consisting of a single Violin flash Memory Array. 3

4 The first platform was a disk-based system with four computing nodes. The configuration for this system is shown in Figure 2 and called disk. The HDD-based cluster consisted of four computing nodes, each with two Intel Xeon Quad Core processors running at 2.53 GHz with hyper-threading enabled. Each node had 24 GB of DRAM and a total of eight 1TB SATA disks. One disk at each node was allocated exclusively to the Cassandra commit log so that the writes to the log were sequential; the other seven disks at each node were allocated for data. All the computing nodes on the HDD platform were running Debian/6.04 Squeeze Linux distribution. Nodes communicated with each other using a LAN switch operating at 10 Gb/sec. The second platform was another four-node system Figure 2: Disk-based System Used for Benchmark attached to a Violin 6616 Series flash Memory Array. The Violin 6616 is based on Single Level Cell (SLC) flash memory and optimized for high I/O per second (IOPS) and low latency while still providing robust RAID protection, ultra-low response times, high transaction rates, and realtime queries of large datasets. The Violin 6616-based cluster consisted of four computing nodes. Each node had two Intel Xeon Quad Core processors running at 2.53 GHz with hyper-threading and 36 GB of DRAM. All four nodes were connected directly to the same Violin 6616 Series Array via PCI- Express. Each of the nodes was running Ubuntu Lucid Linux Distribution. A 10 Gb/s LAN switch provided communication between nodes. This platform, shown in Figure 3, is referred to as flash. Figure 3: Flash-based System Used for Benchmark 4

5 Table 1 summarizes the key system parameter values used in the benchmark. 4 NoSQL Workload The benchmark consisted of two simple workloads. The first, called data ingestion workload, was to insert data into the system and then measure the rate at which data could be ingested. This workload is representative of real-time systems with extremely fast data update rates, such as ingesting network activity log data for network situational awareness applications or ingesting social media data in real-time for real-time sensing of social trends. The second workload, called data analysis workload, consisted of queries on the ingested data where Table 1: Cassandra Benchmark Environment the amount of data retrieved in each query is varied. This workload represents analysis of data in real-time environments, such as identifying the cause of network congestion by scanning a large log of network activity data or scanning the last X minutes of social media data in a specific geographical area to identify the root causes behind an emerging trend/pattern. 5 Performance Analysis 5.1 Data Ingestion Results In these experiments, data was ingested continuously one record at a time. Record size was varied from a few tens of bytes to a few KBs. The data was pumped into the server nodes from five client nodes, with each node running a load program that spawned 100 threads. This method simulates 500 clients simultaneously pumping data into a server backend that has to ingest the data. Each of the five client nodes had two Intel Xeon Quad Core processors running at 2.53 GHz and 24 GB of DRAM memory. All the nodes were running Debian/6.04 Squeeze Linux. This configuration is shown in Figure 4. Figure 4: Benchmark Environment with Data Nodes 5

6 For these experiments, the data was replicated only once and compaction was disabled. Several Cassandra parameters were tuned, as recommended by Cassandra s documentation (and based on our experience) to extract the maximum performance. These parameters are also shown in Table 1 above. These experiments were repeated for records of size 50 bytes, 100 bytes, 1000 bytes, 5000 bytes, and bytes. The throughput was measured and is plotted in Figure Observations Figure 5: Data Ingestion Throughput The Violin flash Memory Array enabled data ingest performance gains of 30% to over 280% compared to the disk platform. In terms of performance trends, as the record size increased, the disk system s throughput decreased predictably. But the throughput for the flash system actually increased at first, even as the record size also increased, before the trend changed and the inserts/second began to decrease. To help understand the trends better, the CPU utilization per node and storage bandwidth for each of the record sizes are plotted in Table 2. With smaller record sizes, we could have expected both the disk and the flash systems to perform at the same level because the write bandwidth is smaller than 100 MB/sec, the typical maximum sequential bandwidth provided by SATA disks. However, this is not the case because of the commit log processing in Cassandra. A copy of all the data must pass through the commit log before being periodically flushed to storage. With disks, the throughput is limited by how fast the commit log thread can compute the Cyclic Redundancy Check (CRC) and flush the commit log. But with flash, more commit logs can be flushed because solid state storage supports more write bandwidth. Table 2: Data Ingestion Write IOPS and CPU Utilization Because of its greater performance capabilities, one would have expected the flash system s throughput to be much higher than what is shown above, since a direct attached Violin 6616 flash Memory Array could provide as much as 1.2 GB/sec per node. However, because all the data must go through a single commit log thread, the CRC computation saturates the CPU core running this thread, thereby limiting 6

7 throughput. Nonetheless, even with Cassandra s architecture limitations, the Violin flash Memory Array achieves higher write IOPS and CPU utilization while nearly doubling the actual record insert performance, and more, in many cases. 6 Data Analysis Workload In these experiments, Cassandra performance was evaluated when executing queries with a focus on point queries. Point queries retrieve the records that satisfy a predicate; the predicate checks if the value of an attribute is equal to the constant specified in the query. Logically, such queries must scan the data to look for all records that match the specified predicate. The number of records retrieved by the query depends on the selectivity of the attribute. If the selectivity is high, then the final result will have a small number of output data items, and vice versa. Experiments were run with different selectivity factors. The physical evaluation of these queries is carried out by either scanning all the records in the table or by using an index on the attribute to retrieve the qualifying records. A user level index was created by introducing a separate column family that used the distinct value of the attribute as the key and the entire record as value. In data processing parlance, this is a de-normalized schema with a materialized view on the index attribute. This scheme presents the most efficient way to answer these queries using an index (though at a higher storage footprint cost). To evaluate these queries, data was loaded into a single column family. Some of the attributes in these records were chosen to have repeating values to ensure certain selectivity. For data analysis workload experiments, 175 million rows were ingested that matched the aforementioned schema with a selectivity query run. The SQL equivalent of this query is: SELECT * FROM data WHERE attr_0001 = 1 For each experiment, 100,000 queries were run, presented to the cluster from a query node running a client program. The client program can be configured to vary the number of threads. Varying the number of threads translates to the number of concurrent queries presented to the system at any given instance. Figure 6: Benchmark Environment with Query Node Figure 6 shows the benchmark environment for presenting queries to the Cassandra cluster. In order to eliminate operating system caching effects, the file system buffers were cleared before each experiment, and each query within the experiment was provided with a distinct value for attr_0001 that was never repeated. The results are plotted in Figures 7 and 8 for two different selectivity factors of % and 0.001% (tagged with the labels and respectively). A selectivity factor of % implies that the query selects one record for every one million input records in the database. 7

8 6.1 Observations For the disk system, as the number of concurrent queries increased the response time to complete the query also increased sharply. The main reason for the sharp increase was an increase in the number of random seeks, because index rows for particular distinct values are scattered across all the index column family files in disk, and each file must be read to retrieve its portion of the result. For flash, the increase was much more linear until 400 queries, after which the response time increased gradually. Because flash can offer more IOPS and the latency of an I/O is in the order of microseconds, it is able to satisfy the I/O requests for the queries quickly, leading to reduced latency. However, after 400 queries, CPU usage starts increasing and at 800 queries some of the queries were consuming 95% of the CPU. On the average, flash utilizes more CPU as compared to disks. This is a function of how fast the storage can provide the data, i.e. read latency. Aggregate CPU utilization for disk stalled at around 35% while the flash continued up to as much as 60%, and ultimately it was CPU saturation that limited Cassandra performance on the flash platform, not the capabilities of the storage itself. Figure 7: Concurrent Queries vs Latency (.0001) Figure 8: Concurrent Queries vs Latency (.001) 7 Yahoo Cloud Serving Benchmark To provide more benchmark breadth, as well as inject more realistic testing environments into the mix, Yahoo Cloud Serving Benchmark (YCSB) was added. YCSB is a new benchmark for web serving workloads characterized by traffic patterns seen in new Web 2.0 applications such as social networking or gaming. YCSB has been used in the past to compare the performance of various NoSQL systems and also of SQL systems that target these newer applications. It provides a set of workloads, each representing a particular mix of 8

9 read/write operations, data sizes, request distributions, and so on. Performance was measured for various workloads: insert only, update heavy, read heavy, and read only. 7.1 Insert Only For this experiment, 175 million records were inserted from five data nodes. Each node used 100 threads to insert 35 million records. The record sizes were varied from 50 bytes to 10,000 bytes and measured the latency and throughput. The results are shown in Figure 9. Figure 9: YCSB Insert Only Record Size vs Latency and Throughput As the size of the record increased, the latency increased and throughput fell, as expected, because when the record size increases, the amount of data passing through Cassandra and saved to storage increases. At smaller record sizes, flash and disk experienced similar latency and achieved similar throughput because Cassandra writes are sequential and disks hadn t yet reached their peak sequential performance of 100 MB/sec. But at higher record sizes, the disk performance degraded and eventually stalled all together because all the records had to pass through the single commit log thread. This thread, in the disk platform, was limited by the sequential write bandwidth. On the other hand, on the flash platform the commit log thread was not limited by bandwidth. It was instead limited by the CRC computation, because flash provides so much more bandwidth/iops capability than HDD. For the remainder of the experiments, 175 million records were loaded with a record size of 1000 bytes. Each record consisted of 10 fields and all fields were the same size. An operation could either read a field of a record or update a record by replacing the value of a field. Before each experiment, the file system caches were flushed to eliminate reading from cache. This workload was started immediately after inserting data, which meant that Cassandra would do compaction, the process of merging multiple insert files into fewer ones to improve read performance. Compaction does increase CPU costs for merging multiple files, and it utilizes I/O for sequential reads and writes. 7.2 Update Heavy 50% Reads and 50% Updates In this experiment, the workload was equally divided between reads and updates. A total of 500,000 operations were executed for each experiment, which was repeated for different target throughputs. The extraordinary results are shown in Figure 10. In this figure, the x-axis represents a gradual increase in the application demand/throughput and the y-axis represents the observed performance. 9

10 As can be seen in the figure, when the throughput is low the flash read latency is 10-30x less than the disk read latency, even in mixed workloads. As the throughput increased, the disk platform was not able to push beyond Figure 10: YCSB Update Heavy Read and Write Latency vs Throughput the threshold of 1000 operations/second. Even if the user provided a target throughput higher than 1000 ops/sec, Cassandra would eventually process the workload but the actual reported ops/sec would never rise beyond 1000 ops/sec. Therefore, no numbers for disk were included after the ceiling of 1000 ops/sec. On the other hand, flash continued to outperform disk and maxed out at a throughput of 10,000 ops/sec. At a target throughput of 1000 ops/sec, the read IOPS for both flash and disk are roughly equal. If that is the case, why is the read latency in disk more than 10x higher than in flash? It is because of the high cost of random seeks. At a target throughput of 1000 ops/sec, each operation issues around four random read I/Os. Since the cost of a random seek can be as high as 30ms, a total of 120ms was required to retrieve data from disk, which dominated the overall read latency. Furthermore, at this target throughput, disks were saturated, attaining their highest levels of performance around IOPS. But in the case of flash, as the throughput increased, the number of read IOPS kept pace and thus the Violin 6616 served the data requests very quickly, because it can handle more than 1M IOPS. In the case of write operations, the latency of flash was still 2-3x lower than disk. This is because all writes in Cassandra are sequential and the cost of sequential writes to the disk platform are 1/10 of the cost of random seeks. Because Cassandra batches several writes into 32 MB-sized memtables, the cost of a write is amortized over several write operations and thus the write latency in the disk system is reasonable. 7.3 Read Heavy 95% Reads and 5% Updates This experiment evaluated the performance of a workload where read operations were very frequent and update operations occurred only occasionally. More specifically, the workload had 95% reads and 5% updates. The results for this workload are shown in Figure 11. From this figure, it can be observed that the read latency of flash was 30-40x less than disk. Again, blame the poor performance of disks on the high cost of random seeks. The read rate for disk at a target throughput of 1000 ops/sec was approximately 4000 IOPS. Hence each query costs four random seeks, which pegs the minimum read latency at 120 ms. 10

11 On the other hand, the write latency for disk was still 5x higher than the flash write latency. Furthermore, the latency does not seem to vary as the target throughput increases. Since the number of write operations is not very large for a target throughput, most of the writes are buffered in memory and periodic flushes of the Figure 11: YCSB Read Heavy Read and Write Latency vs Throughput memory buffers is triggered. Since the periodic flush occurs every one second and the data being written varies only between 50K to 1MB, the write latency does not show much variation. The difference in latency between disk and flash is essentially the cost of the actual writes, which in the case of the disk system is a few milliseconds for sequential I/O and for the flash system is less than one millisecond. 7.4 Read Only 100% Reads This experiment presented a workload consisting only of read operations. The read operation either reads a single field or all the fields of a record. Before each experiment, file cache buffers were flushed. The read latency and read IOPS are shown in Figure 12. Figure 12: YCSB Read Only Read Latency and IOPS vs Throughput 11

12 The observations here are similar to those for the read heavy workload. Flash provides sustained performance for higher targeted throughputs. For disks, the latency suffers from the cost of random seeks, as is evident from the number of read IOPS. 8 Conclusion Benchmark results of Cassandra measured on a traditional HDD-based platform compared to a platform anchored by a single Violin flash Memory Array demonstrate that flash can achieve significant performance improvements in all workload environments. These studies show that flash performs 10-40x faster than disks for queries with different selectivities. And for data loading, flash performs anywhere from 30% to nearly 300% better than disk. Even more interesting, the Cassandra benchmark studies suggest that as software architectures evolve to better utilize the much higher bandwidth and much lower latency of solid state storage devices such as Violin flash Memory Arrays, much greater overall performance gains can be expected. 12

13 Benchmarking Cassandra On Violin About Violin Memory Violin Memory is pioneering a new class of high-performance flash-based storage systems that are designed to bring storage performance in-line with high-speed applications, servers and networks. Violin Flash Memory Arrays are specifically designed at each level of the system architecture starting with memory and optimized through the array to leverage the inherent capabilities of flash memory and meet the sustained highperformance requirements of business critical applications, virtualized environments and Big Data solutions in enterprise data centers. Specifically designed for sustained performance with high reliability, Violin s Flash Memory Arrays can scale to hundreds of terabytes and millions of IOPS with low, predictable latency. Founded in 2005, Violin Memory is headquartered in Mountain View, California. For more information about Violin Memory products, visit Violin Memory. All rights reserved. All other trademarks and copyrights are property of their respective owners. Information provided in this paper may be subject to change. For more information, visit. vmem-13q1-tr-casandra-r1-uslet-en

Benchmarking Hadoop & HBase on Violin

Benchmarking Hadoop & HBase on Violin Technical White Paper Report Technical Report Benchmarking Hadoop & HBase on Violin Harnessing Big Data Analytics at the Speed of Memory Version 1.0 Abstract The purpose of benchmarking is to show advantages

More information

Oracle NoSQL Database and SanDisk Offer Cost-Effective Extreme Performance for Big Data

Oracle NoSQL Database and SanDisk Offer Cost-Effective Extreme Performance for Big Data WHITE PAPER Oracle NoSQL Database and SanDisk Offer Cost-Effective Extreme Performance for Big Data 951 SanDisk Drive, Milpitas, CA 95035 www.sandisk.com Table of Contents Abstract... 3 What Is Big Data?...

More information

Accelerating Enterprise Applications and Reducing TCO with SanDisk ZetaScale Software

Accelerating Enterprise Applications and Reducing TCO with SanDisk ZetaScale Software WHITEPAPER Accelerating Enterprise Applications and Reducing TCO with SanDisk ZetaScale Software SanDisk ZetaScale software unlocks the full benefits of flash for In-Memory Compute and NoSQL applications

More information

Accelerating Cassandra Workloads using SanDisk Solid State Drives

Accelerating Cassandra Workloads using SanDisk Solid State Drives WHITE PAPER Accelerating Cassandra Workloads using SanDisk Solid State Drives February 2015 951 SanDisk Drive, Milpitas, CA 95035 2015 SanDIsk Corporation. All rights reserved www.sandisk.com Table of

More information

Cisco UCS and Fusion- io take Big Data workloads to extreme performance in a small footprint: A case study with Oracle NoSQL database

Cisco UCS and Fusion- io take Big Data workloads to extreme performance in a small footprint: A case study with Oracle NoSQL database Cisco UCS and Fusion- io take Big Data workloads to extreme performance in a small footprint: A case study with Oracle NoSQL database Built up on Cisco s big data common platform architecture (CPA), a

More information

White Paper October 2014. Scaling MySQL Deployments Using HGST FlashMAX PCIe SSDs. An HGST and Percona Collaborative Whitepaper

White Paper October 2014. Scaling MySQL Deployments Using HGST FlashMAX PCIe SSDs. An HGST and Percona Collaborative Whitepaper White Paper October 2014 Scaling MySQL Deployments Using HGST FlashMAX PCIe SSDs An HGST and Percona Collaborative Whitepaper Table of Contents Introduction The Challenge Read Workload Scaling...1 Write

More information

WITH A FUSION POWERED SQL SERVER 2014 IN-MEMORY OLTP DATABASE

WITH A FUSION POWERED SQL SERVER 2014 IN-MEMORY OLTP DATABASE WITH A FUSION POWERED SQL SERVER 2014 IN-MEMORY OLTP DATABASE 1 W W W. F U S I ON I O.COM Table of Contents Table of Contents... 2 Executive Summary... 3 Introduction: In-Memory Meets iomemory... 4 What

More information

EMC XtremSF: Delivering Next Generation Storage Performance for SQL Server

EMC XtremSF: Delivering Next Generation Storage Performance for SQL Server White Paper EMC XtremSF: Delivering Next Generation Storage Performance for SQL Server Abstract This white paper addresses the challenges currently facing business executives to store and process the growing

More information

Best Practices for Optimizing SQL Server Database Performance with the LSI WarpDrive Acceleration Card

Best Practices for Optimizing SQL Server Database Performance with the LSI WarpDrive Acceleration Card Best Practices for Optimizing SQL Server Database Performance with the LSI WarpDrive Acceleration Card Version 1.0 April 2011 DB15-000761-00 Revision History Version and Date Version 1.0, April 2011 Initial

More information

Improve Business Productivity and User Experience with a SanDisk Powered SQL Server 2014 In-Memory OLTP Database

Improve Business Productivity and User Experience with a SanDisk Powered SQL Server 2014 In-Memory OLTP Database WHITE PAPER Improve Business Productivity and User Experience with a SanDisk Powered SQL Server 2014 In-Memory OLTP Database 951 SanDisk Drive, Milpitas, CA 95035 www.sandisk.com Table of Contents Executive

More information

Flash Memory Arrays Enabling the Virtualized Data Center. July 2010

Flash Memory Arrays Enabling the Virtualized Data Center. July 2010 Flash Memory Arrays Enabling the Virtualized Data Center July 2010 2 Flash Memory Arrays Enabling the Virtualized Data Center This White Paper describes a new product category, the flash Memory Array,

More information

DIABLO TECHNOLOGIES MEMORY CHANNEL STORAGE AND VMWARE VIRTUAL SAN : VDI ACCELERATION

DIABLO TECHNOLOGIES MEMORY CHANNEL STORAGE AND VMWARE VIRTUAL SAN : VDI ACCELERATION DIABLO TECHNOLOGIES MEMORY CHANNEL STORAGE AND VMWARE VIRTUAL SAN : VDI ACCELERATION A DIABLO WHITE PAPER AUGUST 2014 Ricky Trigalo Director of Business Development Virtualization, Diablo Technologies

More information

EMC XtremSF: Delivering Next Generation Performance for Oracle Database

EMC XtremSF: Delivering Next Generation Performance for Oracle Database White Paper EMC XtremSF: Delivering Next Generation Performance for Oracle Database Abstract This white paper addresses the challenges currently facing business executives to store and process the growing

More information

Accelerating Server Storage Performance on Lenovo ThinkServer

Accelerating Server Storage Performance on Lenovo ThinkServer Accelerating Server Storage Performance on Lenovo ThinkServer Lenovo Enterprise Product Group April 214 Copyright Lenovo 214 LENOVO PROVIDES THIS PUBLICATION AS IS WITHOUT WARRANTY OF ANY KIND, EITHER

More information

Speeding Up Cloud/Server Applications Using Flash Memory

Speeding Up Cloud/Server Applications Using Flash Memory Speeding Up Cloud/Server Applications Using Flash Memory Sudipta Sengupta Microsoft Research, Redmond, WA, USA Contains work that is joint with B. Debnath (Univ. of Minnesota) and J. Li (Microsoft Research,

More information

Can the Elephants Handle the NoSQL Onslaught?

Can the Elephants Handle the NoSQL Onslaught? Can the Elephants Handle the NoSQL Onslaught? Avrilia Floratou, Nikhil Teletia David J. DeWitt, Jignesh M. Patel, Donghui Zhang University of Wisconsin-Madison Microsoft Jim Gray Systems Lab Presented

More information

Accelerate SQL Server 2014 AlwaysOn Availability Groups with Seagate. Nytro Flash Accelerator Cards

Accelerate SQL Server 2014 AlwaysOn Availability Groups with Seagate. Nytro Flash Accelerator Cards Accelerate SQL Server 2014 AlwaysOn Availability Groups with Seagate Nytro Flash Accelerator Cards Technology Paper Authored by: Mark Pokorny, Database Engineer, Seagate Overview SQL Server 2014 provides

More information

Lab Evaluation of NetApp Hybrid Array with Flash Pool Technology

Lab Evaluation of NetApp Hybrid Array with Flash Pool Technology Lab Evaluation of NetApp Hybrid Array with Flash Pool Technology Evaluation report prepared under contract with NetApp Introduction As flash storage options proliferate and become accepted in the enterprise,

More information

BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB

BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB BENCHMARKING CLOUD DATABASES CASE STUDY on HBASE, HADOOP and CASSANDRA USING YCSB Planet Size Data!? Gartner s 10 key IT trends for 2012 unstructured data will grow some 80% over the course of the next

More information

Data Center Storage Solutions

Data Center Storage Solutions Data Center Storage Solutions Enterprise software, appliance and hardware solutions you can trust When it comes to storage, most enterprises seek the same things: predictable performance, trusted reliability

More information

DataStax Enterprise, powered by Apache Cassandra (TM)

DataStax Enterprise, powered by Apache Cassandra (TM) PerfAccel (TM) Performance Benchmark on Amazon: DataStax Enterprise, powered by Apache Cassandra (TM) Disclaimer: All of the documentation provided in this document, is copyright Datagres Technologies

More information

Evaluation Report: Accelerating SQL Server Database Performance with the Lenovo Storage S3200 SAN Array

Evaluation Report: Accelerating SQL Server Database Performance with the Lenovo Storage S3200 SAN Array Evaluation Report: Accelerating SQL Server Database Performance with the Lenovo Storage S3200 SAN Array Evaluation report prepared under contract with Lenovo Executive Summary Even with the price of flash

More information

Intel Solid- State Drive Data Center P3700 Series NVMe Hybrid Storage Performance

Intel Solid- State Drive Data Center P3700 Series NVMe Hybrid Storage Performance Intel Solid- State Drive Data Center P3700 Series NVMe Hybrid Storage Performance Hybrid Storage Performance Gains for IOPS and Bandwidth Utilizing Colfax Servers and Enmotus FuzeDrive Software NVMe Hybrid

More information

HP SN1000E 16 Gb Fibre Channel HBA Evaluation

HP SN1000E 16 Gb Fibre Channel HBA Evaluation HP SN1000E 16 Gb Fibre Channel HBA Evaluation Evaluation report prepared under contract with Emulex Executive Summary The computing industry is experiencing an increasing demand for storage performance

More information

Removing Performance Bottlenecks in Databases with Red Hat Enterprise Linux and Violin Memory Flash Storage Arrays. Red Hat Performance Engineering

Removing Performance Bottlenecks in Databases with Red Hat Enterprise Linux and Violin Memory Flash Storage Arrays. Red Hat Performance Engineering Removing Performance Bottlenecks in Databases with Red Hat Enterprise Linux and Violin Memory Flash Storage Arrays Red Hat Performance Engineering Version 1.0 August 2013 1801 Varsity Drive Raleigh NC

More information

III Big Data Technologies

III Big Data Technologies III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution

More information

Comprehending the Tradeoffs between Deploying Oracle Database on RAID 5 and RAID 10 Storage Configurations. Database Solutions Engineering

Comprehending the Tradeoffs between Deploying Oracle Database on RAID 5 and RAID 10 Storage Configurations. Database Solutions Engineering Comprehending the Tradeoffs between Deploying Oracle Database on RAID 5 and RAID 10 Storage Configurations A Dell Technical White Paper Database Solutions Engineering By Sudhansu Sekhar and Raghunatha

More information

Maximum performance, minimal risk for data warehousing

Maximum performance, minimal risk for data warehousing SYSTEM X SERVERS SOLUTION BRIEF Maximum performance, minimal risk for data warehousing Microsoft Data Warehouse Fast Track for SQL Server 2014 on System x3850 X6 (95TB) The rapid growth of technology has

More information

Everything you need to know about flash storage performance

Everything you need to know about flash storage performance Everything you need to know about flash storage performance The unique characteristics of flash make performance validation testing immensely challenging and critically important; follow these best practices

More information

EMC Unified Storage for Microsoft SQL Server 2008

EMC Unified Storage for Microsoft SQL Server 2008 EMC Unified Storage for Microsoft SQL Server 2008 Enabled by EMC CLARiiON and EMC FAST Cache Reference Copyright 2010 EMC Corporation. All rights reserved. Published October, 2010 EMC believes the information

More information

JVM Performance Study Comparing Oracle HotSpot and Azul Zing Using Apache Cassandra

JVM Performance Study Comparing Oracle HotSpot and Azul Zing Using Apache Cassandra JVM Performance Study Comparing Oracle HotSpot and Azul Zing Using Apache Cassandra January 2014 Legal Notices Apache Cassandra, Spark and Solr and their respective logos are trademarks or registered trademarks

More information

Performance Characteristics of VMFS and RDM VMware ESX Server 3.0.1

Performance Characteristics of VMFS and RDM VMware ESX Server 3.0.1 Performance Study Performance Characteristics of and RDM VMware ESX Server 3.0.1 VMware ESX Server offers three choices for managing disk access in a virtual machine VMware Virtual Machine File System

More information

NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB, Cassandra, and MongoDB

NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB, Cassandra, and MongoDB bankmark UG (haftungsbeschränkt) Bahnhofstraße 1 9432 Passau Germany www.bankmark.de info@bankmark.de T +49 851 25 49 49 F +49 851 25 49 499 NoSQL Performance Test In-Memory Performance Comparison of SequoiaDB,

More information

Using Synology SSD Technology to Enhance System Performance Synology Inc.

Using Synology SSD Technology to Enhance System Performance Synology Inc. Using Synology SSD Technology to Enhance System Performance Synology Inc. Synology_SSD_Cache_WP_ 20140512 Table of Contents Chapter 1: Enterprise Challenges and SSD Cache as Solution Enterprise Challenges...

More information

SSD Performance Tips: Avoid The Write Cliff

SSD Performance Tips: Avoid The Write Cliff ebook 100% KBs/sec 12% GBs Written SSD Performance Tips: Avoid The Write Cliff An Inexpensive and Highly Effective Method to Keep SSD Performance at 100% Through Content Locality Caching Share this ebook

More information

How to Choose Between Hadoop, NoSQL and RDBMS

How to Choose Between Hadoop, NoSQL and RDBMS How to Choose Between Hadoop, NoSQL and RDBMS Keywords: Jean-Pierre Dijcks Oracle Redwood City, CA, USA Big Data, Hadoop, NoSQL Database, Relational Database, SQL, Security, Performance Introduction A

More information

EMC XTREMIO EXECUTIVE OVERVIEW

EMC XTREMIO EXECUTIVE OVERVIEW EMC XTREMIO EXECUTIVE OVERVIEW COMPANY BACKGROUND XtremIO develops enterprise data storage systems based completely on random access media such as flash solid-state drives (SSDs). By leveraging the underlying

More information

Top Ten Questions. to Ask Your Primary Storage Provider About Their Data Efficiency. May 2014. Copyright 2014 Permabit Technology Corporation

Top Ten Questions. to Ask Your Primary Storage Provider About Their Data Efficiency. May 2014. Copyright 2014 Permabit Technology Corporation Top Ten Questions to Ask Your Primary Storage Provider About Their Data Efficiency May 2014 Copyright 2014 Permabit Technology Corporation Introduction The value of data efficiency technologies, namely

More information

Comparing Couchbase Server with MongoDB 3.0: Benchmark Results and Analysis

Comparing Couchbase Server with MongoDB 3.0: Benchmark Results and Analysis Comparing Couchbase Server 3.0.2 with MongoDB 3.0: Benchmark Results and Analysis Composed by Avalon Consulting, LLC Introduction The data needs of today s Enterprise require a special set of tools. At

More information

Scaling Objectivity Database Performance with Panasas Scale-Out NAS Storage

Scaling Objectivity Database Performance with Panasas Scale-Out NAS Storage White Paper Scaling Objectivity Database Performance with Panasas Scale-Out NAS Storage A Benchmark Report August 211 Background Objectivity/DB uses a powerful distributed processing architecture to manage

More information

Virtualization of the MS Exchange Server Environment

Virtualization of the MS Exchange Server Environment MS Exchange Server Acceleration Maximizing Users in a Virtualized Environment with Flash-Powered Consolidation Allon Cohen, PhD OCZ Technology Group Introduction Microsoft (MS) Exchange Server is one of

More information

Benchmarking Couchbase Server for Interactive Applications. By Alexey Diomin and Kirill Grigorchuk

Benchmarking Couchbase Server for Interactive Applications. By Alexey Diomin and Kirill Grigorchuk Benchmarking Couchbase Server for Interactive Applications By Alexey Diomin and Kirill Grigorchuk Contents 1. Introduction... 3 2. A brief overview of Cassandra, MongoDB, and Couchbase... 3 3. Key criteria

More information

The Flash Transformed Data Center & the Unlimited Future of Flash John Scaramuzzo Sr. Vice President & General Manager, Enterprise Storage Solutions

The Flash Transformed Data Center & the Unlimited Future of Flash John Scaramuzzo Sr. Vice President & General Manager, Enterprise Storage Solutions The Flash Transformed Data Center & the Unlimited Future of Flash John Scaramuzzo Sr. Vice President & General Manager, Enterprise Storage Solutions Flash Memory Summit 5-7 August 2014 1 Forward-Looking

More information

Amadeus SAS Specialists Prove Fusion iomemory a Superior Analysis Accelerator

Amadeus SAS Specialists Prove Fusion iomemory a Superior Analysis Accelerator WHITE PAPER Amadeus SAS Specialists Prove Fusion iomemory a Superior Analysis Accelerator 951 SanDisk Drive, Milpitas, CA 95035 www.sandisk.com SAS 9 Preferred Implementation Partner tests a single Fusion

More information

Cloud Storage. Parallels. Performance Benchmark Results. White Paper. www.parallels.com

Cloud Storage. Parallels. Performance Benchmark Results. White Paper. www.parallels.com Parallels Cloud Storage White Paper Performance Benchmark Results www.parallels.com Table of Contents Executive Summary... 3 Architecture Overview... 3 Key Features... 4 No Special Hardware Requirements...

More information

Evaluation Report: Database Acceleration with HP 3PAR StoreServ 7450 All-flash Storage

Evaluation Report: Database Acceleration with HP 3PAR StoreServ 7450 All-flash Storage Evaluation Report: Database Acceleration with HP 3PAR StoreServ 7450 All-flash Storage Evaluation report prepared under contract with HP Executive Summary Solid state storage is transforming the entire

More information

Data Center Solutions

Data Center Solutions Data Center Solutions Systems, software and hardware solutions you can trust With over 25 years of storage innovation, SanDisk is a global flash technology leader. At SanDisk, we re expanding the possibilities

More information

InfiniteGraph: The Distributed Graph Database

InfiniteGraph: The Distributed Graph Database A Performance and Distributed Performance Benchmark of InfiniteGraph and a Leading Open Source Graph Database Using Synthetic Data Objectivity, Inc. 640 West California Ave. Suite 240 Sunnyvale, CA 94086

More information

Data Center Performance Insurance

Data Center Performance Insurance Data Center Performance Insurance How NFS Caching Guarantees Rapid Response Times During Peak Workloads November 2010 2 Saving Millions By Making It Easier And Faster Every year slow data centers and application

More information

VMware vsphere 6 and Oracle Database Scalability Study

VMware vsphere 6 and Oracle Database Scalability Study VMware vsphere 6 and Oracle Database Scalability Study Scaling Monster Virtual Machines TECHNICAL WHITE PAPER Table of Contents Executive Summary... 3 Introduction... 3 Test Environment... 3 Virtual Machine

More information

Business white paper. HP Process Automation. Version 7.0. Server performance

Business white paper. HP Process Automation. Version 7.0. Server performance Business white paper HP Process Automation Version 7.0 Server performance Table of contents 3 Summary of results 4 Benchmark profile 5 Benchmark environmant 6 Performance metrics 6 Process throughput 6

More information

Best Practices for Deploying SSDs in a Microsoft SQL Server 2008 OLTP Environment with Dell EqualLogic PS-Series Arrays

Best Practices for Deploying SSDs in a Microsoft SQL Server 2008 OLTP Environment with Dell EqualLogic PS-Series Arrays Best Practices for Deploying SSDs in a Microsoft SQL Server 2008 OLTP Environment with Dell EqualLogic PS-Series Arrays Database Solutions Engineering By Murali Krishnan.K Dell Product Group October 2009

More information

Understanding the Benefits of IBM SPSS Statistics Server

Understanding the Benefits of IBM SPSS Statistics Server IBM SPSS Statistics Server Understanding the Benefits of IBM SPSS Statistics Server Contents: 1 Introduction 2 Performance 101: Understanding the drivers of better performance 3 Why performance is faster

More information

The Evolution of Microsoft SQL Server: The right time for Violin flash Memory Arrays

The Evolution of Microsoft SQL Server: The right time for Violin flash Memory Arrays The Evolution of Microsoft SQL Server: The right time for Violin flash Memory Arrays Executive Summary Microsoft SQL has evolved beyond serving simple workgroups to a platform delivering sophisticated

More information

Can Flash help you ride the Big Data Wave? Steve Fingerhut Vice President, Marketing Enterprise Storage Solutions Corporation

Can Flash help you ride the Big Data Wave? Steve Fingerhut Vice President, Marketing Enterprise Storage Solutions Corporation Can Flash help you ride the Big Data Wave? Steve Fingerhut Vice President, Marketing Enterprise Storage Solutions Corporation Forward-Looking Statements During our meeting today we may make forward-looking

More information

Big Fast Data Hadoop acceleration with Flash. June 2013

Big Fast Data Hadoop acceleration with Flash. June 2013 Big Fast Data Hadoop acceleration with Flash June 2013 Agenda The Big Data Problem What is Hadoop Hadoop and Flash The Nytro Solution Test Results The Big Data Problem Big Data Output Facebook Traditional

More information

Intel RAID SSD Cache Controller RCS25ZB040

Intel RAID SSD Cache Controller RCS25ZB040 SOLUTION Brief Intel RAID SSD Cache Controller RCS25ZB040 When Faster Matters Cost-Effective Intelligent RAID with Embedded High Performance Flash Intel RAID SSD Cache Controller RCS25ZB040 When Faster

More information

MS EXCHANGE SERVER ACCELERATION IN VMWARE ENVIRONMENTS WITH SANRAD VXL

MS EXCHANGE SERVER ACCELERATION IN VMWARE ENVIRONMENTS WITH SANRAD VXL MS EXCHANGE SERVER ACCELERATION IN VMWARE ENVIRONMENTS WITH SANRAD VXL Dr. Allon Cohen Eli Ben Namer info@sanrad.com 1 EXECUTIVE SUMMARY SANRAD VXL provides enterprise class acceleration for virtualized

More information

Using VMware VMotion with Oracle Database and EMC CLARiiON Storage Systems

Using VMware VMotion with Oracle Database and EMC CLARiiON Storage Systems Using VMware VMotion with Oracle Database and EMC CLARiiON Storage Systems Applied Technology Abstract By migrating VMware virtual machines from one physical environment to another, VMware VMotion can

More information

Capacity Management for Oracle Database Machine Exadata v2

Capacity Management for Oracle Database Machine Exadata v2 Capacity Management for Oracle Database Machine Exadata v2 Dr. Boris Zibitsker, BEZ Systems NOCOUG 21 Boris Zibitsker Predictive Analytics for IT 1 About Author Dr. Boris Zibitsker, Chairman, CTO, BEZ

More information

VMware Virtual SAN Backup Using VMware vsphere Data Protection Advanced SEPTEMBER 2014

VMware Virtual SAN Backup Using VMware vsphere Data Protection Advanced SEPTEMBER 2014 VMware SAN Backup Using VMware vsphere Data Protection Advanced SEPTEMBER 2014 VMware SAN Backup Using VMware vsphere Table of Contents Introduction.... 3 vsphere Architectural Overview... 4 SAN Backup

More information

SAS Business Analytics. Base SAS for SAS 9.2

SAS Business Analytics. Base SAS for SAS 9.2 Performance & Scalability of SAS Business Analytics on an NEC Express5800/A1080a (Intel Xeon 7500 series-based Platform) using Red Hat Enterprise Linux 5 SAS Business Analytics Base SAS for SAS 9.2 Red

More information

Accelerating MS SQL Server 2012

Accelerating MS SQL Server 2012 White Paper Accelerating MS SQL Server 2012 Unleashing the Full Power of SQL Server 2012 in Virtualized Data Centers Allon Cohen, PhD Scott Harlin OCZ Storage Solutions, Inc. A Toshiba Group Company 1

More information

Dell Reference Configuration for DataStax Enterprise powered by Apache Cassandra

Dell Reference Configuration for DataStax Enterprise powered by Apache Cassandra Dell Reference Configuration for DataStax Enterprise powered by Apache Cassandra A Quick Reference Configuration Guide Kris Applegate kris_applegate@dell.com Solution Architect Dell Solution Centers Dave

More information

Microsoft SQL Server 2014 Fast Track

Microsoft SQL Server 2014 Fast Track Microsoft SQL Server 2014 Fast Track 34-TB Certified Data Warehouse 103-TB Maximum User Data Tegile Systems Solution Review 2U Design: Featuring Tegile T3800 All-Flash Storage Array http:// www.tegile.com/solutiuons/sql

More information

SLIDE 1 www.bitmicro.com. Previous Next Exit

SLIDE 1 www.bitmicro.com. Previous Next Exit SLIDE 1 MAXio All Flash Storage Array Popular Applications MAXio N1A6 SLIDE 2 MAXio All Flash Storage Array Use Cases High speed centralized storage for IO intensive applications email, OLTP, databases

More information

Caching Software Performance: FlashSoft Software 3.8 for Microsoft Windows Server with Hyper-V and SQL Server 2012

Caching Software Performance: FlashSoft Software 3.8 for Microsoft Windows Server with Hyper-V and SQL Server 2012 Technical Brief Caching Software Performance: FlashSoft Software 3.8 for Microsoft Windows Server with Hyper-V and SQL Server 2012 Western Digital Technologies, Inc. 951 SanDisk Drive, Milpitas, CA 95035

More information

Optimizing Dell PowerEdge Configurations for Hadoop

Optimizing Dell PowerEdge Configurations for Hadoop Optimizing Dell PowerEdge Configurations for Hadoop Understanding how to get the most out of Hadoop running on Dell hardware A Dell technical white paper July 2013 Michael Pittaro Principal Architect,

More information

LSI MegaRAID CacheCade Performance Evaluation in a Web Server Environment

LSI MegaRAID CacheCade Performance Evaluation in a Web Server Environment LSI MegaRAID CacheCade Performance Evaluation in a Web Server Environment Evaluation report prepared under contract with LSI Corporation Introduction Interest in solid-state storage (SSS) is high, and

More information

2009 Oracle Corporation 1

2009 Oracle Corporation 1 The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material,

More information

RAID. RAID 0 No redundancy ( AID?) Just stripe data over multiple disks But it does improve performance. Chapter 6 Storage and Other I/O Topics 29

RAID. RAID 0 No redundancy ( AID?) Just stripe data over multiple disks But it does improve performance. Chapter 6 Storage and Other I/O Topics 29 RAID Redundant Array of Inexpensive (Independent) Disks Use multiple smaller disks (c.f. one large disk) Parallelism improves performance Plus extra disk(s) for redundant data storage Provides fault tolerant

More information

Tableau Server 7.0 scalability

Tableau Server 7.0 scalability Tableau Server 7.0 scalability February 2012 p2 Executive summary In January 2012, we performed scalability tests on Tableau Server to help our customers plan for large deployments. We tested three different

More information

Violin Memory Arrays With IBM System Storage SAN Volume Control

Violin Memory Arrays With IBM System Storage SAN Volume Control Technical White Paper Report Best Practices Guide: Violin Memory Arrays With IBM System Storage SAN Volume Control Implementation Best Practices and Performance Considerations Version 1.0 Abstract This

More information

PrimaryIO Application Performance Acceleration Date: July 2015 Author: Tony Palmer, Senior Lab Analyst

PrimaryIO Application Performance Acceleration Date: July 2015 Author: Tony Palmer, Senior Lab Analyst ESG Lab Spotlight PrimaryIO Application Performance Acceleration Date: July 215 Author: Tony Palmer, Senior Lab Analyst Abstract: PrimaryIO Application Performance Acceleration (APA) is designed to provide

More information

SQL Server Virtualization

SQL Server Virtualization The Essential Guide to SQL Server Virtualization S p o n s o r e d b y Virtualization in the Enterprise Today most organizations understand the importance of implementing virtualization. Virtualization

More information

MS Exchange Server Acceleration

MS Exchange Server Acceleration White Paper MS Exchange Server Acceleration Using virtualization to dramatically maximize user experience for Microsoft Exchange Server Allon Cohen, PhD Scott Harlin OCZ Storage Solutions, Inc. A Toshiba

More information

Boost Database Performance with the Cisco UCS Storage Accelerator

Boost Database Performance with the Cisco UCS Storage Accelerator Boost Database Performance with the Cisco UCS Storage Accelerator Performance Brief February 213 Highlights Industry-leading Performance and Scalability Offloading full or partial database structures to

More information

FAWN - a Fast Array of Wimpy Nodes

FAWN - a Fast Array of Wimpy Nodes University of Warsaw January 12, 2011 Outline Introduction 1 Introduction 2 3 4 5 Key issues Introduction Growing CPU vs. I/O gap Contemporary systems must serve millions of users Electricity consumed

More information

FPGA-based Multithreading for In-Memory Hash Joins

FPGA-based Multithreading for In-Memory Hash Joins FPGA-based Multithreading for In-Memory Hash Joins Robert J. Halstead, Ildar Absalyamov, Walid A. Najjar, Vassilis J. Tsotras University of California, Riverside Outline Background What are FPGAs Multithreaded

More information

An Oracle White Paper May 2011. Exadata Smart Flash Cache and the Oracle Exadata Database Machine

An Oracle White Paper May 2011. Exadata Smart Flash Cache and the Oracle Exadata Database Machine An Oracle White Paper May 2011 Exadata Smart Flash Cache and the Oracle Exadata Database Machine Exadata Smart Flash Cache... 2 Oracle Database 11g: The First Flash Optimized Database... 2 Exadata Smart

More information

EMC VFCACHE ACCELERATES ORACLE

EMC VFCACHE ACCELERATES ORACLE White Paper EMC VFCACHE ACCELERATES ORACLE VFCache extends Flash to the server FAST Suite automates storage placement in the array VNX protects data EMC Solutions Group Abstract This white paper describes

More information

Achieving a Million I/O Operations per Second from a Single VMware vsphere 5.0 Host

Achieving a Million I/O Operations per Second from a Single VMware vsphere 5.0 Host Achieving a Million I/O Operations per Second from a Single VMware vsphere 5.0 Host Performance Study TECHNICAL WHITE PAPER Table of Contents Introduction... 3 Executive Summary... 3 Software and Hardware...

More information

SQL Server Business Intelligence on HP ProLiant DL785 Server

SQL Server Business Intelligence on HP ProLiant DL785 Server SQL Server Business Intelligence on HP ProLiant DL785 Server By Ajay Goyal www.scalabilityexperts.com Mike Fitzner Hewlett Packard www.hp.com Recommendations presented in this document should be thoroughly

More information

An Overview of Flash Storage for Databases

An Overview of Flash Storage for Databases An Overview of Flash Storage for Databases Vadim Tkachenko Morgan Tocker http://percona.com MySQL CE Apr 2010 -2- Introduction Vadim Tkachenko Percona Inc, CTO and Lead of Development Morgan Tocker Percona

More information

ioscale: The Holy Grail for Hyperscale

ioscale: The Holy Grail for Hyperscale ioscale: The Holy Grail for Hyperscale The New World of Hyperscale Hyperscale describes new cloud computing deployments where hundreds or thousands of distributed servers support millions of remote, often

More information

Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging

Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging In some markets and scenarios where competitive advantage is all about speed, speed is measured in micro- and even nano-seconds.

More information

Dell Microsoft Business Intelligence and Data Warehousing Reference Configuration Performance Results Phase III

Dell Microsoft Business Intelligence and Data Warehousing Reference Configuration Performance Results Phase III White Paper Dell Microsoft Business Intelligence and Data Warehousing Reference Configuration Performance Results Phase III Performance of Microsoft SQL Server 2008 BI and D/W Solutions on Dell PowerEdge

More information

Fusion iomemory iodrive PCIe Application Accelerator Performance Testing

Fusion iomemory iodrive PCIe Application Accelerator Performance Testing WHITE PAPER Fusion iomemory iodrive PCIe Application Accelerator Performance Testing SPAWAR Systems Center Atlantic Cary Humphries, Steven Tully and Karl Burkheimer 2/1/2011 Product testing of the Fusion

More information

Optimizing SQL Server Storage Performance with the PowerEdge R720

Optimizing SQL Server Storage Performance with the PowerEdge R720 Optimizing SQL Server Storage Performance with the PowerEdge R720 Choosing the best storage solution for optimal database performance Luis Acosta Solutions Performance Analysis Group Joe Noyola Advanced

More information

IBM Systems and Technology Group May 2013 Thought Leadership White Paper. Faster Oracle performance with IBM FlashSystem

IBM Systems and Technology Group May 2013 Thought Leadership White Paper. Faster Oracle performance with IBM FlashSystem IBM Systems and Technology Group May 2013 Thought Leadership White Paper Faster Oracle performance with IBM FlashSystem 2 Faster Oracle performance with IBM FlashSystem Executive summary This whitepaper

More information

Condusiv s V-locity Server Boosts Performance of SQL Server 2012 by 55%

Condusiv s V-locity Server Boosts Performance of SQL Server 2012 by 55% openbench Labs Executive Briefing: April 19, 2013 Condusiv s Server Boosts Performance of SQL Server 2012 by 55% Optimizing I/O for Increased Throughput and Reduced Latency on Physical Servers 01 Executive

More information

Big Data With Hadoop

Big Data With Hadoop With Saurabh Singh singh.903@osu.edu The Ohio State University February 11, 2016 Overview 1 2 3 Requirements Ecosystem Resilient Distributed Datasets (RDDs) Example Code vs Mapreduce 4 5 Source: [Tutorials

More information

Virtuoso and Database Scalability

Virtuoso and Database Scalability Virtuoso and Database Scalability By Orri Erling Table of Contents Abstract Metrics Results Transaction Throughput Initializing 40 warehouses Serial Read Test Conditions Analysis Working Set Effect of

More information

Inge Os Sales Consulting Manager Oracle Norway

Inge Os Sales Consulting Manager Oracle Norway Inge Os Sales Consulting Manager Oracle Norway Agenda Oracle Fusion Middelware Oracle Database 11GR2 Oracle Database Machine Oracle & Sun Agenda Oracle Fusion Middelware Oracle Database 11GR2 Oracle Database

More information

Best Practices for Deploying Citrix XenDesktop on NexentaStor Open Storage

Best Practices for Deploying Citrix XenDesktop on NexentaStor Open Storage Best Practices for Deploying Citrix XenDesktop on NexentaStor Open Storage White Paper July, 2011 Deploying Citrix XenDesktop on NexentaStor Open Storage Table of Contents The Challenges of VDI Storage

More information

Violin Memory 7300 Flash Storage Platform Supports Multiple Primary Storage Workloads

Violin Memory 7300 Flash Storage Platform Supports Multiple Primary Storage Workloads Violin Memory 7300 Flash Storage Platform Supports Multiple Primary Storage Workloads Web server, SQL Server OLTP, Exchange Jetstress, and SharePoint Workloads Can Run Simultaneously on One Violin Memory

More information

Capitalizing on Smarter and Faster Insight with Flash

Capitalizing on Smarter and Faster Insight with Flash 89 Fifth Avenue, 7th Floor New York, NY 10003 www.theedison.com 212.367.7400 Capitalizing on Smarter and Faster Insight with Flash IBM FlashSystem and IBM InfoSphere Identity Insight Printed in the United

More information

Scaling from Datacenter to Client

Scaling from Datacenter to Client Scaling from Datacenter to Client KeunSoo Jo Sr. Manager Memory Product Planning Samsung Semiconductor Audio-Visual Sponsor Outline SSD Market Overview & Trends - Enterprise What brought us to NVMe Technology

More information