Fusion IO Flash Storage Accelerates In-Memory SQL Server 2014 Database up to 4x

Fusion IO has jumped on-board the Microsoft SQL Server 2014 in-memory features and has claimed up to 4x improvements in transaction per second and substantial reduction in data latencies.  A nice little Fusion IO video that introduces SQL Server 2014:

Fusion ioMemory integrates into the SQL Server 2014 database engine buffer pool to improve IO throughput. The lower price points of the PCIe flash card products will no doubt put pressure on flash arrays.

fio100

Fusion IO has written a nice white paper detailing how their cards accelerate SQL Server 2014 by leveraging the new In-Memory features. Using their ioDrive2 Duo cards they substantially accelerated the performance, latency of transactions.

fio101The performance brief shows an increase of 4.4 x more transaction with the ioDrive2 Duo 2.4TB versus an enterprise class disk array. It also shows that it delivers better latency – serving customers up to 73% faster and delivers 3.3x faster database startup times.

fio102

Here is a SQL Server 2014 In-Memory OLTP Overview :

 

More on Clojure and Storm, a Distributed, Real-Time Computation System

In yesterday’s post I brought up Storm. There is a nice presentation on Storm from Nathan Marz of Twitter.  He explains the workings of Storm, a distributed fault-tolerant and real-time computational system currently used by Twitter to keep statistics on user clicks for every URL and domain.

 

Note that that there are some examples of Storm topologies.

storm-starter

 

Here is some details on how Twitter was using Storm in 2011.

Recommended Reading : Clojure and Storm, a Distributed Real-Time Computation System

In looking at more Clojure stuff, I discovered, Storm. Interesting.  There is a nice article on how to build an activity feed system with Storm.  Storm is a distributed real-time computation system. Storm can be looked at a way of being for real-time what Hadoop is to batch computation.

storm

From the Apache Incubator site you can see that Storm is a relatively new project with big aims.  An example of using Clojure and Storm offers more insight into Storm.

Storm02The article goes into good detail about how to build a real-time activity feed system that filters and aggregates the raw event data generation in an application

 

Oracle, GlassFish Application Server and Support

Glassfish Application Server is an excellent Java EE app server and indeed is the reference platform for Java EE editions.  So it was surprising and weird, to say the least, that Oracle announced the following recently. I will not try to interpret it – it seems pretty clear.

glassupport

With all that said, I imagine a lot Glassfish developers and users might be concerned.  Along game the explanation of the previous roadmap :

glassupport2

All of this has served to alarm developers and users of GlassFish.

Adam Bien’s take in November when the original announcement

adambien01

All quite alarming for GlassFish users.  RebelLabs offered the following recently in an effort to help any GlassFish users that were migrating :

rebellabsglassThings do not bode well for GlassFish.  We will have to wait and see where Oracle is going with all this.

 

 

Flash Storage Highlight : Nimbus Data Receives Top Ranking In Flash Memory Storage Arrays in 2014-15 DCIG Buyer’s Guide

One of the flash arrays that has consistently demonstrated technology advantages and practical use-case advantages is Nimbus Data’s Gemini arrays.  I have mentioned some of their advantages in past posts. They just picked up some accolades from the 2014 DCIG Flash Memory Buyer’s Guide.  In evaluating 39 offerings from 20 companies, Nimbus Data’s Gemini F600 and F400 were selected as Best-in-Class (#1) and Recommended (#2) respectively.  You can read more about the selection process and the win here :

NimbusDataBestInClass

 

 

Recommended Viewing : The BlueKai Playbook for Scaling to 10 Trillion Transaction a Month

Good talk on delivering a highly scalable solution. Ted Wallace, VP of Data Delivery at BlueKai discusses how BlueKai scales to 10 trillion data transactions per month.  BlueKai provides data-driven marketing and as a result needs highly scalable solutions. Ted Wallace discussed how they do this. He provides some good details – they use Aerospike to get the high database performance – average read/write response times are between 1 – 2 ms. Six Aerospike clusters with 6 to 10 server in each of three geographically located data centers. They use standard Linux hardware with four Intel 800G SSDs in each and 128 GB to 256 GB of RAM. Lots more details in the talk.  Select the image to go to the talk.

Aerospike_video

Recommended Learning: Conway’s Game of Life in Clojure

An elegant comparison of different languages doing the game of life. You know that screensaver on Sun workstations running SunOS 3.5 (smile)… never mind before a lot of people’s time. :

gollogan

I bumped into this really  tutorial which builds Conway’s game of life in Clojure. Interesting example.

gameoflife

Another Game of Life/Clojure older post :

cljrgol

and more game of life on Github : seangeo and sebastianbenj.

Re-visit this later – I will have more.


clojureiconGo to more posts on clojure or that are clojure-related at http://digitalcld.com/cld/category/clojure.