high_scalability high_scalability-2010 high_scalability-2010-797 knowledge-graph by maker-knowledge-mining
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Introduction: The Changelog Episode 0.1.8 - NoSQL Smackdown! This podcast was recorded at SXSW and features some energetic trash talking by: Stu Hood from Cassandra, Jan Lehnardt from CouchDB, Wynn Netherland from The Changelog, subbing for MongoDB, Werner Vogels CTO at Amazon. It's fun hearing these guys step out of their sober advocacy roles and let loose a little with why they are great and the other products suck, hard. Algorithmic Graph Theory . It's FREE! A GNU-FDL book on algorithmic graph theory by David Joyner, Minh Van Nguyen, and Nathann Cohen. This is an introductory book on algorithmic graph theory. HBase vs Cassandra: why we moved by Dominic Williams. Benchmarking Cloud Serving Systems with YCSB by lots of people from Yahoo! Research. We present the Yahoo! Cloud Serving Benchmark (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report re- sults for four
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1 This podcast was recorded at SXSW and features some energetic trash talking by: Stu Hood from Cassandra, Jan Lehnardt from CouchDB, Wynn Netherland from The Changelog, subbing for MongoDB, Werner Vogels CTO at Amazon. [sent-4, score-0.367]
2 It's fun hearing these guys step out of their sober advocacy roles and let loose a little with why they are great and the other products suck, hard. [sent-5, score-0.179]
3 A GNU-FDL book on algorithmic graph theory by David Joyner, Minh Van Nguyen, and Nathann Cohen. [sent-8, score-0.642]
4 This is an introductory book on algorithmic graph theory. [sent-9, score-0.623]
5 Cloud Serving Benchmark (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. [sent-14, score-0.49]
6 All recordings from NoSQL Live Boston now online! [sent-17, score-0.128]
7 Werner Vogels on SimpleDB adding unique capabilities: By providing developers control over the consistency model, they are now empowered to make the consistency and performance trade-offs 12 Reasons To Be Learning Graph Theory . [sent-24, score-0.279]
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Introduction: The Changelog Episode 0.1.8 - NoSQL Smackdown! This podcast was recorded at SXSW and features some energetic trash talking by: Stu Hood from Cassandra, Jan Lehnardt from CouchDB, Wynn Netherland from The Changelog, subbing for MongoDB, Werner Vogels CTO at Amazon. It's fun hearing these guys step out of their sober advocacy roles and let loose a little with why they are great and the other products suck, hard. Algorithmic Graph Theory . It's FREE! A GNU-FDL book on algorithmic graph theory by David Joyner, Minh Van Nguyen, and Nathann Cohen. This is an introductory book on algorithmic graph theory. HBase vs Cassandra: why we moved by Dominic Williams. Benchmarking Cloud Serving Systems with YCSB by lots of people from Yahoo! Research. We present the Yahoo! Cloud Serving Benchmark (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report re- sults for four
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Introduction: Scalability Perspectives is a series of posts that highlights the ideas that will shape the next decade of IT architecture. Each post is dedicated to a thought leader of the information age and his vision of the future. Be warned though – the journey into the minds and perspectives of these people requires an open mind. Werner Vogels Dr. Werner Vogels is Vice President & Chief Technology Officer at Amazon.com where he is responsible for driving the company’s technology vision, which is to continuously enhance the innovation on behalf of Amazon’s customers at a global scale. Prior to joining Amazon, he worked as a researcher at Cornell University where he was a principal investigator in several research projects that target the scalability and robustness of mission-critical enterprise computing systems. He is regarded as one of the world's top experts on ultra-scalable systems and he uses his weblog to educate the community about issues such as eventual consistency. Information
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Introduction: Update: Social networks in the database: using a graph database . A nice post on representing, traversing, and performing other common social network operations using a graph database. If you are Digg or LinkedIn you can build your own speedy graph database to represent your complex social network relationships. For those of more modest means Neo4j , a graph database, is a good alternative. A graph is a collection nodes (things) and edges (relationships) that connect pairs of nodes. Slap properties (key-value pairs) on nodes and relationships and you have a surprisingly powerful way to represent most anything you can think of. In a graph database "relationships are first-class citizens. They connect two nodes and both nodes and relationships can hold an arbitrary amount of key-value pairs. So you can look at a graph database as a key-value store, with full support for relationships." A graph looks something like: For more lovely examples take a look at the Graph Image Gal
Introduction: On the surface nothing appears more different than soft data and hard raw materials like iron. Then isn’t it ironic , in the Alanis Morissette sense, that in this Age of Information, great wealth still lies hidden deep beneath piles of stuff? It's so strange how directly digging for dollars in data parallels the great wealth producing models of the Industrial Revolution. The piles of stuff is the Internet. It takes lots of prospecting to find the right stuff. Mighty web crawling machines tirelessly collect stuff, bringing it into their huge maws, then depositing load after load into rack after rack of distributed file system machines. Then armies of still other machines take this stuff and strip out the valuable raw materials, which in the Information Age, are endless bytes of raw data. Link clicks, likes, page views, content, head lines, searches, inbound links, outbound links, search clicks, hashtags, friends, purchases: anything and everything you do on the Internet is a valu
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Introduction: I've seen mentioned in few times sites like Digg or LinkedIn using graph servers to hold their social graphs. But the only sort of open source graph server I've found is http://neo4j.org/ . Can anyone recommend an open source graph server? Thanks Aaron
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Introduction: The Changelog Episode 0.1.8 - NoSQL Smackdown! This podcast was recorded at SXSW and features some energetic trash talking by: Stu Hood from Cassandra, Jan Lehnardt from CouchDB, Wynn Netherland from The Changelog, subbing for MongoDB, Werner Vogels CTO at Amazon. It's fun hearing these guys step out of their sober advocacy roles and let loose a little with why they are great and the other products suck, hard. Algorithmic Graph Theory . It's FREE! A GNU-FDL book on algorithmic graph theory by David Joyner, Minh Van Nguyen, and Nathann Cohen. This is an introductory book on algorithmic graph theory. HBase vs Cassandra: why we moved by Dominic Williams. Benchmarking Cloud Serving Systems with YCSB by lots of people from Yahoo! Research. We present the Yahoo! Cloud Serving Benchmark (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report re- sults for four
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Introduction: Submitted for your reading pleasure... On the new year Twitter set a record with 6,939 Tweets Per Second (TPS). Cool video visualizing New Year's Eve Tweet data across the world. Marko Rodriguez in Memoirs of a Graph Addict: Despair to Redemption tells a stirring tale of how graph programming saved the world from certain destruction by realizing Aritstotle's dream of an eudaimonia-driven society. Could a relational database do that? The tools of the revolution can be found at tinkerprop.com , which describes a databases agnostic stack for working with property graphs, they include Blueprints - a property graph model interface; Pipes - a dataflow netowork using process grapphs; Gremlin - a graph based programming language; Rexster - a RESTful graph shell. The never never ending battle of good versus evil has nothing on programmers arguing about bracket policies or sync vs async programming models. In this node.js thread, I love async, but I can't code like this , the batt
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Introduction: With the success of Neo4j as a graph database in the NoSQL revolution, it's interesting to see another graph database, HyperGraphDB , in the mix. Their quick blurb on HyperGraphDB says it is a: general purpose, extensible, portable, distributed, embeddable, open-source data storage mechanism. It is a graph database designed specifically for artificial intelligence and semantic web projects, it can also be used as an embedded object-oriented database for projects of all sizes. From the NoSQL Archive the summary on HyperGraphDB is: API: Java (and Java Langs), Written in: Java , Query Method: Java or P2P, Replication: P2P , Concurrency: STM , Misc: Open-Source, Especially for AI and Semantic Web. So it has some interesting features, like software transactional memory and P2P for data distribution , but I found that my first and most obvious question was not answered: what the heck is a hypergraph and why do I care? Buried in the tutorial was: A HyperGraphD
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Introduction: i didnt find a nearly good solution for this problem yet: imagine, you're responsible for a small CDN network (static images), with two different datacenter. the balancing for the two DC is done with a anycast nameservice (a nameserver in every DC, user gets on nearest location). so, one of the scenario is that one of the datacenters goes down completly. you can do a monitoring on the nameserver and only route to the dc which is still alive, no problem. But what about the TTL from the DNS-Records? Tiny TTLs like 2 min. are often ignored by several ISP (e.g. AOL). so, the client doesn't get the IP from the other Datacenter. what could be a solution in this scenario?
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Introduction: This is a guest post by Ben Lowry, CEO of Playtomic . Playtomic is a game analytics service implemented in about 8000 mobile, web and downloadable games played by approximately 20 million people daily. Here's a good summary quote by Ben Lowry on Hacker News : Just over 20,000,000 people hit my API yesterday 700,749,252 times, playing the ~8,000 games my analytics platform is integrated in for a bit under 600 years in total play time. That's just yesterday. There are lots of different bottlenecks waiting for people operating at scale. Heroku and NodeJS, for my use case, eventually alleviated a whole bunch of them very cheaply. Playtomic began with an almost exclusively Microsoft.NET and Windows architecture which held up for 3 years before being replaced with a complete rewrite using NodeJS. During its lifetime the entire platform grew from shared space on a single server to a full dedicated, then spread to second dedicated, then the API server was offloaded to a VPS pro
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Introduction: The Changelog Episode 0.1.8 - NoSQL Smackdown! This podcast was recorded at SXSW and features some energetic trash talking by: Stu Hood from Cassandra, Jan Lehnardt from CouchDB, Wynn Netherland from The Changelog, subbing for MongoDB, Werner Vogels CTO at Amazon. It's fun hearing these guys step out of their sober advocacy roles and let loose a little with why they are great and the other products suck, hard. Algorithmic Graph Theory . It's FREE! A GNU-FDL book on algorithmic graph theory by David Joyner, Minh Van Nguyen, and Nathann Cohen. This is an introductory book on algorithmic graph theory. HBase vs Cassandra: why we moved by Dominic Williams. Benchmarking Cloud Serving Systems with YCSB by lots of people from Yahoo! Research. We present the Yahoo! Cloud Serving Benchmark (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems. We define a core set of benchmarks and report re- sults for four
Introduction: One stumbling block of the the great march towards virtualization is the relatively poor performance of resource hungry applications like databases. We are told to develop and test using VMs, but deploy without them. Which kind of sucks IMHO. Maybe better virtualization technology can remove this split. This paper talks about a different approach to virtualization called "container-based" virtualization that can reportedly double the performance of traditional hypervisor systems like Xen. It does this by trading isolation for efficiency. Rather than maintaining complete isolation between VMs the container approach shares resources between VMs and thus gives higher performance while still guaranteeing strong fault, resource, and security isolation. It's yet another battle in computing's endless war of creating and destroying abstraction layers. I learned a lot from from this paper because of how it compared and contrasted traditional hypervisor and container based virtualization strateg
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