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	<title>atbrox</title>
	
	<link>http://atbrox.com</link>
	<description />
	<lastBuildDate>Wed, 16 May 2012 13:09:19 +0000</lastBuildDate>
	<language>en</language>
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		<title>A large-scale in-memory storage example – social network data</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/pOsUsHU0I18/</link>
		<comments>http://atbrox.com/2012/05/16/a-large-scale-in-memory-storage-example/#comments</comments>
		<pubDate>Wed, 16 May 2012 08:25:22 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[in-memory]]></category>
		<category><![CDATA[information retrieval]]></category>
		<category><![CDATA[infrastructure]]></category>
		<category><![CDATA[RAM]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2414</guid>
		<description>This posting is a follow-up to the large-scale low-latency (RAM-based) storage related price estimates in my previous posting Main takeaways from Accel’s Big Data Conference. Assume you were to store and index large amounts of social network updates in-memory, e.g. tweets. 1) fetch some tweets curl https://stream.twitter.com/1/statuses/sample.json?delimited=length -uAnyTwitterUser:Password &gt; yourfilename 2) gather some stats about [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/pOsUsHU0I18" height="1" width="1"/&gt;</description>
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		<slash:comments>0</slash:comments>
		<feedburner:origLink>http://atbrox.com/2012/05/16/a-large-scale-in-memory-storage-example/</feedburner:origLink></item>
		<item>
		<title>Main takeaways from Accel’s Big Data Conference</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/H3FVKakjFr4/</link>
		<comments>http://atbrox.com/2012/05/14/main-takeaways-from-accels-big-data-conference/#comments</comments>
		<pubDate>Mon, 14 May 2012 07:24:36 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[cloud computing]]></category>
		<category><![CDATA[big data]]></category>
		<category><![CDATA[disk]]></category>
		<category><![CDATA[ram]]></category>
		<category><![CDATA[ssd]]></category>
		<category><![CDATA[storage]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2384</guid>
		<description>Attended Accel Partners Big Data conference last week. It was a good event with many interesting people, a very crude estimate of distribution: 1/3 VCs/investors, 1/3 startup tech people, 1/3 big corp tech people +-. My personal 2 key takeaways from the conference: Realtime processing: hot topic with many companies creating their own custom solutions, [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/H3FVKakjFr4" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2012/05/14/main-takeaways-from-accels-big-data-conference/feed/</wfw:commentRss>
		<slash:comments>3</slash:comments>
		<feedburner:origLink>http://atbrox.com/2012/05/14/main-takeaways-from-accels-big-data-conference/</feedburner:origLink></item>
		<item>
		<title>atbr now has Apache Thrift support</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/S-a9LzrFP30/</link>
		<comments>http://atbrox.com/2012/05/02/atbr-now-has-apache-thrift-support/#comments</comments>
		<pubDate>Wed, 02 May 2012 10:06:10 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[cloud computing]]></category>
		<category><![CDATA[atbr]]></category>
		<category><![CDATA[c++]]></category>
		<category><![CDATA[nosql]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[thrift]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2368</guid>
		<description>atbr (large-scale and low-latency in-memory key-value pair store) now supports Apache Thrift for easier integration with other Hadoop services. Thrift Example Checkout and install atbr Prerequisite Install/compile Apache Thrift &amp;#8211; http://thrift.apache.org/ Compile a atbr thrift server and connect using python client Python thrift api example Stay tuned for other updates on atbr. Rough roadmap Increased [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/S-a9LzrFP30" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2012/05/02/atbr-now-has-apache-thrift-support/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		<feedburner:origLink>http://atbrox.com/2012/05/02/atbr-now-has-apache-thrift-support/</feedburner:origLink></item>
		<item>
		<title>atbr – supports websocket-based sharding</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/7FZJ_X9cSoA/</link>
		<comments>http://atbrox.com/2012/05/01/atbr-supports-websocket-based-sharding/#comments</comments>
		<pubDate>Tue, 01 May 2012 07:38:50 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[cloud computing]]></category>
		<category><![CDATA[atbr]]></category>
		<category><![CDATA[dictionaries]]></category>
		<category><![CDATA[hadoop]]></category>
		<category><![CDATA[hashtables]]></category>
		<category><![CDATA[in-memory]]></category>
		<category><![CDATA[large-scale]]></category>
		<category><![CDATA[mapreduce]]></category>
		<category><![CDATA[python]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2361</guid>
		<description>atbr (large-scale and low-latency in-memory key-value pair store) now supports websocket-based sharding for parallel deployments. Websocket Sharding Example Checkout and install atbr Start 3 servers loaded with data Start shard server talking to shards Connect to shard server and lookup key=key1 Stay tuned for other updates on atbr, here is a rough roadmap. Increased concurrency [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/7FZJ_X9cSoA" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2012/05/01/atbr-supports-websocket-based-sharding/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		<feedburner:origLink>http://atbrox.com/2012/05/01/atbr-supports-websocket-based-sharding/</feedburner:origLink></item>
		<item>
		<title>atbr – large-scale in-memory hashtables (in Python)</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/TH_Q2oJSKNE/</link>
		<comments>http://atbrox.com/2012/04/25/atbr-large-scale-in-memory-hashtables-in-python/#comments</comments>
		<pubDate>Wed, 25 Apr 2012 14:13:57 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[cloud computing]]></category>
		<category><![CDATA[cloud]]></category>
		<category><![CDATA[ec2]]></category>
		<category><![CDATA[hashtables]]></category>
		<category><![CDATA[python]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2300</guid>
		<description>Large-scale in-memory key-value stores are universally useful (e.g. to load and serve tsv-data created by hadoop/mapreduce jobs), in-memory key-value stores have low latency, and modern boxes have lots of memory (e.g. EC2 intances with 70GB RAM). If you look closely many of the nosql-stores are heavily dependent on huge amounts of RAM to perform nicely [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/TH_Q2oJSKNE" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2012/04/25/atbr-large-scale-in-memory-hashtables-in-python/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		<feedburner:origLink>http://atbrox.com/2012/04/25/atbr-large-scale-in-memory-hashtables-in-python/</feedburner:origLink></item>
		<item>
		<title>Monodroid with Sencha Touch for App Development</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/Ne4Cc--TXog/</link>
		<comments>http://atbrox.com/2012/03/07/monodroid-with-sencha-touch-for-app-development/#comments</comments>
		<pubDate>Wed, 07 Mar 2012 13:28:09 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[Android]]></category>
		<category><![CDATA[iOS]]></category>
		<category><![CDATA[mobile]]></category>
		<category><![CDATA[Windows 8]]></category>
		<category><![CDATA[android]]></category>
		<category><![CDATA[html5]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2276</guid>
		<description>Selecting development tools for an app depend on several criteria, e.g. Should the app run on multiple mobile/tablet/pad platforms? (e.g. Android, iOS, Windows 8 etc.) Access to native features Is the UI &amp;#8220;game like&amp;#8221; (e.g. custom graphics) or &amp;#8220;business like&amp;#8221; (e.g. standardized forms/input fields etc.) Mono &amp;#8211; the opensource version of C#/.net platform &amp;#8211; seems [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/Ne4Cc--TXog" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2012/03/07/monodroid-with-sencha-touch-for-app-development/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		<feedburner:origLink>http://atbrox.com/2012/03/07/monodroid-with-sencha-touch-for-app-development/</feedburner:origLink></item>
		<item>
		<title>Distributed Tracer Bullet Development</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/EtatYbEuZeU/</link>
		<comments>http://atbrox.com/2012/01/24/tracerbullet-hadoop/#comments</comments>
		<pubDate>Tue, 24 Jan 2012 10:18:42 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[information retrieval]]></category>
		<category><![CDATA[infrastructure]]></category>
		<category><![CDATA[tracer bullet development]]></category>
		<category><![CDATA[json]]></category>
		<category><![CDATA[software engineering]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2198</guid>
		<description>Tracer Bullet Development Tracer Bullet Development is finding the major &amp;#8220;moving parts&amp;#8221; of a software system and start by writing enough code to make those parts interact in a real manner (e.g. with direct API-calls, websocket or REST-APIs), and as the system grows (with actual functionality and not just interaction) keep the &amp;#8220;tracer ammunition&amp;#8221; flowing [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/EtatYbEuZeU" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2012/01/24/tracerbullet-hadoop/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		<feedburner:origLink>http://atbrox.com/2012/01/24/tracerbullet-hadoop/</feedburner:origLink></item>
		<item>
		<title>Workshop: Mapreduce’12 – 3rd International Workshop on Mapreduce and its applications)</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/p6g2uvjEZC8/</link>
		<comments>http://atbrox.com/2011/11/30/workshop-mapreduce12-3rd-international-workshop-on-mapreduce-and-its-applications/#comments</comments>
		<pubDate>Wed, 30 Nov 2011 08:16:22 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[hadoop]]></category>
		<category><![CDATA[mapreduce]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2146</guid>
		<description>If you are interested in Mapreduce or Hadoop I recommend submitting to or attending the following workshop. The Third International Workshop on MapReduce and its Applications (MAPREDUCE&amp;#8217;12) June 18-19, 2012 HPDC&amp;#8217;2012, Delft, the Netherlands. http://graal.ens-lyon.fr/mapreduce/ SCOPE ===== Since its introduction in 2004 by Google, MapReduce has become the programming model of choice for processing large [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/p6g2uvjEZC8" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2011/11/30/workshop-mapreduce12-3rd-international-workshop-on-mapreduce-and-its-applications/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		<feedburner:origLink>http://atbrox.com/2011/11/30/workshop-mapreduce12-3rd-international-workshop-on-mapreduce-and-its-applications/</feedburner:origLink></item>
		<item>
		<title>Workshop: Searching for fun</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/qdlf7siQVAs/</link>
		<comments>http://atbrox.com/2011/11/23/workshop-searching-for-fun/#comments</comments>
		<pubDate>Wed, 23 Nov 2011 20:59:59 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[cfp]]></category>
		<category><![CDATA[entertainment]]></category>
		<category><![CDATA[information retrieval]]></category>
		<category><![CDATA[query intent]]></category>
		<category><![CDATA[search]]></category>
		<category><![CDATA[workshop]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2132</guid>
		<description>If you are interested in search I recommend you to consider submitting a paper to or attending the Searching 4 fun workshop* (I just joined as a program committee member) which is going to be held in Barcelona in April 2012. Call for Papers The topics of the workshop will be evaluation focused and include [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/qdlf7siQVAs" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2011/11/23/workshop-searching-for-fun/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		<feedburner:origLink>http://atbrox.com/2011/11/23/workshop-searching-for-fun/</feedburner:origLink></item>
		<item>
		<title>Mapreduce &amp; Hadoop Algorithms in Academic Papers (5th update – Nov 2011)</title>
		<link>http://feedproxy.google.com/~r/atbrox/~3/JYyXHNW2i-Q/</link>
		<comments>http://atbrox.com/2011/11/09/mapreduce-hadoop-algorithms-in-academic-papers-5th-update-%e2%80%93-nov-2011/#comments</comments>
		<pubDate>Wed, 09 Nov 2011 12:26:27 +0000</pubDate>
		<dc:creator>Amund Tveit</dc:creator>
				<category><![CDATA[hadoop]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[mapreduce]]></category>

		<guid isPermaLink="false">http://atbrox.com/?p=2103</guid>
		<description>The prior update of this posting was in May, and a lot has happened related to Mapreduce and Hadoop since then, e.g. 1) big software companies have started offering hadoop-based software (Microsoft and Oracle), 2) Hadoop-startups have raised record amounts, and 3) nosql-landscape becoming increasingly datawarehouse&amp;#8217;ish and sql&amp;#8217;ish with the focus on high-level data processing [...]&lt;img src="http://feeds.feedburner.com/~r/atbrox/~4/JYyXHNW2i-Q" height="1" width="1"/&gt;</description>
		<wfw:commentRss>http://atbrox.com/2011/11/09/mapreduce-hadoop-algorithms-in-academic-papers-5th-update-%e2%80%93-nov-2011/feed/</wfw:commentRss>
		<slash:comments>5</slash:comments>
		<feedburner:origLink>http://atbrox.com/2011/11/09/mapreduce-hadoop-algorithms-in-academic-papers-5th-update-%e2%80%93-nov-2011/</feedburner:origLink></item>
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