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	<title>Andrei Tupitcyn</title>
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	<description>Путешествия, инвестиции, программирование и моя жизнь в Риге</description>
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		<title>Understanding implicit in Scala</title>
		<link>https://andr83.io/en/1384/</link>
					<comments>https://andr83.io/en/1384/#respond</comments>
		
		<dc:creator><![CDATA[andr83]]></dc:creator>
		<pubDate>Thu, 19 Apr 2018 16:20:18 +0000</pubDate>
				<category><![CDATA[Development]]></category>
		<category><![CDATA[fp]]></category>
		<category><![CDATA[implicit]]></category>
		<category><![CDATA[scala]]></category>
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					<description><![CDATA[<span class="rt-reading-time" style="display: block;"><span class="rt-label rt-prefix">Reading Time: </span> <span class="rt-time">4</span> <span class="rt-label rt-postfix">minutes</span></span> During last year I have had several conversations with my friends from Java world about their experience in Scala. Most of them tried to use Scala as better Java of course and after returned back with reason - Scala is too powerful and there are a lot of ways to do one thing. The main concern was about implicit. 
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		<title>How to work with Hive tables with a lot of partitions from Spark</title>
		<link>https://andr83.io/en/1090/</link>
					<comments>https://andr83.io/en/1090/#respond</comments>
		
		<dc:creator><![CDATA[andr83]]></dc:creator>
		<pubDate>Sun, 28 Jan 2018 22:54:48 +0000</pubDate>
				<category><![CDATA[Development]]></category>
		<category><![CDATA[Hive]]></category>
		<category><![CDATA[Spark]]></category>
		<category><![CDATA[Sql]]></category>
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					<description><![CDATA[<span class="rt-reading-time" style="display: block;"><span class="rt-label rt-prefix">Reading Time: </span> <span class="rt-time">3</span> <span class="rt-label rt-postfix">minutes</span></span> One of the common practice to improve performance of Hive queries is partitioning. Partitions are simply parts of data separated by one or more fields...
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