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    <title>Pinboard (jm)</title>
    <link>https://pinboard.in/u:jm/public/</link>
    <description>recent bookmarks from jm</description>
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      <rdf:Seq>	<rdf:li rdf:resource="https://songlh.github.io/paper/go-study.pdf"/>
	<rdf:li rdf:resource="https://labs.spotify.com/2015/01/09/personalization-at-spotify-using-cassandra/"/>
	<rdf:li rdf:resource="http://ferd.ca/beating-the-cap-theorem-checklist.html"/>
	<rdf:li rdf:resource="http://basho.com/how-raid-fits-in-with-horizontal-scalability-on-commodity-hardware/"/>
	<rdf:li rdf:resource="http://lab050.com/blog/2013/3/7/how-we-use-riak-to-model-complexer-data-types"/>
	<rdf:li rdf:resource="http://www.cloudera.com/blog/2011/03/avoiding-full-gcs-in-hbase-with-memstore-local-allocation-buffers-part-3/"/>
	<rdf:li rdf:resource="http://blosc.pytables.org/trac"/>
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  </channel><item rdf:about="https://songlh.github.io/paper/go-study.pdf">
    <title>&quot;Understanding Real-World Concurrency Bugs in Go&quot; (paper)</title>
    <dc:date>2019-03-02T21:21:02+00:00</dc:date>
    <link>https://songlh.github.io/paper/go-study.pdf</link>
    <dc:creator>jm</dc:creator><description><![CDATA['Go advocates for the usage of message passing as the means of inter-thread communication and provides several new concurrency mechanisms and libraries to ease multi-threading programming. It is important to understand the implication of these new proposals and the comparison of message passing and shared memory synchronization in terms of program errors, or bugs. Unfortunately, as far as we know, there has been no study on Go’s concurrency bugs. In this paper, we perform the first systematic study on concurrency bugs in real Go programs. We studied six popular Go software including Docker, Kubernetes, and gRPC.

We analyzed 171 concurrency bugs in total, with more than half of them caused by non-traditional, Go-specific problems. Apart from root causes of these bugs, we also studied their fixes, performed experiments to reproduce them, and evaluated them with two publicly-available Go bug detectors.
Overall, our study provides a better understanding on Go’s concurrency models and can guide future researchers and practitioners in writing better, more reliable Go software and in developing debugging and diagnosis tools for Go.'

(via Bill de hOra)]]></description>
<dc:subject>via:dehora golang go concurrency bugs lint synchronization threading threads bug-detection</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:jm/b:e389d307df9b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:golang"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:go"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:concurrency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:bugs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:lint"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:synchronization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:threading"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:threads"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:bug-detection"/>
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</item>
<item rdf:about="https://labs.spotify.com/2015/01/09/personalization-at-spotify-using-cassandra/">
    <title>Personalization at Spotify using Cassandra</title>
    <dc:date>2015-01-12T22:29:19+00:00</dc:date>
    <link>https://labs.spotify.com/2015/01/09/personalization-at-spotify-using-cassandra/</link>
    <dc:creator>jm</dc:creator><description><![CDATA[Lots and lots of good detail into the Spotify C* setup (via Bill de hOra)]]></description>
<dc:subject>via:dehora spotify cassandra replication storage ops</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:jm/b:13abf373f291/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:spotify"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:cassandra"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:replication"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:storage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:ops"/>
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</item>
<item rdf:about="http://ferd.ca/beating-the-cap-theorem-checklist.html">
    <title>Beating the CAP Theorem Checklist</title>
    <dc:date>2013-08-15T12:52:36+00:00</dc:date>
    <link>http://ferd.ca/beating-the-cap-theorem-checklist.html</link>
    <dc:creator>jm</dc:creator><description><![CDATA['Your ( ) tweet ( ) blog post ( ) marketing material ( ) online comment
advocates a way to beat the CAP theorem. Your idea will not work. Here is why
it won't work:'

lovely stuff, via Bill De hOra]]></description>
<dc:subject>via:dehora funny cap cs distributed-systems distcomp networking partitions state checklists</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:jm/b:93061ca7f005/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:cap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:cs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:distcomp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:networking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:partitions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:state"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:checklists"/>
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</item>
<item rdf:about="http://basho.com/how-raid-fits-in-with-horizontal-scalability-on-commodity-hardware/">
    <title>how RAID fits in with Riak</title>
    <dc:date>2013-06-27T20:51:14+00:00</dc:date>
    <link>http://basho.com/how-raid-fits-in-with-horizontal-scalability-on-commodity-hardware/</link>
    <dc:creator>jm</dc:creator><description><![CDATA[<blockquote>Write heavy, high performance applications should probably use RAID 0 or avoid RAID altogether and consider using a larger n_val and cluster size. Read heavy applications have more options, and generally demand more fault tolerance with the added benefit of easier hardware replacement procedures.</blockquote>

Good to see official guidance on this (via Bill de hOra)]]></description>
<dc:subject>via:dehora riak cluster fault-tolerance raid ops</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:jm/b:b571236a5098/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:cluster"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:fault-tolerance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:raid"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:ops"/>
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</item>
<item rdf:about="http://lab050.com/blog/2013/3/7/how-we-use-riak-to-model-complexer-data-types">
    <title>Riakking Complex Data Types</title>
    <dc:date>2013-03-12T21:47:39+00:00</dc:date>
    <link>http://lab050.com/blog/2013/3/7/how-we-use-riak-to-model-complexer-data-types</link>
    <dc:creator>jm</dc:creator><description><![CDATA[interesting details about Riak's support for secondary indexes.  Not quite SQL, but still more powerful than plain old K/V storage (via dehora)]]></description>
<dc:subject>via:dehora riak indexes storage nosql key-value-stores 2i range-queries</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:jm/b:aff9cce8a81d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:via:dehora"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:indexes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:storage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:nosql"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:key-value-stores"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:2i"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:range-queries"/>
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</item>
<item rdf:about="http://www.cloudera.com/blog/2011/03/avoiding-full-gcs-in-hbase-with-memstore-local-allocation-buffers-part-3/">
    <title>Avoiding Full GCs in HBase with MemStore-Local Allocation Buffers</title>
    <dc:date>2011-10-22T21:20:06+00:00</dc:date>
    <link>http://www.cloudera.com/blog/2011/03/avoiding-full-gcs-in-hbase-with-memstore-local-allocation-buffers-part-3/</link>
    <dc:creator>jm</dc:creator><description><![CDATA[Fascinating. Evading the Java GC by reimplementing a slab allocator, basically]]></description>
<dc:subject>memory allocation java gc jvm hbase memstore via:dehora slab-allocator</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:jm/b:76e4aad99f52/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:jvm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:hbase"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:memstore"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:via:dehora"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:slab-allocator"/>
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</item>
<item rdf:about="http://blosc.pytables.org/trac">
    <title>Blosc</title>
    <dc:date>2010-10-12T14:03:09+00:00</dc:date>
    <link>http://blosc.pytables.org/trac</link>
    <dc:creator>jm</dc:creator><description><![CDATA[A high-performance compressor optimized for binary data -- 'designed to transmit data to the processor cache faster than a traditional, non-compressed, direct memory fetch via memcpy()' (via Bill de hOra)]]></description>
<dc:subject>via:dehora compression memcpy caching l1 software memory optimization performance python pytables</dc:subject>
<dc:identifier>https://pinboard.in/u:jm/b:093f869daa95/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:memcpy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:caching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:l1"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:memory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:performance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:jm/t:python"/>
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