http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2017/11/floss.html
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-                <img src="//crail.incubator.apache.org/img/crail_logo.png"
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-                    alt="Crail" id="logo">
-            </a>
-          </div>
-
-          
-          
-          <h2>Floss</h2>   
-          
-
-          <p>Crail features in the <a 
href="https://twit.tv/shows/floss-weekly/episodes/458?autostart=false";>FLOSS 
weekly podcast</a></p>
-
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-          <div class="footer">
-            <p>Apache Crail is an effort undergoing <a 
href="https://incubator.apache.org/";>incubation</a> at <a 
href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
-          </div>
-
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-
-        <!-- Support retina images. -->
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-            src="//crail.incubator.apache.org/js/srcset-polyfill.js"></script>
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-</html>

http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2017/11/iops.html
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-        <title>The Apache Crail (Incubating) Project: Iops</title>
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-                <img src="//crail.incubator.apache.org/img/crail_logo.png"
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-            </a>
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-
-          
-          
-          <h2>Iops</h2>   
-          
-
-          <p>New blog <a 
href="//crail.incubator.apache.org/blog/2017/11/crail-metadata.html">post</a> 
about Crail’s metadata performance and scalability</p>
-
-
-        <br>
-       <br> 
-          <div class="footer">
-            <p>Apache Crail is an effort undergoing <a 
href="https://incubator.apache.org/";>incubation</a> at <a 
href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
-          </div>
-
-        </div> <!-- /container -->
-
-        <!-- Support retina images. -->
-        <script type="text/javascript"
-            src="//crail.incubator.apache.org/js/srcset-polyfill.js"></script>
-    </body>
-</html>

http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2017/11/rdmashuffle.html
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-        <title>The Apache Crail (Incubating) Project: Spark Shuffle: SparkRDMA 
vs Crail</title>
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-                    alt="Crail" id="logo">
-            </a>
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-
-          
-          
-          <h2>Spark Shuffle: SparkRDMA vs Crail</h2>   
-          
-
-          <p class="meta">17 Nov 2017,  <mark>this is a blog post from a user 
of the Crail project.</mark>  </p>
-
-<div class="post">
-<div style="text-align: justify">
-<p>
-This blog is comparing the shuffle performance of Crail with SparkRDMA, an 
alternative RDMA-based shuffle plugin for Spark.
-</p>
-</div>
-
-<h3 id="hardware-configuration">Hardware Configuration</h3>
-
-<p>The specific cluster configuration used for the experiments in this 
blog:</p>
-
-<ul>
-  <li>Cluster
-    <ul>
-      <li>8 compute + 1 management node x86_64 cluster</li>
-    </ul>
-  </li>
-  <li>Node configuration
-    <ul>
-      <li>CPU: 2 x Intel(R) Xeon(R) CPU E5-2690 0 @ 2.90GHz</li>
-      <li>DRAM: 96GB DDR3</li>
-      <li>Network: 1x100Gbit/s Mellanox ConnectX-5</li>
-    </ul>
-  </li>
-  <li>Software
-    <ul>
-      <li>Ubuntu 16.04.3 LTS (Xenial Xerus) with Linux kernel version 
4.10.0-33-generic</li>
-      <li><a href="https://github.com/zrlio/crail";>Crail 1.0</a>, commit 
a45c8382050f471e9342e1c6cf25f9f2001af6b5</li>
-      <li><a href="">Crail Shuffle plugin</a>, commit 
2273b5dd53405cab3389f5c1fc2ee4cd30f02ae6</li>
-      <li><a href="https://github.com/Mellanox/SparkRDMA";>SparkRDMA</a>, 
commit d95ce3e370a8e3b5146f4e0ab5e67a19c6f405a5 (latest master on 8th of 
November 2017)</li>
-    </ul>
-  </li>
-</ul>
-
-<h3 id="overview">Overview</h3>
-<div style="text-align: justify">
-<p>
-Lately there has been an increasing interest in the community to include RDMA 
networking into data processing frameworks like Spark and Hadoop. One natural 
spot to integrate RDMA is in the shuffle operation that involves all-to-all 
network communication pattern. Naturally, due to its performance requirements 
the shuffle operation is of interest to us as well, and we have developed a 
Spark plugin for shuffle. In our previous blog posts, we have already shown 
that the Crail Shuffler achieves great workload-level speedups compared to 
vanilla Spark. In this blog post, we take a look at another recently proposed 
design called <a href="https://github.com/Mellanox/SparkRDMA";>SparkRDMA</a> (<a 
href="https://issues.apache.org/jira/browse/SPARK-22229";>SPARK-22229 JIRA</a>). 
SparkRDMA proposes to improve the shuffle performance of Spark by performing 
data transfers over RDMA. For this, the code manages its own off-heap memory 
which needs to be registered with the NIC for RDMA use. It supports 
 two ways to store shuffle data between the stages: (1) shuffle data is stored 
in regular files (just like vanilla Spark) but the data transfer is implemented 
via RDMA, (2) data is stored in memory (allocated and registered for RDMA 
transfer) and the data transfer is implemented via RDMA. We call it the 
"last-mile" approach where just the networking operations are replaced by the 
RDMA operations.
-</p>
-<p>
-In contrast, the Crail shuffler plugin takes a more holistic approach and 
leverages the high performance of Crail distributed data store to deliver 
gains. It uses Crail store to efficiently manage I/O resources, storage and 
networking devices, memory registrations, client sessions, data distribution, 
etc. Consequently, the shuffle operation becomes as simple as writing and 
reading files. And recall that Crail store is designed as a fast data bus for 
the intermediate data. The shuffle operation is just one of many operations 
that can be accelerated using Crail store. Beyond these operations, the modular 
architecture of Crail store enables us to seamlessly leverage different storage 
types (DRAM, NVMe, and more), perform tiering, support disaggregation, share 
inter-job data, jointly optimize I/O resources for various workloads, etc. 
These capabilities and performance gains give us confidence in the design 
choices we made for the Crail project.
-</p>
-</div>
-
-<h3 id="performance-comparison">Performance comparison</h3>
-<div style="text-align: justify">
-<p>Lets start by quantitatively assessing performance gains from the Crail 
shuffle plugin and SparkRDMA. As described above, SparkRDMA can be operated in 
two different modes. Users decide which mode to use by selecting a particular 
type of shuffle writer (spark.shuffle.rdma.shuffleWriterMethod). The Wrapper 
shuffle writer writes shuffle data to files between the stages, the Chunked 
shuffle writer stores shuffle data in memory. We evaluate both writer methods 
for terasort and SQL equijoin.
-</p>
-</div>
-<div style="text-align:center"><img 
src="//crail.incubator.apache.org/img/blog/rdma-shuffle/terasort.svg" 
width="550" /></div>
-<p><br /></p>
-<div style="text-align: justify">
-<p>
-First we run <a 
href="https://github.com/zrlio/crail-spark-terasort";>terasort</a> on our 8+1 
machine cluster (see above). We sort 200GB, thus, each node gets 25GB of data 
(equal distribution). We further did a basic search of the parameter space for 
each of the systems to find the best possible configuration. In all the 
experiments we use 8 executors with 12 cores each. Note that in a typical Spark 
run more CPU cores than assigned are engaged because of garbabge collection, 
etc. In our test runs assigning 12 cores lead to the best performance.
-</p>
-<p>
-The plot above shows runtimes of the various configuration we run with 
terasort. SparkRDMA with the Wrapper shuffle writer performance slightly better 
(3-4%) than vanilla Spark whereas the Chunked shuffle writer shows a 30% 
overhead. On a quick inspection we found that this overhead stems from memory 
allocation and registration for the shuffle data that is kept in memory between 
the stages. Compared to vanilla Spark, Crail's shuffle plugin shows performance 
improvement of around 235%.
-</p>
-</div>
-<div style="text-align:center"><img 
src="//crail.incubator.apache.org/img/blog/rdma-shuffle/sql.svg" width="550" 
/></div>
-<p><br /></p>
-
-<div style="text-align: justify">
-<p>
-For our second workload we choose the <a 
href="https://github.com/zrlio/sql-benchmarks";>SQL equijoin</a> with a <a 
href="https://github.com/zrlio/spark-nullio-fileformat";>special fileformat</a> 
that allows data to be generated on the fly. By generating data on the fly we 
eliminate any costs for reading data from storage and focus entirely on the 
shuffle performance. The shuffle data size is around 148GB. Here the Wrapper 
shuffle writer is slightly slower than vanilla Spark but instead the Chunked 
shuffle writer is roughly the same amount faster. The Crail shuffle plugin 
again delivers a great performance increase over vanilla Spark.
-</p>
-</div>
-
-<div style="text-align: justify">
-<p>Please let us know if your have recommendations about how these experiments 
can be improved.</p>
-</div>
-
-<h3 id="summary">Summary</h3>
-
-<div style="text-align: justify">
-<p>
-These benchmarks validate our belief that a "last-mile" integration cannot 
deliver the same performance gains as a holistic approach, i.e. one has to look 
at the whole picture in how to integrate RDMA into Spark applications (and for 
that matter any framework or application). Only replacing the data transfer 
alone does not lead to the anticipated performance increase. We learned this 
the hard way when we intially started working on Crail.
-</p>
-
-</div>
-
-
-</div>
-
-<!-- 
-
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-          <div class="footer">
-            <p>Apache Crail is an effort undergoing <a 
href="https://incubator.apache.org/";>incubation</a> at <a 
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sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
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http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2018/01/apache.html
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-          <h2>Apache</h2>   
-          
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-          <p>Crail is now an Apache Incubator project!</p>
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-       <br> 
-          <div class="footer">
-            <p>Apache Crail is an effort undergoing <a 
href="https://incubator.apache.org/";>incubation</a> at <a 
href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
-          </div>
-
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-
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http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2018/06/apache-release.html
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-                <img src="//crail.incubator.apache.org/img/crail_logo.png"
-                    srcset="//crail.incubator.apache.org/img/crail_logo.png"
-                    alt="Crail" id="logo">
-            </a>
-          </div>
-
-          
-          
-          <h2>Apache Release</h2>   
-          
-
-          <p>Apache Crail 1.0 incubator <a 
href="//crail.incubator.apache.org/download">release</a></p>
-
-
-        <br>
-       <br> 
-          <div class="footer">
-            <p>Apache Crail is an effort undergoing <a 
href="https://incubator.apache.org/";>incubation</a> at <a 
href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
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http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2018/06/dataworks.html
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-
-          
-          
-          <h2>Dataworks</h2>   
-          
-
-          <p>Apache Crail (incubating) to feature in the <a 
href="https://dataworkssummit.com/san-jose-2018/session/data-processing-at-the-speed-of-100-gbpsapache-crail-incubating/";>DataWorks
 Summit</a> on June 21st</p>
-
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-        <br>
-       <br> 
-          <div class="footer">
-            <p>Apache Crail is an effort undergoing <a 
href="https://incubator.apache.org/";>incubation</a> at <a 
href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
-          </div>
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http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2018/06/sparksummit.html
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-                    Documentation
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-                <img src="//crail.incubator.apache.org/img/crail_logo.png"
-                    srcset="//crail.incubator.apache.org/img/crail_logo.png"
-                    alt="Crail" id="logo">
-            </a>
-          </div>
-
-          
-          
-          <h2>Sparksummit</h2>   
-          
-
-          <p>A Spark serverless architecture powered by Crail will be 
presented today at the <a 
href="https://databricks.com/session/serverless-machine-learning-on-modern-hardware-using-apache-spark";>Spark
 Summit</a></p>
-
-
-        <br>
-       <br> 
-          <div class="footer">
-            <p>Apache Crail is an effort undergoing <a 
href="https://incubator.apache.org/";>incubation</a> at <a 
href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
-          </div>
-
-        </div> <!-- /container -->
-
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-        <script type="text/javascript"
-            src="//crail.incubator.apache.org/js/srcset-polyfill.js"></script>
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http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2018/08/sql-p1-news.html
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-<html>
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-        <title>The Apache Crail (Incubating) Project: Sql P1 News</title>
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-                <img src="//crail.incubator.apache.org/img/crail_logo.png"
-                    srcset="//crail.incubator.apache.org/img/crail_logo.png"
-                    alt="Crail" id="logo">
-            </a>
-          </div>
-
-          
-          
-          <h2>Sql P1 News</h2>   
-          
-
-          <p>A new blog <a 
href="//crail.incubator.apache.org/blog/2018/08/sql-p1.html">post</a> 
discussing file formats performance is now online</p>
-
-
-        <br>
-       <br> 
-          <div class="footer">
-            <p>Apache Crail is an effort undergoing <a 
href="https://incubator.apache.org/";>incubation</a> at <a 
href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
-          </div>
-
-        </div> <!-- /container -->
-
-        <!-- Support retina images. -->
-        <script type="text/javascript"
-            src="//crail.incubator.apache.org/js/srcset-polyfill.js"></script>
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-</html>

http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/2018/08/sql-p1.html
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-<html>
-    <head>
-        <meta charset="utf-8">
-        <title>The Apache Crail (Incubating) Project: SQL Performance: Part 1 
- Input File Formats</title>
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-                <img src="//crail.incubator.apache.org/img/crail_logo.png"
-                    srcset="//crail.incubator.apache.org/img/crail_logo.png"
-                    alt="Crail" id="logo">
-            </a>
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-
-          
-          
-          <h2>SQL Performance: Part 1 - Input File Formats</h2>   
-          
-
-          <p class="meta">08 Aug 2018,  <mark>this is a blog post from a user 
of the Crail project.</mark>  </p>
-
-<div class="post">
-<div style="text-align: justify">
-<p>
-This is the first user blog post in a multi-part series where we will focus on 
relational data processing performance (e.g., SQL) in presence of 
high-performance network and storage devices - the kind of devices that Crail 
targets. Relational data processing is one of the most popular and versatile 
workloads people run in the  cloud. The general idea is that data is stored in 
tables with a schema, and is processed using a domain specific language like 
SQL. Examples of some popular systems that support such relational data 
analytics in the cloud are <a href="https://spark.apache.org/sql/";>Apache 
Spark/SQL</a>, <a href="https://hive.apache.org/";>Apache Hive</a>, <a 
href="https://impala.apache.org/";>Apache Impala</a>, etc. In this post, we 
discuss the important first step in relational data processing, which is the 
reading of input data tables.
-</p>
-</div>
-
-<h3 id="hardware-and-software-configuration">Hardware and Software 
Configuration</h3>
-
-<p>The specific cluster configuration used for the experiments in this 
blog:</p>
-
-<ul>
-  <li>Cluster
-    <ul>
-      <li>4 compute + 1 management node x86_64 cluster</li>
-    </ul>
-  </li>
-  <li>Node configuration
-    <ul>
-      <li>CPU: 2 x Intel(R) Xeon(R) CPU E5-2690 0 @ 2.90GHz</li>
-      <li>DRAM: 256 GB DDR3</li>
-      <li>Network: 1x100Gbit/s Mellanox ConnectX-5</li>
-    </ul>
-  </li>
-  <li>Software
-    <ul>
-      <li>Ubuntu 16.04.3 LTS (Xenial Xerus) with Linux kernel version 
4.10.0-33-generic</li>
-      <li>Apache HDFS (2.7.3)</li>
-      <li>Apache Paruqet (1.8), Apache ORC (1.4), Apache Arrow (0.8), Apache 
Avro (1.4)</li>
-      <li><a href="https://github.com/apache/incubator-crail/";>Apache Crail 
(incubating) with NVMeF support</a>, commit 
64e635e5ce9411041bf47fac5d7fadcb83a84355 (since then Crail has a stable source 
release v1.0 with a newer NVMeF code-base)</li>
-    </ul>
-  </li>
-</ul>
-
-<h3 id="overview">Overview</h3>
-
-<p>In a typical cloud-based relational data processing setup, the input data 
is stored on an external data storage solution like HDFS or AWS S3. Data tables 
and their associated schema are converted into a storage-friendly format for 
optimal performance. Examples of some popular and familiar file formats are <a 
href="https://parquet.apache.org/";>Apache Parquet</a>, <a 
href="https://orc.apache.org/";>Apache ORC</a>, <a 
href="https://avro.apache.org/";>Apache Avro</a>, <a 
href="https://en.wikipedia.org/wiki/JSON";>JSON</a>, etc. More recently, <a 
href="https://arrow.apache.org/";>Apache Arrow</a> has been introduced to 
standardize the in-memory columnar data representation between multiple 
frameworks. To be precise, Arrow is not a storage format but it defines an <a 
href="https://github.com/apache/arrow/blob/master/format/IPC.md";>interprocess 
communication (IPC) format</a> that can be used to store data in a stroage 
system (our binding for reading Arrow IPC messages from HDFS is available
  <a 
href="https://github.com/zrlio/fileformat-benchmarks/blob/master/src/main/java/com/github/animeshtrivedi/FileBench/HdfsSeekableByteChannel.java";>here</a>).
 There is no one size fits all as all these formats have their own strengths, 
weaknesses, and features. In this blog, we are specifically interested in the 
performance of these formats on modern high-performance networking and storage 
devices.</p>
-
-<figure><div style="text-align:center"><img 
src="//crail.incubator.apache.org/img/blog/sql-p1/outline.svg" width="550" 
/><figcaption>Figure 1: The benchmarking setup with HDFS and file formats on a 
100 Gbps network with NVMe flash devices. All formats contains routines for 
compression, encoding, and value materialization with associated I/O buffer 
management and data copies routines.<p></p></figcaption></div></figure>
-
-<p>To benchmark the performance of file formats, we wrote a set of 
micro-benchmarks which are available at <a 
href="https://github.com/zrlio/fileformat-benchmarks";>https://github.com/zrlio/fileformat-benchmarks</a>.
 We cannot use typical SQL micro-benchmarks because every SQL engine has its 
own favorite file format, on which it performs the best. Hence, in order to 
ensure parity, we decoupled the performance of reading the input file format 
from the SQL query processing by writing simple table reading micro-benchmarks. 
Our benchmark reads in the store_sales table from the TPC-DS dataset (scale 
factor 100), and calculates a sum of values present in the table. The table 
contains 23 columns of integers, doubles, and longs.</p>
-
-<figure><div style="text-align:center"><img 
src="//crail.incubator.apache.org/img/blog/sql-p1/performance-all.svg" 
width="550" /><figcaption>Figure 2: Performance of JSON, Avro, Parquet, ORC, 
and Arrow on NVMe devices over a 100 Gbps 
network.<p></p></figcaption></div></figure>
-
-<p>We evaluate the performance of the benchmark on a 3 node HDFS cluster 
connected using 100 Gbps RoCE. One datanode in HDFS contains 4 NVMe devices 
with a collective aggregate bandwidth of 12.5 GB/sec (equals to 100 Gbps, 
hence, we have a balanced network and storage performance). Figure 2 shows our 
results where none of the file formats is able to deliver the full hardware 
performance for reading input files. One third of the performance is already 
lost in HDFS (maximum throughput 74.9 Gbps out of possible 100 Gbps). The rest 
of the performance is lost inside the file format implementation, which needs 
to deal with encoding, buffer and I/O management, compression, etc. The best 
performer is Apache Arrow which is designed for in-memory columnar datasets. 
The performance of these file formats are bounded by the performance of the 
CPU, which is 100% loaded during the experiment. For a detailed analysis of the 
file formats, please refer to our paper - <a href="https://www.usenix.org/c
 onference/atc18/presentation/trivedi">Albis: High-Performance File Format for 
Big Data Systems (USENIX, ATC’18)</a>. As a side-note on the Arrow 
performance - we have evaluated the performance of <em>implementation of 
Arrow’s Java library</em>. As this library has been focused on interactions 
with off-heap memory, there is a head room for optimizing the HDFS/on-heap 
reading path of Arrow’s Java library.</p>
-
-<h3 id="albis-high-performance-file-format-for-big-data-systems">Albis: 
High-Performance File Format for Big Data Systems</h3>
-
-<p>Based on these findings, we have developed a new file format called Albis. 
Albis is built on similar design choices as Crail. The top-level idea is to 
leverage the performance of modern networking and storage devices without being 
bottleneck by the CPU. While designing Albis we revisited many outdated 
assumptions about the nature of I/O in a distributed setting, and came up with 
the following ideas:</p>
-
-<ul>
-  <li>No compression or encoding: Modern network and storage devices are fast. 
Hence, there is no need to trade CPU cycles for performance. A 4 byte integer 
should be stored as a 4 byte value.</li>
-  <li>Keep the data/metadata management simple: Albis splits a table into row 
and column groups, which are stored in hierarchical files and directories on 
the underlying file system (e.g., HDFS or Crail).</li>
-  <li>Careful object materialization using a binary API: To optimize the 
runtime representation in managed runtimes like the JVM, only objects which are 
necessary for SQL processing are materialized. Otherwise, a 4 byte integer can 
be passed around as a byte array (using the binary API of Albis).</li>
-</ul>
-
-<figure><div style="text-align:center"><img 
src="//crail.incubator.apache.org/img/blog/sql-p1/core-scalability.svg" 
width="550" /><figcaption>Figure 3: Core scalability of JSON, Avro, Parquet, 
ORC, Arrow, and Albis on HDFS/NVMe.<p></p></figcaption></div></figure>
-
-<p>Using the Albis format, we revise our previous experiment where we read the 
input store_sales table from HDFS. In the figure above, we show the performance 
of Albis and other file formats with number of CPU cores involved. At the right 
hand of the x-axis, we have performance with all 16 cores engaged, hence, 
representing the peak possible performance. As evident, Albis delivers 59.9 
Gbps out of 74.9 Gbps possible bandwidth with HDFS over NVMe. Albis performance 
is 1.9 - 21.4x better than other file formats. To give an impression where the 
performance is coming from, in the table below we show some micro-architectural 
features for Parquet, ORC, Arrow, and Albis. Our previously discussed design 
ideas in Albis result in a shorter code path (shown as less instructions 
required for each row), better cache performance (shows as lower cache misses 
per row), and clearly better performance (shown as nanoseconds required per row 
for processing). For a detailed evaluation of Albis please re
 fer to our paper.</p>
-
-<table style="width:100%">
-  <caption> Table 1: Micro-architectural analysis for Parquet, ORC, Arrow, and 
Albis on a 16-core Xeon machine.<p></p></caption>
-  <tr>
-    <th></th>
-    <th>Parquet</th>
-    <th>ORC</th> 
-    <th>Arrow</th>
-    <th>Albis</th>
-  </tr>
-  <tr>
-    <th>Instructions/row</th>
-    <td>6.6K</td> 
-    <td>4.9K</td> 
-    <td>1.9K</td> 
-    <td>1.6K</td> 
-  </tr>
-  <tr>
-    <th>Cache misses/row</th>
-    <td>9.2</td> 
-    <td>4.6</td> 
-    <td>5.1</td> 
-    <td>3.0</td> 
-  </tr>
-  <tr>
-    <th>Nanoseconds/row</th>
-    <td>105.3</td> 
-    <td>63.0</td> 
-    <td>31.2</td> 
-    <td>20.8</td> 
-  </tr>
-</table>
-<p></p>
-
-<h3 id="apache-crail-incubating-with-albis">Apache Crail (Incubating) with 
Albis</h3>
-
-<p>For our final experiment, we try to answer the question what it would take 
to deliver the full 100 Gbps bandwidth for Albis. Certainly, the first 
bottleneck is to improve the base storage layer performance. Here we use Apache 
Crail (Incubating) with its <a 
href="https://en.wikipedia.org/wiki/NVM_Express#NVMeOF";>NVMeF</a> storage tier. 
This tier uses <a href="https://github.com/zrlio/jNVMf";>jNVMf library</a> to 
implement NVMeF stack in Java. As we have shown in a previous blog <a 
href="//crail.incubator.apache.org/blog/2017/08/crail-nvme-fabrics-v1.html">post</a>
 that Crail’s NVMeF tier can deliver performance (97.8 Gbps) very close to 
the hardware limits. Hence, Albis with Crail is a perfect setup to evaluate on 
high-performance NVMe and RDMA devices. Before we get there, let’s get some 
calculations right. The store_sales table in the TPC-DS dataset has a data 
density of 93.9% (out of 100 bytes, only 93.9 is data, others are null values). 
As we measure the goodput, the expect
 ed performance of Albis on Crail is 93.9% of 97.8 Gbps, which calculates to 
91.8 Gbps. In our experiments, Albis on Crail delivers 85.5 Gbps. Figure 4 
shows more detailed results.</p>
-
-<figure><div style="text-align:center"><img 
src="//crail.incubator.apache.org/img/blog/sql-p1/albis-crail.svg" width="550" 
/><figcaption>Figure 4: Performance of Albis on 
Crail.<p></p></figcaption></div></figure>
-
-<p>The left half of the figure shows the performance scalability of Albis on 
Crail in a setup with 1 core (8.9 Gbps) to 16 cores (85.5 Gbps). In comparison, 
the right half of the figure shows the performance of Crail on HDFS/NVMe at 
59.9 Gbps, and on Crail/NVMe at 85.5 Gbps. The last bar shows the performance 
of Albis if the benchmark does not materialize Java object values. In this 
configuration, Albis on Crail delivers 91.3 Gbps, which is very close to the 
expected peak of 91.8 Gbps.</p>
-
-<h3 id="summary">Summary</h3>
-<div style="text-align: justify">
-<p>
-In this first blog of a multipart series, we have looked at the data ingestion 
performance of file formats on high-performance networking and storage devices. 
We found that popular file formats are in need for a performance revision. 
Based on our analysis, we designed and implemented Albis - a new file format 
for storing relational data. Albis and Crail share many design choices. Their 
combined performance of 85+ Gbps on a 100 Gbps network, gives us confidence in 
our approach and underlying software philosophy for both, Crail and Albis. 
-</p>
-
-<p>
-Stay tuned for the next part where we look at workload-level performance in 
Spark/SQL on modern high-performance networking and storage devices. Meanwhile 
let us know if you have any feedback or comments. 
-</p>
-</div>
-
-</div>
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-    <p class="post-meta"><b>Developer Post</b> by Patrick Stuedi on August 18, 
2017</p>
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http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/page4/index.html
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-    <p class="post-meta"><b>Developer Post</b> by Adrian Schuepbach and 
Patrick Stuedi on November 21, 2017</p>
-    
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-<hr>
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-    <a href="/blog/2017/11/rdmashuffle.html">
-        <h2 class="post-title">
-            Spark Shuffle: SparkRDMA vs Crail
-        </h2>
-        
-    </a>
-               
-    <p class="post-meta"><b>User Post</b> by Jonas Pfefferle, Patrick Stuedi, 
Animesh Trivedi, Bernard Metzler, Adrian Schuepbach on November 17, 2017</p>
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-    <a href="/blog/2017/08/crail-nvme-fabrics-v1.html">
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-            Crail Storage Performance -- Part II: NVMf
-        </h2>
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-    <p class="post-meta"><b>Developer Post</b> by Jonas Pfefferle on August 
22, 2017</p>
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-            Crail Storage Performance -- Part I: DRAM
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-    <p class="post-meta"><b>Developer Post</b> by Patrick Stuedi on August 18, 
2017</p>
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-    <a href="/blog/2017/01/sorting.html">
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-            Sorting on a 100Gbit/s Cluster using Spark/Crail
-        </h2>
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-    </a>
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-    <p class="post-meta"><b>User Post</b> by Patrick Stuedi on January 17, 
2017</p>
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href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
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http://git-wip-us.apache.org/repos/asf/incubator-crail-website/blob/004e8a6d/content/blog/page5/index.html
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-        <h2 class="post-title">
-            SQL Performance: Part 1 - Input File Formats
-        </h2>
-        
-    </a>
-               
-    <p class="post-meta"><b>User Post</b> by Animesh Trivedi on August 8, 
2018</p>
-               
-</div>
-<hr>
-
-<div class="post-preview">
-    <a href="/blog/2017/11/crail-metadata.html">
-        <h2 class="post-title">
-            Crail Storage Performance -- Part III: Metadata
-        </h2>
-        
-    </a>
-               
-    <p class="post-meta"><b>Developer Post</b> by Adrian Schuepbach and 
Patrick Stuedi on November 21, 2017</p>
-    
-</div>
-<hr>
-
-<div class="post-preview">
-    <a href="/blog/2017/11/rdmashuffle.html">
-        <h2 class="post-title">
-            Spark Shuffle: SparkRDMA vs Crail
-        </h2>
-        
-    </a>
-               
-    <p class="post-meta"><b>User Post</b> by Jonas Pfefferle, Patrick Stuedi, 
Animesh Trivedi, Bernard Metzler, Adrian Schuepbach on November 17, 2017</p>
-               
-</div>
-<hr>
-
-<div class="post-preview">
-    <a href="/blog/2017/08/crail-nvme-fabrics-v1.html">
-        <h2 class="post-title">
-            Crail Storage Performance -- Part II: NVMf
-        </h2>
-        
-    </a>
-               
-    <p class="post-meta"><b>Developer Post</b> by Jonas Pfefferle on August 
22, 2017</p>
-    
-</div>
-<hr>
-
-<div class="post-preview">
-    <a href="/blog/2017/08/crail-memory.html">
-        <h2 class="post-title">
-            Crail Storage Performance -- Part I: DRAM
-        </h2>
-        
-    </a>
-               
-    <p class="post-meta"><b>Developer Post</b> by Patrick Stuedi on August 18, 
2017</p>
-    
-</div>
-<hr>
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-<div class="post-preview">
-    <a href="/blog/2017/01/sorting.html">
-        <h2 class="post-title">
-            Sorting on a 100Gbit/s Cluster using Spark/Crail
-        </h2>
-        
-    </a>
-               
-    <p class="post-meta"><b>User Post</b> by Patrick Stuedi on January 17, 
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href="https://incubator.apache.org/";>incubation</a> at <a 
href="https://www.apache.org/";>The Apache Software Foundation (ASF)</a>, 
sponsored by the Apache Incubator PMC. Incubation is required of all newly 
accepted projects until a further review indicates that the infrastructure, 
communications, and decision making process have stabilized in a manner 
consistent with other successful ASF projects. While incubation status is not 
necessarily a reflection of the completeness or stability of the code, it does 
indicate that the project has yet to be fully endorsed by the ASF.
-            </p>
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