[jira] [Created] (HADOOP-15407) Support Windows Azure Storage - Blob file system in Hadoop

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[jira] [Created] (HADOOP-15407) Support Windows Azure Storage - Blob file system in Hadoop

JIRA jira@apache.org
Esfandiar Manii created HADOOP-15407:

             Summary: Support Windows Azure Storage - Blob file system in Hadoop
                 Key: HADOOP-15407
                 URL: https://issues.apache.org/jira/browse/HADOOP-15407
             Project: Hadoop Common
          Issue Type: New Feature
          Components: fs/azure
    Affects Versions: 3.2.0
            Reporter: Esfandiar Manii
            Assignee: Esfandiar Manii

{color:#212121}This JIRA adds a new file system implementation, ABFS, for running Big Data and Analytics workloads against Azure Storage. This is a complete rewrite of the previous WASB driver with a heavy focus on optimizing both performance and cost.{color}
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{color:#212121}{color:#333333}High level design{color}{color}
{color:#212121}At a high level, the code here extends the FileSystem class to provide an implementation for accessing blobs in Azure Storage. The scheme abfs is used for accessing it over HTTP, and abfss for accessing over HTTPS. The following URI scheme is used to address individual paths:{color}
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{color:#212121}ABFS is intended as a replacement to WASB. WASB is not deprecated but is in pure maintenance mode and customers should upgrade to ABFS once it hits General Availability later in CY18.{color}
{color:#212121}Benefits of ABFS include:{color}
{color:#212121}·         Higher scale (capacity, throughput, and IOPS) Big Data and Analytics workloads by allowing higher limits on storage accounts{color}
{color:#212121}·         Removing any ramp up time with Storage backend partitioning; blocks are now automatically sharded across partitions in the Storage backend{color}
{color:#212121}o    This avoids the need for using temporary/intermediate files, increasing the cost (and framework complexity around committing jobs/tasks){color}
{color:#212121}·         Enabling much higher read and write throughput on single files (tens of Gbps by default){color}
{color:#212121}·         Still retaining all of the Azure Blob features customers are familiar with and expect, and gaining the benefits of future Blob features as well{color}
{color:#212121}ABFS incorporates Hadoop Filesystem metrics to monitor the file system throughput and operations. Ambari metrics are not currently implemented for ABFS, but will be available soon.{color}
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{color:#212121}{color:#333333}Credits and history{color}{color}
{color:#212121}Credit for this work goes to (hope I don't forget anyone): Shane Mainali, {color}{color:#212121}Thomas Marquardt, Zichen Sun, Georgi Chalakov, Esfandiar Manii, Amit Singh, Dana Kaban, Da Zhou, Junhua Gu, Saher Ahwal, Saurabh Pant, and James Baker. {color}
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{color:#212121}ABFS has gone through many test procedures including Hadoop file system contract tests, unit testing, functional testing, and manual testing. All the Junit tests provided with the driver are capable of running in both sequential/parallel fashion in order to reduce the testing time.{color}
{color:#212121}Besides unit tests, we have used ABFS as the default file system in Azure HDInsight. Azure HDInsight will very soon offer ABFS as a storage option. (HDFS is also used but not as default file system.) Various different customer and test workloads have been run against clusters with such configurations for quite some time. Benchmarks such as Tera*, TPC-DS, Spark Streaming and Spark SQL, and others have been run to do scenario, performance, and functional testing. Third parties and customers have also done various testing of ABFS.{color}
{color:#212121}The current version reflects to the version of the code tested and used in our production environment.{color}

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