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Category: Hadoop

S3 And HDFS Data Migration

Published 2017-06-06 by Kevin Feasel

Ilya Yalovyy looks at S3DistCp, which allows you efficiently to migrate data back and forth between HDFS and S3:

Raw files often land in S3 or HDFS in an uncompressed text format. This format is suboptimal both for the cost of storage and for running analytics on that data. S3DistCp can help you efficiently store data and compress files on the fly with the --outputCodec option:

$ s3-dist-cp --src s3://my-tables/incoming/hourly_table_filtered --dest s3://my-tables/incoming/hourly_table_gz --outputCodec=gz

The current version of S3DistCp supports the codecs gzip, gz, lzo, lzop, and snappy, and the keywords none and keep (the default). These keywords have the following meaning:

  • “none” – Save files uncompressed. If the files are compressed, then S3DistCp decompresses them.

  • “keep” – Don’t change the compression of the files but copy them as-is.

This is an important article if you’ve got a Hadoop cluster running on EC2 nodes.

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Using Hive As A Power BI Data Source

Published 2017-06-05 by Kevin Feasel

Ust Oldfield shows how to use Hive via Azure HDInsight as a data source for Power BI:

As Hive is part of the Azure HDInsight stack it would be tempting to select the HDInsight or Hadoop connector when you’re getting data. However, note HDFS in brackets beside the Azure HDInsight and Hadoop File options as this means that you’ll be connecting to the underlying data store, which can be Azure Data Lake Store or Azure Blob Storage – both of which use HDFS architectures.

But this doesn’t help when you want to access a Hive table. In order to access a Hive table you will first of all need to install the Hive ODBC driver from Microsoft. Once you’ve downloaded and installed the driver you’ll be able to make your connection to Hive using the ODBC connector in PowerBI.

Read the whole thing.  Connecting to Hive is pretty easy.

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S3 Versus HDFS For Spark Data Storage

Published 2017-06-02 by Kevin Feasel

Reynold Xin, Josh Rosen, and Kyle Pistor argue that you should use blob storage (S3, Azure Blob, etc.) instead of disk when building a cloud-based Spark cluster:

Based on our experience, S3’s availability has been fantastic. Only twice in the last six years have we experienced S3 downtime and we have never experienced data loss from S3.

Amazon claims 99.999999999% durability and 99.99% availability. Note that this is higher than the vast majority of organizations’ in-house services. The official SLA from Amazon can be found here: Service Level Agreement – Amazon Simple Storage Service (S3).

For HDFS, in contrast, it is difficult to estimate availability and durability. One could theoretically compute the two SLA attributes based on EC2’s mean time between failures (MTTF), plus upgrade and maintenance downtimes. In reality, those are difficult to quantify. Our understanding working with customers is that the majority of Hadoop clusters have availability lower than 99.9%, i.e. at least 9 hours of downtime per year.

It’s interesting how opinion has shifted; even a year ago, the recommendation would be different.

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Spark Changes In HDP 2.6

Published 2017-05-25 by Kevin Feasel

Vinay Shukla and Syed Mahmood talk about what’s new with Spark and Zeppelin in the Hortonworks Data Platform 2.6 update:

SPARKR & PYSPARK

Most data scientists use R & Python and with SparkR & PySpark respectively they can continue to leverage their familiarity with the R & Python languages. However, they need to use the Spark API to leverage Machine learning with Spark and to take advantage of distributed computations. Both SparkR & PySpark are evolving rapidly and SparkR now supports a number of machine learning algorithms such as LDA, ALS, RF, GMM GBT etc. Another key improvement in SparkR is the ability to deploy a package interactively. This will help Data Scientists deploy their favorite R package in their own environment without stepping on other users.

PySpark now also supports deploying VirtualEnv and this will allow PySpark users to deploy their libraries in their own individual deployments.

There are several large changes, so check it out.

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Presto On HDInsight

Published 2017-05-23 by Kevin Feasel

Ashish Thapliyal shows how to install Presto on an HDInsight cluster:

What is Presto?

Presto is a distributed SQL query engine optimized for ad-hoc analysis at interactive speed. It supports standard ANSI SQL, including complex queries, aggregations, joins, and window functions. Presto is becoming popular SQL interactive query engine that has grabbed the attention and mind-share in Big data communities.

What are the key advantages of Presto?

1- It’s very fast – Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses.

2- Presto can query data where it lives – Presto supports many data sources via the number of connectors that community has built. You can query HDFS , Hive, Azure Storage or data stored in SQL Server , My SQL , CosmosDB or Cassandra etc.

You can install Presto in one simple step with HDInsight Script Action feature

Read on for instructions and showing how to connect this to other Azure products like CosmosDB and Azure SQL Database.

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Star-Schema Benchmark With Hive + Druid

Published 2017-05-23 by Kevin Feasel

Carter Shanklin and Slim Bouguerra run a Hadoop OLAP system running Hive and Druid against the Star-Schema Benchmark battery of queries:

How did we arrive at the query used to build the OLAP index? There is a systematic procedure:

  1. The union of all dimensions used by the SSB queries is included in the index.
  2. The union of all measures is included in the index. Notice that we pre-compute some products in the index.
  3. Druid requires a timestamp, so the date of the transaction is used as the timestamp.

You can see that building the index requires knowledge of the query patterns. Either an expert in the query patterns architects the index, or a tool is needed to analyze queries or to dynamically build indexes on the fly. A lot of time can be spent in this architecture phase, gathering requirements, designing measures and so on, because changing your mind after-the-fact can be very difficult.

One thing I don’t like so much is that they removed the ORDER BY clauses from some of the queries, as making this change makes it more difficult to use these results for “it’s totally not a comparison so don’t sue us Oracle” purposes.

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Securing Kafka-To-Spark

Published 2017-05-22 by Kevin Feasel

Mark Grover explains how to secure communications between Apache Kafka and Apache Spark:

However, to read data from secure Kafka in distributed fashion, we need Hadoop-style delegation tokens in Kafka (KAFKA-1696), support for which doesn’t exist at the time of this writing (Spring 2017).

We considered various ways to solve this problem but ultimately decided that the recommended solution to read data securely from Kafka (at least until Kafka delegation tokens support is introduced) would be for the Spark application to distribute the user’s keytab so it’s accessible to the executors. The executors will then use the user’s keytab shared with them, to authenticate with the Kerberos Key Distribution Center (KDC) and read from Kafka brokers. YARN distributed cache is used for shipping and sharing the keytab to the driver and executors, from the client (that is, the gateway node). The figure below shows an overview of the current solution.

This turns out to be a bit more difficult than I would have anticipated.

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Multiple R Studio Users On HDInsight

Published 2017-05-18 by Kevin Feasel

Xiaoyong Zhu shows how to set up additional R Studio users in an HDInsight cluster:

Basically speaking, the “http user” will be used to authenticate through the HDInsight gateway, which is used to protect the HDInsight clusters you created. This user is used to access the Ambari UI, YARN UI, as well as many other UI components.

The “ssh user” will be used to access the cluster through secure shell. This user is actually a user in the Linux system in all the head nodes, worker nodes, edge nodes, etc., so you can use secure shell to access the remote clusters.

For Microsoft R Server on HDInsight type cluster, it’s a bit more complex, because we put R Studio Server Community version in HDInsight, which only accepts Linux user name and password as login mechanisms (it does not support passing tokens), so if you have created a new cluster and want to use R Studio, you need to first login using the http user’s credential and login through the HDInsight Gateway, and then use the ssh user’s credential to login to RStudio.

It’s a good read and also includes a sample Spark-R job.

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What’s New In Hadoop 3.0?

Published 2017-05-16 by Kevin Feasel

Shubham Sinha explains some of the changes coming to Hadoop:

Integrating EC with HDFS can maintain the same fault-tolerance with improved storage efficiency. As an example, a 3x replicated file with 6 blocks will consume 6*3 = 18 blocks of disk space. But with EC (6 data, 3 parity) deployment, it will only consume 9 blocks (6 data blocks + 3 parity blocks) of disk space. This only requires the storage overhead up to 50%.

Since Erasure coding requires additional overhead in the reconstruction of the data due to performing remote reads, thus it is generally used for storing less frequently accessed data. Before deploying Erasure code, users should consider all the overheads like storage, network and CPU overheads of erasure coding.

Now to support the Erasure Coding effectively in HDFS they made some changes in the architecture. Lets us take a look at the architectural changes.

There are some nice features coming to Hadoop version 3.

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Optimizing Kafka

Published 2017-05-16 by Kevin Feasel

Yeva Byzek explains different tuning options available within Apache Kafka:

Without needing to make any changes to Kafka configuration parameters, you can setup a development Kafka environment and test basic functionality. Yet the fact that Kafka runs straight off the shelf does not mean you won’t want to do some tuning before you go into production. The reason to tune is that different use cases will have different sets of requirements that will drive different service goals. To optimize for those service goals, there are Kafka configuration parameters that you should change. In fact, the Kafka design itself provides configuration flexibility to users, and to make sure your Kafka deployment is optimized for your service goals, you absolutely should investigate tuning the settings of some configuration parameters and benchmarking in your own environment. Ideally, you should do that before you go to production, or at least before you scale out to a larger cluster size.

We have written a white paper to help you identify those service goals, configure your Kafka deployment to optimize for them, and ensure that you are achieving them through monitoring.

Read the whole thing, especially the part about throughput versus latency.

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