Oct 28, 2018

Don't turn your back on Big Compute! (part 1)

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We started Elastacloud eight years ago by focussing on High Performance Computing on Microsoft Azure. HPC is normally in the purview of Researchers, Engineers and Risk Calculators in finance but it is good for so much more.

Here over the next few posts I'll break down the problem domain and why I didn't use Big Data to solve this.

 

- We have several tens of thousands of files an hour being copied into Azure Blob Storage through a series of feeds

- Each feed comes in a form called the Common Event Format (CEF) which needs to be converted into something which can be processed and ingested into a Data Warehouse

- Each file is gzipped and when inflated can be anywhere between 1K and 200Mb

 

From the outset this is not a case for Big Data technologies even though the ease of modern frameworks like Azure HDInsight and Azure Databricks make this easy, it's not a good use of resources. Here's why:

 

- Gzip doesn't scale well with Hadoop or Spark, as it's not a compression format that can be parallelised

- There are thousands of files which need to be read in a short space of time and at load time this doesn't scale well with IO especially if you're looking at hourly

- Each file needs to heavy processing to output into a new intermediate format which can be ingested

 

Point (3) is especially relevant because the cost doesn't scale well with smaller Spark clusters.

 

In the few short posts that follow I'll build a story of how we can use:

 

- Azure Data Factory

- Azure Batch

- Parquet.NET

- Azure SQL Data Warehouse

 

This is a programmers solution. Nothing comes for free so there is some work to do and good programming practice which needs to be in play.

 

We'll be learning:

 

- To get around the limitations of Azure Data Factory and Custom Activities

- Using Azure Batch to enable pure linear scalability of file conversion

- Using Batch applications to build reusability and reproducable and versioned deployments

 

In the next post we'll be looking at extending Azure Data Factory Custom activities to achieve scale and throughput allowing the conversion of several GB of files in 10-20 mins.

 

New Posts
  • Something that comes up quite frequently when people start using Spark is "How can I filter my DataFrame using the contents of another DataFrame?". People with SQL experience will immediately look to trying to replicate the following. SELECT * FROM table_a a WHERE EXISTS (SELECT * FROM table_b b WHERE b.Id = a.Id) So how do you do this in Spark? Well, some people will try to use the Column.isin method which uses varargs, this is okay for a small set of values but if you have a couple of large DataFrames then it's less than optimal as each row needs to be evaluated against the list. So what's the other choice? We can use joins to do the same thing. There are 2 we can use, a SEMI JOIN which is equivalent to our above example of running EXISTS; the other is ANTI JOIN which is equivalent to a NOT EXISTS. Using the above example and keeping the table names as DataFrame names we could re-write this in Scala as: table_a.join(table_b, Seq("Id"), "left_semi") These 2 joins are unique in that they only return the output of the left DataFrame, without any content from the right DataFrame. So what does this look like in practice. Well using Azure Databricks we can quickly create some sample data to try them out. First lets create a couple of DataFrames. First lets runs a simple query to find heroes which have an arch-enemy. This uses the SEMI JOIN to keep records in the left DataFrame where there is a matching record in the right DataFrame. Now, lets have look for heroes who've been a little more active and have removed their arch-enemies (for now). This time we've used an ANTI JOIN to keep only those records in the left DataFrame where there are no matching records in the right DataFrame. You'll notice that in the examples the join condition uses the slightly longer form, that's because in this example the columns we're joining on have different names, and also because there is a column in both DataFrames which have the same name.
  • Recently I needed to deploy an Azure Data Lake Store - Gen 2 instance and thought I'd take the opportunity to use some custom ARM template functions . These aren't something you often see in the example templates but can be really useful if there's a complex expression which you find yourself writing repeatedly within a template. If, for instance, you routinely create resource names based on a prefix, unique name and a suffix then this could save you a few keystrokes. In essence you are simply parameterizing the expression as follows: In this way you can use this simpler expression where you would have previously used the more complex version. [namespace.function(parameter1, parameter2)] If you want to see what this looks like in a full template then checkout this simple ARM template I put together for creating a Data Lake Store - Gen 2 instance over on GitHub.
  • Documentation is not something people often spend time reading, or if they do then its to quickly find the one thing their after and then get out as quickly as possible, very similar to how I do my Christmas shopping. Sometimes it's worth spending time reading the documentation though as there can be some useful bits of information hidden in summary descriptions, links etc... One such item is the Azure Data Lake Store client. If you find yourself reading or writing a lot of files and your doing it in multiple tasks (or threads, but you should be using Tasks if possible), then reading the docs can really help you out. For instance this snippet taken from the description at the top of the documentation page . If an application wants to perform multi-threaded operations using this SDK it is highly recomended to set ServicePointManager.DefaultConnectionLimit to the number of threads application wants the sdk to use before creating any instance of AdlsClient. By default ServicePointManager.DefaultConnectionLimit is set to 2. Okay, so how bad can things be if you don't read this? Well, to answer that I created an ADLS instance and uploaded a number of small parquet files. Then wrote an application to read each file (using the excellent Parquet .NET ) and return the number of records in the file, each file is processed in it's own Task and each uses the same AdlsClient instance. The simple process being followed here is to get a list of files, call " ProcessPath " on each and then when all the files have been process output the results. The output of this initial version is as follows: It's not too bad, but with multiple tasks I would have expected it to be better. Looking at the documentation snippet above it suggests we need to change the ServicePointManager.DefaultConnectionLimit value, but what to? Well doing some digging around came across a suggestion from Microsoft Support which, for ASP.NET, is to limit the number of requests that can execute at the same time to 12 per CPU (or 12 per core). So let's give that a go and see what happens. The code change for this is pretty simple and we can use System.Environment to get the number of processors available. So does it make much of a difference? Well, yes, quite a lot of difference actually. I ran the code in both variations a few more times to check it wasn't intermittent networking issues, other processes on my laptop interfering etc... but no, it really does make that much of a difference. So next time you're working with multiple tasks sharing resources, maybe spend a bit of time reading the documentation to see if there's anything which can make a difference to your application.