Befriending Dragons

Turn Scary Into Attainable


Leave a comment

Hadoop Likes Big Files

One of the frequently overlooked yet essential best practices for Hadoop is to prefer fewer, bigger files over more, smaller files. How small is too small and how many is too many? How do you stitch together all those small Internet of Things files into files “big enough” for Hadoop to process efficiently?

The Problem

One performance best practice for Hadoop is to have fewer large files as opposed to large numbers of small files. A related best practice is to not partition “too much”. Part of the reason for not over-partitioning is that it generally leads to larger numbers of smaller files.

Too small is smaller than HDFS block size (chunk size), or realistically small is something less than several times larger than chunk size. A very, very rough rule of thumb is files should be at least 1GB each and no more than maybe around 10,000-ish files per table. These numbers, especially the maximum total number of files per table, vary depending on many factors. However, it gives you a reference point. The 1GB is based on multiples of the chunk size while the 2nd is honestly a bit of a guess based on a typical small cluster.

Why Is It Important?

One reason for this recommendation is that Hadoop’s name node service keep track of all the files and where the internal chunks of the individual files are. The more files it has to track the more memory it needs on the head node and the longer it takes to build a job execution plan. The number and size of files also affects how memory is used on each node.

smallpiebigpieLet’s say your chunk size is 256MB. That’s the maximum size of each piece of the file that Hadoop will store per node. So if you have 10 nodes and a single 1GB file it would be split into 4 chunks of 256MB each and stored on 4 of those nodes (I’m ignoring the replication factor for this discussion). If you have 1000 files that are 1MB each (still a total data size of ~1GB) then every one of those files is a separate chunk and 1000 chunks are spread across those 10 nodes. NOTE: In Azure and WASB this happens somewhat differently behind the scenes – the data isn’t physically chunked up when initially stored but rather chunked up at the time a job runs.

With the single 1GB file the name node has 5 things to keep track of – the logical file plus the 4 physical chunks and their associated physical locations. With 1000 smaller files the name node has to track the logical file plus 1000 physical chunks and their physical locations. That uses more memory and results in more work when the head node service uses the file location information to build out the plan for how it will split out any Hadoop job into tasks across the many nodes. When we’re talking about systems that often have TBs or PBs of data the difference between small and large files can add up quickly.

The other problem comes at the time that the data is read by a Hadoop job. When the job runs on each node it loads the files the task tracker identified for it to work with into memory on that local node (in WASB the chunking is done at this point). When there are more files to be read for the same amount of data it results in more work and slower execution time for each task within each job. Sometimes you will see hard errors when operating system limits are hit related to the number of open files. There is also more internal work involved in reading the larger number of files and combining the data.

Stitching

There are several options for stitching files together.

  • Combine the files as they land using the code that moves the files. This is the most performant and efficient method in most cases.
  • INSERT into new Hive tables (directories) which creates larger files under the covers. The output file size can be controlled with settings like hive.merge.smallfiles.avgsize and hive.merge.size.per.task.
  • Use a combiner in Pig to load the many small files into bigger splits.
  • Use the HDFS FileSystem Concat API http://hadoop.apache.org/docs/current/api/org/apache/hadoop/fs/FileSystem.html#concat.
  • Write custom stitching code and make it a JAR.
  • Enable the Hadoop Archive (HAR). This is not very efficient for this scenario but I am including it for completeness.

There are several writeups out there that address the details of each of these methods so I won’t repeat them.

The key here is to work with fewer, larger files as much as possible in Hadoop. The exact steps to get there will vary depending on your specific scenario.

I hope you enjoyed this small bite of big data!

Cindy Gross – Neal Analytics: Big Data and Cloud Technical Fellow  image
@SQLCindy | @NealAnalytics | CindyG@NealAnalytics.com | http://smallbitesofbigdata.com

//

Advertisements


3 Comments

Create HDInsight Cluster in Azure Portal

Creating an HDInsight cluster from the Azure portal is very easy. However, sometimes you want all the choices and best practices explained as well as the “how to”. I have created a series of slides with audio recordings to walk you through the process and choices. They are available as sessions 1-8 of “Create HDInsight Cluster in Azure Portal” on my YouTube channel Small Bites of Big Data.

Playlist Getting Started with HDInsight: https://www.youtube.com/playlist?list=PLAD2dOpGM3s1R2L5HgPMX4MkTGvSza7gv

  1. Why HDInsight: https://youtu.be/J9KzIShLeD8
  2. Azure Subscription: https://youtu.be/lSxMtmRE114
  3. Azure Storage – WASB: https://youtu.be/6OdDDmdaVVE
  4. Metastore: https://youtu.be/1Og_eftYVpA
  5. Create HDInsight: https://youtu.be/SysIo3LwONk
  6. Hive Query: https://youtu.be/DRAuOXsuec0
  7. Load Demo Data: https://youtu.be/XyiOpRPjfUs
  8. Pricing, Automation, and Wrapup: https://youtu.be/78YowrOnNGM

PowerPoint deck: http://www.slideshare.net/cindygross1/create-hd-insightfeb2015

image

Why HDInsight?

HDInsight is Hadoop on Azure as a service.

  • Easy, cost effective, changeable scale out data processing
  • Lower TCO – easily add/remove/scale
  • Separation of storage and compute allows data to exist across clusters
  • Hortonworks HDP is one of the 3 major Hadoop
    distributors, the most purely open source
  • HDInsight *IS* Hortonworks HDP as a service in Azure (cloud)
  • Metastore (Hcatalog) exists independently across clusters via SQL DB
  • #, size, type of clusters are flexible and can all access the same data
  • Hive is a Hadoop component that makes data look like rows/columns for data warehouse type activities

It offers the standard advantages of Hadoop:

  • Scale-out
  • Load data now, add schema later (write once, read many)
  • Fail fast – iterate through many questions to find the right question
  • Faster time from question to insight
  • Hadoop is “just another data source” for BI, Analytics, Machine Learning

In addition you have the advantages of Hadoop in the cloud:

  • Instantly access data born in the cloud
  • Easily, cheaply load, share, and merge public or private data
  • Data exists independently across clusters (separation of storage and compute) via WASB on Azure storage accounts

Recording of why HDInsight on YouTube

Azure Subscription

You have many options to obtain a Microsoft Azure subscription:

Login to Azure Subscription

1. Login on Azure Portal https://manage.windowsazure.com

2. Use a Microsoft Account http://www.microsoft.com/en-us/account/default.aspx
Note: Some companies have federated their accounts and can use company accounts.

image

Choose Subscription

Most accounts will only have one Azure subscription associated with them. But if you seem to have unexpected resources, check to make sure you are in the expected subscription. The Subscriptions button is on the upper right of the Azure portal.

image

image

Add Accounts

Option: Add more Microsoft Accounts as admins of the Azure Subscription.

1. Choose SETTINGS at the very bottom on the left.

2. Then choose ADMINISTRATORS at the top. Click on the ADD button at the very bottom.

3. Enter a Microsoft Account or federated enterprise account that will be an admin.

image

Recording of getting started with an Azure subscription on YouTube

Azure Storage – WASB

I recommend you manually create at least one Azure storage account and container ahead of time. While the HDInsight creation dialogue gives the option of creating the storage account and container for you, that only works if you don’t plan to reuse data across clusters.

Create a Storage Account

1. Click on STORAGE in the left menu then NEW.

2. URL: Choose a lower-case storage account name that is unique within *.core.windows.net.

3. LOCATION: Choose the same location for the SQL Azure metastore database, the storage account(s), and HDInsight.

4. REPLICATION: Locally redundant stores fewer copies and costs less.

image

Repeat if you need additional storage.

Create a Container

1. Click on your storage account in the left menu then CONTAINERS on the top.

2. Choose CREATE A CONTAINER or choose the NEW button at the bottom.

3. Enter a lower-case NAME for the container, unique within that storage account.

4. Choose either Private or Public ACCESS. If there is any chance of sensitive or PII data being loaded to this container choose Private. Private access requires a key. HDInsight can be configured with that key during creation or keys can be passed in for individual jobs.

This will be the default container for the cluster. If you want to manage your data separately you may want to create additional containers.

image

WASB

Additional information about storage, including details on Windows Azure Storage Blobs (WASB) is on http://SmallBitesOfBigData.com.

image

Recording of creating an Azure storage account and container on YouTube.

Metastore (HCatalog)

In Azure you have the option to create a metastore for Hive and/or Oozie that exists independently of your HDInsight clusters. This allows you to reuse your Hive schemas and Oozie workflows as you drop and recreate your cluster(s). I highly recommend using this option for a production environment or anything that involves repeated access to the same, standard schemas and/or workflows.

Create a Metastore aka Azure SQL DB

Persist your Hive and Oozie metadata across cluster instances, even if no cluster exists, with an HCatalog metastore in an Azure SQL Database. This database should not be used for anything else. While it works to share a single metastore across multiple instances it is not officially tested or supported.

1. Click on SQL DATABASES then NEW and choose CUSTOM CREATE.

2. Choose a NAME unique to your server.

3. Click on the “?” to help you decide what TIER of database to create.

4. Use the default database COLLATION.

5. If you choose an existing SERVER you will share sysadmin access with other databases.

image

You can make the system more secure if you create a custom login on the Azure server. Add that login as a user in the database you just created. Grant it minimal read/write permissions in the database. This is not well documented or tested so the exact permissions needed for this are vague. You may see odd errors if you don’t grant the appropriate permissions.

Firewall Rules

In order to refer to the metastore from automated cluster creation scripts such as PowerShell your workstation must be added to the firewall rules.

1. Click on MANAGE then choose YES.

2. You can also use the MANAGE button to connect to the SQL Azure database and manage logins and permissions.

image

Recording of creating the metastore on YouTube.

Create the HDInsight Cluster

Now that we have the pre-requisites done we can move on to creating the cluster.

  • Quick Create through the Azure portal is the fastest way to get started with all the default settings.
  • The Azure portal Custom Create allows you to customize size, storage, and other configuration options.
  • You can customize and automate through code including .NET and PowerShell. This increases standardization and lets you automate the creation and deletion of clusters over time.
  • For all the examples here we will create a basic Hadoop cluster with Hive, Pig, and MapReduce.
  • A cluster will take several minutes to create, the type and size of the cluster have little impact on the time for creation.

Quick Create Option

For your first cluster choose a Quick Create.

1. Click on HDINSIGHT in the left menu, then NEW.

2. Choose Hadoop. HBase and Storm also include the features of a basic Hadoop cluster but are optimized for in-memory key value pairs (HBase) or alerting (Storm).

3. Choose a NAME unique in the azurehdinisght.net domain.

4. Start with a small CLUSTER SIZE, often 2 or 4 nodes.

5. Choose the admin PASSWORD.

6. The location of the STORAGE ACCOUNT determines the location of the cluster.

image

Custom Create Option

You can also customize your size, admin account, storage, metastore, and more through the portal. We’ll walk through a basic Hadoop cluster.

New

1. Click on HDINSIGHT in the left menu, then NEW in the lower left.

2. Choose CUSTOM CREATE.

image

Basic Info

1. Choose a NAME unique in the azurehdinisght.net domain.

2. Choose Hadoop. HBase and Storm also include the features of a basic Hadoop cluster but are optimized for in-memory key-value pairs (HBase) or alerting (Storm).

3. Choose Windows or Linux as the OPERATING SYSTEM. Linux is only available if you have signed up for the preview.

4. In most cases you will want the default VERSION.

image

Size and Location

1. Choose the number of DATA NODES for this cluster. Head nodes and gateway nodes will also be created and they all use HDInsight cores. For information on how many cores are used by each node see the “Pricing details” link.

2. Each subscription has a billing limit set for the maximum number of HDInsight cores available to that subscription. To change the number available to your subscription choose “Create a support ticket.” If the total of all HDInsight cores in use plus the number needed for the cluster you are creating exceeds the billing limit you will receive a message: “This cluster requires X cores, but only Y cores are available for this subscription”. Note that the messages are in cores and your configuration is specified in nodes.

3. The storage account(s), metastore, and cluster will all be in the same REGION.

image

Cluster Admin

1. Choose an administrator USER NAME. It is more secure to avoid “admin” and to choose a relatively obscure name. This account will be added to the cluster and doesn’t have to match any existing external accounts.

2. Choose a strong PASSWORD of at least 10 characters with upper/lower case letters, a number, and a special character. Some special characters may not be accepted.

image

Metastore (HCatalog)

On the same page as the Hadoop cluster admin account you can optionally choose to use a common metastore (Hcatalog).

1. Click on the blue box to the right of “Enter the Hive/Oozie Metastore”. This makes more fields available.

2. Choose the SQL Azure database you created earlier as the METASTORE.

3. Enter a login (DATABASE USER) and PASSWORD that allow you to access the METASTORE database. If you encounter errors, try logging in to the database manually from the portal. You may need to open firewall ports or change permissions.

image

Default Storage Account

Every cluster has a default storage account. You can optionally specify additional storage accounts at cluster create time or at run time.

1. To access existing data on an existing STORAGE ACCOUNT, choose “Use Existing Storage”.

2. Specify the NAME of the existing storage account.

3. Choose a DEFAULT CONTAINER on the default storage account. Other containers (units of data management) can be used as long as the storage account is known to the cluster.

4. To add ADDITIONAL STORAGE ACCOUNTS that will be accessible without the user providing the storage account key, specify that here.

image

Additional Storage Accounts

If you specified there will be additional accounts you will see this screen.

1. If you choose “Use Existing Storage” you simply enter the NAME of the storage account.

2. If you choose “Use Storage From Another Subscription” you specify the NAME and the GUID KEY for that storage account.

image image

Script Actions

You can add additional components or configure existing components as the cluster is deployed. This is beyond the scope of this demo.

1. Click “add script action” to show the remaining parameters.

2. Enter a unique NAME for your action.

3. The SCRIPT URI points to code for your custom action.

4. Choose the NODE TYPE for deployment.

image

Create is Done!

Once you click on the final checkmark Azure goes to work and creates the cluster. This takes several minutes. When the cluster is ready you can view it in the portal.

image

Recording of HDInsight quick and custom create on YouTube

Query with Hive

For most people the easiest, fastest way to learn Hadoop is through Hive. Hive is also the most widely used component of Hadoop. When you use the Hive ODBC driver any ODBC-compliant app can access the Hive data as “just another data source”. That includes Azure Machine Learning, Power BI, Excel, and Tableau.

Hive Console

The simplest, most relatable way for most people to use Hadoop is via the SQL-like, Database-like Hive and HiveQL (HQL).

1.  Put focus on your HDInsight cluster and choose QUERY CONSOLE to open a new tab in your browser. In my case it opens: https://dragondemo1.azurehdinsight.net//

2.  Click on Hive Editor.

image

image

Query Hive

The query console defaults to selecting the first 10 rows from the pre-loaded sample table. This table is created when the cluster is created.

1. Optionally edit or replace the default query:
Select * from hivesampletable LIMIT 10;

2. Optionally name your query to make it easier to find in the job history.

3. Click Submit.

Hive is a batch system optimized for processing huge amounts of data. It spends several seconds up front splitting the job across the nodes and this overhead exists even for small result sets. If you are doing the equivalent of a table scan in SQL Server and have enough nodes in Hadoop, Hadoop will probably be faster than SQL Server. If your query uses indexes in SQL Server, then SQL Server will likely be faster than Hive.

image

View Hive Results

1. Click on the Query you just submitted in the Job Session. This opens a new tab.

image

2. You can see the text of the Job Query that was submitted. You can Download it.

3. The first few lines of the Job Output (query result) are available. To see the full output choose Download File.

4. The Job Log has details including errors if there are any.

5. Additional information about the job is available in the upper right.

image

View Hive Data in Excel Workbook

At this point HDInsight is “just another data source” for any application that supports ODBC.

1. Install the Microsoft Hive ODBC driver.

2. Define an ODBC data source pointing to your HDInsight instance.

3. From DATA choose From Other Sources and From Data Connection Wizard.

image

View Hive Data in PowerPivot

At this point HDInsight is “just another data source” for any application that supports ODBC.

1. Install the Microsoft Hive ODBC driver.

2. Define an ODBC data source pointing to your HDInsight instance.

3. Click on POWERPIVOT then choose Manage. This opens a new PowerPivot for Excel window.

4. Choose Get External Data then Others (OLEDB/ODBC).

Now you can combine the Hive data with other data inside the tabular PowerPivot data model.

image

Recording of querying Hive on YouTube

Load Demo Data

In the cloud you don’t have to load data to Hadoop, you can load data to an Azure Storage Account. Then you point your HDInsight or other WASB compliant Hadoop cluster to the existing data source. There many ways to load data, for the demo we’ll use CloudXplorer.

You use the Accounts button to add Azure, S3, or other data/storage accounts you want to manage.

In this example nealhadoop is the Azure storage account, demo is the container, and bacon is a “directory”. The files are bacon1.txt and bacon2.txt. Any Hive tables would point to the bacon directory, not to individual files. Drag and drop files from Windows Explorer to CloudXplorer.

Windows Azure Storage Explorers (2014)

image

Recording of loading demo data on YouTube

WrapUp

Once you have created the HDInsight cluster you can use it and play with it and try many things. When you are done, simply remove the cluster. If you created an independent metastore in SQL Azure you can use that same metastore and the same Azure storage account(s) the next time you create a cluster. You are charged for the existence of the cluster, not for the usage of it. So make sure you drop the cluster when you aren’t using it. You can use automation, such as PowerShell, to spin up a cluster that is configured the same every time and to drop it. Check the website for the most recent information.

Pricing

image

Automate with PowerShell

With PowerShell, .NET, or the Cross-Platform cmd line tools you can specify even more configuration settings that aren’t available in the portal. This includes node size, a library store, and changing default configuration settings such as Tez and compression.

Automation allows you to standardize and with version control lets you track your configurations over time.

Sample PowerShell Script: HDInsight Custom Create http://blogs.msdn.com/b/cindygross/archive/2013/12/06/sample-powershell-script-hdinsight-custom-create.aspx. If your HDInsight and/or Azure cmdlets don’t match the current documention or return unexpected errors run Web Platform Installer and check for a new version of “Microsoft Azure PowerShell with Microsoft Azure SDK” or “Microsoft Azure PowerShell (standalone).”

image

Recording of Pricing, Automation, and Wrapup on YouTube

Summary

  • HDInsight is Hadoop on Azure as a service, specifically Hortonworks HDP on either Windows or Linux
  • Easy, cost effective, changeable scale out data processing for a lower TCO – easily add/remove/scale
  • Separation of storage and compute allows data to exist across clusters via WASB
  • Metastore (Hcatalog) exists independently across clusters via SQL DB
  • #, size, type of clusters are flexible and can all access the same data
  • Instantly access data born in the cloud; Easily, cheaply load, share, and merge public or private data
  • Load data now, add schema later (write once, read many)
  • Fail fast – iterate through many questions to find the right question
  • Faster time from question to insight
  • Hadoop is “just another data source” for BI, Analytics, Machine Learning

I hope you enjoyed this Small Bite of Big Data! Happy Hadooping!

Cindy Gross – Neal Analytics: Big Data and Cloud Technical Fellow  
@SQLCindy | @NealAnalytics | CindyG@NealAnalytics.com | http://smallbitesofbigdata.com


Leave a comment

Master Choosing the Right Project for Hadoop

Hadoop is the hot buzzword of the Big Data world, and many IT people are being told “go create a Hadoop cluster and do some magic”. It’s hard to know where to start or which projects are a good fit. The information available online is sparse, often conflicting, and usually focused on how to solve a technical problem rather than a business problem. So let’s look at this from a business perspective.

Data-Driven InsightsYodaCool

For the average business just getting into using Hadoop for the first time, you are most likely to be successful if you choose a project related to data exploration, analytics and reporting, and/or looking for new data-driven actionable insights. In many ways Hadoop is ‘just another data source.” Generally most businesses will not start with replacing existing, high-functioning OLTP implementations. Instead you will likely see the highest initial return on investment (ROI) from adding on to those existing systems. Pull some of the existing data into Hadoop, add new data, and look for new ways to use that data. The goal should remain clearly focused on how to use the data to take action based on the new data-driven insights you will uncover.

Success

DataPointer Below are some characteristics that are often present for a successful Hadoop implementation. You don’t need to have all of them to be successful, use the list to brainstorm new ideas.

  • Goals include innovation, exploration, iteration, and experimentation. Hadoop allows you to ask lots of “what-if” questions cheaply, to “fail fast” so you can try out many potential hypotheses, and look for that one cool thing everyone else has missed that can really impact your business.
  • New data or data variations will be explored. Some of it may be loosely structured. Hadoop, especially in the cloud, allows you to import and experiment with data much more quickly and cheaply than with traditional systems. Hadoop on Azure in particular has the WASB option to make data ingestion even easier and faster.
  • You are looking for the “Unknown Unknowns”. There are always lurking things that haven’t come to your attention before but which may be sparks for new actions. You know you don’t know what you want or what to ask for and will use that to spur innovation.
  • Flexible, fast scaling without the need to change your code is important. Hadoop is built on the premise that it is infinitely scalable – you simply add more nodes when you need more processing power. In the cloud you can also scale your storage and compute separately and more easily scale down during slow periods.
  • You are looking to gain some competitive advantage faster than your competition based on data-driven actions. This goes back to the previous points, you are using Hadoop to look for something new that can change your business or help you be first to market with something.
  • There are a low number of direct, concurrent users of the Hadoop system itself. The more jobs you have running at the same time, the more robust and expensive your head node(s) must be and often the larger your cluster must be. This changes the cost/benefit ratio quickly. Once data is processed and curated in Hadoop it can be sent to systems that are less-batch oriented and more available and familiar to the average power user or data steward.
  • Archiving data in a low-cost manner is important. Often historical data is kept in Hadoop while more interactive data is kept in a relational system.

Anti-Patterns

Quite often I hear people proposing Hadoop for projects that are not an ideal use for Hadoop, at least not as you are learning it and looking for quick successes to bolster confidence in the new technology. The below characteristics are generally indicators that you do NOT want to use Hadoop in a project.RosieInTechWIT

  • You plan to replace an existing system whose pain points don’t align with Hadoop’s strengths.
  • There are OLTP business requirements, especially if they are adequately met by an existing system. Yes, there are some components of Hadoop that can meet OLTP requirements and those features are growing and expanding rapidly. If you have an OLTP scenario that requires ACID properties and fast interactive response time it is possible Hadoop could be a fit but it’s usually not a good first project for you to learn Hadoop and truly use Hadoop’s strengths.
  • Data is well-known and the schema is static. Generally speaking, though the tipping point is changing rapidly, when you can use an index for a query it will likely be faster in a relational system. When you do the equivalent of a table scan across a large volume of data and provide enough scaled-out nodes it is likely faster on a Big Data system such as Hadoop. Well-known, well-structured data is highly likely to have well-known, repeated queries that have supporting indexes.
  • A large number of users will need to directly access the system and they have interactive response time requirements (response within seconds).
  • Your first project and learning is on a mission critical system or application. Learn on something new, something that makes Hadoop’s strengths really apparent and easy to see.

And in Conclusion

BeTheChangeChalk Choosing the right first project for your dive into Hadoop is crucial. Make it bite-sized, clearly outline your goals, make sure it has some of the above success criteria and avoid the anti-patterns. Make learning Hadoop a key goal of the project. Budget time for everyone to really learn not only how things work but why they work that way and whether there are better ways to do certain things. Hadoop is becoming ubiquitous, avoiding it completely is not an option. Jump in, but do so with your eyes wide open and make some good up-front decisions. Happy Big Data-ing!


2 Comments

Azure Maximums and Resource Usage from PowerShell

Technorati Tags: ,

Have you ever struggled to find out how many VM cores, HDInsight cores, storage accounts, or other Azure resources your subscription is set to allow or how many you actually use? Maybe you want to use this information in your automation scripts to avoid trying to create components for which you don’t have resources.

quizzical owl

PowerShell to the rescue!

First a couple of key points. There are various maximums in Azure. Today we are talking about finding the currently configured maximums allowed for a specified subscription. There are default maximums (default limit) which you can increase for a given subscription by opening a billing support ticket. There are also hard maximums (maximum limit). However, with some products, such as HDInsight (Hadoop), you can get past some per-subscription maximums for dependent services by combining resources (storage accounts) from multiple subscriptions for a single HDInsight cluster. All the samples below find the current billing quota limitation and actual usage for the current subscription.

Let’s take a look at the information available on the subscription level cmdlet.

Start by checking which subscription is in focus / current for the PowerShell session.

(Get-AzureSubscription -Current).SubscriptionName

(Get-AzureSubscription -Current).CurrentStorageAccountName

If you need information on a different subscription either pass the subscription name (as defined on your client) for the cmdlets that support this or change the focus to a different subscription.

$SubName = “sqlcatwoman”

Select-AzureSubscription -SubscriptionName $SubName

Now we will look at the cores available for Azure virtual machines (VMs / IaaS). Note that HDInsight cores are tracked separately. Be careful with unexpected line wraps that may paste into your PowerShell window (or ISE) incorrectly. The below snippet is 1 comment line and 4 lines of code.

# How many cores are available to create new VMs (or increase size of existing VMs) for the current subscription?

[int]$maxVMCores     = (Get-AzureSubscription -current -ExtendedDetails).maxcorecount

[int]$currentVMCores = (Get-AzureSubscription -current -ExtendedDetails).currentcorecount

[int]$availableCores = $maxVMCores $currentVMCores

Write-Host “Cores available for VMs:” $availableCores

We can get similar information about cloud services:

#how many cloud (hosted) services are available on this subscription

[int]$maxAvl         = (Get-AzureSubscription -current -ExtendedDetails).MaxHostedServices

[int]$currentUsed    = (Get-AzureSubscription -current -ExtendedDetails).CurrentHostedServices

[int]$availableNow   = $maxAvl $currentUsed

Write-Host “Cloud services available:” $availableNow

Some limits and usage are available on cmdlets specific to a particular technology. For example, the HDInsight usage and maximums are available from the Get-AzureHDInsightProperties cmdlet. You can find details and samples on Get HDInsight Properties with PowerShell.

Other times we have to look at different cmdlets for different pieces of the information, such as for storage accounts:

#how many storage accounts are available on this subscription

[int]$maxAvl         = (Get-AzureSubscription -current -ExtendedDetails).MaxStorageAccounts

[int]$currentUsed    = (Get-AzureStorageAccount).Count

[int]$availableNow   = $maxAvl $currentUsed

Write-Host “Storage Accounts available:” $availableNow

We can look at all the extended properties available for a subscription:happy owl

Get-AzureSubscription -currentExtendedDetails

If you know you have a particular component created and this cmdlet shows the “Current” value is zero, take a look at the Get-Azure… cmdlet for that particular type of resource and look for a “Current” value.

Another handy thing to look at is the overall information about what Azure regions exist and what services are available in each region:

Get-AzureLocation 

And you can pull off specific information:

Get-AzureLocation  | Select DisplayName

I hope these small bites of PowerShell help save the day for you in some way!


2 Comments

SQL PASS: All the Magic Knobs – Tools

SQL PASS: All the Magic Knobs – Tools

In my All the Magic Knobs talk at #SQLPASS 2011 I discussed some easy ways to determine if you’re using some of the performance magic for SQL Server. When you have many consolidated, non-tier 1 databases you don’t have a lot of control over, the best way to tune is to provide a solid, performant infrastructure through low effort, high impact choices. The same steps help in your tier 1 environments as well. The quickest way to see how close you are to that standard is to run one of our automated health checks. They check the SQL instance itself and some of the most important Windows settings that help SQL Server operate optimally.

SQL Best Practices Analyzer (BPA) is available for SQL Server 2000, 2005, and 2008/2008 R2. It is an add-in to the Microsoft Baseline Configuration Analyzer (MBCA). Both the MBCA and the SQL BPA are free. You can run the BPA locally or remotely and you can find plenty of sample scripts to run it against multiple instances. You choose your schedule for execution and you can either review the output after each execution manually or write your own program to alert you to what you consider the most serious items.

The System Center Advisor (SCA) is at this point still in pre-release. Licensing details will be available after release, for now you can download a free trial. It works for SQL Server 2008 and newer on Windows 2008 and newer. SCA runs on a schedule and sends alerts when a registered instance is not configured as advised. What it checks can change dynamically as PSS finds new important items.

Several companies, including Microsoft through our Premier Field Engineering (PFE) team, offer various health checks that include knowledge sharing and additional advice to help you decide if, how, when, and where to implement the recommendations.

Of course, you have to actually implement the recommendations to get the benefit; the tools listed above don’t do any remediation on their own. While that should go without saying, in my experience known recommendations often go unimplemented until some problem they would have prevented pops up.

For more SQL Server best practices see some of my other blogs: http://blogs.msdn.com/b/cindygross/archive/tags/best+practices/  

 


Leave a comment

SQL PASS: All the Magic Knobs

SQL PASS 2011 DBA-319-C #SQLPASS

All the Magic Knobs – Low Effort, High Return Tuning

Key points covered:

  • Power Savings = High Performance
  • Smart Virtualization
  • Enough Hardware
  • Control other apps, filter drivers
  • Optimize for ad hoc workloads = ON
  • Compression = ON
  • Set LPIM + Max Server Memory
  • Pre-size files, avoid shrink and autogrow
  • Fast Tempdb
  • Proper Maintenance

My presentation from 10/13/11 is attached.

AllTheMagicKnobs.pptx


Leave a comment

Taming the Tempdb Tempest – WI SQL Server Virtual User Group, 22 Apr 2011

Thanks to the Wisconsin Virtual SQL Server User Group for letting me talk about tempdb today! The slides and demo queries are attached. Once the recording is available I will update this blog with a link to it.

Taming the Tempdb Tempest

Summary:

·         Multiple data files of the same size, one log file

·         Enough data files to avoid contention, not so many to cause problems.

·         Presize for peak periods of next X months, re-evaluate

·         Set autogrow to be rare but “big enough”

·         Instant File Initialization on (small security risk)

·         Fast IO subsystem

·         Change size/settings if you add new features that use tempdb

·         Monitor for approaching full, change in activity/size

·         Performance tune user databases and applications

·         Limit use of versioning or temp objects

 

The demo queries are:

·         sys.dm_db_file_space_usage.sql: How space is used inside tempdb

·         sys.dm_db_file_space_usage_companion1.sql: Show how different activities cause space to be used in tempdb

·         Autogrow.sql: Find autogrow settings for all dbs on an instance

·         TempdbContention.sql: Find contention on tempdb metadata

 

Also see my previous blog post with the same basic data in a different format: Compilation of SQL Server TempDB IO Best Practices http://blogs.msdn.com/b/cindygross/archive/2009/11/20/compilation-of-sql-server-tempdb-io-best-practices.aspx

I also delivered the talk to the Boise SQL Server User Group on 13 Sep 2011 and the updated queries and slide deck are attached below.

TempdbBoiseSQLSep2011.zip