The time it takes to rebuild the index can be substantially longer for ONLINE. Many of us has other things to do with the database during night-time and/or weekends. Yes, these are the typical window in time where we try to find things such as index rebuilds. Say that you do it night-time and it currently take 4 hours. Wouldn’t it be nice if you could cut that time down to 1.5 hours? That would leave more time for imports, massaging of data, CHECKDB and other things you want to do. Sure, you can do it ONLINE, but it will slow down access during the rebuild. Also the more data you modify during the rebuild, the more space you need in tempdb.
Betteridge’s Law of Headlines applies too, so that’s two important principles in one post.
As far as the post goes, Tibor makes a fair point: there is a trade-off between availability and efficiency with index rebuilds. But having worked with clustered columnstore indexes in 2014, you’ll pry the online operations in subsequent versions out of my cold, dead hands.
There is an npm module, “node-webhdfs,” with a wrapper that allows you to access Hadoop WebHDFS APIs. You can install the node-webhdfs package using npm:
npm install webhdfs
After the above step, you can write a Node.js program to access this API. Below are a few steps to help you out.
Click through for examples on how the package works.
Kudu RPC (KRPC) supports asynchronous RPCs. This removes the need to have a single thread per connection. Connections between hosts are long-lived. All RPCs between two hosts multiplex on the same established connection. This drastically cuts down the number of TCP connections between hosts and decouples the number of connections from the number of query fragments.
The error handling semantics are much cleaner and the RPC library transparently re-establishes broken connections. Support for SASL and TLS are built-in. KRPC uses protocol buffers for payload serialization. In addition to structured data, KRPC also supports attaching binary data payloads to RPCs, which removes the cost of data serialization and is used for large data objects like Impala’s intermediate row batches. There is also support for RPC cancellation which comes in handy when a query is cancelled because it allows query teardown to happen sooner.
Looks like there were some pretty nice gains out of this project.
2) Create Databricks Service
Yes you are reading this correctly. Under the hood Data Factory is using Databricks to execute the Data flows, but don’t worry you don’t have to write code.
Create a Databricks Service and choose the right region. This should be the same as your storage region to prevent high data movement costs. As Pricing Tier you can use Standard for this introduction. Creating the service it self doesn’t cost anything.
Joost shows the work you have to do to build out a data flow. This has been a big hole in ADF—yeah, ADF seems more like an ELT tool than an ETL tool but even within that space, there are times when you need to do a bit more than pump-and-dump.
The steps that need to be performed are:
– Allow the new port in the RHEL firewall
– Change the SSH daemon to listen on the new port
– Add an incoming rule in the VM network security group for the new port
– Remove the rule that allows port 22
The Ubuntu process will be pretty close to this as well.
We understand that streaming data isn’t typically considered “Data Science” by itself. However, it’s are often associated and setting up this background now opens up some cool applications in later posts. For this post, we’ll cover how to sink streaming data to Power BI using Stream Analytics.
The previous posts in this series used Power BI Desktop for all of the showcases. This post will be slightly different in that we will leverage the Power BI Service instead. The Power BI Service is a collaborative web interface that has most of the same reporting capabilities as Power BI Desktop, but lacks the ability to model data at the time of writing. However, we have heard whispers that data modeling capabilities may be coming to the service at some point. The Power BI Service is also the standard method for sharing datasets, reports and dashboards across organizations. For more information on the Power BI Service, read this.
Brad has a nice demo, so check it out.
— so, before SWITCHOFFSET existed, …
SELECT SWITCHOFFSET(SYSDATETIMEOFFSET(),'-05:00') AS [EST the easy way], TODATETIMEOFFSET(DATEADD(HOUR, -5, SYSDATETIMEOFFSET()), '-05:00') AS [EST the hard way]
— so, thinking of a DATETIMEOFFSET data type as a complex object
— with many different parts: year, month, day, hour, time zone, etc.
— it looks like SWITCHOFFSET changes two things: time zone and hour
This was an interesting video. I typically think entirely in UTC and let the calling application convert to time zones as needed, but if that’s not an option for you, knowing about
SWITCHOFFSET() is valuable.
I logged a case with MS Support and when they came back to me, they advised that the service principal that is spun up in the background had expired. This service principal is required to allow the cluster to interact with the Azure APIs in order to create other Azure resources.
When a service is created within AKS with a type of LoadBalancer, a Load Balancer is created in the background which provides the external IP I was waiting on to allow me to connect to the cluster.
Because this principal had expired, the cluster was unable to create the Load Balancer and the external IP of the service remained in the pending state.
There were a lot of steps here; click through to see just how many.
The malware spreads via brute-force attacks on weak passwords “or by exploiting one of three vulnerabilities found on Hadoop YARN Resource Manager, Redis [in-memory key-value store service] and ActiveMQ,” Securonix said. Once logged into database services, the malware can for example delete existing databases stored on a server and create another with a ransom note specifying a bitcoin payment.
The security analyst recommends continuous review of cloud-based services like Hadoop and YARN instances and their exposure to the Internet. Along with strong passwords, companies should “restrict access whenever possible to reduce the potential attack surface.”
It’s pretty standard advice: secure your data, password-protect your systems, and minimize the number of computers that get to touch your computers.
As with any neural network, we need to convert our data into a numeric format; in Keras and TensorFlow we work with tensors. The IMDB example data from the
keraspackage has been preprocessed to a list of integers, where every integer corresponds to a word arranged by descending word frequency.
So, how do we make it from raw text to such a list of integers? Luckily, Keras offers a few convenience functions that make our lives much easier.
This is a very nice tutorial if you’re new to the process.