The other question people are likely to ask is why, kernel contributions notwithstanding, is Microsoft listed as the publisher of the distro? The short answer: support.
According to Microsoft’s blog post, the FreeBSD Foundation is a community of mutually supportive users, “not a solution provider or an ISV with a support organization.” The kinds of customers who run FreeBSD on Azure want to have service-level agreements of some kind, and the FreeBSD Foundation isn’t in that line of work.
The upshot: If you have problems with FreeBSD on Azure, you can pick up the phone and get Microsoft to help out — but only if you’re running its version of FreeBSD.
To be honest, I don’t see this as a big deal. I’m glad the image is there, but this hardly seems like a landmark change in anything to me.
The basic idea is to create a lookup table of distinct categories indexed by unique integer identifiers. The way to avoid is to collect the unique categories to the driver, loop through them to add the corresponding index to each to create the lookup table (as
Mapor equivalent) and then broadcast the lookup table to all executors. The amount of data that can be collected at the driver is controlled by the
spark.driver.maxResultSizeconfiguration which by default is set at 1 GB for Spark 1.6.1. Both
broadcastwill eventually run into the physical memory limits of the driver and the executors respectively at some point beyond certain number of distinct categories, resulting in a non-scalable solution.
The solution is pretty interesting: build out a new RDD of unique results, and then join that set back. If you’re using SQL (including Spark SQL), I would use the DENSE_RANK() window function.
AutoAdjustBufferSize property of the SSIS data flow. Done with manually setting the Buffer Size and Buffer Max Rows. Just set this property to true and the data flow takes care of its own performance.
Custom logging levels in the SSIS Catalog. Now I can finally define a logging level that only logs errors and warnings AND set it as the server-wide default level.
The DROP TABLE IF EXISTS syntax. The shorter the code, the better 🙂
I was initially a bit concerned with AutoAdjustBufferSize because I figured I could do a better job of selecting buffer size. Maybe on the margin I might be able to, but I think I’m going to give it a try.
We are excited to announce the public availability of the sql server 2014 express Docker image for Windows Server Core based Containers! The public repo is hosted on Docker Hub and contains the latest docker image as well as pointers to the Dockerfile and the start PS script(hosted on Github). We hope you will find this image useful and leverage it for your container based applications!
Containerization is a huge part of modern administrative world and it’s good to see Microsoft (belatedly) jumping onto the bandwagon.
So I’ve spent a while now looking at 3 competing languages and I did my best to give each one a fair shake. Those 3 languages were F#, Python and R. I have to say it was really close for a while because each language has its strengths and weaknesses. That said, I am moving forward with 2 languages and a very specific way I use each one. I wanted to outline this, because for me it has taken a very long time to learn all of the languages to the level that I have to discover this and I would hate for others to go through the same exercise.
Read on for his decision, as well as how you go from “here’s some raw data” to “here are some services to expose interesting results.”
Kibana is the natural UI choice for partnering Elasticsearch, and it has the advantage of being Web-based and Dockerized, so it’s cross-platform and easy to share. But PowerBI is a lot more powerful, and the multitude of available connectors mean it’s easy to build a single dashboard which pulls data from multiple sources.
Using Elasticsearch for one of those sources is simple, although it will need some custom work to query your indexes and navigate the documents to get the field you want. You can even publish your reports to PowerBI in the cloud and limit access using Azure Active Directory – which gives you a nice, integrated security story.
I tend to be very hard on Kibana, particularly because it makes the easy stuff easy and the hard stuff impossible, so I think that this is an interesting alternative to Kibana.
The customer has lots of waits on RESOURCE_SEMAPHORE_QUERY_COMPILE. To troubleshoot this, we have to look from two angles. First, did customer have many queries needing large amount of compile memory? Secondly, was it possible that other components used too much memory, causing the threshold lowered? In other words, if SQL Server had enough memory, those queries requiring same amount of compile memory would not have been put to wait.
We used this query and captured for several iterations of data to confirm that server didn’t have queries that required large amount of compile memory per se.
It’s nice to have this trick up your sleeve when you simply can’t get a better query in place.
Put differently, we can build a mutex from an auto-reset EventInternal by tacking on an owner attribute, making a rule that only the owner has the right to signal the event, and adding assignment of ownership as a fringe benefit of a successful wait. A nonsignalled event means an acquired mutex, and a signalled event means that the next acquisition attempt will succeed without waiting, since nobody currently owns the mutex. The end result is that our SOS_Mutex class exposes the underlying event’sSignal() method and its own take on Wait(). From the viewpoint of the mutex consumer, the result of a successful wait is that it owns the mutex, and it should act honourably by calling Signal() as soon as it is done using the resource that the mutex stands guard over.
There’s some deep detail here, so this is definitely one of those “after your first cup of coffee” posts to read.
Randolph West has a series of career-limiting moves, which sadly I had missed until now. I’ll make up for that by linking the whole series.
For whatever reason, we ran the script in the Oracle SQL*Plus client, which Wikipedia generously describes as “the most basic” database client.
Cut to the part where we run the script. The
DROP TABLEcommand was run successfully and committed, but the previous step where the data was moved had failed.
The entire table was gone.
My job was to provide technical support to a senior staff member, and I said no because I was busy on something that was, for all intents and purposes, not as important.
This was of course escalated very quickly to the managing director, who in turn shouted at my boss, who in turn shouted at me. If I recall correctly, my boss eventually helped his colleague with her important problem and only reamed me out after the fact.
She explained to me that whether or not that was the case, the language was totally inappropriate and calling a vendor on the weekend for something that did not constitute an emergency was unprofessional. In any number of scenarios, I could have been fired for my behaviour.
Chastened, I took away several important lessons: it doesn’t matter whose fault something is. The job had to be done, and I was around to do it. Furthermore, it is important never to be caught bad-mouthing someone on the record, no matter how good a relationship you have with a vendor. It will always come back to bite you.
His current in the series is Reply All, in which he’s looking for your stories.
I blinked when I saw the warning, “Your password can’t be longer than 16 characters.” I couldn’t believe that I had gotten that warning, so I erased what I had typed for a password and started typing 1, 2, 3, etc., to see if this warning did trip at 17 characters. It did. Why in the world is there a limitation on password length if you’re going to do a hash my password? And if you had to pick a limit, why 16 characters? Why not 50 or 100 or 255?
I’ll go one step further: there is never a good limit to how long a password should be. For services like these, Microsoft should have the plaintext version of the password (which again, should be a string of an arbitrary length) only enough to perform an adequate number of rounds of hashing and salting using an appropriate hashing function (e.g., bcrypt). At that point, once the password gets hashed, the hash is always the same length, meaning the length of the plaintext is irrelevant for storage.