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Fixing question optimization in Presto

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“SQL on all the things” is the tagline related to Presto, the question engine that was initially developed by Fb to quickly analyze huge quantities of information — notably knowledge that lay scattered throughout a number of codecs and sources. Since its launch as an open supply challenge in 2013, Presto has been adopted broadly throughout a whole bunch of enterprises. At the moment, a powerful worldwide neighborhood contributes to its ongoing growth.

A decade or so beforehand, the normal strategy for an organization to deal with its knowledge processing wants was to arrange an information heart, inventory it with CPUs and arduous drives, and purchase the entire related software program to tame, retailer, and analyze the information. This additionally required an funding in a number of software program licenses and related service contracts. These knowledge providers tended for use in bursts — i.e., the start of the week and finish of the quarter dealt with much more site visitors than different occasions. However since these sources had been statically allotted, they needed to be provisioned for peak utilization and left under-utilized the remainder of the time. Moreover, firms would wish to workers a workforce of engineers to maintain this setup operational, guarantee excessive availability, and troubleshoot varied use instances.

Elastic cloud economics is the tectonic shift on this business that now permits enterprises to pay just for the sources they use. They will faucet low-cost knowledge storage providers supplied within the cloud, resembling Amazon S3, and dynamically provision knowledge processing workhorses within the type of digital servers that carefully match the scale of the various workload.

This decoupling of storage and compute permits customers to seamlessly resize their compute sources. Question engines like Presto work nicely on this auto-scaling context, and they’re seeing increased adoption as extra enterprises transfer knowledge to the cloud. Presto has an extensible, federated design that enables it to learn and course of knowledge seamlessly from disparate knowledge sources and file codecs.

Whereas Presto’s federated structure is kind of helpful in having the ability to course of knowledge in place, it engenders vital complexity in producing an optimum execution plan for a question. The remainder of this text will clarify why producing an optimum question execution plan is a tough drawback for Presto and categorical a view on the way in which ahead.

The evolution of the question optimizer 

First, let me take a step again and describe the generic drawback and a number of the options which were developed over the previous a number of a long time. Question optimizers are accountable for changing SQL, expressed declaratively, to an environment friendly sequence of operations which may be carried out by the engine on the underlying knowledge. As such, question optimizers are a essential element of databases.  

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On October 25, Apple will release iOS 15.1 and iPadOS 15.1. What we know so far

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Apple released important updates for iOS 15 and iPadOS 15 on Tuesday, to address several issues and a severe security hole affecting the two platforms. Now, according to reports, Apple is working on iOS 15.1 and iPadOS 15.1 builds for iPhone, iPod touch, and iPads.

Also, Twitter user named RobertCFO received confirmation from an Apple Product Security Team member about the final build’s release date. On October 25th, according to a leaked email that was then deleted from Twitter, iOS 15.1 and iPadOS 15.1 will be released, a week after Apple holds its conference.

This follows Apple’s general software upgrade policy, which is to release new updates a week after its events.

SharePlay, which allows you to remotely watch and listen to streaming material with your friends via FaceTime, ProRes video support, as well as Covid-19 vaccination document support in the Wallet app, are all expected features of iOS 15.1.

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PSA: Mining Chia on an SSD Will Completely Wreck It in No Time Flat

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When SSDs first started transport in shopper merchandise, there have been comprehensible issues about their longevity. Time, steadily enhancing manufacturing methods, and a few low-level OS enhancements have all contributed to solid-state storage’s popularity for sturdiness. With experiences praising SSDs as provisionally extra dependable than arduous drives even beneath heavy utilization, it’s straightforward to see how individuals may not see the brand new Chia cryptocurrency as a serious trigger for concern.

It’s. Chia is first plotted after which farmed, and whereas farming Chia takes little or no in the way in which of processing sources, plotting it should completely hammer an SSD.

It’s been years since we talked about write amplification, but it surely’s a difficulty that impacts all NAND flash storage. NAND is written in 4KB pages and erased in 256KB blocks. If 8KB of information must be changed out of a 256KB block, the drive might want to learn the unique 256KB block, replace it, write the brand new block to a unique location on the drive, after which erase the earlier block.

Write amplification has been an issue for NAND for the reason that starting and a substantial amount of work has gone into addressing these issues, however Chia represents one thing of a worst-case situation. Right here’s an excerpt from a latest Chia blog post:

Producing plot recordsdata is a course of known as plotting, which requires short-term space for storing, compute and reminiscence to create, kind, and compress the information into the ultimate file. This course of takes an estimated 256.6GB of short-term house, very generally saved on SSDs to hurry up the method, and roughly 1.3TiB of writes in the course of the creation.

The ultimate plot created by the method described above is simply 101.3GB. There seems to be an order of magnitude of distinction between the full quantity of drive writes required to create a Chia plot and the storage capability mentioned plot requires when accomplished.

Motherboard producers have gotten in on the motion, with one Chia-compliant board providing 32 SATA backplanes.

Right here’s what this boils right down to: A number of shopper SSDs are actually unhealthy decisions for mining Chia. TLC drives with SLC / MLC caches will not be really useful as a result of they provide poor efficiency. Low-end and midrange shopper drives will not be really useful, as a result of they don’t provide excessive sufficient endurance. It’s important to watch out through which SKUs you buy and enterprise and enterprise drives are extra extremely really useful normally.

Don’t purchase a QLC drive to mine Chia.

Optane would appear to be a near-perfect match for Chia, given its a lot greater endurance, however I can’t discover any data on whether or not individuals have tried deploying it in massive sufficient numbers to have some concept of what efficiency and endurance seem like beneath the 24/7 load Chia plotters are placing on their hardware. Possibly any individual will put a rig collectively utilizing it, as a lot out of curiosity as the rest.

Past that, ExtremeTech recommends customers not try and plot Chia on any SSD they aren’t snug with dropping, and to not purchase an SSD for the aim until you don’t thoughts throwing it away if it dies far more rapidly than anticipated. Chia plotting is a worst-case situation for SSD longevity and it needs to be handled as such.

One notice of fine information: To this point, Chia mining has had a a lot stronger affect on high-capacity arduous drive costs than on SSDs and smaller drives. Hopefully, this continues to be the case.

Now Learn:

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Microsoft adapts OpenAI’s GPT-Three pure language expertise to mechanically write code

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Microsoft CEO Satya Nadella introduces the brand new GPT-Three integration into Energy Apps in a recorded keynote tackle for the corporate’s digital Construct convention.

Microsoft unveiled new tools for automatically generating computer code and formulation on Tuesday morning, in a brand new adaptation of the GPT-Three natural-language expertise extra generally identified for replicating human language.

The aptitude, to be supplied as a part of Microsoft’s Power Platform, is among the fruits of the corporate’s partnership with OpenAI, the San Francisco-based synthetic intelligence firm behind GPT-Three. Microsoft invested $1 billion in OpenAI in 2019.

“The code writes itself,” stated Microsoft CEO Satya Nadella, saying the information in a recorded keynote tackle to open the corporate’s Build developer conference.

The characteristic is named Power Apps Ideas. It’s a part of a broader push by Microsoft and different expertise corporations to make software program growth extra accessible to non-developers, often called low-code or no-code growth.

Microsoft fine-tuned GPT-Three to “leverage the mannequin’s current strengths in pure language enter to offer Energy Apps makers the power to explain logic similar to they’d to a good friend or co-worker, and find yourself with the suitable system for his or her app,” says Ryan Cunningham of the Energy Apps staff in a publish describing the way it works.

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