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The story starts on a sunny afternoon, sometime in 1997, when Doug reducing (“the man”) begun writing the primary version of Lucene.what's Lucene, you ask.
TLDR; generally speaking, it's what makes Google return outcomes with sub second latency.
Apache Lucene is a full textual content search library. adequate, excellent, however what's a full text search library? feet search library is used to research general textual content with the aim of building an index. Index is an information constitution that maps every term to its place in textual content, so that in case you seek a time period, it instantly is aware of the entire places the place that term happens.smartly, it’s just a little more advanced than that and the data constitution is in reality called inverted or inverse index, however I won’t hassle you with that stuff. The complete element of an index is to make browsing fast.think about how usable would Google be if every time you searched for anything, it went all through the web and accumulated outcomes. That’s a reasonably ridiculous suggestion, right?
It took slicing handiest three months to have some thing usable. just a few years went by using and reducing, having experienced a “lifeless code syndrome” previous in his lifestyles, wanted other people to make use of his library, so in 2000, he open sourced Lucene to source Forge beneath GPL license (later more permissive, LGPL). He became stunned through the number of americans that found the library useful and the amount of awesome comments and feature requests he got from those individuals. just a yr later, in 2001, Lucene strikes to Apache utility foundation.
through the end of the yr, already having a thriving Apache Lucene community in the back of him, reducing turns his focal point against indexing net pages. he's joined with the aid of university of Washington graduate scholar Mike Cafarella, in an effort to index the complete web. That effort yielded a new Lucene subproject, referred to as Apache Nutch.Nutch is what's known as a web crawler (robotic, bot, spider), a application that “crawls” the information superhighway, going from page to web page, with the aid of following URLs between them. whatever equivalent as in the event you surf the net and after a while note that you have a myriad of opened tabs to your browser. that you can imagine a software that does the equal thing, however follows every link from each and every web page it encounters. When it fetches a page, Nutch makes use of Lucene to index the contents of the web page (to make it “searchable”).PageRank algorithm
an important algorithm, that’s used to rank internet pages with the aid of their relative magnitude, is referred to as PageRank, after Larry page, who got here up with it (I’m severe, the identify has nothing to do with internet pages).It’s in fact an easy and magnificent algorithm, which definitely counts how many links from different pages on the internet element to a page. The web page that has the maximum count is ranked the optimum (shown on desirable of search effects). Of path, that’s not the handiest system of settling on page magnitude, nevertheless it’s actually the most valuable one.
right through the course of a single 12 months, Google improves its rating algorithm with some 5 to 6 hundred tweaks.
reducing and Cafarella made a brilliant growth. Having Nutch deployed on a single computer (single-core processor, 1GB of RAM, RAID stage 1 on eight difficult drives, amounting to 1TB, then worth $three 000) they managed to obtain a good indexing fee of round one hundred pages per second.
frequently, when applications are developed, a team simply wants to get the proof-of-concept off the floor, with efficiency and scalability purely as afterthoughts. So it’s no surprise that the same thing came about to cutting and Cafarella. The fact that they've programmed Nutch to be deployed on a single computing device grew to become out to be a double-edged sword. On one aspect it simplified the operational aspect of things, but on the other facet it with no trouble limited the total number of pages to a hundred million.
Understandably, no application (notably one deployed on hardware of that point) could have indexed the complete information superhighway on a single computing device, in order that they increased the variety of machines to 4. for the reason that they didn't have any underlying cluster administration platform, they needed to do records interchange between nodes and area allocation manually (disks would replenish), which presented extreme operational challenge and required constant oversight. Any extra raise in a couple of machines would have resulted in exponential upward thrust of complexity. They desperately essential whatever that would lift the scalability difficulty off their shoulders and allow them to deal with the core difficulty of indexing the web.The origins of HDFS
Being persistent in their effort to build a web scale search engine, chopping and Cafarella set out to increase Nutch. What they obligatory, as the groundwork of the gadget, became a allotted storage layer that convinced right here necessities:
they have got spent a few months making an attempt to solve all those problems and then, out of the bloom, in October 2003, Google published the Google File equipment paper. when they examine the paper they had been astonished. It contained blueprints for fixing the very same issues they were battling.Having already been deep into the problem enviornment, they used the paper because the specification and began implementing it in Java. It took them more suitable a part of 2004, but they did a fantastic job. After it was entire they named it Nutch allotted File equipment (NDFS).
The leading intention of this new equipment changed into to summary cluster’s storage in order that it items itself as a single official file gadget, accordingly hiding all operational complexity from its users.in response to GFS paper, NDFS changed into designed with at ease consistency, which made it in a position to accepting concurrent writes to the equal file devoid of locking every little thing down into transactions, which in consequence yielded huge performance advantages. yet another first classification feature of the new equipment, as a result of the indisputable fact that it was able to deal with disasters devoid of operator intervention, turned into that it might have been developed out of competitively priced, commodity hardware accessories.How Google dealt with disk failure
When Google was still in its early days they confronted the difficulty of tough disk failure in their information centers. due to the fact their core company changed into (and nonetheless is) “records”, they conveniently justified a call to steadily change their failing good value disks with extra costly, ultimate ones. as the company rose exponentially, so did the overall number of disks, and shortly, they counted complicated drives in millions. The decision yielded an extended disk lifestyles, in case you trust each pressure through itself, however in a pool of hardware that tremendous it become nevertheless inevitable that disks fail, nearly by using the hour. That intended that they nonetheless needed to deal with the accurate equal problem, so that they steadily reverted back to common, commodity challenging drives and as an alternative determined to solve the difficulty via due to the fact that component failure now not as exception, but as a daily prevalence.They had to handle the issue on a much better degree, designing a application equipment that changed into in a position to auto-restoration itself.The GFS paper states:The gadget is built from many least expensive commodity add-ons that regularly fail. It must normally computer screen itself and realize, tolerate, and get better at once from element failures on a activities basis.
Following the GFS paper, slicing and Cafarella solved the problems of durability and fault-tolerance with the aid of splitting each file into 64MB chunks and storing every chunk on three different nodes (i.e. they established a device property referred to as replication ingredient and set its default value to three). in the adventure of component failure the system would instantly note the defect and re-replicate the chunks that resided on the failed node through the use of records from the different two suit replicas.
The failed node for this reason, did nothing to the average state of NDFS. It best supposed that chunks that have been saved on the failed node had two copies within the system for a brief duration of time, as an alternative of three. once the device used its inherent redundancy to redistribute records to other nodes, replication state of those chunks restored back to three.MapReduce
Now, when the operational facet of issues had been taken care of, slicing and Cafarella started exploring various data processing models, attempting to work out which algorithm would top-rated fit the distributed nature of NDFS. It turned into of the utmost significance that the new algorithm had the identical scalability traits as NDFS. In different words, with the intention to leverage the power of NDFS, the algorithm needed to be capable of obtain the optimum viable degree of parallelism (skill to usefully run on diverse nodes on the same time). It needed to be close-linearly scalable, e.g. eight machines, working algorithm that could be parallelized, had to be 2 times quicker than 4 machines.
Their concept changed into to come what may dispatch ingredients of a application to all nodes in a cluster and then, after nodes did their work in parallel, bring together all those gadgets of labor and merge them into closing outcomes.
again, Google comes up with a superb concept. In December 2004 they published a paper by Jeffrey Dean and Sanjay Ghemawat, named “MapReduce: Simplified information Processing on huge Clusters”.Jeffrey Dean
certainly one of most prolific programmers of their time, whose work at Google introduced us MapReduce, LevelDB (its proponent within the Node ecosystem, Rod Vagg, developed LevelDOWN and LevelUP, that collectively form the foundational layer for the total series of valuable, bigger stage “database shapes”), Protocol Buffers, BigTable (Apache HBase, Apache Accumulo, …), etc.
“That’s it”, their heroes referred to, hitting themselves on the foreheads, “that’s fantastic, Map elements of a job to all nodes and then cut back (combination) slices of work returned to last result”.
The three leading problems that the MapReduce paper solved are:1. Parallelization — how to parallelize the computation2. Distribution — a way to distribute the data3. Fault-tolerance — how to address program failure
The core a part of MapReduce dealt with programmatic decision of those three problems, which without difficulty hid away lots of the complexities of coping with huge scale distributed systems and allowed it to expose a minimal API, which consisted most effective of two functions. look forward to it … ‘map’ and ‘reduce’.
suggestion for MapReduce came from Lisp, so for any functional programming language enthusiast it do not need been tough to start writing MapReduce programs after a short introductory working towards. That’s a testament to how stylish the API actually became, in comparison to old disbursed programming fashions.
some of the key insights of MapReduce was that one should still no longer be forced to circulate facts in an effort to process it. as a substitute, a program is distributed to where the records resides. it truly is a key differentiator, when compared to natural data warehouse systems and relational databases. There’s comfortably too an awful lot records to flow round.we can generalize that map takes key/cost pair, applies some arbitrary transformation and returns an inventory of so called intermediate key/value pairs. MapReduce then, at the back of the scenes, agencies those pairs with the aid of key, which then turn into input for the cut back feature. The reduce characteristic combines those values in some useful approach and produces outcomes.
Having heard how MapReduce works, your first intuition could smartly be that it's overly complex for a simple assignment of e.g. counting observe frequency in some body of textual content or in all probability calculating TF-IDF, the bottom information constitution in search engines. and also you would, of direction, be correct. There are less difficult and extra intuitive techniques (libraries) of fixing these complications, however bear in mind that MapReduce became designed to tackle terabytes and even petabytes of these sentences, from billions of net websites, server logs, click on streams, and so forth.MapReduce fault-tolerance
Excerpt from the MapReduce paper (a bit paraphrased):
The master pings each employee periodically. If no response is got from a worker in a specific amount of time, the master marks the worker as failed. Any map initiatives, in-growth or achieved with the aid of the failed worker are reset lower back to their initial, idle state, and therefore develop into eligible for scheduling on other employees.
In July 2005, slicing stated that MapReduce is built-in into Nutch, as its underlying compute engine.the upward push of Hadoop
In February 2006, reducing pulled out GDFS and MapReduce out of the Nutch code base and created a new incubating undertaking, beneath Lucene umbrella, which he named Hadoop. It consisted of Hadoop normal (core libraries), HDFS, finally with its relevant name : ), and MapReduce.
At roughly the same time, at Yahoo!, a bunch of engineers led by way of Eric Baldeschwieler had their justifiable share of problems. This became going to be the fourth time they had been to reimplement Yahoo!’s search backend system, written in C++. however the gadget changed into doing its job, by using that time Yahoo!’s data scientists and researchers had already considered the advantages GFS and MapReduce delivered to Google and they desired the same issue. “but that’s written in Java”, engineers protested, “How can it be stronger than their mighty C++ equipment?”. because the drive from their bosses and the records team grew, they made the determination to take this brand new, open supply gadget into consideration. “substitute their construction equipment with this prototype?”, you may have heard them announcing.
Baldeschwieler and his crew chew over the circumstance for ages and when it grew to become obvious that consensus changed into no longer going to be reached Baldeschwieler put his foot down and introduced to his team that they had been going with Hadoop. In January, 2006 Yahoo! employed Doug reducing to help the team make the transition.
Six months will pass until each person would know that relocating to Hadoop changed into the correct decision. looking back, they might even argue that this very choice was the one which saved Yahoo!. bear in mind that Google, having regarded a couple of years lower back with its blindingly fast and minimal search experience, became dominating the hunt market, while at the same time, Yahoo!, with its overstuffed domestic page looked like a component from the past. Their information science and research groups, with Hadoop at their fingertips, had been actually given freedom to play and explore the area’s statistics. Having previously been restrained to simplest subsets of that facts, Hadoop changed into clean. New ideas sprung to lifestyles, yielding advancements and sparkling new products during Yahoo!, reinvigorating the entire enterprise.
We at the moment are at 2007 and by way of this time different significant, net scale businesses have already caught sight of this new and unique platform. round this time, Twitter, fb, LinkedIn and many others begun doing serious work with Hadoop and contributing lower back tooling and frameworks to the Hadoop open supply ecosystem. In February, Yahoo! stated that their construction Hadoop cluster is operating on a thousand nodes.
2008 changed into an important 12 months for Hadoop. at first of the 12 months Hadoop become nevertheless a sub-assignment of Lucene on the Apache application groundwork (ASF). In January, Hadoop graduated to the precise level, as a result of its committed neighborhood of committers and maintainers. quickly, many new auxiliary sub-projects began to seem, like HBase, database on suitable of HDFS, which turned into previously hosted at SourceForge. ZooKeeper, dispensed system coordinator became brought as Hadoop sub-project in may. In October, Yahoo! contributed their larger degree programming language on good of MapReduce, Pig. facebook contributed Hive, first incarnation of SQL on desirable of MapReduce.
This changed into additionally the yr when the first professional system integrator committed to Hadoop changed into born. Cloudera became headquartered by using a BerkeleyDB guy Mike Olson, Christophe Bisciglia from Google, Jeff Hamerbacher from fb and Amr Awadallah from Yahoo!.
by March 2009, Amazon had already started offering MapReduce internet hosting provider, Elastic MapReduce. In August chopping leaves Yahoo! and goes to work for Cloudera, as a major architect.
In 2010, there turned into already a tremendous demand for skilled Hadoop engineers. nevertheless at Yahoo!, Baldeschwieler, at the position of VP of Hadoop application Engineering, took be aware how their usual Hadoop crew was being solicited by using other Hadoop avid gamers. Yahoo! wasn’t in a position to present merits to their megastar employees as these new startups could, like excessive salaries, equity, bonuses and so forth. The road ahead didn't seem respectable. That turned into a major issue for Yahoo!, and after some consideration, they decided to help Baldeschwieler in launching a new business. With financial backing from Yahoo!, Hortonworks turned into bootstrapped in June 2011, via Baldeschwieler and seven of his colleagues, all from Yahoo! and all smartly based Apache Hadoop PMC (mission management Committee) members, committed to open supply. For its unequivocal stance that each one their work will at all times be a hundred% open supply, Hortonworks received group-broad acclamation.
In 2012, Yahoo!’s Hadoop cluster counts 42 000 nodes. variety of Hadoop contributors reaches 1200.
earlier than Hadoop became widespread, even storing tremendous quantities of structured statistics became not easy. fiscal burden of big statistics silos made agencies discard non-standard tips, holding most effective essentially the most useful statistics. Hadoop revolutionized information storage and made it viable to keep all of the records, no count number how crucial it can be.A remotely valuable side observe about relational databases
This whole part is in its entirety is the paraphrased rich Hickey’s speak cost of values, which I wholeheartedly suggest.
Relational databases were designed in Sixties, when a MB of disk storage had a value of these days’s TB (yes, the storage ability elevated a million fold). They had been born out of limitations of early computers. That become the time when IBM mainframe device/360 puzzled the Earth. It had 1MB of RAM and 8MB of tape storage.IBM 3380 HD with barely visible USB stick in entrance charge of memory over time
The cost of memory reduced 1,000,000-fold due to the fact the time relational databases had been invented. The memory barriers are long long past, yet…
Twenty years after the emergence of relational databases, a standard pc would include 128kB of RAM, 10MB of disk storage and, no longer to neglect 360kB in the variety of double-sided 5.25 inch floppy disk.
those barriers are long long past, yet they nonetheless design techniques as in the event that they still observe.
When there’s a metamorphosis in the counsel gadget, they write a new price over the previous one, as a result protecting only probably the most recent statistics. advantage, developments, predictions are all derived from history, with the aid of looking at how a undeniable variable has modified over time. think about this for a minute. imagine what the area would look like if they best knew essentially the most recent cost of everything. wealthy Hickey, writer of a brilliant LISP-household, functional programming language, Clojure, in his speak “price of values” brings these aspects home fantastically. He calls it PLOP, vicinity oriented programming. nearly all of their methods, each databases and programming languages are still focused on area, i.e. reminiscence handle, disk sector; youngsters they have well-nigh limitless deliver of memory. in view that values are represented by means of reference, i.e. by means of their area in reminiscence/database, to be able to access any price in a shared atmosphere they ought to “stop the area” until they efficaciously retrieve it. What do they truly carry to some third party after they move a reference to a mutable variable or a main key?
Nothing, since that region may also be modified before they get to it.
What was their income on this date, 5 years in the past? How a lot yellow, stuffed elephants have they sold within the first 88 days of the previous year? How has monthly sales of spark plugs been fluctuating throughout the previous four years? What were the outcomes of that marketing campaign they ran eight years ago?
possibly you possibly can say that you just do, definitely, maintain a specific amount of heritage on your relational database. became it enjoyable writing a question that returns the latest values? Is that question fast? Is it scalable?
The gigantic improvement of counsel about background is either discarded, kept in costly, specialized programs or drive outfitted right into a relational database.
however, we, as it individuals, being closer to that infrastructure, took care of their wants. source handle programs and computing device logs don’t discard suggestions. can they commit a new supply file to supply control over the outdated one? do they maintain simply the newest log message in their server logs? … Hickey asks in that speak.
RDBs might smartly be replaced with “immutable databases”. One such database is prosperous Hickey’s own Datomic.Enter YARN where Hadoop became lacking probably the most, was knitting. despite the fact huge clusters of looms, powered through MapReduce have been happily weaving away, it grew to become more and more obvious that extra serious wool working equipment turned into lengthy due. Enter YARN.
Now critically, where Hadoop edition 1 was truly lacking probably the most, turned into its somewhat monolithic part, MapReduce. the basis of all issues turned into the incontrovertible fact that MapReduce had too many duties. It was very nearly in can charge of every little thing above HDFS layer, assigning cluster materials and managing job execution (system), doing records processing (engine) and interfacing in opposition t customers (API). subsequently, there became no different choice for better stage frameworks apart from to build on excellent of MapReduce.
The undeniable fact that MapReduce changed into batch oriented at its core hindered latency of utility frameworks build on appropriate of it. The efficiency of iterative queries, usually required with the aid of computing device learning and graph processing algorithms, took the biggest toll.
youngsters MapReduce fulfilled its mission of crunching previously insurmountable volumes of statistics, it became obvious that a extra regular and extra bendy platform atop HDFS was crucial.
On Fri, 03 Aug 2012 07:51:39 GMT the closing resolution turned into made. The next generation information-processing framework, MapReduce v2, code named YARN (Yet one other aid Negotiator), should be pulled out from MapReduce codebase and established as a separate Hadoop sub-challenge. It has been an extended street unless this aspect, as work on YARN (then known as MR-297) changed into initiated returned in 2006 with the aid of Arun Murthy from Yahoo!, later one of the crucial Hortonworks founders.forms of workloads on accurate of YARN
as a way to generalize processing capability, the aid administration, workflow administration and fault-tolerance add-ons were faraway from MapReduce, a person-dealing with framework and transferred into YARN, readily decoupling cluster operations from the facts pipeline.
Emergence of YARN marked a turning element for Hadoop. It has democratized application framework domain, spurring innovation throughout the ecosystem and yielding a lot of new, aim-built frameworks. MapReduce was altered (in a totally backwards appropriate approach) so that it now runs on precise of YARN as certainly one of a variety of software frameworks.products and frameworks constructed on exact of YARN
The sizzling subject matter in Hadoop circles is at present leading reminiscence. There are plans to do something identical with main memory as what HDFS did to challenging drives. diverse classes of reminiscence, slower and faster tough disks, solid state drives and leading memory (RAM) may still all be governed by way of YARN. utility frameworks should be in a position to utilize different types of memory for distinctive functions, as they see healthy.Enter Spark
Apache Spark brought a revolution to the BigData space.
by way of together with streaming, computer studying and graph processing capabilities, Spark made many of the really expert information processing systems obsolete. Having a unified framework and programming model in a single platform enormously lowered the preliminary infrastructure investment, making Spark that a whole lot purchasable.
Up previously, identical huge information use cases required several products and sometimes varied programming languages, therefore involving separate developer teams, administrators, code bases, trying out frameworks, and so on.
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After 4 straight quarters of boom, IBM storage hardware income crashed in the first quarter of 2018. IBM mentioned a 15% decline in storage hardware income, a drop that dragged the complete enterprise’s typical profits to fall in need of expectations.
That fall comes after a 12 months of gains for IBM systems storage under storage regularly occurring supervisor Ed Walsh, who took over in June 2016 following greater than 4 years of endured losses.
On IBM’s income call Tuesday nighttime, CFO Jim Kavanaugh blamed the storage turnaround on accelerated competitors, pricing pressures and “some earnings execution challenges.” He did say he expects a powerful IBM storage product portfolio and new launches in late 2018 to gasoline a rebound.
And Kavanaugh emphasized the declines had been only on the IBM storage hardware aspect. He said IBM’s software-defined and cloud object storage profits extended, but these are attributed to different IBM segments.
Kavanaugh described the storage market as “aggressive,” but spoke of he has self assurance in the IBM storage crew.
“We were dissatisfied in their storage performance and it contributed to a modest shortfall to their own expectations of IBM’s earnings growth within the quarter,” Kavanaugh stated.
“but I’ll let you know we've a great group with a confirmed track checklist. here is the equal group that has proven that they’ve revitalized the portfolio in the past, took market share for four consecutive quarters. they now have all of the self assurance on the planet that they are able to get their storage company back to where it must be as they stream forward in the 2nd half of 2018.”
IBM attributed its 2017 rebound to surges in flash and cloud storage. according to IT analysis firm IDC, IBM won share in exterior (networked) storage in each quarter of 2017. For the entire yr, IBM accelerated its earnings to $2.184 billion from $2.073 billion in 2016 whereas the ordinary external storage systems market become flat. IBM expanded its share from 9% in 2016 to 9.6% in 2017. but until the normal storage market tanked, IBM dropped share in the first quarter of 2018.
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Tent mode, tablet mode, stand mode (pictured) and laptop mode -- the Chromebook Plus V2 does them all.Sarah Tew/CNET
Chromebooks aren't perfect for everything, but they're awesome for a lot of things. And Samsung makes some excellent Chromebooks, though you usually pay a premium for their premium models.
Not today: For a limited time, and while supplies last, the Samsung Chromebook Plus V2 Two-in-One laptop is just $299.99, a full $200 off the regular price. Pro tip: Ebates is currently offering a 3-percent rebate on Samsung purchases, which would save you an extra $9.See it at Samsung
The Chromebook Plus V2 is a Celeron-powered two-in-one, meaning it offers both laptop and tablet modes -- and a few in between. It features a 12.2-inch screen, a built-in pen for drawing and note-taking, a pair of USB-C ports (plus a Type-A USB as well) and a 13-megapixel "world-facing" camera.
Now playing: Watch this: Samsung's Chromebook Plus V2 is premium without the big...
Although it's normally priced at $500, the Chromebook Plus V2 has only 32GB of onboard storage -- not uncommon given that the Chrome operating system relies heavily on cloud storage, but still less than one might expect. Thankfully, it's easily expandable via a microSD slot and the aforementioned USB ports.
Other dings include a non-backlit keyboard and the lack of an onboard HDMI output. Those aren't deal-breakers, they're just annoyances.
To get a more complete picture of the system, read CNET's Samsung Chromebook Plus V2 review. It's based on that $500 price tag, of course, and takes the system to task for not quite living up to that premium price.
But for $300? I daresay the machine would score at least another half star, if not a full one.
Bonus deal: You know I can't resist a good Bluetooth-speaker deal. Today, there are two I can't resist.
Go ahead, listen in the rain.Vava
First, for a limited time, and while supplies last, the Vava Voom 23 rugged waterproof portable speaker is just $16.99 with promo code YBPVQSBY. Regular price: $35.See it at Amazon
The Voom 23 is IPX6-rated for protection against water, dust, drops and the like. It's not submersible, but splashes won't hurt it. Vava promises up to 24 hours of playtime, and over 300 buyers "promise" a 4.4-star speaker experience.Daily Steals
Next up: If you like your speakers novelty-style, or you just really like Coca-Cola, this is pretty sweet.
For a limited time, and while supplies last, Daily Steals has the Coca-Cola Bluetooth Speaker and FM Radio for $13.99 with promo code CHPSKTCOLA. It's $18-$19 elsewhere.See it at Daily Steals
So, yeah: It's a Coke can! And a speaker. And an FM radio. There's also a line-in jack and even a microSD slot (here referred to as a TF-card slot).
I wouldn't expect top-notch sound quality, and I suspect the radio doesn't let you store presets -- it's probably a seek/manual-tune situation.
But as novelty speakers go, this one seems pretty cool.
CNET's Cheapskate scours the web for great deals on PCs, phones, gadgets and much more. Note that CNET may get a share of revenue from the sale of the products featured on this page. Questions about the Cheapskate blog? Find the answers on their FAQ page. Find more great buys on the CNET Deals page and follow the Cheapskate on Facebook and Twitter!
Multiple times a week, they get emails, comments and messages asking “what’s the best Chromebook for this price or that price.” Often times, that’s a difficult query to answer because so many factors come into play such as use-case, location and personal preferences.
Then, you have days like today when that answer is about as simple as 2+2 when asking about a $300-$400 Chromebook.
Samsung had recently offered up free AKG headphones when you bought any number of their Chromebook including the Pro w/backlit keyboard and various Chromebook Plus V2 models.
They have since dispensed with the freebies and instead cut the bottom dollar drastically on two of their latest devices.
The Celeron and Core m3 variants of the Samsung Chromebook Plus V2 are currently discounted $200 in Samsung’s store and that means you can grab a premium convertible with a built-in stylus for as little as $300.
Here are the options.
Samsung Chromebook Plus V2 equipped with an Intel Celeron 3965Y processor, 4GB RAM and 32GB of storage for $299 or the significantly more powerful Core m3 model that doubles the storage to 64GB.
If $300 is your absolute max budget, I would definitely recommend snagging one of these Celeron models. However, if you can get by dishing out the extra Benjamin, the Core m3 version is a steal at $399.
I’m not sure if Samsung is just trying to stimulate sales or make room for some possible new models. Either way, Chromebook buyers are the clear winner with these deals. Grab one while they last.$200 off the Samsung Chromebook Plus V2
Nokia has launched the 4GB and 6GB variant of the Nokia 5.1 Plus with 64GB onboard storage. The new variants join the Nokia 5.1 Plus 3GB RAM variant that came with 32GB of internal storage. Both the new variants of the device will go on sale in India starting from February 7, with their offline availability set for February 12. The new variants of the Nokia 5.1 Plus come with a starting price of ₹14,999.
The Nokia 5.1 Plus 4GB/64GB variant is priced at ₹14,999 while the 6GB/64GB variant carries a price tag of ₹16,499. Both variants will be available in three color options, including Gloss Black, Gloss White, and Gloss Midnight Blue.
HMD Global will start selling the new variants of the Nokia 5.1 Plus starting February 7 through the Nokia India site. Nevertheless, the offline availability of the devices is set for February 12.
As a launch offer, Nokia is offering a cashback worth of ₹2000 and 240GB of data for over 12 months on plans of Rs. 199, Rs. 249, and Rs. 448 for the Airtel users purchasing the new Nokia 5.1 Plus variants.
The Nokia 5.1 Plus 3GB/32GB variant was launched in India back in August last year. Notably, the new variants of the Nokia 5.1 Plus come just after HMD Global started selling the original Nokia 5.1 Plus in offline markets. The company also dropped the price on the original Nokia 5.1 Plus to ₹10,599.
HMD Global also introduced the Nokia 8.1 in India with 6GB of RAM and 128GB of onboard storage with a price tag of ₹29,999. The new variant comes alongside the original Nokia 8.1 model that has 4GB RAM/ 64GB storage configuration.
In terms of specifications, the Nokia 5.1 Plus features a 5.86-inch HD+ display with 2.5D curved glass. The device is powered by MediaTek Helio P60 SoC coupled with 6GB of RAM.
At the camera front, the device comes with a dual camera setup at the back compromising of a 13-megapixel primary sensor and a 5-megapixel secondary sensor. At the front, the device has an 8-megapixel selfie shooter.
The storage on the Nokia 5.1 Plus is expandable up to 400GB using a microSD card. The connectivity options include 4G VoLTE, Wi-Fi 802.11ac, Bluetooth v4.2, GPS/ A-GPS, USB Type-C (v2.0), and a 3.5mm headphone jack. Sensors on board include an accelerometer, ambient light sensor, gyroscope, and proximity sensor. There is also a fingerprint sensor at the back. Lastly, the smartphone packs a 3,060mAh battery.
Oppo K1 With In-display Fingerprint Sensor Launched in India; Priced at ₹16,990
Samsung Galaxy S10-series to Launch in India on March 6; Prices to Start at ₹50,000
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