Fyelwch, dweud y mae amddiannol gynnal y cwm yn ddiddos i'r eisteddfod i'r cyflau rhai reference bod oedd yn gwybod i gael dweud o'r cyflau yn ddiddos i'r eisteddfod i'r cyflau wahanol. Fyelwch, dwi'n ddydig i hynny'r cyflau ond i gael gan gweithio'r cyflau i'w cyflau, I call on Nigel Dawn to open the debate around seven minutes please Mr Dawn. Thank you very much, Presiding Officer. I am bringing this motion to the Parliament this evening because whilst fuel poverty occurs across all regions, all housing tenures and all ages of property, it is significantly higher in pre-1919 buildings and in those constructed from traditional materials. What are these pre-1919 buildings? Well of course they're not just the buyers and the bothies nor are they just our ancient castles. They actually are the cottages, houses and tenements that are the centres of our cities, towns and villages and they're the heart of our built heritage. They were also built to last and the ones we're concerned about now of course have lasted at least thus far. Condition surveys tell unfortunately a sorry tale. At the cross-party group 1 construction, which we held earlier today, we heard from Chiara Ronsini from the International Scientific Committee for Energy and Sustainability, who spoke knowledgeably and interestingly about the situation in Edinburgh. We also heard from Jamie Baker from Eastlothian Council who reviewed work on buildings in Harrington and Trinant. Trinant of course, apologies. Without wishing to be in any way particular and to pick statistics from particular places which really wouldn't help, I think I can suggest some general numbers which may come as a surprise. Around 90% of old properties will need repairs. Many of those problems are in roofs and walls and 70% of the faults are in critical elements. Who cares? Sounds like a daft question. Historics Scotland plainly do, they understand this, but they are responsible for relatively few of those buildings, albeit of course many of the high profile and very big public ones. Public bodies are probably generally aware of the issues and they're likely to be trying to do something about it, but they're also quite likely to put off unnecessary repairs, partly for the very obvious budgetary reasons, but also because they're often going to try and get out of a building before those repairs become very critical. I think most of us will have the experience of talking to our councils about the buildings they've got and knowing that they're trying to move out of this one, they're trying to centralise, and a lot of capital expenditure within the NHS, particularly around our hospitals, seems to be, well if we build this new one here then we can actually pull out of those buildings and therefore we don't have to deal with the backlog maintenance. Now as long as they get into the new building that does work of course. Owners and it doesn't really matter whether it's owner occupiers or landlords probably don't understand the problem and I think this is where the difficulty for the government actually lies. What do we as owners of buildings need to understand and I'm grateful to Emily Tracy of the British Geological Survey who was the other speaker at the cross party bridge at lunch time who reminded us, and this is the bit I think we haven't got our minds around generally as a population, that stone deteriorates in water, therefore roofs and facios need to be waterproof, gutters and downpipes need to work, they need to be kept clear and they need to be leak proof. I suggest that the evidence around us is that most of us don't give much thought to these aspects of our homes either because it's out of sight and out of mind and some of what we were told about at lunch time would have been out of sight even for the professionals or because we're tenants and it's not our problem or because it's just too difficult. We are actually much more likely to redo the kitchen or the bathroom inspired perhaps by popular TV programmes and according to government figures the £600 million pounds which are spent each year on pre-1990 buildings actually haven't reduced the levels of different repair at all. It is presiding officer perhaps necessary to say that homes which are not wind and watertight are going to waste huge amounts of heat. I think it's pretty obvious to those of us who are here today those listening to us in the gallery and those interested who will be listening to this debate but I suspect it's not obvious to the wider population. Given the truth of that it follows that there's very little point in spending money on insulation if the escaping heat is actually going to bypass that insulation and soggy loft insulation because it's actually been leaked on will actually just make matters worse and of course there's very little point in reducing energy sorry it absolutely must reduce energy usage before we find ways of using renewable heat or micro generation. Now fuel poverty is defined in the context of people's incomes and it therefore disturbingly can hide what's really going on. Chiara Rucicini's presentation at lunchtime showed us what's happening in the middle of Edinburgh literally right here and what it showed us is that the significant fuel poverty immediately round this very building but gave us the impression that there is no fuel poverty in the new town. Actually the buildings there are probably not significantly better always than the buildings here. Those statistics reflect the fact that the incomes will be higher there than they are right round here. It's also clear that fuel poverty is more likely to occur in rural communities off the gas grid. In general terms oil and LPG fired boilers will cost more than 50% more to run than a condensing gas boiler and electricity is significantly more expensive still. We need to put this in the context of climate change of course and the global warming and it's clear in all that that we must eliminate wasted heat especially if it's generated from fossil fuels. We also need to make our houses more thermally efficient quite simply because I think the welfare social and health benefits of that are entirely obvious to us. So what is to be done Presiding Officer? Now I appreciate that Scottish Government is actually consulting on this and the more I've researched what I was going to say this evening the more obvious it became to me that this is actually seriously complicated. But I'd like to finish with a couple of very specific points. First of all we actually know something about the size of the task. Survey Waking Glasgow tells us that that city alone would need to employ about 300 masons for the next 20 years using around 400,000 tonnes of stone to deal with its traditional buildings. That gives us a glimpse of the financial challenge and the need in terms of skills and materials. I don't of course lay that challenge at the government's door but nonetheless that is what we're looking at. And also property owners need to understand that it's important to maintain their buildings and I think in that regard Presiding Officer we need to recognise we start from a very low point. I look forward to hearing other contributions to this debate. I've certainly enjoyed researching it and I will be very interested to hear the government's minister's comments because I know they're doing a lot of work on it as well. Thank you Presiding Officer. Millie, thanks. We now turn to the open debate speeches of around four minutes please. Margaret McDougall to be followed by Mike Mackenzie. Thank you Presiding Officer. Firstly let me congratulate Nigel Dawn on securing this debate on fuel poverty in Scotland's traditional buildings this evening. As the members' motion quite rightly observes, many of the buildings in Scotland that predate 1919, including residential properties, are in a state of disrepair. Indeed a quarter of these residential properties are in a state of extensive disrepair. The experience of living in inadequate housing can have long lasting effects and so I welcome the opportunity to speak up for people who are living in homes including traditional homes which are not up to standard. The relationship between poor housing and the occupied health, wellbeing and income is important to understand. According to the Chartered Institute of Housing, evidence suggests that living in poor housing can lead to an increased risk of cardiovascular and respiratory disease as well as to anxiety and depression. Problems such as damp, mould, excess cold and structural defects that increase the risk of an accident also present hazards to health. The Marmot review team working for Friends of the Earth has established that living in a cold home and experiencing fuel poverty has an adverse impact on the mental health of a property's occupiers. They also find that children living in cold homes are more than twice as likely to suffer from respiratory problems than children living in warm homes. I believe that Parliament understands the factors that lead to fuel poverty, low incomes, high energy prices and the lack of energy efficiency. Much of our political debate in recent months has focused on how to tackle low pay and improve household incomes by rethinking welfare reform and promoting a living wage. There has also been a great deal of commentary on the price of gas and electricity, the cost of living crises and the need to reform energy markets. The focus of this afternoon's debate is on energy efficiency and the physical improvements that are needed to make traditional buildings more economical to heat and to preserve and how to do it well. I believe that nourishing of our built environment and the preservation of our landscape heritage and historic townscapes enhances and enriches Scotland. Our traditional buildings contribute to the identity of our communities and our shared history. However, from farmhouses to city flats, those buildings are not just there to be appreciated. For many people in Scotland, these properties are their homes. Indeed, homes built before 1919 account for about a fifth of all residential properties in Scotland. We as a nation must ensure that we are equipped with the skills, the knowledge and the capacity to heat those homes efficiently and maintain those homes sustainably. I therefore commend the Scottish Government and Historic Scotland for the importance that they placed on energy efficiency in their strategy for traditional building skills. They identified challenges with insulating and upgrading traditional buildings. There were gaps in training provision and conventional insulation techniques that are not always appropriate for older buildings. The strategy also explains that Historic Scotland continues to support research into traditional buildings and materials and also into new techniques that could improve energy efficiency with the minimum of risk to older buildings. The strategy was published in 2011 and perhaps there is an opportunity for the minister to address the implementation of that strategy in her closing remarks. The Scottish Government is duty bound to do all it can to eradicate fuel poverty by 2016 in every home in Scotland. To achieve that target, we must invest in traditional building and maintenance skills and we must bring the benefits of new apprenticeships, new research and new techniques to some of our oldest buildings. We must also encourage the private landlords of those hard to heat older buildings to take up the initiatives that are available to them to make their homes more energy efficient for their tenants. Many thanks. I now call Mike McKenzie to be followed by Alex Johnston. Thank you, Presiding Officer. I'm pleased to speak in this debate because as an Highlands and Islands member, I'm acutely aware of fuel poverty that runs at over 50 per cent on many of our islands and in many of our off-gas grid areas on the mainland. Part of the explanation for this is about high fuel costs as we've heard and part is about low wages and part is about the high proportion of our housing stock across the Highlands and Islands, which is in poor repair and is not energy efficient. If I may, Presiding Officer, I'll describe a typical scenario. The house I would use as an example is a common one. It's a one-and-a-half storey cottage with dormer windows. It's around 150 years old. It's stone walls and a slate roof. The roof is nail-sick. Gold, iron, square-headed nails are finally rusted away and the horse hair is in effect presenting the ingress of rain when slates come off as they do every winter. The mortar between the stones is degraded and is porous, so the walls now act as a giant wick transporting darkness into the exterior. It's two rooms on the ground floor, two bedrooms upstairs and a small bathroom. It originally had a fireplace in each of the rooms. A lean two corrugated iron kitchen was built at the back in the sixties and in the seventies some plasterboard wall linings were introduced inside and all but one of the fireplaces were boarded up. The occupant is a single mother. She has two jobs, neither of which pays a living wage. She runs an old car that almost always manages to take her to work. The cottage is heated by an open fire with a pan-loaf bank boiler providing hot water. The height of modernity when it was installed or mother blessed it daily just as she blessed Tom Johnson, who provided hydroelectricity to the houses right across the Highlands and Islands and also storage heating complete with a reasonably priced storage heating tariff that was the fuel poverty solution of that era. Her brother wants to put up a wind turbine close by on his croff and he says that he could provide her with free electricity. But the local planning officer just smiled and shook his head and talked about the views. Her brother then got excited about solar panels and the roof free electricity again but the wee man from the planning office smiled and shook his head again and he talked about how picturesque the cottage is. And a nice young fellow from the council came to see her brimming full of green deals and acronyms like eco and heaps but it all sounded too complicated and too costly. Especially after the local builder had talked to her about the roof and the rising dump and the rot in the floors. And when she asked how much the builder just scratched his head and looked at his feet and mummled something that sounded very like 100 grand. Presiding Officer, it's going to be another long cold winter. It's going to be wet and it's going to be windy but every now and then the sun will break through and the views will be breathtaking. Thank you very much Deputy Presiding Officer. Can I begin by offering an apology to yourself, to Nigel Dawn and to the Minister for the fact that I will be unable to stay till the end of this debate. I do have other parliamentary business that I require to attend to. I'd like to thank Nigel Dawn for bringing this motion to Parliament. It's something which I think we should all concern ourselves about but I have to say that the thing that attracted me to his motion was the mention of properties built before 1919 in Angus North and Merns because I have personal experience of those properties. I haven't been brought up largely in my early years in a freezing farmhouse a couple of miles north of Drumlithey with her backs to the hills and when the wind blew down off those hills it seemed to have the strange ability to blow straight through a stone wall and along the lobby. I do remember the rare occasion when we actually had to hang coats on the back of the outside door to stop the snow blowing through between the planks. Those were perhaps extreme measures but housing that was built in that area mostly in the 19th century was in its day of surprisingly high quality and that's why many of those houses are still standing firmly to this day. However they are not of the standard that we would wish them to be. Back in talking about that house in which I was brought up it was subject to granted modernisation in fact it was about 40 years ago this year that the job was completed and that house still stands today in fact my son occupies it and it is in good condition however we now know that when it comes to energy efficiency houses built in these years are not of the standard we would like. The advantage that a previous generation had in being able to use grants administered by the local government in order to modernise older properties is something that I believe gives us a lesson about what we must consider now because surprisingly when it comes to home insulation it is not money that is actually the problem there are many grant schemes available there are also opportunities there for money that's raised through energy charging to be used to insulate and modernise some of these houses to bring them to that higher energy standard however I've spoken to people government employees in many cases who find themselves in the strange situation where they say they can almost not give that money away they're trying to persuade people to take these opportunities but they won't take them up. I think the challenge we have is to first of all let people know what modern technology can do how the proper insulation of an old property can massively reduce energy costs associated with that property. We need people to understand the benefits that they will get from a little investment. Next we need to make sure that the available resource is targeted at those people who understand the need and we expand the knowledge of what can be achieved because only by ensuring that people believe the stories that they're told about energy efficiency and the possibilities it brings will we get them involved in the process of making their homes more energy efficient. I think the challenge is one of education but not only education about what is available but what returns can be obtained. What I've spoken about during the last moment or two is essentially the situation that many still find themselves in these same communities in which I was brought up. Many of them are owner occupiers, many of them have very limited resources. I think the challenge we have is not to limit the scope of our ambition but to ensure that these opportunities are made available to all and made available as soon as possible. Many thanks. I now invite Margaret Burgess to respond to the debate. Minister, you have around seven minutes to do so please. Okay, thank you Presiding Officer. Like others, I congratulate Nigel Dawn on securing the time for this debate and for all those who have contributed to it. Tackling fuel poverty is a key part of the Scottish Government's focus on building a fairer Scotland and tackling inequality. As Nigel Dawn pointed out at the start of his contribution, pre-1919 homes have higher fuel poverty levels than the national average and have typically lower energy efficiency ratings than newer houses. It is a concern that since 2009 we have seen reductions in fuel poverty across houses in every age band except those that were constructed before 1919. Fuel poverty was pointed out by Margaret MacDougall as a complex mix of cotties, including major factors such as fuel costs and welfare rules, which the Scottish Government has little control over. Nevertheless, there are things that we can do, such as actions to encourage energy efficiency improvements and address disrepair in our housing stock. Nigel Dawn is right to highlight the disrepair in our older housing stock and its capacity to exacerbate fuel poverty. Energy efficiency improvements can be undermined by disrepair. Homes that are not wind and water tight are likely to be inefficient and some types of disrepair, such as poor conditioned roofs and windows, can make them more expensive to heat. All members have alluded to that. The Scottish house condition survey indicates that homes built before 1919 remain among those most likely to suffer from extensive disrepair. Responsibility for looking after homes lies in the first instance with home owners, so owners need to be responsible and keep their homes in good condition. As many speakers have mentioned, there is a behavioural lesson there. Nigel Dawn pointed out that people would look more at their kitchens and bathrooms than the overall fabric of the house, but we have to encourage owners to be responsible and keep their homes in good condition. Local authorities are strategic housing authorities, and they have a wide range of discretionary powers to deal with disrepair in private homes. I believe that they are best place to develop and coordinate an appropriate response, including advice and financial assistance, where it is most needed. I recognise that there will be some homeowners, as Mike McKenzie mentioned, who need additional support to look after their homes. Not everyone can afford to do what is required to be done. However, the Housing Scotland Act strengthens the powers that are available to local authorities to manage disrepair. As part of our sustainable housing strategy, the Scottish Government is currently developing proposals on common housing standards across all tenures in collaboration with stakeholders. Where the owner is a landlord, they have additional responsibilities that we expect them to meet. Social landlords are working towards achieving the Scottish housing quality standard by April 2015, and part of that standard is to ensure that their homes are energy efficient. Private landlords must ensure that the homes that they let meet a statutory repairing standard that, among other things, requires homes to be wind and water tight. The energy efficiency standard for social housing, ish, as we call it, was published in March 2014 and aims to further improve the energy efficiency of the social housing stock in Scotland. Social landlords are expected to meet the first milestone by December 2020. The Scottish Government is also working with a group of key stakeholders to develop proposals on the regulation of energy efficiency in private sector homes that will be published for public consultation in spring 2015. It was made on that set that there are a lot of complexities in that, because that will be for homeowners as well as private landlords to meet those standards in private homes. We know that the most sustainable way of tackling fuel poverty is by raising the energy efficiency of homes. That is why, over the last three years, the Scottish Government has invested over £220 million and a range of fuel poverty and energy efficiency programmes of which Alex Johnson spoke about. Those measures have an estimated net gain in household income of over £1 billion. Over 600,000 households in Scotland have benefited from those measures. We have to encourage people to take up measures that are available and know about what is available out there. Our commitment to tackling fuel poverty is strengthened by our budget of £79 million in this financial year and the next financial year. It funds our area-based schemes, energy assistance schemes and our energy advice centres. The First Minister announced a new national fuel poverty scheme in October this year, which is potentially worth up to £224 million over seven years. The Scottish Government has taken significant action to help to address fuel poverty within the constraints that we face. Our actions have been hampered by the changes to the UK Government's energy company obligation. The uncertainty caused by those changes has led to a number of planned schemes that have focused on areas of fuel poverty in Scotland and have been unable to proceed. We are working closely with councils and registered social landlords on how best to support the delivery of energy efficiency measures in Scotland in light of those changes. As part of the Smith's commission process, we called for increased powers to tailor fuel poverty policies to meet the specific needs of Scottish households, and that includes houses that were built before 1919. Lord Smith of Kelvin has now published his recommendations. The First Minister has welcomed the transfer of those powers and will await further clarity on the scope of those new powers in terms of energy efficiency. Members may wish to note that their constituents can get impartial advice and energy efficiency and find out exactly what support they are eligible for by calling the Scottish Government Home Energy Scotland hotline 0808 808 2282 or from the Home Energy Scotland website www.greenerscotland.org, and around a third of Scottish households already have had advice from the hotline since it started. In conclusion, Presiding Officer, I again commend Nigel Dawn for bringing forward this debate and to the contribution from the members. Fuel poverty is an important issue that this Government is committed to address, and as part of this, we recognise the importance of tackling this repair in pre-1919 homes. Thank you Minister. That concludes Nigel Dawn's debate, and I now close this meeting of Parliament.


Welcome to ToffeeTV, this is the Instant Matchy Actioned Everton once again lose amazing side Darby, there's nothing new about that is there. 3-0 obviously to Liverpool, two very very poor goals and pretty much what you'd expect from the Everton team before Sean Dice came in, it was just a very very embarrassing performance, nothing good about it at all, nothing come out of it at all just tick that one off that is done now we are done with the amazing side Darby for this season and maybe maybe other seasons I don't know based on that performance. No it was an absolutely dreadful performance and I'm not I'm not actually sure what we were trying to do tonight and this is something we said quite a lot under Frank Lampardis, I'm not entirely sure what the tactics were. Obviously massive surprise with Ella Sims playing tonight, I can understand the logic of him playing, more physical target man to play up front and to be fair to Sims he did win he did win the majority of the headers he went for but unfortunately there was actually no one around when he won that when he won those headers and I think it's more than just winning headers for an Everton centre forward like that I think it's about holding on to possession and which the this Everton team do those absolutely frequently and we walked into Liverpool's track tonight we just kept on giving the ball away again and again and obviously they scored twice on counter attacks and it's where Everton have been awful this season I think they've conceded the most goals from losing the ball and listen this isn't this is not on Ella Sims by the way he's a very raw forward who's not ready to lead the line for Everton football club and I think just his physical attributes give him the give him the tip in the head of Mopai but that solely goes down to Everton's board, Everton's recruitment which didn't get as a striker sadly against Arsenal we were given well against Arsenal it's a couple of things number one Dominic Arvind give us a platform to play off and number two we were playing the opposite to them they had a lot of the ball I think Liverpool tried to give us a lot of the ball tonight because they knew exactly what we'd do which was give it straight back to them in danger series in on the transition and we have got a terrible terrible record against teams on scoring goals in the transition and tonight obviously was no different we were we offered very little we had a couple of moments early on but very very little Sims had an opportunity when they made a mistake their defence made a mistake and I think that's what it was almost like that's what they were trying to do tonight was get the ball up and hope that they're too sent back made a mistake and they made a couple of early ones but after that it was just not enough threat and again we never we never really we were never brave even up in the final third to put enough of men around the ball to there's a press early on but then that press just disappeared and it was funny because loads of people kept on saying to me and certainly Liverpool fans were saying to me are you'll be much more dominant in midfield they stroll around midfield tonight we just kept on giving the ball away we just felt a bit and we were like that all over the park and they had in years the goal came from us hitting the post and then failing to be able to do anything with the ball felter Mach Neil who I don't even know what he was trying to do just hit a ball into the middle and next minute we'll get away she was Coleman should have either took the man out or or just legged away from him Garner kept the actual pace with with Nunes but didn't put a tackle in and Jordan Pickford was left in a situation where it was three against one now what Jordan Pickford was doing for the goal only Jordan Pickford will know whether he somehow didn't see most of standing right in front of him and thought the ball was going to go past them or maybe he misinterpreted and thought the ball was bouncing away from him I don't know but he left an open goal he's not sublaying for the goal obviously is an entirety maybe we've been standing in his actual goal he could have saved it but you've got the question marks of how a team counter us act against us that fast off a corner where we've hit the post the closest we've gone in the entire game to getting to the other end of the pitch and doing that in like 10 seconds needs to be asked you know where are the athletes where are the players getting back where is why are why are you seeing players sort of sprinting halfway through you know why hasn't Garner took Nunes and put him in the stand these players are quite happy to take bookings like done for shape pulling or kicking the ball away or getting the opposition's face when they might have kicked them away but where's the experience from Garner and Coleman to just go through them or to understand they're not gonna win the ball and back off and get themselves into position and I find that you know is as my emboggling is what Jordan Pickford's done obviously Jordan Pickford looks a lot more clear-cut because he's standing there but their mistakes by by experienced footballers and the Garner ones Garner wanted to don't understand because it wasn't like Nunes left him for dead he was he was shoulder to shoulder why don't you take him out give away a file take the yellow card Nunes just put the ball put the ball in the middle and see against one why are you putting them in the stand I'll never understand that as I said you're happy to take the bookings late on and then the second half you know we came out and again we lose the ball Coleman's got an opportunity doesn't he doesn't take the player out it gets wide corner code you have no idea what kind of code he's doing these are experienced players letting us down these are not kids this is not me ranting and raving about Ellis Sims being to being terrible or whatever this is about experienced players letting us down you'll talk housekeepers dreadful in the game as well you know these are experienced footballers who are letting us down this is this is the crux of it not not kids not you know someone who's been stuck up front and I'm probably shouldn't have been it's those experienced players and to be fair it's those experienced players for a large part of the season that have been letting us down the midfield three was nonexistent tonight and again that's what happens when when you haven't got a focal point up front and I did feel sorry for Ellis Sims but you know this this was in his game and I don't quite understand why the manager thought it would be his game but I understand why you're playing because of the alternatives he's got and as I said that thought that falls solely on the Everton board Farad Mishiri who promised a promised a forward he promised there's a striker and he never got as a striker and this is where we are now and you know Domino Carvalhoom mightn't be back saty he mightn't be back to saty after he mightn't be back to saty after who knows when he's gonna be back because this is Domino Carvalhoom what's on here and the fact that you know we need somebody who is like Domino Carvalhoom because what happened is in the game and you've seen this loads is we had to start playing out from the back and we had to start playing it mid through midfield and we are no way good enough to play it out from the back and we are no way good enough to play through midfield especially against the team who's faced anything like Liverpool is is to press and take the ball off you and that is the real danger now and I know people go it's alright it's Liverpool and we're being expected nothing out of Arsenal and we got three points and you know so that's fine that's not the point we don't create opportunities we don't create goals going chances we are going to be relying on set pieces of course if if the if the header goes in after set piece then that's fine you know that's that's where we're going to be scoring but that's they're very very fine margins and what you find is that you know before you know you're at the other end of the pitch so it's it's it's it's it's a worrying performance it's not the end of the world I appreciate that it's not the end of the world it's just that I just can't quite understand what we were trying to do tonight and it reminded me of last year when Lampard came in and we went away to Newcastle after beating Leeds and we were on a bit of a high and then we were dreadful in that game and it's about finding a consistency it's about finding 90 minutes and whether you win lose or draw then 90 minutes is the 90 minutes are similar and I don't think we can do that if we haven't got done with a car balloon and as I said that's my worry that is my worry it's not about it's about who comes in with replacing Sims can't do that job because I don't think he's got the experience to get hold of the ball he did it early on in the game and then it ebbed away I think it was one way it was a really poor touch and it almost felt like the confidence drained out of him and also the centre back started stepping up then and taking up higher positions and I think our plays as well stop giving it to sims in the in central positions as well and what are the alternatives Mopai, Grey you know these plays are not good enough for everything they're really not and Sean Dice has got a real test on his hands to score goals of football the problem that was under Frank Lampard is still the same problem putting the ball in the back of the net that is the problem for everyone football club we've scored the fewest goals in the Premier League that will only lead and we had Avis at the end shock and header should have put it in the back of the net that was that was absolutely yeah so just just all in all just a just a bad night and then I've got to shake this off quickly and they've got to shake this off as quickly as they can ahead of what is a huge game now against against leads and they have to find a formula of how they're going to score goals because you know it's not just it's not just having a centre forward it's people creating you know Mach Neil tonight back to the Mach Neil that we're used to and I won't be offered nothing you know James Coleman I thought looked poor tonight and Michelenko Salah took him to the cleaners many many times and listen Liverpool would just Liverpool came out and played like Liverpool tonight and it you know they played Fabinho and they played Henderson in midfield and they completely dominated and the young kid played really well as well you know it did there was I think after about 10 minutes they thought these have got nothing because we didn't have nothing and this is the big problem isn't it this is the big problem with Evan football comes when you're playing better teams it's not going to be like that on Saturday of course it's not good as some partly united it's not going to be like that we'll have our card and it'll be different but and these are not the games you expect to win by the way I just expected I just expected more from that team tonight because I expected it so I expected more challenges I expected it to be more physical I expected it to be on the front foot I expected them to be in Liverpool's face and they were none of those things they literally had their bellies tickled for all of the game and that's what annoys me more than anything like I sat here last year after the derby we've beaten 2-0 and I've seen it have a right go at them I've seen his fight and and I had nothing really bad to say tonight I'm just like we had absolutely nothing we never turned up for that game of football we look like we we really we look like we bottled it really really bottled that game of football but I mean bottled don't mean I mean like we're gonna turn up and we're gonna be amazing and we're gonna dominate the game what I mean is we never even went out there and pull any kind of you know any kind of challenges in and late late on when Andy Robinson's laughing in the faces and they're having a little go on and like why why are you why are you doing that because you've done nothing for 80 minutes or 85 minutes you've done absolutely nothing you've run around you haven't put a tackle in you had you had opportunities twice to kick their plays and pull them down take yellow cartons off from scoring and you're haven't and suddenly one of their plays kicked the ball away and you're all hard cases go with that foul nonsense you know it's pathetic if you're not gonna do that all the way through the game if you're not gonna get in people's faces during the game when the game's actually there to be won then then what's the point you know it's pathetic I just don't get that at all and tonight nobody can come off that pitch really and say you know and say they've done anything you know maybe a few people say well there seems at the goal but you know that's we need players who are gonna do more than have a go at this stage you can you can make make excuses for some players and young players and fair enough you can you know you can say someone had plays had no servers and not important there's no one who can come off that pitch tonight and say they offered anything anything at all you know he obviously sometimes you can say that about Jordan Pickford but he can't say that because the goal the first goal from his point of view is an absolute disaster a goalkeeping disaster so yeah just just listen they were miles better than a simple and they didn't have to be miles better than this by the way they just competed from the first minute and as I said after about ten minutes they were like these are not even having to go here they we just lay down for them and then we started doing all stupid like Tarkowski trying to be a hard man and putting leaving a couple on people you know Liverpool were at the you know keep on again people keep on telling me they're at the lowest they're at the lowest blah blah blah they're at the lowest and we're gonna dominate the midfield and we didn't lay a glove on them and that's the fact it's like as I said if we turned up and we'd had a go and we'd we'd we were beaten because they would be in the better side then I'd sit here and go fair play lads because that's all I was expecting tonight all I was expecting tonight was to see an evidence team have a go after seeing what I done again what we've done against Arsenal but that was embarrassing that was embarrassing you know and every year there's a different there's always a different reason isn't there there's always a different reason for that but this year straightforward us just being utterly utterly shite and we we in our three of our next four games are huge games against people who are in and around the the bot man for the table and we've got to take serious points and nothing will change from that maybe tonight the manager's seen the real team and again I said this 12 months ago after the Newcastle game but dice is a different kettle of fish than Frank Lampard is he'll be looking for the answers I'm sure and maybe now he might look it and think if the better be if there's a free transfer out there you know some big girl who can hold the ball up then maybe we go go and see we can get one I don't know I don't love that player exists in Europe but we're gonna have to do something because if we do not get a sense of forward you can hold the ball up and bring the rest of the team into play we are in trouble because we are our own waste enemies we try and play out from the back and we lose it and the opposition come at us on the counterattack and after time it ends up on the back of the net so so they have a tough night but you know nothing not certainly nothing I haven't experienced before and yeah yeah it is it is what it is as I said earlier it could it could be it could be the last time for a while that we have to go through that experience hopefully not I'll put up with doing that twice a year and being you know the target in the waste thing in the world then then then not do it so yeah anyway let me know your let me know your thoughts in the comments I'll be back to do my play ratings in a little bit yeah so join me for that thanks for watching see you later


Meanwhile, Delta State Governor Wright Honourable Sharif Abourowary supports financial autonomy for local governments and its separation of powers. He stated that since his inauguration nine months ago, he has not interfered with local government projects and has seen successful haves. Abourowary believes that if local governments do their own at the state level and the federal government does its own, the hardships felt today will be reduced. Honourable Victor Ebonka, Algon Chairman expressed gratitude to the governor for approving 40 new teachers and over 3,000 primary school staff in Patani local government areas. And he came out to produce. He would manage the funds in local government so they would see how best to produce in local government. Also, they are not returning now. You have seen that hardship of our family will reduce, the hardship will reduce. And I believe that as an assistant, and I believe in this phase of this hardship that we are facing this morning, this phase is to phase out. The project of 14 new teachers in Patani local government was not only to guarantee what to accept, but also to go out of your way to ensure that you facilitates our treatment of the massacring children who are being abused. Hello, hope you enjoyed the news. Please do subscribe to our YouTube channel and don't forget to hit the notification button so you get notified about fresh news updates.


Hi, I'm Kim Palco, and I'm the Product Manager for Big Data across the middleware portfolio and specifically for the JBoss Data Virtualization product in middleware. Well, enterprises today are seeing a big change in data in their environment. So it used to be that there were kind of more relational data sources, and now we've got new data sources coming online all the time, no SQL sources, IoT sources, so data's coming in from sensor devices, big data sources like Hadoop and Spark, and the speed in which they're getting requests for these data sources is unprecedented, really. So with all these new and disparate data sources coming online, they need to be able to correlate the data and combine it quickly. So the time to decision is shrinking all the time, and there's an expiry date for data. It loses value over time. So there's no longer time to be able to create long running ETL processes or big data warehouse projects that can take months to implement. So what data virtualization allows you to do is to be able to connect to a number of different disparate data sources. And then pick and choose just the data that you need out of each source, whether it's in the cloud or on-prem, SQL, relational, no-SQL, legacy, whatever it is, and take just the fields, just the columns, just the data that you need from each source without having to move or copy any data and be able to compose what we call a virtual database that can be consumed by any standard interface. So ODBC, JDBC, REST, Java applications, or OData, which is a standard for transferring data over REST. So what that allows architects to be able to do is to create flexibility in their architecture, kind of a plug-and-play architecture where you can plug new sources in and you can actually pull off the old sources as you move that data over time. You can also plug in new consumers, so new BI or data discovery tools, can plug into this architecture. And while you're doing all of this, that data abstraction layer in the middle, the data virtualization allows, protects any of the consuming applications from disruption. So developers are particularly interested in data virtualization for a couple of use cases. One is when there are multiple different data sources and they have to connect to them. You can do this on your own by writing your own code, but especially when you get more than one data source and you need to say, write to that data source as well, data virtualization lets you do that really clean with kind of a no-code goal in our Eclipse-based GUI tooling. And it allows you to be able to do that very easily and quickly, and as well as be able to write. Another use case that developers are interested in for data virtualization is moving toward microservices. So in a pure microservices architecture, you know, every microservices owns its own data. It's completely isolated, and that's perfect for, you know, new projects. But what we see in a lot of enterprises is data is stuck in monolithic, you know, systems with huge databases. And even if someone wanted to go to pure microservices architecture, that would take a lot of time. So what data virtualization allows you to be able to do is to leave the data on your legacy or traditional systems, and to be able to create virtual databases on top of it so that each microservice can actually have its own virtual database without having to go through the pain and time to break up that whole monolithic system at once. A lot of people are interested in using data virtualization and big data projects to solve a couple of problems. So one issue with big data projects not getting traction is that there's simply not enough data scientists around. There's not enough highly skilled people to be able to use a lot of these technologies. So what data virtualization allows you to do is to be able to consume data from big data systems like Hadoop or Spark and be able to consume it with the tools, the traditional tools that people are accustomed to using. So you can actually run queries from Excel if that's what you like or any BI tool that you're used to using and then let data virtualization do the work in translation and be able to run it on those systems. So there are several new features in data virtualization that we're excited about, especially our new integration with JBoss Data Grid. So we've had integration with JBoss Data Grid now for a couple of years. But what's new is the data grid can now be a materialization target for the data and data virtualization. So as I explained in DataVert, we don't take ownership of that data, but you can materialize it to a source. Well, now you can materialize it to JBoss Data Grid, which is a lightning fast in-memory scalable cache. The other feature that I'm very excited about is integration with Apache Spark. So Apache Spark is just taking the big data world by storm. I just went to a recent conference. It's a very popular technology and it integrates now with data virtualization as both a data source so we can consume from Apache Spark and also Apache Spark can consume data from data virtualization, which allows it to be able to take advantage of some of the performance optimizations we have in data virtualization for connecting with multiple data sources and also our centralized security model, which is a huge benefit when working with Spark. So data virtualization will make its debut in OSC3 in the early fall. And what we're doing there is really acting as a data service kind of API layer in OpenShift so that developers can use a quick and easy web UI to quickly create a data service with writing no or very low code. And it also allows you to be able to connect to data sources, whether they're hosted in the cloud or on-prem. So it acts as kind of a gateway, data gateway for OpenShift.


Okay, let's start. Hi everyone. Today, we're going to talk about new key, which is our new project. We started around zero matter a few months ago So I'm going to first introduce you to our speakers today First we have one year Say hi With a software engineer at Fredat and it has been our dear beloved PTR for the last six months And it's going to be our next PTR again for the next six months The nice year to she walks out me around this as a software engineer to and she's one of four more recent Syllometer contributor. She's been a core developer for the last cycle And myself I walk as a software engineer at Syllometer. I've been the Syllometer PTR for a year Before I went and I'm driving the gnocchi initiative for six months now I'm trying to out with integrating it into Syllometer So before we start talking about gnocchi, I think it's a good idea to Take a step back on Syllometer itself or it started And what what the motivation behind it so when we started Syllometer about two years ago a Bit more than two years ago actually we have a lot of problems. We wanted to solve First use case was billing but obviously we soon discovered that what we wanted to do was metering So we started to meter open stack and to meter everything in open stack We didn't want to change most of open stack. So we started to pull things receive events notifications and build a lot of data from this and We decided back then not to do any kind of trade-off That means we didn't want to we were not really Sure or people were going to use the data So we decided not to lose anything not to do any kind of aggregation not to do anything fancy Just which of the data put them into a store and that's it then build a query around it providing an API for people to use this data and To well do whatever they want with it doing billing monitoring analytics of any kind The problem is that this was a lot of data and it's it's Very hard to use this amount of data when you don't try to do any kind of optimization behind so we did Probably made a few things not totally correct in Syllometer The data model we picked back then Having nothing aggregated nothing Computed on the runtime on crime right time was Very hard to have good performance basically a Lot of queries are optimized in Syllometer For common usage pattern like if you want to a total list of resource is being optimized by most of the storage Driver behind but if you do some fancy queries most queries are Big O and meaning the most simple you have the longer the query will be When you have cloud platform with a few hundred of nodes and you have a few thousand samples per minute You end up very quickly with a few my land millions samples in your DB and doing fancy query on that is very very long The data structure we picked Which we named sample In Syllometer is freeform that means that it can has anything in it It's not entirely of fault When we started Syllometer, we relied a lot of notifications and the notification system in Syllometer in an open stack Is free from two so we just Didn't try to fix it enough in the open stack itself We just use that and we ended up with an API which has no consistency meaning Most of the data that are returned are freeform so it's very hard to Know in advance which kind of fields are going to have some fields are part of the API So you got a contract with Syllometer and it's API. You're sure to retrieve This kind of thing we have this name this type, but sometimes you just don't know So it's out to be queries. It's out to retrieve the data to manage it And to build application around it and last thing is that There's a lot of use cases that we saw especially in billing Situation when you want to retrieve things like one one instance has been posed and resumed one instance has been Resized or these kind of things which are not really meters And it's complicated to have to solve this kind of issue in the API currently You don't have a magic query that can help you Seeing that this instant has been resized or has been moved or has been posed, etc So we decided that it was a good time to solve this for him and to start something Maybe from scratch. So what's what's that's what we did with yaki We change or I think I can say the paradigm behind all we store things The first thing we started to do in yaki is to track resources Like I said in Syllometer of the only data structure we use is sample and sample is just I'm each or something like I get a measure from Cpu chill on the instance and I send everything back to Syllometer The value for the CPU chill and all the information I have about this instance and I do that every time I'm mature anything about this instance That's a lot of data and this doesn't help you knowing when the Resource the instance has been created for real You can guess that from when you see the first sample and when you see the last sample But not sure it's really the last sample or if one sample is going to arrive 10 minutes later all these kind of things So we are about to build Resource list dynamically, but it's it has huge cost and it's not very A good way to tracking it. So in yaki we track resources from scratch like we have a dedicated Part of the API for that We have separated two things so in Syllometer we gained recently the support for storing events Based on notifications. So this is something we did not have two years ago We have that for I think Maybe have an hour I saw something like that So it's pretty recent But we're not able to use that to say okay. There's some kind of data some Patterns on use cases that you're not going to solve with samples We're going to solve with events and in that case in the case on yaki We're going to say we don't meter this because this is an event Like if an instance has been resized. It's not a matrix. It's an event something happened You want to retrieve that you're going to ask Syllometer for these events It's not something we're going to store in samples like we used to do in a Syllometer We have metrics in yaki obviously so we have resource and metrics metrics are time series Data, which means it's only time some value a list of time some value Nothing else nothing fancy like we have in Syllometer with a lot of information you get from the instance when zoom when you meter the CPU till you got everything about this instance Now you don't get that with no key you just get the time stamp and the value of the CPU utilization What we do is that we link the metrics the list of time some values to a resource So you got a list of time some value Which we name the entity a one will explain it to you just after and You got this list of metrics and you link that to the CPU usage of this instance You link that to the network in usage of this etc Another thing we do in yaki. It's that this time we decided to make some kind of trade-off on data We do aggregation and we do it eagerly most Drivers are doing this on the right time. We talked a year ago I think in Hong Kong about doing a location in Syllometer It never happened for various reason many because we think we don't have and many people to work on that but I think we all Noticed that having millions of sample for a year in the Syllometer database is not going to scale very far and most people have I mean, I don't know many people who want to have fine-grained Data from a year ago most people are happy enough with aggregation So we do aggregation as a first feature and yaki I'm going to let Owen talk to you about new key more deeper cool Thank You Julian and so I'm going to continue on this and discussion by doing a little bit of comparing contrast basically identifying some key aspects of Syllometer which might be interpreted as shortcomings and Explaining at a high level initially and then we'll drill down into a bit more detail as to how we're intending to address those shortcomings in The case of no key. All right So very simple kind of and table here the before and after shall we say so One axis in which you can kind of and really see the difference between the approach taken by classic Syllometer and The new approach that and no key is adopting and Julianne alluded to this earlier is this notion of How heavy the actual samples are so a sample in Syllometer terminology is our basic stock and trade It's it's the basic piece of data that we store and we manage and we make available Available so that insight can be surfaced into what's going on in your cloud And the thing about Syllometer surface or Syllometer samples is that are actually quite heavyweight things Right now at a high level really all you're interested in here generally is a number Right, so say it's CPU utilization at one moment. It's 40 percent the next moment That's 39 percent the moment after that is 41 percent So the key piece of data there is a number which is a relatively Lightweight datum right now. It's not going to take up too much space. You can store a lot of those Now in addition to that actual base number We store a bunch of identifying information such as what's the uuid? That's a bigger thing of the resource that it's associated with What's the uuid of the tenant and the user and so on so already this thing has kind of grown and growing right? But at least it's standard for each sample regardless of what the source of the sample is You know you're always going to have an associated resource an associated Project ID Representing the tenant an associated user and so on but in addition to that we actually store a snapshot of the resource metadata associated with or as it as it existed at the time in which the sample was taken so if you think about an instance It would include things like the instant state. Is it active was it suspended has it been resumed that kind of thing But it would also include things that change very very rarely if at all Such as the identity of the image that the instance was booted from The user metadata that was associated with that instance So these things are very in for either static or very infrequently changing But the way salamander stores them via this kind of snapshotting approach means that this effectively the same data is Duplicated over and over and over now that actually gives us a lot of flexibility as to how this data these data are You know interpreted and used going forward, but it actually Creates a very very large storage footprint that has shown to be a problem in reality Now contrast that with what no key does In which case it really strips it down to the bare bones Right it says really all we're talking about here is a number and that number had effect at some particular time So that's all we're going to store right every time we take a new measurement We will store the actual value and the time stamp at which we took that measurement Right and then all of this other data that we had previously snapshot it on a per sample basis We're gonna magic that data up in a different way, right and we'll talk about how we do that later Right, so certain use cases in salamander currently that depend on these sample data being repeated over and over And we'll see that the strategies that we have in mind for actually Enabling those use cases going forward will allow us to do it with no key in a much more lightweight way So that's the first kind of axis of comparison Another point that Julianne alluded to as well is that well how long are you really interested in keeping this data around for? Right forever that might be the case for some data But for a lot of stuff like say CPU utilization its currency is Really key Yeah, so the data that's fresh that's related to say the last hour or the last day That's kind of actionable data You could drive for example alarming off it and use your alarms to trigger auto scaling actions But the data from last week not so much last month last year Again, it's becoming much less useful that that would be the case in CPU util But you could look at some other types of data that was used to drive billing such as the existence of a certain number of instances And that's the type of stuff that you probably want to keep along around for a much longer amount of time You have somebody comes and queries their bill from six months ago You need to have the data there so that you can back it up, right? so one of the problems with salamander is that our initial stab at expiry was completely global All right, so basically data exists in Full resolution until it falls off the cliff and then it no longer exists All right, and that's done in a way that's not selective on the basis of what type of data it is All right, so clearly you want to be able to store different types of data for different amounts of time But also you really want that kind of fallen off the cliff behavior Do you want it to be like full res full res full resonant nothing? Or would it be much more convenient to keep a small amount of full resolution data? Then roll that up at a certain granularity Right and store that for a longer period and then maybe roll it up to an even more coarse-grained granularity and keep that for an Even longer period again So what you get is this gradual aging out of the system of these data as opposed to a sudden expiry And that's exactly what and no key allows us to do We can choose individual time series set an archive policy on each individual time series Selectively and then that archive policy allows it to be gradually rolled up in ever and Courser grain until finally we discard the data or we keep it around forever if necessary But if we are keeping around forever the assumption would be that we're doing it in a in a fairly coarse-grained form Okay, so let's look at another couple of axes of comparison here between classic salameter and no key Again, Julien alluded to this idea of aggregation when we actually do the aggregation right in classic salameter It's all done on demand so by on demand I mean in order to satisfy an individual query So if you query the salameter API and you say the period I'm interested in is early What we will do is in the back end will go and say in Mongo will do a big old map reduce and we'll take these data We'll stick them into buckets based on the granular you chose and We'll say ah the average for that our was whatever the average for the next hour with some other value And we'll do all of that now if you emit exactly the same query Five minutes down the road. We'll do that work again. Yeah, because it's all done on demand Yeah, whereas what no key does is As the data is being ingested right or soon thereafter it eagerly does the roll-up Yeah, so you identify a number of aggregation functions like averages minima maxima Even more exotic things like standard deviations right and as that the data are being ingested basically the aggregation is happening continually Now different storage drivers do it different ways and I'll talk about the pluggable storage driver there Later on some of them do it absolutely eagerly as in as the data point is literally received It's aggregated others do it in a slightly kind of laggy fashion But the effect is is is the same For for queries that basically don't extend over the very recent time period You don't repeat this work over and over because the aggregation is done on demand or sorry done eagerly as the data is received Okay, so let's talk a little about some The basic kind of lingua franca of no key, right? So we got a certain kind of terminology we use with salamander We talk about things like meters and samples and so on so that to distinguish our kind of new way of Imagining this we've used different terminology, right? So first off we have of course our natural concept of a resource So this is common we talk about resources in classic salamander We talk about it no key also a resource basically is just a thing now usually it's a thing in the cloud right a user visible thing such as an instance of volume an image and a Load balancing VIP something like that something that a user can reason over but sometimes also it's something in your infrastructure like a host or even a IPMI sensor, right? It's just a thing, right? So we store some representation of these things and separate it off but linked we store a representation of Data about some aspect of those things right now those aspects we refer to as entities So we wanted to keep the naming ultra ultra ultra generic Yeah, so what would be a typical entity? Well, it would be something like say if the resource was an instance the entity could be CPU utilization Yeah, if the resource was a VIP the entity could be the number of open connections If the resource was a image the entity could be the number of downloads Yeah, so some aspect of that thing that you want to kind of store data or gather data about and generally the mapping would be One to many yeah, so you've got one resource and you want to look at lots of different aspects of that resource and gather data on it now the entities in the kanaki kind of realization of this are Identified by you ID and the standard kind of opens that way But we also have a way of identifying them by name for convenience. So you can say if you know that the Resources X you can find out which entities are associated with that resource or you can just say Associated with resource identified by this you ID give me the entity named CPU util Yeah, so we've got multiple different ways of identifying them And then the last key concept is this idea of a measure a measure is an individual data point Associated with an entity which is itself associated with a resource and Measures in no key and this is the kind of key idea are ultra ultra ultra lightweight. They're feather light All you're talking about here is the number Okay, the value and the time at which the measure was taken. So effectively. It's just a couple It's just a pair a timestamp and a value and that's one Value within a time series and the fact that we stored them in this kind of ultra lightweight way Means the additional storage required for each individual measurement that's taken is very very small and also these values are very convenient for manipulating using libraries like say the pandas Analytics library or for using specialized data stores such as open TSTP or influx. They fit very very closely with with that model Okay, so going a little deeper and some more slightly more advanced concepts in in in no key And what is this idea of an archive policy? So recall earlier? I talked about this kind of falling off the cliff Versus the gradual aging out of the system and it's the archive policy that drives that notion of gradually aging out So an archive policy basically is a set of pairs and each pair defines a granularity Yeah, how fine-grained or coarse-grained these data are and also a retention time span Do you want to keep it around for a week or a year? so typically at a high level you could think of early data being kept for a month and Daily data being kept for a year per second data being kept for a day that kind of idea Okay, so basically the archive policy drives two aspects of of what no key does and At what grain the values are rolled up and how long they're kept around for in effect Okay, so here's just a quick kind of visual representation of the kind of aggregation approach that that that no key takes So if you look at this picture if you look at the kind of the timeline, right? The actual far right-hand side of the slide. That's the here and now, right? That's where we're at Okay, and time is kind of progressing in that direction So all of these bars are looking back over some historical time frame. Yeah So basically here. We have a case where an entity has an archive policy associated with it that has three different granularities per second per minute and per hour and Usually the way you set this up is the more coarse-grained the Archive policy is and the longer the retention will be yeah So you keep coarse-grained data around for longer you keep fine-grained data around for a shorter period in general You don't have to do that, but that's a typical case The green bar at the top is kind of our live window. That's our kind of buffer. Yeah So as we receive data It's going into this green window and that extends over some period the period is actually configurable And we keep that it's at least as long as the move one period of the most coarse-grained But generally you want to have a look back window is a bit further than that right because you might receive laggy data slightly lagging and Backfill and so on so basically what we do is we maintain full resolution data And then as these data are received they're Aggregated into a number of buckets. We got a per second per minute and per hour in this case But you can create archive policies and as an administrative function with whatever retention you'll or whatever granularity you like and Basically depending on the storage driver either that aggregation is done in an ultra eager fashion as you receive each data point bang It's re-aggregated or it's slightly laggy For example influx DB the way we've approached that or we will approach that driver as we complete it will be to use Continuous queries where it's kind of lagging by the period equal to the most coarse-grained in this case and one error And and then the actual aggregation itself is non cascaded That's another point that we're trying to make from this visual So we don't kind of roll up our full resolution data into per second buckets And then roll up from per second into per minute and then roll up per minute into per hour Instead we use non cascading aggregation So we don't get these distortions that you get when you take the bean of beans for example Yeah, so full res is is aggregated independently into each of the the Levels of granularity that you've configured Okay, so at this point I'll hand over to my colleague Dina and She's gonna talk a bit more about performance and so on next question I'd like to cover up is what will we do with all of these data points we've collected because Actually after the cloud has run for a while there are thousands and actually millions of these data points stored So and measuring different entities is the main concept of nookie but all these data points have no actual meaning and Well, they're not really useful without association with entities and resources so actually a nookie index here is something responsible for the connection of All these data points to resources entities and linking all the stuff together so and the key of How nookie should be performant and quick and well behave as we wish it to behave is The fact that the resources are well typed. They have just defined there were well some attributes and All these things is indexed if the resource type is unknown Well, okay, we can use generic tile, but anyway, if we'd like to store some information we definitely will define some kind of resource in nookie and Actually by separating the samples idea. Well measurements here in nookie and Mapper resources to these measurements we get rid of one really interesting problem We are having now in CELOMETER because let's imagine we want to Store information about CPU till for some VM well forever And then we'd like to grab some information from Monday to Wednesday and in this case We need as my colleagues said to just extract all this information stored in the database And then perform the needed request and then just write on the aggregated result but in nookie with this concept of index with concept of separated data storage for the Time series data and just index that links all the things together. We get rid of this Useful useless. I mean thing of extracting all this data from that the base so When we're speaking about nookie, of course we're speaking about providing some kind of time series API for the different kind of storages and For now we have implemented Swift and pandas library based Driver in nookie and Owen and I are working on in Flux DB and open this DB drivers respectively when a fighting through the reviewing process to make it work as it's supposed to and Actually, I'd love to cover one also really interesting question. What's about performance because currently CELOMETER after years of development is not so bad actually and It can process not so well not so small amount of data per minute per hour well, whatever and Let's imagine. We just want to write some data to the nookie plus CELOMETER Here is the result of just writing samples are collected in 100 batches of these Measurements for different resources. So actually that's kind of natural situation We have different resources all these metrics are not connected with each other because all let's say the ends are different and We are writing these information to the open t-stdb driver actually using dispatcher I have just Well, I'll go through all these things like dispatchers we're writing now a little bit later and Okay, so that's kind of Six hundred to seven hundred measurements per second being returned to the just open t-stdb But actually that's exactly the same average number where I have enough or just one collector processing Samples in CELOMETER with MongoDB or HBase back-end. They just do the same number and Here is something we'd like to be close to well in really nearest future because this blue line is actually the Result of the same test the same database open t-stdb But we're writing directly for from CELOMETER dispatcher not nookie dispatcher but CELOMETER dispatcher to the open t-stdb via just well quick socket pushing all this data and That makes about two thousand of writes per second and just before this presentation was met with Owen and Juliana Discussed how should we make this green line closer to blue one and we found lots of law-hanging fruits to be fixed Just well the next days and that will make this green chart really close to the blue one and This result is much Better that we could ever achieve with CELOMETER with the current architecture of CELOMETER because Well, we could do nothing with this Flexible but heavy structure of samples were stored at CELOMETER, but this lightweight samples were well measures We are posted to the nookie is something we can operate with really high speed and Okay, so let's go through current CELOMETER infrastructure to understand How should we hack into this workflow to Integrate nookie and CELOMETER together. So actually this kind of usual workflow We're having amount of open-stack services running in some cloud. If something interesting happens there, okay They are pushing Notifications of about the events that happen to the notification bus. Also, we're having polling agents that actually Appalling these services once a minute an hour Well, whatever you'd like to to collect the information about metrics You'd like to collect and all this stuff is also pushed to the notification bus let's say to the Rebiton queue or some other queue and Then all these samples notifications pushed to the queue are processed by notification agents and They transform to something that can be eaten nice by collectors and the collectors are writing all this data To the better base currently we're having separated storages for the events matters and alarms actually And this was the first step down to Separate these things from one huge storage driver We had and this was step to integrate CELOMETER and nookie because actually we're interested in metrics for that case of nookie and Also, we have an API that can perform different queries Well in the way we just described for some of the cases there is alarm evaluator that is well some Let us call it periodically the API trying to understand what's going on if some alarm has happened Well, whatever and if happened, okay, let's call my alarm to fire and defy services interested in this alarm for example heat about some well some threshold has being well So for sure was received received well, whatever so and The question is where should we hack into these infrastructure these quite complex workflow to make nookie work inside of this scheme not only Well effective, but also performance and in quite of simple way and really here's the answer. We are planning to translate all nookie API to the CELOMETER version free API for the all time series related requests to operate effectively with time series information we are storing in open tstdb or influx db or whatever and Move all these times series database interaction to CELOMETER collector that is actually writing things to the database and The steps we're doing now is integrating nookie as separate piece of code a separate project for now. It's code is Stored on stack forge actually Inside the CELOMETER workflow using CELOMETER database dispatcher mechanism actually that's something collectors using well Just write data to the data store So that looks like a nice place to hack into just as proof of concept of how all this thing might work so Actually, I guess that's all from my side Owen Thank you, Dina. Yeah, so I'll pick it up again and just continue with a couple of kind of concluding Points to talk about so basically I mentioned earlier that one of the key things that we wanted to do here was to make the actual and Unit of data that we store much much more smaller and much more lightweight But as it happens, we've got a number of kind of use cases with CELOMETER that do depend on the more heavyweight more highly Decorated data being stored and one of those is the alarming use case Now the reason why effectively we have alarming within CELOMETER The the kind of motivating use case was the requirement to drive autoscaling in heat Yeah So he basically generally wants to scale out the number of instances that are hosting some particular service like a You know a web server database or something like that and it's generally driven on the basis of the observed trend in say CPU utilization That kind of thing now We've got a group of instances there that are somehow all collected together They're they're associated from a heat point of view. They're all part of the same autoscaling group But that notion doesn't actually extend outside of heat much, right? So there's not really a good way of identifying those instances except for Some of this metadata, right? So basically the way we work is when heat spins up a new instance as part of an autoscaling group It decorates that instance using user metadata and that user metadata includes an identifier for this autoscaling group And then we can query over the CELOMETER data, right and Aggregate over all of the instance data over a certain time period that matches The user metadata that was set on the instance to identify it as being a member of this autoscaling group Yeah, so that's one case where we've actually kind of embedded in our design of another feature a Assumption about how the underlying storage are how the underlying data is stored So how are we going to get around that? Well the approach that we're looking at basically is to support cross entity aggregation in no key All right, and then to identify the set of entities So this would be the set of say CPU utilization time series that we have associated with a bunch of instances All of whom are members of this autoscaling group that we will identify those using strongly typed fairly selectively chosen Metadata or attributes of those resource that we store as first-class citizens in the resource representation Not as freeform Unpredictable data that just sits in a dict and that dictionary may or may not contain those values Yeah, so for example in the case of this particular and usage of the Metadata what we're doing is we're adding a strongly typed server group Attribute to the resource representation and each of the resources in no key is going to have a relatively small number of strongly typed attributes and then we'll be able to kind of Agriate over all of the matching entities that are associated with resources with the same attribute value using this cross entity aggregation mechanism Okay, so second and common use case in salamander that also depends on these heavyweight samples is this notion of Reconstructing the resource as state timeline So taking a particular instance and looking at the span of time for which the instance was active And then there was another span of time for which it was suspended and then you resume that and it becomes active again Yeah, so you can reconstruct directly from the sample data and the actual major life cycle events and that gives us a clue as to how we're going to address this in The lifetime of that resource now as that happens these events are generally quite infrequent Yep, if you take an instance in your cloud, how often do you actually resize it?  I mean it's not something that happens in You know on a daily basis or even a weekly basis or you have many instances that are never resized ever in their lifetime So it's much cheaper to actually store the events that basically represent those state and transitions that we're interested in rather than to continually snapshot the data that's either static or very infrequently changing Another question about Nokia is often leveled basically as well. Wait a minute lads. What are you doing here? Effectively you seem to be just reinventing the wheel in terms of time series storage There are a lot of dedicated specialized metrics oriented databases out there So why are you doing no key? Well, the thing is we're not really reinventing the wheel the idea as in much of OpenStack is for Nokia to be highly pluggable right to use a back-end driver model and To support a variety of different storage drivers Now we've a canonical one that's based on the pandas analytics library and Swift and that's intended to be something that can be run without anything external, right? So you can just use services that you know Are likely to exist in your OpenStack cloud such as Swift for storage and that brings certain advantages It also brings a lot of advantages to us in terms of testing these things in the in the continuous integration gate But going forward we've got underactive development as as Dina mentioned earlier two other storage drivers Both of which are based on specialized metrics oriented databases One of which is OpenTS DB and the other one is influx DB Yeah, and in this case you may well ask the question. Well, what does Nokia actually bring to the table? What value does it add above what influx can already do well what Nokia manages in that case is the key Entity to resource mapping which drives the cross entity aggregation and it also manages an abstract notion of archive policy So that you can define an archive policy and then have the storage driver map that on to say continuous queries With charred base retention in the influx DB case and map it on to something different entirely in the OpenTS DB case So no key presents this very abstract very standardized notion of that archive policy Okay, so very briefly I've almost from at a time as per usual, but Some forward-looking questions things that we have under active consideration These are the hot questions that we're trying to address this week in a lot of our design sessions So as was mentioned previously, no key has been developed somewhat at arms length it's been a stack forage project that Julianne spanned up and We've kept it kind of separated an arms length from the salameter core So it's not to be too disruptive on our continuing that Maintainership of the salameter core. So obviously we're going to have to bring those two things together somehow. Yeah We're going to have to merge the two core teams We're going to have to bring the gnocchi code into the salameter repo or to have a model where we're consuming from multiple repos Yeah, so we have to decide how we're going to do that. We got some gnarly migration questions Clearly there are salameter users out there in the wild. They've built up non-trivial data stores based on the old model So how are we going to transition or manage that migration? Are we going to have some mechanism whereby we can mine this more lightweight data from the previously built up more heavyweight data? Yeah, so there that that's a Non-trivial question that we've got to answer and the other thing is that It's in open stack in general. We've got a fairly well-defined way of deprecating things, right? You put something on the deprecation path It stays in the deprecation path for a number of cycles and then it kind of goes away Yeah, that's how we basically go from things that are legacy features the things that are no longer supported In the case of salameter because we've got such a discontinuous step here We may want to have that deprecation path unusually long We may want to keep the v2 API around for longer than two cycles Yeah, depending on what our our users who've actually got in deployment currently and Would like us to do so that again is it is a is an open question okay, so kind of out of time and Usually we've got this link slide as a as something to look at while questions are being asked And I think I burned most of the potential Q&A time by going on at length about what we're going to do So apologies for that We may have time for maybe one or two questions. If there's anything brief that's on anybody's mind Shoot. Yeah That's a fair point. I think we're concentrating more on the the pre aggregation route and Because we had the case previously where we did no roll-up whatsoever And we found that queries in general tended to apply to fairly coarse-grain time periods And the fact that we're doing this on demand meant there was a lot of computational cost in terms of repeating very similar Map reduce jobs in mongo and so on and If you want to follow a model whereby you do that aggregation on demand and That's something we could potentially accommodate. I've yeah, it's not been our focus to be honest So you have correctly read it the focus has been mainly around increasing the right and fish efficiency decreasing the storage footprint and using eager pre aggregation to decrease or reduce the computational complexity of satisfying queries More indexing. Yeah. Yeah, cool. So one last question Yes, exactly Yes Yeah, so that upfront upfront declaration is exactly what the archive policies are about So you go and an added administrative level create different archive policies and then associated them with other with individual entities And that's your upfront decision-making that you exactly talked about. Cool. Okay It's I think we probably have to move because there's another session starting So if you've any other questions, we're around for the salameter design track and you can approach this individually So thank you for your attention