This is stock number 11075. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used heavy-duty truck and RAM headquarters. Today we are checking out this Super Clean 2018 RAM 2500 Crew Cab Short Box Big Horn. This truck has the 6.7 liter Cummins diesel. It has been fully saved and inspected by our service shop. It has a fresh oil and filter change. All the fluids have been checked and topped off. The truck has been gone through mechanically 100% and it is 100% ready to go. We shoot all of our videos in 1080p, 60 frames per second. So if you have HD capabilities on your computer, tablet, or smartphone device, turn them on right now because it is definitely your best way to check out the quality and condition of the truck before seeing it in person. If you want to check out all the photos on this truck in the upper right hand part of your screen is a link to our website. Click that and check us out there. It comes with 20-inch chromed alloy rims that are in really nice shape. And it has Nettle Ridge Grappler 35x12.5 R20 LT tires. These tires look like they have probably about, I'd say, 60% of the tread left. It does have a suspension lift kit on it. The frame and underbody is in excellent shape. This is a one-owner truck that came out of Idaho. No dents or dings on that front fender. Headlight lenses are nice and clear. And the front bumper is in excellent condition. I didn't see any dents or dings on there. Lower valence looks good. And it does come with the factory fog lights. It has a nice bug shield and the hood is in excellent condition as well. Didn't see any dents or dings on there. Same thing with the passenger side front fender. And the passenger side rim. No scuffs or scrapes on there. Down the rest of this side of the truck. Looks really good. Take note of how clean the body is, how reflective and mirror-like that paint is. We take these HD videos so if you are far away or even if you're close by and just cannot make the trip down but you're still interested in purchasing the vehicle, you can see the truck, hear the truck, and have confidence in the vehicle that you're looking at before you even get here. So that when you do get here, there's absolutely no surprises. And that's why we offer the Summit Auto Fast Pass option in the upper right-hand part of your screen. You'll link right to our website where you can apply for financing. Get pre-approved. See a payment quote. Even appraise the trade off from the comfort of your own home. So that when you get here, all you got to do is drive the truck, love it, and you'll be all set to go. Look how clean that frame and underbody is. I mean like this thing looks like it's brand new. It has all the factory exhaust so it hasn't been altered in any way, shape, or form and it has some nice aftermarket chrome step bars. Back tires have just as much tread as the front tires and this back rim is in excellent shape as well. It does have the rear coil suspension and everything is just super clean on this truck. As we come around to the back, much of the same. Rear bumper is in great shape, no dents or dings on that. It does have the backup parking sensors, full towing package which includes the receiver, H4 pin and 7 pin wiring. And the tailgate is in really nice shape as well. The only thing I saw, one tiny little ding. That's the only imperfection I saw on this entire truck. But other than that, very, very nice. It has spray and bed liner and it does have the LED bed lighting and this is a turbo liner in case you wanted to look that up. But it's a really nice, fine spray and bed liner. It's kind of like a Linux. And as you go down this side of the truck, just as clean as that passenger side. No dents or dings on the box. And for full disclosure, this back rim is in excellent condition as well. Doors are in perfect condition too. It does have the heated fold out tow mirrors. So they have built in directional signals on them.  This one does have the 40-20-40 split bench seating. No rips or tears on those seats. Power driver seat, lumbar, weather tech floor mats throughout. You do get the power windows, power locks and power mirrors. And those mirrors do power fold in. I always like showing both sides so that you know that both sides are working properly. Auto headlamps. And as we hop inside the truck here, I'll turn the ignition on so you can see everything. This one has 17,467 miles. You can see all the tire pressure sensors are working properly. Tire pressure light is going to come on because it says to inflate to 80, but we got them at 66 right now. But instrument cluster is really nice and clean. You got a compass, outside temp display. All that good stuff and a digital speedometer and everything that you can see there. So really, really nice instrument cluster. This one has the heated leather wrap steering wheel. Cruise controls on the right. Bluetooth and information center controls on the left. And then there are audio controls on the back of the steering wheel. This one also has the six speed automatic transmission with the optional tap shift. And then you get the Uconnect 4C radio, AM, FM and Sirius XM radio capabilities. It also has the factory navigation system on it. There's your AM, FM and Sirius. They're heated seats and heated steering wheel controls. You can check out your backup camera pretty much at any time. And that's pretty cool. Then you got Uconnect apps, including projection manager where you can project your cell phone to the screen with Android Auto or Apple CarPlay. We actually did a video on Android Auto in the upper right hand part of your screen if you want to check that out. Check that video out on how to do that. And then down here, if you don't like using the controls on the radio, you got your more tactile controls, dual climate control, volume and tune controls as well. Turned out four wheel drive, factory brake controller, more heated seats and heated steering wheel buttons, stability control, tow haul, factory exhaust. And you can turn those rear parking sensors off. Take note of how clean the carpeting it is and the passenger side format. This truck's never been smoked in. Smells very, very clean inside. Headliner is an excellent shape. And you do get map lights, power sliding rear window and home link buttons up top for your garage door security systems and lighting systems. You also get assist and SOS buttons in that rear view mirror. And we'll take a quick look at the back seats. And then we'll start her up and check out under the hood.  Back seats are just as clean as the front seats. No rips or tears back here.  Power sliding rear window with the removable headrests up there. Underneath here, you get a load floor, which is really nice. If you've got pets, you can put both sides down. They've got a nice flat place to lay her. If you've got muddy boots or tools, put them on there. You're not going to get your nice seats dirty. And then there is storage underneath here. This is a factory winter front that comes with the truck. Weather tech floor mat back here and a factory floor mat. And underneath here, you get in-floor storage with removable containers for easy cleaning. You can put ice and drinks in there. And it's like you have two mini coolers wherever you go because there's one on the driver's side and the passenger side. You've got child safety locks on the back doors. And the bottoms of the doors all look really good. And definitely one of the nicest, cleanest trucks that we have on our lot right now. It's got that nice lift on it. We'll take a quick look under the hood. You can see no check engine lights or anything on there. I would personally like to thank you for checking out the video today. And hopefully from this HD video, you will have been able to tell just how clean this truck is all the way around. Under the hood, no different. 6.7 liter Cummins diesel engine base. Very clean runs, very smooth. This truck has been fully-safetyed and inspected by our service shop. It has a fresh oil and filter change. All the fluids have been checked and topped off. And this one is 100% ready to go. And to see more pictures of this truck or one of our other 450 new and used cars, trucks, SUVs, minivans, Wranglers, halftons, recorder tons, one-tons. You name it, we got it. Click on our website, www.summitauto.com. Full pictures and descriptions of every single vehicle from two locations, all at summitauto.com. And if you'd like to check out more HD videos, you can go to youtube.com slash summitauto. Remember to like, subscribe, and share on this video and all the videos that you see there. In fact, in a second, you'll see a link to subscribe to our YouTube channel on your left. A link to more Ram 2500 truck videos like this one on right. If you have not been to our website, on the bottom link to this vehicle on our website, click those, check us out. And we really look forward to helping you with this Super Clean 2018 Ram 2500 crew cab short box, big horn, and flame red clear coat. Thanks again for checking out the video.


Ooh, that's a hot mug, guys. Hey guys, this is my review for No One Will Save You. This is one that I was kind of interested in because this guy is kind of a back and forth for me. The director writer. Brian Dufffield has a mixture of movies that I have liked and some I haven't liked. I thought Love and Monsters was pretty decent. I thought that Underwater was underrated. I am amazed it has a 5.9 on IMDb, but he's also made other ones that are kind of like, ooh. He was the writer for the last Divergent movie that was apparently so unsuccessful they never got to finish the last movie. The babysitter movies, which there's also Jane Got a Gun, which if you want a really absolutely mind-blowingly infuriating production story about a movie that just absolutely went nowhere, look up Jane Got a Gun.  This film follows Caitlin Devere, who is a young teen who lives in this house by herself, clearly has some issues with the town and then what is believed to be a break-in turns out to be an alien invasion. I'm not spoiling anything here because the trailer literally did that for me. I both like this movie and I don't like it at the same time. Stephen King made a note about how this is a very good Twilight Zone movie and I agree it did not need to be this long. I have seen short films basically have the exact same concept and the same execution and they did it better than this one. Not to say that this film doesn't have positives because it does. It actually has a pretty great performance by Caitlin who I know we all say that we wish that she had played Ellie in the Last of Us show but oh well, she does a pretty great job in this movie. A lot of people are talking about how she doesn't say anything in the movie and there's maybe like two times where it's like okay, I know you're reaching to have her not say anything but she is by herself for a large portion of the movie and we see her have to interact with these situations with these issues with both her personal self and the aliens. I like for the most part how she handles it but I have to talk about the aliens next and they're a two-way street for me. One is despite the fact that they're fairly generic in their design like the gray men with the big black eyes all of them have different variations which I liked. I thought that was a cool idea of you have a limited budget but you're able to still give some variety, give some variation, give some difference and some kind of intrigue into the film. However, I can't believe that this species invaded earth because a lot of them are just moronic. The first one that she encounters that whole interaction makes sense. The second one, okay, your sense of disbelief is allowing you to kind of feed into it but then every other one afterwards is just bonkers. Like these are some of the dumbest aliens you've ever seen. However, as that comes into the conclusion I both like how it ends and I don't like how it ends. It is a good pseudo look into her character, her reaction, her relationship with the town and the consequences of her previous actions. However, it seems to kind of glorify the idea that what she did being this thing that has caused such strife with her family, with the town and everything is in turn into a blessing sort of. If you're looking for kind of like a normal narrative ending in terms of like, okay, you know the character alert their lesson, that doesn't happen. This is very much a Twilight Zone like ending which is why I'm torn on how it ends. One half of me is like, that's actually kind of witty. That's actually a pretty cool idea. The other half of me is like, no, I don't feel sympathy for the main character. Of course you're hated for what you did. It's a back and forth a little bit and that's more so just a personal opinion. How the film interacts with its tension, with its score, with its character acting, with the aliens. I was thoroughly entertained if not like raising my eyebrow about like halfway through the ceiling at a couple of points. I like how simplistic most of the idea is and how it's executed. I do like most of the CG in the film even if sometimes fire still looks like outer trash but the aliens look pretty good for the most part.  We're kind of accustomed to that now but you could definitely feel that this movie was trying to take inspiration from Prey but Prey did that well, R10 Cloverfield Lane which also did that way better. Both of those are movies that could work as shorts but they also worked as feature films. This is a movie that definitely would have worked as a short but I don't think it's worth the 90 minute run time. Could have honestly been 60 minutes or 70 minutes but then you don't have a full feature. For me personally, I just enjoyed bits of it but after it was done, I didn't care about it anymore. I was interested in seeing what some people's reactions are and there's a very wide discrepancy. There's people who don't like it that she doesn't talk which I'm like, the visual storytelling guys have you never heard of it? It's very decent in this movie for the most part. Some people who just don't like how she doesn't say anything when she goes into the town at one point when shit starts to go down. That part is like the dumbest part of the whole movie.  There's so many movies where the town drunk or the town bum who's not taken seriously whatsoever still tries to say, hey guys, shit's happening. In the end, I'm gonna give, no one will save you a three out of seven. Yes, that's a negative but I still feel it is a decent film. I feel that it is something worth your time. The loss of point is for me never wanting to watch it again, never caring to watch it again. But anyways, those are my thoughts. What did you guys think about this movie? I'm actually kind of curious. Admittedly, I did originally not want to do a review about this movie but after just seeing the discussion about it, I was curious and seeing what my subscribers and just anyone on YouTube had their thoughts on this movie. So let me know in the comments below and I'm interested to see what you guys had to say. Anyways, guys, that's all for me. I hope you enjoyed this review. If you did, leave a like and if you're interested in more subscribe and yes, I'm back. We're gonna start doing a few more reviews in the next little while but I hope you guys enjoyed this one and I'll see you guys in the next one.


What's up guys take down here welcome back to the channel today. I am sharing with you guys my Conor McDavid collection I do have some cards. I do have some collectibles So I'm going to share everything that I have with you guys next year If I keep getting more Conor McDavid stuff and expand my collection next year I'll give you guys an update and share everything that I have then But this is as far as I got and so far. This is my first year of collecting. I'm glad I got What I have here and I just want to share that with you. So let's just get right into it So I have his 2017 2018 series 2 Base card. I know it's a base card. Not everybody likes base cards, but for my Conor McDavid collection That is a great looking card in my opinion and it fits in really well. I don't have any that have his home jerseys, so I do like this one a lot Next up I have the Tim Hortons 2017 2018 a Game day action card Which I think looks amazing So I'm glad I have that in my collection Also from Tim Hortons this year. I have the Statmakers the gold statmakers card, which is a great addition to my collection. I'm really happy I have this one And my last Tim Hortons one is the triple exposure Conor McDavid card Really love this card Wasn't expecting the value of it But I'm gonna keep it in my collection. So it guess it doesn't really matter what the value is. I'm not selling it So I'm keeping it But that is amazing that is has to be one of my favorite Conor McDavid cards but I also have his Upper deck portraits. I was really surprised I got this one. I have Ovechkin I thought Ovechkin was going to be the top upper deck portrait I pulled but Conor McDavid That's the top one I have right now. That is amazing. I'm really happy I love the portraits this year. This is my first year collecting, but these portraits look amazing It's a close-up headshot black and whites. I do like the Cement cinematography that's they used and I do have an award winner Upper deck Conor McDavid card These are actually collectible cards. These ones apparently aren't in stores or limited Edition card and it's for individual collectors not for resell. It's not in stores At least that's what it says at the back It says congratulations. You've received a limited edition Conor McDavid card This card has only been made available for individual collectors as a thank you for making an upper deck your card choice So that is I'm not sure how many they have I'm not sure the value or anything like that But that is a great addition to my collection I'm not sure how rare it is or anything or if anybody else has any of those But an award winner for Conor McDavid is really cool Next up I have the box and it was a gift set the Walmart exclusive with Conor McDavid on the front for NHL 18 I know not everybody plays ps4 Video games or anything like that that might be watching this but to me I keep this box I put my other collectible up on it And it's a great backdrop will put my cards in front of it and actually looks pretty cool I actually had to argue with the lady It was on sale for Black Friday for $49.99 the game and gift set combo And she was saying that it was just the game, but it wasn't so I ended up getting the last one my Walmart had So I'm really happy about that. But what you get with the gift set is the Conor McDavid figure Which is really cool. I Love it. I haven't had any like this. This is my first figure that I got glad it's Conor McDavid fits in Perfect with my collection Love it a great addition right there And then last up. I know not everybody's into these things. I wish to get more, but I don't have room to display them but my first one that I got for the pop figure Is Conor McDavid? And I decided I'll keep it in the box Down the line. I might take me out of the box just to save some space But for now he's going to stay in the box I'd love to get more. They do have Marvel which I'm heavily into Marvel as well I'd like to get a whole bunch of them put up on my dresser here But I don't have room or the money for that right now. Maybe in the future maybe next year, but My first one's Conor McDavid and it was cheap. I see this going on Amazon for about 20 dollars and up I actually have where I get all my hockey card supplies collector, buddy They actually sell them for 12.99 or sorry 11.99 something like that. So It's how much they're worth their value at 12.99 on average But a lot of people are selling this for $20 amazon is ebay is selling it and it's going through the roof just because of the player But it's only worth about 12 dollars So don't get shipped go to collector buddy if you guys see any of that And you guys wanting it for yourself for your own collection, but they have more than nhl They do have a bunch of nhl, but they have everything but This is my Conor McDavid collection. I hope to expand it in the future I'd love to get his chl card his rookie card. I doubt I'll ever find one of those But you never know, I guess I'm gonna leave this video here Hopefully in this 2018 the end of 2018 whenever I start collecting 2018 2019 Hopefully I can expand my Conor McDavid collection A little bit and if I do I will give you guys an update on that In the future whenever I get the chance to expand it, but for now This is my collection as of april 2018. I'll leave this video here. Hope you guys do enjoy. Please take care. Peace


One of the deep assumptions of the myth of modernity, the story of separation, is that the way to create change in the world is to exert force, to make something change. How do we make a change happen? How do I force somebody to change? How do I pressure corporations to change? Or how do I convince somebody that they're wrong? How do I win a debate? Like there's this oppositional mindset that comes from really Newtonian physics. Nothing changes unless you exert a force on it. Otherwise, the pendulum just keeps swinging forever. But if there's some air resistance, okay, then it will slow down. So there's a force against it. That way of, that oppositional way of relating to the world and to each other is based on a metaphysical assumption, and it's not actually true. Quantum mechanics has shown us that it's not true. When we understand, and to deep program from that actually takes time, that's the space between stories that you referenced earlier, to deep program from that way of thinking, you have to traverse a territory of bewilderment, of everything I thought I knew was wrong, who I thought I was, I am not really, and I don't know what's on the other side of it. Yet, on some level, you know that there's something on the other side of it. Some part of you understands that this descent into darkness has a purpose. And in those moments, we need allies actually to remind us of what we know. When we go through that process, then we're able to act from a different place, from a different consciousness, and the key tenet of that state is the understanding that we are not the only intelligence in the world, that things happen, and not just randomly, that are expressions of a larger intelligence, and that our power to enact change doesn't depend on how accurately and powerfully we can exert a force, and how big an audience we have on our podcast, and how big an audience our writing reaches and all that. There are other very mysterious principles of change at work, and that this inconceivable orchestrating intelligence will deploy each of us in the right place, at the right moment, with the right gifts, and with the right instructions to do what needs to be done. And the instructions come through our emotions, not through our calculations.


And that being prepared to change, having that plan B, having those scenarios, and then also continuing to go digital to all of your information should be easily accessible by those that need it. And we refer to that as our digital nervous system, all of the different systems that you have that will give you the important information that your organization and those in it need to run your business. And we are having upcoming I'm going to pitch a webinar that we just did in November, and I think you guys are going to love the name, it was called let's get digital. You know, so you know that song. Oh yes. So, in that webinar, and, and you can see it, you can get on our website. We talked all about, you know, all the different electronic systems that are in play right now, including electronic or cloud based accounting systems, electronic bill pay system electronic expense reporting how electronic invoicing, and then the, how you can group all that information from all of those various sources in from your digital nervous system into dashboards and other summaries that are going to give you a bird's eye view of your key performance indicators, and what you need to manage those things that you need to manage on a regular daily weekly monthly basis, and to get that information to all the key people in your organizations and your board that need to see it regularly. Timely relevant, all of the key performance indicators.


Good afternoon everyone and welcome. Actually it's not even afternoon, it feels like afternoon because I've been up since four o'clock this morning but it is actually morning. I was up at four o'clock this morning because my dad had to leave really early and woke me up. But it is actually only 10, 19 in the morning out of this one I did now that I consider it. And actually now that I consider the fact of that I'm like, oh my Lanta that I even did such a great trade today because I am exhausted. All right, let's look at it. Journey women's off to Canada, see you in September. Excellent, email me when you get back or you'll have your email with you. All right, let's go over the train today for everyone in the world. So CSIQ was a gap, it was a gap down. Close the night before up here at 24.69, gap down this morning and open at 21.45. Saw the gap down only this morning. I didn't have time to look at anything whatsoever yesterday and I usually don't look at stuff at night anyways unless for some reason I'm bored or have nothing to do which is not the case at all anymore ever. But the reality was this was a good gap. I liked it this morning, I looked at this morning and I realized guess what, N-C-O-F. And I wrote it in the room and I said no chance of failure that CSIQ is the one today. I did rate a couple of the things but I really knew that there was no point in doing anything other than that. And then guess what I wrote this morning which I wrote fairly early and at the time that I wrote it this was not even here. So then I saw this drop and open here and I was like okay but I just did it. I just did it and you know what it ended up being completely exactly the way that I wanted it to set up even despite the fact that it dropped into the open because I said an ideal world if it doesn't rally at all and falls of a planet into the open and must set up really by 935. So in an ideal world I wanted it to not rally which it did not fall off a planet if this isn't falling off a planet I don't know what is. So it did fall off a planet and it absolutely set up by 935. So ASAP is really what I wanted this to do and it did. So what is this actually? This is actually selling action and it's a nice selling action. And if you didn't short this aggressively today and get this you didn't get the dollar moving it. Now could you have shorted this here? Yes. Where did this go actually in here? You could have got this was money here actually but this would have been very this would have been like what I would have called as a comma cosy to short this here and do it here this late after this drop off after this gap and the solid fall off and the hard fall would have been a comma cosy. Now as it turns out it worked but it really was a comma cosy trade in here to do this late and most traders probably did this here or not at all because you would have missed this. So we got this whole thing. I mean this was a nice, nice, nice call and really I mean fell hard prior to the open and actually could have pooped us out but that's one of the reasons why we put the start where we did I am doing a fantastic job this year of actually keeping us in trains getting us to make sure that we get them in case they do go and fall off a planet like this didn't I wanted it to and knew that it should have and it did but if it had pooped us over it it would have kept us in just like the one from yesterday in the second call in WMT which kept us in when it did that thing. So I really know what the numbers are but I will tell you the beauty of my system is the fact that you can make this money so fast so, so, so, so quickly and to be honest with you I don't mind taking risk I really don't have a problem with risk at all I mean look at the risk I took in this today but I'm telling you that I don't wanna be in it that long and so I really wanted to go I wanted to set up by 10 and I wanna be at as quickly as humanly possible because I know that I can be perfect and I've said this before for 30 minutes or even an hour if I have to be but it is extremely challenging to be perfect all day long therefore people should not trade all day long and you get the tireder the more tired even if you're not up at 4 a.m. you get tired by noon by 1130 by 1115 by one o'clock by two o'clock you're exhausted if you're in trades and still trading and you're back and forth back and forth and you're not thinking clearly at all it's too stressful and it's just too annoying so the best thing to do is to make the money quickly out of the gate as soon as you can if the trade doesn't work you're out if the trade works you're up and you're up quickly and you're not at risk for that long and you get the rest of the day to yourself and that is one of the best things about my system but I will tell you that what I really appreciate about myself today and so a lot of gratitude for is the fact that I can see something like this and know that I have 100% conviction without a shadow of a doubt that it's actually gonna work and we just do it and we just do it and I just call it in when I'm as aggressive as I need to be and actually I even wrote this down too here now let me just read this this is what I said about this today actually now I just forgot this here this is on the sheet can do anything this could do anything today even reverse whoosh and I'm gonna take it I actually said that about this today I actually said about this today and I don't say that about every NCOF I actually wrote that down about this today look at that oh that's the other thing my dad said last night he said he said a really nice thing when he said that he's gotta hand it to me that I've accomplished this figure now I gotta trade and now all of the television stuff and persevering through every difficulty on my own without a husband or boyfriend but he also said that I have just a tremendous amount of confidence and he said what's another word you use he said it was really cute he's like what's another word you use he's like it's not confidence although you have confidence he's like you have a lot of confidence he's like what's another word I said conviction dad he said yeah that's what you have Melissa I said yeah that is what I have and I'll tell you when you have conviction you're able to take risk in your trades and you're able to do it and not sweat a bullet, not worry and no and have 100% convictions gonna work just like this one yesterday and I gave a little lecture yesterday when I was talking about it and I said if you don't here this is working here did anyone just do this I just called this and I didn't do it even but I'm done for the day but did anyone just do this this is working nice New Jersey trader no worries you can come another time anyways the we were talking about this yesterday with WMT because I was saying that this was very sloppy yesterday until it broke and very choppy and probably a lot of traders didn't know what to do with it some were going long some were going short it was back and forth and you had to have conviction that it was a short and it was a short and it was a short so that's again where having a system like the 26 point rating system really tells you if something is a long and a short and you have to know where you're going with it and look this went to the this went to the target today and I did say 1960 and this basically that's it I mean that might be the low of the day all right this is Melissa with stockswush.com have a great day everyone if you're interested in the next golden gap class is August 29th and 30th thank you everyone and have a great day email me and Melissa at the stockswush.com for more information


What's going on everyone? Sam here with late sports cards ripping one box at 22 tops chrome sonic baseball late for Jeff s It's good luck as always Jeff. Let's do it buddy. Let me get you a super short printed rookie Pulled the gold railway j-rod earlier today. That was kind of wild Let me get some by Goldie and Judge buybacks as well Also be cool Here we go All right, cool appreciate that Carl for sure appreciate you buddy What's your up? There's a green re-wave to 99 as large new bar lads newt back my truck Cool cool cool, Carl Dan to be swanson Brandon Crawford that'll probably that'll probably be the most likely scenario I would say but again if I could sneak in at the end. I will I will do it Brandon Marsh Ray wave rookie Youth quake Shane boss rookie Evan Long, Gordon.  Here's Jonathan India And Salvi presides if you like new bar, then that's that's a good card to get right there for sure show Core ray wave Nico Horner Salty by the way, you're up next if you are still here you are going up next That was boom prism Trevor Rogers Glass now and Matt Scherzer. I got you Carl. I got you appreciate you buddy round the cuneo refractor John Lester Josiah Gray at a youth quake Cole Calhoun If you said REM Starling Marta right all right. No, I know what you mean All the 288 to 299 G's nice one there max month one Soto and Prism Brian Reynolds That'll do it for your break there. Jeff. Thanks as always for your support buddy. Do appreciate it. We'll get him right out to you


Welcome to another edition of RCE. Again, this is Brock Palin. You can find us online at rce-cast.com And we're coming up very very close on episode 100 Once again, I have Jeff Squire from Cisco Systems and one of the authors of open MPI Jeff. Thanks a lot for your time Hey, Brock. Yeah today. We're actually this is actually fairly timely because The the HPC community was just kind of rocked in a little turmoil with that certain little blog post for mr. Jonathan Dursey About how HPC is dying and MPI is killing it and one of the big things that he cited in there as as a comparison point Was that the big data community seems to be growing much faster than the HPC community? And it's actually just completely coincidental that we are actually interviewing somebody from that community today Yeah, so one of the things he referred to was a spark and spark something I've been screwing around with and having a lot of fun with but one thing Jonathan pointed out was that you're able to still write He actually wrote diffusion using spark and much less space and it takes to use MPI and he had job postings our stuff So that that was really interesting And I thought a lot of it was because people just don't know these things exist Which is part of the reason you and I do this and I wrote about that at failure as a service which is my blog and You know, basically, I think people need to learn about these things So I gave it away. We're talking about spark today. So let's go right in Matei Take a moment to introduce yourself Hi, yeah, thanks for having me on the show. I'm Matei Zaharia I'm an assistant professor at MIT working in distributed systems And I started the spark project while I was a grad student Before that and I also spent the past two years Commercializing it through this company called Databricks. So I wonder if could you give us the quick run out? What is spark give us the you know, the quick answer and then we'll dive into more specifics. Yeah, sure So spark is a parallel Computing framework for data processing on clusters. It lets you use Some concise APIs in Python Java or Scala to process data at scale So people are probably most familiar with the MapReduce paper, which is actually quite old now and you know, the big data Analytics community really kind of got a run in when MapReduce kind of we got brought to everyone's mind How is this different than MapReduce? Yeah, a great question. Yeah, so spark is actually inspired by MapReduce and It tries to Generalize the MapReduce programming model so that you can capture more types of algorithms or of computations But still keep the nice properties of MapReduce Such as fault tolerance and automatic distribution and placement of computation near the data So it started our research group was actually doing research on MapReduce And as we saw people trying to use it for new applications. We designed this new model So Hadoop is known as you know kind of the the front runner in terms of a MapReduce framework that's out there right now But Hadoop offers a lot of things to spark replace everything about Hadoop Yeah, good questions So spark spark does not Try to replace everything in Hadoop The part it it replaces or you know The part you can use it for is the distributed processing part, which is what MapReduce would do But the Hadoop stack also contains a lot of other projects It contains storage systems such as the Hadoop file system and spark can use those but it's agnostic to those It doesn't try to provide its own file system or anything like that and likewise all the key value stores and Things like that that are in the Hadoop stack don't get replaced So spark is used most often in Hadoop environments alongside the other components of the Hadoop stack So what then is the value of spark? So if I'm using it inside Hadoop and at least to me a novice in the big data kind of world Why wouldn't I just use pure Hadoop all the things that come with Hadoop that allow me to do MapReduce and the like Yeah, good. Yeah, good question. So the main part is that the the spark execution engine lets you do more types of computation and Often faster or more easily easier to program than you would using MapReduce So there's kind of two pieces of that one is that the engine is designed to be Highly general so it can do not just batch computations like MapReduce But also streaming computations or interactive queries where you're sitting at a console and typing in queries and getting answers so basically it's this wide range of things and then the APIs for it are quite a bit easier to program with than the MapReduce API. So it's these high-level functional programming based APIs in Python and Java and Scala and many of the things that you'd have to write custom MapReduce code for in Hadoop There's already a library function to do it in Spark. So it's just faster to put together programs So as Spark itself utilize any of the MapReduce algorithms or is it pretty much taking what you saw a lot of people doing with MapReduce and worked with it It it it utilizes the some of the ideas that were in the MapReduce You know architecture or design so for example ideas like sending computations to the nodes that contain the data and You know figuring out tracking enough about the computation to figure out how to recover from failure But it doesn't use the code in Hadoop MapReduce. The only part of Hadoop it interacts with is the storage systems and it can use the the same interfaces that Everything else in Hadoop uses to read from any Hadoop supported storage system So now when you say that Spark is faster, how does it get that speed up? Yeah, good question. So there's two main things that contribute one of them is that Spark can Spark lets you Control and use Distributed memory in your program So if you have a bunch of machines, you can choose to load data sets into memory across of them Keep them there across queries and if you have any kind of iterative algorithm or any kind of interactive queries where you're Asking multiple queries on the same data set at once that part is quite a bit faster In in traditional MapReduce, you don't have any Control over memory you can't have like a variable that you load into memory and then you share across MapReduce steps So this is the first thing and then the second thing is that the engine Also supports more general sort of computation graphs So if you look at MapReduce, it's just these two phases map and reduce And if you have an algorithm that needs to do more phases of communication For example, you have to run it as separate MapReduce jobs And there's quite a bit of overhead to starting each job and feeding data from one of them into the next one because it has to go into a file and With Spark you can have a more general graph with multiple stages and this also Helps improve performance even if you're not using the in-memory features at all So that's an interesting point you raise right there one of the big performance Criticisms of typical MapReduce slash Hadoop And all of its clones is that it has to read and write to files all the time And there are a lot of reasons why it was architected that way But am I reading between the lines of your answer here that you don't do that or you don't have to do that in Spark? Yeah, exactly. Yeah, we can avoid that in a lot of cases And in particular in MapReduce the part that's really Slow really time-consuming is the output of each MapReduce job has to go into a file in a distributed file system So it actually has to be replicated across nodes for fault tolerance And it's very expensive to do that right and then you do that and then immediately you just read it into another Hadoop job So in Spark if you just merge those jobs together into one Execution graph you can avoid all of that So Spark tries to do a lot of this stuff in memory, but do I have to do anything special if Say the data set I'm working with is is larger than the piece of memory Do I kind of have to manually manage this in-memory out of memory? Yeah, the way it's designed you don't have to do that. So it will Gracefully sort of spill out to use the disk if it can't fit things into memory Of course if you want you can try to manage it You can try to tell it well, you know, you loaded a data set and you want to remove it And so on but even if the data sets don't fit the idea is that you shouldn't have to Change your program based on the size of the cluster you're running on and how many resources there are And this is also helpful actually if machines fail in the middle of your program Or if your job needs to be resized down because you want to launch another job or stuff like that Okay, now that's an interesting point right there, too So how what level of abstraction do you provide? So you mentioned at the very beginning that you have library calls for common operations above just the raw underlying Message passing and whatever Loading of data and things like that and so Assumably those are indicated for are targeted at specific types of tasks that and use cases that that you have created Is the fault tolerance baked into that so you just say hey solve You know for X and halfway through half of the nodes die You know what what level of fault tolerance do you have will it restart the computation or or or how does that go? Yeah, good question. Yeah, so let me let me explain that in a bit of detail so the way the programming model works is you work with these distributed collections these distributed data sets and It's maybe in some ways you can compare it to some of the array Programming languages for HPC where you had distributed arrays so you build a data set You know it's spread out across multiple machines and it contains Objects of any type so for example say Java objects if you're doing things in Java and then you have Operations on top of these data sets for example you can do a map that gives you back a new data set or you can do a group by or a filter or something of that nature and Spark Make sure that if you use these operations it can always recover You know the result of any sequence of operations so each data set is you know can always be Recomputed because spark tracks the operations that you used to build it and if you lose only a fraction of a data set like say you started With something and you did a map and then you lost you know one of the results It can also do the computation on only the part that was missing and we build just that part and not have to Roll everything back to a checkpoint or like redo the whole computation So it it tries to provide this fine-grained fault recovery where you only recover the pieces that are missing And all the abstractions are set up. So basically everything has this kind of fault tolerance From out of the box you'd have to go and do something special if you didn't want to have this But all the operations provide this already So you kind of touch on it there, but these are the RDD's Can you give a little bit more like tell what RDD stands because everything is based around that and then also tell about some of their characteristics such that Gives you the parallel performance the constraints you put into place, but gives it the performance Right. Yeah. Yeah, so these distributed data sets you worked with you work with are called RDD's or resilient distributed data sets and basically there are a few interesting things with them so they They can contain sort of arbitrary types of objects as I said so like Java objects for example if you're programming in Java and each RDD is spread out across the nodes of your cluster so it can be partitioned in sort of any way and RDD's are Immutable so once you create one you can't modify it you just create new ones from it And this is what allows us to sort of track exactly how it was built and basically what happens is you start by Creating an RDD for instance by referring to a file in your file system You can say okay like view that as a collection of strings You know for example and then you do transformations on it such as a map and spark tracks The the graph of operations that you use to build a specific RDD Which is called its lineage and then it can use that to recompute any Partition of the RDD at any time So you work by just defining these and and chaining together these transformations to create new data sets And then once you've built them our spark figures out a way to execute them on the cluster It's very similar to Working with collections in a functional language. So actually it came from the you know from looking at the Scala Programming language like at its collection library and it's also similar to the various Functional APIs and Python like the iterator tools and so on Okay, so you said something in there like you know, they're immutable you basically are just Transforming it and making new RDD's and you can kind of build them up Is there any type of like optimizer where it'll kind of look at all your transformations before it actually starts moving the data and kind of Limits data movement. Yeah, exactly. Yeah Yeah, there are several things that are done here and actually this is one of the areas where we're trying to Extend the API to enable even more optimizations so Couple of things that are done. So one is with regard to data partitioning you can ask spark to partition a data set in a given way So for example, if you have key value pairs like say the key is I don't know It's like a URL for a web page and the value is the content of the web page You can do something like say partitioned them by domain name So then pages, you know from the same domain name on the same machine and you can do things with them Faster that way and then many of sparks Operations that you run on these are aware of the partitioning and try to take advantage of it So if you have a data set that's already grouped for instance here by domain name and you want to Do a reduced operation on it. It knows that you know these pages are already on the same machine So it can avoid some communication or if you have two data sets that you want to join Which means bringing together elements with the same key from from the two data sets It also looks at how they're partitioned and tries to figure out the way that will minimize communication And then the other optimization that it does which is helpful is pipelining things So for example, if you do a map function and then you do another map function, you know On the result of that it fuses these together into a single function and then it applies both of them at once So you don't have to you know read the data once, you know Do the first thing and save it somewhere and then start reading it again. You can do both of them in one scan So these things help You know the reason they're nice is because when you write a program You can structure your program in terms of these small Simple operations like map and then at the end of the day you still get an execution plan That's you know, that's pretty good even though you got this this really modular program that that you used to build it Now let me ask a little more about these optimizations because you you mentioned some very interesting ones there about trying to minimize communication because obviously This is one of your limiting steps is shuttling all the data around and as the name implies big data You know it just takes time to send stuff across networks And it also consumes resources and all these kinds of things that could be used for computing So do you have other types of optimizations or are you looking at things like network topology awareness and even So outside the box right network topology things like staying on a leaf switch rather than going through a core switch and things like that and and other types of of optimization inside the box so for numa awareness and and Stuff like that. Yeah, definitely. There's a bunch of in of stuff in there already and there's a bunch of new stuff Going on as well So in terms of network topology For input actually both for input data and for data that you save Into memory spark is aware of the network topology and you can do stuff like you can specify, you know Which machines are in the same rack and be aware of the rack topology as well So it it understands that there's this Hierarchical like tree to eat apology. So when it's reading input data from something like the Hadoop file system It knows which machines have each block of data and same thing when when you cache data and memory It it tries to place computations, you know as close as possible to the machines that have it we've also Try to optimize some of the specific, you know communication operations So for example one That we spent a bunch of time on is doing Broadcast and we tend to run in these sort of ethernet Commodity networks, which don't have any special built-in Permatives for these So we we do actually for broadcast we have this Implementation of bit turned more or less that the nodes can use to send data around and that's that's pretty important in Some applications that tend to broadcast a large Sort of parameter vector to everyone So apart from this networking Stuff the the other area where there's active work So apart from this networking stuff There's also Some interesting work going on with doing relational optimizations in spark. So these are the same kinds of optimizations That you do in databases and there are things you can do when you have a more Structured model for the data. So for example, you have a table of records And you know that each record has say an integer field and a float field And then the database community has done a lot of work and storing and querying these efficiently You know including things like column oriented databases and doing processing directly on compressed data and so on So in spark, we have a project Called spark sequel which lets you Store data with a known schema and then query it using sequel and we also have an api called Data frames which is similar to the data frames in the r programming language and the pandas and python you know if you've ever looked at these python data processing libraries and data frames, uh, you know, let you have a Distributed table with a you know with a known schema and do these kinds of relational operations on it from inside a normal programming language like python and we optimize these operations then using these Techniques and you can get actually quite a bit better performance even then with the plain spark apis Okay, so that's yet another interesting point. I feel like I've been saying that the whole interview here So you're talking about making optimizations because you can know the structure of things which kind of flies in the face of what one of the perceived values Of these kinds of systems is which is unstructured data So can you talk a little bit about comparing and contrasting? You know structured data versus unstructured data and the kind of optimizations that you can do Yeah, good question. Yeah, so so, um, uh, a lot of the big data systems I used Process what starts out as unstructured data. So for example text files, you know, representing logs or You know documents from the web or anything like that But as you process them you generally tend to add some kind of structure So you extract some fields from each record or even with something like json data, even though Technically it can contain, you know, all kinds of weird Elements in there you select some of the fields that you care about and you do things with them So as data flows through a pipeline in most places and Hadoop deployments as well It becomes more and more structured. So this is where these apis come in now when you know something about the structure of the data There are several types of things you can do. So the first thing is in terms of storing it There's been a lot of work in in doing very high performance analytical databases and for example, one of the main things that has come up is column oriented storage and processing. This is similar to basically the Struct of arrays as opposed to array of structs that you sometimes have In scientific programming, it basically means, you know, if your queries tend to access only a few fields in the record It's better to store it together the same field into one Address and memory instead of having to scan through a whole lot of memory and only extract out a small percent of the data And once you once you store data by column, you can also compress each column and there are some algorithms that can Operate directly on the compressed data to do simple things like sums and so on where you never have to uncompress it Which is also kind of neat and then apart from these storage optimization The other really big class is a kind of algebraic optimization So when you have a query like you you start out with a table and you say, okay I want to pull out field one and then plus two times field two and then I want to group by something You know, and then I want to filter out where let's say just the top 10 values You can actually rearrange the expressions the user give you and move them to sort of minimize the amount of work So for example, if you know that they're filtering out something You can do the filtering before you do the computation of the other values that they want to select If you know they're grouping, you know, and then discarding some groups You can just skip stuff that is not in the correct group at the beginning And these things are You know, basically these are all things you'd have to do by hand If if you were writing a map reduce essentially and if you can instead write a structured query or write an expression using data frames There's basically a compiler that comes up with the best query plan for you So you've been talking about a lot of examples that really do come from the data world now Been seeing some examples around of actually using spark for more traditional Heavy computation data rate on the data many many times you see spark being used for that much and you know What's what's your opinion of spark being used for that sort of work? Yeah, we've we've seen a few Neat use cases there and actually i'm definitely interested in you know and finding More of them and in trying to apply this this kind of technology to hpc as well so One one set is just heavy Computation but on these kind of you know these these commodity clusters. So as examples of that There's a group In neuroscience at janelia farm institute that's using spark to process Data from brain imaging basically they have A way to image the brains in in sort of live animals and to see which neurons are active as they're doing certain things So, you know, for example, they have this fish and they show at different patterns And they can see which neurons are firing in it and they get a lot of image data And then they want to do some analysis on it often in close to real time because they're actually running an experiment there So they want to do things like, you know, just basic Filtering and processing of the images or they want to do things like figure out which neurons are correlated with each other Which would include some kind of clustering algorithm or some some kind of pca To reduce the dimensionality of the data and try to understand it So this is an application that's using spark even though it's it's mostly You know, it's very cpu intensive. It's still useful to be able to do things interactively or even in a streaming fashion Another area where it's being used is for genomic data. So that is closer to sort of big data processing as opposed to big compute But there's a team at at UC Berkeley that's using it for that to to build, you know, highly parallel Processing pipelines for that and then we've also seen it used in In some computationally intensive machine learning. So for example Image processing pipelines or things like Like expectation maximization using some more Complicated models that require a lot of compute. So it's not, you know, it's not going to be a fit for everything That's compute heavy but things that You know that don't have Super fine-grained Communication patterns it can actually be pretty good for and pretty convenient to use for those Now one of the traditional complaints About some of these additional frameworks or at least from the perspective of my community the hpc community is They're really looking to extract every cycle possible. And so using higher level languages Tends to be Relatively faddish and things like that and that's that's somewhat changing because the world is is going to rapid prototyping And you know if my computation takes a little bit longer It's still better for me to be able to code it easier with a higher level framework and language and things like that where Is your community falling on this because the languages that you have chosen to export spark in Is java, scala and python and those are all very high level things. So what's what's the trade-off? And uh, you know, how is the valuation of that in the big data and the spark community in particular Yeah, there is definitely a trade-off Although people are working to close the gap as well to let you Use native code at least in in some Specific cases in in spark and haduba applications I think the the reason that The big data community started with these fairly high level languages Namely the ones based on java is because There was a lot of experimentation And a lot of sort of time pressure to you know to build a new algorithm for example That you might try out and then you might throw away So these you know all these projects started from web companies Which have you know, it's it's not like the web companies have one algorithm that you know They've known about for 10 years and and they just want to run that at the highest level possible And it it will run there for months. So they want to make it very fast These these companies are experimenting with new algorithms all the time And they also have a lot of ad hoc queries, you know, someone comes in and wants to explore a data set and maybe find out something new So they valued The speed of development You know over making any specific algorithm very efficient And that's the same thing, you know, we've seen with spark We we have a lot of people especially the ones using that for interactive queries that really need the high level language now in terms of You know Closer to sort of bare metal performance. There are two types of things that people have been doing One is trying to use Coronals or libraries on each machine that are optimized, you know, and that are written as optimized code So for example, the machine learning library and spark uses Blast on each machine and you can you know use your own like favorite implementation of that To do linear algebra. So it's not as good as writing all of your algorithms You know and see from the beginning But at least for a lot of them you get the inner loop part can be pretty fast And the similar thing is for these Relational queries the sequel and data frames actually a lot of frameworks now are Generating code at runtime based on your sequel query and then you get, you know Code that's, you know, that's that's optimized to run this way. So I think I think we will see some You know some more efforts to let you get this kind of performance, but there's there is fundamentally this trade-off between, you know people trying to build something, you know In a few hours or type a query even in a few minutes and see what happens versus people Trying to build something that they know, you know will run for, you know, many weeks or months into the future We mentioned that, you know Scala python and java are things available now, but you've mentioned that some other things been worked into it What's the spark framework itself written? Is it written in one of those or is it written in its own thing? Yeah, it's actually written in scala. So scala is basically It's a it's a Statically typed language on the jvm. You can think of it as sort of a higher level java. So A lot of this stuff has similar performance to what you'd get using java basically Now just out of curiosity. What's the largest job that you've heard of or or the largest r.r.d? Yeah, the So the the largest job that we've seen We've seen some jobs that that processed Over a petabyte of data in in the same job and ran for I think close to a week So this was actually at one of the Chinese web companies That actually I should look up which one Let me let me just look it up actually because I can I can then give give you the right answer So so the largest job we're aware of Is over one petabyte of data and this is a job that actually ran for something like a week This this was on at Ali Baba the Chinese web company To do image processing. So they had one petabyte of images that that they wanted to process And there are some other web companies that are also processing You know around one petabyte of data per day, although not necessarily in in the same job using spark the largest cluster we're aware of is is about 8 000 nodes and In terms of in-memory rdd's I'm not sure what the biggest one is that I I don't think too many people have say a petabyte scale of memory in a cluster But we've definitely seen jobs with say 10 terabytes of memory being used With in one spark job So I want to touch on a little bit of history this too. Can you tell us a little bit about what the amp lab is? Sure. So the amp lab is a Research lab at UC Berkeley that I was part of actually, you know, when when we started spark it's kind of Multidisciplinary sort of lab in big data. So it's it's bringing together people from Distributed systems databases And machine learning and trying to you know, look at the problem together from these perspectives And it's also it also has a few people in there doing application so for example the genomics application I talked about or other applications at UC Berkeley So it was a lab of you know about 50 people where we worked on various systems that tried to do these and Part of why spark started is because we saw for instance machine learning researchers, you know trying to run algorithms that didn't work well on map reduce and and we thought We saw that basically as a problem Okay, now as for the future though, tell us a little bit more about Databricks Yeah, so Databricks is a startup company commercializing spark That began out of the amp lab About one and a half years ago. So I was part of the founding team along with You know, quite a few other members of you know of the research group and We're now we're you know, we're basically the the largest organization Contributing to spark and we continue to build spark as an open source project and then the way We commercialize it is by offering a hosted service on amazon. So Basically, you know spark as a service and tools on top to make it very easy to run and manage Now you mentioned open source there. That's kind of key. What license is a spark available under It's the Apache license and it's hosted at the Apache software foundation. So anyone Can sort of join and contribute, you know using this well-defined process that they have there Now something I ask all projects who have developers in them pure curiosity question because there tends to be a lot of passion on on every side of this is What version control system do you use and why? Yeah, so We we use git. I think we started with git from the beginning and really You know, I I know there are many pros and cons to these even within Apache, you know, many many people have different opinions, but For me a major reason to use that was actually github just the interface online For reviewing patches was, you know, pretty nice pretty convenient to use and then we also found that a lot of Developers were familiar with github and and they found it easier to contribute to spark using github So I think that's that's probably the main reason why why we did that Okay, so we've talked about everything spark is what what are you guys looking at adding into the future?  The the whole community has gone really fast So now there are many different people working on new libraries and new features for spark the biggest Sort of direction we've been going in in the past two years is actually having, you know building up Really rich standard library on top of the core spark engine So when spark started out and similar with map reduce and so on it was just about the programming model and we said like Oh, isn't that very exciting that you can write say k-means clustering and 30 lines of code or something like that? Like wow, that's amazing before you would have had to build, you know a whole distributed system But in practice nobody wants to write k-means clustering and in 30 lines of code They want to call it from a library in one line of code So we're we're building a bunch of standard libraries on spark that all operate on rdd's You know all of them can share data with each other But that have a lot of the you know most common algorithms for data processing built in and optimized For a distributed setting. So we have a machine learning library Graph processing library the spark sequel system for structured data And also a stream processing library to do things like sliding windows and aggregating data Across time and most of the activity in spark is in these libraries now adding new algorithms and you know new ways to run the existing ones apart from this i'd say the other Sort of big thing that I think will happen You know in the next few months is the r interface to spark So I don't know in in the hpc world Maybe r isn't you know as popular as say python for data analysis But definitely in the sort of industrial like data analysis space. It's one of the most popular languages And you'll be able to call spark from r and call the machine learning algorithms and so on on You know distributed data sets that you build using r and I think this will again increase the the community of people Who can use that? So, you know overall Our goal is is to have you know to look at the The tools and and libraries people are using for data processing on a single machine And try to provide you know equally high level or equally convenient ones that can scale out of the cluster So that's what we've been trying to do Okay, another question we'd like to ask people with software projects is what is the The weirdest or the most unexpected use of spark that you have heard of Yeah, I'll have to think about that Yeah, so when we started the project one pretty unexpected use but Actually pretty neat and hindsight Was people building web applications like user facing web applications that run spark rays on the back end So for example, there are a number of Applications for say advertisers or You know, basically this kind of data products where Uh an advertiser can go in and look at how well their campaign is doing and then they can slice and dice the data So for example, they can say, oh, let me filter it down to one demographic or one Region, you know in the world or one time frame and as they change the filters for the data The application actually calls spark on the back end and he computes a result and with the in-memory Data sets you can set up you can actually get back these results and you know Half a second or like a few hundred milliseconds so you can actually have an interactive web application That's you know running these queries over a cluster in the back end So when I saw those initially, you know, I I definitely had not expected People to use it that way and you know, I was very excited about it And we've tried to do things to make it easier for people to build these Oh, thank you very much for your time. Where can people find more information about spark? Yeah, you can you can you know find out more download the project at spark.opachi.org And you know, I should also say if you want to try it, you don't actually need a cluster You can on it on your laptop. There's a local mode that you know works very well and that you know Most people use for development and you just need to have java on your laptop And also python if you want to use python Thank you very much. All right. Thank you. This was great. Yeah, thanks for having me


The fiery horse with a speed of light, a cloud of dust, and a hockey hyosilver, the moon rangers. With his faithful Indian companion, Tonto, the daring and resourceful masked rider of the planes led the fight for law and order in the early western United States. Nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear. From out of the past come the thundering hoofbeats of the Great Horse Silver. The lone ranger rides again. Hank Webster ran the general store in Broken Bow. He also ran the post office in one corner of his store. His duties as postmaster took but little of his time, because the stagecoach came in only once a week to deliver the mail in a small pouch and pick up outgoing mail, if any. Frequently, because of holdups, letters bound for the east didn't go beyond the badlands that began at the edge of town. For three days, Hank had been increasingly uneasy. He paced the floor from the front door of the store through the living quarters in the rear. In every few minutes, he would go to the front porch where his 10-year-old grandson sat listening to the tall tales of Hank's old friend called Idaho. Thanks a lot, Hank. Idaho, have you seen anything of it? They were strangers in town. Yeah? No, Dad, glad it, Hank. You asked that a half hour, go on a half hour before that.  Ram, you keep busting in on the story, Idaho's telling me. What lie are you telling the youngster this time, Idaho? That ain't no lie. It's about the time the lone ranger whipped a whole army of outlaws. Another lone ranger yarn and... I want to hear the rest of it, Idaho. Dad, just a minute, Bobby, just a minute. Hank, what makes the man you're looking for so all fired important? Who is he? Where's he coming from? Virginia City. Well, does that make him important? No, but Jay Stewart Hamilton does. I've heard of him. I reckon you have, Bobby. Hamilton's one of the biggest men in these parts. Isn't he the man who's back in the new railroad? Yep. Most every night while I'm working in the cafe, I hear someone talking about Mr. Hamilton. And, Hank, I understand Hamilton's sending a million dollars worth of jewels to St. Joe's. Where'd you hear that?   Where'd you hear about the jewels? In the cafe. It's general talk that Mr. Hamilton aims to sell his wife's jewels so he can raise the cash to finish that railroad that he's interested in. Oh, God. I hope that'll be kept secret. Not much secret about it. Everyone's wondering where he'll sell a million dollars worth of jewels. It ain't that much, but it's a lot of jewelry. Hank, you'll have to sell them in an eastern city, won't you? Yep. And Virginia City's west of here. That means you'll have to send them through this town. Surely will. That's just why I'm so fired-worried. Well, wouldn't be no chance for cooks to steal them between Virginia City and here. No. But after they leave here, they'll have to be taken across the badlands no matter where they're going. That's going to be downright risky. Don't I know it? No. Go now, we set the message you would get here. He's bringing the jewels? No. I don't know, I'll tell you something. Yeah? The jewels have been hidden in my store for the last week.  I've been waiting word as to what I should do with them. Why don't Hamilton let you know? Well, he's got to find out where to send them. Where he can sell them. He's waiting here from several agents in St. John Omaha and other places. As soon as he hears, he'll send a messenger to me. And I sure wish he'd come. I don't know. You were telling me how the Lone Ranger saved your life when you were up against a gang of outlaws. Yes. They were shooting at you from all directions when you heard hoofbeats coming fast. Yeah. You looked up and saw a big white horse charging down the hill. There he is. Here comes the messenger. You see that stranger on the white horse? Oh, yeah.  Oh, rider isn't mad. Yes, he couldn't be the Lone Ranger. Oh, oh, oh. Yeah, stranger, you looking for me? I don't know whether I'm looking for you or not. You don't know? I'll shake your leg if you don't know who does. I'm looking for Hank Webster. That's me, that's me. Can't you see the sign there on my store? Hey, Mr. Whatcher, what's your horse's name? Horse's name? I call him Chief. Why? I'd call him Silver if I owned him. You wouldn't? Like the Lone Ranger. You know the Lone Ranger? Nope, I wish I did. Idaho knows him. Don't shy to him. Well, that is... Idaho don't know him any more than I do. But get to business. You got something for me? Yeah, we'll go inside the store. Yep. I'd like a witness to sign the receipt. Would you step inside to Idaho? Sure thing. Hurry back, I won't finish your story. All right, Bobby. I came from Mr. Hamilton in Virginia City. I guess you've been expecting me. I sure have. And I'm glad you got here. Are you going to take the jewel? Careful. That's all right. I can trust Idaho with my life. I hope so. To answer your question, no. Huh? The man who's going to take them will be here today or tomorrow. Does he know whether to go? That's contained in this letter. He'll be riding a white horse. Yeah. And he'll be wearing a mask. A mask? Yeah. He'll identify himself by showing you a silver bullet. Great day. You seem to know who I mean. The Lone Ranger? Yeah. He's the one man who could be trusted on a mission like this. You'll see that those jewels get across the bad land safely. Just give him this letter. It has all the information he'll need as to where they're going. Meanwhile, Bobby was wide eyed as he left the porch and stood beside the messenger's white horse. To the boy who resembled the horse of the Lone Ranger. Daddy, I won't hurt you. I just want to scratch your neck. I'd sure call you silver if you were my horse. Silver, old boy. Stand still, silver. I'll put the reins over your head where they belong. There. I can get to that saddle. Steady silver. I owe silver. Don't fall. We shall run away. Come back here. We got to get after him. Hey, don't get hushy. I'll get him.  Let me borrow a horse. Bobby made a brave effort to stay in the saddle. The stirrups were out of reach, but he clung to the white mane of the runaway horse for several minutes. Then his fingers became numb. His frenzied grip on the horse's mane relaxed. He was pitched off to fall in a dense thicket at the bottom of an arroyo that paralleled the trail. He lay there motionless, while the messenger's horse continued along the route toward Virginia City. When Bobby regained consciousness, he heard men's voices at the top of the arroyo. The boy couldn't see the speakers, and he was too weak to leave the place where he lay. I'm going to carry him, or what direction he'll be heading, or when he'll go. I know how to find the answer to both questions. Hal, I got my information from a gent who works for Hamilton in Virginia City. You see, Hamilton has put all the instructions in a letter to the postmaster broken bull. All we've got to do is get that letter. How can we get it? Steal it from the post office. Listen, Slick. An old dilute named Webster runs the post office in connection with his store. Should be hard to find the letter? Only thing is, we've got to act fast before the jewels leave broken bull. Or when Webster learns that the letter is gone, he'll tell Hamilton it was stolen, the plans will be changed. Oh, no, I got it all worked out. That letter will likely be locked in Webster's safe. Now, you wait around until the store is closed, then get inside and bust open the safe. You're good at that. I can crack most any safe it's made. Bust the safe and take whatever cash there is in it as well as the letter. Then make a copy of the letter. When we know how the jewels are being sent across the bad lands, it'll be easy to get them. All right. I copy the letter after taking it and the cash from the safe. Then what? And take the letter and the cash to the living quarters behind the store. A man named Idaho sleeps in the small bedroom. Tuck the cash and the letter under Idaho's mattress. And join me at the cafe. You'll be waiting? Yeah. Idaho works there. We start a fight and Idaho gets killed accidentally. Oh, wait a minute. I don't like mixing murder into this. It'll be called an accident. We'll see to that. What then? Webster finds his money in the letter gone. Later he finds that same beneath Idaho's mattress and thinks the old man stole it. Now I savvy your scheme, Baxter. Idaho being dead, he can't deny stealing the letter and the cash. Of course he can't. And Webster, figuring Idaho is the only one who saw the instructions for sending the Hamilton jewels, won't have any reason to change the plan. Baxter, I have to hand it to you. You sure worked it out good. You might have known I had a good scheme on a send for you. Yeah, but I didn't think it would be this good. Now you go over to the post office, wait around till the store is closed, then get started on your part of the job. All right, Baxter. I'm all my way. I'll meet you later in the cafe. Get it. Get it. Come on. The boy lying at the bottom of the arroyo tried to move out of the brushwood. He had heard every word of the conspirators. He sobbed in pain and anger. Gotta get away from here. The weekend days Bobby tried to stand. He clutched the bushes for support. As he gained his feet, fierce pain shot from his ankle. It was more than he could endure. Unconscious for the second time, the brave lad slumped to the ground. It was sunset when Bobby again opened his eyes. He found himself in a woodland camp near a small stream. Before he could speak, he heard the low voice of a man who sat beside him, a masked man. Take it easy. You've had a bad fall. Who are you? Come over here, Tato. He's awake. You're mad. How...how I feel?  He didn't get a bad bump. That hurt, huh? I thought it was my ankle. Your ankle's badly sprained. You'll be all right in two or three days. Where am I? We found you near the trail. Were you thrown from your horse? Yeah. Where am I now? Oh, a few hundred yards from where you fell. We needed water, so we brought you here. Well, now I remember. You two weren't talking. Oh, so you heard us.  You bet I heard you. Hold on, hold on. I know all about your play. Oh, no, no. You locked it up yet. You can't keep me here. Steady, son. Steady.  Oh, you... Tell me, tell me. Why did you call us murderers?  Oh, who is your grandpa?  What's your name?  I'll get away with it somehow. Now, listen to me. Why should I? Because you've made a mistake. I know what I heard. You couldn't possibly have heard anything to make you think we're murderers. One of you's called Slick and the other Baxter. We met on the trail from Virginia City. Did those men sound like us? I don't remember. Now, please, please tell us who you are. It's important. I'm unconscious, Dan. Toto, you heard what he said. Men called Slick and Baxter planning murder. We must win the lads' confidence and learn who he is before it's too late. The Captain falls on the first act of our Lone Ranger adventure. Before the next exciting scenes, please permit us to pause for just a few moments. Now, to continue. After the messenger's white horse had been found riderless, there had been a long and prudeless search for Bobby. At sunset, the messenger rode back toward Virginia City. Hank and Idaho returned to town. Idaho went to the house and waited, while Hank asked the sheriff to send men in search of the missing boy. The old storekeeper was exhausted when he joined Idaho at the store. Any word, Idaho? No. What's the sheriff saying, Hank? Well, he'll put two of his best men on the tracks of that horse. Did you tell him we'd already followed the tracks? Yeah. But he said Bobby had to leave the saddle somewhere. It's just a case of finding the where and following the tracks. Well, Idaho, you may as well go. There's nothing you can do here. But I hate to leave you here alone. I don't mind being alone. You've got your job. Yes, I reckon so. Toto went to town for a doctor, but found there was none available. He returned as darkness gathered over the Lone Ranger's camp. The masked man had given first aid and Bobby lay in a coma. From time to time, his lips moved. His voice was barely audible. Help. The Lone Ranger leaned close, trying to catch a word that would tell something about the boy's identity. Can you hear me? Maybe we take a boy to town. Maybe someone know him. No, Toto, he's too ill to be moved. Wait, wait, he's trying to speak. Silver. Silver? Ranger, where's the Lone Ranger? Hey, what's Bobby? Oh, yeah, no. Can you hear me, son? Bobby said no more. He lay motionless and silent. The minutes dragged heavily while the masked man and Toto watched the lads pale face in the flickering light of a small well-concealed fire. Presently, the boy's eyelids moved. Toto, I think he's regaining consciousness. All right. It looked that way. We've only learned something about him. Got to help. Help. You have help. We're here to help you. Can you understand what I'm saying? You. What are you? Now, listen to me before you try to speak. You were talking about a horse named Silver. Silver. We're not outlaws, lad. We're going to help you fight the outlaws. My horse is named Silver. Silver. The Lone Ranger. He's getting stronger, Toto. Let me prove I'm telling the truth. The horse named Silver. He's waiting. Now tell me, who are you? My name is Bob. Bob Webster. Bob Webster? Your grandfather's Hank Webster? Yes. They're going to rob him. Bobby, you overheard men talking. Remember? Yes. I remember. I must know everything those men said. Hank Webster had been pacing the floor impatiently while he waited in the slender hope that one of the sheriff's men would bring word of his grandson. Presently, he heard a horse stop in the rear of his home. Why are they stopping in the back? He waited, listening, then started toward the rear. I'm curious. He opened the door to Idaho's room, then fell back in surprise. Where he went to? Where? Bobby sent me. Bobby? Where is he? What's happened to him? Tell me. What do you know about the boy? He's going to be all right. He's in my camp. He's had a bad fall. There was a slight concussion, and I thought it was better to keep him in camp until morning. Where's your camp? Why couldn't we find traction? Daddy Webster, look at this. Money? I mean this. A letter? Where did you get that money? The letter's more important than the money. It's from Hamilton. Here, see for yourself. Where did you get that? It was beneath the pillow on Idaho's bed. No, no, I don't believe that. Not Idaho. Give me that letter. Now, wait. That letter was in my shape. Do you know a man named Baxter? No, I've never heard of him. Or anyone who's called Slick? No, no. I want to know about him. They're the ones who stole this letter. They put it under Idaho's pillow, so he'd get the blame. Now, where is Idaho? Oh, she's here. How do I know you're telling the truth? How do I know you're not the one who busted the shape? The letter tells you about the man who will carry the jewels from here. Yeah, but... He's to identify himself with a silver bullet. Yes. Here's the bullet. Huh? Great chichili. You mean to say... Now, tell me where Idaho went. Is he out looking for the boy? Oh, no. He's going to work as usual. He went to the cafe. That's where Slick and Baxter plan to kill him. Kill Idaho? Find the sheriff or deputy or anyone with authority and come to the cafe at once. I'll go ahead and hope I'm not too late. Oh, but wait. That letter... I'll take the letter with me. Baxter and the man called Slick to just outside the cafe. They glanced through the doors long enough to locate old Idaho near the rear of the large room. He was waiting on a table. You see him, Slick? Yeah. I'll go in and start talking to him, Slick. We've been over the scheme before. We'll go over it again so you don't make any mistake. I'll go in and start talking to him. You come along and bump into me. Sammy? Yeah. Then you get sore about it. Right. We start an argument that leads to drawing our guns. I know. When men in the cafe see gunplay start, they'll duck for cover. There'll be plenty of confusion during which some bullets will fly wild. One will hit Idaho. You'll see to that. Yeah. All right, let's get it over with. Go in and start talking to Idaho. Howdy. Oh, uh... Good evening, Mr. Got a vacant table at this side of the place? Well, now I can maybe give you one in a few minutes. The chance over y'all is almost through. Well, I guess I can... Wait! Hey, where are you going? I want a table. You needn't try to bump me out of the place to get it. Who says I bumped you? I do. No, I can't. Please, yes. Wait a minute. Who you calling a liar? You! My son, you said that right to my face.  You take that back. You're spoiling for a fight. You'll come to the right side. Please, please, yes. There's no need to fight. This here's a peaceful place.  I'm not spoiling for a fight, Mr. But I won't take being called that by you. You take back what you said.   Get that draw and I'll slap leather. That's a dare. Then take it. Idaho, come here. Who's that? I'll take your dare. Slick's gun came out and the bullet hit the big lamp near the ceiling. There was a bedlam of confusion as men scrambled over chairs and tables to find safety from gunplay. The lone ranger couldn't use his gun because old Idaho was in the line of fire. Acting with lightning speed, he dashed forward, dived over a table and grabbed the old man's legs. Idaho went down fast and the bullet split the air right where his head had been. Let go of me. Let me up here. Get him. Let's go to it. Baxter fired at the two men on the floor. The bullet tugged the masked man's shirt. The lone ranger rolled to the side as he brought up a gun. Two shots from their marks. One struck Baxter's gun. The other broke Slick's wrist. The masked man leaped to his feet with guns drawn. If there's any more gunplay, I'll be the one who makes it. My arm... My arm is smashed.  All the rest of you stand where you are. What's the idea of starting this ruckus? Idaho, what happened anyhow? Gosh, Wash, I... I don't really know. These two started jarring each other and left it gunplay. Then this masked man came in like a tornado and knocked me down. My arm... Is your name Baxter? No. Then they called you Slick. You must be Baxter. Yeah, what up? Idaho, Idaho, are you all right? Hank, things happened here. He's the sheriff and the deputy. I found him just starting out on another search for Bobby. Hank told me about you, mister. That saves explanation of my mask. I told the sheriff I've been robbed. Here are your men, Sheriff. Slick and Baxter. What's that? You rob Webster's safe. That's not true. You can't prove that. Where'd you get that idea? An important letter and a sum of money we're stolen from Webster. Isn't that right, Webster? Well, you can't say we did it. What's your proof? We may find the proof in your pocket. What makes you think so? Stand still. I'll see for myself. Sheriff, this is a downright insulting way to treat strangers in town? Here. What's this? Some kind of message, isn't it? Better look at it, Hank. Hey, there's a copy of the message to me. That's what it is. Here's the original, Sheriff. You might compare the two and keep the information confidential. Yeah. Oh, you Stephen Paul catch. You can't deny opening my safe. How else could you get that information? I... How about you, Baxter? Did you go with Slick to rob the safe? No, of course not. I was right here in the cafe at the time. At what time? Why, well... How do you know what time the robbery took place? You were in on it and that bruised us. It was your idea, Baxter. It was all your idea. You're in as deep as I am.  I won't take the rap alone. You got me into this, too.  That will do for a start, Sheriff. When you check your hand-bells, you may find these too worn for some other hold-ups in the badlands. Come on, you crooks. Get going. All right. Probably I never seen the light of it. That mass man snapped them two shots from the door of the cafe, and both of them went straight to the mark. Golly, that was your fine shoe. The one that I... It sure was. Are Slick and Baxter in jail now? Yeah. We took them there last night before the mass man brought us to where you were sleeping. Then we brought you home. Well, you feeling all right, buddy? I'm all right now, Grandpa. Well, I'll sit here in the porch with you for a minute. I sure feel relieved that the Hamilton Jews are off my mind. Off your mind? How's that, Hank? They've been hit under the floor for the past week. Well... That's why I've been so worried. I wish fit to be tired waiting for word as to how I was to send them east from here. And now you've sent them? Yep. According to the instructions he came yesterday, the letter said I was to turn them over to the masked man who showed me a silver bullet. He's the one that saved my life. The same? You want to found me? That's right, Bobby. By now, he's halfway across the Badlands, and I reckon Hamilton can just relax and feel sure he'll get his cash to finish the railroad. Hank, who intonation is that mask man? Well, you should know, Idaho. You've been bragging to Bobby how he saved your life so many times. Huh? He's the Lone Ranger. This is a feature of the Lone Ranger Incorporated, created by George W. Trendle, produced by Trendle Campbell Enterprises, and directed by Charles D. Livingston. Tonight's drama was written by Fran Stryker. The part of the Lone Ranger is played by Brace Beemer.


Hi, this is Barry Coleman at the Northern Canola Gores Association. Thank you for attending this year's tour. The Northern Canola Gores Association has a broad representation of board members from across the state who work hard representing the industry. We allocate over $300,000 in annual research funding to strengthen the canola crop in this region. Much of the research focus has been on disease identification and control, insect management, hybrid development specific to our growing region, finding new uses for canola oil, and improving management and stand establishment. The past few years have been challenging for agriculture due to the low prices for commodities. We had the market facilitation program for two years, which has now been replaced by the coronavirus food assistance program or a CFAP, and the canola industry is fortunate in this regard as canola has been included in this program, unlike the USDA shutout of our crop, as well as many other crops from the market facilitation program from prior years. We're providing input to Senator Hoven on the continued need for assistance and the wave price supports for the 2020 crop year. The CFAP announced in May of this year did provide over $17 million to the canola industry. This came out to about $9 an acre, and while those in agriculture are certainly appreciative of this support for agriculture at this time, it's far short of what is needed for the problem of low prices this year. This is based off of the USDA calculating the drop in canola price from January to April of this year and then applying a reduction factor on that when determining how much money would be allocated to growers. The ag industry has been told the reason for the reduction of the funds was that USDA was limited by the funds that had available in the CCC, therefore the amounts need to be reduced. This points to the need for more assistance from the USDA, and Senator Hoven's office has indicated they do indeed intend to provide more assistance. We should be hearing more in the next few weeks from the Senate in this regard. And the NCJ will continue to provide any information needed to ensure that canola gets its fair share of assistance from any future programs. Now in addition to the CFAP program, the NCJ predicts that canola PLC payments for this year will reach the highest levels ever. Total payments will likely exceed $90 million for the canola industry. Now North Dakota has 1.35 million base acres of canola in the program, which represents about 95% of the total canola base acres in the country. Average payments for these base acres is roughly $74 an acre. And we worked closely many years ago with the US Canola Association and other canola growers from other states to ensure that canola was included in this important price support program. And it continues to provide needed financial support to canola growers in a decoupled manner. So our working with other allies and other canola growers and other regions of the country certainly paid dividends in that regard to this program. Now some other issues that the NCJ has been working on are getting canola eligible for inclusion in renewable diesel. Back when the biodiesel program started over a decade ago, it was found that canola met the life cycle analysis for biodiesel. But not the higher threshold that was in place at the time for renewable diesel. Now at the time there was no company in the US even contemplating renewable diesel production. There was nothing in the works, much less RD production using canola oil. So at the time it was decided to go ahead with the analysis that had been looked at by the EPA and just have canola approved for biodiesel use. Now that EPA assured us that they would would likely okay it in time, but they haven't. So now there's an ongoing shift in the biofuels market towards more renewable diesel production. Production of our renewable diesel has been up more than 46% in the last year alone. So we've asked the EPA to complete its assessment that they've had on their desk for the last decade and complete that analysis, which we believe will show that canola oil meets the higher threshold to be included as an eligible feedstock for renewable diesel production. Now this would allow a local source of renewable diesel production in North Dakota, the plant in South Hart, for instance, to be able to add canola oil to its list of eligible commodities that could be used. And this would fit well with our target objective of finding new uses for canola oil. Finally, we hope by this winter that things will return to normal when we plan on having our 23rd annual canola expo in Langdon on Tuesday, December 8th. And as always, we encourage growers who want to become members of the Association to just log on to our website at northerncanola.com, where you can click on a website link to become a member of the Association. We look forward to seeing you at our canola expo in Langdon on Tuesday, December 8th. Thank you.