The story you are about to hear is true. The names have been changed to protect the innocent. Fatima Cigarettes. Best of all, king-sized cigarettes brings you dragnet on both radio and television.  You're assigned the Wilshire Division, by detail. An informant sends you word about an extortion racket going on in your area. The victim, a bookmaker. The suspect, a police officer. Your job, check it out. Compare Fatima with any other king-sized cigarettes. Yes, compare Fatima with any other king-sized cigarettes. One, Fatima's length filters the smoke 85 millimeters for your protection. Two, Fatima's length cools the smoke for your protection. Three, Fatima's length gives you those extra puffs, 21% longer than standard cigarette size. Fatima gives you more for your money. And in king-sized Fatima, you get an extra mild and soothing smoke, plus the added protection of Fatima quality. Five, Fatima, in the bright, sunny yellow pack. Best of all, king-sized cigarettes. Dragnet, the documented drama of an actual crime. For the next 30 minutes in cooperation with the Los Angeles Police Department, you will travel step-by-step on the side of the law through an actual case transcribed from official police files. From beginning to end, from crime to punishment, Dragnet is the story of your police force in action. It was Tuesday, October 18. It was hot in Los Angeles. We were working the day watch out of Wilshire Division. Vice-detail. My partner's Frank Smith. The boss is Captain Hayes. My name's Friday. We were on the way downtown from the office, and it was 9.35 a.m. when we got to the felony section of the main jail. The interview room. Sit down, Mr. Hawks. Thanks. Got your message when we checked in this morning. What's it about?  I've had enough of this. See, either one of you got a cigarette or left mine back in the cell. Yeah, sure. Here you are, Lawrence. Oh, thanks. There's a match for you. Now, what's the story? What's on your mind? Well, I've been thinking about it most of the night. I didn't get very much sleep. So you remember yesterday?  Yeah.  I admit it. I've been taking a few bets. It wasn't any big operation.  We know all that, Lawrence. We went through that yesterday, didn't we? Yeah, I know. I just wanted to make sure we got it straight. We aren't any big operators. We just ran the bookmaking as a side line. Business hadn't been too good lately. Had to make it up somehow. All right, sorry, Lawrence. We can't feel bad about it. We were tagged for making a book before. You should have learned your lesson. Oh, I know. I know. I know. I'm not singing with blues. I'm not trying to halibut. I didn't call you down here for that. Well, I know. What's this all about? Well, like I told you, I've been thinking about it a lot. I didn't sleep hard at all last night.  All right, what's that? I know, Stuley. I don't want to get anybody in trouble, but there's one thing I believe in, and that's playing it square, no matter what you do. Just like now, I ain't beating about the rest. You know, what are you getting at? What do you want to tell us? Oh, all right. Here it is. You know what the record is? I stood a pinch for bookmaking a year and a half ago. I got the thing all squared away, and it came back to my business. I learned my lesson. I didn't want any more trouble, so I laid off the books. All right. Fill us in. What about this policeman? I'm getting to it. Just a minute. Hey, I'm sorry. You've got a cigarette, please. I'm a little jumpy this morning. Yeah, I'll show you. Thanks. Oh, thanks a lot. Well, like I was saying, you know, when I kid up the last beef, I've got all about the horses. I was awful for good. The wife made me promise. I wouldn't have anything more to do with them.  All right, go ahead. Everything was going fine. The business wasn't too good, but we made out. Then the trouble started. My oldest boy, that's Harry Jr., he had an accident on the job, and he broke his hip. I had to help his family out while he was in the hospital. Like I say, the business wasn't making too much money, but we made out, though. Yeah, well, then just as soon as Harry got well, the wife came down, gallbladdered. She had to have an operation. It really cost money, you know. Well, the wife was resting at home. She didn't come down to the store. I started with the books again, you know, just taking a few bets on the side, just a few. I didn't really want to do it. Just one of those things, you know. I didn't have anybody to go to. The bills had to be paid. Where does this policeman fit in with this Ray Wiggins? Well, I'm just coming to it. Well, he came in the store. He said he wanted to make a bet. I didn't know him from Adam, so I played careful, you know. I told him I wasn't making books. And then he said he was a good friend of Myra's. Myra, that's a guy who runs a beauty parlor on Crenshaw. She used to make books, but she hasn't now. Yeah, we know the spot. Well, when he said that he knew Myra, I think he was all right, so I took the bet. That's when it started. As soon as he makes the bet, he comes out and says he's a copper working for vice. I thought he was just kidding. And he flashes this identification. What kind of identification? Bag. What kind of a bag? Well, I don't know. Just a bag. Looked all right. I'd have a look. This one here, was it like this one? I don't know. I didn't get too good a look at it. I might have been something like that. I don't know. Do you ask for any other identification? I don't want to show you any. No. Well, I didn't ask for any. I was scared. I just figured I was arrested again. That's all. He seemed to know all about the first pinch. I stood for making books. He told me that I'd been operating for about a month and a half. He said he had his eye on me. What did he do after he told you he was a policeman? Well, I started to explain to him how the thing shaped up when I was making books again, how I had to pay up those doctor's bills. I tried to beg off. I told him that I'd quit right now. Then he changed on and tells me I don't have to. What do you mean? Well, that's just what he said. He didn't make sense to me either. Then he told me I was operating in his distance. He said to be okay if I went right along the way I was. He'd see to it that I didn't stand any pinches. Yes, sir. What else did he say?  He said there was his pardon. A couple of guys over him they'd have to be taken care of too. He said the juice would cost me 100 bucks a week. I tried to explain to him that stuff. Well, I told him that I wasn't operating big. I was just on the side. I said I wasn't making that much a week. I thought I was doing it about 50. That's all just 50. Then he said, well, he says, all right, if that's what you feel about it, I'll just take you down, book you now. Well, I told him I didn't want to stand the pinch again and I'd do the best that could. So he finally said I'll give you two weeks to go ahead and open this thing up. He said I'd have to do more business because the juice was just going to cost me 100 a week. He didn't care how I got it. This policeman told you to go ahead and open up. For 100 a week, you could go ahead and make a book and nobody bought it. Yeah. Did your wife ever see this policeman and his fellow Williams? Yeah, she saw him. She saw him a couple of times. She saw me giving him the money. Well, when dollars start? About the first of July, paying him $100 every week since then.  I mean, he was running in the ground. He had me in the bind. You said the man's name was Ray Williams, right? Yeah, that's right. That's right. He said he had a partner. Did you ever see him? No. No, I never did. Did Williams ever come in with anybody? Did you ever see him with anybody? Well, no. No matter the fact, I never did. One time I asked him who his partner was, he says none of my business. What kind of a car does he drive? Well, I don't know. Never came to my place in the car. He always walked. He came on foot. You know anything else about him? Where does he live? I don't know. He never told me. He never told me anything, you see? Nothing. Once I asked him what station he was working out of, who his boss was. He got a little hard with me. He told me not to get nosy, not to ask so many questions. Is there anything else you can tell us about Williams? Maybe where he hangs out, people he knows, anything you figure will help him? Well, he works out at Wiltshire. You are not. Ray Williams. Yeah, Ray Williams.  Not in my book. Frank and I continued questioning the prisoner, Harold Lawrence, and he gave us a complete description of the extortionist, the man he knew was Officer Ray Williams. Frank got on the phone, notified the commander of internal affairs division of the latest development, and he advised us to bring the prisoner to his office immediately. We signed Lawrence out of the main jail for investigation and took him to the city hall, room 37. With the help of sergeant's Lloyd Reiner and Floyd Phillips, we started checking through the personnel files for the subject. Neither Frank nor myself recognized the name or the description of Williams as given us by Lawrence. The first step was to show the prisoner photographs of every police officer in the department, regardless of rank or detail. He was unable to make an identification. We then showed him photographs of all police officers, retired or otherwise, he failed to identify any of them as Williams. We showed him photographs of all civilian employees of the department. Again, no identification. We then showed him photographs of all police officers, of the department. Again, no identification. He was unable to find anyone who even closely resembled the extortionist. Not satisfied with this, the prisoner was shown photographs of all law enforcement agencies personnel in the area. The same results, no identification. 4.18 p.m., while Reiner and Phillips continued checking on their end, we returned to prisoner to the main jail, and then Frank and I drove out to see his wife, Mrs. Mildred Lawrence. We located her at Lawrence's place of business, at a pet shop just off Wilshire Boulevard. What is it this time? Haven't we got enough trouble? What do you want now? We just got through talking to your husband, ma'am. A few things we'd like to clear up. I'd like to talk to you a few minutes if we could. Talk to Harry? How is he? He's all right, ma'am. We've been with him all day. We're still looking into the case. Why can't you let him alone? He admitted what he was doing. He wasn't lying. He told you everything. Yes, ma'am. We appreciate that. You wanted to let him alone, let me alone. Trouble, that's all we've had. Sickness and trouble, no end of it. Well, we realize that, Ms. Lawrence, we don't like to bother you, but there's something we have to straighten out here. I haven't anything to tell you. I'm busy. Not alone here to run the store. No health, I'm sick. Shouldn't even be down here. Let us alone. Let me alone. Harry was wrong. He'll pay for it. We're not asking you for anything. We're asking you for something, ma'am. We'd like you to help us. Help you? Why? Policemen, all alive. As far as I'm concerned, you're nothing but trouble. Reason for all I trouble. Well, that's what we'd like to talk to you about, ma'am. I haven't anything to say. I told you.  I had a lot of work to do. You mind stepping inside there?  Can't just stand around and talk.  Haven't even cleaned their litter yet. We appreciate how you feel, Ms. Lawrence. We won't take up too much of your time. Can't you give us just a few minutes, please? Now, you listen to me. I've tried to be nice to you. I've got no reason to either. I could tell you a lot of things about police and things you wouldn't want to listen to. I don't think you're so smart. Maybe Harry was doing wrong, but he's not the only one. I could tell you plenty if I wanted to. Well, that's what we're here for, ma'am.  Then why don't you let me alone? I talked to one cop already today. I told him to stay away and you do the same. I told him just what I thought of him. Low down thief. What was this policeman you talked to today? You know who he is. We're at the same place with you. Big honest policeman. You never do anything wrong. That Ray Williams ought to be in jail just like Harry is. Ray Williams. Is that the man who was here today? That's what I said, wasn't it? Now, can you get out of here? Now, look, Ms. Lawrence, just a minute, please. We talked to your husband and he told us all about this man, William. Why don't you do something about it?  That's what we're trying to do, if you'll just help us. Williams isn't a policeman. What do you mean? Yes, ma'am. That's what we've been trying to tell you. We talked to your husband and he told us the whole story. How can you tell? How do you know he's not a policeman? He's not, ma'am. We're sure of that. Harry said he was. Showed Harry his badge. Well, that doesn't make him a policeman, ma'am. We had your husband look at pictures of every officer in this area. He convinced himself that Williams isn't a policeman. Well, if he isn't a policeman, who is he? That's what we want to find out. Will you help us? Mrs. Lawrence. You'll have to excuse me. I didn't mean to be nasty. I guess that I've been sick. I was in trouble, Harry, in jail. Yes, ma'am. We understand that. Now, you say Williams was here today. About what time was that? It was before lunch, about 11.30, quarter to 12, I think. He came here to get money. That's what he always came for. Did he say he'd be back? No, I told him never to come back. I told him Harry was in jail. All the money we gave him, what good did it do? Said I'd go to the city hall if he ever came back. What did he say to that? He said Harry had to expect an arrest now and then. I told him Harry wasn't going to get arrested anymore. He was through with all that junk. People like him, too. Then he swore at me. He told me some real nasty names. He told me I'd better keep my mouth shut, or I'd get worse than Harry did. Then he went out and slammed the door. Was he driving a car? Did you notice? No, I never saw him in one. Is there anything you know about Williams at all? I mean anything that would help us locate him, maybe. No, I don't think so. All I know is, he said he was a policeman. His name was Ray Williams. Couldn't Harry tell you anything? Maybe he knows. No, ma'am. He couldn't tell us much. Oh, excuse me, please. Yes, ma'am. Right to his pet shop. Yes? What's that? Oh, what? Hello? She hung up. Ma'am? It was Ray Williams. But he just drove by here. He saw us. What do you want? He saw me talking to you.  Told me to keep my mouth shut, said if I don't, he's coming back. Yeah? Said he'd kill me. Before we left, we called in and arranged for a 24-hour stakeout on Lawrence's pet shop and also on the Lawrence home. Until the suspect was apprehended, she would be given a 24-hour protection. The next day, we picked up Mrs. Lawrence and brought her downtown to the city hall, room 23. We went through the same procedure with hers we had with her husband. She was unable to identify any of the photographs of police officers in the area. In addition to the personnel photographs, Mrs. Lawrence also went through a group of pictures which sergeants Reiner and Phillips came up with. They'd had a run made through the staff's office on all known extortionists using the same M.O. Williams used, posing as a police officer. From this group, both Mr. and Mrs. Lawrence gave us two partial identifications. Both men were picked up, questioned and eliminated. Meantime, we'd gotten out a local broadcast and the teletype was sent to George Brereton, C.I.I. Sacramento, asking for assistance in identifying the extortion suspect. The investigation went on. Two days passed. No leads on the suspect. In checking on other possible victims of the extortionist, Ray Williams, we drove to interview Myra Thomas, operator of a beauty shop out on Crenshaw. She was a tall good-looking brunette in her early 30s. This man we're looking for is Thomas Ray Williams. We understand he's a friend of yours. Please, sergeant. You described the man to me. It doesn't mean a thing to me. I don't know him. Well, how'd he get your name, miss? I'm sure I don't know. I'm sorry I can't help you. Well, now, apparently the man knows you or he knows about you. I don't think I understand you. You should think this man, Williams, knows me. We've already told you, ma'am. Mr. Lawrence says that when Ray Williams first approached him, he used your name as a means of introduction. Now, it's not a coincidence. I think you'll admit that. Well, I just don't understand what it has to do with me. You know how you feel. I have an idea of what you're thinking. I was in trouble once, that bookmaking thing. I learned my lesson, though I'm true with that business. We're not here about that old arrest, ma'am. We don't want to bother you. Just thought you might be able to help us locate Williams. I'm sorry. I don't think I can. Possibly he might have been using another name, ma'am. I still wouldn't know him. Description doesn't sound like anybody I ever knew. You know the Lawrence's pretty well, do you? Yes, I wouldn't say pretty well, but I do know them. Have you seen them lately, either one of them? No, I haven't. You haven't talked with Mrs. Lawrence the last couple of days? No, why? We thought Mrs. Lawrence might have told you what kind of a man this Ray Williams is. What do you mean? What's so bad about posing as a police? Well, it's more in that, ma'am. He telephoned Mrs. Lawrence, threatened her if she talked to the police about him. What? Yes, ma'am.  Oh, no. Yes, ma'am, that's right. Couldn't be. He wouldn't do a thing like that. Do you know him that well? You are listening to Dragnet, authentic stories of your police force in action. Because of its quality, Fatima is the one king-sized cigarette that stands up. Here's the practical way to prove that yourself. Compare Fatima with any other king-sized cigarette. One, Fatima's length filters the smoke 85 millimeters for your protection. Two, Fatima's length cools the smoke for your protection. Three, Fatima's length gives you those extra puffs 21% longer than standard cigarette size. Fatima gives you more for your money. And in king-sized Fatima, you get an extra mild and soothing smoke, plus the added protection of Fatima quality. Because of its quality, its extra mildness and superbly blended tobaccos, more and more smokers coast to coast are switching to Fatima every day. Five, Fatima, your money back plus postage. If you're not convinced, Fatima is better than the king-sized cigarette you are now smoking. Just return the pack and the unsmoked Fatimas by August 1st, 1952. Fatima, box 37, New York 1. Add to your smoking enjoyment. Smoke king-sized Fatima, extra mild and soothing, plus the added protection of Fatima quality. Get Fatima in the bright, sunny yellow pack. Best of all, king-sized cigarettes. The idea of an extortionist posing as a policeman to obtain money certainly was nothing new. The idea is as old as crime itself. It's one of the most vicious types of extortion in the eyes of law enforcement agencies because so many such cases go unreported to proper authorities by the victims. Usually, the victim is a person of circumstances with something to hide. The extortionist discovers the person's secret by one means or another, and he begins the never-ending process of draining the victim of his last dollar, all under the guise of being a police officer. If the victim's intelligent enough, at the first approach by the extortionist, he takes the matter to the proper authorities. If he isn't, and he's lucky enough eventually to escape the extortion trap, he goes on for the rest of his life believing that a man who was hired to protect him was the source of all his trouble. And for the rest of his life, he hangs on to his idea, and he passes it on to others, a complete disrespect for law and law enforcement officers. Saturday, October 22, 215 p.m., Frank Smith and I continued questioning the beauty shop operator Myra Thomas. The more we talked with her, the more we were convinced that she was acquainted with the suspect, Ray Williams. In order to convince her of the suspect's viciousness, we drove her over to see Mrs. Lawrence at the pet shop. After their talk, we drove Myra Thomas back to the beauty shop, and she told us the story. I guess I have to admit it. He certainly had me fooled. I thought he was a policeman. You say he showed you his identification, ma'am. I mean when he first approached you. Yes, he showed me a badge. It looked authentic to me. The way he talked, the things he said. I thought he was a policeman. I really did. Now you've heard our end of the story, Mrs. Thomas. You've talked to Mrs. Lawrence. Are you willing to help us now? I won't get around, will I? Won't be in the newspapers? They won't get it from us, no. You won't tell my husband, will you? He doesn't have to know all about this. No, ma'am. Not unless you want him to. Well, I don't. We're not getting along too well as it is. Williams says it's a trouble, too. I guess I should have told you when you first asked me. What's that, ma'am? Just like the Lawrence's. I was paying him off, too. A lot longer than the Lawrence's. Every week I paid him and I couldn't tell anyone. I couldn't even tell my husband. What's that, ma'am? I guess I thought I was smart. The book-making thing, I mean. I started taking a few bets from the girls in the shop. Just small ones. I didn't mean anything by it. And this Williams came in one day. Said his girlfriend made a bet with me. He wanted a place one, too. Did you take it? Yes. Like I told you, I thought I was smart. When the guy took the bet, he showed me the badge, told me he was a police officer. That's when it started. Same thing. Like the Lawrence's. How about after you arrested Miss Thomas? What happened with Williams then? Oh, after I got it settled, Williams came back to the shop again. I told him then I was through with the horses. Wouldn't have anything to do with it again. After that, I didn't see him for a while. Sure was glad. You've seen him since? You know where we can locate him now? No, I don't. I have seen him since, though.  You give me a word. Miss Thomas, if you don't want your husband to know about this, it's your business. He'll never hear it from us. It's got to be that way. Believe me. I love my husband. Ray Williams has caused us nothing but trouble. Can't tell you the worries he's caused me. I mean, besides all the money I paid him, too. You mean you're still paying him? Yes, that's right. Every week. You know how low that man is. I can't begin to tell you. I thought he was a policeman.  Am I telling a sad? No, I guess I don't mind. I've gone this far. All right. As well, my husband and I were separated. We had a fight. We were separated. I started going out on dates. Nothing serious. One night, I got out with a fella from out of town. Nice fella. Ray Williams saw me. And the fella sitting in the car. Parking out right in back of the bar. We were necking. Williams come by and knew that I saw him. I see. There wasn't anything wrong. Really, I love my husband. He wouldn't understand about the fella that night, though. I mean, if Williams ever told him. That's what he's been holding over your head, Henry? Yes, it's more than a year now. I pay him every week. I'll tell you the truth, Sergeant. I can't go on much longer. I can't. Well, you don't have to worry about it anymore. Just help us locate the man. That's all you have to do now. I don't know where he lives, but I know he's going to contact me. He always does. That's what we'd like to know. How does he contact you? He phones usually once a week. Maybe every other week. Calls me and tells me to meet him someplace and bring him money. $50 every week. Do you have a regular time that he calls you? No, almost any time. It's never the same day, never the same place I meet him. I haven't heard from him in over a week now. He should be calling pretty soon, I guess. He usually calls here? Does he phone you at home? Both places. He seems to know where to reach me. I don't know what I'm going to do this time. I just don't know. Ma'am. I haven't got the money for him. I'm broke. Four or five dollars. That's all I have. Well, don't worry about it. But he's going to want his pay off. You know that. What am I going to do? This time it's on us. Before we left Myra Thomas at the beauty shop, we had a definite understanding that as soon as she heard from the extortionist, Ray Williams, she was to call us immediately, any time of the day or night. Then we called the office and made arrangements for stakeouts to be placed both at the beauty shop and at Myra Thomas's home in case Williams decided to contact her in person. The Thomas woman was given a plain envelope containing $50 in marked bills, which she was to hand over to the suspect the next time he demanded his regular pay off. Two days went by. Tuesday, October 25th. No word on the suspect. Thursday, the 27th. Still no word. Saturday morning, October 29th. I got it. Right. Vice detail, Smith. How's that? Oh, yeah. Uh-huh. I see. Hold on, huh? Pencil, Joe? Yeah. Here you go. Over that place again? Okay. The time? Yeah, surely. In about half an hour. Guess, man. We'll be there. Goodbye. What do you got, Frank? Anything? Yeah, Myra Thomas. He wants to see us. I told her we'd be over in about half an hour. What's the deal? Williams called her. He wants his money. 11.15 a.m. Frank and I got in the car and drove out to see Myra Thomas at her apartment. She told us that the suspect, Ray Williams, had telephoned her shortly before 11 o'clock that morning and set up a date to meet her for his regular pay off at 10 o'clock that night. The designated meeting place was a small nightclub out on Santa Monica Boulevard. The Thomas woman was to meet Williams at the bar. We told her to get there a few minutes ahead of time, and if she happened to spot Frank and I in the nightclub, she was to give no sign of recognition. As far as the actual pay off was concerned, we instructed her to hand over the envelope containing the money to the suspect when he demanded it without any argument. Shortly after 9 o'clock that night, Frank and I entered the nightclub and got a table directly adjoining the bar. It was a modernistic affair, lighted from below with a blue fluorescent lamp. A four-piece orchestra was playing at the rear of the place. Nobody was dancing. Only a few of the tables were occupied, but the bar was doing a good business. We waited. 9.30, 9.45. We spotted Myra Thomas as she entered the club. She went to the far end of the bar, sat down on one of the stools in order to highball. 9.50, 10 p.m. We kept waiting. 10.15, 10.30. Still no sign of the suspect. We watched Myra Thomas order another highball. Getting laid, Joe? Yeah, what time you got? 25 to 11. Joe? Just came in. Coming this way, gray suit? Yeah. What do you think? Description bits, doesn't it? Yeah, it must be. Going up to Miss Thomas talking to her. All right. He's got the envelope. All right. Just a minute. Let's give him room. He's leaving. Going for the front door. What do you say? Let's go. Not wasting any time. Come on, let's hurry. Down the street. Come on. All right, Mr. Holden. What's the matter? Where do you want? Police officers. Like talk to you. Have you got your identification? Well, sure, right here. What do you want? We'd like to look at it. Oh, sure. I hear you. Oh, excuse me. That's your badge? Oh, no, it's nothing. Where'd you get that phony badge? A friend of mine. Why, what's the matter? What do you got in your right-hand pocket? Well, nothing. Just an envelope. Why? The money in that envelope? What's that? I said, is there any money in that envelope? Currency. Yeah, I think so. Why? What's the difference? You know the serial numbers on the bills in that envelope? Look, I don't know what you mean. Serial numbers on the money. What are you getting at? The bills in four fives. Yeah, what about it? Extortion, mister. Do you know the pitch? Now, come on, let's go. Now, hold it. Wait a minute. Let's get this straight, huh? So, I got an envelope in my pocket. An ordinary envelope. That's supposed to make me a criminal? No. No more in that badge. It makes you a cop. Come on. The story you have just heard was true. The names were changed to protect the innocent. On January 8th, trial was held in Superior Court, Department 86, City and County of Los Angeles, State of California. In a moment, the results of that trial. Now, here is our star, Jack Webb. Thank you, George Veneman. Friends, our sales records show that more and more smokers every day are switching to king-sized Fatima. Now, this makes us feel especially good because it backs up what we've been telling you, that Fatima gives you more for your money. No other king-sized cigarette can match Fatima quality. When you buy your first pack and compare them with the cigarette you've been smoking, you'll notice Fatimas do have a better flavor and aroma.  Next time, buy Fatima. Best of all, king-sized cigarettes. Raymond Edward Williams was tried and convicted of extortion one count and impersonating an officer one count. He received sentences as prescribed by law and is now serving his term in the state penitentiary. Extortion is punishable by imprisonment for not less than one nor more than 10 years. Impersonation of an officer is punishable by a fine of not more than $1,000, or by imprisonment for not more than one year, or by both such fine and imprisonment. Friends, sometime during the next two weeks, you'll be asked to buy a poppy in memory of those who've fought to keep freedom alive. Give generously when you're asked and wear your poppy proudly. You have just heard Dragnet, the series of authentic cases from official files. Technical advice comes from the office of Chief of Police W. H. Parker, Los Angeles Police Department. Technical Advisors Captain Jack Donahoe, Sergeant Marty Wynn, Sergeant Vance Frazier. Heard tonight were Herb Ellis, Vic Rodman, June Whitley, scripted by Jim Moser, music by Walter Schumann. Howl Gibney speaking. King Size Fatima, made by the same people who make popular Chesterfield cigarettes, has brought you Dragnet, transcribed from Los Angeles. Stay tuned for Counter-Spy next on NBC.


One of the most important pieces of having a beautiful desktop is having a beautiful color scheme I'm talking about the color scheme for your terminal emulators and your text editors today I want to share with you my top nine color schemes for your terminal emulators and your text editors So let me switch over to my desktop and for this example today I'm gonna open up a terminal emulator and a text editor and I'm just gonna open up the terminal and the text editor that I Use so for the terminal I typically use alacrity So I'm gonna open up my alacrity terminal and for my text editor I typically use do me max so I will open up do me max and I will just open up a document So you guys can see as the colors change and then the alacrity terminal I'm just gonna run an LS the first color scheme I want to talk about is actually my personal favorite It's the one I'm using these days and that is the doom one color scheme now doom one Is the default color scheme for doom emacs it was heavily inspired by the atom text editor Which has a dark theme called one dark the doom emacs guys just Reimplemented that as doom one. So that's the default theme and doom emacs I also took the doom one color scheme and implemented that in my alacrity terminal config that way that they've got the same color scheme And you can see it's really nice really a good contrast very easy on the eyes And it's just a beautiful color scheme and for me, you know my eyes my I've got old eyes, right? I'm in my mid 40s and sometimes color schemes that don't have a lot of contrasts. They're kind of tough for me They're kind of tough on my eyes, but this you know This actually is pretty good for me if I run a neofetch you can see more of the color scheme here do it LS If I open something in Vim, I'll open my bash RC You can see some of the colors here in as far as bash scripting here What those colors would look like matter of fact? I like doom one so much that I've actually implemented it in my xmo bar panel at the top right now I'm using xmo net as my window manager xmo bar as my panel And I've used the doom one colors in the widgets on my xmo bar and it just looks lovely in my opinion So doom one definitely my number one color scheme the next color scheme I want to talk about is a very popular color scheme called Dracula and Doom emacs has a Dracula built into it if you do space HT and doom emacs It will actually load all the themes that are available and you can see doom dash Dracula That is the Dracula color scheme and a Lackard II I also in my a Lackard II configs if you go to my get lab my dotfalls get lab repository and take a look at my A Lackard II config I have Dracula available in my config as one of the color schemes I can choose I've got about eight or nine different color schemes in my a Lackard II config and Dracula is one of them I also for purposes of this video I went ahead and themed my xmo bar for the Dracula colors too just so you can see more of the complete desktop Experience although it's not quite right because in this side with the widgets is the Dracula colors I did not adjust these here because these are actually part of the xmo nade config itself rather than the xmo bar config And I didn't want to adjust that for each time I changed the color scheme on this video So these colors not quite right, but that's a minor gripe overall I actually like Dracula too kind of like the doom one color scheme before it's very contrasty Right, it's good contrast very easy on the eyes The colors are more a pastel kind of colors and instead of more of a dark gray Or almost black background now it's more of a dark purple kind of background It's got you know some purplish and pinkish kind of hues to it and honestly It's a it's a different kind of look right and then it's not bad It's one of the reasons I think that the Dracula color scheme has become probably the most popular color scheme You see people using out there and the good thing about Dracula if you go to the Dracula website They've created themes for e-max and Vim and Adam and VS code and g-edit and pretty much every terminal emulator and text editor You can think of they have an official Dracula plug-in for that terminal or text editor So you can really theme out your entire desktop environment with Dracula if you choose Moving on to the next color scheme another very popular one that honestly is one that you know Originally when I first tried it years ago I thought it was the ugliest thing I've ever seen and then it kind of grew on me a little bit and then You know then I realized now it's really not that great and then I use it again It's I've got a love-hate relationship with this particular color scheme. This is gruff box dark now There is a light variant of gruff box It's not gonna be a part of this video because although I have a love-hate relationship with gruff box dark I actually just have a hate-hate relationship with gruff box light I typically don't like light color schemes anyway and gruff box light is is really bad in my opinion But gruff box dark many people love it many people are kind of mediocre lukewarm on it for me It's not too bad. It's not quite as Contrasty as doom one or Dracula, but it doesn't have such little contrast that it's painful to view I mean this is this is easy on my eyes. I mean I could use it One thing as far as just being an attractive color scheme I do think these earthy tones the fact that it's so brown You know this almost the poop color kind of color scheme I don't want to you know bring any toilet humor into this But you know this is a little different than the you know Standard black and gray and purplish themes that you saw in the previous color schemes this with all the brown for me This isn't the most attractive color scheme But of course we're talking about function as well And if you're in a terminal or a text editor all the time you spent hours a day in a text editor For example, you want something that's easy on your eyes and honestly gruff box dark for me I would be just fine working in it. So it definitely makes my top nine color schemes Next up is the awesome color scheme known as monikai pro monikai pro is another I guess I like a lot of the same kind of color schemes because it's very similar to something like doom one and Dracula as far as it's dark It has a lot of good contrast a lot of the colors a similar kind of pastel colors almost to you know Something like Dracula itself. It doesn't have quite as much purple going on with it But it's very easy to read. It's got good contrast very easy again on my old eyes If I run a neo fetch you can see that there if I do something in vim You see a lot of orange and purple at least here in shell scripting Of course that is not using monikai pro like any kind of official Vim color scheme that is just the terminal color scheme set to monikai pro and then me opening like a default Config of Vim and once again in the XMO bar I adjusted the widget colors to also be the monikai pro background and the monikai pro colors And honestly, I think that looks quite nice I could actually live happily ever after in monikai pro if doom one didn't exist I would certainly strongly consider this as my default color scheme Now moving on to something really quite different than any of the color schemes previously is Nord Nord actually comes in two different variants. There's traditional Nord Which is what you're looking at with this bluish grayish kind of theme There's also a Nord light, but again, I don't particularly care for light color scheme So Nord light is not going to be a part of this list But the standard Nord color scheme is actually quite nice I actually kind of like bluish silvery blue backgrounds bluish green backgrounds There's another popular color scheme that shares that similar kind of background later in the list that I quite like as well Nord the only problem with Nord and why I've never really used it long-term is for me Although I kind of like the blue colors as far as the background I don't really like the actual 16 colors of the color scheme themselves because there's just not enough contrast for Me in my eyes. You see I've adjusted the colors in the widgets here at the x-mobar panel at the top and some of these I mean I can read them. It's not like they're unreadable, but it doesn't have that same kind of pop You know that the other color schemes that we've reviewed so far Nord is definitely the one with the least amount of contrast so far on the list, but I will say it is attractive I just don't know for my old eyes how I would like using this long term And I mentioned I would be showing another bluish kind of color scheme This is oceanic next and I've actually used this color scheme in years past and this one unlike Nord does have some nice Contrast mainly because the background is actually quite a bit darker It's a really dark bluish kind of greenish background and the colors a little less of that light pastel More contrast you can see in the panel I adjusted the x-mobar colors again in the widgets and these really pop you can really the Small font here and the panel really stands out much more so with oceanic next than with Nord Now I do apologize for blinding you guys with a light theme But this is really the only light theme I would ever consider running This is solarized light Solarized comes in two different variants solarized dark and solarized light and they're really fantastic They're very readable themes. The only real difference between solarized light and solarized dark is the background the other 16 color color schemes you see the x-mobar panel here the widgets these colors These are the same in both light and dark variants The only thing is in solarized light you have this really light yellowish background and in solarized dark You have a dark bluish green background Now while I said I didn't hate solarized light I can say I absolutely love Solarized dark solarized dark is an old color scheme that's been around for a while and it's very popular It's typically built-in to pretty much every terminal emulator text editor any text editor especially You guys oftentimes on Linux distros that I do if I open up a text editor whatever the default text editor on that distribution might be and I Immediately don't like their color scheme typically. I'll go in their preferences and if solarized dark is available Oftentimes, that's the one I choose because it's just so comfy. I mean it is just so good good contrast It's just an attractive color scheme, too It's really unique right because it's got this almost blue green aqua marine kind of background But it's a dark background. It's definitely not light good colors You know some pastels of pinks blues purples Then just a good all-around color scheme and I have actually lived in solarized dark for a long term like a month set of time I've actually not since starting the YouTube channel, but years ago I used to theme everything with solarized dark at one point. I remember in my Tiling window manager themes and my open box themes. I was finding solarized dark GTK themes for the GUI Applications I use and I think the terminal my desktop environment. I found solarized dark Wallpapers so you can find pretty much anything to go with this particular color scheme And you know, it's not that popular as far as like when you go to screenshot Subreddits like a r slash Unix porn, you know You don't see a whole lot of solarized on that because people typically like those really black dark themes for that But when you're talking about just easy on the eyes solarized dark may be The best as far as you know for those of you looking for something that you work at a computer In the terminal in a text editor long term. It's probably the best one for that kind of work And the final color scheme that I want to talk about today is another one that I have used in years past that I found Very easy on the eyes good contrast and this color scheme is called tomorrow night And there are two different tomorrow color schemes. There's tomorrow night, which is the one typically most people are gonna use That's a dark theme. There's also tomorrow light and it's like most light themes I don't particularly care for for its particular light version But tomorrow night is actually quite easy on the eyes and once again good contrast This is much more of a traditional kind of dark theme It kind of has that gruff box kind of feel to it as far as it's got some brown tones to it But it's not so brown It doesn't have that poopy kind of feel to it that the gruff box dark theme has is more of a really really dark brown Just a hint of some brown tones in it and honestly tomorrow night would probably be a great color scheme this time of year especially if you have any kind of fall or autumn related wallpapers because just the earthy tones and with the Pinks and orange and yellows to it. It's got those fall colors to it And of course we're coming up on Thanksgiving here in the US as well It would really go a long way, you know, if you were theming your desktop for the holidays It's probably a great Selection for that. So there you have it my top nine color schemes for your terminal emulator and text editor And of course all of this is subjective. I know many of you guys are gonna disagree You're gonna say some of the color schemes I showed are absolute garbage and you're gonna say, you know I should have shown this other color scheme that you really like Hey share it in the comments below because other people are gonna be interested and Honestly, you may share some color schemes that I don't know about I might be interested as well now before I go I need to thank a few special people. I need to thank the producers of this episode I'm talking about Gabe James Mitchell Paul Scott Wess a commie Allen Chuck commander angry Kurt Diolchi David Dylan Gregory Hickey Lee Maxim Michael Mike nitrix Surion Alexander peace arch and fedora polytech raver red prophet Stephen and Willie These guys they're my highest-eared patrons over on patreon without these guys this episode about my favorite color schemes would not Have been possible the shows also brought to you by each and every one of these ladies and gentlemen as well all these names You're seeing on the screen. These are all my supporters over on patreon because I don't have any corporate sponsors It's just me and you guys the community and if you like my work and want to help support me Please subscribe to distro tube over on patreon. All right guys peace After seeing solar eyes dark again, I might switch back to it


Over 2,000 drums of illegal premium motor spirits, vehicles and properties worth millions of Nariah have been destroyed in Adamawa as explosion rocks black market yesterday night. Eyewitnesses describe the incident as negligence of the security operatives who are aware of the illegal business of PMS but fail to take action. They will ever appeal to the state government and security agencies as a matter of urgency take drastic steps in order to stop those involved in the illegal business of PMS within and outside the city capital. So it's almost getting to 2,000 drums. This is not the first time they have been doing these types of business for a long time.  So this business I don't know what what are you going to call it we can call it a illegal business or what I don't know so the things is very very sad.


President of the United States and the Vice President. I will bear true faith and allegiance to the same, that I take this obligation freely without any mental reservations or purpose of evasion, and that I will well and faithfully discharge the duties of the office on which I'm about to enter. Mr. Vice President, and now, Mr. Secretary and Mrs. Cavazos, members of the Congress, are here, and ladies and gentlemen, and thank all of you, as I'm sure you know we're here today to celebrate a bit of good news for the nation. They're swearing in that it has just taken place of Dr. Loro F. Cavazos as our country's fourth Secretary of Education, and I'm honored to add as the first Hispanic Cabinet Mem Officer in American history. I know all of you join me in extending, and you've shown that, to Dr. and Mrs. Cavazos and to their family and friends, a proud welcome to the White House. Of course, September is the month when American education goes back to work, so now seems a perfect time to introduce a new Secretary of Education. Just two weeks ago, many American students, teachers, and administrators returned to their classrooms and offices in pursuit of knowledge and its matchless rewards. We sometimes take this for granted, but theirs is a high calling, a grand enterprise of individual effort and collective national purpose. And I'm pleased to say they could not have a finer colleague, advocate, and spokesman than Larry Cavazos, whose own life and career pay eloquent tribute to the ennobling possibilities of good education. The son of a foreman at the legendary King Ranch in South Texas, Larry Cavazos went on to earn master's and doctoral degrees in zoology and physiology to teach anatomy and biology as a professor at the Medical College of Virginia at Tufts University and at Texas Tech University. And to serve first as anatomy department chairman and then as dean of the Tufts University School of Medicine. For the past eight years, Dr. Cavazos has been president of Texas Tech University, winning for that great institution and for himself a well-deserved national reputation for educational leadership and innovation. Lastly, and maybe most important, I might also point out that he and his wife Peggy have raised 10 children. Now that's what I call real homework. But with his scholarship and his work in academic administration, through his membership in innumerable national, inter-American, and international organizations, and by his participation in the movement for school reform where it counts at the grassroots, Larry Cavazos has already made a major contribution to American education. And he brings us to his new post, a record of solid accomplishment that promises even bigger contributions in the future. But along with high distinction, Dr. Cavazos brings something else to the cabinet and to the Department of Education. Something even more important to our schools and to our students. Something I've made a cornerstone of this administration's policy since day one. And that something is the idea of educational excellence and a passionate commitment to quality schooling for every American child. I know that Dr. Cavazos shares my pride in the substantial progress American education has made since 1981. And I'm sure too that he believes, as I do, that we can and must do better still. And I have every confidence that his work as Education Secretary will continue to lead the way, that his voice will continue to reflect the great good sense Americans have always had about our children and our schools, and that his effort will be devoted to keeping a national spotlight of attention on education. You know, I've spent some of the happiest days of my life, and at my age that's saying a lot. Visiting American classrooms, meeting American teachers and students, and watching them work and learn. Larry Cavazos will do the same. Larry is to be sure a sterling example of the magnificent contributions Hispanic Americans have made to our national life. But I know his service in the Cabinet will speak to all Americans, from every background in every circumstance, of the real hope and opportunity that good education always provides. With Secretary Cavazos, and that sounds good, doesn't it? American education will be in sure and loving hands. Larry and Peggy, congratulations, and welcome aboard. It's a proud moment for you and me. Thank you very much. Thank you. Mr. President, Mr. Vice President, members of the Congress, ladies and gentlemen, what a tremendous privilege it is for me to be here today. Mr. President, I do so appreciate your confidence and trust in selecting me for this position as Secretary of Education. You've brought great leadership to our nation, and your commitment to education has truly helped this country make significant gains and strides in the battle to always educate every person. I so much appreciate the warm, the unanimous support of Labor and Human Resources Committee and the confidence of the Senate. I'm grateful for their expeditious handling of my nomination. And I wish to express thanks to the many people that guided us and assisted us through the confirmation process. There were very many of them, and we deeply appreciate what you did. My sincere thanks and love to my wife Peggy and to our 10 children and their spouses. One of our grandsons is here, the other two couldn't make it there, only two and three years old, and all of our family. Most of them are here with us today. This journey in education started in 1949 when I was a teaching assistant. Over the years, the support and confidence and love of my family has sustained me and has helped me. And I would not be here today, I'm convinced that if it were not for them and the support that they have given me, and on top of that many, many others who are here in this room with us today, who have contributed to my education, who have supported me in difficult times and who have encouraged me. There are many of you in this room and I'd like to thank each one of you personally. And lastly, the work ahead for us is truly enormous. We must awaken America to renew its commitment to education. We must all work together, all of us work together. We must care for every person, the highly motivated, achieving child, the undereducated, the dropout, the illiterate, and the handicapped. All persons, majority and minority, must reach their fullest level potential of education so that they will become in turn responsible citizens and in this manner they will contribute to the greatness of America. Mr. President, I look forward to working with you in the days ahead and what I consider perhaps the most important mission of this nation, its effort in education. Thank you for your confidence, sir.


Yeah, thanks a lot for coming everyone special greets to everyone watching this stream at the moment Today we want to talk a little bit around our experiences and story of how we've applied stream processing over the years what we've learned and Towards the ends. We're gonna have an exciting announcement of how we think we can make stream processing with Python quite a lot better So in short, who are we? We are both two developers at Wynton, which is a global investment management and Data science company you might have already talked to us quite a lot at our stand downstairs and we've been in the game for quite a while now for 20 years and Our core thing really is that we believe that the scientific method can be applied profitably to financial markets Which means we run a lot of data processing on financial and non-financial data and try to make a profit out of that and As part of that we apply a lot of technology and data science and this talk is really about One part of that of how we do real-time stream processing I'm interested so quick hands up, please if you've ever heard of Kafka or done some stream processing. Oh Very good. That's good. Okay, then I go about this a bit shorter Basically what stream processing is is that you instead of doing the classic batch processing Where you would go at the end of the day and you collect all of your data for today Write it into a big file and then run for example a huge spark job or something on that on it You work on your data as a stream Which means data comes in throughout the entire day and you process it as it comes in Which is quite nice because then you get real-time and interactive data. You can put it on a nice dashboard and Often it also makes thinking about your processing quite a lot simpler because you don't have those Random interruptions where your one batch shops stops and the next one starts and One example for such stream processing in Finance we've actually been doing stream processing for quite a long time now like this is just the natural thing you do For example, if you buy and sell stocks at an exchange You can subscribe to their market data feed and basically what they do is every time someone buys something or sells something You get a little message that tells you for example Someone at 1015 has bought some stock in Apple and they've bought 10 shares at the price of 144 dollars for example and you really get this flowing in you get a massive amount of messages every second and Then you do various things with it One of the things we do for example is we showed in various dashboards and It feeds into various dragging algorithms and they don't necessarily always care about every single trait that happens But like in the question for the talk before Often we want to have like a little bit of an aggregate. So we want for example to know every minute How many Apple has been traded in total for what average price for example and that's something that one of our stream processors would do so we take a Nice input stream of high frequency events Run a bidding process and then group it down into slower streaming events that are closer to what we really care about and then Our downstream consumers don't really need to do that much work if they're not interested in all the details And over the time we found The streaming model it really works well. So now it's expanded from just handling market data Which is this core financial data of the stock has been traded to alternative data Which means we run some web scraping we look at news we look at various other Not really core financial data If nowadays got an entire office in San Francisco Focusing on this and the other thing that we found was really useful is just normal business events So something happens on the day and you want to make sure that once that happens This other job runs or you want to update some monitoring page And there we've really found the combination of stream processing where we would send for example This job has run on an event bus in Combination with a more traditional database is really useful So you would write your normal data into the database and then you write a little notification Onto your event stream and that then can tell various applications to the right thing for example We run monitoring on it. We do our risk management based of those events we do our trading decisions and there's various other things and What I really want to focus on today It's kind of the thing in the middle where we get various raw events in and then we process it a little bit via various transformations Into the format that we really care about in the end And if you're talking about stream processing nowadays, what you really mean usually is Apache Kafka Which is the project which has won the before mentioned streaming wars really Apache Kafka is a message broker, which means it's a little bit like a database So you install it on a server and it provides you with something called topics where you would write various Data that belongs together on a single topic from a producer and then various consumers can pick up that data and do things with it and One important thing around it is the topics are Subdivided into different partitions and that gives you Parallelism so for example if you have trades you would send some of your trades on Partition one someone partition two and then you can run multiple consumers each consumer gets a petition and then they can share their work a little bit and And the other nice thing around Kafka is it makes it really easy to or it has a really nice Underlying model of how it deals with data. So within a partition every message Is ordered and you get it out in exactly the same order and it's really quite beautiful and elegant how it's implemented And because it's so popular It has huge support around to big data ecosystem So we talked before we heard their system can interact with Kafka There's a bunch of others which can do it So there's a lot of projects just have sprung up lately in the last couple of years that try to Make it easier for you for you to do stream processing on those events we've looked at pretty much all of them and We weren't really too happy with them Basically for two main reasons if you're interested in details come talk to me later. I can talk about this for hours But really the two core reasons Where we had issues with those projects is that usually they're pretty heavy-weight So kind of if you want to use spark screaming to do your stream processing You need to run and maintain an entire spark cluster and then your code runs inside the spark cluster And there's quite a bit of overhead in that just installing it setting it up Configuring it correctly and then doing things like scaling up And the other thing is pretty much all of those projects are written for the JVM, which means Python is a bit of a second-class citizen and then doesn't really fit in or you talk With the actual system via this text JSON input output, which isn't really great for performance There's one nice outline Pretty much one year ago Confluent and the creators of Apache Kafka Introduced a new API for Kafka that does very similar things what these other stream processing frameworks do but Does it as just a simple library? Which means it turns around the relationship between your code and The framework so in the others you would submit your code You need to package it up and then it runs somewhere Inside the storm server and what you can do with Kafka screams is It's just a library which means the master of everything is still just your normal Python process You can run it any way you want you can run it on the AWS if you auto scale it You just all the scale completely the normal way because it's just a normal process It it's also got proper real Event by event processing, which means unlike for example spark streaming You don't need to batch events up so you can get pretty good latency and it helps you a lot with all those Really tricky things around stream processing that gets difficult if you want to do some stateful processing for example for the binning or if you want to join Different streams together if you want to do some aggregations and also for tolerance and Distributed processing is solved quite nicely, but and it's now nowadays part of the main Apache Kafka client But the big drop back for us is So far, it's Java only and it's not that easy to write bindings to that Java interface And that's a bit of a problem for us because it went and we are big Python users we are around 450 people in total and Really at least half of them know some Python they're not all developers But we have lots of data scientists researchers Operations people and if you want to know a little bit more about our Python usage We had a talk last year by some of our colleagues that showed a little bit What we do with Python Yep, and what we really what would want is to give all those people who know Python the ability to do stream processing nicely and easily and What we've done so far is kind of this hipster programming which means Every time you do it you roll it on your own you just grab a Kafka client. There's various good ones and you connect to your cluster and In four lines you can connect it you subscribe to messages you get a callback for every single message and To get started with this is actually a really good idea. And we've been running quite a lot just like this various processes that update monitoring and so on If you want to go down that route there's two clients I would recommend there is one written by Donna powers. Thanks a lot for writing that which is Native Python client, they've written all of the protocol interactions in Python and they especially have a really nice Pyphonic interface. It feels very natural to use it. It's really nice and robust. It handles for example Your cluster scaling correctly and the other recommendation I would have is the confluent Kafka Client which is a wrapper on the C library, which means it's really really high performance So we've pumped something like a million messages per second for it pretty easily And yeah, they're both good since we had parsley talking earlier. Thanks to them as well They've also built a Python client, but I haven't really used it that much But I thought I shouldn't mention it So so what we've done is we've wrote our own straight streaming framework quite a couple of times Every time you start with those ten lines or four lines, maybe You start building it and then you run it in production in your demo setup. It all looks fine and You let it lose and things slightly break or are slightly wrong because streaming is actually really really hard For example, if you want to do the spinning from earlier Where you say you group some traits together, then you do an aggregation over them and calculate some average or total Really need to be careful that you handle every message exactly once because if you double count them your aggregation is just wrong and That's really something that people have actually struggled with Stateful processing is difficult if you want to do joins or if you want to cache like the last bits You kind of run into the same problems that If you keep a local database As a lookup you need to be really really careful that you don't apply things twice and That's hard then distributing load is quite different can be quite difficult Because while the client supported usually what you have to do is you need to handle certain callbacks that get triggered When the rebalancing happens correctly and that can be quite tricky It's fine if you've been doing it for a while But if you really just a data scientists who wants to write some algorithm and some code and run that It can be quite difficult and then if you want performance out of your streaming You usually want to do micro batching which means that you buffer up. You don't Do like a database write on every single message you do but you group them together and after a while like for example every second then you Process all the messages you got as one chunk and that gives you quite a lot of speed up and Because we want all of that we've decided Yeah We've started an open source project on our new open source organization To basically bring Kafka streams what which we think has solved all those problems really really nicely for Java And we're doing a re-implementation of that just in Python pure pyphonic Which means you don't have any dependencies over and above a simple Kafka server And my colleague Rob will now show you a little demo of how it really looks like and what it can do Hi, I'm very excited a little nervous to do a live demo, but it was it was a So I'm going to start up. Ah, it's good. Oops. Sorry Now I'm going to start up all the components required for the application Starting with zookeeper so Kafka uses zookeeper in the background for cluster management for storing the topics leadership elections For example, so let's start up our zookeeper Start a Kafka server now. I'm going to start just one instance in production. You really would want to say three instances Three replicas for extra resilience and so on. So let's go ahead and start up Kafka That's now running I'm going to start up a consumer This is just the default console consumer that comes with Kafka It'll take whatever is in the topic bin prices. Sorry. Can you see this? Is it big enough? great So it will write out whatever finds in bin prices out to the console There's currently nothing in there. We're not running our application just yet. So Let's Now just before I let's let's start up the application There it is it started we can see that it's listening to the prices topic So I think was at the fourth the fifth line down It says that it's listening to the prices topic. It will write out to the bin prices topic We've currently not got no data in the bin in the prices topic though. I'm going to start one more consumer first That's here in my Jupyter notebook So let's do the imports Set up bokeh Set up our consumer. This is very similar to the the lines code. You just saw Andreas Show you and Our plot so there's our plot ready We've got one more thing to do and that's just to start up this loop down here Which will loop on the consumer to read in the prices from the input topic. So let's start it now again Like the previous consumer. There's no output yet because there are no inputs in there. So one last thing to do Let's get some inputs Right, we've got this generator script and the generator script is going to generate some prices Just random prices that Normally distributed returns for the prices. So let's start generating some prices Go back to our application. We can see it's already begun to process those Go to this consumer here. We see there's nothing there yet There's nothing there because we're binning the prices our definition of a bin is when we reach the end of a minute For the purpose of the example in fact two minutes We will push the last the last value on the bin The last two values, sorry The last two minutes out to the output topic. So let's have a look inside Chrome At our plot we can see That our input prices are being parsed Or being read in our bin prices will appear as these red dots here So once we reach two minutes I have sped this up you you can run the the generator in approximately real time So one moment more and we should see the dots There we go. So they so as I say in production if we were running similar code We would obviously be interested in pushing out the bin price every minute for the purpose of the example here We've just waited for two prices to be available before pushing them out This will just carry on plotting We'll just leave it go there We'll move back here You can see in the consumer the console consumer here. It's also found the two prices So these are two perfectly independent applications neither Knows about the other but both are consuming the same data. So what's the code behind this look like? Here we go So the first thing you probably want to do is to write a topology Our topology comprises at least one source One sink the source is your input topic. So prices our sink is the output product topic bin prices And you will want at least one processor in between The processor is where the meter your calculation really takes place so in here in the binning class is The processor so we've got some initialization fairly standard stuff and we've had this Process function process method It the process method will be called for every value that is read in from the input topic and then we convert it to the output When we find two so you can see down here when our store contains two outputs, so we obviously Transitioned into the third minute say We will call punctuate punctuate simply forwards the values the bin values out to the output topic So that's what that's the flow from input to output and what we saw in the bokeh plot in Chrome So we've seen the application. We've seen some code What comes next so right so The first thing and most importantly really is to finish our implementation. We're at a very early stage We're very early stage, but we absolutely encourage contributions. We'd love you to check out our code start on github file some issues Right tests right documentation, whatever you like just get involved We'd really like to experiment with a more pythonic API in particular for the DSL DSL is the domain specific language That sits on top of the streams API in Kafka We feel that there is a very good opportunity to leverage Python strengths to make it even easier to set up a topology Using the domain specific language optimize Python is a great language. We all love it. We wouldn't be here if we didn't but we're under no illusions It does have it can hit limits and if when we hit limits, we will work on optimizing the performance further This could include batching it could include using some of the the great Python libraries NumPy pandas are out there or even leveraging say new new projects like Apache arrow Lastly, well lastly on this slide, but really the roadmap is longer There are many more advanced features of Kafka The 011 release recently Introduced exactly one semantics for example. So yes, as I said before and I'll repeat again get in touch There's the github Page I can't seem to click on up So it is there the the github page get involved check it out and Are there any questions? Hello. Hello. I am thank you very much very interesting. I waited for this for half a year now. So thank you I would check it out just now. I haven't found it. So if you Google for this one doesn't show up So we have to do some search engine optimization It was it was out of just today Okay, so but I already tweeted it so excellent. Thank you Questions hi so in the last presentation we saw Apache storm and how supposedly nice it is with like Tracing the workflows and like sync errors and stuff like that and can you have that like easily out of the block box with Kafka streams and your library It's a bit more lightweight so parts of them you can have especially confluent is I think happy to sell you an entire seat of tools on the other side Because it's just a library. It's pretty lightweight and doesn't do like spinning up of your processes, which means that if you already have existing Infrastructure to do all of that for example. We run a lot of our code on Kubernetes nowadays You can just do it on there and it's integrated with what you already have Thanks Another one Thanks for the way talk. Have you tried some kinds of iter tools? Algebra for the stream processing part In particularly Not yet, but I would love to talk to you about that how you think this could rock Because this is exactly something that we would really like to make nice and pyphonic So please come to our booth maybe later I would say that if you if you look at the the current code base It's it follows the Java code base reasonably closely Which we felt was you know a reasonable way to get started get get to learn the code behind the Java implementation But yes, it would be fantastic to introduce a New code where you know where we can leverage as I said before Python strengths When I walked one comment, so if Someone is asking yourself Should I use streaming at all there's a talk from him tomorrow why you should use streaming? Yeah, thank you And it's really great to see the project you started because I would have also said that this is missing in Python at the moment So I'm very glad to try this out Looked also at Dreams and one thing I found particularly Interesting is the scaling approach and the state management approach. Is that already included in your approach or is that on the roadmap? The scaling we do have working in a in a simple fashion. So basically You can run two different Python processes and they will share the load between them and balance it The state processing is a bit further out. So before we want to do that Oh, basically, that's what we're working on next But it's really tricky to implement which is why we want to put it in this library But it's coming. I think we are already over isn't No, so first It's good that you're making this open source. I'm not familiar with Kafka streams, but I use I'm a Hipster Okay, so I Kind of like feel the pain you were describing and my question is I try to combine RX Python to these reactive extensions Yep with Kafka And it sort of works, but you know, you have to Write a little code. It's I'm not familiar with how Kafka stream works So it's similar to that concept of having operation of the streaming or the events are coming Through Kafka and window and stuff like that. Yes, exactly. It's basically exactly this So it's the same you get your stream input object And then you can map a function onto it or you can group by and you can split it up It's basically exactly that we have time for Maybe two more questions So do you use some Binary protocol to get events from Kafka like is it performant is fast Okay, so To talk to cut so basically what this is the sits on top of an existing Kafka client and basically Takes away all the boilerplate that you have to write and beneath it at the moment. We're using the Confluent Kafka Client with it does one the second one and basically that's talking Just a normal binary Kafka protocol, but this highly optimized C implementation. It's really really fast Where I think the current bottleneck would be is what goes from C? Implementation where you get one call back for every single message into Python code and basically what we've seen is that we really need to include some Batching that for example you get a lumpy array of all your events back because if you do Processing with Python of a million records per second If you look at the profiler really every single if shows up and it's it's getting a bit hard to optimize there But yeah in short. Yeah, we're using a really optimized Kafka client underneath and it Talks a binary protocol. Okay one last question Thank you we recently implemented the exact same solution at my company Kinesis which is very similar to Kafka. Yeah, and I was wondering if you found any way to Test your applications using this library without having the need of a full Kafka installation in your local development environment Okay, so I think this library makes it a bit easier because you Aren't so low-level so it's relatively if you write a processor You have a really nicely defined Python interface and basically we will test against that But I feel your pain around if you stitch more of them together at some point We've managed or we've mostly gone for integration tests Because there's so many small things that are really hard to unit test correctly I decode Andreas's comments I would just say though that the code that I showed the binning top I was the full application. So it should be Not necessarily easy, but you know not very difficult to to abstract that in a way to make it more amenable to testing Because that is that is everything Okay, so thank you very much for for your talk and for all the sourcing this thing so