WEBVTT 00:00.120 --> 00:06.870 So the next thing we need to discuss around tables are data types data types are really really important 00:07.260 --> 00:12.000 and to show you what I mean and first of all when I'm talking about when I say data types Let's return 00:12.000 --> 00:14.700 back to our catch table. 00:14.970 --> 00:19.380 So we have these three headers in three columns name breed and age. 00:20.040 --> 00:23.220 And here's an example I changed some of the data just a little bit. 00:23.220 --> 00:29.320 So the names are the same the breeds are the same but if you look over at age Blue is one year old rock 00:29.450 --> 00:30.640 it is three. 00:31.020 --> 00:37.310 But Montie now is 10 the word 10 and Sam is crazy. 00:37.380 --> 00:39.510 Sam's age is I am young. 00:39.540 --> 00:40.130 Correct. 00:40.170 --> 00:44.370 So what I'm trying to illustrate here is that we have inconsistent data. 00:44.730 --> 00:47.130 This is all text in text. 00:47.130 --> 00:52.170 But here we've got numbers mixed with text and that is not good and it's actually not allowed in school 00:52.170 --> 00:52.860 at all. 00:52.860 --> 00:59.750 When you create a new table you actually have to specify name has to be text. 00:59.760 --> 01:01.040 Breed has to be text. 01:01.050 --> 01:05.890 Age has to be a number and there are a whole bunch of different data types. 01:06.180 --> 01:10.070 It's not just text and number in fact those aren't even data types. 01:10.080 --> 01:12.440 Those are just what I'm calling them the English version. 01:12.450 --> 01:18.060 But you'll see them in just a second but to illustrate why it's so important that your data matches 01:18.420 --> 01:22.960 that you don't have this inconsistent data and why sequel forces you to do this. 01:23.460 --> 01:25.210 Here's a little example. 01:25.710 --> 01:34.350 So let's say that you have this data in your database in a table where hypothetically in a fictional 01:34.350 --> 01:37.290 world you can have data that isn't all matching. 01:37.290 --> 01:42.720 So we have this you know numeric data mixed with text under age. 01:42.720 --> 01:47.820 So let's say I wanted to make a really really simple app that would take each cat's age in the database 01:48.300 --> 01:53.570 and then print out the converted or equivalent cat age. 01:53.610 --> 01:59.910 So from human years to cat years basically which is sort of interesting actually in making these slides. 02:00.000 --> 02:06.810 I learned quite a bit about how you calculate cat age because for dogs or this colloquialism you can 02:06.810 --> 02:08.380 just multiply it by seven. 02:08.430 --> 02:11.060 For cats it's a little more complex. 02:11.280 --> 02:14.040 Apparently it's not linear. 02:14.040 --> 02:19.460 So if you look at the scale when a cat is 2 months old it's three years in human years. 02:19.680 --> 02:26.200 But then they get to two years old but suddenly 24 and when they're 14 at 72 so it's not consistent. 02:26.220 --> 02:30.210 So I just decided you know what let's just go with multiplying by 7. 02:30.240 --> 02:31.460 It really doesn't matter. 02:31.830 --> 02:36.330 So our app takes the age of blue multiplies by seven no problem. 02:36.360 --> 02:37.700 Our app takes three. 02:37.740 --> 02:38.720 Multiply that by seven. 02:38.730 --> 02:42.230 No problem but it has no idea what to do here. 02:42.420 --> 02:45.710 Our code doesn't expect text and it can't do that. 02:45.790 --> 02:48.670 The same thing with I am young cat. 02:48.710 --> 02:50.020 TIME 7. 02:50.100 --> 02:51.090 It doesn't even know what to do. 02:51.100 --> 02:52.260 It just freaks out. 02:52.500 --> 02:55.000 So really trivial stupid example I know. 02:55.140 --> 03:00.560 But the idea here is important that we have to have these data types are established and that my Eskew 03:00.740 --> 03:02.070 will enforce them. 03:02.070 --> 03:08.910 So when we create tables we have to say exactly what data type we're looking for because this type of 03:08.910 --> 03:11.880 situation is really really not good. 03:11.880 --> 03:13.300 Also known as bad. 03:13.710 --> 03:20.460 So returning to our catch table when we're creating it we'll have to say this must be text breed must 03:20.460 --> 03:24.590 be text and age must be a number. 03:24.640 --> 03:28.910 So in reality there are a lot of different my Escuela data types. 03:29.260 --> 03:32.970 Like I said text and number are not real data types. 03:32.980 --> 03:38.950 In fact there are a ton in my opinion too many and it gets pretty confusing especially when you're just 03:38.950 --> 03:39.400 starting out. 03:39.400 --> 03:43.130 It can be intimidating just to show you exactly what I'm talking about. 03:43.360 --> 03:45.730 I put together a list of all the different data types. 03:45.850 --> 03:51.430 So all the numeric types basically the different ways of representing numbers we've got. 03:51.430 --> 03:59.090 It's small it's tiny and medium and big and decimal numeric float double. 03:59.150 --> 04:01.890 But then under string types. 04:01.900 --> 04:10.600 So these are ways of representing text basically char var char binary var binary blob tiny medium blob 04:10.600 --> 04:16.080 long blob text tiny text medium text long text and then you know. 04:16.540 --> 04:20.190 And then finally we've got data types of different ways of representing dates. 04:20.320 --> 04:24.560 We've got date date time time stamp time and year. 04:24.760 --> 04:27.810 So yeah it's kind of crazy. 04:27.850 --> 04:32.740 I know fortunately you don't have to know all of those at any given time. 04:32.740 --> 04:34.060 Absolutely not. 04:34.060 --> 04:39.010 I had to spend a decent amount of time even digging through the documentation just to find all of these 04:39.460 --> 04:44.110 because the reality is you don't use them most of the time or you don't use all of them. 04:44.890 --> 04:48.570 You figure out which ones you need depending on a given situation. 04:48.730 --> 04:54.280 You use a subset normally that you're comfortable with and then eventually you may have to switch things 04:54.280 --> 05:00.430 out and change from using a medium into a big bigot are actually more realistically an end to a big 05:00.430 --> 05:01.710 event or something like that. 05:02.140 --> 05:04.460 But we'll get there for now. 05:04.780 --> 05:06.180 We're going to hope that it's crazy. 05:06.400 --> 05:09.360 And we're just going to whittle it down and focus on two. 05:09.470 --> 05:12.500 The first one is it for a numeric type. 05:12.520 --> 05:14.970 And then Vardar for our string type. 05:15.040 --> 05:19.720 So to represent numbers we're working with Ant and that's represent text strings. 05:19.780 --> 05:21.240 Work with var char. 05:21.820 --> 05:23.190 So we'll start off with it. 05:23.500 --> 05:25.340 So it represents a whole number. 05:25.360 --> 05:26.980 So we can't use it for decimals. 05:28.290 --> 05:32.240 And it ranges up to this gigantic number here. 05:32.490 --> 05:34.390 Basically it's big. 05:34.470 --> 05:39.180 However if you need to store numbers larger than that that's when you can start looking into some of 05:39.180 --> 05:41.620 these other data types but like bigot. 05:41.640 --> 05:47.520 But we're not going to worry about that because for now we're storing things like cat age. 05:47.580 --> 05:49.110 So this is an it. 05:49.290 --> 05:55.970 Here's another and it can be negative or zero or big or 42. 05:55.970 --> 06:04.060 So that's pretty much it to its then Vardar is a way of representing text or strings and it's variable 06:04.060 --> 06:04.650 length. 06:04.730 --> 06:09.060 So you may have noticed that there was char on there and Vardar. 06:09.070 --> 06:11.520 Well char is a fixed length. 06:11.620 --> 06:15.530 So that means that everything in that column has to be the same length. 06:15.730 --> 06:17.620 It has to be 10 characters or something. 06:17.710 --> 06:24.370 But Vardar allows it to have variation in that which is what we want most of the time and it's between 06:24.370 --> 06:26.740 1 and 255 characters. 06:27.850 --> 06:35.320 So things like coffee or negative nine thousand nine hundred ninety nine string in quotes. 06:35.530 --> 06:42.700 So it's not a number or this whatever this is or capital L or a sentence as long as it's fewer than 06:42.700 --> 06:44.630 255 characters. 06:44.680 --> 06:51.100 So returning to our acads table we would say that name which must be text would be Vardar. 06:51.310 --> 06:54.900 And notice that I have these parentheses here and a number inside. 06:54.910 --> 06:58.310 So when you use Farje are you have to specify a maximum length. 06:58.330 --> 07:00.550 So in my case I chose 100. 07:00.670 --> 07:06.670 I could go up to 255 but I just don't want to have any giant names but that's all that it does here. 07:06.730 --> 07:09.620 So it specifies where the cutoff point is. 07:09.620 --> 07:15.700 And if I go beyond 100 characters if I write 300 or 200 characters all that will be stored is from the 07:15.700 --> 07:18.130 first character to the one hundredth character. 07:18.340 --> 07:25.750 So three I did the same thing Vardar and I did a 100 again but that could change if we wanted them. 07:25.780 --> 07:32.320 If we want to restrict breeds to be shorter if we could change it to be 50 characters and then for age 07:32.320 --> 07:35.340 which must be a number we just use it. 07:35.440 --> 07:38.260 We're just working with whole numbers we're not going to worry about. 07:38.260 --> 07:42.230 My cat is one point 2 years old or anything like that. 07:42.300 --> 07:42.680 OK. 07:42.700 --> 07:45.150 So that's all we're going to do for now with data types. 07:45.220 --> 07:46.780 Basically high level overview. 07:46.840 --> 07:50.450 And we saw two of them in particular char and it. 07:50.470 --> 07:52.150 Next up we have a really quick activity.