1
00:00:01,900 --> 00:00:09,670
Now with logical operators done, let's have a look at element operators and there we got two, exists and

2
00:00:09,670 --> 00:00:10,540
type.

3
00:00:10,540 --> 00:00:15,530
Now what do these do? Now to show what these operators do,

4
00:00:15,530 --> 00:00:19,740
I'll switch to a new database which I'll name user which does not exist yet

5
00:00:20,010 --> 00:00:24,600
and there, I'll have my users collection where

6
00:00:24,840 --> 00:00:29,550
I will insert many users, so some quick insertion training here.

7
00:00:29,550 --> 00:00:33,070
Let's insert some users with a name, Max,

8
00:00:33,120 --> 00:00:39,590
now I'll add some hobbies here which actually is an array where I have embedded documents in the array,

9
00:00:39,600 --> 00:00:47,230
so not just text but there I have hobbies with a title, let's say sports and then also with a frequency,

10
00:00:47,460 --> 00:00:50,490
let's say three times a week, whatever

11
00:00:50,880 --> 00:00:58,700
and then I got another hobby here, let's say title cooking and I do that six times a week,

12
00:00:58,750 --> 00:01:00,740
let's say.

13
00:01:00,920 --> 00:01:03,090
So this is one user, now I'll

14
00:01:03,200 --> 00:01:08,100
also add another field for that user, that's a phone number,

15
00:01:08,120 --> 00:01:11,230
not my real number by the way which might be this number,

16
00:01:11,290 --> 00:01:13,610
again this is not my real number.

17
00:01:13,610 --> 00:01:17,750
Now let's add another user, name

18
00:01:17,750 --> 00:01:31,070
Manuel, hobbies is also an array here with title cooking, frequency five let's say,

19
00:01:31,160 --> 00:01:33,330
not sure if that's the correct one

20
00:01:33,570 --> 00:01:40,310
and then we got another hobby, cars with a frequency of well hard to measure

21
00:01:40,310 --> 00:01:43,490
that, let's say it's looking for new cars twice a week,

22
00:01:43,620 --> 00:01:44,400
I don't know.

23
00:01:44,490 --> 00:01:46,470
So let's say that is this data,

24
00:01:46,480 --> 00:01:47,790
now we also add a phone number

25
00:01:47,820 --> 00:01:49,510
and here it is some text,

26
00:01:49,680 --> 00:01:54,630
so also a number but stored as text, also not his real number.

27
00:01:54,630 --> 00:02:00,660
Now additionally here, we also got an age field of 30. So I added two new documents,

28
00:02:00,810 --> 00:02:03,430
we can have a quick look at them with find pretty,

29
00:02:03,560 --> 00:02:05,070
here's the data from Manuel

30
00:02:05,070 --> 00:02:06,510
and here's my data.

31
00:02:06,510 --> 00:02:09,340
Now I deliberately have different types for phone

32
00:02:09,480 --> 00:02:14,580
and I deliberately added this extra field because that will help us with exists and type.

33
00:02:14,970 --> 00:02:20,940
You might already be able to guess what these operators do, let's say in our huge collection which

34
00:02:20,940 --> 00:02:29,190
we have here with two elements, we want to find all persons who have an age field.

35
00:02:29,240 --> 00:02:30,510
Now we can do that,

36
00:02:30,590 --> 00:02:39,370
users is the collection name and we can pretty print the result by finding all documents where age

37
00:02:39,500 --> 00:02:46,550
and then we pass a document to configure our value for age, where age exists is true, we could also check

38
00:02:46,550 --> 00:02:51,290
for false and we would find all documents where no field exists, with true,

39
00:02:51,380 --> 00:02:54,910
we find all the documents where it does exist.

40
00:02:55,250 --> 00:03:00,900
And if I now hit enter, we should find Manuel who does have an age field.

41
00:03:00,950 --> 00:03:03,820
Now of course, we can also use that together with other arguments,

42
00:03:03,890 --> 00:03:09,520
so we could also say age should exist and should be greater than 30. 

43
00:03:09,650 --> 00:03:16,160
If we do that, we find no element because Manuel's fields does exist, he does have an age but he's not greater

44
00:03:16,160 --> 00:03:21,390
than 30, greater than or equal of course would work and would return Manuel again,

45
00:03:21,590 --> 00:03:25,900
so this is exists. Now exist is pretty straightforward,

46
00:03:25,910 --> 00:03:30,400
now let me add one more user to my database here with insert one,

47
00:03:30,540 --> 00:03:37,410
so to my collection, to my users collection. Name is Anna, let's add some hobbies here,

48
00:03:38,410 --> 00:03:45,280
title is sports, frequency, let's say it's two

49
00:03:45,440 --> 00:03:54,590
and here we have another title yoga, frequency is three, let's say. Now for Anna,

50
00:03:54,700 --> 00:04:00,300
I will also add a phone number, let's say it's also text and also not the real one

51
00:04:00,340 --> 00:04:06,110
and then I also add an age here but I'll set it to null, so it's not this closed

52
00:04:06,110 --> 00:04:12,910
you could say. Now technically that makes a difference because if I now find all elements real quick,

53
00:04:13,290 --> 00:04:18,910
we see that for Anna, there is an age field, the value just is null which is a special value which is,

54
00:04:19,090 --> 00:04:23,310
well it just means there is no value there but the field exists.

55
00:04:23,360 --> 00:04:33,190
Now if I repeat my query from before where I just check for the age field existing, I do find Anna too because

56
00:04:33,220 --> 00:04:36,660
the field exists, it just happens to be null.

57
00:04:36,890 --> 00:04:43,180
Now sometimes you want to find all users where it exists and where there is a valid value in there though,

58
00:04:43,190 --> 00:04:45,190
so now we could check false,

59
00:04:45,200 --> 00:04:51,770
maybe it's included in there but no, with exists false, you only find Max who doesn't have an age field.

60
00:04:51,830 --> 00:04:58,820
So if you want to make sure that age exists but also, that there is a value in there, you have to check

61
00:04:59,030 --> 00:05:01,950
that it also is not equal to null,

62
00:05:01,950 --> 00:05:09,150
so now I check for all documents where the age field exists and that should of course be true and not

63
00:05:09,170 --> 00:05:09,660
age,

64
00:05:09,830 --> 00:05:14,420
so where the age field exists and where the age field is not equal to null

65
00:05:14,510 --> 00:05:16,890
and now this will return Manuel again.

66
00:05:17,000 --> 00:05:22,130
So this is how you can use exists and especially this combination with not equal null is quite

67
00:05:22,130 --> 00:05:27,820
helpful for making sure you really only have documents where there is a value in that field.