Grace  Lesch

Grace Lesch

1622457757

How To Query JSON in Couchbase via Collections and Scopes

The first day saw many sessions but I knew the N1QL update section would have a lot of technical innovations. N1QL is the SQL-like query language used on JSON documents stored in Couchbase. With Couchbase, you are not just using a key-value store but a full JSON document database that also exposes data through queries like a relational database (among other things).

Most of N1QL is identical to SQL, but there are nuances that add even more power than standard SQL provides — especially for JSON-specific features like sub-objects, array handling, and, now more refined logical data management.

#database #query json #couchbase #scopes

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How To Query JSON in Couchbase via Collections and Scopes
Grace  Lesch

Grace Lesch

1622457757

How To Query JSON in Couchbase via Collections and Scopes

The first day saw many sessions but I knew the N1QL update section would have a lot of technical innovations. N1QL is the SQL-like query language used on JSON documents stored in Couchbase. With Couchbase, you are not just using a key-value store but a full JSON document database that also exposes data through queries like a relational database (among other things).

Most of N1QL is identical to SQL, but there are nuances that add even more power than standard SQL provides — especially for JSON-specific features like sub-objects, array handling, and, now more refined logical data management.

#database #query json #couchbase #scopes

Brandon  Adams

Brandon Adams

1625637060

What is JSON? | JSON Objects and JSON Arrays | Working with JSONs Tutorial

In this video, we work with JSONs, which are a common data format for most web services (i.e. APIs). Thank you for watching and happy coding!

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Endpoint Example
http://maps.googleapis.com/maps/api/geocode/json?address=13+East+60th+Street+New+York,+NY

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#jsons #json arrays #json objects #what is json #jsons tutorial #blondiebytes

Joseph  Murray

Joseph Murray

1621559580

Collection vs Collections in Java: Difference Between Collection & Collections in Java

Introduction

This article will be looking into one of the most popular questions in Java Language – What is Collection in Java? Also, what do you mean by Collections in Java? Are Collection and Collections the same or different in Java?

What is Collection?

What is Collections?

Conclusion

#full stack development #collection #collection vs collections in java #collections in java #difference between collection and collections in java

Ahebwe  Oscar

Ahebwe Oscar

1620185280

How model queries work in Django

How model queries work in Django

Welcome to my blog, hey everyone in this article we are going to be working with queries in Django so for any web app that you build your going to want to write a query so you can retrieve information from your database so in this article I’ll be showing you all the different ways that you can write queries and it should cover about 90% of the cases that you’ll have when you’re writing your code the other 10% depend on your specific use case you may have to get more complicated but for the most part what I cover in this article should be able to help you so let’s start with the model that I have I’ve already created it.

**Read More : **How to make Chatbot in Python.

Read More : Django Admin Full Customization step by step

let’s just get into this diagram that I made so in here:

django queries aboutDescribe each parameter in Django querset

we’re making a simple query for the myModel table so we want to pull out all the information in the database so we have this variable which is gonna hold a return value and we have our myModel models so this is simply the myModel model name so whatever you named your model just make sure you specify that and we’re gonna access the objects attribute once we get that object’s attribute we can simply use the all method and this will return all the information in the database so we’re gonna start with all and then we will go into getting single items filtering that data and go to our command prompt.

Here and we’ll actually start making our queries from here to do this let’s just go ahead and run** Python manage.py shell** and I am in my project file so make sure you’re in there when you start and what this does is it gives us an interactive shell to actually start working with our data so this is a lot like the Python shell but because we did manage.py it allows us to do things a Django way and actually query our database now open up the command prompt and let’s go ahead and start making our first queries.

#django #django model queries #django orm #django queries #django query #model django query #model query #query with django

Arrays in JSON: Modeling, Querying and Indexing Performance

Abstract

JSON array gives you flexibility in the type of elements, number of elements, size of the elements, and the depth of the elements. This adds to the flexibility of operational JSON databases like Couchbase and MongoDB. The performance of queries with an array predicate in operational databases depends on the array indexes. However, the array indexes in these databases come with significant limitations. E.g. only one array key is allowed per index. The array indexes, even when created, can only process AND predicates efficiently. The upcoming Couchbase 6.6 release removes these limitations by using a built-in inverted index to be used to index and query arrays in N1QL. This article explains the background and the workings of this novel implementation.

Introduction

An array is a basic type built into JSON defined as _An array is an ordered collection of values. An array begins with [left bracket and ends with ]right bracket. Values are separated by ,comma. _An array gives you flexibility because it can contain an arbitrary number of scalar, vector, and object values. A user profile can have an array of hobbies, a customer profile an array of cars, a member profile an array of friends. Couchbase N1QL provides a set of operators to manipulate arrays; MongoDB has a list of operators to handle arrays as well.

Before you start querying, you need to model your data in arrays. All JSON document databases like Couchbase, MongoDB recommend you to denormalize your data model to improve your performance and appdev. What that means is, transform your 1:N relationship into a single document by embedded the N into 1. In JSON, you’d do that by using an array. The example below, the document(1) contains 8 (N) likes. Instead of storing a foreign key reference to another table, in JSON, we store data inline.

JSON

1

"public_likes": [

2

    "Julius Tromp I",

3

    "Corrine Hilll",

4

    "Jaeden McKenzie",

5

    "Vallie Ryan",

6

    "Brian Kilback",

7

    "Lilian McLaughlin",

8

    "Ms. Moses Feeney",

9

    "Elnora Trantow"

10

],

Values here are arrays of strings. In JSON, each element can be any valid JSON type: scalars (numeric, string, etc), or objects or vectors (arrays). Each hotel document contains an array of reviews. This is the process of denormalization. Converting multiple 1:N relationships to a single hotel object that contains N public_likes and M reviews. With this, the hotel object embeds two arrays: public_likes and reviews. There can be any number of values of any type under these arrays. This is the key contributing factor to JSON Schema flexibility. When you need to add new likes or reviews, you simply add a new value or an object to this.

JSON

1

"reviews": [

2

    {

3

        "author": "Ozella Sipes",

4

        "content": "This was our 2nd trip here...",

5

        "date": "2013-06-22 18:33:50 +0300",

6

        "ratings": {

7

            "Cleanliness": 5,

8

            "Location": 4,

9

            "Overall": 4,

10

            "Rooms": 3,

11

            "Service": 5,

12

            "Value": 4

13

        }

14

    },

15

    {

16

        "author": "Barton Marks",

17

        "content": "We found the hotel ...",

18

        "date": "2015-03-02 19:56:13 +0300",

19

        "ratings": {

20

            "Business service (e.g., internet access)": 4,

21

            "Check in / front desk": 4,

22

            "Cleanliness": 4,

23

            "Location": 4,

24

            "Overall": 4,

25

            "Rooms": 3,

26

            "Service": 3,

27

            "Value": 5

28

        }

29

    }

30

],

Like the hotel object above, you denormalize your data model into JSON, there could be many arrays for each object. Profiles have arrays for hobbies, cars, credit cards, preferences, etc. each of these can be scalars (simple numeric/string/boolean values) or vectors (arrays of other scalars, arrays of objects, etc).

Once you model and store the data, you need to process it — select, join, project. Couchbase N1QL (SQL for JSON) provides an expressive language to do these and more. Here are common use cases.

SQL

1

1\. Find all the documents with a simple value can be done by either of the following queries.

2

3

SELECT *

4

FROM `travel-sample`

5

WHERE type = "hotel"

6

    AND ANY p IN public_likes SATISFIES p = "Vallie Ryan" END

7

8

9

SELECT t

10

FROM `travel-sample` t

11

      UNNEST t.public_likes AS p

12

WHERE t.type = "hotel"

13

    AND p = "Vallie Ryan"

SQL

1

2\. Find all the documents that match a range.  In this case, we try to find all the documents that have atleast one rating has “Overall” > 4

2

3

SELECT COUNT(1)

4

FROM `travel-sample`

5

WHERE type = "hotel"

6

    AND ANY r IN reviews SATISFIES r.ratings.Overall > 4 END

7

8

SELECT COUNT(1) 

9

FROM `travel-sample` t

10

UNNEST reviews AS r

11

WHERE t.type = "hotel"

12

    AND r.ratings.Overall > 4

13

GROUP BY t.type

SQL

1

3\. Find all the documents where every rating for  “Overall” > 4

2

3

SELECT * FROM `travel-sample` WHERE type = ‘hotel’ AND ANY AND EVERY r in reviews SATISFIES r.Overall > 4 END

4

5

SELECT COUNT(1)

6

FROM `travel-sample`

7

WHERE type = "hotel"

8

    AND ANY

9

    AND EVERY r IN reviews SATISFIES r.ratings.Overall > 4 END

10

11

SELECT reviews[*].ratings[*].Overall

12

FROM `travel-sample`

13

WHERE type = "hotel"

14

    AND ANY

15

    AND EVERY r IN reviews SATISFIES r.ratings.Overall > 4 END

16

limit 10;

17

18

    [

19

    {

20

        "Overall": [

21

            5

22

        ],

23

        "name": "The Bulls Head"

24

    },

25

    {

26

        "Overall": [

27

            5,

28

            5,

29

            5,

30

            5,

31

            5

32

        ],

33

        "name": "La Pradella"

34

    },

35

    {

36

        "Overall": [

37

            5,

38

            5,

39

            5

40

        ],

41

        "name": "Culloden House Hotel"

42

    },

43

    {

44

        "Overall": [

45

            5

46

        ],

47

        "name": "Auberge-Camping Bagatelle"

48

    },

49

    {

50

        "Overall": [

51

            5,

52

            5

53

        ],

54

        "name": "Avignon Hotel Monclar"

55

    }

56

    ]
Array Indexing:

Indexing arrays are a challenge for B-tree based indexes. However, the JSON database has to do it to meet the performance requirements: MongoDB does itCouchbase does it. However, both come with limitations. You can only have one array key within an index. This is true of MongoDB; this is true of Couchbase N1QL. The core reason for this limitation is when you index elements of an array, you need separate index entries.

Plain Text

1

Consider the array:

2

3

Document key: “bob”

4

{

5

   “Id”: “bob123”

6

   “A”: [1, 2, 3, 4]

7

   “B”: [521, 4892, 284]

8

}

9

10

Indexing of the field “id”  simply requires 1 entry in the index:   

11

         “bob123”:bob

12

13

Indexing of the field “a” requires 4 entries in the index: 

14

        “1”:”bob”, 2:”bob”, 3:”bob”, 4:”bob”

15

16

Indexing of the composite index (id, a) requires 4 entries:

17

   “bob123”, 1: bob

18

   “bob123”, 2: bob

19

   “bob123”, 3: bob

20

   “bob123”, 4: bob

21

22

Indexing of the composite index (id, a, b) requires the following 12 entries:

23

   “bob123”, 1, 521: bob

24

   “bob123”, 1,4982: bob

25

   “bob123”, 1, 284: bob

26

   “bob123”, 2, 521: bob

27

   “bob123”, 2,4982: bob

28

   “bob123”, 2, 284: bob

29

   “bob123”, 3, 521: bob

30

   “bob123”, 3,4982: bob

31

   “bob123”, 3, 284: bob

32

   “bob123”, 4, 521: bob

33

   “bob123”, 4,4982: bob

34

   “bob123”, 4, 284: bob

The size of the index grows exponentially as the number of array keys in the index and the number of array elements in the index. Hence the limitation. Implications of this limitation are:

  • Push only one array predicate to the index scan and handle other predicates after the index scan.
  • This means queries with multiple array predicates could be slow
  • Avoid composite indexes with array keys to avoid huge indexes.
  • This means queries with complex predicates on array keys will be slow

#nosql #performance #mongodb #json #couchbase #index #arrays #modeling and querying