In NodeJs with MS-SQL, I want to retrieve two three table data in the form of array of objects
Hello there my name is Shaziya, please help me out (●´⌓`●)
Actually i have done NodeJs from YouTube,
I want learn NodeJS with MS-SQL, do you or any friends have such course for Advance understanding,
Like how to connect 4 5 tables and show data in array of objects format
How to make nested query or subquires like...
I mean if wanna do table match with two table product and order
Product ID with Order table by matching product ID
data {
productId : 1:
productName : "abc",
[{
orderId : 1
orderName : "xyz"
},
{
orderId : 2
orderName : "pqr"
}
]
}
At least i got some course or solution for that where i stuck
Related
I have two kind of record mention below in my table staudentdetail of cosmosDb.In below example previousSchooldetail is nullable filed and it can be present for student or not.
sample record below :-
{
"empid": "1234",
"empname": "ram",
"schoolname": "high school ,bankur",
"class": "10",
"previousSchooldetail": {
"prevSchoolName": "1763440",
"YearLeft": "2001"
} --(Nullable)
}
{
"empid": "12345",
"empname": "shyam",
"schoolname": "high school",
"class": "10"
}
I am trying to access the above record from azure databricks using pyspark or scala code .But when we are building the dataframe reading it from cosmos db it does not bring previousSchooldetail detail in the data frame.But when we change the query including id for which the previousSchooldetail show in the data frame .
Case 1:-
val Query = "SELECT * FROM c "
Result when query fired directly
empid
empname
schoolname
class
Case2:-
val Query = "SELECT * FROM c where c.empid=1234"
Result when query fired with where clause.
empid
empname
school name
class
previousSchooldetail
prevSchoolName
YearLeft
Could you please tell me why i am not able to get previousSchooldetail in case 1 and how should i proceed.
As #Jayendran, mentioned in the comments, the first query will give you the previouschooldetail document wherever they are available. Else, the column would not be present.
You can have this column present for all the scenarios by using the IS_DEFINED function. Try tweaking your query as below:
SELECT c.empid,
c.empname,
IS_DEFINED(c.previousSchooldetail) ? c.previousSchooldetail : null
as previousSchooldetail,
c.schoolname,
c.class
FROM c
If you are looking to get the result as a flat structure, it can be tricky and would need to use two separate queries such as:
Query 1
SELECT c.empid,
c.empname,
c.schoolname,
c.class,
p.prevSchoolName,
p.YearLeft
FROM c JOIN c.previousSchooldetail p
Query 2
SELECT c.empid,
c.empname,
c.schoolname,
c.class,
null as prevSchoolName,
null as YearLeft
FROM c
WHERE not IS_DEFINED (c.previousSchooldetail) or
c.previousSchooldetail = null
Unfortunately, Cosmos DB does not support LEFT JOIN or UNION. Hence, I'm not sure if you can achieve this in a single query.
Alternatively, you can create a stored procedure to return the desired result.
I want to return the count and data by writing it in a single Cosmos sql query.
Something like
Select *, count() from c
Or if possible i want get the count in a json document.
[
{
"Count" : 1111
},
{
"Name": "Jon",
"Age" : 30
}
]
You're going to have to issue two separate queries - one to get the total number of documents matching your query, and a second to get a page of documents.
How can I save array of objects in cassandra?
I'm using a nodeJS application and using cassandra-driver to connect to Cassandra DB. I wanted to save records like below in my db:
{
"id" : "5f1811029c82a61da4a44c05",
"logs" : [
{
"conversationId" : "e9b55229-f20c-4453-9c18-a1f4442eb667",
"source" : "source1",
"destination" : "destination1",
"url" : "https://asdasdas.com",
"data" : "data1"
},
{
"conversationId" : "e9b55229-f20c-4453-9c18-a1f4442eb667",
"source" : "source2",
"destination" : "destination2",
"url" : "https://afdvfbwadvsffd.com",
"data" : "data2"
}
],
"conversationId" : "e9b55229-f20c-4453-9c18-a1f4442eb667"
}
In the above record, I can use type "text" to save values of the columns "id" and "conversationId". But not sure how can I define the schema and save data for the field "logs".
With Cassandra, you'll want to store the data in the same way that you want to query it. As you mentioned querying by conversatonid, that's going to influence how the PRIMARY KEY definition should look. Given this, conversationid, should make a good partition key. As for the clustering columns, I had to make some guesses as to cardinality. So, sourceid looked like it could be used to uniquely identify a log entry within a conversation, so I went with that next.
I thought about using id as the final clustering column, but it looks like all entries with the same conversationid would also have the same id. It might be a good idea to give each entry its own unique identifier, to help ensure uniqueness:
{
"uniqueid": "e53723ca-2ab5-441f-b360-c60eacc2c854",
"conversationId" : "e9b55229-f20c-4453-9c18-a1f4442eb667",
"source" : "source1",
"destination" : "destination1",
"url" : "https://asdasdas.com",
"data" : "data1"
},
This makes the final table definition look like this:
CREATE TABLE conversationlogs (
id TEXT,
conversationid TEXT,
uniqueid UUID,
source TEXT,
destination TEXT,
url TEXT,
data TEXT,
PRIMARY KEY (conversationid,sourceid,uniqueid));
You have a few options depending on how you want to query this data.
The first is to stringify the json in logs field and save that to the database and then convert it back to JSON after querying the data.
The second option is similar to the first, but instead of stringifying the array, you store the data as a list in the database.
The third option is to define a new table for the logs with a primary key of the conversation and clustering keys for each element of the logs. This will allow you to lookup either by the full key or query by just the primary key and retrieve all the rows that match those criteria.
CREATE TABLE conversationlogs (
conversationid uuid,
logid timeuuid,
...
PRIMARY KEY ((conversationid), logid));
I'm a bit of a noob with MongoDB, so would appreciate some help with figuring out the best solution/format/structure in storing some data.
Basically, the data that will be stored will be updated every second with a name, value and timestamp for a certain meter reading.
For example, one possibility is water level and temperature in a tank. The tank will have a name and then the level and temperature will be read and stored every second. Overall, there will be 100's of items (i.e. tanks), each with millions of timestamped values.
From what I've learnt so far (and please correct me if I'm wrong), there are a few options as how to structure the data:
A slightly RDMS approach:
This would consist of two collections, Items and Values
Items : {
_id : "id",
name : "name"
}
Values : {
_id : "id",
item_id : "item_id",
name : "name", // temp or level etc
value : "value",
timestamp : "timestamp"
}
The more document db denormalized method:
This method involves one collection of items each with an array of timestamped values
Items : {
_id : "id",
name : "name"
values : [{
name : "name", // temp or level etc
value : "value",
timestamp : "timestamp"
}]
}
A collection for each item
Save all the values in a collection named after that item.
ItemName : {
_id : "id",
name : "name", // temp or level etc
value : "value",
timestamp : "timestamp"
}
The majority of read queries will be to retrieve the timestamped values for a specified time period of an item (i.e. tank) and display in a graph. And for this, the first option makes more sense to me as I don't want to retrieve the millions of values when querying for a specific item.
Is it even possible to query for values between specific timestamps for option 2?
I will also need to query for a list of items, so maybe a combination of the first and third option with a collection for all the items and then a number of collections to store the values for each of those items?
Any feedback on this is greatly appreciated.
Don't use timestamp if you are not modifying the ObjectId.
As ObjectId itself has time stamp in it.
So you will be saving a lot of memory by it.
MongoDB Id Documentation
In case if you dont require the previous data then you can use update query in MongoDB to update the fields every second instead of storing.
If you want to store the updated data each time then instead of updating store it in flat structure.
{ "_id" : ObjectId("XXXXXX"),
"name" : "ItemName",
"value" : "ValueOfItem"
"created_at" : "timestamp"
}
Edit 1: Added timestamp as per the comments
Forgive me for asking something that is probably explained elsewhere, but I didnt found a simple and plain conversion/explanation of SQL model to Cassandra Model.
Lets say I've a use-case of designing a DB structure for employee details and records in a organization. In SQL(having years of experience), I could have modelled it using normalization techniques, but coming into the world of NoSQL, it would take me sometime to have hold over designing DB for NoSQL, hence I'm here (for better understanding).
Can someone transform this SQL model into a NoSQL(Cassandra) model, thereby giving a lot of newbies(like me) a simple and plain transformation of SQL to NoSQL migration.
Since SO works on a concept Try First and then ask, so Ive thought of a structure as well. Let me know if that works well.
Since data can be denormalized in Cassandra, I thought of this structure.
Emploee(ColumnFamily) = {
"01234"(EmployeeId) : {
"EmpName" : "Jack",
"mail" : "Jack#xyz.com",
"phone" : ["9999900000","8888888888"],
"DOB": 4/1/91,
"Contact":{ "Street" : XYZ2 , "City":ABC, "Pincode":PQR},
"UnitID":{ "UnitName" : XYZ , "UnitHead":ABC},
"RoleID":{ "Designation" : Manage , "Band":Something},
},
"01235"(EmploeeId) : {
"EmpName" : "Jackyyy",
"mail" : "Jackyyy#xyz.com",
"phone" : ["99565600000","88888846468"],
"DOB": 4/1/91,
"Contact":{ "Strreet" : XYZ2 , "City":ABC1, "Pincode":PQR},
"UnitID":{ "UnitName" : XYZ1 , "UnitHead":ABC1},
"RoleID":{ "Designation" : Faculty , "Band":Something},
},
and so on...
}
Projects(ColumnFamily) = {
"1213"(ProjectId) : {
"EmpID" : [01234,01235],
"StartDate" : 4/1/2001,
"EndDateDate": 4/1/2012,
"ClientName": Apple
"Description": "Something",
},
and so on...
}
Attentance Detail(ColumnFamily) = {
"1213"(DetailId) : {
"EmpID" : 01234,
"SwipeInTime" : Something,
"SwipeOutTime": Something,
"Status": Apple
},
and so on...
}
Firstly please let me know, if this structure is correct. If yes, how would I design queries for the following?
1) Select employee whose phone number = something;
2) Select employees who lives in 'XYZ' location;
3) Select employees whose age is > 40 years ;
4) Select employee whose Designation is a 'Manager' of Unit Name 'XYZ' ;
5) Select employees who work for over 1o hours a day;
6) Get names(not IDs) of all employees wh were working for client 'Apple';
Let me know If I can provide more clarity on the question!!!
Your structure is not correct because you won't be able to express any of your 6 queries :-(
The main rule of Cassandra modeling is: start from your queries and denormalize. In your case, you would have 6 tables employee_by_phone, employee_by_location, employee_by_age and so on.
http://www.datastax.com/dev/blog/basic-rules-of-cassandra-data-modeling
However if you have a lot of multi-criteria queries like these, Cassandra (Datastax Enterprise edition) has SolR extension which will let you express richer queries. In this case your model may be right.