I am new to Quick Sight. My understanding of Quick sight Query and retrieval is as below
Each graph in a sheet is a query by itself being fired to the database
The filters that are attached to a graph acts like a Where condition for data retrieval and not after data retrieval
Do I stand right here ? Please let me know
Musthafa
Related
I have two chart tables both with different data sources. I want one table to act as the filter to the other table.
Here is the problem...
I tried a custom query for my data source which used the email parameter to filter the data source.
The problem is every time a user changes a filter on any page a query is executed in BigQuery, slowing the results and exponentially increasing my BigQuery monthly charges.
I tried blending the two tables.
The problem is the blended data feature only allows for 10 dimensions to be added to the resulting blended data source and is very slow.
I tried creating a control filter using a custom field on the "location" column on each table sharing the same "Field Id".
The problem is that the results table returns all the stores until you click on a location in the control list. And I cannot let a user see other locations.
Here is a link to a data studio sample report you can clearly see what I am trying to do.
https://datastudio.google.com/reporting/dd33be45-ab13-4881-8a3b-cabafa8c0dbb
Thanks
One solution which i can recommend to over come your first challenge, i.e. High cost. You can customize cost by using GCP-Memorystore, depending on frequency of data that is getting updated.
Moreover, Bigquery also cashes data for a query if you are not using Wild cards on tables and Time partitioned tables. So try to customize your solution over analysis cost if it is feasible over your solution. Bigquery Partition and Clusting may also help you in reducing BQ analysis cost.
How to check whether my query is transformed or not by the Query Transformer?
I've searched on the Internet but couldn't find a solution.
Justin Cave: "it would be unusual to care whether any transforms were done"
I disagree. There are excellent reasons for MTO or anyone else to want to know if query transformation has occurred. Here are four:
Learning. See in action specific characteristics that cause the optimizer to transform or to not transform a query.
Get an idea if a query that doesn't transform is missing a needed bitmap index.
Check the timing and resource usage of a transformed query Vs. the original.
Having that great quality we share as Oracle professionals: curiosity!
I have a situation to write search query in elasticsearch having data as follows
{id:"p1",person:{name:"name",age:"12"},relatedTO:{id:"p2"}}
{id:"p2",person:{name:"name2",age:"15"},relatedTO:{id:"p3"}}
{id:"p3",person:{name:"name3",age:"17"},relatedTO:{id:"p1"}}
scenario:- user's want to search people related to p2,and using each related person find who they are related to
1.first find who are related to p2 answer= p1
2.Now find people related to p1 answer=p3. (the requirement as of now is to go only 1-level) so no need to find people related to p3.the final result should be p2,p1,p3.
Normal scenario's we will write a nested sql to get results.How do we achieve this using elastic query language in one-shot
With one shot you will need to use Parent-Child-Relationships, but I wouldn't recommand it to you in the first place, because it is not very performant. Btw: also Grandparents and Grandchildren are supported.
You could also use Application Side Joins - meaning you execute several queries, until you get what you want. (Be aware that the first result sets should be very tiny, otherwise this could get costly)
What I would really recommand to you is read this docu and rethink your use case.
In case you want to model relationships like in facebook or google+ I would tell you to look for a NoSQL Graph Database.
Note: Ideally in Elasticsearch the data is flat, which means denormalized.
I am working with node.js and mongodb.
I am going to have a database setup and use socket.io to have real-time updates that will have the db queried again as well or push the new update to the client.
I am trying to figure out what is the best way to filter the database?
Some more information in regards to what is being queried and what the real time updates are:
A document in the database will include information such as an address, city, time, number of packages, name, price.
Filters include city/price/name/time (meaning only to see addresses within the same city, or within the same time period)
Real-time info: includes adding a new document to the database which will essentially update the admin on the website with a notification of a new address added.
Method 1: Query the db with the filters being searched?
Method 2: Query the db for all searches and then filter it on the client side (Javascript)?
Method 3: Query the db for all searches then store it in localStorage then query localStorage for what the filters are?
Trying to figure out what is the fastest way for the user to filter it?
Also, if it is different than what is the most cost effective way, then the most cost effective as well (which I am assuming is less db queries)...
It's hard to say because we don't see exact conditions of the filter, but in general:
Mongo can use only 1 index in a query condition. Thus whatever fields are covered by this index can be used in an efficient filtering. Otherwise it might do full table scan which is slow. If you are using an index then you are probably doing the most efficient query. (Mongo can still use another index for sorting though).
Sometimes you will be forced to do processing on client side because Mongo can't do what you want or it takes too many queries.
The least efficient option is to store results somewhere just because IO is slow. This would only benefit you if you use them as cache and do not recalculate.
Also consider overhead and latency of networking. If you have to send lots of data back to the client it will be slower. In general Mongo will do better job filtering stuff than you would do on the client.
According to you if you can filter by addresses within time period then you could have an index that cuts down lots of documents. You most likely need a compound index - multiple fields.
,This question is likely subjective, but a lot of "grid" Javascript plugins have come out to help paginate and sort tables. They usually work in 2 ways, the first and simplest is that it takes an existing HTML <table> and converts it into a sortable and searchable information. The second is that it passes info to the server and has the server select info from the database to be displayed.
My question is this: At what point (size wise) is it more efficient to use server-side processing vs displaying all the data and have the "grid plugin" convert it to a sortable/searchable table client-side?
Using datatables as an example, I have to execute at least 3 queries to get total rows in the table, total filtered results for pagination, and the filtered results to be displayed for the specific selected page. Then every time I sort, I am querying again. Every time I move to another page, or search in the table, more queries.
If I was to pull the data once when the client visits the page, I would be executing a single query, and then formatting and pushing the results to the client all at once. This increases the page size, and possibly delays loading of the page once it gets too big. The upside is there will only one query, and all the sorting, searching, and pagination is handled by the plugin, so no waiting for a response and no more queries.
If I was to have just a few rows, I imagine just pushing the formatted table data to the client at the page load would be the fastest. But with thousands of rows, switching to server-side would be the most efficient way.
Where is the tipping point? Is there a tipping point, or is server-side or client-side the way to go 100% of the time?
The answer on your question can be only subjective. So I explain how I personally understand the problem and give me recommendation.
In my opinion the data with 2-3 row and 3-4 column can be displayed in HTML table without usage any plugin. The data you display for the user the more important will be that the user will be able to grasp the information which will be displayed. So I think that the information for example have to be good formatted and marked with colors and icons for example. This with help to grasp information from probably 10 rows of data, but not much more. If you just display table with 100 rows or more then you overtax the user. The user will have to analyse the data to get any helpful information from the table. Scrolling of the data makes this not easier.
So I think that one should give the user comfortable or at least convenient interface to sort and to filter the data from the table. The exact interface is mostly the matter of taste. For example the grid can have an additional filter bar
For filtering and even for sorting of the data it's important to have not pure strings, but to be able to distinguish the data types like integer (10 should be after 9 and not between 1 and 2), numbers (correct interpret '.' and ',' inside of numbers), dates (3/20/2012 should be grater as 4/15/2010) and so on. If you just convert HTML table to some grid you will have problems with correct filtering or sorting. Even if you use pure local JavaScript data to display in grid it would be important to have datasource which has some kind of type information and then to create the grid based in the data. In the case you can gives date as JavaScript Date or as ISO 8601 string "2012-03-20" and in the grid display the data corresponds the specified formatter as 3/20/2012 or 20-Mar-2012.
Whether you implement filtering, sorting and paging on the server side or on the client side is not really important for the user who open the page. It's important only that all works quickly enough. The exact choose of the grid plugin, the filtering (with filter toolbar or external controls) and styling of the grid depend on your taste and the project requirements.