I've a data series that contains ID of the users. I can print the chart for 1,2,3 where the number are the IDs of the user. Now, i've in a file the name of the users and their IDs, thus 1=Alice, 2=Bob etc.
Is there a way to rename the series such that the chart show Alice or Bob instead of numbers?
Note that I user *, I've a data series of this type request. where as USER-ID i put * and graphite+grafana shows the 3 charts, one for each USER-ID.
This is what I get, and I would like to have for each series the name of the user instead of the ID (which is half covered for security)
Related
My organization has acquired the HERE Navstreets data set. It wishes to update the content while still adhering to the HERE Navstreets data model and relationships.
In this context, it is deemed of value to:
Retain the LINK_ID column as the unique identifier for each street segment.
Make a distinction between the original HERE LINK_ID values and the one added by my organization.
Retain the ability to ingest streets updates from HERE should my organization decide to do so.
In this context, we would like to use a different range of LINK_ID values from the one used by HERE. As an example, if HERE uses values between 10,000,000 and 100,000,000, we would assign only LINK_ID values that are within the range 1,000-9,999,999 (this is only for illustration purposes).
Is this approach already accounted for by HERE for the street data they may get from Map Creator? Is there a specific HERE (for Review or Work in Progress) range of LINK_ID values we should consider?
Based on the HERE KB0015682: Permanent ID concept in HERE Data
Entities with Permanent IDs
Generally, the following feature do have permanent IDs in the HERE Map products:
Lane
Face
Point Features
Administrative Areas (for , Built-up Areas, Districts, and Administrative Areas)
Complex Features (this includes Complex Administrative Area Features as well as Complex Intersections and Complex Roads)
Permanent IDs are globally unique within a specific Object, e.g., a Link ID occurs once globally. However the same Permanent ID can be used among different Object types (e.g., Node, Link, condition, etc.). Note: When a map is upgraded to Intermediate or HERE map, or when a country undergoes administrative restructuring, there may be a change in Permanent IDs.
The following are examples of permanent IDs in the RDF:
Address Point ID
Admin Place ID
Association ID
Building ID
Carto ID
Complex Feature ID
Condition ID
Country ID (which is one of the Admin Place IDs)
Face ID
Feature Point ID
Lane ID
Lane Nav Strand ID
Link ID
Name ID (with some exceptions)
Nav Strand ID
Node ID
POI ID
Road Link ID
Sign ID
Zone ID
Numeric Range of Permanent IDs
Map object IDs (PVIDs) in the extracts use 32-bit integer values to fit in a N(10) scheme. Note: Exception to N(10) scheme can exist. For example, Lane ID is N(12) in length.
The entire range is divided as follows:
Range----------------------------Designation
0000000001 to 0016777215 -> Non-permanent IDs
0016777215 to 2147483647 -> Permanent IDs
The range dedicated to permanent IDs are used for any entity.
The range dedicated to non permanent IDs are used in rare situations where an update is made in a copy of the database instead of in the live database itself and this update results in a new ID. This new ID in the database copy would be in the non-permanent range. The update would also be applied into the live database and this update would receive a permanent ID available in the next scheduled release. A cross-reference is not provided between non-permanent IDs and the eventual permanent ID from the live database.
I'm new to Kibana, i have two log patterns in same index pattern
Example
log line 1 : rejected with ID 123456
log line 2 : rebooking is successful for ID 123456 for US country
I need to create bar chart for country base on the matched ID , something like this "select country from log where message contains "rebooking" AND ID is in (select ID from log where message contains "rejected") "
Please help !
The logs that you're inserting into the ES cluster should be normalized further.
Index should have fields created on which you want your conditions/actions to be based on.
Right now it is as good as searching a text document. There is no way to make sure that the verbiage of the logs will not change causing visualization to be inaccurate.
What is the procedure to do if I have 2 tables: From table-1 I get all the users I want, and then, after I have the list of user ids, I want to compare each user with current user with a field found on table-2. The first task is easy, I just have an onDataChange that populates the users list with their ids. But now that I have this list, how to iterate each user, and compare it with the current user based on a specific field from table-2.
What I currently try is to use a for loop to iterate each user on the list with each having onDataChange call to table-2, and then I populate the necessary dataset. But when this for loop ends, this dataset is no longer visible.
I hope what I try to achieve in this post is understandable.
I'll try to demonsrate with tables:
Assuming I get user list from table-1 based on data1:
table-1
|
|_____data1
|____uid20
|____uid30
|____uid44
Now I have list of 3 users: uid20, uid30, uid44. Then, I need to compare the list of users, with current user, call it user1, from table-1, based on another field (timestamp). What I mean is, after I have list of users, I want to filter these to have a timestamp that's close to the current user, for up to certain amount of time. So in my example, I want to have only users that are within 2 minutes of the current user timestamp.
table-2
|
|______uid1
| |____timestamp: <some_timestamp>
|
|______uid20
| |____timestamp: <some_timestamp>
|
|______uid30
| |____timestamp: <some_timestamp>
|
|______uid44
|____timestamp: <some_timestamp>
But every time there is something that's out of scope of the new listener, and also it looks like it's not the right procedure. Maybe I first need to save what's found on table-1 locally ? Or, it can be done somehow purely with Firebase calls?
**This is some code:
Getting the current user, is easy:
final FirebaseUser user = FirebaseAuth.getInstance().getCurrentUser();
final String userId = user.getUid();
So I always have it visible at any scope
First, as I understand, you can use the startAt and endAt to get a range of the values within two minutes of difference. What I mean is that before getting each value from your table-2 you can just get the values that matches your use case, in this case, values that are 2 minutes of the current timestamp.
For example, in your table 2, I would query like this:
ref.orderByChild('timestamp').startAt(yourCurrentTimeStamp).endAt(yourCurrentTimeStamp+120000);
where 120000 is 2 seconds in miliseconds
and then when looping through this elements I would use getKey to get each key of the values filtered by this query, so I would get only the users with 2 minutes of difference, and then compare them with the first for loop you did in order to see if they match.
to compare 2 users ID you can use equals, since it's a String:
if(snapshotUserTable1.getKey().equals(snapshotUserTable2.getKey())){
/...
}
I am trying out some timelion visualizations in Kibana. In it I am not sure how to provide filter tab for the data.
For example, yearly country population data and filter tab to filter it based on zone and state.
Usually a timelion expression is just like ".es()" and some functions which can be appended to it like
.es(*).bars().label("Population")
I don't want to specify filter under ".es(q=???)" as this will make the filter static and the user won't be able to use filtering directly. ( In this case, every time user has to go to edit timelion window and edit filter there.)
you can specify a lucene query string as the q argument to .es() as in
.es(q=field:value)
That query string would only affect this time series and not interfere with the rest of your dashboard.
Let's say I have three basic models: a User, a Company, and a Visit. Every time a User goes to a Company, a Visit is recorded in this format (user_id, company_id, visit_date).
I'd like to be able to calculate the average time between visits for a company. Not visits overall, but specifically how long on average one of their customers waits before returning to the store.
For example, if one user visited on Tuesday, Wednesday, and Friday that gives one "gap" of one day, and one "gap" of two days => (1, 2). If another user visited on Monday and Friday, that gives one gap of 4 days => (4). If a third user visited only once, he should not be considered. The average time between user visits for the company is (1 + 2 + 4) / 3 = 2.333 days.
If I have thousands of users, taps, and companies and I want to calculate a single figure for each company, how should I go about this? I've only done basic MapReduce applications before and I can't figure out what my Map and Reduce steps would be to get this done. Can anyone help me figure out a MapReduce in pseudocode? Or is there some other method of distributed calculation I can reasonably perform? For the record, I'd like to perform this operation on my database every night.
The overly simplistic approach would be to have two job steps.
First job step has a mapper to write key values in the form "company:user" and "visit_date". In the example above, the mapper would write something like:
"user1:companyA" -> "2012/07/16"
"user1:comapnyA" -> "2012/07/17"
"user1:comapnyA" -> "2012/07/19"
"user2:comapnyA" -> "2012/07/15"
"user2:comapnyA" -> "2012/07/19"
...
This means that each call to the reducer will pass all of the visits by a single user to a single company. That means that one call to the reducer will pass in:
"user1:companyA" -> {2012/07/16, 2012/07/17, 2012/07/19}
and another call will pass in:
"user2:companyA" -> {2012/07/15, 2012/07/19}
I'm assuming the set of dates (passed in as an Iterable value) is easily managed as you sort it, figure out the gaps and write a record for each gap as a key value pair in the form "company" and "gap". For example, when passed:
"user1:companyA" -> {2012/07/16, 2012/07/17, 2012/07/19}
The first job's reducer will write to the context:
"companyA" -> 1
"compnayA" -> 2
The second job has a pass-through mapper that just passes the company/gap info on to the reducer. Each call to the reducer gives an Iterable value of gaps for a specific company. Iterate through the data to produce an average and write the key value pair in the form "company" and "average_gap".
If the original set of visits gets too big, we can talk about getting hadoop to do the sorting for you with some custom comparators.