(This may be a webapp question.) I would like to use Trac 1.0.1 activity for time tracking. For example, closing a ticket, editing a wiki page or leaving a comment
I was imagining output something like this:
| Time | Ticket | Custom field | Summary | Activity |
| 2013-05-08 10:00 | 4123 | Acme | Ticket title | Ticket closed |
| 2013-05-08 10:00 | 4200 | Sierra | Title here | Comment left on ticket |
| 2013-05-08 10:00 | - | - | - | Edited /wiki/Acme/Coyote |
| 2013-05-08 10:00 | - | - | - | Committed /git/Apogee.txt|
I would like to include basically everything that appears in the timeline, including comment activity. For ticket-related activity, I would like to include ticket number and a custom field.
Which tables should I be looking at? (A pointer to relevant docs or code would suffice.)
I believe you are just asking for trac database schema which can be viewed here, you can also view the source for timeline here.
Related
I have a data table in druid and which has missing rows and I want to fill them by generating the missing timestamps and adding the precedent row value.
This is the table in druid :
| __time | distance |
|--------------------------|----------|
| 2022-05-05T08:41:00.000Z | 1337 |
| 2022-05-05T08:42:00.000Z | 1350 |
| 2022-05-05T08:44:00.000Z | 1360 |
| 2022-05-05T08:47:00.000Z | 1377 |
| 2022-05-05T08:48:00.000Z | 1400 |
And i want to add the missing minutes either by forcing it in the side of druid storage or by query it directly in druid without passing by other module.
The final result that I want will be look like this:
| __time | distance |
|--------------------------|----------|
| 2022-05-05T08:41:00.000Z | 1337 |
| 2022-05-05T08:42:00.000Z | 1350 |
| 2022-05-05T08:43:00.000Z | 1350 |
| 2022-05-05T08:44:00.000Z | 1360 |
| 2022-05-05T08:45:00.000Z | 1360 |
| 2022-05-05T08:46:00.000Z | 1360 |
| 2022-05-05T08:47:00.000Z | 1377 |
| 2022-05-05T08:48:00.000Z | 1400 |
And thank you in advance !
A Driud time series query will produce a densely populated timeline at a given time granularity like the one you want for every minute. But its current functionality either skips empty time buckets or assigns them a value of zero.
Doing other gap filling functions like LVCF (last value carried forward) that you describe seems like a great enhancement. You can join the Apache Druid community and create an issue that describes this request. That's a great way to start a conversation about requirements and how it might be achieved.
And/Or you could also add the functionality and submit a PR. We're always looking for more members in the Apache Druid community.
I’m interested to know if someone here has ever come across a situation where the source is not always unique when dealing with snapshots in DBT.
I have a data lake where data arrives on an append only basis. Every time the source is updated, a new recorded is created on the respective table in the data lake.
By the time the DBT solution is ran, my source could have more than 1 row with the unique id as the data has changed more than once since the last run.
Ideally, I’d like to update the respective dbt_valid_to columns from the snapshot table with the earliest updated_at record from the source and subsequently add the new records to the snapshot table making the latest updated_at record the current one.
I know how to achieve this using window functions but not sure how to handle such situation with dbt.
I wonder if anybody has faced this same issue before.
Snapshot Table
| **id** | **some_attribute** | **valid_from** | **valid_to** |
| 123 | ABCD | 2021-01-01 00:00:00 | 2021-06-30 00:00:00 |
| 123 | ZABC | 2021-06-30 00:00:00 | null |
Source Table
|**id**|**some_attribute**| **updated_at** |
| 123 | ABCD | 2021-01-01 00:00:00 |-> already been loaded to snapshot
| 123 | ZABC | 2021-06-30 00:00:00 |-> already been loaded to snapshot
-------------------------------------------
| 123 | ZZAB | 2021-11-21 00:10:00 |
| 123 | FXAB | 2021-11-21 15:11:00 |
Snapshot Desired Result
| **id** | **some_attribute** | **valid_from** | **valid_to** |
| 123 | ABCD | 2021-01-01 00:00:00 | 2021-06-30 00:00:00 |
| 123 | ZABC | 2021-06-30 00:00:00 | 2021-11-21 00:10:00 |
| 123 | ZZAB | 2021-11-21 00:10:00 | 2021-11-21 15:11:00 |
| 123 | FXAB | 2021-11-21 15:11:00 | null |
Standard snapshots operate under the assumption that the source table we are snapshotting are being changed without storing history. This is opposed to the behaviour we have here (basically the source table we are snapshotting is nothing more than an append only log of events) - which means that we may get away with simply using a boring old incremental model to achieve the same SCD2 outcome that snapshots give us.
I have some sample code here where I did just that that may be of some help https://gist.github.com/jeremyyeo/3a23f3fbcb72f10a17fc4d31b8a47854
I agree it would be very convenient if dbt snapshots had a strategy that could involve deduplication, but it isn’t supported today.
The easiest work around would be a stage view downstream of the source that has the window function you describe. Then you snapshot that view.
However, I do see potential for a new snapshot strategy that handles append only sources. Perhaps you’d like to peruse the dbt Snapshot docs and strategies source code on existing strategies to see if you’d like to make a new one!
I’ve been working on a Windows Form App using vb.net that retrieves information from a SQL database. One of the forms, frmContract, queries several tables, such as Addresses, and displays them in various controls, such as Labels and DataGridViews. Every year, the customer’s file is either renewed or expired, and I’m just now realizing that a change committed to any record today will affect the information displayed for the customer in the past. For example, if we update a customer’s mailing address today, this new address will show up in all previous customer profiles. What is the smartest way to avoid this problem without creating separate rows in each table with the same information? Or to put it another way, how can versions of a customer’s profile be preserved?
Another example would be a table that stores customer’s vehicles.
VehicleID | Year | Make | Model | VIN | Body
---------------------------------------------------------------
1 | 2005 | Ford | F150 | 11111111111111111 | Pickup
2 | 2001 | Niss | Sentra | 22222222222222222 | Sedan
3 | 2004 | Intl | 4700 | 33333333333333333 | Car Carrier
If today vehicle 1 is changed from a standard pickup to a flatbed, then if I load the customer contract from 2016 it will also show as flatbed even though back then it was a pickup truck.
I have a table for storing individual clients.
ClientID | First | Last | DOB
---------|----------|-----------|------------
1 | John | Doe | 01/01/1980
2 | Mickey | Mouse | 11/18/1928
3 | Eric | Forman | 03/05/1960
I have another table to store yearly contracts.
ContractID | ContractNo | EffectiveDate | ExpirationDate | ClientID (foreign key)
-----------|------------|---------------|-------------------|-----------
1 | 13579 | 06/15/2013 | 06/15/2014 | 1
2 | 13579 | 06/15/2014 | 06/15/2015 | 1
3 | 24680 | 10/05/2016 | 10/05/2017 | 3
Notice that the contract number can remain the same across different periods. In addition, because the same vehicle can be related to multiple contracts, I use a bridge table to relate individual vehicles to different contracts.
Id | VehicleID | ContractID <-- both foreign keys
---|-----------|------------
1 | 1 | 1
2 | 3 | 1
3 | 1 | 2
4 | 3 | 2
5 | 2 | 3
6 | 2 | 2
When frmContract is loaded, it queries the database and displays information about that particular contract year. However, if Vehicle 1 is changed from pickup to flatbed right now, then all the previous contract years will also show it as a flatbed.
I hope this illustrates my predicament. Any guidance will be appreaciated.
Some DB systems have built-in temporal features so you can keep audit history of rows. Check to see if your DB has built-in support for this.
In my Fitnesse Tests I want to enter dates through datepicker elements. Sometimes it works. But most of the time a different date, unlike the date that was entered, appears. Here is an example:
| ensure | do | type | on | id=field_id | with | |
| ensure | do | type | on | id=field_id | with | 05.05.1997 |
| check | is | verifyValue | on | id=field_id | [28.05.1997] expected [05.05.1997] |
(To make sure that the field isn't already filled, I pass an empty String first.)
Mostly, the 'day'-statement is different from what was entered. Do you know the reason for this behavior? How can I solve this?
Thanks in advance!
This is related to how you wrote your fixture and not FitNesse, the problem is that it returns a different value and also implies that the previous line didn't work - | ensure | do | type | on | id=field_id | with | 05.05.1997 |
I have reworked our API's logging system to use Azure Table Storage from using SQL storage for cost and performance reasons. I am now migrating our legacy logs to the new system. I am building a SQL query per table that will map the old fields to the new ones, with the intention of exporting to CSV then importing into Azure.
So far, so good. However, one artifact of the previous system is that it logged 3 times per request - call begin, call response and call end - and the new one logs the call as just one log (again, for cost and performance reasons).
Some fields common are common to all three related logs, e.g. the Session which uniquely identifies the call.
Some fields I only want the first log's value, e.g. Date which may be a few seconds different in the second and third log.
Some fields are shared for the three different purposes, e.g. Parameters gives the Input Model for Call Begin, Output Model for Call Response, and HTTP response (e.g. OK) for Call End.
Some fields are unused for two of the purposes, e.g. ExecutionTime is -1 for Call Begin and Call Response, and a value in ms for Call End.
How can I "roll up" the sets of 3 rows into one row per set? I have tried using DISTINCT and GROUP BY, but the fact that some of the information collides is making it very difficult. I apologize that my SQL isn't really good enough to really explain what I'm asking for - so perhaps an example will make it clearer:
Example of what I have:
SQL:
SELECT * FROM [dbo].[Log]
Results:
+---------+---------------------+-------+------------+---------------+---------------+-----------------+--+
| Session | Date | Level | Context | Message | ExecutionTime | Parameters | |
+---------+---------------------+-------+------------+---------------+---------------+-----------------+--+
| 84248B7 | 2014-07-20 19:16:15 | INFO | GET v1/abc | Call Begin | -1 | {"Input":"xx"} | |
| 84248B7 | 2014-07-20 19:16:15 | INFO | GET v1/abc | Call Response | -1 | {"Output":"yy"} | |
| 84248B7 | 2014-07-20 19:16:15 | INFO | GET v1/abc | Call End | 123 | OK | |
| F76BCBB | 2014-07-20 19:16:17 | ERROR | GET v1/def | Call Begin | -1 | {"Input":"ww"} | |
| F76BCBB | 2014-07-20 19:16:18 | ERROR | GET v1/def | Call Response | -1 | {"Output":"vv"} | |
| F76BCBB | 2014-07-20 19:16:18 | ERROR | GET v1/def | Call End | 456 | BadRequest | |
+---------+---------------------+-------+------------+---------------+---------------+-----------------+--+
Example of what I want:
SQL:
[Need to write this query]
Results:
+---------------------+-------+------------+----------+---------------+----------------+-----------------+--------------+
| Date | Level | Context | Message | ExecutionTime | InputModel | OutputModel | HttpResponse |
+---------------------+-------+------------+----------+---------------+----------------+-----------------+--------------+
| 2014-07-20 19:16:15 | INFO | GET v1/abc | Api Call | 123 | {"Input":"xx"} | {"Output":"yy"} | OK |
| 2014-07-20 19:16:17 | ERROR | GET v1/def | Api Call | 456 | {"Input":"ww"} | {"Output":"vv"} | BadRequest |
+---------------------+-------+------------+----------+---------------+----------------+-----------------+--------------+
select L1.Session, L1.Date, L1.Level, L1.Context, 'Api Call' AS Message,
L3.ExecutionTime,
L1.Parameters as InputModel,
L2.Parameters as OutputModel,
L3.Parameters as HttpResponse
from Log L1
inner join Log L2 ON L1.Session = L2.Session
inner join Log L3 ON L1.Session = L3.Session
where L1.Message = 'Call Begin'
and L2.Message = 'Call Response'
and L3.Message = 'Call End'
This would work in your sample.