Angular NormalizedCollection is now working when reference of 'bookings.reservationFor.apartmentId' is passed. It was working perfect when I created a sample firebase entry and defined apartmentId as a value of 'reservationFor' directly and passed 'bookings.reservationFor' in select() parameter.
I am new to angularFire. Please let me know what is wrong with this code.
Using Firebase v2.2.9; AngularJs v1.5.6; AngularFire v1.2.0;
firebase structure
FB
|
--apartment
| |
| --apartment1
| |
| --name: "test name"
| --address: "test address"
| --apartment2
| |
| --name: "test name"
| --address: "test address"
|
--booking
|
--"-JFZG3coHOAblHZ7XSjK"
| |
| --date: "booking date 1"
| --reservatonFor:
|
--apartmentId: "apartment1"
|
--"-KJKJASDIUOPIWE9WEeJ"
| |
| --date: "booking date 2"
| --reservatonFor:
|
--apartmentId: "apartment2"
|
--"-YtUTRGJLNL876F3SSwS"
| |
| --date: "booking date 3"
| --reservatonFor:
|
--apartmentId: "apartment1"
|
Controller
function mainCtrlFunc($scope, $firebaseArray) {
var baseRef = new Firebase(firebaseUrl);
var norm = new Firebase.util.NormalizedCollection(
[baseRef.child("booking"), "bookings"]
[baseRef.child("apartment"), "apartments", "bookings.reservationFor.apartmentId"]
).select(
"bookings.date",
"apartments.name"
)ref();
$scope.bookings = $firebaseArray(norm);
}
Error:
Firebase.child failed: First argument was an invalid path: "[object Object]". Paths must be non-empty strings and can't contain ".", "#", "$", "[", or "]"
Related
I am trying to parse the below data in Kusto. Need help.
[[ObjectCount][LinkCount][DurationInUs]]
[ChangeEnumeration][[88][9][346194]]
[ModifyTargetInLive][[3][6][595903]]
Need generic implementation without any hardcoding.
ideally - you'd be able to change the component that produces source data in that format to use a standard format (e.g. CSV, Json, etc.) instead.
The following could work, but you should consider it very inefficient
let T = datatable(s:string)
[
'[[ObjectCount][LinkCount][DurationInUs]]',
'[ChangeEnumeration][[88][9][346194]]',
'[ModifyTargetInLive][[3][6][595903]]',
];
let keys = toscalar(
T
| where s startswith "[["
| take 1
| project extract_all(#'\[([^\[\]]+)\]', s)
);
T
| where s !startswith "[["
| project values = extract_all(#'\[([^\[\]]+)\]', s)
| mv-apply with_itemindex = i keys on (
extend Category = tostring(values[0]), p = pack(tostring(keys[i]), values[i + 1])
| summarize b = make_bag(p) by Category
)
| project-away values
| evaluate bag_unpack(b)
--->
| Category | ObjectCount | LinkCount | DurationInUs |
|--------------------|-------------|-----------|--------------|
| ChangeEnumeration | 88 | 9 | 346194 |
| ModifyTargetInLive | 3 | 6 | 595903 |
I need the messages in Azure AppInsights grouped by the existence of particular substrings in the messages and the counts of these messages.
At the end, here is what the grouping would look like
messages count
-------- -------
foomessages <say, 300>
barmessages <say, 450>
:
:
where
foomessages = All messages containing the substring "foo" etc.
How can I construct a query for this ?
datatable(log: string) [
"hello world",
"this is a test",
"this is a world test",
"another test"
]
| summarize
LogsWithWorld = countif(log has "world"),
LogsWithTest = countif(log has "test")
| project Result = pack_all()
| mv-expand Result
| extend Message = tostring(bag_keys(Result)[0])
| extend Count = tolong(Result[Message])
| project Message, Count
The produced result is:
| Message | Count |
|---------------|-------|
| LogsWithWorld | 2 |
| LogsWithTest | 3 |
|---------------|-------|
Entity Model:
I've read AWS Guide about create a Modeling Relational Data in DynamoDB. It's so confusing in my access pattern.
Access Pattern
+-------------------------------------------+------------+------------+
| Access Pattern | Params | Conditions |
+-------------------------------------------+------------+------------+
| Get TEST SUITE detail and check that |TestSuiteID | |
| USER_ID belongs to project has test suite | &UserId | |
+-------------------------------------------+------------+------------+
| Get TEST CASE detail and check that | TestCaseID | |
| USER_ID belongs to project has test case | &UserId | |
+-------------------------------------------+------------+------------+
| Remove PROJECT ID, all TEST SUITE | ProjectID | |
| AND TEST CASE also removed | &UserId | |
+-------------------------------------------+------------+------------+
So, I model a relational entity data as guide.
+-------------------------+---------------------------------+
| Primary Key | Attributes |
+-------------------------+ +
| PK | SK | |
+------------+------------+---------------------------------+
| user_1 | USER | FullName | |
+ + +----------------+----------------+
| | | John Doe | |
+ +------------+----------------+----------------+
| | prj_01 | JoinedDate | |
+ + +----------------+----------------+
| | | 2019-04-22 | |
+ +------------+----------------+----------------+
| | prj_02 | JoinedDate | |
+ + +----------------+----------------+
| | | 2019-05-26 | |
+------------+------------+----------------+----------------+
| user_2 | USER | FullName | |
+ + +----------------+----------------+
| | | Harry Potter | |
+ +------------+----------------+----------------+
| | prj_01 | JoinedDate | |
+ + +----------------+----------------+
| | | 2019-04-25 | |
+------------+------------+----------------+----------------+
| prj_01 | PROJECT | Name | Description |
+ + +----------------+----------------+
| | | Facebook Test | Do some stuffs |
+ +------------+----------------+----------------+
| | t_suite_01 | | |
+ + +----------------+----------------+
| | | | |
+------------+------------+----------------+----------------+
| prj_02 | PROJECT | Name | Description |
+ + +----------------+----------------+
| | | Instagram Test | ... |
+------------+------------+----------------+----------------+
| t_suite_01 | TEST_SUITE | Name | |
+ + +----------------+----------------+
| | | Test Suite 1 | |
+ +------------+----------------+----------------+
| | t_case_1 | | |
+ + +----------------+----------------+
| | | | |
+------------+------------+----------------+----------------+
| t_case_1 | TEST_CASE | Name | |
+ + +----------------+----------------+
| | | Test Case 1 | |
+------------+------------+----------------+----------------+
If I just have UserID and TestCaseId as a parameter, how could I get TestCase Detail and verify that UserId has permission.
I've thought about storing complex hierarchical data within a single item. Something likes this
+------------+-------------------------+
| t_suite_01 | user_1#prj_1 |
+------------+-------------------------+
| t_suite_02 | user_1#prj_2 |
+------------+-------------------------+
| t_case_01 | user_1#prj_1#t_suite_01 |
+------------+-------------------------+
| t_case_02 | user_2#prj_1#t_suite_01 |
+------------+-------------------------+
Question: What is the best way for this case? I appreciate if you could give me some suggestion for this approach (bow)
I think the schema below does what you want. Create a Partition Key only GSI on the "GSIPK" attribute and query as follows:
Get Test Suite Detail and Validate User: Query GSI - PK == ProjectId, FilterCondition [SK == TestSuiteId || PK == UserId]
Get Test Case Detail and Validate User: Query GSI - PK == TestCaseId, FilterCondition [SK = TestSuiteId:TestCaseId || PK = UserId]
Remove Project: Query GSI - PK == ProjectId, remove all items returned.
Queries 1 and 2 come back with 1 or 2 items. One is the detail item and the other is the user permissions for the test suite or test case. If only one item returns then its the detail item and the user has no access.
The first question you should ask is: why do I want to use key-value document DB over relational DB when I clearly have strong relations in my data?
The answer might be: I need a single-digit millisecond queries at any scale (millions of records). Or, I want to save money using dynamodb on-demand. If this is not the case, you might be better with a relational DB.
Let’s say you have to go for dynamodb. If so, most of patterns applicable for relational DBs are anti-patterns when it comes to NoSQL. There is a useful talk from last re-invent about design patterns for dynamodb and advice to watch it https://youtu.be/HaEPXoXVf2k.
For your data I’d think about taking similar approach, and having two tables: users and projects.
Projects should store sub-set of test suits as map of array of objects and test cases as map of array of objects. Plus you could add list of user ids in the map of strings. Of course you will need to maintain this list when users join or leave the project/s.
This should satisfy your access patterns.
I have a dataset in which I paste values in a dplyr chain and collapse with the pipe character (e.g. " | "). If any of the values in the dataset are blank, I just get recurring pipe characters in the pasted list.
Some of the values look like this, for example:
badstring = "| | | | | | GHOULSBY,SCROGGINS | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | CAT,JOHNSON | | | | | | | | | | | | BURGLAR,PALA | | | | | | | | |"
I want to match all the pipes that occur more than once and delete them, so that just the names appear like so:
correctstring = "| GHOULSBY,SCROGGINS | CAT,JOHNSON | |BURGLAR,PALA |"
I tried the following, but to no avail:
mutate(names = gsub('[\\|]{2,}', '', name_list))
The difficulty in this question is in formulating a regex which can selectively remove every pipe, except the ones we want to remain as actual separators between terms. We can match on the following pattern:
\|\s+(?=\|)
and then replace just empty string. This pattern will remove any pipe (and any following whitespace) so long as what follows is another pipe. A removal would not occur when a pipe is followed by an actual term, or when it is followed by the end of the string.
badstring = "| | | | | | GHOULSBY,SCROGGINS | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | CAT,JOHNSON | | | | | | | | | | | | BURGLAR,PALA | | | | | | | | |"
result <- gsub("\\|\\s+(?=\\|)", "", badstring, perl=TRUE)
result
[1] "| GHOULSBY,SCROGGINS | CAT,JOHNSON | BURGLAR,PALA |"
Demo
Edit:
If you expect to have inputs like | | | which are devoid of any terms, and you would expect empty string as the output, then my solution would fail. I don't see an obvious way to modify the above regex, but you can handle this case with one more call to sub:
result <- sub("^\\|$", "", result)
We also might be able to modify the original pattern to use an alternation covering all cases:
result <- gsub("\\|\\s+(?=\\|)|(?:^\\|$)", "", badstring, perl=TRUE)
I need to do a rule with no constraints in a decision table.
i.e.:
rule ...
when
$p : Person()
then
$p.setCity("none");
end
I tried these:
| 1 | RuleTable example |
| 2 | CONDITION | ACTION |
| 3 | p:Person() | |
| 4 | name | p.setCity("$param"); |
| 5 | description | config person |
| 6 | | none |
But when I run application throws this exception:
person cannot be resolved
Exception in thread "main" java.lang.IllegalArgumentException: No se puede parsear base de conocimiento.
Probably it fails because you have no real condition in your table.
Try putting $param == $param as condition
Use condition like as shown in picture. It will generate DRL as:
rule "XYZ"
when
doc:Document()
then
doc.setX("Y");
end