We Keep Coding

Syntax highlighting in Vscode for type hints

I am using the Dark++ theme but personalising a lot of colours.
Everything works fine but one small thing: only the basic types are properly highlighted.
For example this
"editor.tokenColorCustomizations": {
"comments": "#707070",
"keywords": "#adc5ee",
"types": "#bbbbbb",
"strings": "#bdceb7"
}
gives me the following picture:
I would like the type hints in the function declaration to be grey+italic, as it happens correctly for the type "str". I understand it is not straightforward for npt.NDArray as that comes from the typing module, but why is this not working even for "list" and "dict"?
And do you know of a workaround I could use?
There are not special tokens for these types as far as I know, so no way to access them other than just customising the general token "types".
I tried using regex expressions with the "Highlight" extension but that is not optimal, because I also want to keep the functionality that if I comment out part of that text, it should be greyed-out (using "Highlight" it doesn't).

To close this topic and for future reference, this is how I solved my issue: I ended up using semantic highlighting for type hints as rioV8 suggested.
This is done by adding the following to my Vscode settings JSON file:
"editor.semanticTokenColorCustomizations": {
"rules": {
"*.typeHint": {
"foreground": "#bbbbbb",
"fontStyle": "italic"
},
"class.typeHint.builtin": {
"foreground": "#bbbbbb",
"fontStyle": "italic"
}
}
}

DynamoDB Query by by nested array

I have a Companies table in DynamoDB that looks like this:
company: {
id: "11",
name: "test",
jobs: [
{
"name": "painter",
"id": 3
},
{
"name": "gardner"
"id": 2
}
]
}
And I want to make a scan query that get all the companies with the "painter" job inside their jobs array
I am using python and boto3
I tried something like this but it didn't work
jobs = ["painter"]
response = self.table.scan(
FilterExpression=Attr('jobs.name').is_in(jobs)
)
Please help.
Thanks.

It looks like this may not be doable in general, however it's possible that the method applied in that link may still be useful. If you know the maximum length of the jobs array over all of your data, you could create an expression for each index chained with ORs. Notably I could not find documentation for handling map and list scan expressions, so I can't really say whether you'd also need to check that you're not going out of bounds.

MongoDB Update with Array Filters [duplicate]

I am trying to update a value in the nested array but can't get it to work.
My object is like this
{
"_id": {
"$oid": "1"
},
"array1": [
{
"_id": "12",
"array2": [
{
"_id": "123",
"answeredBy": [], // need to push "success"
},
{
"_id": "124",
"answeredBy": [],
}
],
}
]
}
I need to push a value to "answeredBy" array.
In the below example, I tried pushing "success" string to the "answeredBy" array of the "123 _id" object but it does not work.
callback = function(err,value){
if(err){
res.send(err);
}else{
res.send(value);
}
};
conditions = {
"_id": 1,
"array1._id": 12,
"array2._id": 123
};
updates = {
$push: {
"array2.$.answeredBy": "success"
}
};
options = {
upsert: true
};
Model.update(conditions, updates, options, callback);
I found this link, but its answer only says I should use object like structure instead of array's. This cannot be applied in my situation. I really need my object to be nested in arrays
It would be great if you can help me out here. I've been spending hours to figure this out.
Thank you in advance!

General Scope and Explanation
There are a few things wrong with what you are doing here. Firstly your query conditions. You are referring to several _id values where you should not need to, and at least one of which is not on the top level.
In order to get into a "nested" value and also presuming that _id value is unique and would not appear in any other document, you query form should be like this:
Model.update(
{ "array1.array2._id": "123" },
{ "$push": { "array1.0.array2.$.answeredBy": "success" } },
function(err,numAffected) {
// something with the result in here
}
);
Now that would actually work, but really it is only a fluke that it does as there are very good reasons why it should not work for you.
The important reading is in the official documentation for the positional $ operator under the subject of "Nested Arrays". What this says is:
The positional $ operator cannot be used for queries which traverse more than one array, such as queries that traverse arrays nested within other arrays, because the replacement for the $ placeholder is a single value
Specifically what that means is the element that will be matched and returned in the positional placeholder is the value of the index from the first matching array. This means in your case the matching index on the "top" level array.
So if you look at the query notation as shown, we have "hardcoded" the first ( or 0 index ) position in the top level array, and it just so happens that the matching element within "array2" is also the zero index entry.
To demonstrate this you can change the matching _id value to "124" and the result will $push an new entry onto the element with _id "123" as they are both in the zero index entry of "array1" and that is the value returned to the placeholder.
So that is the general problem with nesting arrays. You could remove one of the levels and you would still be able to $push to the correct element in your "top" array, but there would still be multiple levels.
Try to avoid nesting arrays as you will run into update problems as is shown.
The general case is to "flatten" the things you "think" are "levels" and actually make theses "attributes" on the final detail items. For example, the "flattened" form of the structure in the question should be something like:
{
"answers": [
{ "by": "success", "type2": "123", "type1": "12" }
]
}
Or even when accepting the inner array is $push only, and never updated:
{
"array": [
{ "type1": "12", "type2": "123", "answeredBy": ["success"] },
{ "type1": "12", "type2": "124", "answeredBy": [] }
]
}
Which both lend themselves to atomic updates within the scope of the positional $ operator
MongoDB 3.6 and Above
From MongoDB 3.6 there are new features available to work with nested arrays. This uses the positional filtered $[<identifier>] syntax in order to match the specific elements and apply different conditions through arrayFilters in the update statement:
Model.update(
{
"_id": 1,
"array1": {
"$elemMatch": {
"_id": "12","array2._id": "123"
}
}
},
{
"$push": { "array1.$[outer].array2.$[inner].answeredBy": "success" }
},
{
"arrayFilters": [{ "outer._id": "12" },{ "inner._id": "123" }]
}
)
The "arrayFilters" as passed to the options for .update() or even
.updateOne(), .updateMany(), .findOneAndUpdate() or .bulkWrite() method specifies the conditions to match on the identifier given in the update statement. Any elements that match the condition given will be updated.
Because the structure is "nested", we actually use "multiple filters" as is specified with an "array" of filter definitions as shown. The marked "identifier" is used in matching against the positional filtered $[<identifier>] syntax actually used in the update block of the statement. In this case inner and outer are the identifiers used for each condition as specified with the nested chain.
This new expansion makes the update of nested array content possible, but it does not really help with the practicality of "querying" such data, so the same caveats apply as explained earlier.
You typically really "mean" to express as "attributes", even if your brain initially thinks "nesting", it's just usually a reaction to how you believe the "previous relational parts" come together. In reality you really need more denormalization.
Also see How to Update Multiple Array Elements in mongodb, since these new update operators actually match and update "multiple array elements" rather than just the first, which has been the previous action of positional updates.
NOTE Somewhat ironically, since this is specified in the "options" argument for .update() and like methods, the syntax is generally compatible with all recent release driver versions.
However this is not true of the mongo shell, since the way the method is implemented there ( "ironically for backward compatibility" ) the arrayFilters argument is not recognized and removed by an internal method that parses the options in order to deliver "backward compatibility" with prior MongoDB server versions and a "legacy" .update() API call syntax.
So if you want to use the command in the mongo shell or other "shell based" products ( notably Robo 3T ) you need a latest version from either the development branch or production release as of 3.6 or greater.
See also positional all $[] which also updates "multiple array elements" but without applying to specified conditions and applies to all elements in the array where that is the desired action.

I know this is a very old question, but I just struggled with this problem myself, and found, what I believe to be, a better answer.
A way to solve this problem is to use Sub-Documents. This is done by nesting schemas within your schemas
MainSchema = new mongoose.Schema({
array1: [Array1Schema]
})
Array1Schema = new mongoose.Schema({
array2: [Array2Schema]
})
Array2Schema = new mongoose.Schema({
answeredBy": [...]
})
This way the object will look like the one you show, but now each array are filled with sub-documents. This makes it possible to dot your way into the sub-document you want. Instead of using a .update you then use a .find or .findOne to get the document you want to update.
Main.findOne((
{
_id: 1
}
)
.exec(
function(err, result){
result.array1.id(12).array2.id(123).answeredBy.push('success')
result.save(function(err){
console.log(result)
});
}
)
Haven't used the .push() function this way myself, so the syntax might not be right, but I have used both .set() and .remove(), and both works perfectly fine.

Python: Mutability and dictionaries in config

I want to keep some large, static dictionaries in config to keep my main application code clean. Another reason for doing that is so the dicts can be occasionally edited without having to touch the application.
I thought a good solution was using a json config a la:
http://www.ilovetux.com/Using-JSON-Configs-In-Python/
JSON is a natural, readable format for this type of data. Example:
{
"search_dsl_full": {
"function_score": {
"boost_mode": "avg",
"functions": [
{
"filter": {
"range": {
"sort_priority_inverse": {
"gte": 200
}
}
},
"weight": 2.4
}
],
"query": {
"multi_match": {
"fields": [
"name^10",
"search_words^5",
"description",
"skuid",
"backend_skuid"
],
"operator": "and",
"type": "cross_fields"
}
},
"score_mode": "multiply"
}
}
The big problem is, when I import it into my python app and set a dict equal to it like this:
with open("config.json", "r") as fin:
config = json.load(fin)
...
def create_query()
query_dsl = config['search_dsl_full']
return query_dsl
and then later, only when a certain condition is met, I need to update that dict like this:
if (special condition is met):
query_dsl['function_score']['query']['multi_match']['operator'] = 'or'
Since query_dsl is a reference, it updates the config dictionary too. So when I call the function again, it reflects the updated-for-special-condition version ("or") rather than the the desired config default ("and").
I realize this is a newb issue (yes, I'm a python newb), but I can't seem to figure out a 'pythonic' solution. I'm trying to not be a hack.
Possible options:
When I set query_dsl equal to the config dict, use copy.deepcopy()
Figure out how to make all nested slices of the config dictionary immutable
Maybe find a better way to accomplish what I'm trying to do? I'm totally open to this whole approach being a preposterous newbie mistake.
Any help appreciated. Thanks!

How to get a list of all document types

I use elesticserach_dsl in Python to do searching, and I really like it. But the thing I do not know how to impement, is how to get a list of all different document types. The catch is type field plays for me almost the same role as table name in SQL, and what I want to do, is to somehow mimic SHOW TABLES command.

I don't know how to do this in Python, but from Elasticsearch point of view, this is how the request looks like:
GET /_all/_search?search_type=count
{
"aggs": {
"NAME": {
"terms": {
"field": "_type",
"size": 100
}
}
}
}