i'm not sure if that's the right place to ask this kind of questions, but I feel like the way i'm doing things now is 'dumb way' and there's room for improvement in my code.
I'm trying to build stock data website as my side project, and im using rust for backend. One microservice i'm writing is responsible for scraping data from web and then saving it in database. The result of web scraping is 2d vector where each row is responsible for one attribute of struct i'll later construct. Then I save rows to variables.
Then i use izip! macro from itertools to make iterate over all those attributes and create struct.
izip!(
publication_dates,
quarter_dates,
income_revenue,
...
)
.for_each(
|(
publication_date,
quarter_date,
income_revenue,
...
)| {
Financials {
ticker: self.ticker.to_owned(),
publication_date,
quarter_date,
...
},
})
My issue is the fact, that one data table can have more than 40 attributes, to saving data from just one page can be over 250 lines of code so i'd have total of 2000 lines just to store webscraped data, most of it repetitive (parsing rows to correct data types). I'm pretty sure that's not correct approach since any changes i'd like to make would have to be done in many places.
One of my ideas to make it better was to create enum with desired types, then create vector of those enums like vec!([dataType::quarter_date, dataType::int32, dataType::int32 ...]) and iteratoe over both rows and new vector, and use match statement to use according function for data processing. That would get shorten rows allocation part a bit, but probably not by much.
Do you have any advice? Any hint would be great help, i just need a direction that i can later explore by myself :-)
If you want to only reduce the code duplication, I would recommend using a macro for that. A simple example is this (playground):
macro_rules! create_financials {
($rows:ident, $($fun:ident > $column:ident),+) => {{
$(
let $column = $rows
.next()
.ok_or("None")?
.into_iter()
.flat_map($fun);
)+
itertools::izip!($($column,)+).map(
|($($column,)+)| {
Financials {
$($column,)+
}
}
).collect::<Vec<_>>()
}}
}
Note that I removed the .collect::<Vec<_>>() part, it is not needed and allocates additional memory.
I also replaced the for_each with map to return a Vec from the macro which could be used outside of the macro.
The macro can be used simply like this:
let financials: Vec<Financials> = create_financials!(
rows,
quarter_string_date_to_naive_date > quarter_date,
publish_date_string_to_naive_date > publication_date,
income_revenue > income_revenue
);
To remove the code duplication of parsing to the different data types, look if the data types implement FromStr, From or TryFrom. Else you could define your own trait which does the conversion and which you can implement for each data type.
Related
I need to transform a large array of JSON (that can have over 100k positions) into a CSV.
This array is created directly in the application, it's not the result of an uploaded file.
Looking at the documentation, I've thought on using parser but it says that:
For that reason is rarely a good reason to use it until your data is very small or your application doesn't do anything else.
Because the data is not small and my app will do other things than creating the csv, I don't think it'll be the best approach but I may be misunderstanding the documentation.
Is it possible to use the others options (async parser or transform) with an already created data (and not a stream of data)?
FYI: It's a nest application but I'm using this node.js lib.
Update: I've tryied to insert with an array with over 300k positions, and it went smoothly.
Why do you need any external modules?
Converting JSON into a javascript array of javascript objects is a piece of cake with the native JSON.parse() function.
let jsontxt=await fs.readFile('mythings.json','uft8');
let mythings = JSON.parse(jsontxt);
if (!Array.isArray(mythings)) throw "Oooops, stranger things happen!"
And, then, converting a javascript array into a CSV is very straightforward.
The most obvious and absurd case is just mapping every element of the array into a string that is the JSON representation of the object element. You end up with a useless CSV with a single column containing every element of your original array. And then joining the resulting strings array into a single string, separated by newlines \n. It's good for nothing but, heck, it's a CSV!
let csvtxt = mythings.map(JSON.stringify).join("\n");
await fs.writeFile("mythings.csv",csvtxt,"utf8");
Now, you can feel that you are almost there. Replace the useless mapping function into your own
let csvtxt = mythings.map(mapElementToColumns).join("\n");
and choose a good mapping between the fields of the objects of your array, and the columns of your csv.
function mapElementToColumns(element) {
return `${JSON.stringify(element.id)},${JSON.stringify(element.name)},${JSON.stringify(element.value)}`;
}
or, in a more thorough way
function mapElementToColumns(fieldNames) {
return function (element) {
let fields = fieldnames.map(n => element[n] ? JSON.stringify(element[n]) : '""');
return fields.join(',');
}
}
that you may invoke in your map
mythings.map(mapElementToColumns(["id","name","element"])).join("\n");
Finally, you might decide to use an automated for "all fields in all objects" approach; which requires that all the objects in the original array maintain a similar fields schema.
You extract all the fields of the first object of the array, and use them as the header row of the csv and as the template for extracting the rest of the elements.
let fieldnames = Object.keys(mythings[0]);
and then use this field names array as parameter of your map function
let csvtxt= mythings.map(mapElementToColumns(fieldnames)).join("\n");
and, also, prepending them as the CSV header
csvtxt.unshift(fieldnames.join(','))
Putting all the pieces together...
function mapElementToColumns(fieldNames) {
return function (element) {
let fields = fieldnames.map(n => element[n] ? JSON.stringify(element[n]) : '""');
return fields.join(',');
}
}
let jsontxt=await fs.readFile('mythings.json','uft8');
let mythings = JSON.parse(jsontxt);
if (!Array.isArray(mythings)) throw "Oooops, stranger things happen!";
let fieldnames = Object.keys(mythings[0]);
let csvtxt= mythings.map(mapElementToColumns(fieldnames)).join("\n");
csvtxt.unshift(fieldnames.join(','));
await fs.writeFile("mythings.csv",csvtxt,"utf8");
And that's it. Pretty neat, uh?
In the Google Getting started with Node.js tutorial they perform the following operation
data = {...data};
in the code for sending data to Firestore.
You can see it on their Github, line 63.
As far as I can tell this doesn't do anything.
Is there a good reason for doing this?
Is it potentially future proofing, so that if you added your own data you'd be less likely to do something like data = {data, moreData}?
#Manu's answer details what the line of code is doing, but not why it's there.
I don't know exactly why the Google code example uses this approach, but I would guess at the following reason (and would do the same myself in this situation):
Because objects in JavaScript are passed by reference, it becomes necessary to rebuild the 'data' object from it's constituent parts to avoid the original data object being further modified by the ref.set(data) call on line 64 of the example code:
await ref.set(data);
For example, in MongoDB, when you pass an object into a write or update method, Mongo will actually modify the object to add extra properties such as the datetime it was insert into a collection or it's ID within the collection. I don't know for sure if Firestore does the same, but if it doesn't now, it's possible that it may in future. If it does, and if your original code that calls the update method from Google's example code goes on to further manipulate the data object that it originally passed, that object would now have extra properties on it that may cause unexpected problems. Therefore, it's prudent to rebuild the data object from the original object's properties to avoid contamination of the original object elsewhere in code.
I hope that makes sense - the more I think about it, the more I'm convinced that this must be the reason and it's actually a great learning point.
I include the full original function from Google's code here in case others come across this in future, since the code is subject to change (copied from https://github.com/GoogleCloudPlatform/nodejs-getting-started/blob/master/bookshelf/books/firestore.js at the time of writing this answer):
// Creates a new book or updates an existing book with new data.
async function update(id, data) {
let ref;
if (id === null) {
ref = db.collection(collection).doc();
} else {
ref = db.collection(collection).doc(id);
}
data.id = ref.id;
data = {...data};
await ref.set(data);
return data;
}
It's making a shallow copy of data; let's say you have a third-party function that mutates the input:
const foo = input => {
input['changed'] = true;
}
And you need to call it, but don't want to get your object modified, so instead of:
data = {life: 42}
foo(data)
// > data
// { life: 42, changed: true }
You may use the Spread Syntax:
data = {life: 42}
foo({...data})
// > data
// { life: 42 }
Not sure if this is the particular case with Firestone but the thing is: spreading an object you get a shallow copy of that obj.
===
Related: Object copy using Spread operator actually shallow or deep?
I have a list of valid values that I am storing in a data store. This list is about 20 items long now and will likely grow to around 100, maybe more.
I feel there are a variety of reasons it makes sense to store this in a data store rather than just storing in code. I want to be able to maintain the list and its metadata and make it accessible to other services, so it seems like a micro-service data store.
But in code, we want to make sure only values from the list are passed, and they can typically be hardcoded. So we would like to create an enum that can be used in code to ensure that valid values are passed.
I have created a simple node.js that can generate a JS file with the enum right from the data store. This could be regenerated anytime the file changes or maybe on a schedule. But sharing the enum file with any node.js applications that use it would not be trivial.
Has anyone done anything like this? Any reason why this would be a bad approach? Any feedback is welcome.
Piggy-backing off of this answer, which describes a way of creating an "enum" in JavaScript: you can grab the list of constants from your server (via an HTTP call) and then generate the enum in code, without the need for creating and loading a JavaScript source file.
Given that you have loaded your enumConstants from the back-end (here I hard-coded them):
const enumConstants = [
'FIRST',
'SECOND',
'THIRD'
];
const temp = {};
for (const constant of enumConstants) {
temp[constant] = constant;
}
const PlaceEnum = Object.freeze(temp);
console.log(PlaceEnum.FIRST);
// Or, in one line
const PlaceEnum2 = Object.freeze(enumConstants.reduce((o, c) => { o[c] = c; return o; }, {}));
console.log(PlaceEnum2.FIRST);
It is not ideal for code analysis or when using a smart editor, because the object is not explicitly defined and the editor will complain, but it will work.
Another approach is just to use an array and look for its members.
const members = ['first', 'second', 'third'...]
// then test for the members
members.indexOf('first') // 0
members.indexOf('third') // 2
members.indexOf('zero') // -1
members.indexOf('your_variable_to_test') // does it exist in the "enum"?
Any value that is >=0 will be a member of the list. -1 will not be a member. This doesn't "lock" the object like freeze (above) but I find it suffices for most of my similar scenarios.
I'm using the latest protobuf.js with Node.js 4.4.5.
I currently struggle to get protobuf.js to output the string definitions of enums instead of integers. I tried several suggestions, but none of them worked:
https://github.com/dcodeIO/ProtoBuf.js/issues/97
https://github.com/dcodeIO/protobuf.js/issues/349
I guess it's because of API changes in protobuf.js for the first one. For the second one, I can use the suggested solution partially, but if the message is nested within other messages, the builder seems to fall back to using the integer values, although the string values have been explicitly set.
Ideally, I'd like to overwrite the function which is used for producing the enum values, but I have a hard time finding the correct one with the debugger. Or is there a better way to achieve this for deeply nested objects?
The generated JS code from protoc has a map in one direction only e.g.
proto.foo.Bar.Myenum = {
HEY: 0,
HO: 1
};
Rationale for this is here but you have to the reverse lookup in your own JS code. There are lots of easy solutions for this. I used the one at https://stackoverflow.com/a/59360329/449347 i.e.
Generic reverse mapper function ...
export function getKey(map, val) {
return Object.keys(map).find(key => map[key] === val);
}
UT ...
import { Bar } from "js/proto/bar_pb";
expect(getKey(proto.foo.Bar.Myenum, 0)).toEqual("HEY");
expect(getKey(proto.foo.Bar.Myenum, 1)).toEqual("HO");
expect(getKey(proto.foo.Bar.Myenum, 99)).toBeUndefined();
I used a code, generated from slick code generator.
My table has more than 22 columns, hence it uses HList
It generates 1 type and 1 function:
type AccountRow
def AccountRow(uuid: java.util.UUID, providerid: String, email: Option[String], ...):AccountRow
How do I write compiled insert code from generated code?
I tried this:
val insertAccountQueryCompiled = {
def q(uuid:Rep[UUID], providerId:Rep[String], email:Rep[Option[String]], ...) = Account += AccountRow(uuid, providerId, email, ...)
Compiled(q _)
}
I need to convert Rep[T] to T for AccountRow function to work. How do I do that?
Thank you
;TLDR; Not possible
Explanation
There are two levels of abstraction in Slick: Querys and DBIOActions.
When you're dealing with Querys, you have to access your schema definitions, and rows, Reps and, basically, it's very constrained as it's the closest level of abstraction to the actual DB you're using. A Rep refers to an hypothetical value in the database, not in your program.
Then you have DBIOActions, which are the next level... not just some definition of a query, but the execution of it. You usually get DBIOActions when getting information out of a query, like with the result method or (TADAN!) when inserting rows.
Inserts and Updates are not queries and so what you're trying to do is not possible. You're dealing with DBIOAction (the += method), and Query stuff (the Rep types). The only way to get a Rep inside a DBIOAction is by executing a Query and obtaining a DBIOAction and then composing both Actions using flatMap or for comprehensions (which is the same).