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# Search feature |
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To address the elephant in the room: search is supposed to be "performed on the merged json object of movies", i.e. including the data from OMDB. |
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For example, `?director=Frank Miller` should return movie with internal ID 3532674 (imdb ID tt0401792), |
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even though our "database" does not have any information about Frank Miller having directed this movie, |
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only OMDB has this data. |
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And OMDB does not support searching by director name (the only kind of search it seems to support is by title, optionally with year, |
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and it only returns a single entry at a time). |
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So the only way to figure out which of the movies in the "database" are directed by Frank Miller is |
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to fetch data for _every_ movie present in our "database" from OMDB, and then |
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either do a full scan or build some kind of search index. |
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**Were this a real-world task, I would say that, depending on what problem are we trying to solve, |
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the solution proposed in README is most likely a wrong solution. |
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And that enriching local data with OMDB data should be a responsibility of the service that populates the DB, not of the one that reads from it; |
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that the right approach would be for DB to contain all the data used in search, |
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not for API returning data from that DB to fetch all the entries from DB and then fetch all the corresponding entries from OMDB one-by-one |
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(regardless of whether that happens on startup or on every search).** |
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But as I understand, this is supposed to be a coding challenge, not a requirements analysis challenge. |
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So let's get to the topic of implementing that potentially incorrect solution. |
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Our "database" here only contains four movies, but README implies that this is just for (extreme) simplicity; |
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an actual database would presumably contain thousands of titles, and even simple test database would probably contain a hundred. |
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(If we could rely on the "database" only containing these four movies, then we wouldn't even need to call it a "database", |
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we could just hardcode enriched data in the application and use it in searches, without making any external requests, |
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but I guess that's not what this test assignment is about.) |
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So fetching data from OMDB on every search request is out of question; we must cache OMDB responses somehow, |
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not as an optimization strategy but as a requirement. |
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Additionally, since "Constraints" section mentions to not introduce any system dependencies, we need to cache it in-memory. |
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Building an index on the cached data (and maintaining it) is clearly outside of the scope of this assignment, |
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so we'll have to do a full scan on every search request, and at this point we might as well |
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only do full scans in "database" as well. |
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This means that it is enough for DB interface to only have three methods: |
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returning "our own data" for a specific movie by "our internal ID", by IMDB ID, and returning data for all movies. |
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At this point we already made so many assumptions that it won't hurt to make another couple, for simplicity of implementation: |
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1. That data in local DB doesn't change while the service is running |
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(it physically cannot, with "local DB" actually being JSON files inside the project directory). |
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Otherwise that would open an entirely new can of worms regarding "cache" invalidation: |
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e.g. what if IMDB ID of an existing movie changes between requests? |
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2. That data in OMDB for the movies in the local DB doesn't change while the service is running. |
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Otherwise we would have to constantly refetch all data for all movies just to update the searchable fields, |
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so that search provides the correct and relevant results. |
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So the final implementation idea is: |
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1. Preload all entries from the local "database" on startup; |
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2. For each of them, fetch the relevant data from OMDB on startup; |
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3. Create "merged movie objects", keep them in memory; |
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4. For every search request: go through all objects, find matching ones, return them |
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(with zero calls to OMDB API or to the local "database" in the process). |
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# Technical decisions made |
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## "Additional tasks" |
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### AWS Lambda Function |
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Considering everything that was said above regarding the search, I don't think this service |
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(or any other reasonable implementation of the assignment, as reasonable as it might be) |
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is suitable for serverless deployment. |
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In order to have the search working, we should fetch data from OMDB for every movie in the local "database", one by one. |
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Either we do it on every search request (which is unreasonable, and can easily get us above 1000 requests/day quota, |
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even for the sample database with only four movies in it), or we have to store the fetched data in some kind of somewhat persistent state. |
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Since the assignment said to not introduce any system dependencies, the only somewhat persistent state we can use in Lambda is in its memory. |
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However, we should not rely on the way Lambda instances are cold started and shut down; this is what "serverless" basically means. |
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And even if we would rely on that: if our Lambda will get too many requests at the same time, AWS will spin up several instances, |
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and each one will have to fetch all data from OMDB, which is not really a good idea. |
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On the other hand, if our Lambda will only get infrequent requests, AWS will shut it down and cold-start it again on the next request, |
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meaning that we would have to refetch everything from OMDB again. |
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Reasons why this service cannot reasonably / should not be packaged as a serverless function are the same as |
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the reasons why the assignment as described in README seems to be a really wrong solution to an (unknown) problem. |
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### Caching |
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Preloading everything into local memory on application startup can reasonably be called "caching". |
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In this implementation TTL is infinite, because of the assumptions described near the end of "Search feature" section. |
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## Efficiency, application scaling |
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In this implementation, search and get request handlers just do a full scan of preloaded data. |
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This is not very efficient (the time scales as O(number of movies)), but good enough for low enough numbers of movies |
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(definitely if it is below a thousand); latency on small enough local database should be low. |
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And if local database contains over a thousand movies, we have a bigger problem anyway because we won't be able to |
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fetch all the needed data from OMDB before hitting OMDB API usage limits. |
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I didn't do performance measurements because I didn't have enough time for that. |
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But at least this implementation can be scaled horizontally, improving performance (in terms of numbers of requests handled per second) |
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linearly with the number of instances, at the expense of extra requests to OMDB API from each instance. |
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## Framework |
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I decided to implement this API with Nest.js, because I already used it on test assignments before, |
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and it's good and convenient enough for API prototyping. |
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I'm also using TypeScript with "strictest" config, along with `@typescript-eslint` with "strict-type-checked" config. |
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## OMDB format |
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Unfortunately, I couldn't find any clear documentation on OMDB response format. |
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Instead, I just picked a few actual OMDB responses (mostly the ones listed in [`omdb/converters.spec.ts`](src/integration/movies/omdb/converters.spec.ts)), |
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and adapted my type definitions and converters to them. |
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As a result, I assume that all responses from OMDB satisfy |
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`OmdbResponse` type defined in [`omdb/types.ts`](src/integration/movies/omdb/types.ts) and do not contain any extra fields; |
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and additionally that `'N/A'` values are only allowed in string fields mentioned in [`omdb/converters.ts`](src/integration/movies/omdb/converters.ts) |
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in `createOptionalField` context (i.e. `Awards`, `BoxOffice`, `Rated`, `DVD`, `Episode`, `Season`, `seriesID`, `totalSeasons`, and `Website`). |
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Additionally, I couldn't find any example of OMDB entry with `Production` value different from `'N/A'`, |
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so I have no idea what does this field mean, and I decided to (explicitly) ignore it. |
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## Merging and mapping |
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I decided to have my own `MovieData` structure (defined in [`types.ts`](src/types.ts)), |
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and to convert data from both OMDB and DB format to this structure. |
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Instead of using any pre-existing libraries, I decided to do it manually (in two `converters.ts` files), |
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because for this small one-off assignment it was easier to do it by hand than integrate another library, |
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and because I implemented the conversion in a type-safe way with compile-time checks that I didn't forget about anything, |
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and with runtime checks that there were no extra fields in the input I should have handled. |
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Thi has a drawback that adding any new field to the source data will result in error; |
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on the other hand this means that adding any new field to the source data will require one to explicitly describe |
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how it should be mapped and merged. |
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The assignment says that OMDB `Title` overrides our own `title`, and that OMDB `Plot` overrides our own `description`. |
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But from the sample data, it seems that OMDB always has both in English, and our "database" always has both in German, |
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and that these fields are always present and non-empty, so I thought that it might make sense to preserve both |
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(with `title` and `description` in merged result containing the English data from OMDB, |
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and `localTitle` and `localDescription` containing the German data from our "database"). |
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Besides title, description, runtime and ratings, there is only one other field present both in OMDB response and in our "database": language. |
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There was no clear way to reconcile the data from OMDB with the data from sample JSONs: |
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sample JSONs only have one language per movie, as a two-letter code, |
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while IMDB data contains a list of languages, not all of them even having two-letter codes |
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(because there are more languages in the world than two letter-codes): |
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for example, tt7394674 has `Language: 'English, Micmac'` in OMDB response; |
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a blockbuster tt11866324 (shot mostly in Comanche) has `Language: 'English, North American Indian, French'`. |
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So I decided to ignore the language field from OMDB, and only use the language field from the internal database. |
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After all these adjustments, the merging algorithm was as simple as doing `const mergedData = { ...normalizedOmdbData, ...normalizedInternalData }`, |
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with a single exception of having to manually merge `ratings` fields (just by concatenating the corresponding arrays). |
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One more thing to note is that OMDB is somewhat inconsistent in its naming; |
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it has `Director`, `Writer`, `Genre` and `Country` fields (singular) but `Actors` (plural); while our "database" has `studios` (plural), |
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even though all six fields can (and typically do) contain several values, and are ultimately represented as `string[]` in the merged object. |
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I decided to use plural names for all six. |
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## API |
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The original assignment didn't mention anything about how should one handle requests for movies |
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that are present in our "database" but not OMDB, or vice versa. |
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On practice, all four entries present in sample JSONs have corresponding entries in OMDB. |
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For simplicity (and in order to guarantee that `/api/movies/:id` endpoint always returns full merged `MovieData` object), |
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I decided to return 404 when at least one of the sources does not have any data. |
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Changing the implementation so that API returns partial data when only one of the sources knows about the requested movie would be trivial. |
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The assignment seems to be a bit contradictory regarding the way how search should work; |
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it implies that search field name is the same as merged object field name, but then it provides an example as `?director=Frank Miller` |
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(as I mentioned above, I decided to go with plural forms in the merged object), |
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and also the original OMDB object has `Actors` and our "database" has `studios`. |
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It feels weird to have `actor: ['Mark Hamill', 'Harrison Ford', 'Carrie Fisher']` (in singular), |
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and it also feels weird to have `?actors=Mark Hamill` in the search query, |
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so ideally API should probably have plural forms in the merged object but singular forms in search query. |
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But also this is very much outside of the scope / timebox for this assignment, so I decided to go with plural forms both in merged object and in search. |
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Another thing regarding search is that it seems that for most fields, it does not make any sense to search by them |
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(for example, who would search (and why) by `?awards=Won 6 Oscars. 65 wins & 31 nominations total` or by `?boxOffice=$460,998,507` or by full plot description?) |
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So I limited the search feature to a subset of fields, as described in `SearchFilters` type in [`types.ts`](src/types.ts) |
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and in `SearchDto` in [`movies.controller.ts`](src/movies.controller.ts). |
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And finally, it does not make a lot of sense to search by an empty query (we probably don't want to just list our entire database to anybody |
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who did not enter any search filters), so I return a 400 Bad Request errors for request to search endpoint with empty query. |
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## Testing |
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The code is mostly (according to `npm run test:cov`, plus e2e tests) covered by tests. |
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I did not have any time to come up with good test cases, so most tests work on samples of data from OMDB and on provided sample JSONs. |
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Regular tests (ran with `npm test`) do not have any external dependencies. |
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Additionally there are tests that connect to remote OMDB API; these are ran with `npm run test:e2e`. |
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## Git |
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The assignment has two contradicting messages: |
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that I should use git to document my work ("_We will look at the git history of your pull request to determine the way you approached this_"), |
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without squashing or bundling; |
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but also that you will evaluate the proper use of git. |
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I decided to go with the first message, and use git to document work history, not to showcase git use that would be proper for PRs in a professional environment. |
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## Configuration |
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OMDB API token is passed to the app and e2e tests as an environment variable (hardcoded in package.json, but can easily be extracted from there). |
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# How to use |
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To start: `npm run start`; it is available on `localhost:3000` by default. |
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To run tests: `npm test`, `npm run test:e2e` (see "Testing" section for more details). |
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To lint: `npm run lint` (see "Framework" section for more details). |
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Reference in new issue