Ace Your Haskell Company Interview: Top Questions and Answers Revealed

Use our engineer-created questions to interview and hire the most qualified Haskell developers for your organization.

Many people still use Haskell, a programming language that only works with functions, because it has strong static typing and type inference, lazy evaluation for faster code, algebraic data types, and pattern matching.

Our team has come up with practical coding tasks and interview questions that are designed to test developers’ Haskell skills during coding interviews. We have also put together a list of best practices to make sure that your Haskell interview questions accurately measure how skilled the candidates are.

Senior Haskell interview questions

What is a monad in Haskell, and how does it help with the order of computations? Give an example of a monad in Haskell and explain how it can be used.

Answer:A monad in Haskell is a computational context that facilitates sequencing of computations. It allows us to compose computations with side effects in a structured manner while maintaining referential transparency. Monads give you a set of operations, like return and

One example of a monad in Haskell is the Maybe monad. The Maybe monad represents a computation that can potentially produce a value or fail. It helps you deal with situations where a value might not be there (Nothing) or be there (Just a).

Here’s an example usage of the Maybe monad:

In this case, safeDivide is a function that takes two integers as input and divides them. If the second argument is zero, the function returns Nothing, so there is no division by zero. The performComputation function uses the Maybe monad to handle the sequencing of computations. It first performs the division using safeDivide and binds the result to a. Then it divides it again with 10 as the numerator and a as the denominator, and the result is linked to b. Finally, it returns the sum of a and b within the Maybe monad context.

The computations in performComputation are set up so that if any step returns Nothing, the next steps are skipped, and the final result is also Nothing. This is done with the Maybe monad. This allows for safe and concise handling of potentially failing computations.

Question:You are building a web application in Haskell using a framework like Yesod or Scotty. Talk about the pros and cons of using a Haskell web framework instead of a language like Ruby or Python.

Answer: Using a Haskell web framework, like Yesod or Scotty, has some pros and cons compared to languages like Ruby or Python.

Benefits of using a Haskell web framework:

• Strong static typing: Haskell’s static type system helps find mistakes at compile time, which makes it less likely that web apps will have errors at runtime. This leads to improved reliability and easier refactoring.
• High performance: Haskell is a good language for making fast web apps because it has a good runtime system and an optimized execution model.
• Concurrency and parallelism: Haskell’s functional nature and built-in support for concurrency and parallelism make it possible to use multi-core processors efficiently and handle a large number of requests at the same time.
• Haskell’s functional programming paradigm lets you write code that is both clear and concise. This makes it easier to understand, maintain, and reason about complex web applications.
• Strong focus on purity and immutability: Haskell supports pure functions and immutable data, which makes web applications more modular, testable, and easier to understand how they work.

Drawbacks of using a Haskell web framework:

• Time it takes to learn: Haskell takes longer to learn than popular languages like Python or Ruby. Because of its unique ideas and functional programming paradigm, developers may need to spend time learning the language and the frameworks that go with it.
• Smaller ecosystem: Haskell’s ecosystem is still smaller than languages like Python or Ruby, even though it is growing slowly. This could mean that there are fewer libraries and resources for certain web development needs.
• Haskell is a niche language, so there may not be as many community resources, online forums, and Stack Overflow discussions for it compared to languages that are used by more people. Finding specific solutions or troubleshooting may require more effort.
• Support for tools and IDEs: Haskell has mature support for tools and IDEs, but it may not be as complete or well-done as in other languages. Lots of IDEs and development tools might not have all the features or functions you need.
• Few developers are available: It may be harder to find experienced Haskell developers for a project or team than for more popular languages. This could affect how development resources can be scaled up and made available.

When choosing a Haskell web framework, it’s important to think about these things and see if the benefits fit with the needs of the project and the team’s knowledge of the language.

Question:You are working on a Haskell project that requires interacting with a relational database. Explain how you would typically connect to a database, execute queries, and handle the results in Haskell.

Haskell programmers usually use a library like persistent or HDBC to connect to a relational database, run queries, and deal with the results when working with a relational database.

Here’s a step-by-step overview of the process:

• It is important to add the necessary database library as a dependency in the cabal or stack configuration file for your project.
• Set up a connection: To connect to the database, use the library’s functions or methods and give them the host, port, username, password, and database name. Usually, this step involves making a connection pool to better manage resources.
• Query execution: To write and run SQL queries, use the library’s functions or methods. A lot of libraries come with a Domain-Specific Language (DSL) that lets you build queries safely, or you can use raw SQL queries. For instance, persistent has a built-in DSL, and HDBC lets you run raw SQL queries.
• How to handle results: There are different ways to handle query results depending on the library. Common approaches include:

• Giving back the results as a list of records, where each record is a result set row It is easier to work with the records if they are mapped to Haskell data types.
• Using streaming or pagination to quickly process large sets of results without using too much memory
• Using certain functions or combinators to group, filter, or change the results as needed

• Handling errors: Deal with any problems or exceptions that come up during the connection setup, query execution, or result processing in the right way. Libraries usually have ways to handle errors and spread them in a controlled way.
• Resource management: To stop resource leaks, make sure that resources are properly released after they are used. For example, close the database connection or return connections to the connection pool.

Overall, the process includes connecting to the database, running queries using the library’s API, handling errors, processing the results, and making good use of resources.

Question:You are working on a Haskell project that requires interacting with external REST APIs. How would you typically make HTTP requests and handle the responses in Haskell?.

Answer: The most common way to use external REST APIs in Haskell is to make HTTP requests and handle responses using a library like http-client or req.

Here’s an overview of the process:

• It is important to add the necessary HTTP library as a dependency in the cabal or stack configuration file for your project.
• Making HTTP requests: To make and receive HTTP requests, use the library’s functions or methods. Specify the request method (GET, POST, PUT, DELETE, etc. ), URL, headers, request body (if any), and any other parameters that are important.
• Responding: The library usually has functions or combinators that can handle the HTTP response. Some common approaches include:

• You can read the response body as JSON using libraries like aeson, json, or the built-in JSON response parsing in http-client.
• Getting specific data from the response headers, like authentication tokens or information about the pages that were requested
• Looking at the status code and responding appropriately to different response statuses (success, client, or server errors)
• Dealing with errors or exceptions that might happen while the request is being carried out, like when the network fails or the response is invalid

• If you need to send or receive JSON data for your project, use a library like aeson or json to serialize your Haskell data types to JSON and deserialize JSON responses to Haskell data types.
• Handling errors: Deal with any exceptions or errors that come up during HTTP request or response handling in the right way. Libraries usually have ways to handle errors and spread them in a controlled way.
• Resource management: To stop resource leaks, make sure that resources are properly released after they are used. For example, close network connections and free up memory.

In general, the process includes creating and sending HTTP requests, dealing with responses, parsing JSON data (if needed), fixing errors, and making good use of resources.

Question:You are working on a Haskell project and need to perform concurrent or parallel computations for improved performance. Explain how you would normally achieve concurrency or parallelism in Haskell and talk about the pros and cons of each method.

Answer:There are several ways to achieve concurrency or parallelism in Haskell, such as using pure concurrency, parallel strategies, or libraries such as async or stm.

• For pure concurrency, Haskell supports “green threads” or “Haskell threads,” which are lightweight threads that are built in. Using functions like forkIO or libraries like unliftio, you can make computations that run at the same time and independently in the same Haskell process. Pure concurrency lets you have fine-grained control and works well for tasks that need to do a lot of I/O or when shared mutable state is not a problem.

Advantages: Lightweight and easy to create; suitable for I/O-bound tasks; fine-grained control over concurrency.

Considerations: No automatic load balancing; shared mutable state can lead to race conditions and require explicit synchronization.

• Parallel strategies: Haskell’s Control. Parallel. Strategies module provides strategies to express parallel computations. You can use multiple CPU cores to do computations in parallel by marking them with parallel combinators like parMap, parList, or using. Tasks that can be broken down into separate parts work well with parallel strategies. For example, parallel map or reduce operations work well for these types of tasks.

Advantages: Simple to use with built-in strategies; suitable for data parallelism; automatic load balancing.

Things to think about: the task needs to be broken up into subtasks that can run in parallel; shared mutable state can cause race conditions and needs clear synchronization.

• Haskell has libraries like async and stm (Software Transactional Memory) that can handle more complex situations where two or more tasks run at the same time or in parallel. These libraries give you higher-level ways to handle multiple tasks at once, synchronization, and putting together computations. Async lets you manage multiple computations at the same time and cancel them if necessary. stm, on the other hand, provides atomic transactions for changing shared state while multiple computations are running.

Advantages: Higher-level abstractions for concurrent programming; support for cancellation and shared state management.

Considerations: Learning curve for advanced features; potential overhead in certain scenarios; careful design required for shared mutable state.

Whether you use concurrency or parallelism depends on the needs and features of the problem you are trying to solve. To find the best way to do something, you should think about things like the type of computation, the number of independent tasks that are available, the need for shared state management, and performance.

Question:You are working on a Haskell project and need to handle and manipulate large amounts of data efficiently. Describe how you would normally manage and optimize memory in Haskell to get the best performance and avoid using too much memory.

To answer your question, there are a number of effective ways to manage and optimize memory when working with large amounts of data in Haskell. Here are some common practices:

• Haskell uses lazy evaluation by default, which can cause memory thunks and memory leaks when working with large data structures. Use strict data types instead. To mitigate this, consider using strict data types (Data. Text instead of String, Vector instead of List, etc. ) or strict evaluation annotations (!) to make strictness happen when it’s needed
• Streaming and incremental processing: Instead of loading the whole dataset into memory, you could use streaming libraries like conduit or pipes to process the data in small chunks. These libraries let you process data in a streaming way, which saves memory and makes it easier to work with large datasets.
• Use techniques for data compression. If your data can be compressed, you might want to use libraries for compression like zlib or lz4 to shrink it before storing it or sending it. This can cut memory use and I/O overhead by a lot, especially when working with big files or communicating over a network.
• Optimize memory allocations: Pay attention to how memory is allocated in sections that are very important for performance To avoid allocating too much memory, use methods like array fusion (vector library) or custom allocation strategies. If you can help it, use efficient combinators or specialized functions instead of intermediate data structures.
• Use profiling tools (RTS -p, ghc-prof, ghc-events, etc.) to profile and benchmark your code. ) to identify memory hotspots and bottlenecks in your code. With profile-guided optimization, you can find the areas that need work and make performance improvements.
• Employ memory-safe techniques: Haskell provides memory-safe constructs by default. But be careful of memory leaks or bugs that affect memory when working with lower-level libraries or FFI. Proper resource management (e. g. To keep memory safe, you must free memory when you need to and be very careful when using memory-allocated functions or libraries.
• Think about using libraries for specific tasks. For example, Haskell has many specialized libraries like vector, unordered-containers, and bytestring that make it easy to work with and process data. By using these libraries, you can get data structures and algorithms that work better, use less memory, and run faster.

Remember that optimization should be performed based on actual profiling results rather than premature optimization. Measure, analyze, and change your code over and over to find the most important places to improve it and make sure that the changes you make meet your performance goals.

Question:You are developing a Haskell project that requires handling asynchronous tasks or event-driven programming. Describe how you would normally handle asynchronous programming in Haskell and talk about the pros and cons of each method.

Answer:There are several ways to handle asynchronous tasks or event-driven programming in Haskell. These include using the async library, STM (Software Transactional Memory), or specific event-driven frameworks like eventstore or reactive-banana.

Here are some common approaches with their advantages and considerations:

• async library: The async library is a lightweight concurrency abstraction that lets you do asynchronous tasks, cancel them, and combine them. You can run multiple tasks at once and wait for them to finish by using the async functions async, wait, and cancel.

Advantages: Simple and lightweight; built-in cancellation support; good for managing concurrent tasks with fine-grained control.

Considerations: Manual

management of concurrency; requires explicit synchronization for shared mutable state.

• STM stands for “Software Transactional Memory.” It is a built-in feature of Haskell that lets multiple programs run at the same time and share state. Using TVar (transactional variables) and STM monad, you can coordinate operations that are happening at the same time and make sure they are consistent.

Benefits: It lets you handle shared state in a safe and modular way; when transactions conflict, they are automatically rolled back; it supports retry and orElse semantics by default.

Considerations: Requires understanding of transactional memory concepts; can be less performant than fine-grained concurrency in some cases.

• Event-driven frameworks: Haskell has event-driven frameworks like reactive-banana and eventstore that make it easier to handle events that happen at different times and manage event-driven architectures. These frameworks often utilize functional reactive programming (FRP) concepts.

Advantages: Higher-level abstractions for event-driven programming; declarative and compositional; well-suited for complex event handling and GUI applications.

Considerations: Learning curve for FRP concepts; specialized frameworks may have limited community support or require additional dependencies.

The choice of approach depends on the specific requirements and characteristics of the project. To find the best solution, think about things like how hard it is to handle events, whether shared mutable state is needed, whether cancellation or composition is needed, and how familiar you are with the current method.

Question:You are building a Haskell project that requires interacting with a message queue system like RabbitMQ or Kafka. Explain how you would typically integrate and work with message queues in Haskell, including consuming and producing messages.

Answer: You need to use the right client libraries, like amqp for RabbitMQ or haskell-kafka-client for Kafka, to connect to and work with message queue systems like RabbitMQ or Kafka in Haskell. Here’s an overview of the typical process:

• Take care of dependencies: In your project’s cabal or stack configuration file, add the message queue client library you need as a dependency.
• Connecting to the message queue: To connect to the message queue system, use the functions or methods in the library. Give the connection information that is asked for, like the host, port, authentication information, and the names of the queue or topic.
• Adding messages: To add messages to the message queue, use the library’s functions or methods. Name the recipient’s queue or topic and describe what the message is about. Depending on the library, you might need to serialize the message to a format that the library can understand, like JSON or binary.
• Message consumption: Set up a message consumer to get messages from the message queue and process them. Message handlers or callback functions can be set up in different libraries so that they are called when a new message arrives. The library handles the message retrieval and acknowledgment process.
• Message processing: Put the logic you need to process the received message in the message handler or callback function. This could include de-serializing the message content, running business operations based on the message, and, if desired, letting the message queue system know that the message was successfully processed.
• Error handling and retries: Take care of any errors or exceptions that come up while processing or reading messages. Depending on the library, you may be able to try again if the message processing fails or set up error handling strategies.
• Resource management: Make sure that resources are properly released after they are used. For example, close the connection to the message queue system and free up any resources that are connected to it to stop leaks or connection limits.

It’s important to look at the message queue client library’s documentation and examples to find out how to implement things correctly and what the best practices are.

You need to use OAuth to authenticate and authorize users when working on a Haskell project that needs to interact with external APIs. Describe how you would normally handle the OAuth flow in Haskell, including the steps you would take and any libraries or frameworks you would need.

Answer: Getting access tokens, making authenticated requests, and handling the OAuth protocol are some of the steps that are usually needed to handle the OAuth flow in Haskell. Here’s an overview of the process:

• It is important to add the necessary OAuth library as a dependency in the cabal or stack configuration file for your project. Common libraries for OAuth in Haskell include oauth and oauth2.
• Register your app: Before you can connect to an OAuth provider, you need to register your app with that provider so that you can get the credentials you need, like a client ID and a client secret. Each provider may have its own registration process.
• Get the authorization URL: Create the authorization URL by giving it the right parameters, such as the client ID, redirect URI, and scopes. To start the OAuth flow and get user permission, the authorization URL is used.
• Request redirect and authorization code: Once the user agrees, the OAuth provider sends them back to your app’s redirect URI along with an authorization code. Capture and store this code for the next step.
• Exchange authorization code for access token: To trade the authorization code for an access token, use the functions or methods in the library. To do this, you need to send a request to the OAuth provider’s token endpoint with the authorization code, client ID, client secret, and redirect URI. The OAuth library typically handles this token exchange process.
• Manage and store access tokens: Once you have an access token, make sure it is kept safely (e.g. g. , in a session, a database, or an environment variable) for requests that have already been authenticated The OAuth library often has functions to handle storing and getting tokens.
• Requests that are valid: To make valid requests to the external API, use the access token. Depending on what the API needs, put the access token in the request headers or query parameters. Adding authorization headers is often possible with libraries like http-client or specialized OAuth clients.
• Token refresh: Once the access token has expired, you need to take care of the token refresh process. To do this, you need to send a request to the OAuth provider’s token endpoint with either a refresh token (if one is given) or go through the process of getting a token again.
• Handling errors and revoked tokens: Take care of OAuth errors like expired tokens, invalid credentials, or requests that aren’t authorized. If necessary, think about putting in place ways to handle errors and remove tokens.

For specific implementation details and best practices, it’s important to look at the OAuth library’s documentation and examples. This is because the steps and methods may be different depending on the library and OAuth provider.

You are working on a Haskell project that needs to connect to a relational database, run complicated queries, and convert the results to Haskell data types. Why is it important to handle query composition, result mapping, and abstraction layers in Haskell in a way that makes database interactions clean and easy to maintain?

When using Haskell to connect to a relational database, libraries such as persistent, opale, and HDBC can be used to handle query composition, result mapping, and abstraction layers. Here’s an overview of the typical approach:

• Pick a database library: Pick a good database library based on the needs and wants of your project. High-level abstractions are provided by libraries like persistent. On the other hand, lower-level approaches with more control over SQL queries are provided by libraries like Opaleye.
• Set up the database schema: To set up the database schema, use the library’s DSL or declarative methods. To do this, you need to define Haskell data types for the database’s tables and columns, as well as any constraints or relationships between them.
• Query composition: To put together database queries, use the library’s DSL or functions. Type-safe query composition with combinators or operators is possible with libraries like persistent that offer a rich DSL. Libraries like Opaleye let you use functions to build queries directly.
• Set up mapping functions or type classes to turn the query results into Haskell data types. Usually, this means setting up instances of type classes like FromRow or ToRow for persistent or giving Opaleye mapping functions. The conversion between the database representation and Haskell data types is taken care of by these mapping functions or instances.
• Abstracting database operations: To hide database operations and give a clean way to interact with the database, you might want to make an abstraction layer or repository pattern. This can include writing functions or methods that handle common CRUD operations, complicated queries, or transactions.
• Managing migrations: If your project involves changing the database schema, you might want to use migration tools like persistent-migration or migrant to keep track of changes and versions. You can make small changes to the schema with these tools and apply them in a controlled way.
• Error handling and transactions: Take care of any errors or exceptions that come up while the database is running. Use the library’s built-in transaction management tools or explicitly manage transactions to make sure they are handled correctly.

By following these practices, you can achieve a clean and maintainable database interaction layer in Haskell. Type-safe query composition, separation of concerns, and abstraction of database operations are all made possible by it. This makes code easier to maintain and read.

Help us design a parking lot

Hey candidate! Welcome to your interview. Boilerplate is provided. Feel free to change the code as you see fit. To run the code at any time, please hit the run button located in the top left corner.

Goals: Design a parking lot using object-oriented principles

Here are a few methods that you should be able to run:

• Tell us how many spots are remaining
• As a whole, how many parking spots are there?
• Tell us when the parking lot is full
• Tell us when the parking lot is empty
• Tell us when certain spots are full e. g. when all motorcycle spots are taken.
• Tell us how many spots vans are taking up

Assumptions:

• The parking lot can hold motorcycles, cars and vans
• Some spots are for motorcycles, some are for cars, and some are big.
• A motorcycle can park in any spot
• A car can park in either a regular spot or a single tight spot.
• There is room for a van, but it will take up three space
• These are just a few assumptions. If you need to, you can ask your interviewer about more assumptions.

Haskell Interview Questions and Answers 2019 | Haskell Interview Questions | Wisdom Jobs

FAQ

Does the Haskell company drug test?

What was the interview process like at the Haskell Company?

I interviewed at The Haskell Company Informal meeting with members of the local office. Asked mostly about my background and customer service history. No questions were super technical. Some team members did forget about the meeting Phone screen to discuss the job and my experience. Person was very pleasant.

How long was the interview at the Haskell Company (Dallas-Fort Worth)?

I interviewed at The Haskell Company (Dallas-Fort Worth) in Nov 2022 Zoom interview that lasted about 30 minutes . Some questions surprised me but was good preparation for in person interview which I had two weeks later . After the in person I waited to hear back for next steps but was not contacted I applied online.

Is Haskell a good company?

Haskell is ranked among the 2020 leaders in numerous categories and climbed in the overall rankings. Haskell has climbed to No. 1 among U.S. contractors for Manufacturing Food & Beverage in the 2020 ENR Top Contractors Sourcebook 2020.

What Haskell covered in his career?

Haskell’s career is marked by award winning coverage of some of the biggest stories and important events. From the Miracle on the Hudson Plane Crash, eight Political Conventions, seven World Series, Super Storm Sandy, the Sandy Hook School Shooting and more than 20 New York City Marathons.