Product Perfect

The Orc programming language is a novel computational model specifically designed for concurrent and distributed systems. Developed by the University of Texas at Austin, it was introduced in 2004 to address the complexities of modern computing environments. The name 'Orc' stands for 'ORestrial Computing', symbolizing its role in managing and coordinating complex interactions across networks. It uses "sites", "combinators", and "operators" as core elements to enable efficient orchestration of concurrent tasks. As an open-source project, it offers programmers a robust toolset for building high-performance distributed systems.

Ask the right questions secure the right Orc talent among an increasingly shrinking pool of talent.

First 20 minutes

General Orc app knowledge and experience

The first 20 minutes of the interview should seek to understand the candidate's general background in Orc application development, including their experience with various programming languages, databases, and their approach to designing scalable and maintainable systems.

How would you describe the basic structure of an Orc file?

An ORC file includes a stripe of rows data, followed by a footer that holds statistics. The file also includes a postscript that contains the size of the compressed footer and the type of compression used.

What are the key features of Orc?

Key features of ORC include efficient compression, complex types, column projection, and a choice between fast and slow compression algorithms.

Describe the difference between Orc and Parquet.

ORC is more efficient in terms of reading, writing, and processing data compared to Parquet. However, Parquet is more efficient when it comes to storage space as it uses a columnar storage format.

How would you implement column pruning in Orc?

Column pruning in ORC can be implemented by simply selecting the columns needed for a particular query, thus reducing the I/O operations.

What are the types of compression supported by Orc?

ORC supports three types of compression: ZLIB, SNAPPY, and LZO.

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What you’re looking for early on

Does the candidate have a strong understanding of Orc programming language?

Has the candidate demonstrated problem-solving skills during the interview?

Has the candidate shown an ability to work well in a team?

Does the candidate have a good understanding of other relevant technologies and languages?

Next 20 minutes

Specific Orc development questions

The next 20 minutes of the interview should focus on the candidate's expertise with specific backend frameworks, their understanding of RESTful APIs, and their experience in handling data storage and retrieval efficiently.

Describe the difference between row-based and column-based storage.

In row-based storage, data is stored in a sequence of rows, while in column-based storage, data is stored in a sequence of columns. ORC uses column-based storage which provides a better compression ratio and is more effective for read-heavy workloads.

How would you optimize performance of Orc files?

Performance of ORC files can be optimized by choosing appropriate stripe size, using the right compression algorithm, implementing column pruning and using predicate pushdown.

What are the benefits of using a columnar storage format like Orc?

Columnar storage format like ORC provides high compression ratio, efficient use of storage, and improved query performance. It is particularly effective for analytics and reporting workloads.

Describe the difference between ORC and Avro file formats.

ORC is a columnar storage format providing high compression and improved query performance while Avro is a row-based storage format that is more suitable for write-heavy workloads.

How would you handle schema evolution in Orc?

ORC supports schema evolution. You can add, drop, or rename columns and the schema changes are tracked in the file footer.

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At this point, a skilled Orc engineer should demonstrate strong problem-solving abilities, proficiency in Orc programming language, and knowledge of software development methodologies. Red flags include lack of hands-on experience, inability to articulate complex concepts, or unfamiliarity with standard coding practices.

Digging deeper

Code questions

These will help you see the candidate's real-world development capabilities with Orc.

What does this simple Orc code do?
def hello() = 
	println('Hello, World!')

hello()
This code defines a function named 'hello' which prints out 'Hello, World!' when called. The function is then called.
What does this Orc code do?
def add(x, y) = 
	val z = x + y
	z

add(5, 3)
This code defines a function named 'add' which takes two arguments, x and y, adds them together and assigns the result to a variable named 'z'. The function then returns 'z'. The function is then called with the arguments 5 and 3.
What will be the output of this Orc code?
def sumList(lst) = 
	lst.foldLeft(0)(_ + _)

sumList(List(1, 2, 3, 4, 5))
This code defines a function named 'sumList' which takes a list as an argument and sums all the elements in the list using the 'foldLeft' method. The function is then called with a list of integers from 1 to 5. The output will be 15.
What does this Orc code do?
def threadExample() = 
	Thread.sleep(1000)
	println('Thread Example')

threadExample()
This code defines a function named 'threadExample' which makes the current thread to sleep for 1000 milliseconds (or 1 second) and then prints out 'Thread Example'. The function is then called.

Wrap-up questions

Final candidate for Orc Developer role questions

The final few questions should evaluate the candidate's teamwork, communication, and problem-solving skills. Additionally, assess their knowledge of microservices architecture, serverless computing, and how they handle Orc application deployments. Inquire about their experience in handling system failures and their approach to debugging and troubleshooting.

What are the limitations of ORC?

Limitations of ORC include lack of support for real-time processing, limited language support, and the need for conversion when working with non-Hadoop systems.

Describe the difference between ORC and CSV file formats.

ORC is a columnar storage format providing high compression and efficient querying while CSV is a text-based format that is human readable but less efficient in terms of storage and querying.

How would you handle large files in Orc?

Large files in ORC can be handled by splitting them into smaller stripes. This allows for parallel processing and improves query performance.

Orc application related

Product Perfect's Orc development capabilities

Beyond hiring for your Orc engineering team, you may be in the market for additional help. Product Perfect provides seasoned expertise in Orc projects, and can engage in multiple capacities.

Orc Consultive assessments

Orc Application Conversion Assessment Orc Application Integration Assessment Orc Data & Database Integration Assessment

Custom Orc development projects

Custom Orc App Conversion Orc Custom App Development Process App UI Design for Orc Facelifts

Hiring guide for Orc Engineers

Orc Developer Hiring Guide

What you’re looking for early on

The ideal back-end app developer

What you’re looking to see on the Orc engineer at this point.

Orc Consultive assessments

Custom Orc development projects