Google Interview Question for Software Developers


Team: Developer Tools
Country: United States
Interview Type: Phone Interview




Comment hidden because of low score. Click to expand.
3
of 3 vote

Sum is write-only var. Order of writing don't matter. You can send jobs to several multithreading servers and accomulate result. Divide and Conquer multithreading optimization.

- lalalka March 25, 2017 | Flag Reply
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2
of 2 vote

For loop will be blocked at each sumOfFile call, where it could have been processed in parallel. So that the next file can be processed without waiting for the sumOfFile call to complete for the current file.
Instead of holding the entire file in the memory, read the file in segments and simultaneously do the summation till you meet the end.
Once you're out of threads in thread pool, the processing of the remaining files will wait till someone gets free to pick up the next file.

- Antriksh March 25, 2017 | Flag Reply
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1
of 1 vote

Multiple issues:
1) Performance - assuming 1M numbers to be added take 10ms. Doing sequential summing on a single machines means 10000*10=100s. Too bad.
2) Overflow - Here total are 10Bill integers, cant bank on max int limit of ~2.15Bill. Long is safer @ max of 9.2BillBill. Best is go for BigInteger. Can also go with long as it can be faster....so decide the trade off based on data range.
3) Beware of OOM due to heap size limits. With ints, each data set is 4MB+(4B per int + object overhead), with longs ~16MB . For BigInteger plan more, as it internally uses int[].

So assuming max heap configured at 8GB, assuming 50% is available for you ...so you can process (4GB/16MB) ~250 files per node in a multi-threaded implementation, needed 40 nodes.
Assuming each node takes 20-30ms to do the job. Have workers/jobs to delegate job to 20 machines with file paths(each machine can read the target files directly from a central mount location). Kick them off in parallel. Sum up the 40 results...each taking 20-30ms each... account for say 2-5ms of network latency...you can get final answer in ~35-40ms.
Optimize further as per your perf SLA.

Comments welcome!!

- gg_poks June 11, 2018 | Flag Reply
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0
of 0 vote

Multiple issues:
1) Performance - assuming 1M numbers to be added take 10ms. Doing sequential summing on a single machines means 10000*10=100s. too bad.

- Anonymous June 11, 2018 | Flag Reply
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-2
of 2 vote

/* 
1. bucketize on thread pool.
2. Shoot.
*/

// per thread num of file 
bucket_size = 100 
// assume list of files are here 
files // yep, files as in like list of strings 
num_files = size(files)
def process_files ( start_inx ){
  end_inx = start_inx + bucket_size 
  end_inx = end_inx > num_files ? num_files : end_inx
  sum ( [start_inx : end_inx ] ) -> { sumOfFile( files[$.o] ) }
} 
num_buckets = ceil ( num_files / bucket_size )
start_inx = 0 
threads = list ( [0:num_buckets] ) -> {
  start_inx += bucket_size
  thread( ) -> { process_files( start_inx ) }
}
// do not get into a loop, ever 
succeeded = poll( 42, 300 ) :: { !exists( threads ) :: {  $.o.alive } }
// zoomba threads return value, proper they are option monad 
assert ( succeeded , "Why you fail in timeout???" )
total_sum = sum ( threads ) -> { $.o.value }
println( total_sum ) // should be cool

- NoOne March 26, 2017 | Flag Reply


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