partitioning

The problem: Let P be the set of all possible ways of partitioning string s into adjacent and possibly null substrings. I'm looking for an elegant algorithm to solve this problem. Background context: Given a tuple of strings (s, w), define P(s) and P(w) as above. There exists a particular partition R ∈ P(s) and T ∈ P(w) that yields the least number of substring Levenshtein (insertion, deletion and substitution) edits. An example: Partition string "foo" into 5 substrings, where ε is a null substring: [ε, ε, f, o, o] [ε, f, ε, o, o] [ε, f, o, ε, o] [ε, f, o, o, ε] [f, ε, ε, o, o] [f, ε, o, ε, o]

I was playing around with cassandra-stress tool on my own laptop (8 cores, 16GB) with Cassandra 2.2.3 installed out of the box with having its stock configuration. I was doing exactly what was described here: http://www.datastax.com/dev/blog/improved-cassandra-2-1-stress-tool-benchmark-any-schema And measuring its insert performance. My observations were: using the code from https://gist.github.com/tjake/fb166a659e8fe4c8d4a3 without any modifications I had ~7000 inserts/sec. when modifying line 35 in the code above (cluster: fixed(1000)) to "cluster: fixed(100)", i. e. configuring my test data

Given a list of integers l , how can I partition it into 2 lists a and b such that d(a,b) = abs(sum(a) - sum(b)) is minimum. I know the problem is NP-complete, so I am looking for a pseudo-polynomial time algorithm i.e. O(c*n) where c = sum(l map abs) . I looked at Wikipedia but the algorithm there is to partition it into exact halves which is a special case of what I am looking for... EDIT: To clarify, I am looking for the exact partitions a and b and not just the resulting minimum difference d(a, b) To generalize, what is a pseudo-polynomial time algorithm to partition a list of n numbers

Scenario I have a 10 million row table. I partition it into 10 partitions, which results in 1 million rows per partition but I do not do anything else (like move the partitions to different file groups or spindles) Will I see a performance increase? Is this in effect like creating 10 smaller tables? If I have queries that perform key lookups or scans, will the performance increase as if they were operating against a much smaller table? I'm trying to understand how partitioning is different from just having a well indexed table, and where it can be used to improve performance. Would a better

I would like to avoid Impala nodes unnecessarily requesting data from other nodes over the network in cases when the ideal data locality or layout is known at table creation time. This would be helpful with 'non-additive' operations where all records from a partition are needed at the same place (node) anyway (for ex. percentiles). Is it possible to tell Impala that all data in a partition should always be co-located on a single node for any HDFS replica? In Impala-SQL, I am not sure if the "PARTITIONED BY" clause provide this feature. In my understanding, Impala chunks its partitions into