partitioning

问题 As I partitioned the target table for interval partitioning by month, and only keep 27 months data(so need drop the eldest partition monthly). After I use below SQL to drop it,I ran the SP, the SP was very slowly. alter table target_table drop partition target_eldest_partition; So I cancel the SP and analyzed the table ANALYZE TABLE target_table COMPUTE STATISTICS; but it encountered an error Error starting at line : 12 in command - ANALYZE TABLE per_limra COMPUTE STATISTICS Error report -

问题 i have a big table with several partition. my partition name is like below: P_13931203 P_13931204 P_13931205 P_13931206 i have a select for create partition name dynamically as below: select 'P_' || to_char(sysdate-1,'yyyymmdd','nls_calendar=persian') from dual; example Output: P_13931204 when i select as below everything is OK: select * from <table_name> partition (P_13931205); but when i select as below i get error: select * from <table_name> partition (select 'P_' || to_char(sysdate-1,

问题 I will be using a large amount of files structured as follows: /day/hour-min.txt.gz with a total of 14 days. I will use a cluster of 90 nodes/workers. I am reading everything with wholeTextFiles() as it is the only way that allows me to split the data appropriately. All the computations will be done on a per-minute basis (so basically per file) with a few reduce steps at the end. There are roughly 20.000 files; How to efficiently partition them? Do I let spark decide? Ideally, I think each

问题 Suppose you are provided with the following function declaration in the C programming language. int partition(int a[], int n); The function treats the first element of a[] as a pivot and rearranges the array so that all elements less than or equal to the pivot is in the left part of the array, and all elements greater than the pivot is in the right part. In addition, it moves the pivot so that the pivot is the last element of the left part. The return value is the number of elements in the

问题 I want to partition a pandas DataFrame into ten disjoint, equally-sized, randomly composed subsets. I know I can randomly sample one tenth of the original pandas DataFrame using: partition_1 = pandas.DataFrame.sample(frac=(1/10)) However, how can I obtain the other nine partitions? If I'd do pandas.DataFrame.sample(frac=(1/10)) again, there exists the possibility that my subsets are not disjoint. Thanks for the help! 回答1: use np.random.permutations : df.loc[np.random.permutation(df.index)] it

问题 I have two arrays ( x1 and x2 ) of equal length that have overlapping ranges of values. I need to find a value q such that l1-l2 is minimized, and l1 = x1[np.where(x1 > q)].shape[0] l2 = x2[np.where(x2 < q)].shape[0] I need this to be reasonably high-performance since the arrays can be large. A solution using native numpy routines would be preferred. 回答1: There may be a smarter way to look for a value, but you can do an exhaustive search as follows: >>> x1 = np.random.rand(10) >>> x2 = np

问题 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

问题 I have a table with hundreds of thousands of records. How to partition the table, month-wise? 回答1: You can ALTER TABLE to create new PARITIONS on it. ALTER TABLE table_name PARTITION BY RANGE (MONTH(date_column) ( PARTITION JAN VALUES LESS THAN (2), PARTITION FEB VALUES LESS THAN (3), ... PARTITION DEC VALUES LESS THAN MAXVALUE ); 来源： https://stackoverflow.com/questions/11862051/how-make-month-wise-partitioning-in-existing-mysql-table

问题 I have around 300 tables which are located in different partition and now these tables are not in use for such huge data as it was. Now, I am getting space issue time to time and some of but valuable space is occupied by the 150 filegroups that was created for these tables so I want to change table's filegroup to any one instead of 150 FG and release the space by deleting these filegroups. FYI: These tables are not holding any data now but defined many constraints and indices. Can you please

问题 I'm trying to implement an algorithm to build a decision tree from a dataset. I wrote a function to calculate the information gain between a subset and a particular partition, then I try all the possible partition and want to choose the "best" partition, in the sense that it's got the lowest entropy. This procedure must be recursive, hence, after the first iteration, it needs to work for every subset of the partition you got in the previous step. These are the data: X = {{1, 0, 1, 1}, {1, 1,