performance

问题 I created a function that whenever I run the AppendRow script every row that does not have a dot (".") at the AY column, an array with every information/column that I want from that sheet will be transfered to my main sheet that has around 13k rows atm. Usually about 20-40 rows get pasted into the first sheet everyday and this script automatically re-arranges the columns to my main sheet. PROBLEM: The problem is that each row that gets appended to my main sheet takes around 8-15sec to get

问题 I have some raw text that is usually a valid UTF-8 string. However, every now and then it turns out that the input is in fact a CESU-8 string, instead. It is possible to technically detect this and convert to UTF-8 but as this happens rarely, I would rather not spend lots of CPU time to do this. Is there any fast method to detect if a string is encoded with CESU-8 or UTF-8? I guess I could always blindly convert "UTF-8" to UTF-16LE and then to UTF-8 using iconv() and I would probably get the

问题 I have a problem where i need to walk through an object graph and pick out a particular property value. My original solution caches a linked list of property names that need to be applied in order to get from point A to point B in the object graph. I then use apache commons PropertyUtils to iterate through the linked list calling getProperty(Object bean, String name) until i have reached point B. My question is around how this will perform compared to perhaps cahing the Method objects for

问题 I am decoding h264 rtp stream with ffmpeg on android. I found a strange problem: if I don't call avformat_find_stream_info ,decoding P frame takes tens of micro seconds, by contrast, calling avformat_find_stream_info before decoding will reduce P frame decoding time to less than 1 ms on average. However, avformat_find_stream_info itself is time consuming on network streams. Are there anything I can do to make decoding fast without calling avformat_find_stream_info ? 回答1: When avformat_find

问题 I am decoding h264 rtp stream with ffmpeg on android. I found a strange problem: if I don't call avformat_find_stream_info ,decoding P frame takes tens of micro seconds, by contrast, calling avformat_find_stream_info before decoding will reduce P frame decoding time to less than 1 ms on average. However, avformat_find_stream_info itself is time consuming on network streams. Are there anything I can do to make decoding fast without calling avformat_find_stream_info ? 回答1: When avformat_find

问题 I am decoding h264 rtp stream with ffmpeg on android. I found a strange problem: if I don't call avformat_find_stream_info ,decoding P frame takes tens of micro seconds, by contrast, calling avformat_find_stream_info before decoding will reduce P frame decoding time to less than 1 ms on average. However, avformat_find_stream_info itself is time consuming on network streams. Are there anything I can do to make decoding fast without calling avformat_find_stream_info ? 回答1: When avformat_find

问题 I am decoding h264 rtp stream with ffmpeg on android. I found a strange problem: if I don't call avformat_find_stream_info ,decoding P frame takes tens of micro seconds, by contrast, calling avformat_find_stream_info before decoding will reduce P frame decoding time to less than 1 ms on average. However, avformat_find_stream_info itself is time consuming on network streams. Are there anything I can do to make decoding fast without calling avformat_find_stream_info ? 回答1: When avformat_find

问题 Let's say I have a pandas DataFrame (loaded from a csv file) with this structure (the number of var and err columns is not fixed, and it varies from file to file): var_0; var_1; var_2; 32; 9; 41; 47; 22; 41; 15; 12; 32; 3; 4; 4; 10; 9; 41; 43; 21; 45; 32; 14; 32; 51; 20; 40; Let's discard the err_ds_j and the err_mean columns for the sake of this question. I have to perform an automatic comparison of the values of each row, with the values of the other rows; as an example: I have to compare

问题 I am looking for a way to calculate the percentage a base-polygon is overlayed by another (selection) polygon. Take this as an example. I want to calculate the area of red AND blue over the area of red. group_a <- data.frame(x = c(1, 4, 1), y = c(2, 4, 4), group = "base") group_b <- data.frame(x = c(2, 5, 2), y = c(3, 3, 5), group = "selection") dat <- rbind(group_a, group_b) dat x y group 1 1 2 base 2 4 4 base 3 1 4 base 4 2 3 selection 5 5 3 selection 6 2 5 selection library(ggplot2) ggplot

问题 I am creating a multi tenant db for an application. I have gone with the TenantID in every table approach and it works very well. I am at the performance tuning stage. My question is should each TenantID in every table be indexed for optimized searching as every query on the db will filter on this column? Look forward to any advice. Thanks 回答1: Although there are many considerations you need to make when indexing, In my experience, the (unique) clustered index works well as tenantId + PK All