portgl.blogg.se - Redshift copy command truncation

For optimum parallelism, the ideal size is between 1 MB and 125 MB after compression.

Load data files should be split so that the files are about equal size,īetween 1 MB and 1 GB after compression.

Serialized load, which is much slower than a parallel load.

Loading data from a single file forces Redshift to perform a.Regarding the number of files and loading data in parallel, the recommendations are: You should also read through the recommendations in the Load Data - Best Practices guide: Is there any source or calculator to give an approximate performance metrics of data loading into Redshift tables based on number of columns and rows so that I can decide whether to go ahead with splitting files even before moving to Redshift. Is it really worthy to split every file into multiple files and load them parallelly? I've gone through Īt this point, I'm not sure how long it's gonna take for 1 file to load into 1 Redshift table. I wanted to improve the performance even more. Maximum of 20000 rows for every iteration for 20 tables.

With the current system that I have, each CSV file may contain a maximum of 1000 rows which should be dumped into tables. And for next iteration, new 20 CSV files will be created and dumped into Redshift. I'm now creating 20 CSV files for loading data into 20 tables wherein for every iteration, the 20 created files will be loaded into 20 tables. I want to upload the files to S3 and use the COPY command to load the data into multiple tables.įor every such iteration, I need to load the data into around 20 tables. I'm working on an application wherein I'll be loading data into Redshift.