Hi, dev: Recently I found the bug in compressing sort temp file and tried to fix this bug in PR#1632 (https://github.com/apache/carbondata/pull/1632). In this PR, Carbondata will compress the records in batch and write the compressed content to file if we turn on this feature. However, I found that the GC performance is terrible. In my scenario, about half of the time were wasted in GC. And the overall performance is worse than before. I think the problem may lie in compressing the records by batch. Instead of this, I propose to compress the sort temp file in file level, not in record-batch level. 1. Compared with uncompressed ones, compressing the file in record-batch level leads to different layout of file. And it also affects the reading/writing behavior. (The compressed: |total_entry_number|batch_entry_numer|compressed_length|compressed_content|batch_entry_numer|compressed_length|compressed_content|...; The uncompressed: |total_entry_number|record|record|...;) 2. During compressing/uncompressing the record-batch, we have to store the bytes in temporary memory. If the size is big, it directly goes into JVM old generation, which will cause FULL GC frequently. I also tried to reuse this temporary memory, but it can only be reusable in file level -- We need to allocate the memory for each file. If the number of intermediate files are big, frequent FULL GC is still inevitable. If the size is small, we will need to store more `batch_entry_numer`(described in point1). Note that, the size is rowSize*batchSize. In previous implementation, Carbondata use 2MB bytes to store one row. 3. Using file level compression will simply the code since CompressedStream is also an Stream, which will not affect the behavior in reading/writing compressed/uncompressed files. 4. After I used file level compression, the GC problem disappeared. Since my cluster has crashed, I didn't get the actual performace enhanced. But seeing from the Carbondata maven tests, the most time consuming module `Spark Common Test` takes less time to complete comparing with uncompressed. Time consumed in `Spark Common Test` module: | Compressor | Time Consumed | | --- | --- | | None | 19:25min | | SNAPPY | 18:38min | | LZ4 | 19:12min | | GZIP | 20:32min | | BZIP2 | 21:10min | In conclusion, I think file level compression is better and I plan to remove the record-batch leve compression related code in Carbondata. 发自网易邮箱手机版
