[Public] Hi Bowen,
Thanks for the reply and it helped to identify the failure point. Tested compaction throughput with different values and threads active in compaction reports "java.lang.OutOfMemoryError: Map failed" error with 1024 MB/s earlier compared to other values. This shows with lower throughput such issues are going to come up not immediately but in days or weeks. Test results are given below. +------------+-----------------------+-----------------------+-----------------+ | Records | Compaction Throughput | 5 large files In GB | Disk usage (GB) | +------------+-----------------------+-----------------------+-----------------+ | 2000000000 | 8 | Not collected | 500 | +------------+-----------------------+-----------------------+-----------------+ | 2000000000 | 16 | Not collected | 500 | +------------+-----------------------+-----------------------+-----------------+ | 900000000 | 64 | 3.5,3.5,3.5,3.5,3.5 | 273 | +------------+-----------------------+-----------------------+-----------------+ | 900000000 | 128 | 3.5, 3.9,4.9,8.0, 15 | 287 | +------------+-----------------------+-----------------------+-----------------+ | 900000000 | 256 | 11,11,12,16,20 | 359 | +------------+-----------------------+-----------------------+-----------------+ | 900000000 | 512 | 14,19,23,27,28 | 469 | +------------+-----------------------+-----------------------+-----------------+ | 900000000 | 1024 | 14,18,23,27,28 | 458 | +------------+-----------------------+-----------------------+-----------------+ | 900000000 | 0 | 6.9,6.9,7.0,28,28 | 223 | +------------+-----------------------+-----------------------+-----------------+ | | | | | +------------+-----------------------+-----------------------+-----------------+ Issues observed with increasing compaction throughput. 1. Out of memory errors 2. Scores reduces as throughput increased 3. Files size grows as throughput increased 4. Insert failures are noticed After this testing, I feel that this change is beneficial for workloads where data is not kept/left on nodes for too long. With lower throughput large system can ingest more data. Does it make sense ? Thanks, Amit From: Bowen Song via dev <dev@cassandra.apache.org> Sent: Friday, July 22, 2022 4:37 PM To: dev@cassandra.apache.org Subject: Re: [DISCUSS] Improve Commitlog write path [CAUTION: External Email] Hi Amit, The compaction bottleneck is not an instantly visible limitation. It in effect limits the total size of writes over a fairly long period of time, because compaction is asynchronous and can be queued. That means if compaction can't keep up with the writes, they will be queued, and Cassandra remains fully functional until hitting the "too many open files" error or the filesystem runs out of free inodes. This can happen over many days or even weeks. For the purpose of benchmarking, you may prefer to measure the max concurrent compaction throughput, instead of actually waiting for that breaking moment. The max write throughput is a fraction of the max concurrent compaction throughput, usually by a factor of 5 or more for a non-trivial sized table, depending on the table size in bytes. Search for "STCS write amplification" to understand why that's the case. That means if you've measured the max concurrent compaction throughput is 1GB/s, your average max insertion speed over a period of time is probably less than 200MB/s. If you really decide to test the compaction bottleneck in action, it's better to measure the table size in bytes on disk, rather than the number of records. That's because not only the record count, but also the size of partitions and compression ratio, all have meaningful effect on the compaction workload. It's also worth mentioning that if using the STCS strategy, which is more suitable for write heavy workload, you may want to keep an eye on the SSTable data file size distribution. Initially the compaction may not involve any large SSTable data file, so it won't be a bottleneck at all. As more bigger SSTable data files are created over time, they will get involved in compactions more and more frequently. The bottleneck will only shows up (i.e. become problematic) when there's sufficient number of large SSTable data files involved in multiple concurrent compactions, occupying all available compactors and blocks (queuing) a larger number of compactions involving smaller SSTable data files. Regards, Bowen On 22/07/2022 11:19, Pawar, Amit wrote: [Public] Thank you Bowen for your reply. Took some time to respond due to testing issue. I tested again multi-threaded feature with number of records from 260 million to 2 billion and still improvement is seen around 80% of Ramdisk score. It is still possible that compaction can become new bottleneck and could be new opportunity to fix it. I am newbie here and possible that I failed to understand your suggestion completely. At-least with this testing multi-threading benefit is reflecting in score. Do you think multi-threading is good to have now ? else please suggest if I need to test further. Thanks, Amit From: Bowen Song via dev <dev@cassandra.apache.org><mailto:dev@cassandra.apache.org> Sent: Wednesday, July 20, 2022 4:13 PM To: dev@cassandra.apache.org<mailto:dev@cassandra.apache.org> Subject: Re: [DISCUSS] Improve Commitlog write path [CAUTION: External Email] >From my past experience, the bottleneck for insert heavy workload is likely to >be compaction, not commit log. You initially may see commit log as the >bottleneck when the table size is relatively small, but as the table size >increases, compaction will likely take its place and become the new bottleneck. On 20/07/2022 11:11, Pawar, Amit wrote: [Public] Hi all, (My previous mail is not appearing in mailing list and resending again after 2 days) Myself Amit and working at AMD Bangalore, India. I am new to Cassandra and need to do Cassandra testing on large core systems. Usually should test on multi-nodes Cassandra but started with Single node testing to understand how Cassandra scales with increasing core counts. Test details: Operation: Insert > 90% (insert heavy) Operation: Scan < 10% Cassandra: 3.11.10 and trunk Benchmark: TPCx-IOT (similar to YCSB) Results shows scaling is poor beyond 16 cores and it is almost linear. Following settings are the common settings helped to get the better scores. 1. Memtable heap allocation: offheap_objects 2. memtable_flush_writers > 4 3. Java heap: 8-32GB with survivor ratio tuning 4. Separate storage space for Commitlog and Data. Many online blogs suggest to add new Cassandra node when unable to take high writes. But with large systems, high writes should be easily taken due to many cores. Need was to improve the scaling with more cores so this suggestion didn't help. After many rounds of testing it was observed that current implementation uses single thread for Commitlog syncing activity. Commitlog files are mapped using mmap system call and changes are written with msync. Periodic syncing with JVisualvm tool shows 1. thread is not 100% busy with Ramdisk usage for Commitlog storage and scaling improved on large systems. Ramdisk scores > 2 X NVME score. 2. thread becomes 100% busy with NVME usage for Commiglog and score does not improve much beyond 16 cores. Linux kernel uses 4K pages for mapped memory with mmap system call. So, to understand this further, disk I/O testing was done using FIO tool and results shows 1. NVME 4K random R/W throughput is very less with single thread and it improves with multi-threaded. 2. Ramdisk 4K random R/W throughput is good with single thread only and also better with multi-threaded Based on the FIO test results following two ideas were tested for Commitlog files with Cassandra-3.1.10 sources. 1. Enable Direct IO feature for Commitlog files (similar to [CASSANDRA-14466] Enable Direct I/O - ASF JIRA (apache.org)<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fissues.apache.org%2Fjira%2Fbrowse%2FCASSANDRA-14466&data=05%7C01%7CAmit.Pawar%40amd.com%7C830520176c7a4657417b08da6bd26c94%7C3dd8961fe4884e608e11a82d994e183d%7C0%7C0%7C637940848767120963%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=wxXA9jVlNsqbOyBBaX3fAxcPY%2F%2BN1Yxou%2F5GlBsMf64%3D&reserved=0> ) 2. Enable Multi-threaded syncing for Commitlog files. First one need to retest. Interestingly second one helped to improve the score with "NVME" disk. NVME disk configuration score is almost within 80-90% of ramdisk and 2 times of single threaded implementation. Multithreading enabled by adding new thread pool in "AbstractCommitLogSegmentManager" class and changed syncing thread as manager thread for this new thread pool to take care synchronization. Only tested with Cassandra-3.11.10 and needs complete testing but this change is working in my test environment. Tried these few experiments so that I could discuss here and seek your valuable suggestions to identify the right fix for insert heavy workloads. 1. Is it good idea to convert single threaded syncing to multi-threading implementation to improve the disk IO? 2. Direct I/O throughput is high with single thread and best fit for Commitlog case due to file size. This will improve writes on small to large systems. Good to bring this support for Commitlog files? Please suggest. Thanks, Amit Pawar
