Select timings later hence these were just tangential observations. Interested in exploring data set loading timings here, as data set Introducing an explicit PostgreSQL commit after each PostgreSQL, while the inserts were being done in a book-by-book manner Process, leading to batch-like insertion of all the books with Probably due to the delayed commit statement at the end of the load The data load was significantly faster with PostgreSQL this is Here are timing results of the data loading step: Note the extra column in PostgreSQL which We can see that both MongoDB and PostgreSQL lead to databases of These are stored as UTF-8 strings in MongoDB and PostgreSQL. U'title': u'Engineering Turbulence Modelling and Experiments 6: ERCOFTAC International Symposium on Engineering Turbulence and Measurements - ETMM6', It may be interesting to note that internally json.loads() parses execute ( 'INSERT INTO test (recid,data) VALUES ( %s, %s ) ', ( book, json. insert ( book ) def postgresql_load_books (): for book in json. Also, big thanks to Dom for helping me with the benchmarking tool and preparing for Percona Live Europe together.Def mongodb_load_books (): for book in json. I did learn loads of stuff while writing these blog posts. With this MongoDB issue, it’s not fair to make a statement which database is faster with JSON/JSONB. MongoDB has also acknowledged this problem with new Jira ticket to track this issue. On the other hand, PostgreSQL has an extremely stable and predictable throughput. The design of MongoDB/WiredTiger needs to be improved to minimise the impact during checkpoint. However, the maximum throughput and WILL drop significantly during checkpoints. Indeed, we have seen MongoDB achieving a higher maximum throughput than PostgreSQL. We have reproduced the issue with CentOS 7 on XFS. However, you will get a predictable database performance in return. Yes, this means that your throughput will be lower. In PostgreSQL, the checkpoint_completion_target will allow you to spread the checkpoint writes over a period of time continuously until the next checkpoint. This means that every 60s, there’s a massive performance drop in throughput since checkpoint will flush everything to disk and fill up the disk IO queue.
The now-durable data act as a checkpoint in the data files. During checkpoint, WiredTiger writes all the data in a snapshot to disk in a consistent way across all data files. MongoDB cache eviction/checkpoint bug?īased on our diagnosis, we discovered that the issue happens during checkpoint (every 60s). MongoDB’s performance behaviour is not acceptable in this scenario. IMO, a good database should provide consistent and predictable performance. This probably co-relate to the 1s to 3s response times in the INSERTs graph above. Although MongoDB does outperform PostgreSQL tremendously sometimes, there are other times where it takes 4x the amount of time as compared to PostgreSQL. MongoDB’s graph fluctuates ALOT whereas PostgreSQL has a stable and consistent graph. MongoDB – Time taken to write in nanoseconds vs throughput PostgreSQL – Time taken to write in nanoseconds vs throughput Now, we can measure application file system operations. It allows you to write scripts to hook into the kernel to see what the database is waiting on. With FreeBSD, we get an extremely powerful tool called dTrace. For a platform with an SLA of 1s response time, if your database takes 1s to complete a request, you are in a bad situation. This means that it has a significant amount of operations taking more than 1s. However, when we look at the 99th percentile graph, we noticed something strange.įrom the graph, we can see that MongoDB has a very long “tail”. In our results, MongoDB takes lesser time to insert 10 million records. Throughput (Number of operations per second).99th percentile (Latency of the operations).
Being able to do 1 million operations in 10 minutes is not good if it takes 5s to complete an operation or 10% of the operations complete in 10s. Doing this will hide important details about the performance. In naive performance benchmarking, people will measure the average latency or the time taken to perform x amount of transactions. The correct way to do performance benchmark It uses very little memory and doesn’t use any locks! Dominic and myself also presented the results at Percona Live Europe 2017. Hence, I enlist the help of my colleague, Dominic, to write a new tool in Golang to do it. I didn’t want to use existing benchmarking tool like YCSB because it wasn’t fast enough to push the databases to its limit. I also ran the same test on newer versions of both MongoDBand PostgreSQL. In part I and part II of this blog series, I shared about my opinions on the test results that were released. Thoughts on PostgreSQL JSON Vs MongoDB JSON Performance – Part III (Final)
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