![]() Your user clicks around, data gets replicated from the backend, another browser tab writes data. Sometimes this is easy, as instead of iterating over a documents list and calling single inserts, with RxDB you could use the bulk methods to store many document at once.īut most of the time is not so easy. To fix your IndexedDB performance problems you have to make sure to use as less data transfers/transactions as possible. This makes clear that the limiting factor to IndexedDB performance is the transaction handling, not the data throughput. Interestingly if we increase the document size to be 100x bigger, it still takes about the same time to store them. Inserting 1k documents with one transaction per write, takes about 2 seconds. I forked the comparison tool here and changed it to use one transaction per document write. But the key point here is that all these documents get written in a single transaction. It is quite fast and it is very unlikely that you want to store that many document at the same time at the client side. ![]() When you run tests on Nolans Browser Database Comparison you can see that inserting 1k documents into IndexedDB takes about 80 milliseconds, 0.08ms per document. So before we start complaining, lets analyze what exactly is slow. Even sending data over the internet to the backend can be faster then storing it inside of an IndexedDB database. Time which can be critical for a fast page load. Not slow like a database on a cheap server, even slower! Inserting a few hundred documents can take up several seconds. You start developing your app and everything goes fine.īut as soon as your app gets bigger, more complex or just handles more data, you might notice something. It becomes clear that the only way to go is IndexedDB. For RxDB you can use the OPFS RxStorage to get about 4x performance improvement compared to IndexedDB. UPDATE April 2023: Since beginning of 2023, all modern browsers ship the File System Access API which allows to persistently store data in the browser with a way better performance. It can store json data and iterate over its indexes. IndexedDB is an indexed key-object database.The FileSystem API could be used to store plain binary files, but it is only supported in chrome for now.Storing and reading data can fully block the JavaScript process so you cannot use LocalStorage for more then few simple key-value pairs. LocalStorage is a synchronous API over asynchronous IO-access.WebSQL is deprecated because it never was a real standard and turning it into a standard would have been too difficult. ![]() Cookies are sent with each HTTP request, so you cannot store more then a few strings in them. ![]() So you have a JavaScript web application that needs to store data at the client side, either to make it offline usable, just for caching purposes or for other reasons.įor in-browser data storage, you have some options: For storing around 100 million rows of around 90 byes each, what do you expect the best case insertion rate to be? I'm trying to set my own expectations of what we can achieve with our current approach.Why IndexedDB is slow and what to use instead.Why does memcpy() hog so much of the CPU when we're only storing around ~1.5MB/sec? Is this normal?.97% of the time is spent in sqlite3BtreeMovetoUnpacked(). I see that around 75% of the time is spent in memcpy_repmovs(). We compile sqlite with SQLITE_WIN32_MALLOC and SQLITE_WIN32_HEAP_CREATE. My machine is on Azure with 12 cores and has 128 GB of RAM. Currently it takes around 90 minutes which means the insertion rate is ~18000 rows/second. The DB grows to ~9GB after it's completely populated. We need to create the DB from scratch on launch and populate 100 million rows in the table. We have an in-memory database with a simple table(3 integers and a string, the first 3 numbers form the primary key, no other indexes at this point ). ![]()
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