Readwrite think insert12/19/2023 ![]() I need good performance for SELECT * FROM table WERE SEGMENT_ID = ? And partitioning is the pretty much only solution that works nicely with ORM's.Īny really, why dynamically? Pregenerate. Otherwise the answer really is hardware, particularly because 100 millions are not a lot. It really is partitioning, but only if your insert/delete scenarios make it efficient. Which you possibly should - once you hit hundreds of billions of rows. No need to be dynamically here, well, though you sort of can - the main point is that you never WORK with the sub-tables, so ORM and queries stay the same. It will not make it faster to read or write, but deletes of WHOLE partitions can speed up significantly. A sort google actually tells me postgres also has it - are you aware of that? From waht I see it is quite a little less elegant than SQL Server (seems to create indices on each partition, instead of this handled by the database transparently). This is one thing where professional databases have something called partitioning. The most obvious approach is creating new table for SEGMENT_ID I mean, my age old VM (sql server, using 48gb memory, 6 cores and around 10 dedicated SSD) is handling 64 million row insert/delete jobs in less than a second WITHOUT anything particular. Dual Socket, 16 cores (I just go by the minimum licensing Windows Standard here, bit it matches for example the low end of an AMD EPYC), possibly 128GB RAM and an all SSD Setup, at least a heavily SSD cached thing. ![]() Not running it on a phone? I mean, seriously 100s of millions of rows is not particularly large on modern mid range hardware. What are the common approaches to boost read/write performance of table with up I'm compiling Angular apps in the background. You can pick one up for a few hundred bucks and install Xubuntu it. That's on a 5 year old x230 with a "Intel(R) Core(TM) i5-3230M CPU 2.60GHz" and a single SSD. ![]() That means we created the table in 1.5 mins. Here is a table exactly as you described, worst case 100 million rows with 1 million rows per SEGMENT_ID explain analyzeįunction Scan on generate_series gs (cost=0.00.15.00 rows=1000 width=32) (actual time=21740.904.57589.405 rows=100000000 loops=1) Maybe there is some kind of magical index which will help me to avoid creating new tables dynamically or configuring sharding? But will my inserts be clustered if I insert all segment-related data at once?Īlso Postgres propose to use partitioning to handle very big tables. You can also use sharding, right? Does database create new tables under the hood? In other words you finish with code that smells. The most obvious approach is creating new table for SEGMENT_ID dynamically, but dynamic tables means hacks with ORM or even native SQL query-framework. Reads - are pretty often, I need good performance for SELECT * FROM table WERE SEGMENT_ID = ?. Writes - all rows for SEGMENT_ID are inserted at once, no updates for SEGMENT_ID afterwards. Table has column SEGMENT_ID INT NOT NULL, where each segment has about 100.000-1.000.000 rows. What are the common approaches to boost read/write performance of table with up to 100 millions of rows?
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |